Search Results (428)

Structural parameters, such as strain or deflection, were collected by sensors and analyzed to assess the bridge’s structural condition and obtain a reliable reference for bridge maintenance. In the data acquisition and transmission process, sensor data inevitably contains noise and interference, resulting in anomalies, especially data distortion during wireless transmission. These anomalies significantly impact data analysis and structural evaluation. To mitigate the effects of these abnormalities, we conducted the cause analysis. The Sanxia Viaduct was used to design a strain monitoring method as a bridge model. We analyzed vibrating string sensor data collected in the cold environment using the Nair method to eliminate outlier data. The analysis results of strain and temperature trends showed that the data fusion method developed in this study showed high precision and stability and effectively reduced the impact of noise and data anomalies. By monitoring actual bridges, the effectiveness and practicality of the method were validated. The model provides significant information on the development and application of bridge health monitoring technology. Full article

Thermal effects critically influence the design and construction of steel-concrete composite cable-stayed bridges, where material thermal mismatch complicates structural responses. Current code-specified temperature gradient models inadequately address long-span bridges. This study employs in-situ monitoring of the Chibi Yangtze River Bridge to propose a refined vertical temperature gradient model, utilizing an exponential function for the concrete deck and a linear function for the steel web. Finite element analysis across six construction stages reveals: (1) Under negative temperature gradients, the concrete deck develops tensile stresses (2.439–2.591 MPa), approximately 30% lower than code-predicted values (3.613–3.715 MPa), highlighting risks of longitudinal cracking. (2) At the maximum double-cantilever stage, transverse stress distributions show pronounced shear lag effects, positive shear lag in deck sections connected to crossbeams and negative shear lag in non-connected sections. The proposed model reduces tensile stress conservatism in codes by 30–33%, enhancing prediction accuracy for composite girders. This work provides critical insights for thermal effect management in long-span bridge construction. Full article

Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation. Full article

Spintronics, an interdisciplinary field merging magnetism and electronics, has attracted considerable interest due to its potential to transform data storage, logic devices, and emerging quantum technologies. Among the materials explored for spintronic applications, metal oxide nanostructures synthesized via sol–gel methods offer a unique combination of low-cost processing, structural tunability, and defect-mediated magnetic control. This comprehensive review presents a critical overview of recent advances in sol–gel-derived magnetic oxides, such as Co-doped ZnO, La_1−xSr_xMnO₃, Fe₃O₄, NiFe₂O₄, and transition-metal-doped TiO₂, with emphasis on synthesis strategies, the dopant distribution, and room-temperature ferromagnetic behavior. Key spintronic functionalities, including magnetoresistance, spin polarization, and magnetodielectric effects, are systematically examined. Importantly, this review differentiates itself from the prior literature by explicitly connecting sol–gel chemistry parameters to spin-dependent properties and by offering a comparative analysis of multiple oxide systems. Critical challenges such as phase purity, reproducibility, and defect control are also addressed. This paper concludes by outlining future research directions, including green synthesis, the integration with 2D materials, and machine-learning-assisted optimization. Overall, this work bridges sol–gel synthesis and spintronic material design, offering a roadmap for advancing next-generation oxide-based spintronic devices. Full article

The safe exploitation of marine natural gas hydrates, a promising cleaner energy resource, is hindered by reservoir instability during drilling. The inherent temperature–pressure sensitivity and cementation of hydrate-bearing sediments leads to severe operational risks, including borehole collapse, gas invasion, and even blowouts. This review synthesizes the complex instability mechanisms and evaluates the state of the art in inhibitive, wellbore-stabilizing drilling fluids. The analysis first deconstructs the multiphysics-coupled failure process, where drilling-induced disturbances trigger a cascade of thermodynamic decomposition, kinetic-driven gas release, and geomechanical strength degradation. Subsequently, current drilling fluid strategies are critically assessed. This includes evaluating the limitations of conventional thermodynamic inhibitors (salts, alcohols, and amines) and the advancing role of kinetic inhibitors and anti-agglomerants. Innovations in wellbore reinforcement using nanomaterials and functional polymers to counteract mechanical failure are also highlighted. Finally, a forward-looking perspective is proposed, emphasizing the need for multiscale predictive models that bridge molecular interactions with macroscopic behavior. Future research should prioritize the development of “smart”, multifunctional, and green drilling fluid materials, integrated with real-time monitoring and control systems. This integrated approach is essential for unlocking the potential of marine gas hydrates safely and efficiently. Full article

In order to upgrade the potential of cereal bran arabinoxylan for advanced hydrogel applications, a deep understanding of its gelation process is required. This work provides a comprehensive and systematic analysis of the laccase-catalyzed cross-linking of feruloylated arabinoxylan (FAX) to establish a clear link between processing conditions and final hydrogel properties. Endo-1,4-xylanase was used to obtain corn bran FAX rich in ferulic acid moieties, and then we demonstrated that gel formation is driven by the oxidative coupling of these feruloyl monomers into diferulic acid bridges, e.g., 8-5′, 5-5′, 8-O-4′, and 8-5′ benzofuran diferulic acids. A systematic investigation revealed that hydrogel properties were significantly affected by the processing conditions, i.e., FAX concentration, enzyme dosage, reaction pH, and reaction temperature during the enzymatic gel formation catalyzed by laccase. While gel strength peaked at a FAX concentration of 30 mg/mL, an optimal temperature of 25 °C and pH 6 were identified. Notably, we discovered a critical trade-off with enzyme concentration: higher laccase levels accelerated the reaction but compromised the final hydrogel’s mechanical strength and water retention. Gelation failed completely at pH ≥ 9 due to laccase inactivation. Meanwhile, scanning electron microscope analysis revealed that the microstructure of the FAX hydrogels was significantly affected by changes in the processing conditions. These findings offer crucial insights for the rational design of FAX-based hydrogels, enabling their tailored fabrication for food industry applications. Full article

The temperature field distribution and temperature effects of concrete box girder bridges were found to be critical to their long-term service safety. Based on long-term structural health monitoring data, the temperature field and temperature effects of a curved continuous concrete box girder bridge in Wuhan were investigated. A finite element model of the temperature field was established through the combined application of finite element software. Extreme weather files were constructed to analyze the bridge’s temperature field and temperature effects. To enhance data reliability, wavelet analysis was employed for denoising the monitoring data. The results indicate a strong correlation between girder temperature and ambient temperature. Under solar radiation, significant vertical temperature differences and certain lateral temperature differences are observed within the concrete box girder. The accuracy of the finite element model was validated through comparison with measured data. Temperature field models featuring the most unfavorable vertical and transverse temperature gradient distribution patterns for concrete box girder bridges under extreme weather conditions in the Wuhan region were established. A distinct temperature difference not covered by specifications exists at the webs and bottom slabs of the bridge. Strong correlations were observed between both pier–girder relative displacement and bottom slab stress with the girder temperature. Full article

Rising urban temperatures due to climate change, limited green spaces, and dense urban areas impact public health and human well-being, highlighting the need for innovative grey infrastructure solutions where conventional green spaces are not feasible. This study aims to bridge the gap between objectively measured and perceived benefits of urban heat mitigation by combining social and technological methods within socio-ecological systems. First, a literature review of 759 articles, with 64 meeting the review criteria, and a bibliometric analysis examined the recent extensive research on participation and the connections between participation, resilience, and sustainability. Second, a chain of evidence as a qualitative method demonstrated how Active Bottom Surface Cooling (ABSC) can enhance outdoor thermal comfort (OTC). This emphasised the importance of participatory innovation and novel cooling technologies for urban resilience: hybrid (digital and analogue) participation can raise the awareness, acceptance, and effectiveness of such technical innovations. It revealed the need for an integrated framework, leveraging synergies: (1) community engagement tailors solutions to urban needs, (2) adaptability ensures effectiveness across diverse settings, (3) improved thermal comfort enhances citizen well-being, and (4) resilience strengthens the climate change response. By conceptualising cities as urban systems, the integrated framework fosters reciprocal socio-ecological benefits between people, nature, and the environment. Through hybrid participation and ABSC, it boosts community engagement, OTC, and well-being for sustainable urban development. Full article

Chip-level cooling has become a thermal bottleneck in next-generation data centers. Although previous studies have optimized evaporator wick structures, they are limited to a single condensation path and ignore the combined effects of the loop heat pipe (LHP) orientation on the capillary wick (CW) replenishment and reflux subcooling. To bridge this gap, this study successfully designed an innovative flat-plate evaporator water-cooled LHP with a parallel condensation pipeline. Experiments were conducted with a 20 °C coolant and at a 4 L/min flow rate across nine orientations. The heat transfer characteristics of LHPs with parallel and series condensation pipelines were compared. The analysis focused on the relationship between the working fluid (WF) replenishment of the CW and the WF reflux temperature in the compensating chamber (CC). The experimental results demonstrated that the parallel condensation LHP could sustainably dissipate 750 W without thermal runaway. At this power, the minimum junction temperature of 82.34 °C was measured at orientation 2 (+60°). For low power and at the nine orientations, the series LHP generally had lower temperatures. However, when the power exceeded 600 W, the parallel LHP showed lower temperatures at orientations 1 (+90°), 2 (+60°), and 3 (+30°). At orientation 9, the parallel LHP had lower temperatures when the power surpassed 200 W. Theoretical analysis indicated that the orientation changes affected the heat transfer via the WF reflux temperature, reflux resistance, and CW replenishment rate. Furthermore, the LHP system we developed in this study is capable of fully satisfying the cooling requirements of data center server chips. Full article

Climate change has magnified health disparities across the Southern African Development Community (SADC) region by destabilizing the critical natural systems, which include water security, food production, and disease ecology. The IPCC (2007) underscores the disproportionate impact on low-income populations characterized by limited adaptive capacity, exacerbating existing vulnerabilities. Rising temperatures, erratic precipitation patterns, and increased frequency of extreme weather events ranging from prolonged droughts to catastrophic floods have created favourable conditions for the spread of waterborne diseases such as cholera, dysentery, and typhoid, as well as the expansion of vector-borne diseases zone also characterized by warmer and wetter conditions where diseases like malaria thrives. This study employed a comparative analysis of climate and health data across Malawi, Zimbabwe, Mozambique, and South Africa examining the interplay between climatic shifts and disease patterns. Through reviews of national surveillance reports, adaptation policies, and outbreak records, the analysis reveals the existence of critical gaps in preparedness and response. Zimbabwe’s Matabeleland region experienced a doubling of diarrheal diseases in 2019 due to drought-driven water shortages, forcing communities to rely on unsafe alternatives. Mozambique faced a similar crisis following Cyclone Idai in 2019, where floodwaters precipitated a threefold surge in cholera cases, predominantly affecting children under five. In Malawi, Cyclone Ana’s catastrophic flooding in 2022 contaminated water sources, leading to a devastating cholera outbreak that claimed over 1200 lives. Meanwhile, in South Africa, inadequate sanitation in KwaZulu-Natal’s informal settlements amplified cholera transmission during the 2023 rainy season. Malaria incidence has also risen in these regions, with warmer temperatures extending the geographic range of Anopheles mosquitoes and lengthening the transmission seasons. The findings underscore an urgent need for integrated, multisectoral interventions. Strengthening disease surveillance systems to incorporate climate data could enhance early warning capabilities, while national adaptation plans must prioritize health resilience by bridging gaps between water, agriculture, and infrastructure policies. Community-level interventions, such as water purification programs and targeted vector control, are essential to reduce outbreaks in high-risk areas. Beyond these findings, there is a critical need to invest in longitudinal research so as to elucidate the causal pathways between climate change and disease burden, particularly for understudied linkages like malaria expansion and urbanization. Without coordinated action, climate-related health inequalities will continue to widen, leaving marginalized populations increasingly vulnerable to preventable diseases. The SADC region must adopt evidence-based, equity-centred strategies to mitigate these growing threats and safeguard public health in a warming world. Full article

This study adopts a multi-scale, simulation-driven approach to evaluate the performance of different passive roof types in mitigating Urban Heat Island (UHI) in hot arid climate. A comparative analysis was performed for selected roof types; green, pond, cool, and dark roofs. At the urban scale, ENVI-met v5.7.1 was employed to simulate microclimatic impacts, including Mean Radiant Temperature (MRT) at the pedestrian street level (1.4 m) and above building canopy level (25 m). The results revealed that green roofs were the most effective in mitigating UHI on the urban scale, reducing MRT by 1.83 °C at the pedestrian level and by 3.5 °C at the above canopy level. Surprisingly, dark roofs also performed well, with MRT reductions of 1.81 °C and 3.5 °C, respectively, outperforming pond roofs, which showed reductions of 1.80 °C and 0.31 °C. While cool roofs effectively reduced MRT at the pedestrian level by 1.80 °C, they had adverse effect at the canopy level, increasing MRT by 15.58 °C. At the building scale, Design Builder v7.3.1, coupled with Energy Plus, was used to assess indoor thermal and energy performance. Pond and cool roofs reduced operative temperature by 0.08 °C and 0.07 °C, respectively, followed by green roofs, with a 0.05 °C reduction, while dark roofs increased it by 0.07 °C. In terms of energy performance, green roofs yielded the greatest benefit, reducing cooling load by 3.3%, followed by pond roofs, with a 1.32% reduction; cool roofs showed negligible reduction, while dark roofs increased it by 1.2%. Finally, a Python-based Multi criteria Decision Making (MCDM) analytical framework integrated these findings with additional factors to optimize thermal comfort, environmental impact, sustainability, and feasibility and rank strategies accordingly. The analysis identified green roofs as the optimal solution, followed by pond roofs and then cool roofs tied with the base case, leaving dark roofs as the least favorable strategy. This study’s key contribution lies in its integrated simulation–decision analysis methodology, which bridges urban climatology and building performance to provide actionable insights for sustainable urban design. By validating green roofs as the most effective passive strategy in hot arid regions, this work aids policymakers and planners in prioritizing interventions that support climate-resilient urbanization. Full article

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. To address this challenge and gain deeper insights into the environmental controls of LAI, this study aims to accurately simulate LAI in the Loess Plateau using deep learning models and to elucidate the spatiotemporal influence of soil moisture and temperature on LAI dynamics. For this purpose, we used three deep learning models, namely Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Interpretable Multivariable (IMV)-LSTM, to simulate LAI in the Loess Plateau, only using soil moisture and temperature as inputs. Results indicated that our approach outperformed traditional models and effectively captured LAI variations across different vegetation types. The attention analysis revealed that soil moisture mainly influenced LAI in the arid northwest and temperature was the predominant effect in the humid southeast. Seasonally, soil moisture was crucial in spring and summer, notably in grasslands and croplands, whereas temperature dominated in autumn and winter. Notably, forests had the longest temperature-sensitive periods. As LAI increased, soil moisture became more influential, and at peak LAI, both factors exerted varying controls on different vegetation types. These findings demonstrated the strength of deep learning for simulating vegetation–climate interactions and provided insights into hydrothermal regulation mechanisms in semiarid regions. Full article

Root phenology significantly influences ecosystem processes yet remains poorly characterized across biomes. This study synthesized data from 59 studies spanning Arctic to tropical ecosystems to identify woody plants root phenological patterns and their environmental drivers. The analysis revealed distinct climate-specific patterns. Arctic regions had a short growing season with remarkably low temperature threshold for initiation of root growth (0.5–1 °C). Temperate forests displayed pronounced spring-summer growth patterns with root growth initiation occurring at 1–9 °C. Mediterranean ecosystems showed bimodal patterns optimized around moisture availability, and tropical regions demonstrate seasonality primarily driven by precipitation. Root-shoot coordination varies predictably across biomes, with humid continental ecosystems showing the highest synchronous above- and belowground activity (57%), temperate regions exhibiting leaf-before-root emergence (55%), and Mediterranean regions consistently showing root-before-leaf patterns (100%). Winter root growth is more widespread than previously recognized (35% of studies), primarily in tropical and Mediterranean regions. Temperature thresholds for phenological transitions vary with climate region, suggesting adaptations to environmental conditions. These findings provide a critical, region-specific framework for improving models of terrestrial ecosystem responses to climate change. While our synthesis clarifies distinct phenological strategies, its conclusions are drawn from data focused primarily on Northern Hemisphere woody plants, highlighting significant geographic gaps in our current understanding. Bridging these knowledge gaps is essential for accurately forecasting how belowground dynamics will influence global carbon sequestration, nutrient cycling, and ecosystem resilience under changing climatic regimes. Full article

Preserving historical porous materials requires careful monitoring of surface humidity to mitigate deterioration processes like salt crystallization, mold growth, and material decay. While microclimate monitoring is a recognized preventive conservation tool, its role in detecting surface-specific moisture risks remains underexplored. This study evaluates the relationship between indoor microclimate fluctuations and surface moisture dynamics across 13 historical sites in Northern Italy (Lake Como, Valtellina, Valposchiavo), encompassing diverse masonry typologies and environmental conditions. High-resolution sensors recorded temperature and relative humidity for a minimum of 13 months, and eight indicators—including dew point depression, critical temperature–humidity zones, and damp effect indices—were analyzed to assess the moisture risks. The results demonstrate that multivariate microclimate data could effectively predict humidity accumulation. The key findings reveal the impact of seasonal ventilation, thermal inertia, and localized air stagnation on moisture distribution, with unheated alpine sites showing the highest condensation risk. The study highlights the need for integrated monitoring approaches, combining dew point analysis, mixing ratio stability, and buffering performance, to enable early risk detection and targeted conservation strategies. These insights bridge the gap between environmental monitoring and surface moisture diagnostics in porous heritage materials. Full article

Carbon black (CB)-filled rubber materials are extensively used in civil engineering seismic isolation. However, CB-filled rubber materials often experience mechanical property degradation because of exposure to environmental factors. To better understand the influences of thermo-oxidative, ultraviolet and ozone aging on mechanical property degradation, uniaxial tension and dynamic mechanical analysis (DMA) tests were carried out. In the uniaxial tension tests, the stress strength and elongation decreased with an increase in aging time. In the DMA tests, the effective temperature ranges decreased by 3.4–14%. And the neo-Hookean model was applied to simulate the hyperelasticity of CB-filled rubber materials. The relationship between the elastic modulus (a constant of the neo-Hookean model) and aging time was established, which provided a qualitative relationship between crosslink density and aging time. In addition, the dispersion of the CB aggregate was investigated using an atomic force microscope (AFM). The results indicated that the mechanical property degradation might be closely related to the aggregate diameter. This paper establishes a bridge between the microstructure and mechanical properties of CB-filled rubber materials, which can improve the understanding of the mechanical property degradation mechanisms of rubber materials and the fabrication of rubber components. Full article