Eng, Volume 7, Issue 1 (January 2026) – 54 articles

This paper presents a closed-loop price–dispatch framework for park-scale virtual power plants (VPPs) with coupled electric–thermal processes under high penetrations of photovoltaics (PVs) and electric vehicles (EVs). The outer layer clears time-varying prices for operator electricity, operator heat, and user feed-in using an improved particle swarm optimizer with adaptive coefficients and velocity clamping. Given these prices, the inner layer executes a lightweight linear source decomposition with feasibility projection that enforces transformer limits, combined heat-and-power (CHP) and boiler constraints, ramping, energy balances, and EV state-of-charge requirements. PV uncertainty is represented by a small set of scenarios and a conditional value-at-risk (CVaR) term augments the welfare objective to control tail risk. On a typical winter day case, the coordinated setting aligns EV charging with solar hours, reduces evening grid imports, and improves a social welfare proxy while maintaining interpretable price signals. Measured outcomes include 99.17% PV utilization (95.14% self-consumption and 4.03% routed to EV charging) and a reduction in EV charging cost from CNY 304.18 to CNY 249.87 (−17.9%) compared with an all-from-operator benchmark; all transformer, CHP/boiler, and EV constraints are satisfied. The price loop converges within several dozen iterations without oscillation. Sensitivity studies show that increasing risk weight lowers CVaR with modest welfare trade-offs, while wider price bounds and higher EV availability raise welfare until physical limits bind. The results demonstrate an effective, interpretable, and reproducible pathway to integrate market signals with engineering constraints in park VPP operations. Full article

Chitosan hydrogels are effective wound dressings that promote healing through the synergy of chitosan’s inherent biological properties and the moist environment they maintain. We previously developed hydrogel microparticles using a highly biocompatible chitosan derivative with superior therapeutic effects. This study aimed to enhance their clinical translation for Endoscopic Sinus Surgery (ESS) by optimizing preparation conditions to achieve an extrusion force of <20 N, facilitating ergonomic, single-handed administration by surgeons. While reducing the particle size alone was insufficient to lower the extrusion force significantly, the introduction of a mechanical “kneading” process to de-agglomerate microparticle aggregates resulted in a substantial reduction in the required force from 213 ± 80 N to approximately 47 N. By further optimizing the polymer concentration to 5.0% (w/v), we successfully reduced the maximum extrusion force to below 20 N (17 ± 1 N). These results demonstrate that the optimized injectable chitosan gel microparticles achieve the practical usability required for precise surgical maneuvers during ESS. Full article

Mosque buildings have symbolic significance, which makes them ideal candidates for implementing energy-efficient building design strategies. Mosques located in hot climates face several challenges in achieving thermal comfort while meeting energy efficiency requirements due to their distinct architectural features and intermittent occupancy patterns. Addressing these challenges requires integrating innovative energy-efficient retrofit strategies that cater to the characteristics of existing contemporary mosque buildings. Thus, this study provides a review of these approaches, considering both passive and active strategies. Passive strategies include thermal insulation, glazing upgrades, and shading improvements, while active ones include Heating, Ventilation, and Air Conditioning (HVAC) zoning and smart control, lighting upgrades, and the integration of photovoltaic panels. The findings highlight the potential of combining both passive and active retrofitting measures to achieve substantial energy performance improvements while addressing the thermal comfort needs of mosque buildings in hot climates. However, more research is needed on smart control systems and advanced building materials to further enhance energy performance in mosque buildings. By adopting these strategies, mosques can serve as models of energy-efficient design, promoting sustainability and resilience in their communities. Full article

Fault diagnosis in the power systems of Hybrid Electric Vehicles (HEVs) is crucial for ensuring vehicle safety and energy efficiency. This study proposes an innovative CNN-LSTM fusion model for diagnosing common faults in HEV power systems, such as battery degradation, inverter anomalies, and motor failures. The model integrates the feature extraction capabilities of Convolutional Neural Networks (CNN) with the temporal dependency handling of Long Short-Term Memory (LSTM) networks. Through data preprocessing, model training, and validation, the approach achieves high-precision fault identification. Experimental results demonstrate an accuracy rate exceeding 95% on simulated datasets, outperforming traditional machine learning methods. This research provides a practical framework for HEV fault diagnosis and explores its potential in real-world applications. Full article

With the rapid development of industries, hard sealing ball valves are increasingly adopted in extreme working conditions, especially for the advantage of high sealing performance. However, current research works on ball valves are lack of leakage rate prediction, which is an important issue. In this paper, a typical hard sealing ball valve is selected as the research object. Mathematical equations for sealing pressure are derived on both fixed and floating ball valves. The sealing pressure on the hard sealing side of the ball valve is analyzed, and the accuracy of the theoretical equation is verified. Meanwhile, the relationship between sealing performance factor and sealing pressure is fitted, and a prediction method of hard sealing ball valve is proposed, and also is validated experimentally. Results indicate that the sealing pressure obtained from the theoretical equation is conservative, as the actual pressure on the sealing surface exhibits a U-shaped distribution. The sealing performance factor varies with sealing pressure according to a piecewise function. It increases in the form of a power function when the pressure is less than 110 MPa and decreases in the form of a quadratic function when the pressure is higher than 110 MPa. The R² of the fitting equation is greater than 0.98%. Furthermore, the theoretical predictions are consistent with the experimental results in magnitude, confirming the reliability of the proposed prediction method. When the roughness is below 0.2, further reduction in the roughness has little effect on the sealing performance. Both roughness and sealing pressure should be considered comprehensively to enhance sealing performance. This work can benefit the leakage rate prediction and further study for the sealing performance improvement of hard sealing ball valves. Full article

In response to the growing demand for environmentally friendly and sustainable yet functional technical textiles, this research developed a spun-bonded nonwoven from the biodegradable thermoplastic starch-based biopolymer BIOPLAST^®, incorporating fruit extracts as natural sources of polyphenolic compounds and surface-active additives. Extracts from Vaccinium myrtillus L. and Sambucus nigra L. were applied onto a nonwoven’s surface via aerographic spraying using a water/ethanol system. The resulting materials were characterized in terms of morphology, physicochemical and mechanical behavior, surface characteristics, and stability under accelerated ageing and hydrolytic conditions. Treatment with the extracts increased the tensile strength by roughly 38% and elongation at break by about 50%, and it changed the surface from hydrophobic (contact angle of 115°) to hydrophilic, with contact angles of 83° for the blueberry-modified nonwoven and 55° for the elderberry-modified nonwoven. The modified nonwovens also showed sustained release of polyphenolic compounds over 72 h, which is beneficial for biomedical, healthcare, and cosmetic applications, where short-term use, controlled release of active compounds, and bioactivity are more important than long-term durability. Overall, the results indicate that BIOPLAST^®-based spun-bonded nonwovens can serve as fully bio-based carriers for fruit extracts in MedTech-related technical textiles, offering a straightforward way to introduce additional functionality into biodegradable nonwovens. Full article

As the retrieval of unconventional oil and gas resources extends to the deep and ultra-deep domains, the issue of cement sheath failure in shale oil wellbores seriously endangers wellbore safety, making it imperative to uncover the relevant damage mechanism and develop effective assessment approaches. In response to the limitations of conventional finite element methods in representing mesoscopic damage, in this study, we determined the mesoscopic parameters of cement paste via laboratory calibrations; constructed a 3D casing–cement sheath–formation composite model using the discrete element method; addressed the restriction of the continuum assumption; and numerically simulated the microcrack initiation, propagation, and interface debonding behaviors of cement paste from a mesomechanical viewpoint. The model’s reliability was validated using a full-scale cement sheath sealing integrity assessment apparatus, while the influences of fracturing location, stage count, and internal casing pressure on cement sheath damage were analyzed systematically. Our findings indicate that the DEM model can precisely capture the dynamic evolution features of microcracks under cyclic loading, and the results agree well with the results of the cement sheath sealing integrity evaluation. During the first internal casing pressure loading phase, the microcracks generated account for 84% of the total microcracks formed during the entire loading process. The primary interface (casing–cement sheath interface) is fully debonded after the second internal pressure loading, demonstrating that the initial stage of cyclic internal casing pressure exerts a decisive impact on cement sheath integrity. The cement sheath in the horizontal well section is subjected to high internal casing pressure and high formation stress, resulting in more frequent microcrack coalescence and a rapid rise in the interface debonding rate, whereas the damage progression in the vertical well section is relatively slow. Full article

With the increasing frequency of precipitation events under global warming, understanding rainfall-induced disruptions to urban mobility has become increasingly important. While prior studies primarily focus on road traffic, the lagged and threshold effects of rainfall on urban rail transit (URT) passenger flow remain insufficiently explored. This study analyzes 109 days of automatic fare collection data from Tianhe District, Guangzhou, in combination with hourly meteorological records and station-level built environment attributes. A rainfall threshold-aware gradient boosting framework is proposed to capture nonlinear response regimes, and an explainable learning approach is used to quantify the relative importance of rainfall, temporal factors, and built environment characteristics. The proposed framework outperforms the baseline model, with the root mean squared error (RMSE) and mean absolute error (MAE) reduced by over 5.38% and 5.93%, respectively. Results further indicate that lagged rainfall intensity exerts the strongest influence on passenger flow variation, with impact magnitudes varying systematically across station types. These findings enhance understanding of the nonlinear, time-dependent effects of rainfall on URT demand and provide practical guidance for passenger flow management and operational planning under rainfall conditions. Full article

The present study aimed to evaluate the effect of air-abrasion as a dentin pre-treatment on the bond strength of contemporary adhesive systems. The bonding approaches included etch-and-rinse (ER), self-etch (SE) and universal (UN) adhesive systems, with the latter applied in both ER and SE modes. Twenty-eight third molars were used, each sectioned in four parts. All specimens were embedded in acrylic resin, ground with silicon carbide papers, and divided into eight experimental groups (n = 14) based on the combination of surface pre-treatment (air-abrasion or none) and adhesive approach. Subsequently, a resin cylinder was bonded to each surface following the respective treatment. Shear bond strength (SBS) was evaluated at a cross-head speed of 0.7 mm/min using a shear-testing machine (OM100 Odeme, Luzerna, Brazil). The data were analyzed with one-way ANOVA and Tukey’s post hoc test. No statistically significant increase in SBS after air-abrasion of dentin was found for any of the experimental groups (p > 0.05). Among the adhesive strategies, the ER system presented higher SBS values (32.81 ± 9.04 MPa) than the UN adhesive applied in SE mode (21.68 ± 5.85 MPa) (p < 0.05). Mixed failures were the most common failure type across all groups. In particular, 20.5% of the specimens exhibited adhesive failure, 14.3% cohesive failure within resin composite, 12.5% cohesive failure within dentin and 52.7% specimens demonstrated mixed failure types. Dentin pre-treatment with air-abrasion using 29 μm Al₂O₃ did not significantly increase the SBS of the three tested contemporary adhesive systems; however, the choice of adhesive strategies influenced the SBS outcomes. Full article

Prestack seismic waveform inversion starts with an initial model and computes synthetic or predicted seismic data using a wave equation-based approach. Then, by matching these predicted data with the observed seismic data, it iteratively modifies the initial model using an optimization method until the predicted and observed data reasonably match. This method has been demonstrated to be superior to amplitude-variation-with-angle inversion. Because of the wave equation-based approach, computational cost is, however, one major drawback of the method. In the presence of well-logs with borehole measurements of the subsurface properties such as the P-wave velocity, S-wave velocity, and density, it is possible to provide a good initial model, and the method quickly converges to the true model at well locations. However, for locations away from the wells, the initial models are obtained by interpolating the initial models at the well locations over the interpreted geological horizons. These models can be far from the true models and inverting prestack data for these locations using wave equation-based method is computationally challenging. Because of these computational challenges, amplitude-variation-with-angle inversion is the current state-of-the-art method for routine seismic inversion applications. In this work, we provide an attribute-guided framework to generate initial models and demonstrate its applicability, which can potentially overcome computational challenges of prestack seismic waveform inversion. Furthermore, we also discuss the feasibility of using this attribute-guided approach to characterize reservoirs for underground hydrogen storage. Full article

Robot-assisted rehabilitation has demonstrated significant efficacy in improving motor function among patients with physical and neurological impairments. The development of effective rehabilitation robots requires careful integration of mechanical design and control systems to ensure safe, compliant, and intention-oriented human–robot interaction while delivering appropriate therapeutic assistance and feedback. Parallel robot manipulators have increasingly gained attention in rehabilitation applications due to their superior precision, structural stiffness, and high load capacity compared to their serial counterparts. This paper presents a scoping review of control strategies specifically implemented in parallel rehabilitation robots between 2015 and 2025. The control strategies include position control, force control, compliance control, adaptive control, intelligent control, and hybrid control. Our analysis showed a progressive shift from traditional position-based control toward more sophisticated adaptive and intelligent strategies that better accommodate patient-specific needs and therapeutic requirements. Full article

Photocatalytic membrane reactors (PMRs) are an innovative technology for water treatment, effectively combining membrane filtration and photocatalysis to enhance contaminant removal while enabling the regeneration of fouled membranes. In this study, a new porous film of chitosan that was impregnated with TiO₂ was developed and coated onto a ceramic support by spin coating to form a new porous immobilized PMR. The formed membrane was tested for two reasons: the removal of methylene blue dye by a dead-end filtration process and to demonstrate its ability to self-regenerate under UV exposure. The selective layer of the membrane was characterized using FTIR spectroscopy, X-ray diffraction, scanning electron microscopy (SEM), and water permeability tests. The results confirmed the formation of an amorphous film with no chemical interaction between chitosan and TiO₂. The membrane exhibited an average water permeability of 10.72 L/m²·h·bar, classifying it as either ultrafiltration (UF) or nanofiltration (NF). Dead-end filtration of methylene blue (10 mg L⁻¹) achieved 99% dye removal based on UV–vis analysis of the permeate, while flux declined rapidly due to fouling. Subsequent UV irradiation removed the deposited dye layer and restored approximately 50% of the initial flux, indicating partial self-regeneration. Overall, spin-coated chitosan–TiO₂ layers on ceramic supports provide high dye removal and photocatalytically assisted flux recovery, and further work should quantify photocatalytic degradation during regeneration. Full article

This paper presents an integrated study on the design, simulation, manufacturing, and experimental testing of a three-blade tritoroidal propeller compared to a conventional two-blade configuration for small UAVs. The aerodynamic analysis was performed in ANSYS Fluent 2022 R1 using the k–ω SST turbulence model at 6000 rpm, while structural integrity was assessed through FEM simulations in ANSYS Mechanical 2022 R1. Both propellers were fabricated via SLA additive manufacturing using Rigid 4000 resin and evaluated on an RCbenchmark 1585 test stand. The CFD results revealed smoother flow attachment and reduced tip vortex intensity for the tritoroidal geometry, while FEM analyses confirmed lower deformation and a more uniform stress distribution. Experimental tests showed that the tritoroidal propeller produces thrust comparable to the conventional one (within 1%) but at a 58% higher torque, resulting in slightly lower efficiency. However, vibration amplitude decreased by up to 70%, and the SPL was reduced by 0.1–6.2 dB at low and moderate speeds. These results validate the tritoroidal concept as a structurally robust and acoustically efficient alternative, with strong potential for optimization in low-noise UAV propulsion systems. Full article

Accurate and efficient defect detection in printed circuit boards (PCBs) is critical for manufacturing quality control. Existing methods predominantly rely on manually extracted features such as surface texture, color, and shape for defect recognition and classification within small-dimensional feature datasets. A convolutional neural network (CNN) model was developed via transfer learning. Feature extraction involves diverse operations across different CNN layers. Essential features were selected, and dimensionality was reduced via either t-distributed stochastic neighbor embedding (t-SNE) or principal component analysis (PCA). Defect classification was subsequently performed by clustering the reduced features with either the K-means or K-nearest neighbors (KNN) algorithm. Compared with alternative model feature learning classifiers, the proposed small-dimensional CNN model performs significantly better. A defect recognition accuracy of 97.33% was achieved, with processing completed in approximately 60 s. This approach, which integrates transfer learning-based CNN feature extraction with dimensionality reduction and clustering techniques, provides a fast and effective method for high-precision defect detection and classification in PCBs. Full article

Arid and semi-arid soils represent extreme habitats where microbial life is constrained by high temperature, low water availability, salinity, and nutrient limitation, yet these ecosystems harbor unique bacterial communities that sustain key ecological processes. To explore the diversity and functional potential of prokaryotic assemblages in Algerian drylands, we compared soils from three contrasting sites: The Oasis of Djanet (RM1), the hyper-arid Tassili of Djanet desert (RM2), and the semi-arid El Ouricia forest in Sétif (RM3). Physicochemical analyses revealed strong environmental gradients: RM2 exhibited the highest pH (8.66), electrical conductivity (11.7 dS/m), and sand fraction (56%), whereas RM3 displayed the greatest moisture (10.9%), organic matter (7.6%), and calcium carbonate (20.7%) content, with RM1 generally showing intermediate levels. High-throughput 16S rRNA gene sequencing generated >60,000 effective reads per sample with sufficient coverage (>0.99). Alpha diversity indices indicated the highest bacterial richness and diversity in RM2 (Chao1 = 3144, Shannon = 10.0), while RM3 showed lower evenness and the dominance of a few taxa. Across sites, 66 phyla and 551 genera were detected, dominated by Actinobacteriota (38–45%) and Chloroflexi (13–44%), with Proteobacteria declining from RM1 (17.5%) to RM3 (3.3%). Venn analysis revealed limited overlap, with only 58 operational taxonomic units shared among all sites, suggesting highly habitat-specific communities. Predictive functional profiling (PICRUSt2, Tax4Fun, FAPROTAX) indicated metabolism as the dominant functional category (≈50% of KEGG Level-1), with carbohydrate and amino acid metabolism forming the metabolic backbone. Notably, transport functions (ABC transporters), lipid metabolism, and amino acid degradation pathways were enriched in RM2–RM3, consistent with adaptation to osmotic stress, nutrient limitation, and energy conservation under aridity. Collectively, these findings demonstrate that Algerian arid and semi-arid soils host diverse, site-specific bacterial communities whose functional repertoires are strongly shaped by soil chemistry and climate, highlighting their ecological and biotechnological potential. Full article

Refractory geopolymers derived from aluminosilicate sources and alkaline activation are a promising alternative to traditional fired bricks, particularly when low-cost, waste-derived raw materials are used. This study improves the workability of a refractory brick formulated with clays (Kaolin and Tepozan–Bauwer), seashell waste, sodium silicate, potassium hydroxide, and water by incorporating sodium lignosulfonate (LS) and polycarboxylate (PC) plasticizers. Clays from Comonfort, Guanajuato, Mexico, and seashells were ground and sieved to pass a 100 Tyler mesh. A base mixture was prepared and evaluated using the Mini Slump Test, varying plasticizer content from 0 to 2% relative to the solid fraction. Based on workability, 0.5% LS and 1% PC (by solids) increased the slump, and a blended plasticizer formulation (1.5% by solids, 80%PC+20%LS) produced the highest workability. These additives act through different mechanisms, with LS primarily promoting electrostatic repulsion and PC steric repulsion. Bricks with and without plasticizers exhibited thermal resistance up to 1200 °C. After four calcination cycles, compressive strength values were 354.74 kg_f/cm² for the brick without plasticizer, 597.25 kg_f/cm² for 1% PC, 433.63 kg_f/cm² for 0.5% LS, and 519.05 kg_f/cm² for 1.5% of the 80%PC+20%LS blend. Strength was consistent with changes in porosity and apparent density, and 1% PC provided a favorable combination of high workability and high compressive strength after cycling. Because the cost of clays and seashells is negligible, formulation selection was based on plasticizer cost per brick. Although 1% PC and the 1.5% of 80%PC+20%LS blend showed statistically comparable strength after cycling, 1% PC was selected as the preferred option due to its lower additive cost ($0.0449 per brick) compared with the blend ($0.0633 per brick). Stereoscopic microscopy indicated pore closure after calcination with no visible cracking, and SEM–EDS identified O, Si, and Al as the significant elements, with traces of S and K. Overall, the study provides an integrated assessment of workability, multi-cycle calcination, microstructure, and performance for refractory bricks produced from readily available clays and seashell waste. Full article

Growing pressure from shrinking freshwater supplies and worsening pollution has heightened the demand for more effective water treatment solutions, especially those that promote reuse. This review synthesizes findings from 235 peer-reviewed papers examining plant-, mineral-, and other naturally derived coagulants used in surface water purification. Overall, these materials demonstrate turbidity reduction performance on par with conventional chemical coagulants across a wide range of initial turbidity levels (roughly 50–500 NTU). They are generally inexpensive, biodegradable, low in toxicity, and produce smaller volumes of residual sludge. Most function through mechanisms such as polymer-chain bridging or charge neutralization. However, their deployment at scale is still constrained by limited commercialization pathways, technical integration issues, and uneven public acceptance. Continued cross-disciplinary work is required to refine their performance and broaden their use, particularly in regions with limited resources or rural infrastructure. Full article

This paper presents an experimental and numerical investigation of continuous reinforced concrete (RC) beams made of self-compacting concrete (SCC) strengthened with fiber-reinforced polymer (FRP) bars using the Near-Surface Mounted (NSM) method. While the majority of previous studies have focused on simply supported beams, this work examines two-span continuous beams, which are more representative of real structural behavior. Four SCC beams were tested under static loading to evaluate the influence of the FRP reinforcement position on flexural capacity and deformational characteristics. The beams were strengthened using glass FRP (GFRP) bars embedded in epoxy adhesive within pre-cut grooves in the concrete cover. Experimental results showed that FRP reinforcement significantly increased the ultimate load capacity, while excessive reinforcement reduced ductility, leading to a more brittle failure mode. A three-dimensional finite element model was developed in Abaqus/Standard using the Concrete Damage Plasticity (CDP) model to simulate the nonlinear behavior of concrete and the bond–slip interaction at the epoxy–concrete interface. The numerical predictions closely matched the experimental load–deflection responses, with a maximum deviation of less than 3%. The validated model provides a reliable tool for parametric analysis and can serve as a reference for optimizing the design of continuous SCC beams strengthened by the NSM FRP method. Full article

Reliability and safety of high-voltage transmission lines are essential for stable and continuous operation of a power system. Environmental factors such as pressure, temperature, surface contamination, humidity, etc., significantly affect the dielectric strength of air, often causing unpredictable voltage breakdowns. This research presents a novel machine learning-based predictive framework that integrates Paschen’s Law with simulated and empirical data to estimate the breakdown voltage $\left(V_{bk}\right)$ of transmission lines in various environmental conditions. The main contribution is to demonstrate that data-driven prediction of breakdown voltage $\left(V_{bk}\right)$ using a hybrid machine learning model is in agreement with physical discharge theory. The model achieved root mean square error (RMSE) of 5.2% and mean absolute error (MAE) of 3.5% when validated against field data. Despite the randomness of avalanche breakdown, model predictions strongly match experimental measurements. This approach enables early detection of insulation stress, real-time monitoring, and optimises maintenance scheduling to reduce outages, costs, and safety risks. Its robustness is confirmed experimentally. Overall, this work advances the prediction of avalanche breakdown behaviour using machine learning. Full article

5G deployment in Malaysia is increasing the need for safe and efficient radio-frequency (RF) scanning at telecommunication towers, but service providers lack a clear, structured way to choose among available methods. This study develops a decision framework using a hybrid Delphi–Analytic Hierarchy Process (AHP) approach. A literature review identified criteria, sub-criteria, and six RF scanning alternatives. Ten experts then participated in three Delphi rounds: Rounds 1 and 2 confirmed five criteria and twenty-five sub-criteria, while Round 3 produced an expert ranking of the six alternatives, with drone-based and human-based scanning as the top priorities. Thirty practitioners subsequently completed AHP pairwise comparisons based on the Delphi-validated hierarchy. The AHP results show that Safety and Environment are the most important criteria, with ‘Fall’ and ‘Thunderstorm’ having the highest global weights. Drone-based scanning ranks highest, followed by human-based and ground-based methods, and the AHP ranking closely matches the expert ranking. The study provides a clear decision method for industry and policymakers to improve worker safety, guide inspection decisions, and strengthen telecommunication infrastructure in line with SDG 8 (Decent Work), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities), and SDG 13 (Climate Action). Full article

This study investigated the efficacy of electrochemical treatment of a water-soluble cutting fluid (SCF) solution using Al, Fe, and stainless steel (SUS304) scraps as three-dimensional (3D) electrode packing materials. The SCF solution had an initial COD_Cr of approximately 109,000 mg·L⁻¹, a TOC of approximately 25,000 mg·L⁻¹, and an initial pH of 9.65. During treatment, the pH remained in the alkaline range (9.99–10.67), and the solution conductivity was approximately 1000 μS·cm⁻¹. Using a conventional two-dimensional (2D) configuration, Al exhibited the highest removal efficiencies (TOC: 58.55%; COD_Cr: 57.12%). An applied current of 0.8 A, corresponding to a current density of 5.00 mA·cm⁻² based on the geometric electrode area, and an inter-electrode distance of 40 mm provided an optimal balance between treatment performance and energy consumption. Under these optimized conditions, the introduction of metal scraps as 3D packing media significantly enhanced treatment efficiency. Al scrap (20 g) achieved the highest TOC removal (69.55%), while Fe scrap showed superior COD_Cr removal (87.42% at 40 g) with the lowest specific energy consumption (0.27 kWh·kg⁻¹ COD_removed). The energy consumption of the baseline D system was 0.46 kWh·kg⁻¹ COD_removed(cage O) and 0.72 kWh·kg⁻¹ COD_removed(cage X). Overall, scrap-based 3D electrodes effectively improved organic removal and energy performance, demonstrating their potential as low-cost and sustainable electrode materials for the electrochemical pre-treatment of high-strength oily wastewater. Full article

The shift toward digital and smart manufacturing requires an accurate prediction of cutting behavior, such as cutting forces. Controlling cutting forces in machining is important for maintaining product quality, particularly in steels such as X5CrNi18-10. This steel has high toughness, which resists cutting, thereby increasing overall cutting forces. Proper selection of machining parameters and conditions can help reduce cutting forces during machining. Several studies have been dedicated to understanding the influence of cutting parameters on cutting forces. However, limited attention is given to the influence of the cutting conditions on cutting forces. The primary objective of this study is to understand the behavior of cutting forces in chromium-nickel alloy steel by varying machining parameters, specifically cutting conditions (dry and wet), using a full factorial (3¹ × 2²) design of experiments (DoE). The secondary objective is to develop a multilinear regression model to predict cutting forces. The root mean square (RMS) values of the cutting force components were calculated from the acquired data and analyzed using OriginPro 2025b. In addition, this study analyzes the effects of cutting parameters and cutting forces on root mean square (RMS) surface roughness (R_q) to understand their impact on quality using the AltiSurf 520 profilometer. The results suggest a significant effect of the selected machining parameters and conditions on cutting force reduction and on improved surface quality when cutting forces are low. This research provides a valuable insight into optimizing the machining process for hard steels. Full article

This study assessed the production and characterisation of biochars derived from the pyrolysis and co-pyrolysis of urban biosolids (BSs) combined with two lignocellulosic biomasses: rice husk (RH) and pruning waste (PW). The treatments were conducted at 300, 400, and 500 °C to evaluate the influence of temperature and mass ratio on the physicochemical, structural, and biological properties of the material. Co-pyrolysis significantly improved the material’s properties, enhancing carbon content, surface area, porosity, and pH, while reducing ash and heavy metal concentrations. RH promoted greater porosity and alkalinity, whereas PW increased carbon content and improved maize germination. Biochars produced at 400–500 °C met the stability criterion (H/C < 0.7) set by the International Biochar Initiative (IBI) and the European Biochar Certificate (EBC). However, zinc (Zn) remained the most limiting element for certification. Overall, the findings demonstrate that the co-pyrolysis of BSs with agroforestry biomasses is an effective and sustainable strategy for generating stable and environmentally safe biochars, suitable for use as soil amendments and for the sustainable valorisation of BSs. Full article

This study investigates rim seal flow in axial turbine configurations through a combined experimental–numerical approach, with the objective of identifying sealing-flow conditions that minimize ingestion while limiting aerodynamic losses. Experimental measurements from the University of BATH are used to validate computational methodology, ensuring consistency with established sealing-effectiveness trends. The work places particular emphasis on the influence of computational domain selection and interface treatment, which is shown to strongly affect the prediction of ingestion mechanisms. A key contribution of this study is the systematic assessment of multiple domain configurations, demonstrating that a frozen rotor MRF formulation provides the most reliable steady-state representation of pressure-driven ingress, whereas stationary and non-interface domains tend to overpredict sealing effectiveness. A simplified thin-seal model is also evaluated and found to offer an efficient alternative for global performance predictions. Furthermore, a statistical orifice-based model is introduced to estimate minimum sealing flow for different rim seal geometries, providing a practical engineering tool for purge-flow scaling. The effects of pre-swirl injection are examined and shown to substantially reduce rotor wall shear and moment coefficient, contributing to lower windage losses without significantly modifying sealing characteristics. Unsteady flow features are explored using a harmonic balance method, revealing Kelvin–Helmholtz-type instabilities that drive large-scale structures within the rim seal cavity, particularly near design-speed operation. Finally, results highlight a clear trade-off between sealing-flow rate and turbine isentropic efficiency, underlining the importance of optimized purge-flow management. Full article

This article offers a comprehensive technical and mechanical review of the Tesla turbine, an innovative device conceived by Nikola Tesla. The core research question guiding this review is: How can the design and application of the Tesla turbine be optimized to overcome its current efficiency limitations and unlock its full potential across various energy recovery technologies? The analysis focuses on the mechanical design of the turbine, illustrating the configuration of co-axial discs without blades mounted on a central shaft, and on the fluid dynamic phenomena that generate torque through the viscous boundary layer between the discs. Mathematical models based on the equations of viscous motion and CFD simulations are used to evaluate mechanical and fluid-dynamic losses, such as viscous friction, edge losses, and inlet duct losses. The work describes mechanical engineering challenges related to efficiency and performance, highlighting optimization techniques for the number and spacing of the discs, nozzle geometry, and thermal management to mitigate the risk of overheating. Finally, potential application areas in microturbine technology for low-enthalpy thermal cycles and energy recovery are examined. The article makes a significant contribution to applied mechanical engineering, offering design guidelines and an updated overview of the challenges and opportunities of Tesla turbine technology. Full article

Environmental health concerns remain a major global challenge. In many nations, the adoption of measures to mitigate the negative environmental impacts of construction-related activities has been slow. Prior research has clarified that further study/advancement are required to improve environmental health/sustainability (EHS). To determine the focus of previous studies, this study attempts to identify, analyse, and visualise the trends in research concerning EHS in construction-related domains. The data were obtained from the Scopus database, and the study employed a bibliometric approach. The following keywords were used to search the database: ‘environmental health’ OR ‘ecological health’ OR ‘environmental sustainability’ OR ‘ecological sustainability’ OR ‘Environmental safety’ OR ‘ecological safety’ AND ‘construction industry’ OR ‘building industry’ to retrieve relevant documents. The analysis included co-citation analysis, keyword co-occurrence and trend mapping. The findings revealed four themes: Environmental Sustainability and Energy-Oriented Decision-Making, Low-Carbon Cementitious Materials and Mechanical Performance of Concrete, Waste Management and Circular Economy Practices, and Life Cycle Assessment and Carbon Emission Analysis. The keyword findings revealed very scant research in environmental health unlike environmental sustainability. Spain, China, and Saudi Arabia are the top three in terms of citation-to-publication ratio, indicating strong influence in literature sources. However, India has the highest number of publications. The findings also suggest that more relevant studies are required in African nations and South Asian countries. It further highlighted a knowledge gap that emerging economies must address to enhance the sustainability and environmental performance of construction projects. This bibliometric analysis is unique in its integrated examination of environmental sustainability and environmental health in the construction industry, employing strategic thematic mapping to reveal system-level linkages, contextual gaps, and targeted directions for future research. The conclusions provide scholars and stakeholders in the built environment with a solid theoretical basis, enhancing the industry’s preparedness to mitigate the adverse environmental and climatic impacts of traditional construction methods. Full article

Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify thermal stresses in the devices during normal operation. The IR acquisitions were used to build a dataset for specialized AI model’s training, which combines thermal anomalies segmentation using U-Net with a Multilayer Perceptron (MLP) classifier for heat distribution patterns. The Taguchi method determines the optimal configuration of the selected parameters, while Analysis of Variance (ANOVA) evaluates the effect of each factor on the F1-score response. These techniques statistically validated the AI performance, confirming the optimal set of selected hyperparameters and quantifying their contribution to F1-score. The novelty of the study lies in the integration of real-time infrared thermography with an interpretable AI pipeline and a Taguchi–ANOVA statistical framework, which enables both optimisation and rigorous validation of AI performance under real-time operating conditions. Full article

Flexible photovoltaic (PV) support systems, referring to cable-supported structural systems that carry conventional rigid PV modules rather than flexible thin-film modules, have attracted increasing attention as a promising solution for photovoltaic construction in complex terrains due to their advantages of broad-span design and simplified installation. However, the deformation behavior of flexible PV supports remains insufficiently understood, which restricts its application and engineering optimization. To address this issue, a three-dimensional finite element model of a flexible PV support system was developed using an in-house Python code to investigate its deformation characteristics. The model discretizes the structure into beam and cable elements according to their mechanical properties, and the coupling relationship between their degrees of freedom is established by means of a multi-point constraint. The validation of the proposed model is confirmed by comparison with theoretical solutions. Simulation results reveal that the deformation of flexible PV supports is more sensitive to horizontal loads, indicating that their overall deformation performance is primarily governed by lateral rather than vertical loading. Furthermore, dynamic analyses show that higher loading frequencies induce noticeable torsional de-formation of the structure, which may compromise the stability of the PV panels. These findings provide valuable theoretical guidance for the design and optimization of flexible PV support systems deployed in complex terrains. Full article

The present research demonstrates the utility of the linear canonical transform (LCT) in constructing the characteristic function of real random variables. We refer to this construction as the linear canonical characteristic function (LCCF). The proposed LCCF aims to address the limitations of the classical characteristic function in both theoretical and applied aspects. Using this approach, we investigate its properties, such as Hermitian symmetry, continuity, convolution, and derivatives, which are generalized forms of the classical characteristic function in the literature. Finally, we implement the obtained results by calculating several probability density functions in the LCCF domains. Full article

Understanding the flow characteristics of tight sandstone reservoirs is crucial for improving resource recovery efficiency. During fluid flow in porous media, surfactant components in the fluid can adsorb onto solid surfaces, forming a boundary layer. This boundary layer has a pronounced impact on fluid movement within tight sandstone formations. In this study, digital core analysis is employed to investigate how the boundary layer influences non-Darcy flow behavior. A computational model is first developed to quantify the thickness and viscosity of the boundary layer, followed by the construction of a mathematical flow model based on the Navier–Stokes equations that incorporates boundary layer effects. Using CT scan data from actual core samples, a pore network model is then built to represent the reservoir’s complex pore structure. The impact of boundary layer development on microscale flow is subsequently analyzed under varying pore conditions. The results indicate that both boundary layer thickness and viscosity significantly influence fluid transport in microscopic pores. When the relative boundary layer thickness is 0.5, and the relative viscosity reaches 10, the actual outlet flow rate drops to only 12.89% of the value obtained without considering boundary layer effects. Furthermore, in tight reservoirs with smaller pore throat sizes, the boundary layer introduces considerable flow resistance. When boundary layer effects are incorporated into the pore network model, permeability initially increases with pressure gradient and then stabilizes. Full article