Search Results (34,953)

This work explores the key capabilities of emerging sensing technologies in the context of Structural Health Monitoring (SHM) of civil infrastructures, aiming to contribute to research on integrated and intelligent systems for more accessible and efficient monitoring solutions. As a case study, it focuses on the analysis of the static and dynamic behavior of the Edgar Cardoso stay-cable bridge during its rehabilitation, using fully customized transducers and equipment. The developed system integrates sensors capable of measuring accelerations, displacements, and temperature, which are connected to an autonomous data acquisition and transmission network. A digital interface was also developed to store, process, and visualize the collected data, enabling remote access for subsequent interpretation and analysis. The main contribution of this research lies in the use of optimized wireless monitoring systems with extended autonomy. This is achieved by employing edge computing techniques to minimize energy consumption during data transmission, as well as by managing the sleep modes of the sensor nodes. At same time, a methodology was proposed for the automatic and real-time estimation of axial forces in cables. This approach relies on the use of innovative edge computing tools, combined with the taut string theory as a simplified modelling framework. The results confirm the effectiveness of the developed system in achieving long-term operation without compromising monitoring performance. In addition, the developed system enabled the identification of the structure’s dynamic properties, particularly natural frequencies. The temperature profiles in critical sections, as well as displacements in the expansion joint were also measured and evaluated. The results demonstrate the potential of customized sensing solutions as effective tools for the management, maintenance, and long-term preservation of strategic infrastructures. Full article

The increasing demand for platinum group metals (PGMs) and critical base metals (BMs) underscores the critical roles these metals play in renewable energy and advanced technologies, enabling more efficient, environmentally sustainable operations. A hydrometallurgical approach to Au, Pd, and Pt tailings, derived from the glycine leaching of low-grade nickel and iron sulfide flotation concentrates, is investigated. The proposed process evaluates two glycine-based systems: glycine combined with KMnO₄ and catalyzed by cyanide under starvation conditions. Leaching with glycine in the presence of KMnO₄ (72 h, 25% solids, 60 °C, pH 11, dissolved oxygen 10 ppm, 126.7 kg/t glycine, and 7 kg/t KMnO₄) achieved extraction efficiencies of up to 66.7% Au, 89.1% Pd, and 95.8% Pt. In comparison, the cyanide-starved glycine system (72 h, 30% solids, 60 °C, pH 11, dissolved oxygen 20 ppm, 98.5 kg/t glycine, and 3.3 kg/t cyanide) resulted in up to 80.8% Au, 78.3% Pd, and 14.3% Pt. Activated carbon and Amberlite resin demonstrated selective adsorption of Au and PGMs. For activated carbon, Au adsorption exhibited a non-linear dependence on carbon dosage, reaching a maximum of 77.61% at 20 g/L, then decreasing to 50.85% at 25 g/L, and finally increasing to 65.04% at 30 g/L, indicating variable adsorption behavior. In contrast, Amberlite resin exhibited more consistent, progressive adsorption with increasing dosage. Au adsorption remained high across all conditions, increasing from 88.06% at 10 g/L to 99.67% at 30 g/L. Similarly, Pd and Pt adsorption improved significantly with resin dosage, reaching maximum values of 81.32% and 83.36% at 25 g/L, respectively, followed by a slight decline at 30 g/L. Implementing a two-stage process using carbon + resin (30 g/L) increased PGM recovery, achieving 99.89% Au, 81.8% Pd, and 92.4% Pt. Elution tests showed that Au (61.97%) and Pd (60.55%) were desorbed efficiently using thiourea (2% w/v) and HCl (0.5 M), whereas Pt elution proved difficult and required alternative strategies. The findings confirm glycine-based technologies as a promising, environmentally friendly alternative to conventional methods and provide a basis for further process development and optimization. Full article

Body measurement traits are key indicators for evaluating growth performance, production potential, and breeding value in Yili horses. However, studies investigating the association between body measurement traits and mutation loci in Yili horses remain limited. In this study, 255 adult Yili mares were used as the study population, including 152 speed-type and 103 meat-type individuals. Whole-genome resequencing was performed, and four phenotypic traits and body weight were measured. A mixed linear model (MLM)-based genome-wide association study (GWAS) was conducted using GEMMA (v 0.98.5), incorporating age, farm effects, and top three principal components as covariates. In parallel, a machine learning-based GWAS (ML-GWAS) framework integrating Lasso regression for feature selection and Random Forest (RF) with five-fold cross-validation was applied to improve the detection of complex genetic signals. Using both conventional GWAS methods and machine learning-based GWAS approaches, a total of 238 mutation loci significantly associated with body measurement traits were identified, and 277 candidate genes were annotated. These genes may play a role in several biological processes, including skeletal development, muscle formation, cell growth, energy metabolism, and protein synthesis. The findings suggest that genetic variations have already manifested among the studied groups. The results indicate that genetic differences have already emerged among different Yili horse populations at the genomic level. Furthermore, this study demonstrates that integrating machine learning with conventional GWAS effectively improves the detection efficiency of loci associated with complex traits, while also providing new molecular evidence for understanding the genetic mechanisms underlying differences in body measurement traits among Yili horse groups. Full article

Drone-based Urban Air Mobility (UAM) shows immense potential in urban logistics and emergency response; however, evidence regarding its systemic sustainability remains fragmented. In a systematic review using the PRISMA methodology, this study analyzes 301 core articles to construct an evaluation framework spanning environmental, economic, social, and systemic effectiveness dimensions. Given technical similarities, electric Vertical Take-off and Landing (eVTOL) findings are integrated to anticipate operational challenges. Results highlight a clear consensus: drone delivery is time-efficient in high-sensitivity scenarios, though noise, equity, and safety remain critical bottlenecks. Meanwhile, deep controversies persist across some dimensions. Environmental benefits are highly context-dependent, contingent on operating models, battery life cycles, and clean energy proportions from a Life Cycle Assessment (LCA) perspective. Economically, a mismatch between high costs and low willingness to pay (WTP) necessitates optimized pricing strategies. Socially, public acceptance is sensitive to the balance between perceived benefits and risks. Furthermore, systemic effectiveness depends on the coupling between vertiports and ground infrastructure. Concluding that sustainable drone-based UAM is a multistakeholder systemic endeavor, we urge future research to prioritize LCA, pricing strategies, public acceptance surveys, and integrated air-ground coordination to resolve controversies and foster sustainable systems. Full article

Ground-source heat pump (GSHP) systems are widely used because of their energy-saving and environmentally friendly characteristics. However, the long-term operation of a standalone GSHP system leads to heat accumulation in the soil for cooling load-dominated buildings, which results in a decline in system performance. To address this issue, in this study, a high-speed railway station in Jinan was considered as the research object, and a hybrid system scheme in which a GSHP is coupled with an air-source heat pump (ASHP) was developed. The system uses the outdoor dry-bulb temperature as the control parameter and establishes a multi-unit operation control strategy. A dynamic simulation model of the hybrid system was constructed using TRNSYS software, and then the energy consumption, soil thermal balance, economics and environmental benefits of the system under various schemes and operating conditions were simulated and analyzed. Through a comparative analysis of the operating strategies, the optimal strategy that achieved the best performance was determined. Under the optimal strategy, the soil thermal imbalance rate after 10 years of operation was only 1%, the total energy consumption was significantly lower than that of a standalone ASHP system, and the initial investment was clearly lower than that of a standalone GSHP system. The results demonstrate that the hybrid system ensures soil thermal balance and high-efficiency operation while providing significant energy savings (a 28% primary energy savings rate compared to a standalone ASHP) and environmental benefits (reducing annual CO₂, SO₂, NOx, and dust emissions by 56.5 t, 384.2 kg, 361.6 kg, and 339 kg, respectively). Therefore, the emission of atmospheric pollutants such as CO₂, SO₂, NOx, and dust can be effectively reduced, thus providing an important reference for the development of building energy-saving technologies under the “dual carbon” goals. Full article

Direct welding of fused silica to pure titanium (Ti) foil using conventional methods faces significant challenges, such as poor interfacial wettability, insufficient joint strength, and the need for interlayers or surface pretreatments. Existing femtosecond (fs) laser welding techniques for these materials often require high-energy millijoule (mJ)-level pulses or alloy interlayers. Moreover, reports on direct microjoule (μJ)-level fs laser welding of Ti foil to fused silica remain scarce. This study successfully demonstrates a direct welding process for pure Ti foil and fused silica using μJ-level fs laser pulses under ambient conditions, achieving joints with a maximum shear strength of 9.19 MPa. Microstructural analysis revealed an elemental interdiffusion region at the weld interface, supported by mechanical interlocking effects. X-ray photoelectron spectroscopy (XPS) confirmed the occurrence of interfacial chemical reactions, forming titanium silicide (TiSi₂) and titanium oxide (TiO₂). Additionally, a 24 h water immersion test of a square sealed cavity revealed outstanding hermeticity, with no water ingress. This work provides a simple, efficient, and robust solution for high-strength, additive-free bonding of fused silica to Ti foil under low-energy processing conditions. Full article

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), dynamic line ratings (DLRs), and flexible loads is reshaping modern power systems while introducing significant operational uncertainties. Reinforcement learning (RL) has gained attention as a data-driven solution for optimal coordination and control under uncertainty. However, existing studies that used RL for optimal coordination reviewed in this research primarily address uncertainties from DERs and load variability, largely neglecting DLRs and EVs as a time-varying network constraint. Moreover, long training times and limited interpretability hinder the practical deployment of RL-based controllers. This paper presents a comprehensive review of RL applications in power system operational control, categorizing approaches based on uncertainty sources, control objectives, and learning architectures. The review highlights the operational advantages of incorporating DLR uncertainty, including improved line utilization, congestion mitigation, enhanced renewable hosting capacity, and increased system flexibility. A critical research gap is identified in the absence of integrated RL frameworks that jointly consider DLRs and learning efficiency. To address this gap, a future research direction integrating a Belief–Desire–Intention (BDI) framework within RL is proposed, enabling faster convergence, constraint-aware decision-making, improved transparency, and enhanced resilience in modern power system coordination and control. Full article

Thermal management is a key factor for the efficiency, performance, and reliability of battery electric vehicles (BEVs), particularly in systems with strongly coupled components and heterogeneous thermal dynamics. This study proposes a centralized nonlinear model predictive control (NMPC) strategy for component cooling in BEVs, designed to maintain temperatures within optimal ranges while minimizing energy consumption and respecting actuator constraints. A reduced-order physics-based model is developed in MATLAB/Simulink R2024b, and the NMPC is implemented using CasADi, incorporating coolant temperatures as stabilizing states and a systematic parametrization of sampling time, prediction horizon, and weighting factors. The considered thermal management system consists of hydraulically coupled subsystems with different overall time constants, for which a single-horizon NMPC formulation is applied. Simulation results show that the proposed controller accurately tracks thermal dynamics across components with varying inertia and effectively captures cross-coupling effects. Sensitivity analyses indicate that variations in sampling time and prediction horizon have a limited impact on temperature trajectories and energy consumption, demonstrating robustness and real-time applicability. Compared to a rule-based controller, the NMPC achieves up to 30% reduction in energy consumption depending on ambient conditions and driving cycles, while improving temperature regulation, particularly for the high-voltage battery, with up to 2 K lower peak temperatures and a more balanced temperature distribution. These findings demonstrate that centralized NMPC is a suitable and efficient approach for thermal management in directly coupled BEV subsystems with heterogeneous dynamics. Full article

This paper presents a finite element study of friction ring springs, with emphasis on the internal stress distribution between inner and outer rings and their damping capacity. A detailed two-dimensional axisymmetric model was developed and compared against experimental measurements, showing close agreement in load–displacement response. In parallel, the classical analytical approach was validated in terms of stress and deformation values. To enable efficient parametric studies, a reduced one-element finite element model representing the periodic structure of the spring was also developed. This simplified model reproduces the response of the complete axisymmetric model while reducing the computational cost by over 80%. Beyond reproducing global mechanical behavior, the study provides detailed insight into the ring interactions as a function of the cone angle, friction coefficient, and the ratio of inner to outer cross-sectional areas. The results show that an optimal design should favor higher circumferential stresses in the inner rings, as their compressive stress state and radial confinement make them more resistant to buckling and crack initiation than the outer rings, which are subjected to tension. The findings provide useful guidelines for the modeling and design of friction ring springs and contribute to the broader understanding of friction-based energy-dissipation systems. Full article

This study investigates the dry pneumatic separation of wheat flour using a newly designed rotating air classifier to obtain starch- and protein-enriched fractions. The process is based on differences in particle density and size, enabling separation without water or chemical reagents. The influence of key process parameters, including air flow velocity 6–12 m/s, classifier geometry, and particle size distribution, was investigated. Statistical analysis confirmed that the air flow velocity and orifice diameter significantly affect the separation efficiency. The optimal conditions of 9–10 m/s and 1.8 mm resulted in a starch fraction with a purity of about 89% and a protein-enriched fraction containing approximately 45% protein. Regression models (R² > 0.99) demonstrated a strong relationship between the process parameters and fraction yield. Compared with conventional wet fractionation, the proposed method reduces energy consumption by approximately 28% and eliminates water use. These results confirm the feasibility of dry pneumatic classification as a sustainable and efficient technology for producing functional wheat-based ingredients. All experiments were conducted in triplicate (n = 3), and the results are presented as mean ± standard deviation. The reported yields correspond to the fraction mass, while the composition values indicate component purity within each fraction. Full article

Background: Optimized nitrogen (N) application and planting density can enhance sweet potato yield. However, the agronomic mechanisms underlying their effects on photosynthetic efficiency and carbohydrate metabolism in sweet potato remain unclear. Methods: To address this, a two-year field experiment was conducted using a split-plot design with two varieties (YS-25 and GX-14), three N levels (60, 90, and 120 kg/ha; designated N60, N90, and N120, respectively), and three planting densities (D1–D3: 50,000, 62,500, and 83,333 plants/ha). Each treatment was replicated three times. Results: The results showed that the N60D2 treatment (60 kg/ha N; 62,500 plants/ha) optimized canopy light distribution by significantly increasing IPAR, light transmission rate, and extinction coefficient (K). This treatment enhanced individual plant photosynthetic capacity (higher photosynthetic rate: Pn, Ci, Gs, and Tr) and light energy use efficiency (Fv/Fm, Y(II), ETR, and qP), and promoted carbohydrate metabolism (sucrose, starch, fructose, and glucose) by increasing enzyme activities (Rubisco, SuSy, SPS, NI, SSS, and AGPase) in functional leaves and roots. These effects improved source–sink coordination, ultimately increasing storage root yield by 63.27–95.47% compared with the control plants (N120D1). Correlation analysis revealed that single-plant root weight and medium-sized root count were important yield determinants for both varieties. Conclusions: These results indicate that reducing nitrogen fertilizer combined with dense planting shapes a reasonable canopy structure for light distribution at the population level and optimizes light and carbon use efficiency at the individual plant level, thereby improving storage root yield and commercial characteristics of sweet potato. Full article

With rising energy prices, increasing the efficiency of the mechanical mixing process is becoming an important design issue. It is expected that with propeller impellers, efficiency can be increased by changing the shape and/or pitch of the blades. Three-blade propeller mixers with five different blade shapes and four strokes (from p_b/D = 0.5 to p_b/D = 2) were tested. The mixing power (by measuring the torque on the shaft), the pumping capacity (by measuring the axial velocity using the PIV method) and the pressure on the bottom of the stirred tank were determined. Based on the results of the research, it was found that the shape, surface and pitch of the blades affect the pumping efficiency. However, for a specific blade stroke, the effect of blade shape on mixing efficiency is small. Impellers with a small stroke show the best efficiency, and increasing the blade stroke reduces the efficiency of the process. However, the small pitch of the blades means that the liquid stream pumped by the impeller may turn out to be too small for the proper conduct of the process, e.g., obtaining slurries. Therefore, the most commonly used p_b/D = 1 pitch turns out to be a good solution. Determining the relationship between pumping efficiency and bottom pressure allows you to determine the liquid stream pumped by the impeller based on the bottom pressure measurement. It has been confirmed that increasing pumping efficiency causes an increase in power demand according to pump theory because propeller impellers show similarities to the rotors of pumps and ship propellers. The theory of ship propellers is much better developed than the theory of mixing. Therefore, the possibility of using it to describe the mixing process could facilitate further research of propeller impellers. It was found that not all dependencies for ship propellers can be used to describe the mixing process. Full article

To improve the visible-light-driven photocatalytic degradation efficiency of g-C₃N₄-based photocatalysts toward organic pollutants, a MoS₂/g-C₃N₄ composite precursor was employed in this work, and the phase composition and defect environment of Mo species were regulated by post-annealing under air and N₂ atmospheres, respectively, thereby constructing Mo-based/g-C₃N₄ (MCN) composites with distinct structural evolution characteristics. The results showed that the photocatalytic activity of the as-sonicated MCN composite toward methylene blue (MB) was only moderately improved, among which the 15% loading sample exhibited the best performance with a degradation efficiency of about 42.0% within 60 min. In contrast, annealing at 400 °C under N₂ resulted in only a slight activity change, whereas the sample treated at 400 °C in air (Air-15% MCN) achieved an MB degradation efficiency of 99.9% within 60 min, together with a much higher pseudo-first-order reaction rate constant than that of the air-treated sample at a lower temperature. XRD, FT-IR and XPS analyses revealed that air annealing induced the conversion of MoS₂ into highly crystalline MoO₃ (or MoO₃₋_x), leading to the formation of a reconstructed MoO₃₋_x/g-C₃N₄ composite interface. Meanwhile, the increased high-binding-energy component in the O 1s spectrum and the EPR signal around g ≈ 2.00 further suggested the presence of more abundant defect-related centers in the air-treated sample. Although Air-15% MCN possessed a lower specific surface area than the untreated and N₂-treated samples, it displayed enhanced visible-light absorption, higher transient photocurrent response, lower interfacial charge-transfer resistance, and accelerated carrier dynamics, indicating that the activity enhancement mainly originated from atmosphere-induced phase transformation, interfacial reconstruction, defect-related active centers, and improved charge separation/transfer, rather than from the surface area effect. Based on the above results, a possible interfacial charge-transfer pathway is tentatively proposed for the g-C₃N₄/MoO₃₋_x interface formed after air treatment, which contributes to the efficient utilization of photogenerated carriers and the rapid degradation of MB. This work demonstrates that atmosphere-induced phase transformation is a simple and effective strategy for regulating the structure and photocatalytic performance of Mo-based/g-C₃N₄ composites, and provides useful guidance for the design of efficient visible-light photocatalysts. Full article

The electrochemical behavior of metallic rhenium was investigated using voltammetry and ex situ X-ray photoelectron spectroscopy (XPS) in aqueous acidic methanol solutions. Capacitance–potential analysis revealed that the double-layer current is governed by an adsorption–desorption surface process involving oxygen and sulfate species, as confirmed by XPS. The hydrogen evolution reaction (HER) proceeds via a Volmer–Heyrovsky mechanism, with hydrogen adatoms, physisorbed oxygen, and chemisorbed sulfate molecules as key intermediates. Methanol does not inhibit hydrogen gas production, and oxygenated species actively participate in the HER pathway. Voltammetric measurements demonstrated that rhenium cathodes are highly efficient for methanol electrolysis in membraneless systems, suggesting their potential application in electrolysis processes involving unpurified wastewater. These findings highlight rhenium as a promising electrode material for use in sustainable energy conversion technologies. Full article

Reliable detection of internal defects in pressure vessel structures remains essential for structural safety and condition-based maintenance. This study presents a low-complexity structural health monitoring framework based on fiber Bragg grating (FBG) sensing and multiresolution wavelet analysis for void detection in curved pressure vessel structures under guided wave excitation. Guided waves are introduced using piezoelectric actuators, while the FBG sensors capture the resulting strain-induced wavelength variations. Due to the limited bandwidth of the optical interrogator, the recorded signals represent the strain envelope response associated with guided wave interaction rather than the resolved ultrasonic carrier waveform. To characterize defect-induced changes, the acquired signals are analyzed using continuous wavelet transform (CWT) for time–frequency interpretation, and discrete wavelet transform (DWT) and wavelet packet transform (WPT) for energy-based multiresolution feature extraction. Experimental results show that void defects lead to consistent redistribution of wavelet-domain energy and increased non-stationarity in the measured strain responses. These trends are further supported by finite-element simulations, which reproduce similar energy redistribution patterns between intact and damaged cases. The proposed framework provides a physically interpretable and computationally efficient approach for defect detection using low-bandwidth FBG sensing, without reliance on high-speed acquisition or data-intensive learning models. The results demonstrate the feasibility of using energy-based multiresolution analysis of FBG strain signals for practical and scalable structural health monitoring of pressure vessel systems. Full article