Search Results (73)

Mass concrete is prone to temperature cracks at an early age due to concentrated hydration heat, significant temperature gradients, and complex constraints, which affect structural durability and service safety. This paper reviews the relevant measures for preventing and controlling such temperature cracks, analyzing that the cracks are caused by the coupling effects of hydration heat, temperature gradients and stress distribution, material properties, environmental factors, and structural dimensions. It elaborates on two types of prevention and control measures: material optimization (low-heat cement, mineral admixtures, chemical admixtures, phase change materials, etc.) and construction process improvement (reasonable placement, cooling systems, external thermal insulation). Among these, phase change materials (PCMs) have become a research focus due to their active temperature regulation function of “peak shaving and valley filling”. This paper also introduces temperature, stress, and crack width monitoring technologies, as well as monitoring-based feedback control and intelligent systems. It summarizes the progress of numerical simulations in temperature field, stress field, and cracking prediction, with particular emphasis on their role in improving the understanding and prevention of early-age thermal cracking. The review further identifies shortcomings in multi-factor coupling mechanisms and integrated material–construction design, and proposes future research directions—such as low-heat-of-hydration binders, PCM optimization, and intelligent monitoring integration—to support more effective crack-control practices in mass concrete. Full article

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks. Full article

Addressing the core issue of rock mass failure and deformation induced by local water-induced uneven expansion in expansive soft rock tunnels, this study systematically analyzes the stress–displacement response of the rock mass under various working conditions. This analysis integrates physical model testing with FLAC3D 6.0 numerical simulation and covers four typical expansion zone configurations (vault, spandrel, haunch, invert) as well as multiple stages of stress loading. Leveraging the mathematical analogy between heat conduction and fluid seepage and combining it with a thermo-hydraulic coupling approach, the FLAC3D temperature field module precisely simulates the moisture-induced stress field. This overcomes the limitations of traditional tools for direct moisture field simulation and enables quantitative assessment of how localized expansion impacts tunnel lining failure. The study reveals that horizontal expansion zones significantly increase the risk of shear failure in tunnel structures. Expansion zones at the tunnel crown and base (invert) pose critical challenges to overall safety and exhibit a pronounced nonlinear relationship between stress loading and displacement. This research deepens the theoretical understanding of the interaction between localized non-uniform expansion and the surrounding rock mass and provides crucial technical guidance for optimizing tunnel support systems and improving disaster monitoring and prevention measures. Full article

Climatic heat stress in Hong Kong’s construction industry has been exacerbated by global climate change in recent times and the city has been taking proactive measures in protecting its workforce. Heat stress management systems refer to integrated frameworks, including policies, technologies, and practices, designed to monitor, mitigate, and prevent heat-related risks to workers’ health and productivity in hot environments. This scoping review investigates the existing heat stress management systems within Hong Kong’s construction industry, analyzing policies and academic research, and highlighting challenges and proposing solutions. A systematic scoping method was used to review and synthesize findings from 49 peer-reviewed articles (updated to 2025) and nine policy documents. This study highlights the interplay between research innovations like AI-driven models and wearable cooling technologies and policy frameworks. The results indicate substantial progress in Hong Kong’s drive to manage heat strain and accidents among construction workers over the years, with advancements in real-time advisory systems and protective equipment, improving worker safety and productivity. However, limited scalability, costs, socio-cultural compliance issues, gaps in addressing equity concerns among vulnerable workers, policy implementation, and other challenges persist. This review underscores the importance of building resilient systems against the escalating heat stress risks by proposing the integration of research-based technological innovation with policies and socio-organizational considerations. It contributes to providing the first updated scoping review post-2020, identifying implementation gaps (e.g., 40% non-compliance rate) and proposing a concrete action framework for future interventions. Recommendations for future research include cross-regional adaptations, cost-effective solutions for medium-sized construction enterprises, and the continuous re-evaluation and improvement of current interventions. Full article

Background/Objectives: The role of the molecular chaperone heat shock protein 90 (Hsp90) in pain and analgesia has been recognized; however, no study to date has investigated its role in facial allodynia during headache. In the current study, we examined the role of Hsp90 and its possible connection to the endocannabinoid system utilizing a rodent model of cortical spreading depression (CSD). Methods: CSD, a physiological phenomenon associated with headache disorders, was induced by cortical injection of KCl in female Sprague Dawley rats. To selectively inhibit Hsp90, 17-AAG was applied on the dura mater 24 h before CSD induction. Periorbital allodynia was assessed by von Frey filaments, while tissue samples were subjected to LC-MS, qPCR, Western immunoblotting, and the GTPγS coupling assay. Results: Increased expression of Hsp90 was selectively observed in the periaqueductal gray (PAG) harvested 90 min after cortical KCl injection, suggesting increased cellular stress from CSD induction. Application of 17-AAG (0.5 nmol) on dura mater 24 h before CSD induction significantly prevented facial allodynia as measured by von Frey filaments. This effect was blocked by injection of the CB₁R antagonist rimonabant (1 mg/kg, ip). The pretreatment with 17-AAG significantly increased the level of anandamide (AEA) in PAG 90 min after cortical insult, as measured by LC-MS. This effect was accompanied by reduced expression of FAAH and increased expression of NAPE-PLD in the same nuclei. Conclusions: These results suggest that Hsp90 inhibition positively modulates the endocannabinoid system, causing pain relief through descending pain modulation in PAG post-CSD. Full article

Residual stress remains a significant challenge in the widespread adoption of the Laser Powder Bed Fusion (LPBF) process, due to its detrimental impact on dimensional accuracy and post-processing requirements and hinders further processing with methods such as welding. Different strategies have already been explored to reduce or mitigate these stresses, including preheating, alternating scan strategies, and heat treatments. In this study, a less commonly investigated approach is examined: the influence of melt pool geometry—specifically layer height and track width—on the residual stresses in LPBF-manufactured 316L stainless steel. By systematically varying these parameters, the resulting internal stress states are compared by distortion measurements of cantilever parts to determine potential correlations and mechanisms of influence. The findings aim to contribute to a deeper understanding of process–structure–property relationships in LPBF and to offer a new avenue for stress control through geometrical process parameter optimization. It can be concluded that among all the strategies for preventing and mitigating residual stress in LPBF, the examined approach has a relatively small influence. The results show that increasing layer thickness and decreasing spot diameter have beneficial effects on the resulting deformations. Full article

The early determination of the type and severity of stresses caused by nutrient deficiency is necessary for taking timely measures and preventing a remarkable yield reduction. This study is an effort to investigate the performance of a machine learning-based model that identifies the type and severity of nitrogen, phosphorus, potassium, and sulfur in rice plants by using the plant microRNA data as model inputs. The concentration of 14 microRNA compounds in plants exposed to nutrient deficiency was measured using an electrochemical biosensor based on the peak currents produced during the probe–target microRNA hybridization. Subsequently, several machine learning models were utilized to predict the type and severity of stress. According to the results, the biosensor used in this work exerted promising analytical performance, including linear range (10⁻¹⁹ to 10⁻¹¹ M), limit of detection (3 × 10⁻²¹ M), and reproducibility during microRNA measurement in total RNA extracted from rice plant samples. Among the microRNAs studied, miRNA167, miRNA162, miRNA169, and miRNA395 exerted the largest contribution in predicting the nutrient deficiency levels based on feature selection methods. Using these four microRNAs as model inputs, the random forest with hyperparameters optimized by the genetic algorithm was capable of detecting the type of nutrient deficiency with an average accuracy, precision, and recall of 0.86, 0.94, and 0.87, respectively, seven days after the application of the nutrient treatment. Within this period, the optimized machine was able to detect the level of deficiency with average MSE and R² of 0.010 and 0.92, respectively. Combining the findings of this study and the results we reported earlier on determining the occurrence of salinity, drought, and heat in rice plants using microRNA biosensors can be useful to develop smart biosensing platforms for efficient plant health monitoring systems. Full article

The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of data on climate-sensitive diseases and related hospital visits in these areas. This study explored the prevalence of such diseases using the Delphi method through focus group discussions with 493 healthcare professionals from 153 hospitals in 156 upazilas across 21 districts and ten zones. Participants were selected by district Civil Surgeons. Key climate-sensitive diseases identified included malnutrition, diarrhea, pneumonia, respiratory infections, typhoid, skin diseases, hypertension, cholera, mental health disorders, hepatitis, heat stroke, and dengue. Seasonal surges in hospital visits were noted, influenced by factors like extreme heat, air pollution, floods, water contamination, poor sanitation, salinity, and disease vectors. Some diseases were zone-specific, while others were widespread. Regions with fewer hospital visits often had higher disease burdens, indicating under-reporting or lack of access. The findings highlight the need for area-specific adaptation strategies and updates to the Health National Adaptation Plan. Strengthening resilience through targeted investment and preventive measures is crucial to reducing health risks from climate change. Full article

Difficult, extreme operating conditions of parabolic antennas under precipitation and sub-zero temperatures require the creation of effective heating systems. The purpose of the research is to develop a multilayer coating containing two metal-ceramic layers, epoxy composite layers, carbon fabric, and an outer layer of basalt fabric, which allows for effective heating of the antenna, and to study the properties of this coating. The multilayer coating was formed on an aluminum base that was subjected to abrasive jet processing. The first and second metal-ceramic layers, Al₂O₃ + 5% Al, which were applied by high-speed multi-chamber cumulative detonation spraying (CDS), respectively, provide maximum adhesion strength to the aluminum base and high adhesion strength to the third layer of the epoxy composite containing Al₂O₃. On this not-yet-polymerized layer of epoxy composite containing Al₂O₃, a layer of carbon fabric (impregnated with epoxy resin) was formed, which serves as a resistive heating element. On top of this carbon fabric, a layer of epoxy composite containing Cr₂O₃ and SiO₂ was applied. Next, basalt fabric was applied to this still-not-yet-polymerized layer. Then, the resulting layered coating was compacted and dried. To study this multilayer coating, X-ray analysis, light and raster scanning microscopy, and transmission electron microscopy were used. The thickness of the coating layers and microhardness were measured on transverse microsections. The adhesion strength of the metal-ceramic coating layers to the aluminum base was determined by both bending testing and peeling using the adhesive method. It was established that CDS provides the formation of metal-ceramic layers with a maximum fraction of lamellae and a microhardness of 7900–10,520 MPa. In these metal-ceramic layers, a dispersed subgrain structure, a uniform distribution of nanoparticles, and a gradient-free level of dislocation density are observed. Such a structure prevents the formation of local concentrators of internal stresses, thereby increasing the level of dispersion and substructural strengthening of the metal-ceramic layers’ material. The formation of materials with a nanostructure increases their strength and crack resistance. The effectiveness of using aluminum, chromium, and silicon oxides as nanofillers in epoxy composite layers was demonstrated. The presence of structures near the surface of these nanofillers, which differ from the properties of the epoxy matrix in the coating, was established. Such zones, specifically the outer surface layers (OSL), significantly affect the properties of the epoxy composite. The results of industrial tests showed the high performance of the multilayer coating during antenna heating. Full article

Hyperthermal environments are encountered in many situations, and significant heat stress can exacerbate the fatigue perception of individuals and potentially threaten their safety. Heat acclimation (HA) interventions have many benefits in preventing the risk of incidents. However, whether HA interventions in specific environments can cope with other different hyperthermal environments remains uncertain. In this study, forty-three young male participants were heat-acclimated over 10 days of training on a motorized treadmill in a fixed hyperthermal environment, and they were tested in different hyperthermal environments. Physiological indices (rectal temperature (T_r), heart rate (HR), skin temperature (T_sk), and total sweat loss (M_sl)) and subjective perception (rating of perceived exertion (RPE) and thermal sensation votes (TSVs)) were measured during both the heat stress test (HST) sessions and HA training sessions. The results show that HR and T_sk significantly differed between pre- and post-heat acclimation (p < 0.05 for all) following the acclimation program. However, after heat acclimation training, the reduction in T_r (ΔT_r) was more notable in lower-ET* environments, and M_sl showed distinct changes in different ET* environments. The RPE and TSV decreased after HA interventions, although the difference was not significant. The results indicate that HA can effectively reduce the peak of physiological parameters. However, when subjected to stronger heat stress, the improvement effects of heat acclimation on human responses will be affected. In addition, HA can alleviate physiological thermal strain, thereby reducing the adverse effects on mobility, but it has no effect on the supervisor’s ability to perceive the environment. This study suggests that additional HA training can reduce the risk of activities in high-temperature environments but exhibits different effects under different environmental conditions, indicating that hot acclimation suits have selective effects on the environment. This study provides recommendations for additional HA training before high-temperature activities. Full article

This study investigates a lithium-ion battery failure in heating insoles that ignited during normal walking while powered off. Through comprehensive material characterization, electrical testing, thermal analysis, and mechanical gait simulation, we systematically excluded electrical or thermal abuse as failure causes. X-ray/CT imaging localized the ignition source to the lateral heel edge of the pouch cell, correlating precisely with peak mechanical stress identified through gait analysis. Remarkably, the cyclic load was less than 10% of the single crush load threshold specified in safety standards. Key findings reveal multiple contributing factors as follows: the uncoated polyethylene separator’s inability to prevent stress-induced internal short circuits, the circuit design’s lack of battery health monitoring functionality that permitted undetected degradation, and the hazardous placement inside clothing that exacerbated burn injuries. These findings necessitate a multi-level safety framework for lithium-ion battery products, encompassing enhanced cell design to prevent internal short circuit, improved circuit protection with health monitoring capabilities, optimized product integration to mitigate mechanical and environmental impact, and effective post-failure containment measures. This case study exposes a critical need for product-specific safety standards that address the unique demands of wearable lithium-ion batteries, where existing certification requirements fail to prevent real-use failure scenarios. Full article

Reproductive failure has significant socioeconomic impacts on smallholder pig farms. This systematic review was conducted to compile the types of reproductive failures and their underlying causes reported in smallholder pig farms from East and Southeast Asia and to identify relevant knowledge gaps. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 26 peer-reviewed studies met the eligibility criteria and were included in our synthesis. These studies were conducted in 11 countries, with Vietnam, China, and Thailand representing the highest share (53.8%). Only six studies (23%) investigated reproductive failure as their primary objective. Stillbirth, mummification, late-term abortion, and weak-born piglets were the predominant reproductive failures reported from smallholder pig farms across the region. The most frequently cited viral pathogens associated with these failures were porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV-2). Common non-infectious risk factors included extreme climate conditions (e.g., heat stress), poor diet and housing, and suboptimal boar management. Our synthesis highlighted a dearth of research focused on reproductive failure in smallholder pig farms in the region and emphasised the need for more targeted studies to clarify the biological, environmental, and managerial risk factors contributing to reproductive failure. This will facilitate the development of targeted prevention and control measures that account for the unique farming conditions and challenges smallholder farms face in East and Southeast Asia. Full article

The impetus of the present work is to propose a comprehensive methodology for the numerical evaluation of drying/curing, as one of the most complex and energy-consuming stages in the paint shop plant, to guarantee a decrease in energy costs without sacrificing the final paint film quality and manufacturability. Addressing the complexities of vehicle assembly, such as intricate geometry and multi-zoned ovens, our approach employs a sophisticated conjugate heat transfer (CHT) algorithm, developed under the OpenFOAM framework, providing efficient heat transfer with the accompaniment of the Large Eddy Simulation (LES) turbulence model, thereby delivering high-fidelity data. This algorithm accurately simulates turbulence and stress in the oven, validated through heat sink cases and closely aligning with experimental data. Applying modifications for the intake supply heated airflow rate and direction leads to optimal recirculation growth in the measured mean temperature within with the curing oven and along the car body surface, saving a significant amount of energy. Key adjustments in airflow direction improved temperature regulation and energy efficiency while enhancing fluid dynamics, such as velocity and temperature distribution. Furthermore, the study integrates machine learning to refine the oven’s heat-up region, which is crucial for preventing paint burnout. A data-based model using a variational auto-encoder (VAE) and an artificial neural network (ANN) effectively encodes temperature and velocity fields. This model achieves an impressive 98% accuracy within a 90% confidence interval, providing a reliable tool for predicting various operational conditions and ensuring optimal oven performance. Full article

Underground coal gasification (UCG) is a clean and automated coal technological process that has great potential. Environmental hazards such as the risk of ground surface subsidence, flooding, and water pollution are among the problems that restrict the application of UCG. Overburden rock stability above UCG cavities plays a key role in the prevention of the mentioned environmental hazards. It is necessary to optimize the safety pillar width to maintain rock stability and ensure minimal coal losses. This study focused on the investigation of the influence of pillar parameters on surface subsidence, taking into account the non-rectangular shape of the pillar and the presence of voids above the UCG reactor in the immediate roof. The main research was carried out using the finite element method in ANSYS 17.2 software. The results of the first simulation stage demonstrated that during underground gasification of a thin coal seam using the Controlled Retraction Injection Points method, with reactor cavities measuring 30 m in length and pillars ranging from 3.75 to 15 m in width, the surface subsidence and rock movement above gasification cavities remain within the pre-peak limits, provided the safety pillar’s bearing capacity is maintained. The probability of crack initiation in the rock mass and subsequent environmental hazards is low. However, in the case of the safety pillars’ destruction, there is a high risk of crack evolution in the overburden rock. In the case of crack formation above the gasification panel, the destruction of aquiferous sandstones and water breakthroughs into the gasification cavities become possible. The surface infrastructure is therefore at risk of destruction. The assessment of the pillars’ stability was carried out at the second stage using numerical simulation. The study of the stress–strain state and temperature distribution in the surrounding rocks near a UCG reactor shows that the size of the heat-affected zone of the UCG reactor is less than the thickness of the coal seam. This shows that there is no significant direct influence of the gasification process on the stability of the surrounding rocks around previously excavated cavities. The coal seam failure in the side walls of the UCG reactor, which occurs during gasification, leads to a reduction in the useful width of the safety pillar. The algorithm applied in this study enables the optimization of pillar width under any mining and geological conditions. This makes it possible to increase the safety and reliability of the UCG process. For the conditions of this research, the failure of coal at the stage of gasification led to a decrease in the useful width of the safety pillar by 0.5 m. The optimal width of the pillar was 15 m. Full article

The implementation of real-time dynamic monitoring of disaster formation and severity is essential for the timely adoption of disaster prevention and mitigation measures, which in turn minimizes disaster-related losses and safeguards agricultural production safety. This study establishes a low-temperature disaster (LTD) monitoring system based on machine learning algorithms, which primarily consists of a module for identifying types of disasters and a module for simulating the evolution of LTDs. This study firstly employed the KNN model combined with a piecewise function to determine the daily dynamic minimum critical temperature for low-temperature stress (LTS) experienced by winter wheat in the Huang-Huai-Hai (HHH) region after regreening, with the fitting model’s R², RMSE, MAE, NRMSE, and MBE values being 0.95, 0.79, 0.53, 0.13, and 1.716 × 10⁻¹¹, respectively. This model serves as the foundation for determining the process by which winter wheat is subjected to LTS. Subsequently, using the XGBoost algorithm to analyze the differences between spring frost and cold damage patterns, a model for identifying types of spring LTDs was developed. The validation accuracy of the model reached 86.67%. In the development of the module simulating the evolution of LTDs, the XGBoost algorithm was initially employed to construct the Low-Temperature Disaster Index (LTDI), facilitating the daily identification of LTD occurrences. Subsequently, the Low-Temperature Disaster Process Accumulation Index (LDPI) is utilized to quantify the severity of the disaster. Validation results indicate that 79.81% of the test set samples exhibit a severity level consistent with historical records. An analysis of the environmental stress-mitigation mechanisms of LTDs reveals that cooling induced by cold air passage and ground radiation are the primary stress mechanisms in the formation of LTDs. In contrast, the release of latent heat from water vapor upon cooling and the transfer of sensible heat from soil moisture serve as the principal mitigation mechanisms. In summary, the developed monitoring framework for LTDs, based on environmental patterns of LTD formation, demonstrates strong generalization capabilities in the HHH region, enabling daily dynamic assessments of the evolution and severity of LTDs. Full article