Authors: Linxi Lei Chao Xu Tian Zeng Zhao Wang Di Song Jinmin Shi
Polarimetric space–time adaptive processing (PSTAP) introduces polarization domain information alongside the space–time domain. By exploiting the differences in polarimetric scattering characteristics between targets and clutter, PSTAP significantly improves the detection performance for slow-moving targets and has become an important technology in the airborne radar community. However, the Conventional polarimetric STAP still faces the severe range ambiguity problem caused by high pulse repetition frequency (PRF) in high-speed scenarios. To overcome range ambiguity, this paper introduces the frequency diverse array (FDA) into polarimetric STAP and develops an FDA-PSTAP framework. First, we establish the fundamental signal processing procedure for FDA-PSTAP. Second, the full-dimensional (FD) FDA-PSTAP is developed according to the MVDR principle. Finally, to reduce the sample requirement for training samples, a reduced-dimensional (RD) FDA-PSTAP is proposed by designing a proper reduced-dimension transformation matrix. Numerical results demonstrate that the proposed FDA-PSTAP effectively suppresses range-ambiguous clutter. Under limited training samples, the proposed reduced-dimensional FDA-PSTAP reduces the output SINR loss by 15–17 dB compared with the full-dimensional counterpart in the main clutter region. When the number of training samples is doubled, the proposed method approaches the optimal performance with a gap of 1–2 dB across the entire Doppler spectrum, confirming its robustness and practical applicability.
]]>Authors: Yixiong Gan Ye Cheng Jinghu Yang Wei Sun
Earthquake-induced soil liquefaction poses a severe threat to the structural safety of underground infrastructure. This paper presents a shaking table model test conducted on sand sites, where two site models—namely, uniform saturated sand and layered dry–saturated sand—are designed to systematically analyze the influences of seismic wave amplitude, seismic wave frequency, and structure burial depth on the development of pore water pressure. The research findings indicate the following: (1) As the input peak ground acceleration (PGA) increases, the output surface peak ground acceleration rises, while the acceleration amplification factor decreases. Under high-intensity earthquakes, sand boiling and water gushing are observed in the uniform saturated sand site, and the overlying unsaturated layer can effectively inhibit the liquefaction development of the underlying saturated sand layer. (2) When the predominant frequency of the input seismic wave is close to the natural vibration frequency of the test site, the surface acceleration response is significantly enhanced, while the resonance effect of low-frequency seismic components is relatively weak. (3) The pore pressure ratio is higher in shallow soil layers, indicating that shallow strata are more prone to liquefaction. Both the pore pressure accumulation rate and the peak pore pressure ratio under uniform saturated sand conditions are higher than those measured in the layered dry–saturated sand site. (4) The sensitivity ranking of the three influencing factors is input PGA > burial depth > seismic wave frequency, where the input PGA is the dominant influencing factor, and the effect of the seismic wave frequency is not statistically significant. The conclusions of this study can provide a reference for seismic response assessment and anti-liquefaction design of shallow-buried structures in liquefiable sites.
]]>Authors: Guangye Si Genfu Liang Gen Li Chongzheng Zhu
To improve the aging resistance of SBS-modified asphalt (SBSMA), this study prepared two metal oxide/montmorillonite nanocomposites, TiO2/montmorillonite (TM) and CeO2/montmorillonite (CM), and investigated their effects on the rheological properties and microstructure of SBSMA before and after long-term aging. The performance differences between the two nanocomposites under different aging modes were also compared. Conventional physical property tests, dynamic shear rheometer tests (temperature sweep and linear amplitude sweep), dynamic mechanical analysis (DMA), and fluorescence microscopy were employed to evaluate the physical properties, high- and low-temperature rheological properties, fatigue resistance, and the microscopic evolution of SBSMA. The results show that before aging, the incorporation of TM and CM significantly improved the high-temperature deformation resistance of SBSMA, enhanced the SBS polymer dispersion state, and promoted the formation of a continuous network structure of polymer phase. After long-term thermal-oxidative aging, both nanocomposites effectively retarded the aging of the asphalt matrix and the SBS network; CM exhibited superior thermal-oxidative aging resistance. After long-term ultraviolet (UV) aging, both nanocomposites also showed significant protective effects, and TM outperformed CM in UV aging resistance. In summary, TM and CM show differentiated advantages in UV protection and thermal-oxidative protection, respectively, providing a theoretical basis for the design of anti-aging materials for SBSMA based on service environments.
]]>Authors: Zhang Luo Fayou A Shiqiang He Haifeng Jia Ruoxi Lin Shiqun Yan
Rainfall-induced instability of highway slopes with a soil–rock binary structure may be strongly influenced by the hydraulic barrier effect of low-permeability shale. This study investigated the right-side slope along the D-ramp section from DK0+230 to DK0+660 at Deze Interchange on the Zhanhui Expressway, China. A two-dimensional coupled seepage–stress model was developed based on the engineering geological conditions and rainfall records to simulate the slope response under a 72 h extreme rainfall scenario with an intensity of 175.6 mm/d. Field displacement monitoring data were used to validate the modeled deformation pattern under natural conditions. Under natural conditions, the reinforced toe zone remained stable, deformation was concentrated along the interface between the block-stone layer and strongly weathered limestone in the middle and rear portions of the slope, and the factor of safety was 1.1344, indicating a basically stable state. During prolonged rainfall, infiltrating water accumulated near the interface between the strongly weathered shale and the underlying shale owing to the hydraulic barrier effect of the low-permeability shale, forming a continuous transient saturated zone. The plastic zone progressively extended from the upper shallow weak interface to the lower deep interface and eventually became fully connected, while the factor of safety decreased to 0.9886, indicating overall instability. The results reveal a coupled mechanism involving interfacial water accumulation, increased pore-water pressure, the formation of a transient saturated zone, and a shift in the controlling zone of slope deformation and failure from shallow to deeper layers. These findings provide a reference for disaster prevention and mitigation of similar soil–rock binary-structure slopes.
]]>Authors: Joel Artemio Morales-Viscaya Leonardo Corral-Trigueros Merlín Octavio Maravilla Marco Antonio Castro-Liera Martin Moreno Alberto Traslosheros-Michel
Permanent Magnet Direct Current (PMDC) motors are widely used in applications requiring precise speed control due to their efficiency and high torque-to-inertia ratio. This work proposes a feedforward speed control strategy for PMDC motors based on model inversion, complemented by a disturbance rejection feedback term. A gray-box model is developed using only four concentrated parameters, avoiding the overdetermination problem of classical seven-parameter identification. These parameters are identified experimentally from step-response data using a nonlinear optimization approach. The proposed control law is validated on two commercially available PMDC motors with distinctly different dynamics: a fast-response motor (FC130SA) and a slower motor with a gearbox (GM25-370). Experimental results show that the proposed feedforward controller with disturbance rejection achieves lower or comparable Integral Squared Error (ISE) than optimally tuned PID/PI controllers, while significantly reducing overshoot (up to 66% in the fast motor) and maintaining lower or comparable Control Input Area (CIA), a metric commonly used in the literature to provide an indirect indication of control effort. Unlike classical controllers, the proposed method requires no per-reference gain tuning. These results show compelling evidence that inversion-based control with disturbance rejection is a viable, energy-efficient alternative to PID control for PMDC motor speed regulation.
]]>Authors: Yoisdel Castillo Alvarez Reinier Jiménez Borges José Pedro Monteagudo Yanes Perla Yazmín Sevilla-Camacho José Billerman Robles-Ocampo Luis Angel Iturralde Carrera Juvenal Rodríguez-Reséndiz
In the feed industry, energy efficiency is typically assessed using aggregate consumption indicators (kWh/t) or the efficiency of the electric motor in isolation, which makes it impossible to pinpoint where energy is lost along the conversion chain. This study formalizes a three-phase methodology that breaks down the useful electrical efficiency of each Significant Energy Use (SEU) into its successive stages—motor, transmission, and process—based on field-measurable variables, linking electrical conversion with the useful power model of each machine. The process efficiency of hammer mills is normalized using the Swiss Institute of Feed Technology (SFT) reference index; this constitutes a load-sensitive performance ratio, not an absolute thermodynamic efficiency. Its demonstration at the “Piensos Cienfuegos” plant (Cuba), using data from a 2015 industrial campaign, yielded overall efficiencies of 72% for the bucket elevator—conditional on the adopted nominal throughput and nameplate power factor, with a plausible range of 43–89% under coupled systematic-bias scenarios—26–28% for the hammer mills—despite motors operating at 90–92% efficiency—and 17.3% for the screw conveyor (24.7% at the processing stage). Grinding efficiency fell from 31.1% to 9.7% as the throughput of Mill III was reduced from 16 to 5 t/h, corresponding to an increase in normalized shaft-specific energy consumption from 3.50 to 11.2 kWh/t. The expanded measurement uncertainty was 11.8% (k=2), and systematic sources of uncertainty were quantified through a sensitivity analysis. The scope of this work is diagnostic: no retrofit or operational intervention was implemented at the plant, and consequently no before–after energy savings are measured or claimed. The reported efficiencies characterize the baseline condition and identify where intervention would be effective.
]]>Authors: Lu Li Xiaodong Wang
To address the limited cooling range of ventilation in deep, high-temperature development headings and the lack of coordinated design between coil-based cooling and the ventilation system, this study proposes a coupled “twin-forcing–coil” cooling scheme. Building on conventional overlap (forcing–exhausting) ventilation, a rear-mounted second forcing duct is added to the conventional overlap (force–exhaust combined) auxiliary ventilation system, forming a dual-duct forcing, single-exhausting configuration—hereafter termed the “twin-forcing–single-exhausting” (TFSE) system—that provides a booster (relay) air supply to mitigate the along-path attenuation of cooling capacity and the short-circuiting of cold air; an in situ heat-exchange coil wall further provides supplementary cooling where ventilation-based temperature control weakens. Using a development heading at the 790 m level of a metal mine in Yunnan as the engineering background, a three-dimensional numerical model coupling the roadway, ventilation system, and coil wall was established and validated against nine field monitoring points, showing average relative errors of approximately 1% for temperature and 2–3% for humidity, comparable to the measurement uncertainty of the field instrumentation. Because the numerical model does not account for evaporative and condensation phase-change processes, two supplementary development headings with standing water at the face were used for validation; results showed that model error increases with water accumulation and heading length, indicating the model’s applicability is limited to conditions with intact surrounding rock and minimal seepage. Six operating cases were designed with duct placement and coil spacing as variables. Results show that single-duct ventilation cooling decays markedly beyond 30 m from the face, whereas twin-forcing booster (relay) air supply effectively extends the cooling range, reducing the 30–70 m section temperature by 2.7–2.9 K; the second duct should be positioned where the first duct’s cooling capacity begins to attenuate but is not yet depleted. Based on only two spacing configurations tested (10 m and 15 m), coil-staggered spacing showed limited effect on cooling performance under the field conditions examined; this preliminary finding requires validation across a broader range of spacings. Among the chilled-water conditions tested, an inlet temperature of 280.65 K and a flow velocity of 0.5 m/s offered a reasonable trade-off between cooling uniformity and economic efficiency. Under the boundary conditions and equipment parameters of this case, energy consumption estimates further indicate that the cooling effect per unit electricity consumption of twin-forcing ventilation is roughly 6–8 times that of coil-based cooling, primarily due to pumping losses over the ~240 m chilled-water delivery distance. This energy penalty indicates that coil-based cooling is better suited as a localized, short-distance supplementary measure rather than as a means of extending the cooling range over long distances.
]]>Authors: Chuanwen Wei Hongke Gao Yuexiang Li Fenglin Ma Xinjie Man Xintang Wang Bo Pang
Fractures are weak surfaces of rock; they are common in underground engineering and are prone to causing engineering disasters. The fracture parameters of rock are the crucial foundation for stability evaluation in engineering. The accurate identification of rock fractures is important for engineering support design, as it is helpful in preventing and reducing engineering accidents caused by fractures. At present, there are few technical methods for fracture identification. Digital drilling test technology provides a new approach to rock fracture identification. In this study, a multi-functional rock mass drilling test system is employed to conduct testing in fractured rock. The response laws of drilling parameters to different fracture positions and angles are analyzed, and an identification model for rock mass fracture parameters while drilling is developed. The results from tests show that the average error in identifying rock mass fracture positions using the fracture parameter identification model is 5.34 mm, and the average error in identifying fracture angles is 2.01°. This study provides a theoretical basis for on-site testing and assessment of rock mass fractures in underground engineering.
]]>Authors: Jiandong Jia Meng Su Yulong Zhang Bin Zhao Jing Xu Jie He
The slow evolution and inconspicuous nature of buffer-layer ablation in high-voltage cables pose a significant challenge for early fault diagnosis. To tackle this issue, we propose a hybrid diagnostic approach that integrates frequency-domain impedance measurement with a convolutional neural network (CNN). A cable simulation model is first established using transmission-line theory and a distributed-parameter framework. We examine the impedance and phase responses at the cable’s sending end, revealing a consistent decreasing trend with rising frequency alongside periodic resonant peaks. The simulator generates a diverse set of spectral signatures corresponding to various cable health states. The CNN then extracts discriminative features from these waveforms, and a probabilistic clustering preprocessing step further refines the input data. Experimental results on a test set of 78 samples—comprising 52 experimentally measured normal spectra and 26 experimentally calibrated simulated spectra for mild and severe ablation—demonstrate a classification accuracy of 0.95, confirming that the proposed methodology enables reliable, non-intrusive detection of buffer-layer ablation without cable disassembly.
]]>Authors: Marisol Coba-Martínez Jarniel García-Morales Gerardo-Vicente Guerrero-Ramírez Marisol Cervantes-Bobadilla Esteban-Osvaldo Guerrero-Ramírez Ivetteh-Viginia Medina-Medina Manuel Adam-Medina
Green hydrogen is a key energy carrier in Power-to-Liquid (PtL) pathways for the production of sustainable synthetic fuels, contributing to the decarbonization of the industrial and transport sectors. However, the intermittent nature of renewable energy sources and the variable hydrogen requirements needed to maintain the appropriate stoichiometric ratio for synthesis processes necessitate regulating hydrogen production according to process demand, rather than maximizing its generation. This article proposes an intelligent control strategy for alkaline water electrolysis, in which the hydrogen production target is determined from the stoichiometric requirements of synthetic methanol production, based on available carbon dioxide. ANN models were developed using the experimental data, incorporating both classical and conformable activation functions in the hidden layer. Based on the selected models, the ANNi was formulated, and PSO and GA were used to determine the required feed current according to hydrogen demand. The proposed methodology was evaluated under a dynamic hydrogen-demand profile derived from the stoichiometric requirements of methanol synthesis. The results show that the proposed controllers closely track changes in hydrogen demand. After each change in the setpoint, the H2/CO2 ratio returned to a ±2% band around the stoichiometric setpoint in approximately 0.98 s for ICANNi-PSO and 0.96 s for ICANNi-GA. Furthermore, some conformable activation functions achieved performance comparable to that of classical activation functions while using fewer neurons in the hidden layer. Both optimization algorithms provided comparable tracking performance under the evaluated conditions.
]]>Authors: Bin Zhang Ruizhe He Lijun Ma Yongzai Chang Shijia Yuan Yang Liu Peng Liu Peng Ding
To address the challenges associated with operational ventilation in high-altitude tunnels, this study investigates the distribution patterns of ventilation flow fields and optimizes the spatial layout parameters of jet fans to determine the most effective configuration. Using a case study from the Zhuohe Expressway tunnel, numerical simulations were conducted to analyze four key design parameters: the lateral clear distance (L) between two jet fans in a single group, the vertical distance (H) from the fan center to the tunnel lining, the axial distance (T) from the fan to the tunnel entrance, and the longitudinal spacing (S) between two groups of fans. The results indicate that for a single-fan group, when the parameter L is 1.25D (D is the fan diameter), pressure rise and comprehensive influence coefficients reach peak values of 20.090 Pa and 0.886, respectively. As well as the parameter H between 1.20 m and 1.25 m, the diffusion of the vertical wind field velocity is continuously reduced due to the constraint of the tunnel lining on Section BB of the tunnel fan’s symmetry axis, and the interference of the tunnel lining on the stable flow state of the fan’s outlet airflow is relatively small. Moreover, parameter T has a relatively low sensitivity impact on the increase in pressure and the variation of the influence coefficient. When the parameter T is within the range of 50 m to 100 m, the airflow at the entrance of the tunnel is smoothly connected with the airflow at the suction section of the fan. Additionally, the pressure rise and the influence coefficient increase by the parameter T. Both the fan’s pressure rise and the influence coefficient reach their maximum values when the parameter T is 100 m. Furthermore, in the case of two-fan groups, the gas is fully mixed in the tunnel when the parameter S is 150 m, and the fan pressure rise and the influence coefficient increase as well as parameter S. The gas between the two sets of fans has been fully mixed in the parameter S at 175 m, and the pressure rise and the coefficient influence reach their maximum values of 40.231 Pa and 0.887, respectively. In light of these findings, the following optimal parameters ranges are recommended for similar tunnel ventilation designs: parameter L is 1.25D for two jet fans within a single group, parameter H is between 1.20 m and 1.25 m, parameter T is 100 m from the tunnel entrance, and parameter S is between 150 m and 175 m for two groups of fans.
]]>Authors: Kun Cai Yesen Zhu Jie Fu Yifeng Han Guanggui Cheng Haixiang Huan Jun Wang Wan Sun
As a key energy-conversion component of tower gravity energy storage systems (T-SGESs), the hoisting structure is susceptible to large-amplitude coupled vibrations when the dominant frequency of a continuous external excitation approaches one of its natural frequencies, potentially compromising operational stability and safety. To characterize this behavior, a two-degree-of-freedom nonlinear dynamic model is developed based on Hamilton’s principle. Eigenvalue and modal analyses are performed to determine the natural frequencies and modal characteristics of the coupled system, while the second-mode primary resonance is further analyzed using the method of multiple scales and validated through numerical frequency-sweep simulations. Near the second-mode primary resonance, the system exhibits a pronounced hardening-type nonlinear response characterized by multistability, saddle-node bifurcations, jump transitions, and hysteresis. Parametric analysis indicates that greater attention should be paid to short-rope and low-payload operating conditions, under which the system tends to exhibit stronger nonlinear responses and larger payload swing amplitudes near the second-mode primary resonance. Meanwhile, the nonlinear resonance response of the hoisting structure can be effectively mitigated through enhanced equivalent stiffness and damping, which substantially narrow the multistable frequency interval. At a damping ratio of 0.04, the system transitions from a multivalued response to a single stable branch, with a marked reduction in payload swing amplitude. These findings identify the second-mode primary resonance as a critical nonlinear operating regime and provide a quantitative basis for resonance avoidance and parameter regulation in T-SGES hoisting systems.
]]>Authors: Smith Eiamsa-ard Sathaporn Liengsirikul Suriya Chokphoemphun Varesa Chuwattanakul Paisan Naphon Manoj Kumar Monsak Pimsarn
Enhanced circular tubes are widely employed in shell-and-tube heat exchangers, power-generation condensers, chemical reactors, refrigeration systems, and air-cooled heat exchangers, where improved convective performance can reduce the heat-transfer area required for a specified thermal duty. Helical screw tapes (HSTs) are passive inserts that promote sustained swirling motion and enhance convective heat transfer within such tubes. Although helical screw tapes and multiple-insert arrangements have been investigated previously, the combined thermohydraulic and second-law effects of increasing the number of co-rotating HSTs under fixed geometric ratios remain insufficiently quantified. In this investigation, turbulent airflow in a heated round tube was numerically investigated to examine the effect of tape number on heat transfer, pressure drop, thermal performance, total entropy generation (Stotal), and exergy destruction (ExD). Six HST configurations containing one to six tapes were examined over a Reynolds-number range of Re = 5000–20,000 in a circular tube with an inner diameter of DT = 31 mm, which was also adopted as the characteristic length for the Reynolds number, Nusselt number, and friction factor. The helical pitch P, screw diameter Ds, tape width W, and tape thickness t were 60 mm, 30 mm, 4.5 mm, and 0.2 mm, respectively, giving a pitch ratio of P/Ds = 2.0 and a width ratio of W/Ds = 0.15. A plain tube (PT) served as the baseline case. The results show that increasing the number of tapes intensifies swirl flow and enhances heat transfer but also leads to a continuous increase in pressure loss. For the optimum three-tape arrangement, the Nusselt number is increased by 126.0–158.8% and the thermal performance factor by 4.5–19.5% relative to the plain tube, while the total entropy generation and exergy destruction are simultaneously reduced by 7.9–61.0%. Among the configurations examined, HST-P2.0-W0.150-3, comprising three tapes at a pitch ratio of P/Ds = 2.0 and a width ratio of W/Ds = 0.15, achieved the best overall performance by delivering the highest thermal performance factor and the lowest total entropy generation and exergy destruction among the HST cases.
]]>Authors: Penggang Tian Chongyang Fu Jianhui Niu Kai Wang Ergang Xiong
Sixteen reinforced concrete beams were tested under symmetric concentrated loading to investigate the mechanical behavior of beams designed using the compressive force path (CFP) method, in comparison with specimens designed according to the Chinese Code for Design of Concrete Structures (GB 50010-2010). The test variables included shear-span ratios (4.0, 3.0, 2.5, and 2.0) and sectional dimensions (150 × 300 mm and 250 × 550 mm). The test process and test results were systematically analyzed. The results show that the stress transmitted along the compressive force path is the main factor governing the shear capacity. The CFP beams achieved peak loads comparable to those of the GB beams while using 5.88–39.99% fewer stirrups, with larger savings observed for smaller shear-span ratios. The CFP method predicted the shear capacity with an error of approximately 10% (ranging from 2.24% to 12.45%). The shear strength of the CFP beams decreased with increasing shear-span ratio and effective depth. Overall, the CFP-designed specimens met the expected mechanical performance requirements, verifying the accuracy and applicability of the CFP method.
]]>Authors: Arif Ibrahim József Sárosi
Hydrogen leakage detection and mapping are essential in hydrogen-rich environments to ensure safe utilization in industrial and commercial applications. In this study, a wireless IoT-enabled hydrogen leak-mapping system was developed using machine learning and a LoRa-coupled wireless sensor network. A miniature model of a hydrogen production system was used, featuring a functioning electrolyzer that generates pure hydrogen by splitting water. To perform efficient leakage mapping, the leak location and watch time were varied, and six readings from commercial hydrogen gas sensors were recorded for better analysis. The relative sensor responses recorded by the six hydrogen sensors were used as input features for the machine learning models. The model accuracy was approximately 88.13%. LoRa communication technology was also used to demonstrate its use in harsh conditions, along with the IoT protocol, to deliver data over the Internet for better accessibility and monitoring. The developed localization technology enables safe monitoring of hazardous, highly flammable hydrogen gas, and machine learning can help prevent fatal accidents.
]]>Authors: Vania Aparecida Rosario de Oliveira Geraldo Cesar Rosario de Oliveira Erick Siqueira Guidi Valério Antonio Pamplona Salomon
The industrial sector faces one of the biggest challenges in decarbonization, mainly due to the high costs associated with the development and implementation of low-carbon, energy-efficient technologies and solutions. The long lifespan of industrial assets and infrequent replacement contribute to maintaining high levels of energy consumption and emissions. As electric motors represent a significant portion of energy consumption in industries, improving their efficiency generates substantial reductions in consumption, energy demand, and emissions, thus optimizing overall energy performance. This article proposes an integrated guideline for the application of multi-criteria decision-making (MCDM) methods, computational thinking (CT), and technical standards in industrial energy management problems. To validate this proposal, the guidelines were applied to a real-world case of electric motor selection in an industrial complex. In this context, the structured analysis of the problem, when based on computational thinking, MCDM methods, and technical standards, provides transparency and traceability to decisions. The motor-selection case study, which incorporated computational thinking and MCDM tools (AHP/TOPSIS) aligned with technical standards, demonstrated that these integrated guidelines can substantially improve decision-making in industrial contexts by structuring selection problems and aligning them with the strategic objectives of organizations.
]]>Authors: Stuart N. Riddick
Methane emission estimates from offshore facilities are increasingly used for regulatory reporting and climate assessment, yet it remains unclear under what atmospheric conditions such estimates are physically meaningful. This study defines three necessary conditions for valid offshore methane quantification: plume detectability, adequate sampling (interception), and reliable inference. A simplified Monte Carlo modelling framework was used to examine how these conditions are affected by atmospheric regime. Results suggest that the ability to obtain a physically meaningful emission estimate is strongly regime dependent. Under well-mixed conditions, successful quantification is achieved in most simulations, with uncertainty dominated by limitations in the inversion method. Under shallow marine boundary layers, quantification becomes increasingly conditional, with success probabilities reduced to approximately 15–20% depending on sampling configuration. Under strongly stratified conditions, plume observability is limited and valid emission estimates are not obtained within the illustrative model framework. To place these findings in context, ERA5 reanalysis data were used to assess atmospheric regime occurrence at representative offshore locations. Well-mixed and neutral conditions occur approximately 70% of the time in the North Sea, whereas the Gulf of Mexico is dominated by shallow boundary layer conditions (~90%), with stratified conditions occurring more frequently (~5%). These results suggest that offshore methane quantification is not a universally achievable measurement capability, but a regime-dependent and probabilistic outcome controlled by atmospheric structure. Atmospheric conditions therefore determine when physically meaningful emission estimates can be obtained and when measurement results should be interpreted with caution.
]]>Authors: Mohammed Sabah Akram Elmitwally Abdelfattah A. Eladl
The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform.
]]>Authors: Emilios Sideridis Efstathios E. Theotokoglou
This experimental work aims at the study by non-destructive and destructive testing of the mechanical and acoustical properties of cold-setting epoxy resins plasticized with amounts of plasticizer and of PMMA (Plexiglas), both belonging to the two basic categories (thermosetting and thermoplastics respectively) of polymeric materials, which usually can be modified because of polymerization rate and curing, change in temperature and frequency, by the addition of plasticizers and/or inclusions as well as due to discontinuities (defects, voids and porosity) where stress concentration exists. On the other hand, ultrasound is a mechanical, elastic wave of very high frequency, and can be used for material testing. Using ultrasounds, defects, discontinuities, and damage can be detected, and moduli can be evaluated accurately. It should be noted that the moduli determined in this way are the dynamic moduli and differ from the static ones for any material. Here, the authors focus their study on plasticized epoxy resins and PMMA and apply this NDT method to estimate mechanical properties and correlate the results with those from destructive tests. Finally, the glass-transition temperature of plasticized epoxies was also evaluated from thermal experiments to determine the effect of the plasticizer.
]]>Authors: Olga Masko Olga Mansurova
This study presents a numerical model of electromagnetic and thermal processes characteristic of a submerged arc furnace. Because direct modeling of a full-scale industrial furnace is complex and difficult to validate experimentally, a laboratory system without an electric arc is considered at this stage. The system reproduces the main features of current supply and energy distribution in the conductive region of the furnace bath. The model is implemented in ANSYS Fluent 2020 R1 using user-defined scalar equations for the electric potential, the components of the magnetic vector potential, and their time derivatives. The implementation was assessed in terms of mesh independence, time-step sensitivity, current and energy balances. The calculations yielded consistent distributions of electric potential, current density, magnetic flux density, Joule heat generation, and temperature. Heating was described using a two-stage scheme: the transient electromagnetic problem is first solved to obtain period-averaged Joule heat generation, which is then used as a source term in the energy equation. The model represents the first stage of a computational framework for submerged arc furnace modeling: at this stage, it is developed and assessed using a simplified laboratory configuration without an electric arc, while in future work it can be supplemented with an arc-channel description and extended to industrial furnace conditions.
]]>Authors: Huayang Li Shijie Zhu Chi Zhang Youchen Wang
Overpressure prediction is critical for safe and efficient drilling, yet remains challenging in complex basins with multiple genetic mechanisms. This study systematically investigates the overpressure origins in the Xihu Sag, East China Sea, a prolific hydrocarbon-bearing sag with widespread overpressure and complex pressure regimes. By integrating well logging data and direct pore pressure measurements from nine wells across three major structural units, the Western Slope Belt, the Western Sub-sag and the Central Inversion Belt, a multi-method diagnostic framework is employed. This combines Bowers’ effective stress analysis with sonic-density cross-plots to discriminate between loading and unloading mechanisms. Results show obvious vertical zoning of pore pressure—normal-pressure zone, overpressure zone, and pressure reversal zone—with distinct horizontal heterogeneity. Results reveal a distinct spatial differentiation in dominant overpressure mechanisms. In the Western Slope Belt, overpressure in the deep Pinghu Formation primarily results from a composite of undercompaction (creating initial pressure seals) and subsequent hydrocarbon generation-induced fluid expansion. In contrast, in the Central Inversion Belt and Western Sub-sag, overpressure is predominantly driven by hydrocarbon charging along faults coupled with tectonic compression, with minimal undercompaction signatures. Previous studies on overpressure genesis in the Xihu Sag have largely focused on the Western Slope Belt. This study expands the analytical scope to the Western Sub-sag and Central Inversion Belt, and conducts a systematic comparative analysis of overpressure genesis across multiple tectonic units. The value of this work lies in the systematic application of classical diagnostic methods to fill the regional research gap regarding the overpressure characteristics of the Huagang Formation and the composite nature of overpressure. With accurately constrained genetic mechanisms, the findings can provide support for optimized drilling fluid design and wellbore stability management, and effectively mitigate deep hydrocarbon exploration risks in this sag and analogous overpressured basins.
]]>Authors: Andreas Poyias Panayiotis Mourtopallas Diamanto Platanou Chrysa Politi Despoina Georgopoulou Antonis Peppas
The decarbonization of industrial steam production, representing up to 57% of energy use in the food industry, is critical for achieving EU climate neutrality goals. This study developed an integrated digital framework for the research project Hy4GreenSteam to optimize green-hydrogen integration through advanced predictive modeling. The employed LightGBM gradient-boosting algorithms were trained on 68,697 PV power measurements and 57,000 meteorological observations from 2020 to 2022. A “Production-Split” methodology was introduced for 24 h ahead forecasting, segmenting training into high (>2 kW) and low (≤2 kW) production regimes to manage solar heteroscedasticity. Results show the 15 min model achieved an R2 of 0.868 and the 1 h model an R2 of 0.832, while the day-ahead model—trained exclusively on information available at forecast issue time—achieved an R2 of 0.701, a 70% relative improvement over same-time-yesterday persistence. A complementary regime analysis shows that the production regime is predictable with 90.7% accuracy and quantifies the accuracy headroom of regime-specialized models (oracle R2 0.794). These methods were integrated into a real-time React-based platform that calculates optimal H2/CH4 blending; for the reference pilot configuration, driven by measured on-site PV generation, the computed CO2 emission reduction reaches 34% relative to natural-gas-only operation during high-solar operating intervals. Predictive modeling combined with a Digital Twin interface provides a TRL 6 decision-support solution, demonstrated in a relevant industrial environment, for managing renewable sources in industrial hydrogen applications.
]]>Authors: Nastasija Nikolic Djordje Milojevic Ivan Macuzic Petar Todorovic Marko Djapan
Small- and medium-sized enterprises (SMEs) face significant challenges in adopting robotic solutions due to limited financial resources, insufficient technical expertise, and uncertainty regarding operational and economic outcomes. Existing automation approaches are often technologydriven and provide limited support for systematic decisionmaking. This study proposes the Agile Robotics Implementation Model (ARIM), an iterative framework integrating Lean Manufacturing, Lean Robotics, and Lean Startup principles. ARIM combines process assessment, key performance indicator (KPI)-based evaluation, and iterative experimentation within the Robotic Startup Cycle, supported by a decision-support software tool. The framework was developed using a Design Science Research (DSR) approach and validated through an industrial case study. Results demonstrate strong agreement between predicted and realized KPI values. The implemented solution achieved a 24.5% return on investment (ROI), with a payback period of approximately 2.1 years, reduced labor demand by 3900 h, and improved productivity, ergonomics, and quality. The findings indicate that ARIM supports reliable and data-driven robotics implementation in the studied SMEs; broader transferability requires validation across multiple cases.
]]>Authors: El Mokhtar El Hafidi Farah Dimade Abdelaziz Amine El Ghaouti Chahid Reddad El Moznine Mouhaydine Tlemçani Abdelowahed Hajjaji Said Laasri
The rise in the need for sustainable energy has facilitated the advancement of perovskite solar cells (PSCs) as potential substitutes for traditional photovoltaic technologies. Nevertheless, their performance is very sensitive to environmental factors, especially temperature, which influences the charge transport and recombination processes. This paper examines the thermal effect on the electrical characteristics and impedance response of lead-free PSCs in accordance with the FTO/ETL (C60, PCBM, SnS2, ZnSe)/CH3NH3SnBr3/Cu2O configuration. The experiments were performed with SCAPS-1D under usual illumination, using a combination of current-voltage analysis and impedance spectroscopy between 270 and 400 K. The findings indicate that there is a significant reduction in open-circuit voltage with higher temperature, whereas the short-circuit current density does not change much. The enhancement of the fill factor increases and then decreases with increased temperature, leading to a net decrease in power conversion efficiency because of the increased recombination. The impedance analysis is also an indicator of lower recombination resistance and accelerated charge carrier dynamics. These results demonstrate that thermal control and interface optimization can be important for enhancing PSC performance.
]]>Authors: Tiancheng Fang Baoyi Shen Yingliang Yang Jiwei Wang Guoqing Guan Abuliti Abudula
Cold-climate dwellings can face coincident electricity and domestic hot-water shortfalls in winter, when solar availability is at its lowest. This study evaluates an integrated residential system for Aomori, Japan, combining photovoltaics, evacuated-tube solar water heating, and battery storage with electrolysis, compressed-hydrogen storage, and a PEM fuel cell operated in combined-heat-and-power mode. Building on a screening-level annual-balance analysis, a coupled annual TRNSYS simulation with a 0.125 h time step resolved battery dispatch, electrolyzer part-load operation, hydrogen compression and finite storage, seasonal fuel-cell operation, and heat recovery. The results show that the principal value of seasonal hydrogen lies in improving winter supply adequacy, dispatchability, and heat recovery rather than annual conversion efficiency. Fuel-cell heat recovery increased the number of days satisfying the hot-water screening indicator—a daily mean tank temperature of at least 43 °C—from 221 to 332. A reserve-aware criterion identified a 225 W electrolyzer operating-power cap as the positive-reserve case; 205 W was near-cyclic with a negligible margin, whereas the original 475 W cap was substantially oversized. The hydrogen pathway remained markedly less efficient than direct photovoltaic and solar-thermal use, and the estimated storage hardware’s lower bound substantially exceeded the break-even capital ceiling supported by the annual operating value. Seasonal hydrogen can therefore strengthen winter energy adequacy and heat recovery but is not yet cost-effective at the single-dwelling scale under the investigated conditions.
]]>Authors: Begzat Akhmetov Assylbek Nurumgaliyev Oleg Zayakin Talgat Zhuniskaliyev Nurbek Aitkenov Murat Kuanyshev Nurgazy Saukhanov Assylbek Abdirashit Yesmurat Myngzhassar
The aim of this study was to investigate the effect of reduction roasting parameters on the phase transformations of the Ushkatyn-III ferruginous manganese ore and the efficiency of subsequent magnetic separation. The experimental procedure included preliminary high-intensity magnetic separation, reduction roasting at 650 °C for 3–5 h using 20–30 wt.% coal as the reducing agent, and low-intensity dry magnetic separation at magnetic field intensities of 0.1–0.6 T. Chemical composition was determined by standard analytical methods, while phase composition was analyzed by X-ray diffraction (XRD). Preliminary magnetic separation increased the manganese content in the magnetic pre-concentrate to 30.71–35.09 wt.%. The optimum results were obtained after roasting for 5 h with 30 wt.% coal, followed by magnetic separation at 0.2 T, producing a low-iron concentrate containing 33.26 wt.% Mn and 0.67 wt.% Fe, with a manganese recovery of 87.79% and an Mn/Fe ratio of 49.6. XRD analysis confirmed the partial reduction of hematite to magnetite (Fe3O4), providing the basis for efficient magnetic separation. The proposed process offers an effective approach for upgrading low-grade ferruginous manganese ores for manganese ferroalloy production.
]]>Authors: Daniyar Bazarbayev Natalya Ryvkina Matija Orešković Khrystyna Moskalova
This article presents the results of a computational parametric study, a global sensitivity analysis, multi-objective optimization, and a technical and economic evaluation of the parameters of phase-change materials (PCMs) incorporated into the building envelope of an office building in a sharply continental climate (using Astana, Kazakhstan, as an example). The study was conducted using simulation modeling, incorporating dynamic thermal calculations in the EnergyPlus software package and the NSGA-II genetic algorithm. The CondFD algorithm was used, for which results of independent verification and experimental validation conducted by other researchers have previously been published. This study used this validated implementation without conducting additional experimental verification of the structure under consideration. Based on the results of a parametric analysis (1232 calculations) and an optimization run (≈25,000 calculations), the range of quasi-optimal phase transition temperatures for the PCM was determined to be 23–25 °C. For further analysis and a technical–economic evaluation, a value of 24 °C was selected as the recommended compromise solution, with a PCM layer thickness of 16 mm and a distance of 15 mm from the inner surface of the wall. This compromise solution reduces annual specific energy consumption for heating and cooling by 22% and hours of thermal discomfort by 42% compared to a reference concrete wall without PCM. A technical and economic assessment, based on post-processing of the simulation results using current electricity rates and market data on the cost of PCM, shows a simple payback period ranging from 3.8 to 38 years, depending on the assumed cost of the encapsulated PCM layer. The results are limited to the specific case considered (south-facing orientation, standalone office module, and continuous ventilation) and are intended for subsequent experimental verification. The information in this article can be used by architects and engineers in the early stages of designing energy-efficient office buildings in regions with a sharply continental climate.
]]>Authors: Arailym Smail Alibek Batyrbek Karshiga Smagulova Zoya Gelmanova Zukhra Bayassilova Viktor Kovalenko Oleksii Bilous
The article is aimed at studying the features of the hot rolling mill CWBRM-1700 of JSC “Qarmet”, which negatively affect the operation of the distribution network of the workshop. Such factors are frequent shock loads of technological mechanisms with high installed capacity of the equipment. Experimental studies of the distribution network of the rolling production on the buses of the 10 kV substation showed that shock loads of synchronous electric drives of roughing stands lead to periodic voltage drops of up to 13% lasting 5–6 s. Mathematical modeling in the MATLAB/Simscape/Electrical environment, the results of which coincide with the data of the experimental study, showed that the most significant factor affecting the quality of electricity are abrupt changes in the reactive power of the synchronous motor from −0.5 to +0.5 MVAR. To solve the problem, it is proposed to use a controlled filter-compensating device. Variants of circuit solutions for such devices are considered. The choice was made in favor of a three-phase adjustable LLC filter with diode–transistor keys. The article develops a method for calculating the electromagnetic parameters of such a filter and establishes that in order to reduce the level of harmonic distortion of voltage, it is necessary to use a triangle connection of the controlled reactive compensator and select the PWM frequency of the transistors, a multiple of the tripled frequency of the power grid. Two options for creating a closed-loop control system for energy modes are studied: a reactive power stabilization system and a voltage stabilization system in a distribution network node, which reduce the duration of transient processes to 0.5 s and reduce the voltage drop in the network node to −4 to + 1% in the first case and to −4 to + 3% in the second, also reducing reactive power consumption to 0.02 MVAR and 0.25 MVAR, respectively. The advantage of a closed-loop control system with voltage stabilization is the ability to use a technically less complex voltage sensor.
]]>Authors: Mana Poyai Dechrit Maneetham Petrus Sutyasadi
Sensorless speed estimation replaces fragile shaft encoders in cost-sensitive Permanent Magnet Direct Current (PMDC) motor drives, but classical model-based observers degrade under brush friction, commutation ripple, and thermal drift, while purely data-driven estimators sacrifice physical interpretability. This paper presents a Hybrid Physics-Data-Driven Observer (HPDDO) that couples an identified lumped-parameter electrical model with a compact multilayer-perceptron residual compensator, which is executed in real time on a low-cost ESP8266 microcontroller. Global parameters are identified from a short labeled recording, after which the network corrects only the nonlinear residual that the physics model cannot explain. Under a strictly time-series-aware evaluation (chronological 80/20 split), the proposed estimator achieves an average root mean square error (RMSE) of 4.11 RPM across dynamic PWM sweeps, abrupt load transitions, and a long-duration thermal-drift test, outperforming an extended Kalman filter (9.49 RPM), a sliding mode observer (10.89 RPM), and a pure neural-network estimator (7.14 RPM) implemented on the identical dataset. An ablation study shows that accuracy is insensitive to network size, with a 0.9 kB variant matching the deployed model, and a residual-clamping safeguard bounds the estimation error under unseen operating conditions. The framework provides an accurate, interpretable, and computationally lightweight solution for industrial PMDC drives without dedicated speed sensors.
]]>Authors: Geqiang Li Shengtao Liu Zhichong Qi Dan Lyu Shuai Wang Zhenle Dong
To address delayed attitude correction, limited adaptability of single-actuator systems, and reduced tunneling efficiency in full-face shaft-boring machines (SBMs), this study proposes a PSO-LSTM-based hybrid steel strand–support shoe attitude correction strategy. A coupled dynamic model with a 45° offset configuration is developed to enable coordinated multi-actuator control. A PSO-optimized Long Short-Term Memory (PSO-LSTM) network is employed to predict inclination deviation over a 5 s horizon, providing anticipatory information for proactive control. Based on this prediction, a hierarchical control strategy with adaptive torque allocation is designed to seamlessly coordinate fine correction via steel strand cables and high-torque correction via support shoes. Simulation results demonstrate that the proposed model achieves a prediction accuracy within ±0.02°. Under inclination conditions of 0.05°, 0.3°, and 1.0°, rapid attitude correction is achieved. Compared with independent support shoe control, the maximum horizontal displacement is reduced from 64 mm, 131 mm, and 160 mm to 6.3 mm, 65 mm, and 100 mm, corresponding to reductions of 90.2%, 50.4%, and 37.5%, respectively. The results further indicate that small-angle deviations can be compensated by the steel-strand system without additional support-shoe operations, while medium- and large-angle deviations can be regulated through coordinated actuation of multiple correction systems according to deviation magnitude. Simulation results demonstrate that the proposed method improves attitude correction performance and dynamic response under the investigated simulation conditions. The proposed framework provides a potential solution for intelligent attitude control of SBMs, while further field validation is required before practical engineering deployment.
]]>Authors: To Anh Dung Nguyen Huu Minh Trinh Trong Chuong Hoang-Giang Vu
Three-level neutral-point-clamped (NPC) inverters are widely used in medium- and high-power drives and grid-connected applications due to their reduced device voltage stress, improved output power quality, and lower switching losses relative to conventional two-level topologies. Among the potential failure modes, clamping diode open-circuit faults are difficult to detect because the clamping diodes conduct only during the zero-voltage states, and their failure produces only subtle distortions in the phase current waveform. This paper proposes a fault diagnosis method for clamping diode open-circuit faults in three-level NPC inverters. The method is based on a current asymmetry index defined as the ratio of the per-cycle mean phase current to the per-cycle mean absolute phase current. During healthy operation, this index is approximately zero in all phases. A fault causes the index to deviate markedly from zero, while the polarity of this deviation identifies the failed diode. The method requires only phase-current measurements already available in the inverter control system. Consequently, no additional sensors, hardware modifications, or changes to inverter operation are required. Simulation results obtained for a 10 kW three-level NPC inverter demonstrate successful fault detection within approximately one to two fundamental cycles for open-circuit failures of both the upper and lower clamping diodes in all three phases.
]]>Authors: Juan Carlos Lozano-Medina Cristina Jiménez-Marcos Amparo Verdu-Vazquez Julia Claudia Mirza-Rosca
Super duplex stainless steels are widely used in seawater desalination plants due to their high mechanical strength and excellent corrosion resistance in chloride-rich environments. However, during reverse osmosis processes, the salinity of the reject stream increases progressively, generating concentrated brines with concentrations close to 7 wt.% NaCl, which represent a chloride-rich service environment that may affect passive film stability and promote localized corrosion. This study investigates the effect of novel La Geria-inspired microstructures (LGMs) generated by laser surface texturing on the microstructure, microhardness, and electrochemical behavior of UNS S32750 super duplex stainless steel in 3.5 wt.% and 7.0 wt.% NaCl solutions, simulating seawater and concentrated desalination brine. Electrochemical results show that textured surfaces exhibit improved corrosion resistance, with more stable corrosion potentials, lower corrosion current densities, and higher impedance values. Microhardness measurements revealed a homogeneous mechanical response, confirming that laser texturing does not alter the mechanical integrity of the material. Microstructural observations showed reduced surface degradation and improved preservation of the duplex ferrite–austenite structure in textured samples after exposure to chloride solutions. These findings demonstrate that biomimetic laser surface texturing enhances corrosion resistance by modifying interfacial conditions and stabilizing the passive film, providing experimental evidence of the beneficial effect of LGMs in aggressive desalination environments.
]]>Authors: Giorgos Varras Michail Chalaris
The Greek power system includes 42 non-interconnected islands grouped into 28 autonomous electrical systems operated by the Hellenic Electricity Distribution Network Operator. Although these systems possess substantial wind and solar potential, the technical constraints of isolated microgrids lead to systematic renewable energy curtailment. Building on our previous methodology for estimating curtailed wind energy and hydrogen production, this study develops and evaluates a dispatch-oriented power-to-power pathway in which curtailed wind electricity is converted into hydrogen and subsequently reconverted into electricity. The study integrates hydrogen-to-power technology selection, annual energy recovery, dispatch strategy, and operational environmental and economic benefits for a representative non-interconnected island. A comparative assessment of commercially relevant hydrogen-to-power technologies identified proton exchange membrane fuel cells as the most suitable option because of their absence of direct CO2 and NOx emissions, rapid start-up, load-following performance, modularity, and compatibility with remote island operation. Applying the previously developed curtailment methodology to 2024 data yielded 9334.5 MWh of exploitable curtailed wind energy. This energy could produce 155.6–233.4 tonnes of hydrogen and recover 2437.1–4277.9 MWh of electricity annually. Two dispatch strategies were evaluated: continuous integration of hydrogen-derived electricity into the island’s generation mix, and strategic hydrogen storage with priority dispatch during periods of emergency diesel generator operation. Under the reference case, both strategies recovered approximately 2935.1 MWh annually, avoided 1868.9 tonnes of CO2 emissions, and reduced fuel expenditure by €359,000.
]]>Authors: Gabriel-Petre Badea Daniel-Eugeniu Crunteanu
Autonomous acoustic sensing systems are increasingly investigated for unmanned aerial vehicle (UAV)-based surveillance and environmental monitoring applications due to their passive operation and relatively low computational requirements. However, the integration of acoustic classification and direction-of-arrival estimation on UAV-mounted microphone arrays remains challenging, particularly because realistic flight conditions introduce propulsion noise, aerodynamic flow, vibration, and complex acoustic interference. This paper presents a static ground validation of an AI-assisted acoustic target detection and azimuth estimation framework integrated on a flying-wing vertical take-off and landing (VTOL) UAV equipped with a distributed microphone array. The proposed system combines MFCC-based chainsaw sound classification using a Random Forest model with amplitude-based and SRP-PHAT-based azimuth estimation. Four HiFiBerry measurement microphones were mounted on a 4 m wingspan flying-wing VTOL UAV and connected to a Raspberry Pi 5 processing unit. Experimental validation was conducted under controlled indoor laboratory conditions using loudspeaker playback, with the UAV propulsion system inactive and only the acoustic acquisition and processing subsystem powered. The tests included single-source angular measurements, simultaneous multi-source acoustic scenarios, and source height variation. The SRP-PHAT method achieved a mean angular error of 3.55° in the single-source tests and 4.81° in the multiple-source tests, outperforming the amplitude-based baseline. The results support the feasibility of the proposed acoustic-processing framework under static ground conditions. However, because propulsion noise and in-flight aerodynamic effects were not included in the present validation, future work must address simulated propulsion noise injection, propulsion-on static testing, outdoor validation with real chainsaw sources, and eventual in-flight experiments. Because propulsion noise, aerodynamic flow, and in-flight vibration were not included in the present experimental campaign, the results should be interpreted as baseline static ground validation results rather than evidence of in-flight robustness.
]]>Authors: Mark Talesnick Noa Dolev Shay Nachum
Accurate pressure measurements in soils and other particulate media are essential for the development and validation of engineering models. Pressure transducers based on deflecting membranes remain widely used despite longstanding recognition that sensor deformation may influence the measured response. This study investigates the extent to which hysteresis observed during load–unload cycles originates from sensor–material interaction rather than intrinsic material behavior. Controlled experiments were conducted on dune sand and uniform glass beads using sensors operating in membrane-deflection mode and Null mode, together with direct measurements of local material deformation. The results show that even small membrane deflections generate pronounced apparent hysteresis and that conventional calibration procedures cannot reliably eliminate this effect, even when performed under matching test conditions. The resulting measurement errors become particularly significant during unloading. Direct observations reveal that during loading, the surrounding soil accommodates the elastic deflection of the membrane. During unloading, however, the surrounding soil does not accommodate membrane recovery, preventing the membrane from rebounding along its elastic path. In contrast, Null-mode measurements exhibit a nearly unique response with substantially reduced hysteresis and little dependence on sensor stiffness. The findings indicate that a significant portion of hysteresis commonly reported in pressure measurements may reflect measurement-induced effects rather than intrinsic material behavior, highlighting the importance of accounting for sensor–material interaction when interpreting experimental data and validating engineering models.
]]>Authors: Vasyl Ravlyuk Alona Lovska Ján Dižo Mykola Ravliuk
The paper is focused on the problem of increasing the reliability of determining the critical speed of railway vehicles during measurements in bench tests, due to the difference in the geometry of the contact interaction in the “wheel-roller” and “wheel-rail” systems. It is shown that existing approaches in dynamic stability analysis and the processing of measurement signals do not consider the systematic influence of equivalent conicity on the measurement results, which leads to a shift in the assessment of the threshold for loss of stability even with the high accuracy of the measuring equipment. An information-measuring approach is proposed, which is based on an analytical description of the relationship between the equivalent conicity of the wheel/rail contact and the critical speed. A generalized error model is developed integrating geometric, metrological and random components. A method for correcting the results of bench tests based on a coefficient considering the ratio of equivalent conicities is also proposed. It was found through the results of the research that neglecting the geometric mismatch of the contact interaction leads to an error in determining the critical speed at the level of 8 to 15%, while the application of the proposed approach reduces it to less than 1 to 2%. Additionally, frequency analysis of the oscillatory process was used. It allowed us to identify the critical mode of the frequency range of about 8 Hz and to establish its connection with the critical speed of movement. It was experimentally confirmed that the corrected results correspond with the calculated values within 1%. The obtained results provide an increase in the metrological consistency of bench and operational studies, expand the possibilities of interpreting test results and can be used in the development of methods for diagnostics, certification and prediction of the limit modes of operation of railway vehicles. The proposed approach provides increased reliability in determining the critical speed during bench tests and it can be used for analytical compensation of a systematic error without upgrading the test equipment.
]]>Authors: Edward Piscitelli Jerry Jose Erum Ahmed Rejath Jose Milan Toma Randy Stout Sheldon Yao
Background: Traditional osteopathic manipulative medicine (OMM) instruction relies on laboratory sessions constrained by scheduling, faculty availability, and practice partner variability. Virtual reality (VR) may address these limitations by providing on-demand access to standardized training scenarios. This pilot feasibility and acceptability study evaluated first-year osteopathic medical students’ motivational responses and perceptions of a novel VR training module focused on thoracic spine assessment techniques. Methods: This pilot single-arm observational study enrolled 45 first-year students at the New York Institute of Technology College of Osteopathic Medicine to assess the feasibility and acceptability of VR-based OMM instruction. A VR training module developed using Unity 3D and deployed on Oculus Quest headsets included 40 interactive assessment items covering thoracic diagnostic techniques. Participants received one-week access to the program then completed the Reduced Instructional Materials Motivation Survey (RIMMS) based on the Attention, Relevance, Confidence, and Satisfaction (ARCS) model and a custom 10-item feedback survey. Session duration and assessment scores were automatically recorded. Descriptive statistics and Pearson correlation analysis were performed. Results: The Relevance domain achieved the highest RIMMS composite mean (3.98), while Attention demonstrated the greatest opportunity for enhancement (3.49). The overall RIMMS composite score was 3.81, indicating favorable motivational reception. Feedback survey results showed that 89.2 percent of participants endorsed the VR experience as educationally positive, and 81.1 percent supported expansion to additional OMM procedures. Performance analysis revealed minimal correlation between session duration and assessment scores (R2 = 0.0169). Conclusions: First-year osteopathic medical students demonstrated positive motivational responses and favorable perceptions toward VR-based OMM training. Based on student perception and feasibility, these findings suggest VR is well-received and perceived by students as a potentially viable supplement to traditional OMM instruction, though attention-capturing elements and interface usability warrant refinement. This pilot study did not measure learning outcomes or skill acquisition.
]]>Authors: Miroslav Šmelko Katarína Draganová Karol Semrád Martin Fiľko
Belt conveyors are essential systems for the continuous transport of various materials in many sectors and applications, including heavy industry or mining, which are characterized by demanding environmental and operational conditions. Our research is focused on the development of the system based on magnetic sensors for the detection of ferromagnetic objects. These detection systems are designed to prevent damage to conveyor belts and the downstream vehicles, machines, and processing equipment involved in material transport and processing. By detecting hazardous foreign objects, they help avoid belt damage or tearing, thereby reducing operational disruptions and the associated maintenance and repair costs. Our study confirmed that in addition to the development of the hardware and software solutions, it is also necessary to develop methods for the processing and evaluation of the data recorded by the detection system, as the data represent a valuable source of information not only for the operational workers but also for the managers and are very helpful in the creation of the sustainable transportation system. The article describes an innovative application of the Weibull distribution for the operational enhancement of the system and its comparison to the conventionally used histograms. In addition to that, the utilization possibilities of the obtained statistical data to evaluate the belt conveyor loading for a better planning of the process, to monitor the work of the operational or other employees’ quality of the supported material, or to reveal failures of the detection system or even of the belt conveyor are overviewed and discussed.
]]>Authors: Jorge A. Lizarraga Luis F. Luque-Vega Javier Ruiz-Leon Rocío Carrasco-Navarro Marcela E. Mata-Romero Jesús Antonio Nava-Pintor Fabián García-Vázquez Luis O. Solís-Sánchez Héctor A. Guerrero-Osuna
This paper addresses experimental PID tuning for DC motor actuators when an accurate plant model is unavailable or impractical to obtain. Controller tuning is formulated as a constrained closed-loop optimization process in which each PID gain set is deployed on the physical system, produces an experimental dataset, and is evaluated through a performance index. The objective function combines tracking error, control effort, and control-signal variation, while penalty terms identify and penalize actuator saturation, excessive overshoot, settling-time violations, steady-state error, and divergent responses. Candidate controllers are generated using a constrained Gaussian Cross-Entropy Method and evaluated directly on the physical actuator. The proposed host-embedded architecture separates two computational time scales: PID execution, encoder processing, and data acquisition are performed in real time on the embedded platform, whereas population sampling, candidate ranking, and distribution updates are executed on the host computer between experiments. Within the reported experimental campaign, the sampling distribution progressively concentrates toward gain regions associated with lower closed-loop cost under the prescribed admissibility criteria. The resulting framework provides a structured and traceable procedure for physical controller deployment, data acquisition, constrained performance evaluation, and episodic PID retuning without explicit plant identification. The reported results demonstrate the feasibility of the architecture on the considered platform, without implying comparative superiority, run-to-run statistical convergence, or analytical closed-loop stability.
]]>Authors: Christina Gioti Dimitrios Moschovas Apostolos Avgeropoulos Constantinos E. Salmas Simeon Agathopoulos Athanasios B. Bourlinos Michael A. Karakassides
The present study demonstrated the fabrication of a sustainable, high-performance composite by upcycling waste denim fabric within a cyanoacrylate matrix reinforced with B4C micro-particles (4, 7, and 10 wt%). XRD and SEM analyses confirmed the successful integration of the ceramic filler and effective fiber encapsulation, with particle agglomeration identified at 10 wt% loading. The 7 wt% B4C composition exhibited the highest static mechanical performance, with a tensile strength of ~20.3 ± 1.3 MPa and Young’s modulus of 1.17 GPa—improvements of ~22.8% and ~41% over the unreinforced denim–cyanoacrylate composite—while at 10 wt% B4C the ultimate tensile strength declined below the unreinforced baseline and the Young’s modulus reverted to a comparable value, likely reflecting agglomeration-induced stress concentration as a major contributing factor. Under dynamic impact loading, the peak contact force increased monotonically from 0.34 kN to 1.31 kN with increasing B4C content yet remained well below the thresholds specified by international impact protection standard thresholds across all compositions. This divergence between static and dynamic responses indicates that the increase in contact force with B4C content reflects progressive stiffening and more direct load transfer rather than improved energy attenuation, whereas tensile performance depends critically on dispersion quality and fiber–matrix adhesion. Surface wettability analysis showed a marked increase in hydrophobicity, with the water contact angle rising from 105 ± 4° to 133 ± 4° (for the DSGB-7 wt% composite), consistent with a Cassie–Baxter wetting regime. These preliminary results suggest that B4C-reinforced waste denim–cyanoacrylate composites merit further investigation as candidate materials platform for protective textile applications, pending validation through larger-scale, standardized testing.
]]>Authors: Bo Yuan Shun Liu Yu Wu Fu Xu Yinghao Chen Zhengdong Luo
Fly ash-metakaolin geopolymer has considerable potential for low-carbon and high-strength applications. However, its performance is highly dependent on preparation parameters and curing conditions, and its mix proportion optimization and nano-modification mechanism still require further clarification. In this study, under the condition that the mass ratio of metakaolin to fly ash was fixed at 9:1, an L16 (45) orthogonal experiment was carried out using nano-SiO2 content, liquid-to-solid ratio, alkali equivalent, sodium silicate modulus, and curing temperature as independent variables. Range analysis and analysis of variance were employed to investigate the response patterns of slurry fluidity, setting time, and compressive strength under variations in these factors, while XRD, SEM-EDS, and FTIR were used to reveal the modification mechanism of nano-SiO2. The results show that the early-age compressive strength is governed by alkali equivalent, whereas the later-age strength is jointly affected by multiple factors, with the differences among their effects gradually decreasing. Alkali equivalent and liquid-to-solid ratio have comparable effects on slurry fluidity, with contribution rates of 33.83% and 30.97%, respectively. Setting time is most sensitive to changes in sodium silicate modulus, which contributes 92.76% and 90.54% to the initial and final setting times, respectively. After the incorporation of an appropriate amount of nano-SiO2, the amorphous gel characteristics, Si-O-T bonding structure, and fracture-surface compactness of the specimens were all enhanced. However, excessive incorporation tends to cause particle agglomeration and increase the water demand of the system, weakening the continuity of geopolymerization. The specimen with better overall performance was prepared with 1% nano-SiO2, a liquid-to-solid ratio of 0.84, an alkali equivalent of 24%, and a sodium silicate modulus of 1.4, and cured at 40 °C.
]]>Authors: Kamil Kušnirák Oto Haffner Erik Kučera Ondrej Kolimár
A camera-guided warehouse robot keeps its bearings by repeatedly estimating its pose against known visual references, and it must relocalize whenever that estimate is lost. What limits this process is often not identification but the availability of a usable reference along the route. The references used in practice are usually single-sided planar fiducial markers such as QR-like codes, ArUco markers, and AprilTags, which stay readable only within a limited cone about their surface normal; a spatial reference, by contrast, can in principle be recognized from any azimuth. We quantify what that difference is worth at the navigation level. The framework is built in Unity with NavMesh navigation and a purely geometric-availability model, rather than an image-based recognizer, whose single switchable property is the availability rule. In the idealized all-azimuth spatial-reference regime (the spatial regime), a reference is available from any direction; in the single-sided, angularly constrained planar-reference regime (the planar regime) it is available only within ±20° of the surface normal. A full-factorial experiment with 54 configurations (3×3×2×3) and n=100 paired replications, 10,800 runs in all, was run in both regimes over four deployment factors: deployment scheme, camera field of view, recovery step, and detection range. Under this geometric model, the spatial regime reached 5.7× higher reference coverage (41.6% vs. 7.3%) and a mission-completion rate 30 percentage points higher (86.2% vs. 55.9%). A paired Wilcoxon signed-rank test confirms the coverage difference (p<0.001, matched-pairs dz=1.84), and McNemar’s test together with a logistic regression confirms the completion difference. In a factorial analysis of variance, the detection range dominates (partial η2=0.903), and a strong deployment × range interaction concentrates the advantage in the rack aisles, where a planar reference is seen edge-on. Three further analyses point the same way: an angular-threshold sweep from 10° to 60°, an equal-count deployment control, and route- and time-normalized visibility and relocalization metrics. The advantage also held across square, L-shaped, and U-shaped aisle layouts (32,400 runs in total), with a negligible regime × layout interaction. All these numbers are model-based estimates under an explicitly stated availability model: they measure the navigation-level value of azimuthal reference availability and do not validate any particular physical object, decoding algorithm, or AR device.
]]>Authors: Suwilai Phumpho Kriengkrai Nabudda Pongthep Poungthong Apichart Artnaseaw
This study presents an integrated computational framework combining Computational Fluid Dynamics (CFD), experimental validation, and surrogate modelling to analyse and optimise the thermal performance, thermodynamic stability, and energy efficiency of a refrigerated truck compartment. CFD simulations were conducted to investigate airflow distribution and temperature uniformity under operating temperatures ranging from 0 to 5 °C. The results showed that airflow circulation was primarily governed by the evaporator outlet, while recirculation zones enhanced air mixing but were insufficient to completely eliminate localised hotspots. Increasing the operating temperature from 0 to 5 °C resulted in a rise in the maximum compartment temperature from 6.26 to 10.01 °C. Thermodynamic stability analysis revealed that operation within the 3–5 °C range provided more stable thermal conditions due to reduced refrigeration load and improved temperature uniformity. Experimental measurements of airflow velocity and evaporator surface temperature demonstrated good agreement with CFD predictions, confirming the reliability of the numerical model. Furthermore, CFD-based optimisation reduced the electrical energy consumption of the eTRU system by 10.0%, decreasing the energy intensity from 0.117 to 0.105 kWh km−1, while maintaining improved thermal stability throughout the refrigerated compartment. The surrogate model achieved excellent predictive performance with R2 = 0.9576 and RMSE = 0.0386, demonstrating its suitability for rapid optimisation of refrigerated transport systems. The proposed framework offers an effective tool for improving thermal management, enhancing energy efficiency, and supporting the development of sustainable refrigerated transport systems.
]]>Authors: Wei-Qiang Hu Yang-Bing Li Cheng Liu Li-Tao Ma Jian-Qi Chen Qing-Xiang Meng
This paper proposes an intelligent analysis method of rock sheet based on the segment anything model (SAM) with zero samples, which combines small sample training with deep learning to realize high-precision automatic identification and segmentation of rock minerals, and then converts the segmentation results into vectorized data by using image processing technology to construct the numerical model of rock minerals, and ultimately realizes rock sheet from image identification to physical and mechanical research. The results show that the SAM-based zero-sample segmentation method can accurately and efficiently identify different mineral components in multi-component complex rock flakes. Numerical simulation results show that the numerical model of rock minerals generated by the method can effectively reflect the microstructural characteristics of rocks and accurately predict their mechanical behaviors, and the resulting elastic modulus matches well with the existing literature data, with a relative error of only 3.4%, suggesting that the proposed method provides reasonable predictive capability for rock mechanical behavior. Compared with the traditional measurement methods, this method realizes the automation and intelligence of rock thin-section analysis and enhances the adaptability to different rock samples, providing an efficient tool means for geological exploration, petroleum engineering, and geotechnical research.
]]>Authors: Obada Al-Khatib Ali Hellany Mohamad Nassereddine Ghalia Nassreddine Tosin Famakinwa
Electric vehicle charging stations (EVCSs) are increasingly deployed as grid-interactive energy assets that combine power electronic converters, sensing devices, communication interfaces, photovoltaic (PV) generation, battery energy storage systems (BESS), and multiple charging ports. This complexity creates reliability challenges because abnormal behavior may originate from electrical, thermal, sensing, communication, port-level, or grid-side sources. This paper proposes a fault-aware decision-support framework for renewable-powered EVCSs using multi-source explainable learning. The framework integrates electrical, thermal, session/port, grid/PV/BESS, and communication/data-quality indicators into a unified health-monitoring representation. Supervised models diagnose known fault classes, anomaly-detection models flag unknown or anomalous events, and a source-level explainability layer supports candidate-source interpretation and maintenance-oriented risk mapping. A scenario-controlled EVCS benchmark is developed with PV generation, BESS operation, grid import, charging-port behavior, communication/data-quality indicators, and six injected fault/anomaly categories. An extended 180-day benchmark further assesses longer-horizon operation, seasonal/weather diversity, drift/ageing proxies, and event-level behavior. The strongest closed-set classifier, LightGBM with class weights, achieved 98.45% accuracy and 0.9792 macro-F1, while the Random Forest model used for explainability and decision-layer analysis achieved 97.35% accuracy and 0.9626 macro-F1. Full multi-source monitoring improved Random Forest macro-F1 from 0.6922 under electrical-only monitoring to 0.9626, demonstrating within the controlled benchmark the diagnostic value of heterogeneous EVCS observability. Open-set performance was source dependent: sensor/measurement and communication/data anomalies were more detectable, whereas thermal/cooling and port/session unknowns remained difficult at the selected threshold. Under nominal scenario-based response assumptions, unavailable port hours and unmet charging energy decreased by 49.01% and 38.33%, respectively, relative to reactive operation; sensitivity analysis showed that these outcomes depend on intervention effectiveness and response delay. These findings establish controlled-benchmark feasibility for explainable multi-source EVCS decision support. Field validation using charger telemetry, maintenance-confirmed labels, and operator-calibrated response policies remains necessary.
]]>Authors: Xin Xu Jun Zhang Shuangping Li Junxing Zheng Zhaogen Hu Bin Zhang Tengteng Cao Zuqiang Liu Han Tang Jianhua Liu Yonghua Li Huawei Wang Chenyu Yang Wenqi Shi
Dam displacement forecasting is important for assessing whether long-term structural responses remain consistent with established operational behavior. In multi-point monitoring systems, however, irregular survey-line layouts and unequal numbers of monitoring points make it difficult to organize long-term records while preserving their engineering meaning. This study develops a field-oriented forecasting framework by reconstructing daily observations into a structured displacement-field object defined by survey-line order, monitoring-point alignment, and three displacement components. A valid-position-aware protocol is introduced to distinguish actual monitoring locations from structural padding, ensuring that model training and evaluation remain restricted to the same physical monitoring definition. Using long-term operational records from the Tianshengqiao First Dam, four representative models, namely SimVP, SimVPv2, PatchTST, and TimesNet, are evaluated under the same chronological split, causal forward-fill-only preprocessing, input window, prediction horizon, and evaluation boundary. All four models achieve strong predictive performance, with R2 values above 0.97 in the X direction and above 0.99 in the Y and Z directions. No single trained model or forecasting route exhibits a consistent advantage across all displacement components and evaluation metrics. Under the present single-dam, case-specific setting, the relative ranking varies with displacement direction and forecasting horizon and should not be interpreted as evidence of general direction-specific suitability for any particular architecture. At the route level, the field-based route retains a slight advantage in Y-direction forecasting and overall MAE, whereas the sequence-based route remains competitive for Z-direction displacement and longer-horizon X-direction prediction. The proposed framework provides a practical and physically consistent digital representation for organizing irregular monitoring records, comparing forecasting routes, and supporting deployment-oriented model selection and subsequent model adaptation.
]]>Authors: Yiming Hu Chao Wang
Two-Phase TMR conserves energy by partitioning redundancy operations into two stages and making the execution of the third task copy optional, yet it remains susceptible to permanent faults. Reactive TMR (R-TMR) counters this by isolating faulty cores, handling both transient and permanent faults. However, the lightweight hardware required by R-TMR not only increases complexity but also becomes a single point of failure itself. To bypass isolated node constraints, this paper proposes a Fault Tolerance and Isolation TMR (FTI-TMR) algorithm for interconnected multicore systems. We construct a stability metric characterized by its prior and posterior estimates to identify the most reliable nodes in the system. These nodes then perform periodic diagnostics to isolate permanent faults. The experimental results show that FTI-TMR reduces task workload by approximately 30% compared with baseline TMR, while achieving higher permanent-fault coverage.
]]>Authors: Mario Sokac Tatjana Puskar Danijela Radumilo Natasa Puskar Predrag Vucinic Aleksandar Milosevic Zeljko Santosi Djordje Vukelic
Digital-to-physical orthodontic workflows combine intraoral scanning, 3D printing, rescanning and thermoforming, but each step may introduce dimensional deviation affecting final appliance geometry. This pilot methodological study evaluated relative dimensional deviations across selected stages of a biomedical manufacturing workflow for orthodontic appliances. Anonymized intraoral scan datasets from two patients with eugnathic occlusion were used, providing four arch-level datasets. CAD inspection compared IOS data, printed models, powder-coated printed models, and thermoformed appliances, with an analysis of global RMS surface deviation, anterior/posterior regional differences, spray-related surface offset and material-thickness effects. The highest mean global RMS deviation was found for IOS vs. a printed model (0.090 mm), followed by IOS vs. a powder-coated model (0.088 mm), whereas the direct printed model vs. powder-coated model comparison showed a lower mean RMS deviation of 0.039 mm. Regional analysis showed higher RMS deviations posteriorly (0.077 mm) than anteriorly (0.047 mm). Thermoformed appliance adaptation was affected by material thickness, with a mean RMS deviation of 0.207 mm for 1.0 mm material and 0.282 mm for 1.5 mm material. Local incisor analysis estimated the spray-related surface offset at approximately 0.030 mm. The main dimensional discrepancy was associated with printed model fabrication, while posterior regions and thicker thermoformed material showed greater deviation.
]]>Authors: Santiago Martin-Castresana Maria Castro Heriberto Pérez-Acebo
Managing vehicle speeds in rural-to-urban transition zones—where two-lane roads traverse small population centres—remains a critical challenge for road safety engineering. While various traffic calming measures (TCMs) are employed to enforce speed limits, empirical evidence comparing their relative effectiveness in sequential applications is often limited. This study presents a field analysis conducted on the BI-2604 road in Gordexola (Spain). Using radar counters at 24 sequential control points, a dataset of 23,021 valid vehicle passages was analysed to evaluate seven distinct calming configurations. The results indicate that, within this corridor, purely visual countermeasures were associated with high non-compliance: standard crosswalks (paint only) recorded a non-compliance rate of 92.9%, while the Speed Monitoring Display (SMD) registered a 72.1% violation rate. Regarding physical measures, a safety–compliance paradox was identified. Speed humps located in 50 km/h zones achieved the highest statistical compliance (41.7% violation). However, raised crosswalks in 30 km/h zones, despite registering higher non-compliance (63.6%), achieved the lowest mean speeds (approximately 34 km/h; V85 ≈ 45 km/h), a range that the previous literature associates with lower pedestrian injury risk. The findings suggest that, within the investigated corridor, physical vertical deflection was associated with lower speeds than the analysed visual/signalling measures, although it remains an imperfect solution that generates significant negative externalities (noise, emissions, and discomfort) and fails to guarantee strict legal adherence to 30 km/h limits. These limitations highlight the urgent need for alternative solutions, setting the stage for future research on optimised perceptual countermeasures.
]]>Authors: Alicia Páez-Pavón Andrea Galán-Salazar Carlos Talayero Federico R. García-Galván Artemia Loayza Isabel Lado-Touriño
Binder systems based on polyethylene (PE) and wax are widely used in Metal Injection Molding (MIM). However, their application raises environmental concerns related to material waste and the possible alteration of binder properties during repeated processing. This study aims to provide a comprehensive understanding of the behavior and properties of PE–wax systems to establish a reference framework for the evaluation and future development of alternative binder formulations. Model PE–wax blends without metallic powder were characterized using differential scanning calorimetry, tensile testing, and X-ray diffraction. The results showed that polymer and wax type influence the mechanical response of the blends, with the HDPE–octadecane formulation exhibiting a lower Young’s modulus than the HDPE–paraffin wax blend at the same wax content. Thermal analysis revealed distinct transitions associated with the polymer and wax components, together with small variations between successive heating and cooling cycles, indicating thermal-history-dependent behavior. Young’s modulus, Poisson’s ratio, thermal conductivity, and density were also predicted using Digimat software. The predicted Young’s modulus values showed good agreement with the experimental tensile results. The results provide a preliminary framework for screening polyethylene–wax binder formulations.
]]>Authors: Mirna Valdez-Hernández Alberto Baeza-Pérez Jiliany Nabet Rosa M. Woo-García Francisco López-Huerta Dulce Y. Medina-Velázquez Abimael Rodríguez-Sánchez Mariana E. Callejas-Jiménez Edith Osorio-de-la-Rosa
Solar photovoltaic (PV) expansion in fast-growing tropical regions requires engineering-based frameworks that compare deployment pathways in terms of electricity generation, spatial footprint, and economic performance. This study develops a techno-spatial and economic assessment of rooftop versus land-based PV deployment in a biodiversity-sensitive region of the Mexican Caribbean. The novelty lies in linking the technical, spatial, ecological, and financial dimensions of rooftop and land-based PV deployment within a single place-based framework. The framework uses a common annual electricity-output basis and integrates three modules: rooftop PV technical potential estimated from housing-census data and conservative performance assumptions; an equivalent-generation land-based PV counterfactual to estimate spatial footprint and conditional ecological exposure; and a household-scale discounted cash-flow assessment under Mexico’s subsidized residential tariff category 1C and high-consumption residential tariff (DAC, Doméstica de Alto Consumo). Under baseline assumptions, rooftop PV could provide approximately 227 megawatt-peak (MWp) of installed capacity and 330 gigawatt-hours per year (GWh yr−1) without additional land occupation. Producing the same output through land-based PV would require about 486 hectares (ha) and, under a forest-overlap scenario, could imply 89,600–95,300 tonnes of carbon dioxide (t CO2) in potential conversion-related emissions. Rooftop PV showed positive economic performance under both tariffs, with stronger returns under DAC conditions. The study provides a bounded engineering-oriented comparison of PV deployment pathways rather than predictions of siting, land conversion, or adoption.
]]>Authors: Ivan Dimchev Penka Zlateva Angel Terziev
The use of hydrogen for heating is one of the pathways toward zero-emission buildings. Two possibilities for using hydrogen in combination with heat-pump systems were analyzed: an electrically driven heat pump powered by a fuel cell and a mechanically driven heat pump powered by a hydrogen internal combustion engine (ICE). Heat-pump operation was adapted to local climatic conditions using typical meteorological year datasets for 30 representative locations across Bulgaria. The seasonal coefficient of performance (SCOP) values were further adjusted toward actual operating conditions using a field-performance correction factor. The results reveal a thermodynamic transition between the two pathways, defined by a critical seasonal coefficient of performance, SCOPcrit. For the reference ICE heat-recovery fraction of 0.45 and without direct fuel-cell heat recovery, SCOPcrit is 2.53. Above this threshold, the fuel-cell-driven pathway requires less hydrogen, whereas below it, the mechanically driven pathway requires less hydrogen. A strong correlation was identified between pathway dominance and the mean outdoor temperature during the heating period, with a transition temperature of about 2.4 °C. The sensitivity analysis shows that SCOPcrit ranges between 1.40 and 3.52. Overall system efficiencies ranged from approximately 1.2 to 1.8 on a lower heating value (LHV) basis, exceeding typical direct hydrogen-combustion efficiencies.
]]>Authors: Tomás José Fontalvo Herrera Enrique J. Delahoz-Domínguez Neiser Rodelo Barrios
The operational dynamics of the steel industry constitute one of the work systems with the highest severity and accident rates. To address this, this research multidimensionally evaluates and stochastically predicts the preventive capability of the safety system in a steel plant. Using a quantitative, evaluative, and longitudinal three-phase design, the retrospective evaluation of nine preventive variables was employed using Six Sigma metrics (DPMO, Z, Y), along with the evaluation of overall performance through the Geometric Capability Indicator (GCI) and the Arithmetic Capability Indicator (ACI), and the stochastic modeling of the process using Markov chains. It was demonstrated that evaluating processes in isolation hides structural inefficiencies, as four variables showed an Excellent individual performance (Z≈6.0), but the comprehensive multivariate evaluation revealed a Deficient systemic state (GCI of 0.471 and ACI of 0.493). Furthermore, Markov modeling on the compliance of the process management index predicted a 100% probability of long-term stagnation in a deficient absorbing state (x1=1). It is concluded that the proposed method functions as a rational anticipation system that provides potential managerial benefits by offering early warning indicators of operational degradation, supporting corrective decision-making on unstable preventive indicators.
]]>Authors: Huasen Sang Jiahe Zhang Chenming Yang Yufei Sun Qiuwan Shen Jicang Si Shian Li
Perovskite materials have emerged as promising candidates for gas sensing owing to their tunable structures, adjustable compositions, rich defect chemistry, and efficient charge transport properties. These characteristics enable the effective regulation of active sites, oxygen vacancies, heterointerfaces, and band alignment, thereby enhancing gas adsorption and sensing performance. This review summarizes recent advances in perovskite-based gas sensors, focusing on synthesis and fabrication strategies, structural engineering, sensing mechanisms, theoretical simulations, and intelligent sensing applications. The effects of doping, defect engineering, morphology control, and heterojunction construction on sensitivity, selectivity, response and recovery behavior, humidity tolerance, and stability are discussed. In addition, the roles of first-principles calculations and artificial intelligence in elucidating sensing mechanisms, identifying gases, predicting concentrations, and suppressing interference are highlighted. Finally, the remaining challenges and future perspectives are discussed to guide the development of stable, low-power, selective, and intelligent perovskite-based sensing systems.
]]>Authors: Hiroshi Kimura Akito Inoue
Text Correction [...]
]]>Authors: Hadi Sarvari
This study presents a systematic taxonomic review of risk modelling and assessment methods in construction projects over the past 35 years (1990–2025). Through a structured four-stage process, 91 peer-reviewed articles from 15 leading journals were analysed. The taxonomic approach enabled the classification and mapping of methods according to chronological evolution, study type, authorship patterns, and focus areas, while thematic analysis was employed to synthesise key themes, trends, and research gaps. The review examines publication trends, geographical distribution of research contributions, and methodological developments. The findings reveal that the probability-impact (P-I) model remains the dominant approach, despite its well-documented limitations in capturing risk interdependencies and their cascading effects on project quality and overall performance. Fuzzy Set Theory (FST), Analytic Hierarchy Process (AHP), and Monte Carlo Simulation (MCS) emerged as the most frequently adopted techniques. The analysis demonstrates a clear evolution in the field: from predominantly basic probabilistic methods in the 1990s to increasingly sophisticated hybrid, fuzzy logic-based, and AI-enhanced approaches after 2010. Notwithstanding these advancements, significant gaps persist, particularly the lack of integrated frameworks capable of simultaneously addressing risks across multiple project objectives—cost, time, quality, and performance. This review synthesises the state of knowledge in the field, identifies persistent theoretical and practical shortcomings, and offers a comprehensive roadmap for future research. Key directions include the development of machine learning applications, dynamic modelling techniques, and holistic multi-objective risk assessment frameworks to better align risk management theory with the complex realities of modern construction projects.
]]>Authors: Ivan Jakovljević Olja Čokorilo Ljubiša Vasov
This paper presents a validation framework for automated aircraft mass and balance operations, combining statistical process control with integrated risk scoring and addressing the critical gap between traditional centralised load control and emerging automated operations. A dual-validation system integrates univariate control limits, a weighted multivariate T2-type statistic, and composite scoring with operational risk assessment; risk scoring quantifies centre of gravity proximity and maximum mass margins. The framework was validated using 48 flights operated by Airbus A350-900/1000 aircraft. The framework was evaluated using a two-phase design—flights 1–30 for calibration (Phase I) and flights 31–48 for prospective evaluation (Phase II). Across all 48 flights it yields an 81.25% automation-eligibility rate (39 GREEN), with 4.17% YELLOW (non-blocking supervisor notification within a defined service window) and 14.58% RED (mandatory review). RED cases result from cargo offloads, dangerous goods changes, or significant baggage/ZFW variations, while cargo operations show exceptional stability (the exclusively cargo-loaded Compartment 2 has σ = 9 kg, versus 219 kg for total cargo). The framework enables transition from centralised load controller roles to exception-based supervision, with risk scoring outputs driving targeted, proportional safety measures. To the best of the authors’ knowledge, it provides the first systematic method for determining when automated load control can be trusted without human intervention—a critical requirement as the industry transitions towards full automation.
]]>Authors: Yunchuan Yue Guangtao Li Dengpan Qiao Zhonghua Ruan Jinhui Sun Dehong Feng Yang Chen
This study addresses stability degradation of cemented unclassified tailings backfill (CTB) under seepage-stress coupling in deep water-rich metal mines. CTB specimens with diverse mix proportions were prepared to explore their mechanical and permeability responses under varying seepage water pressures, and a response surface regression model was built to quantify multi-factor interactive effects on permeability. Results reveal that increased seepage pressure degrades CTB mechanical performance, while the degradation rate gradually declines. Higher cement-tailings ratios amplify the weakening effect of seepage pressure on elastic modulus. The permeability-strain evolution curve of CTB resembles its σ-ε, and the strain at peak permeability kmax always exceeds peak stress strain. The interaction between cement-tailings ratio and seepage water pressure dominates the variation in kmax and kmin. This work deepens the understanding of CTB seepage-mechanical behaviors and offers experimental references for proportion design and stability assessment of CTB in water-rich underground mines.
]]>Authors: Limbert Vega-Gonzalez G. Michel Guzman-Acevedo Juan A. Quintana-Rodriguez J. R. Millan-Almaraz J. Guadalupe Monjardin-Quevedo Aaron Gutierrez-Lopez J. Ramon Gaxiola-Camacho
Existing bridges represent a significant challenge for structural engineering due to aging, increased traffic demands, and lack of original design information. This paper presents an investigation of the structural behavior of the Miguel Hidalgo Bridge, located in Culiacán, Sinaloa, México, which has remained in service for over a century. A preliminary visual inspection identified deterioration mechanisms, including cracking, exposed reinforcement, damaged bearings, and localized structural deformations. Given the absence of original design documentation, a structural health monitoring (SHM) strategy was implemented to assess its condition. The SHM methodology integrates field instrumentation using accelerometers for operational modal analysis under ambient vibration and GPS receivers for displacement measurements, complemented by a geometric survey and material characterization through non-destructive techniques. These data were used to develop and calibrate a three-dimensional finite element (FE) model, which was employed to evaluate structural performance under service loads in accordance with AASHTO provisions. Results indicate that the bridge satisfies serviceability criteria; however, certain components exhibit deficiencies in load-carrying capacity. These findings highlight the importance of integrating monitoring data with numerical modeling to support decision-making related to maintenance, rehabilitation, and extension of service life in existing bridge infrastructure.
]]>Authors: Zhaohui Liu Qing Li
To address the limited endurance of Unmanned Surface Vehicles (USVs) in time-varying sea conditions, this study investigates global energy-efficient path planning that balances obstacle avoidance and energy efficiency. First, based on ship seakeeping theory, an energy consumption model incorporating wave height, wave period, speed, and wave-encounter angle is constructed to represent the impact of dynamic sea states on resistance. The model is assessed through formula–program consistency verification, a multi-factor input ablation on a physics-constrained benchmark, and external trend validation against published towing-tank added-resistance data. Second, the planning problem is modeled as a Partially Observable Markov Decision Process (POMDP). Built upon the Soft Actor-Critic (SAC) algorithm, a Bimodal Spatio-Temporal Feature Fusion Network (BSTFN) is proposed to achieve deep fusion of spatial perception information and historical temporal sea state sequences for decision-making. Furthermore, a composite reward function is designed, integrating energy consumption penalties, heading guidance, and smoothness constraints. Simulation results demonstrate that the proposed method effectively utilizes favorable encounter angles to avoid high sea state regions. While maintaining high task success rates, it significantly reduces average energy consumption, effectively enhancing the endurance and robustness of USVs in complex dynamic environments.
]]>Authors: Nguyen Gia Hien Vu Yifan Tang Rey Lim Yifan Yang Hang Ma Ke Wang G. Gary Wang
Combinatorial Route Optimization (CRO) problems, such as the Vehicle Routing Problem (VRP) or the Travelling Salesman Problem (TSP), are commonly seen in scheduling, logistics, and transportation. While current machine learning (ML) methods can overcome certain limitations of traditional approaches, including exact and heuristic algorithms, they typically require substantial computational resources, large training datasets, and carefully designed models, thereby limiting their scalability and practical deployment. In this paper, we develop a method to address such concerns in a data-efficient and near-training-free manner using foundation models. We select TSP, one of the most well-known combinatorial optimization problems, to solve in our experiments and employ the Tabular Prior-Data Fitted Network (TabPFN), one of the newly designed foundation models. Specifically, we develop a node-based formulation that converts TSP into a sequence of localized prediction tasks and constructs a complete route through in-context learning provided by TabPFN. The proposed method enables TabPFN, a model developed for regression and classification, to be applied to CRO problems with only one TSP sample for fine-tuning. We evaluate the proposed method across varying TSP instance sizes and demonstrate that our approach generalizes effectively without retraining, maintains competitive solution quality, and exhibits promising scalability. These findings suggest that CRO problems can be approached through foundation models, enabling scalability as well as generating rapidly deployable solutions with near-training-free adaptation.
]]>Authors: Rodrigo Limana Salla Elisa Henning Helena Paula Nierwinski Adriana Goulart dos Santos Carina Silvani Auanny Alícia Alves da Silva Daniel Hastenpflug
The resilient modulus is one of the primary parameters used in mechanistic–empirical pavement design methods and plays a fundamental role in pavement structural analysis and performance prediction. In Brazil, the implementation of the MeDiNa pavement design framework has increased the demand for accessible tools capable of supporting nonlinear modeling and coefficient interpretation. However, many available approaches still rely on proprietary software or spreadsheet-based workflows, limiting reproducibility, accessibility, and usability in teaching and research applications. This study presents the development of an open-source application based on R and Shiny for resilient modulus coefficient interpretation using the composite model adopted in the Brazilian MeDiNa methodology. The proposed application allows spreadsheet-based data importation, automated nonlinear coefficient estimation through nonlinear least-squares regression, graphical visualization of resilient modulus behavior, and generation of interactive three-dimensional response surfaces. Experimental datasets obtained from soil–aggregate mixtures, and published resilient modulus databases were used for validation. The results showed that the application successfully estimated the model coefficients and reproduced the stress-dependent resilient modulus behavior of the analyzed materials. The proposed tool provides an accessible and reproducible computational workflow for resilient modulus interpretation in pavement engineering research, teaching activities, and preliminary laboratory analyses associated with the MeDiNa framework.
]]>Authors: Gulnar Bektursunova Akmaral Sagybekova Abdi Kiyalbayev Bakhytzhan Abiyev Kabdolgazy Nauruzbayaev Darkhan Yelemes Assiya Mashekenova Nazym Shogelova
Road transport fuel consumption is strongly affected by pavement conditions and road infrastructure, whereas conventional normative approaches primarily consider vehicle characteristics and operating conditions while neglecting pavement friction and infrastructure-induced driving cycles. This study develops and validates an integrated physical–mechanistic model for predicting actual fuel consumption on high-category roads by combining vehicle energy-balance equations with pavement diagnostic parameters and infrastructure constraints. Unlike existing mechanistic or empirical approaches, the proposed framework explicitly incorporates pavement friction as a quantitative indicator of both skid resistance and road-related energy losses, enabling the simultaneous assessment of traffic safety and fuel efficiency within a unified engineering model. The methodology was applied to the A-350 Almaty–Taldykorgan highway using telemetry from 150 independent vehicle-route observations, 840 complete corridor transits, approximately 4.8 million CAN-GPS records, and 340 pavement friction measurements. The results indicate that actual fuel consumption exceeded normative values by an average of 9.2%, while a reduction in pavement friction below the regulatory threshold was associated with a substantial increase in fuel consumption, further amplified by pedestrian crossings and urban road sections. Model calibration and independent validation demonstrated that the proposed model achieved strong predictive performance (R2 = 0.94; MAE = 1.8%) under the observed operating conditions. This study extends existing fuel consumption modeling by reinterpreting pavement friction as a dual-purpose engineering indicator for both safety assessment and energy-efficiency diagnostics. The proposed methodology provides a scientific basis for corridor-level pavement management and infrastructure-related fuel-efficiency assessment and provides a conceptual foundation for future integration into intelligent transportation systems, subject to broader multi-corridor and seasonal validation.
]]>Authors: Fernando Jurado-Pérez Erick-Alejandro Gonzalez-Barbosa Jorge R. Parra-Michel José-Joel González-Barbosa
Optical ground wire (OPGW) cables are subjected to extreme electromagnetic stresses from lightning and short circuits. Existing models have three main limitations: (i) they assume uniform current distribution, (ii) they use constant material properties, and (iii) they do not couple the electromagnetic, thermal, and mechanical domains. This paper proposes a coupled multiphysics model that incorporates non-uniform current distribution with μr dependent on the magnetic field, temperature-dependent properties, and differentiated failure criteria. The model was implemented in COMSOL Multiphysics and was validated against experimental short-circuit tests (15–30 kA) conducted at the HPT-Laboratory (FEC). For the lightning scenario (10/350 μs impulse), the model predictions were compared with experimental results reported in the literature, showing good agreement in temperature rise and damage patterns. Results show that including a non-uniform current distribution modifies the predicted maximum temperature by 15.8% and shifts its location from the center to the outer aluminum layers. The model reproduces the experimental temperature with an RMSE of <7 °C and a relative error of <8%. A combined failure criterion (thermal + mechanical) predicts strand breakage with 89.2% accuracy, outperforming the purely thermal (72.5%) and mechanical (78.3%) criteria. Specific I−t and I2t curves were generated for two commercial OPGW cable configurations (Manufacturer A and Manufacturer B), with I2t capacities at 500 ms of 128 kA2s and 98 kA2s, respectively. The proposed model provides a useful tool for protection selection and coordination in transmission lines with OPGW cables.
]]>Authors: Haifeng Jia Fayou A Ruoxi Lin Zhang Luo Shiqiang He Shiqun Yan Jingxuan Yu
An excavation-induced clayey landslide in Jianshui County, Yunnan Province, China, threatens a national refined oil pipeline near the rear slope. Field investigation, borehole logging, laboratory testing, and three-dimensional finite-element analyses were integrated to investigate the excavation–rainstorm instability mechanism and evaluate circular anti-slide piles with toe backfilling. Under natural excavation, the reported factor of safety was 1.39, and the maximum displacement was 2.35 mm. Under a 60 mm/day rainstorm, increased pore-water pressure and saturation in the shallow sliding mass and strongly weathered claystone, together with saturated-state strength parameters, reduced the shear-strength reserve. The deformation and stability analyses yielded a maximum computed displacement of 1.36 m and a factor of safety of 0.95, respectively, indicating pronounced pre-failure deformation and loss of stability. After mitigation, the factor of safety increased to 1.41, while the maximum slope and pile-head displacements were both approximately 6.90 mm. The pile row redistributed nonuniform landslide thrust and reduced deformation transfer toward the pipeline. The results are site-specific engineering estimates for the investigated rainfall and parameter conditions.
]]>Authors: Lohami Valentin Landry Gnoumou Halidou Bamogo Abdel Aziz Tinto Issiaka Sanou Jean-Emmanuel Aubert Younoussa Millogo
The limited utilization of raw clays in the rubber industry is primarily attributed to an inadequate understanding of the complex interactions between clay minerals, their accessory minerals, and the polymer matrix. This study compares two local raw clays (SIT and KLE) with a reference industrial clay (REF) to demonstrate how differences in mineralogical composition affect the mechanical properties of natural rubber (NR)-based composites. Mineralogical characterization techniques (XRD, TGA-DSC, and IR) reveal distinct profiles: REF exhibits well-crystallized kaolinite as its dominant phase, whereas SIT contains a high proportion of quartz with less crystalline kaolinite, and KLE shows a complex mineral assemblage including swelling minerals (montmorillonite and chlorite). Microstructural analysis of NR/clay composites reveals a physical dispersion of clay particles within the polymer matrix without evidence of intercalation, with greater homogeneity observed for REF and SIT. Rheological properties indicate that curing times increase with increasing filler content, a trend that is particularly pronounced for KLE due to its interactions with the curing system. NR/REF composites outperform NR/SIT and NR/KLE in all evaluated mechanical properties, including the modulus at 300% elongation (6.1 MPa vs. 3.3 and 2.7 MPa), tensile strength, hardness, and tear resistance. This study establishes that reinforcement efficiency is directly linked to high kaolinite crystallinity, the absence of swelling minerals, and low concentrations of accessory minerals. Based on these findings, untreated local clay soils are not suitable for the manufacture of NR inner tubes and require appropriate pre-treatment prior to use.
]]>Authors: Xiaoli Zhu Liansheng Deng Yajie Li Yaogai Hu
To overcome the limitations imposed by the natural ionosphere on high-frequency (HF) communication systems, the generation of artificial electron clouds through space-based release of ionizable materials offers a controllable means of constructing localized plasma environments. However, a systematic mapping between release parameters and radio wave propagation characteristics is still lacking, which restricts engineering applications. This paper establishes a full-chain simulation framework that links release parameters, cloud morphology, and propagation characteristics by combining a two-fluid hydrodynamic model with ray tracing. Results show that as release altitude rises from 150 km to 210 km, the cloud undergoes pronounced stretching along the magnetic field (the cloud scale grows from 17.28 km to over 80 km), resulting in a broader signal shadow zone (expands from 116 km to 170 km) and a marked increase in multipath delay spread. Increasing mass from 4 kg to 12 kg raises peak electron density from 7.416 × 1012 to 2.154 × 1013 m−3 and widens the shadow zone from 144 km to 174 km. Raising the ionization rate from 20% to 80% broadens the enhancement zone but reduces the shadow zone. Higher altitudes favor broad shielding, larger masses enhance scattering strength and duration, while higher ionization rates accelerate cloud evolution for rapid response and lower rates provide stable coverage. These findings can provide theoretical guidance for the precise design of artificial electron clouds tailored to different application requirements.
]]>Authors: Tonglei Wang Jiabi Liang Qiaogen Zhang Jianjun Liu Peng Wu
Oil-immersed power transformers and high voltage reactors may experience abnormal mechanical vibration during operation, especially under complex electromagnetic and load conditions. Such vibration can induce periodic pressure fluctuations in narrow oil–paper gaps, promoting bubble formation, collapse, and associated characteristic gas production. Since characteristic gases are important indicators for insulation condition assessment, vibration-induced gas generation may affect the interpretation of dissolved gas analysis and fault diagnosis. However, the gas production behavior and underlying mechanism of oil–paper insulation under combined mechanical vibration and electric field stress remain insufficiently understood. In this work, an equivalent oil–paper gap model was developed to experimentally investigate the effects of vibration parameters and electric field strength on gas generation under vibration–electric field coupling. The bubble collapse dynamics under vibration were further analyzed using a modified Rayleigh–Plesset (R-P) equation. Results indicate that the localized high-temperature region produced during bubble collapse in the positive-pressure phase of vibration initiates pyrolysis of insulating oil and paper, generating characteristic gases including H2, CO, CO2, CH4, C2H4, C2H6, and C2H2, among which CO2, CO, H2, C2H4, and CH4 are the dominant components under test conditions. At low electric field strength (before partial discharge inception), the additional pressure contributed by electrostatic forces intensifies bubble collapse, increasing the concentrations of H2, COx, and THC by 15.9%, 7.6%, and 29.8%, respectively. At high electric field strength (after partial discharge inception), discharge-induced decomposition of oil and paper further increases the concentrations of H2, COx, and THC by approximately 47.7%, 30.0%, and 44.9%, respectively. These findings provide theoretical and data support for evaluating insulation conditions and understanding failure mechanisms in oil-immersed power equipment subjected to vibration.
]]>Authors: Yue Zhao Zhao Wang Xuecong Li Chao Xu Di Song Jinmin Shi
Space-time adaptive processing (STAP) is regarded as a highly effective approach for clutter mitigation in airborne radar applications. However, in practical range-ambiguous scenarios, the clutter suppression performance of traditional STAP algorithms can be severely degraded. Element-pulse coding (EPC) radar introduces extra controllable freedoms by assigning frequency offsets among array elements, which provides a promising means to cope with the influence of range ambiguity. On this basis, EPC-assisted STAP techniques have attracted increasing attention. Even so, the large number of adaptive degrees of freedom (DoFs) required by EPC-STAP makes its performance highly dependent on sufficient training snapshots, and a noticeable degradation may occur when only limited snapshots are available. To overcome this limitation, this study focuses on reduced-dimension (RD) STAP for airborne EPC radar under range-ambiguous conditions and develops an efficient reduced-dimension EPC-STAP scheme. Specifically, the received signal model of the airborne EPC-STAP system is first formulated to characterize the data structure. Subsequently, a tailored linear mapping matrix is constructed to compress the original high-dimensional observation space. The resulting RD-EPC-STAP processor is then obtained according to the minimum variance distortionless response principle using the transformed lower-dimensional data. Numerical experiments verify that the proposed algorithm provides improved clutter rejection capability while retaining strong tolerance to array gain and phase errors.
]]>Authors: Junwei Chao Xianling Zhang
The overhead ground wire (OGW) may fracture at the suspension clamp under short-circuit faults, posing a serious threat to the safe operation of transmission lines. However, the dominant damage mechanism—whether Joule heating or arc discharge—remains unclear. This study investigates a 110 kV OGW breakage accident through combined experimental and numerical approaches. Fracture analysis using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) revealed composite damage featuring both melting and tensile necking, with no fatigue characteristics. A real-scale short-circuit test platform was constructed, which, for the first time, directly captured intense arc discharge phenomena inside the suspension clamp during current flow. A multi-physics finite element model was then developed to decouple and quantify the thermal contributions of Joule heating and arc heating. Results show that Joule heating alone raises the local temperature to only 49.27 °C—far below the melting points of aluminum (660 °C) and steel (1450 °C). In contrast, arc heating elevates the temperature to over 26,000 °C locally, causing rapid melting of aluminum strands and heating of the steel core above 1450 °C within milliseconds. This extreme heat reduces the effective load-bearing cross-section and tensile strength, ultimately leading to fracture under normal operating tension. The findings demonstrate that arc discharge, rather than Joule heating, is the decisive factor in such failures. This study provides a quantitative theoretical basis for fault protection and hardware design optimization of overhead transmission lines.
]]>Authors: Yasser Ibrahim Mohamed Thariq Hameed Sultan Jan Lean Tai Navaneetha Krishna Chandran
The convergence of Lean Six Sigma and Industry 4.0 has emerged as a critical pathway toward intelligent manufacturing transformation. However, existing integration studies remain fragmented across technologies, DMAIC phases, and industrial contexts, with limited deployment architectures tailored to Electronics Manufacturing Services environments. This study addresses this gap by conducting a systematic literature review of Lean Six Sigma–Industry 4.0 integration research and developing a unified DMAIC-based conceptual framework specifically designed for EMS. Using a PRISMA-guided systematic review methodology, the selected studies were evaluated through quality appraisal, multidimensional analytical coding, quantitative diagnostic analysis, and cross-dimensional synthesis to move beyond descriptive literature mapping toward mechanism-based interpretation. The findings reveal that Industry 4.0 technologies are heavily concentrated in the Measure and Analyze phases, whereas Define and Control remain underdeveloped, resulting in structurally imbalanced maturity. To address these limitations, this study proposes an EMS-oriented conceptual framework that adapts established DMAIC practices by integrating Industry 4.0 technologies, deployment readiness criteria, digital traceability, and data-driven decision support into unified conceptual architecture. Rather than introducing a new DMAIC methodology, the framework contextualizes existing Lean Six Sigma principles for Electronics Manufacturing Services (EMS), providing a structured approach for intelligent continuous improvement in defect-sensitive manufacturing environments. Unlike previous frameworks that primarily associate Industry 4.0 technologies with individual DMAIC phases, the proposed framework introduces deployment readiness assessment, adaptive decision-gate mechanisms, enterprise-specific conceptual guidance, and EMS-oriented operational integration within a unified conceptual architecture.
]]>Authors: Fauzun Fauzun Muhammad Arkan Febian Hermada Fakhreza Areli Arsyadani Tsalisul Ilham Fariz Galang Nusantara Putra Setiawan Samsul Ma’arip Cahyo Wibi Yogiswara
Unlike many previous high-speed train Heating, Ventilation, and Air Conditioning (HVAC) studies that combined numerical and experimental analyses within the cabin, this study evaluates the supply duct independently, offering a more targeted assessment of internal duct airflow and thermal performance. The research comprised two numerical stages and an experimental validation. The first stage evaluated four ducting geometry variations and identified the ducting system arrangement of 20 supply diffusers with a 45° air grille blocking angle as optimal for the Kereta Cepat Merah Putih (KCMP) case, keeping air velocity within the thermal comfort range for all simulated passengers. This geometry was then manufactured as a full-scale prototype and assessed in a second numerical stage against experimental measurements. The air velocity yielded a mean absolute deviation (MAD) of 0.57 m/s and a relative mean deviation (RMD) of 34.22%, while the air temperature showed an MAD of 2.13 °C and an RMD of 8.16% at duct planes, and 0.80 °C and 3.06% at duct outlets. These results demonstrate that the proposed approach of testing the supply duct independently of the cabin is able to capture the general internal duct airflow trends, even though, particularly for the temperature parameters, a large temperature discrepancy occurs at downstream locations traced to three factors: uncontrolled ambient thermal conditions in a large-scale indoor facility, simplified thermal boundary conditions in the simulation, and additional uncertainty from the non-standardised temperature measurement method. Nonetheless, it offers a promising preliminary reference for HVAC system development, prior to integrated carbody testing, although further method refinements are still required to strengthen the reliability of this approach.
]]>Authors: Mayank Kapadia Vishnu S. Pendyala
In today’s increasingly complicated network environments, effective cybersecurity analytics necessitate scalable data processing systems that can handle massive amounts of diverse traffic data. This article compares relational, graph-based, and streaming data systems for cybersecurity analytics using the CICIDS2017 dataset. We specifically compare a columnar cloud data warehouse (Amazon Redshift) with a graph database (Neo4j) using example analytical queries to investigate trade-offs in query expressiveness, performance, and data modeling flexibility. In addition, we evaluate a real-time data intake pipeline built on Apache Kafka and Apache Cassandra to investigate ingestion throughput and low-latency storage features under simulated streaming workloads. The systems are examined independently to highlight their strengths and weaknesses in batch analytics, relationship-centric analysis, and real-time monitoring. The findings offer practical insights into how alternative data models and processing paradigms impact cybersecurity analytical tasks, as well as recommendations for selecting optimal data systems for network traffic analytics and intrusion detection use cases.
]]>Authors: Candy Abboud Serge Khalil
The rapid growth of Intelligent Transportation Systems (ITSs) and Internet of Things (IoT) technologies has generated massive volumes of distributed traffic data, creating significant challenges related to privacy, scalability, communication overhead, and heterogeneous traffic patterns. To address these challenges, this paper proposes FedTraffic, a hierarchical federated learning framework for traffic flow forecasting that integrates Edge–Fog–Cloud computing, hybrid deep learning, adaptive federated optimization, and Explainable Artificial Intelligence (XAI). The proposed framework combines a Temporal Convolutional Network–Conditional Variational Autoencoder (TCN–CVAE) with traffic-behavior clustering, adaptive client selection, and hierarchical model aggregation to enable accurate, privacy-preserving, and interpretable traffic prediction under heterogeneous non-IID environments. Extensive experiments demonstrate that FedTraffic achieves a best Mean Absolute Error (MAE) of 2.12, a Root Mean Square Error (RMSE) of 4.28, a Mean Absolute Percentage Error (MAPE) of 5.47%, and an R2 score of 0.966. Compared with the strongest federated baseline, it improves MAE by up to 18.77%, RMSE by 16.41%, and MAPE by more than 22%, while reducing communication overhead through an 8:1 latent representation compression ratio. These results demonstrate the effectiveness of FedTraffic as a scalable, privacy-preserving, and interpretable solution for next-generation intelligent transportation systems.
]]>Authors: Dan Iudean Calin Muresan Laszlo Rapolti Marius Bolba Rares Pop Radu Adrian Munteanu
The paper explores the underutilized potential of geothermal energy in Romania, focusing on both shallow and deep geothermal systems as sustainable alternatives for heating applications. Romania’s unique geological features, particularly the Pannonian Depression, offer significant geothermal resources, yet these remain largely untapped in many regions. This study presents an analysis of the country’s geothermal reservoirs, highlighting their spatial distribution, thermophysical properties, and technical potential. In-depth data on aquifer characteristics, including temperatures, depths, and capacities, supports a comprehensive assessment of the energy output achievable from these natural systems. Current applications of geothermal resources are reviewed, including successful projects in Oradea and Timiș County, which demonstrate the viability of geothermal systems for residential and commercial heating. Furthermore, this study discusses Romania’s regulatory landscape and incentive frameworks aligned with European Union standards, identifying key barriers and opportunities for growth. With a robust foundation of technical and spatial data, this paper underscores the strategic role geothermal energy can play in Romania’s transition to a low-carbon economy, supporting both environmental sustainability and energy resilience. The results demonstrate how geomatics-based resource mapping and spatial analysis support evidence-based planning of geothermal energy systems at regional and national scales.
]]>Authors: Yibo Wang Guangxiang Li Qi Gao Lin Lin Kun Yang Linlin Fei Yanhua Liu Qi Zhang
For the newly designed direct-acting centrifugal speed limiter, this paper employs a joint simulation method using ADAMS 2019 and ANSYS 2024, and combines the experimental data to analyze the dynamic characteristics and structural strength reliability of the speed limiter. The results show that the error between the simulated trigger angular velocity and the set value is only 1.31%, and the error with the mathematical model is 3.23%. The maximum structural stress is only 2.62% of the material yield strength, and the first six natural frequencies ranging from 201 to 377 Hz are much higher than the elevator operating frequency band, eliminating the risk of resonance. The relative errors between the experimental test data, theoretical models, and simulation results are all less than 6%, verifying the accuracy and reliability of the design. The speed limiter exhibits sensitive triggering and excellent dynamic stability, providing a methodological reference for the design and verification of elevator safety components.
]]>Authors: Carlos A. Martinez-Felix J. R. Millan-Almaraz Omar Chavez-Alegria Munawar Shah José Carlos Domínguez-Lozoya Angela Melgarejo-Morales
Global Navigation Satellite System Reflectometry (GNSS-R) platforms, such as the CYGNSS constellation, provide unprecedented spatial coverage for monitoring ionospheric scintillation via the S4 index. However, the operational utility of GNSS-R for space weather is substantially degraded by surface-induced signal contamination when sharp land–water boundaries (coastlines) trigger massive, false-positive S4 pseudo-scintillations that imitate true ionospheric plasma irregularities. In this study, a robust machine learning (ML) methodology to autonomously distinguish surface-induced reflections from true atmospheric volumetric scattering was proposed. Using 1 Hz Level 1 continuous Signal-to-Noise Ratio (SNR) time-series data, morphologic features (e.g., maximum amplitude, peak prominence, and standard deviation) were extracted to train a Random Forest (RF) classifier. The model achieves 98% accuracy in differentiating coastal boundaries from ionospheric scintillation, evaluated on a global dataset of over ~450,000 anomalous events. Moreover, a multi-sensor case study of the historic May 2024 G5 geomagnetic storm is presented to validate the geophysical fidelity of the filtered data. The ML-isolated CYGNSS anomalies demonstrate strong spatial correlation with COSMIC-2 Radio Occultation (RO) F2-peak electron density (NmF2) variations and ground-based Rate of TEC Index (ROTI) maps. Furthermore, temporal cross-validation with 1 Hz localized ground magnetometer data in Northwest Mexico reveals positive synchronization between CYGNSS scattering events and localized electrodynamic disturbances. Finally, the results demonstrate that ML-de-aliased GNSS-R data can reliably link the oceanic observational gaps inherent to ground-based networks, offering a powerful new tool for global space weather monitoring.
]]>Authors: Hossein Naderi Alireza Shojaei Lifu Huang
Automating activities in unstructured environments, such as construction sites, has been challenging due to unpredictable events, limiting robot adoption compared to structured settings. Recently, pre-trained foundation models, particularly Large Language Models (LLMs), have shown promise in addressing this challenge through superior generalization capabilities. This study employed a multi-dimensional method that systematically reviews the field from different perspectives of foundation models in robotics and unstructured environments, and synthesizes them with deliberative acting theory. The findings revealed that LLMs’ linguistic capabilities are primarily used to improve perception and human–robot interactions in robotic tasks, while applications in project management, safety, and natural hazard detection are the most utilized applications of foundation models in unstructured environments. Our synthesis shows an empirical gap in the field where fewer identified studies within unstructured environments validated their foundation model applications using physically deployed robots. We positioned the current state-of-the-art on a five-level automation scale of conditional automation. These findings inform future scenarios, challenges, and solutions toward autonomous safe unstructured environments. Our study serves as a benchmark to track our progress toward that future.
]]>Authors: Hussein A. Jasim Mohd Fadlee A. Rasid Fazirulhisyam Hashim Syamsiah Mashohor
Dynamic uplink power allocation is a critical challenge in cell-free massive MIMO (CF-mMIMO) networks, where distributed access points (APs) jointly serve multiple user equipment (UEs) under mobility, time-varying propagation conditions, and strong inter-user interference. Conventional optimization-based methods can improve fairness or spectral efficiency, but they often require repeated numerical solving and are usually designed for a specific objective. Learning-based approaches can reduce online decision time after training; however, their effectiveness depends strongly on the reward design and the selected operating objective. In response to these challenges, we propose a Deep Hybrid Intelligent (DHI) architecture designed to evaluate dynamic uplink power management within cell-free massive MIMO environments. The framework uses Soft Actor-Critic (SAC) learning to generate continuous uplink transmit-power decisions and evaluates objective-specific configurations for fairness, signal-to-interference-plus-noise ratio (SINR) improvement, and spectral-efficiency enhancement. In addition, three optimization-based strategies, namely max-min fairness, max-product SINR optimization, and max-sum-rate maximization, are incorporated to analyze the trade-off among fairness, signal quality, throughput, and computational cost. Limited-memory Broyden-Fletcher-Goldfarb-Shanno with bound constraints (L-BFGS-B) optimization is employed for the max-product and max-sum-rate objectives, while the max-min strategy is evaluated through a fairness-oriented feasibility procedure. Simulation results show that the fairness-oriented configuration achieves the highest Jain’s fairness index, reaching 0.989 at 120 access points, whereas the sum-rate-oriented configuration provides stronger SINR and user-rate performance. The results also indicate execution-time reductions of 51.6%, 83.7%, and 85.0% for the evaluated max-min, max-product, and max-sum-rate strategies, respectively, compared with conventional optimization-based implementations. These execution-time gains are accompanied by a clear performance trade-off: the max-min strategy provides the strongest fairness behavior, the max-sum-rate strategy improves total spectral efficiency and user-rate performance, and the max-product strategy offers a balanced operating point between collective SINR improvement and user-service balance. Therefore, the proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions. These results indicate that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks.
]]>Authors: Eddie Gazo-Hanna Ossama Mokhiamar Semaan Amine
Stroke is a major cause of lower-limb paresis, and clinical practice has long shown that early, repetitive gait training can accelerate motor recovery. End-effector gait trainers mounted on a wheeled walker provide a portable and low-cost alternative to bulky treadmill-based exoskeletons. This paper presents a geared five-bar linkage as the trajectory-generating mechanism of a Walker Gait Trainer (WGT), a single-actuator rehabilitation device for over-ground use. The two cranks of the five-bar linkage are linked by a gear train made up of two identical spur gears and an intermediate idler. This arrangement reduces the dimensional synthesis problem from four defect constraints, Grashof, order, and two circuit constraints, to a single order constraint, because branch and circuit defects are removed by design when both cranks are compelled to rotate continuously. Dimensional synthesis is formulated as a path-generation problem based on seven precision points obtained from normative gait data, and the mechanism dimensions are found through a systematic design-by-analysis search carried out interactively in the GIM kinematic simulation environment, using the closed-form kinematic model developed in this study. The final mechanism is then reconstructed in GIM as a kinematic cross-check of the closed-form model. The geared five-bar linkage reproduces the typical teardrop ankle path of healthy gait with one fewer link than conventional six-bar designs, while also adding the gear ratio as an extra parameter for shaping the trajectory, which is not available in six-bar topologies. The paper also presents the full device integration, in which the input crank is powered by a single speed-controlled actuator mounted on the walker frame, together with a three-dimensional CAD assembly model.
]]>Authors: Abdurra’uf M. Gora Abdullahi Mohammed Shettima Sadi I. Haruna Aminu Darda’u Rafindadi Yasser E. Ibrahim
The increasing demand for sustainable building materials has motivated the search for alternative supplementary cementitious materials to reduce the use of Portland cement while maintaining concrete performance. The present study aims to investigate the synergistic effects of bagasse ash (BA) and rice husk ash (RHA) as partial cement replacements in ternary blended concrete. Previous studies used agricultural ashes individually or in binary form only, whereas in the present work, the synergistic effect of BA and RHA is systematically studied, and Response Surface Methodology (RSM) is used to develop predictive models and optimize the performance of concrete. The slump, compressive strength and splitting tensile strength were evaluated using a Central Composite Design (CCD) to determine the effect of the levels of replacement of BA and RHA. Quadratic regression models were built and evaluated using analysis of variance (ANOVA). All models were statistically significant (p < 0.05) and had high predictive accuracy (R2 > 0.92). The results revealed that increases in BA and RHA contents reduced the workability because of their high specific surface areas and porous structures, while moderate combinations improved the compressive and splitting tensile strengths due to the synergistic filler effects, secondary pozzolanic reactions, and matrix densification. The multi-objective optimization based on the desirability function provided an optimal mixture of 5% BA and 15% RHA with an overall desirability of 92.8%, which provided the best compromise between workability and mechanical performance. Experimental validation of the optimized mixture showed good agreement of the model predictions with prediction errors of less than 5%, confirming the reliability and robustness of the developed RSM models. The results show that synergistic use of BA and RHA is a feasible and sustainable solution for producing high-performance ternary blended concrete and provides a reliable framework for the optimization of the mixture.
]]>Authors: Maen Takruri Mohammad Rabih Lucas Mouhannad Dbeiss Hanen Shall Sufian A. Badawi Marc Al Atem Mohamad Arnaout
The increasing integration of renewable energy resources, distributed energy systems, and intelligent sensing technologies has accelerated the transformation of conventional power grids into interconnected cyber–physical smart energy ecosystems. In this context, the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a key enabler for intelligent monitoring, adaptive energy management, resilient grid operation, and sustainable energy coordination. Although numerous studies have investigated AI, IoT, blockchain, and cybersecurity technologies individually, many existing reviews focus on isolated domains without adequately addressing the interactions between intelligent operational control, communication infrastructures, decentralized coordination, sustainability, and cyber resilience. Accordingly, this paper presents a comprehensive system-level review of AI–IoT-enabled smart energy ecosystems, focusing on smart grids, microgrids, intelligent energy management, blockchain-enabled decentralized coordination, carbon emissions monitoring, and cyber-resilient energy infrastructures. Unlike existing surveys that primarily emphasize individual technologies or algorithmic performance, this work highlights the cross-layer integration and architectural interdependencies between AI-driven operational intelligence, IoT-enabled monitoring, secure communication frameworks, and sustainability-oriented energy management. The paper also discusses key challenges related to interoperability, scalability, cybersecurity, communication latency, and distributed coordination, in addition to future research directions toward resilient, autonomous, and sustainable intelligent energy ecosystems.
]]>Authors: Aws Khalid Ibrahim
Laser Powder Bed Fusion (LPBF) is widely employed for manufacturing complex metallic components; however, process-induced distortion, residual stress, and thermal accumulation remain major challenges affecting dimensional accuracy and structural integrity. In the present study, a three-dimensional thermo-structural finite element model was developed to investigate the combined influence of component orientation and base plate material on the thermo-mechanical behavior of LPBF-fabricated Inconel 718 components. In addition, an integrated optimization methodology combining response normalization, a weighted sum performance index, and radar chart analysis was adopted to realize the optimal combination of the process variables by a simultaneous consideration of distortion, residual stress, and temperature. Accordingly, five part orientations and five different base plate materials were examined under identical processing conditions through 25 simulation cases. The obtained results revealed that component orientation represents the dominant factor controlling distortion and residual stress development, whereas both orientation and base plate material significantly affect component temperature. The inclined orientations generated the highest distortion levels, while the vertical configuration exhibited the highest residual stresses. In contrast, the horizontal orientation along the X-direction demonstrated the most balanced thermo-mechanical performance. Furthermore, AlSi10Mg base plate material provided the lowest thermal accumulation due to its high thermal conductivity. The integrated optimization analysis based on radar chart assessment and performance index evaluation identified the Horizontal-X/Ti-6Al-4V and Horizontal-X/AlSi10Mg configurations as the most favorable LPBF conditions. These findings provide practical guidelines for selecting build orientation and base plate material to reduce thermo-mechanical defects, thereby improving the dimensional accuracy and manufacturing reliability of LPBF-fabricated Inconel 718 components.
]]>Authors: Kriengkrai Nabudda Pongthep Poungthong Wirote Ritthong P. V. Elumalai
This study presents an integrated Computational Fluid Dynamics (CFD) and machine learning framework for analyzing and optimizing classical wire coating processes involving non-Newtonian power-law fluids. A two-dimensional axisymmetric CFD model was developed in ANSYS Fluent 2024R1 to investigate the effects of the power-law index (n = 0.3–1.0) on flow, pressure, temperature, and density fields under non-isothermal conditions. A Latin Hypercube Sampling-based Design of Experiments was coupled with surrogate modelling and Sobol sensitivity analysis to evaluate process performance and identify optimal operating conditions. The results showed that velocity distributions were highly dependent on fluid rheology, with shear-thinning fluids producing broader plug-like flow regions and more uniform velocity profiles. In contrast, pressure, temperature, and density fields exhibited limited sensitivity to variations in the power-law index. Optimization indicated that low power-law indices, moderate pressure gradients, and low-to-moderate wire speeds maximize coating thickness while minimizing material loss. Ridge Polynomial Regression achieved excellent predictive accuracy for all response variables (R2 > 0.995). Sensitivity analysis revealed that the initial die gap is the dominant factor governing coating thickness, whereas material loss is influenced by combined effects of die geometry, fluid rheology, and wire speed. The proposed framework provides an efficient tool for process optimization and material conservation in industrial wire coating applications.
]]>Authors: Yuhang Yin Lin Guo Zuxin Luo Qinghua Cui Xiangkui Wan
This paper proposes a lightweight vital-sign detection framework based on Frequency-Modulated Continuous Wave (FMCW) radar. By integrating time-domain complex differencing and adaptive trend reconstruction, the proposed framework mitigates distortions in chest micro-motion signals caused by multipath reflections, radar cross-section (RCS) variations, high-frequency impulsive noise, and body-motion artifacts in practical monitoring scenarios. The framework first employs a Time-Domain Complex Differencing and Sliding Accumulated Energy (TDCD-SAE) algorithm to precisely lock onto the target range-bin, followed by phase-difference extraction utilizing Complex Conjugate Multiplication (CCM). To eliminate non-physiological interference, an Adaptive Threshold-Based Bidirectional Trend Reconstruction (AT-BTR) algorithm is introduced to restore the corrupted phase profile. The optimized phase signal is converted into chest-wall displacement, followed by body motion detection to realize the joint estimation of the respiration rate and the heart rate. The experimental results demonstrate that the proposed system achieves a high precision, yielding low absolute errors of 1.24/1.71 BPM (supine) and 1.39/2.24 BPM (lateral), thereby validating its efficacy and robustness across diverse sleeping postures in resource-constrained environments.
]]>Authors: Bojan Milošević Nenad Kojić Žarko Petrović Vladimir Mandić Andrija Zorić
This study investigates the application of artificial neural networks (ANNs) for estimating the compressive strength of clay masonry walls based on the mechanical and geometrical properties of their constituent materials. A multilayer perceptron (MLP) neural network was developed using a hybrid dataset derived from Eurocode 6 empirical formulations and representative commercially available masonry units and mortars, enabling systematic generation of realistic input–output relationships. Input parameters included masonry unit dimensions and compressive strength, mortar compressive strength, masonry unit classification group, and mortar type. Different ANN topologies with ReLU, tanh, and logistic activation functions were analyzed, while training was performed using the Adam optimization algorithm. Model performance was evaluated using MSE, MAE, RMSE, R2, and 5-fold cross-validation. The proposed ANN model achieved high prediction accuracy, with R2 values approaching 0.98 for the optimal configuration. Sensitivity, SHAP, and partial dependence analyses confirmed that the constituent material strengths, together with the masonry unit classification and mortar type, are the most influential inputs, in agreement with the Eurocode 6 formulation. The developed model provides a practical tool for preliminary engineering assessment and rapid comparative analysis of masonry wall configurations, reducing reliance on repetitive empirical calculations. The model is based on a Eurocode 6 synthetic dataset and is intended for predictive approximation and engineering support rather than replacement of experimental testing. In this study, the ANN is explicitly framed as a surrogate model of the Eurocode 6 formulation rather than as a replacement for it: its added value lies in providing a fast, continuously differentiable, and interpretable approximation that enables large-scale parameter exploration, interpretability analysis, and deployment as a real-time decision-support web service. To confirm its robustness, the surrogate was benchmarked against Linear Regression, Random Forest, and XGBoost models on the identical dataset.
]]>Authors: Suleiman Abdulrahman Sadi Ibrahim Haruna Yasser E. Ibrahim Nura Shehu Aliyu Yaro Abdulwarith Ibrahim Bibi Farouk
Warm mix asphalt (WMA) provides a sustainable way of lowering production temperatures, reducing energy use for sustainable pavement construction; however, moisture damage affects its durability. Reclaimed asphalt pavement (RAP) contains aged binder that is stiffer, harder, and more brittle than virgin binder, resulting in asphalt mixtures with higher stiffness/modulus. This review examines the effect of incorporating RAP to amend the moisture damage susceptibility of WMA. It surveys the various moisture-damage failures reported in the literature on WMA with RAP mixes, including adhesive and cohesive failures, as well as hydraulic scouring and aggregate fracture. The analysis further explains the influence of WMA technology, RAP content, rejuvenation, and interfacial chemistry on the moisture durability of WMA-RAP mixtures. The strengths and limitations of the conventional and emerging moisture damage evaluation tests, including AASHTO T 283 tensile strength ratio (TSR), boiling water test (BWT), surface free energy (SFE), and fracture-energy-based approaches, were compared. This mechanistic synthesis linking production-related moisture sources, RAP heterogeneity and practical mitigation strategies highlights why reliance on TSR alone can conceal moisture-cracking vulnerability. The synthesis clarifies how RAP changes the moisture damage susceptibility of WMA to retain the environmental, economic and social benefits and circularity without compromising durability. This review proposes a practical roadmap based on technology-specific screening, multi-metric performance evaluation, and construction quality control for more reliable WMA-RAP specifications.
]]>Authors: Kaiyu Cai Jose Castro
Injection molding is one of the most widely adopted manufacturing methods in the plastics industry, owing to its high efficiency, design flexibility, and mass production capabilities. Throughout the development and application of Injection molding technology, trade-offs are pervasive, arising from competing requirements such as processability versus material performance, productivity versus quality, and simplicity versus functionality. Injection molding simulation itself embodies such trade-offs, as it is used to design increasingly complex processes and mold systems to achieve improved material properties and part performance, while inevitably balancing physical accuracy against computational efficiency and modeling cost. This review examines typical modeling options in injection-molding simulation from an accuracy–complexity trade-off perspective. The modeling strategies adopted in the primary stages of the molding cycle—namely, the injection, packing, and cooling phases—are systematically reviewed, with emphasis on how simplifying assumptions are introduced to manage numerical complexity. By organizing existing research through the lens of qualitative trade-offs, this review aims to support a more structured understanding of model selection in injection molding simulation for both academic studies and industrial applications.
]]>Authors: Aidana Imasheva Madiar Beisebekov Sana Kabdrakhmanova Kydyrmolla Akatan Nurgamit Kantay Zhanar Ibraeva Ainur Kabdrakhmanova K. S. Joshy Krishna S. Nair Sabu Thomas Saule Nauryzova
The efficient utilization of natural resources and agricultural wastes aligns well with the UN Sustainable Development Goals. Sunflower seed husks are an affordable and renewable source of cellulose that can be used as an alternative to wood-based resources. However, the yield and quality of cellulose are affected by the presence of components such as phytomelanin, hemicellulose, and lignin. In this study, cellulose pulp (CP) was extracted from untreated, water-treated, and water and alkali-treated SFH. The optimal peroxyacetic acid (PAA) to biomass ratio was established to assess the influence of pre-treatment on CP properties. Water and alkali pre-treatments significantly increased CP yield and reduced residual lignin, hemicellulose, and ash compared to untreated samples. The optimal yield of CP for SFH-NaOH was 55.73%. All microcrystalline cellulose (MCC) types exhibited comparable α-cellulose content, confirmed by the IR band at 1430 cm−1. XRD showed lower crystallinity in untreated CP-SFH relative to pre-treated samples. SEM revealed porous fibrous structures across all MCCs. Pre-treatment also improved the thermal stability and ζ-potential of cellulose nanocrystals (CNCs) obtained from MCC, without altering morphology. CNC yields were determined for all three CP variants. The CP-SFH-NaOH sample had the maximum CNC yield of 52.12%. Phytomelanin with admixture of lignin was recovered from alkaline extracts (8.56%) and fully characterized. Overall, the findings demonstrate the potential of integrated SFH utilization to produce high-quality cellulose derivatives and phytomelanin with admixture of lignin.
]]>Authors: Ali Khouilid Erroumayssae Sabani Hicham Mastouri Chouaib Ennawaoui Abdessamad El Ballouti
Proactive detection of faults in turbine bearings is key to ensuring system reliability in industrial systems. This work introduces the use of Moving Average (MA) and Exponentially Weighted Moving Average (EWMA) control charts for diagnosing bearing faults in the context of predictive maintenance. Unlike other statistical control charts, MA and EWMA control charts provide an advantage in that they employ a dynamic method of identifying trends over time through smoothing out variations as well as quickening the detection of gradual trends in system performance. MA charts are well-suited to find mean behavioral trends since they offer a strong perspective of medium-term behavioral changes. EWMA diagrams show better accuracy in spotting minor, slow variations, which is especially helpful for early-stage fault detection in high-sensitivity settings like turbine bearings. Implementation of the proposed method for real-world turbine operating conditions is shown to demonstrate the potential of using MA and EWMA control charts in monitoring vibration as well as speed anomalies prior to and after maintenance. Results are presented as proof of efficacy in identifying faults, aiding in decision-making on maintenance, and improving the lifespan and system operability of turbomachinery.
]]>Authors: Erroumayssae Sabani El Mehdi Loualid Hicham Mastouri Chouaib Ennawaoui Azeddine Azim
Monitoring the health of bearings in industrial rotating machines is a major challenge for ensuring the reliability and continuous operation of installations. Conventional fault detection methods, based on multivariate control charts such as Hotelling’s T2, multivariate exponentially weighted moving average, or multivariate cumulative sum control chart, are limited by the complex nonlinear dynamics of the system. In this article, we propose an innovative monitoring approach based on the Koopman operator, allowing the linearization of a nonlinear system in an observed space and the application of drift detection techniques via an extended T2 control chart. The study is based on two experimental approaches: one using controlled simulated data to analyze the responsiveness and robustness of the model, and the other applied to real data from an industrial turbogenerator monitoring the vibrations, temperatures, and speeds of the front and rear bearings. Comparative results show that the Koopman-based T2 map detects defects earlier, with better accuracy under noise and a reduced false alarm rate compared to conventional methods. The integration of wavelet preprocessing, statistical feature extraction by sliding windows, and PCA representation of the trajectories enhances the robustness and interpretability of the model.
]]>Authors: Tianfei Wang Kun Zou
Steel manufacturers currently rely on optical sensing systems that capture surface images for quality control, but these systems still face practical challenges in industrial environments. Early machine vision methods used handcrafted features, and their accuracy dropped when lighting changed or the background texture became complex. Deep learning improved detection, yet small defects that blend into background noise or share visual patterns with other classes still cause missed detections and false positives on factory floors where hardware resources are tight. GEA-YOLO integrates different refinement strategies into the Backbone, Neck, and training stage. C2DGA replaces C2PSA in the Backbone. Deformable attention adapts to defect structures that deviate from fixed sampling patterns, and a dynamic gate fuses global contextual information with local texture features. EMA modules are inserted into the Neck, where they recalibrate features independently at each scale and reduce the influence of background interference. DetectAux provides auxiliary supervision for hard samples during training. Unlike approaches that introduce attention at a single fixed position, GEA-YOLO places each module at the stage where it can improve the corresponding representation. We evaluated GEA-YOLO on the NEU-DET and GC10-DET datasets. On NEU-DET, the model reached 80.3% mAP@0.5, 2.6 percentage points above YOLOv11s, while keeping a reasonable balance between accuracy and computational cost and maintaining an inference speed of 169.5 FPS. Cross-dataset validation on GC10-DET further confirmed generalization, yielding 74.9% mAP@0.5, 2.9 percentage points above YOLOv11s, showing strong potential for real-time steel surface inspection. Ablation results confirm that each modification fixes a different weakness in the detection pipeline, but the full gain only appears when all three are used together. These results indicate that GEA-YOLO is promising for real-time optical inspection in controlled benchmark settings.
]]>Authors: Anibal Mantilla-Guerra Christian Mejia-Escobar Jorge Azorin-Lopez Jose Garcia-Rodriguez
The global expansion of renewable energy has increased the strategic importance of photovoltaic (PV) power plants and the demand for comprehensive, high-quality solar datasets to support energy planning, optimization, and data-driven applications. However, existing datasets are often constrained by limited attribute integration, incomplete information, and insufficient global coverage. This study presents a standardized, reliable, and multidimensional global dataset for photovoltaic power plant analysis, together with a largely reproducible and documented methodology that automates the collection, generation, and integration of heterogeneous solar-related data from multiple sources. Using this methodology, 27 geographic, topographic, logistical, climatic, and power-related attributes were integrated into a unified dataset comprising 58,978 photovoltaic plant records worldwide. Descriptive statistical analyses were performed to characterize the dataset and assess its informational richness and consistency. The results demonstrate that both the proposed methodology and the resulting dataset provide a robust foundation for photovoltaic energy research and decision-making across diverse application domains. By making this resource publicly available, this work facilitates reproducible research and supports the development of advanced analytical, predictive, and optimization models for academic, industrial, and policy-oriented applications.
]]>Authors: Christos Tsiafis Constantine David Apostolos Korlos
Rolling-element bearings remain a leading cause of unplanned downtime in industrial machinery, while vibration-based condition monitoring has matured, the post-2018 literature has converged on machine-learning classifiers whose interpretability layer remains restricted to expert analysts. This paper presents an operator-centred visualization framework supported by two time-frequency methods: the Zhao–Atlas–Marks Distribution (ZAMD), a Cohen’s-class representation with a cross-term-suppressing cone kernel, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), evaluated through its Hilbert spectral analysis output. Both methods produce two-dimensional time-frequency artefacts with a similar visual structure—impact-related energy bursts that recur at the characteristic fault frequencies—and are presented in side-by-side form for each fault class. A four-stage framework wraps either method with the characteristic fault frequencies (supplied as a comparison reference) and colour-coded, healthy baseline-referenced scaling. The framework is demonstrated on a laboratory bearing rig (KOYO 6302, 600 RPM) across inner-race, outer-race, and ball-spin fault classes. A preliminary readability assessment of annotated ZAMD-generated artefacts, with twelve non-specialist participants from a brewing and packaging industrial context, recorded 89.8% aggregate classification accuracy (194 of 216 trials) at a mean response time of 15.4 s. Because no label-free or alternative-format control conditions were included, this result characterises the annotated artefact as a whole and does not isolate the contribution of the time-frequency representation from that of the annotation layer; it is established for the ZAMD engine only. The two methods are compared as visualization engines—qualitatively, through the structure of their side-by-side time-frequency artefacts, and quantitatively, through computational cost—whereas the non-specialist readability assessment characterises the ZAMD-based framework specifically. CEEMDAN is positioned as a candidate alternative engine whose time-frequency output is shown to be structurally similar but whose operator readability has not been tested with human participants and is identified as future work.
]]>Authors: Aadith Yadav Govindarajan Anirudh Yamunan Govindarajan Pauline Hayes Tülin Sezer Govindarajan Narayanan
Many models exist to describe the nature of sloshing, an intricate phenomena, including the linear mechanical model and the Duffing oscillator. This paper attempts to provide an alternative model that describes the maximum slosh wave height (MSWH) of different sloshing liquids, to more accurately predict the nuanced phenomena as well as to better understand the inherent nature of sloshing. We explore three different models, the classical linear mechanical model, the non-linear Duffing model and finally the novel fractional visco-elastic model. The latter illustrates promising results after being validated via experimentation. The paper leads to the conclusion that sloshing of liquids, particularly low-viscosity fluids, is better described to have a spectrum of fractional-order visco-elastic forces at near and post resonant excitation frequencies rather than independent viscous and elastic forces as assumed by the linear and non-linear mechanical models.
]]>Authors: Lijuan Wang Yanbin Nian
Previous studies on construction site layout often simplified the site as a rectangle, with little consideration of adaptability to complex terrain and multiple functional constraints. An optimization method was developed for irregular construction sites, based on Systematic Layout Planning (SLP) and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), to address the limited adaptability of traditional methods in multi-objective and multi-constraint scenarios. A mathematical model for site layout was constructed using a rasterization method, with transportation time, transportation cost, and noise level as the optimization objectives. High-quality initial populations were generated by quantifying logistics and non-logistics relationships using the SLP method. The NSGA-II algorithm was enhanced with an adaptive penalty function, two-point crossover encoding, dynamically adjusted crossover and mutation probabilities, and a population restart mechanism. This improved its global search efficiency and convergence performance in complex solution spaces. Case validation results indicate that SLP-INSGA-II outperforms NSGA-II and SLP-NSGA-II while maintaining comparable performance to INSGA-II on some indicators. Without degrading overall optimization performance, incorporating SLP-based engineering priors can enhance search guidance, leading to layout solutions that are both feasible and engineering-interpretable. This study provides a modeling and solution approach for layout optimization in irregular construction sites.
]]>Authors: Warin Keaitnukul Pichit Kaewkosum Amit Joshi Sunil Chamoli Monsak Pimsarn Chinaruk Thianpong Suriya Chokphoemphun Arnut Phila Smith Eiamsa-ard
This study investigates heat transfer enhancement in the cooling channels of gas turbine blade turbulators using modified transverse baffles with isosceles triangular sawtooth perforations. The proposed baffle design aims to improve convective heat transfer by promoting flow mixing and disrupting the thermal boundary layer. Experiments were conducted in a rectangular channel with an aspect ratio of 3.75 under constant heat flux conditions using air (Pr = 0.7) as the working fluid. The effects of Reynolds number (Re = 6000–24,000), sawtooth width ratio (a/W = 0.0, 0.0625, 0.125, 0.25, and 0.5), and sawtooth height ratio (b/e = 0.0, 0.25, 0.5, 0.75, and 1.0) were systematically investigated. The blockage ratio (e/H) and pitch ratio (P/H) were maintained at 0.3 and 1.5, respectively. Heat transfer characteristics were evaluated using the thermochromic liquid crystal (TLC) technique, while thermal–hydraulic performance was assessed in terms of the Nusselt number (Nu), friction factor (f), and thermal performance factor (TPF). The results demonstrate that introducing sawtooth perforations significantly enhances heat transfer compared with a smooth channel, yielding Nusselt number ratios (Nu/Nus) between 1.6 and 2.6. The highest heat transfer enhancement was achieved at a/W = 0.0625 and b/e = 0.25, where the relatively small sawtooth openings generated stronger jet impingement, enhanced flow mixing, and more effective disruption of the thermal boundary layer. However, these geometric modifications also increased the pressure loss due to intensified flow blockage and recirculation, resulting in friction factor ratios (f/fs) ranging from 8.9 to 14.9. The maximum pressure-drop penalty occurred at b/e = 0.25 because the smaller openings produced stronger turbulence and increased flow resistance. Despite the increased friction loss, the optimum configuration (a/W = 0.0625 and b/e = 0.25) achieved the highest thermal performance factor of 1.2 at Re = 6000.
]]>Authors: Roohollah Farzalizadeh Abdolreza Osouli Prabir Kumar Kolay
Shear modulus degradation curves are fundamental inputs in nonlinear site response analyses and are conventionally normalized by the small-strain shear modulus, Gmax, defined at shear strains on the order of γ ≈ 10−4%. In shaking table experiments, however, reliable measurements at very small strains are often unattainable due to instrumentation resolution and strain demand limitations. Consequently, normalization is frequently performed using the shear modulus at a higher reference strain (γ = 0.01%). The impact of this alternative normalization on the resulting shear modulus degradation relationship has not been systematically evaluated. This study investigates the influence of normalization strain level on shear modulus degradation behavior using stress–strain relationships reconstructed from shaking table acceleration records. The shear modulus values were computed from individual hysteresis loops. The shear modulus normalized by Gmax estimated from empirical correlations was compared with the shear modulus normalized by its value at γ = 0.01% directly obtained from shaking table measurements. Results indicate that normalization at γ = 0.01% produces slightly lower normalized modulus values for shear strains exceeding 0.01% compared with the curve normalized by Gmax. Normalization using Gγ=0.01% resulted in reduced scatter and uncertainty.
]]>Authors: Timur-Vasile Chis Oana Roxana Chivu Catalina-Ioana Enache Delia-Andreea Rusan Monica Tegledi
The rehabilitation of aging educational buildings has become increasingly important in the context of sustainable campus development and adaptive reuse of existing infrastructure. This study proposes an integrated BIM-based framework for the rehabilitation of underutilized academic spaces through the combined application of Building Information Modeling (BIM), biophilic interior design principles, and Artificial Intelligence (AI) predictive modeling. The methodology was implemented in a case study involving non-functional areas within the Faculty of Aerospace Engineering at the National University of Science and Technology POLITEHNICA Bucharest. Autodesk Revit was employed to develop a parametric digital model of the existing structure, support spatial reconfiguration, and assess environmental and functional performance indicators throughout the rehabilitation process. To evaluate the effectiveness of the proposed framework, multiple performance criteria were considered, including spatial efficiency, daylight performance, material sustainability, acoustic quality, and user-perceived visual comfort. Furthermore, a synthetic dataset generated through parametric simulation was utilized to train and compare four machine learning algorithms (Multiple Linear Regression, Support Vector Regression, Random Forest, and Artificial Neural Networks) to predict user comfort based on spatial and environmental variables. The rehabilitation strategy resulted in an 18% increase in usable floor area, a 26% improvement in average daylight factor, a 25% increase in renewable material utilization, and a 38% reduction in estimated acoustic reverberation time. Simultaneously, the predictive modeling revealed that the Artificial Neural Network (ANN) provided the highest accuracy (R2 = 0.91) in capturing the non-linear relationship between biophilic design elements and perceived interior quality. By integrating Gilbreth’s principles of cognitive ergonomics, the AI framework actively prevents the rigid, purely quantitative optimization associated with “Digital Taylorism, The findings demonstrate that the proposed BIM-integrated rehabilitation framework can support both technical optimization and user-centered environmental enhancement in educational facilities. The study contributes a transferable digital methodology for sustainable academic building transformation, combining geometric precision, predictive environmental performance assessment, and human-centered design principles within a unified rehabilitation workflow.
]]>Authors: Bing Li Zhongli Jiang Xinfu Wang Jinxian He Hao Li Xiaofang Zhou Xiaoqing Wang Xiaosheng Liu Heng Zhao Mei Zhang Yunpeng Li
This study characterizes the physicochemical properties of coal gangue in Huainan, a typical coal resource-based city in China, and evaluates variations in its chemical composition and associated ecological risks. The results show that the coal gangue in the Huainan mining area is composed mainly of quartz and clay minerals, with SiO2 and Al2O3 together accounting for over 86% of the total composition. Rare earth element concentrations are generally higher than background levels, whereas heavy metal concentrations are generally below the risk screening values for soil contamination of agricultural land. Complex associations are observed among the elements in coal gangue. The correlation coefficients between SiO2 and the other oxides or heavy metals range from −0.750 to −0.993, indicating significant negative correlations and suggesting that the silicate mineral phase occurs independently of other element-enriched phases. The potential ecological risk index (RI) for heavy metals ranges from 33.75 to 300.71 and is driven primarily by Hg and Cd. The RI for rare earth elements ranges from 98.9 to 220.3, with Lu as the key influencing element. The predicted probability of adverse biological effects is 14–15%. Overall, classified management of coal gangue in the Huainan mining area is recommended, together with strengthened continuous monitoring of Hg and Cd and optimization of ecological disposal strategies by integrating potential ecological risk assessment with analysis of adverse biological effects, thereby further supporting the green transition of resource-depleted cities.
]]>