Search Results (6,899)

The structural safety of lithium-ion battery systems in electric vehicles (EVs) has become increasingly important with growing concerns over battery-related accidents. In particular, external impacts on the battery pack case (BPC) can cause cell deformation or short-circuiting, potentially leading to thermal runaway. In this study, the mechanical integrity of a commercial BPC was evaluated using both drop-weight impact tests and finite element method (FEM) simulations. A 10 kg hemispherical or cylindrical weight was dropped from a height of 7 m to generate high-energy vertical impacts. The deformation and potential failure of the BPC were examined experimentally, and equivalent FEM simulations were conducted to analyze stress and deformation responses. In both cases, the BPC maintained structural integrity without cracking or intrusion into the battery cell region. The hemispherical impact resulted in relatively shallow, distributed deformation, whereas the cylindrical impact produced more localized indentations due to stress concentrations. The close agreement between experiment and simulation confirms the suitability of FEM for pre-assessment of BPC impact on safety. These findings provide a useful basis for establishing mechanical design criteria and improving battery protection strategies for EV applications. Full article

Designing sustainable pedestrian infrastructure in hyper-arid cultural landscapes requires balancing visitor experience, heritage protection, and environmental constraints. This study develops a statistically grounded model for planning sustainable walking trails in Al-Ula, Saudi Arabia, using multi-spectral remote sensing data integrated with expert-based evaluation. A GIS-based Multi-Criteria Decision-Making (MCDM) framework was applied to assess topographic slope, vegetation cover (NDVI), built-up density (NDBI), Land Surface Temperature (LST), and solar exposure. Indicator weights were validated through a three-round Delphi survey involving fifteen experts. The results indicate strong consensus among experts, identifying LST (21%) and slope (20%) as the most influential determinants of trail suitability in desert environments. These findings highlight the critical role of thermal stress in shaping safe and sustainable pedestrian mobility in hot climates. The optimized 44.5 km trail network, classified into three difficulty levels, improves energetic efficiency by reducing caloric expenditure by 24% compared to conventional routing. In addition, the proposed network has the potential to reduce carbon emissions associated with heritage-related travel by approximately 75% through modal shift from vehicles to walking. The framework provides a practical decision-support tool for planners seeking to develop low-carbon, climate-responsive tourism infrastructure aligned with the objectives of Saudi Arabia’s Vision 2030. Full article

Climate change exerts growing pressure on the built environment, intensifying urban heat stress, altering microclimatic conditions, and increasing energy demand and health risks. Urban areas, characterized by dense construction and extensive soil sealing, are particularly susceptible to thermal anomalies such as Urban Heat Islands (UHIs), making thermal assessment a crucial element in adaptation and mitigation strategies. This research provides an updated and critical review of methodologies for the thermal evaluation of the built environment, with a focus on remote sensing as an emerging and integrative measurement paradigm. The study presents a comprehensive framework of detection systems, including satellite and aerial remote sensing, ground-based monitoring, and hybrid approaches, complemented by analytical and modeling techniques that combine physical and data-driven methods. A comparative assessment of open-access satellite sensors is carried out, analyzing spatial, spectral, and temporal resolutions and their relevance to urban-scale applications. The integration of remote sensing data with artificial intelligence, machine learning, and cloud-based processing is highlighted as a key advancement for improving interpretative, predictive, and decision-support capabilities. The findings indicate that such integration represents a new frontier for multiscale thermal analysis, supporting resilient urban planning, enhanced energy efficiency, and effective climate change mitigation policies. Full article

Liquid hydrogen (LH₂) as an energy source is viewed as a potential path to achieve carbon neutral commercial aviation, albeit accompanied by a plethora of structural, thermal and safety challenges that still need to be resolved. With respect to a LH₂ tank’s structural integration aspect, static, damage tolerance and impact/crashworthiness responses need to be investigated. Ιn the present work, an efficient structural integration concept of LH₂ tanks into a Regional Commercial Aircraft fuselage is proposed, analyzed and preliminary designed, as part of the Clean Aviation project HERFUSE. The main purpose of the work is the feasibility assessment of introducing adhesively bonded solutions in the connection of LH₂ tanks to the aircraft fuselage. The initial design of the potential mounting system configuration was investigated via a finite element parametric simulation model that was developed for this purpose and used to analyze different variations in the proposed concept, under certification relevant load cases. Different variations in the mounting system were assessed, considering their effect on the stress concentrations developed in the fuselage and the tank structure, as well as induced deformations and potential joints debonding. The results indicated that the utilization of adhesive bonding elements in the design of an LH₂ tank integration system is conceptually efficient, although the specific configuration-related shortcomings that were identified need to be tackled. As far as the preliminary design study results are concerned, the minimum required number of joining elements were identified and an initial mass prediction of the mounting system was performed to be used as initial value in the entire hybrid–electric novel aircraft design loop. Future studies on the detailed design and sizing of the mounting system, as well as to incorporate dynamic crash analyses and implementation of energy absorbing elements are needed. Full article

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework. Full article

Preeclampsia (PE) is associated with systemic oxidative stress and vascular dysfunction, yet its effects on red blood cell (RBC) stability and mechanics remain incompletely understood. Here, we investigate the structural and nanomechanical alterations of RBCs in third-trimester pregnancies complicated by non-severe and severe PE, compared with normotensive controls. RBCs are analyzed using differential scanning calorimetry (DSC) to assess protein thermal stability and atomic force microscopy (AFM) to determine membrane elasticity (Young’s modulus) during in vitro aging. Linear mixed-effects models аre applied to evaluate the effects of disease severity, storage time, and their (group × storage time) interaction. DSC reveals that Band 3 and hemoglobin exhibited pronounced destabilization in PE, with severe cases showing earlier and larger reductions in transition temperatures and heat capacities, indicative of disrupted membrane–cytoskeletal interactions. AFM confirms that these molecular changes translate into functional consequences: control and non-severe PE RBCs show physiological softening over time, whereas severe PE RBCs undergo pathological stiffening. Statistical modeling demonstrates strong time, group, and interaction effects for both thermodynamic and mechanical parameters. Together, these findings identify the Band 3–hemoglobin macrocomplex as a primary target of PE-induced RBC alterations and suggest that combined thermodynamic–nanomechanical profiling can serve as a sensitive approach to detect early subclinical RBC damage not detectable by routine hematological tests. Full article

High-geothermal tunnels are subjected to complex thermo–hydro–mechanical (THM) coupling effects, where the interaction of temperature, seepage, and stress significantly influences the stability of surrounding rock. To address the limitations of conventional models assuming uniform initial temperature, a THM-coupled numerical model incorporating an in situ temperature gradient is established based on the Sangzhuling Tunnel. The concept of the thermal influence zone is quantitatively defined by an equivalent-radius method, and its spatiotemporal evolution is systematically investigated. In addition, the distinct roles of temperature and pore water pressure in controlling deformation and plastic-zone evolution are comparatively clarified. The results show that the thermal influence zone expands nonlinearly with increasing initial rock temperature and gradually stabilizes over time. Temperature and pore water pressure both promote the development of the plastic zone, which predominantly propagates along directions approximately 45° to the horizontal. Under the geological and boundary conditions considered in this study, temperature plays a dominant role by inducing thermal stress and degrading mechanical properties, leading to significant expansion of the plastic zone and increased vault deformation. In contrast, pore water pressure mainly reduces effective stress, thereby influencing deformation distribution, especially at the tunnel invert. Overall, THM coupling significantly amplifies surrounding rock failure compared with single-field conditions. The findings provide quantitative insights into the evolution of the thermal influence zone and its coupled control on deformation and plasticity, offering a theoretical basis for support design and stability control in high-geothermal tunnels. Full article

Siponimod, a selective sphingosine 1-phosphate (S1P) receptor modulator, represents a next-generation therapeutic drug for active secondary progressive multiple sclerosis. This study conducted in-depth forced degradation studies of siponimod in solid state subjected to acidic, alkaline, oxidative, photolytic, and thermal conditions, in compliance with ICH guidelines Q1A (R2) and Q3A (R2). An HPLC method was developed to quantify siponimod and separate its degradation products (DPs). The DPs were characterized using LC-HRMS/MS and LC-MSⁿ techniques. Moreover, the toxicological profiles of siponimod and its DPs were evaluated through the in silico tools ProTox 3.0 and ADMETlab 3.0, with molecular docking and dynamics simulations assessing their binding to the S1P1 receptor. Siponimod was stable to light but degraded under acidic, alkaline, oxidative, and thermal stress, producing five products: DP-1 (acidic), DP-2/3 (oxidative), DP-4 (hydrolytic), and DP-5 (thermal). The toxicity prediction suggested that neither siponimod nor its DPs exhibited carcinogenic or mutagenic potential, and the molecular modeling analysis revealed that DP-2 and DP-3 demonstrated favorable binding affinities, with stable dynamic profiles and thermodynamic properties that closely resembled those of siponimod. As far as we know, this is the first study on the structural elucidation of the DPs of siponimod by LC-HRMS/MS and LC-MSⁿ. Full article

Influenza A virus (IAV) surveillance in swine relies heavily on molecular detection, yet RNA stability in diagnostic specimens such as oral fluids can be rapidly compromised when cold-chain conditions are not maintained. This pilot study evaluated the ability of four molecular-grade carbohydrates (20% trehalose, sorbitol, sucrose, and mannitol) and two commercial nucleic acid stabilizers (PrimeStore^® MTM and RNAlater^®) to preserve RT-qPCR-detectable IAV RNA in swine oral fluids exposed to field-relevant stress conditions. Oral fluid samples collected from pigs experimentally infected with H1N2 (Study 1: n = 150; DPIs 2, 3, 4) or with H1N2 and H3N2 (Study 2: n = 58; DPI 5) were subjected to storage at 25 °C for up to 144 h (Study 1) or 2, 5, 10, or 15 freeze–thaw cycles (Study 2), with DPIs (Study 1) or subtypes (Study 2) serving as biological replicates, given the limited sample size. IAV detection was quantified as efficiency standardized Cq values (ECq) and analyzed using a linear mixed-effects model. Overall, both carbohydrates (trehalose, sorbitol, sucrose) and commercial stabilizers maintained higher ECq values than untreated oral fluids under both thermal and freeze–thaw stress conditions. Due to the limited sample size, these findings should be interpreted cautiously, yet they demonstrate the potential utility of carbohydrates as a low-cost, non-inactivating alternative for stabilizing IAV RNA in field-collected oral fluids. Full article

Traditionally, the calibration of welding heat source model parameters mainly relies on empirical trial-and-error methods, which lack clear guidance and generally lead to low computational efficiency. To address this problem, this paper establishes a quantitative relationship between heat source parameters and weld pool dimensions, which significantly improves the efficiency and accuracy of the simulation. Furthermore, the influence of laws of key parameters of the double-ellipsoid heat source and welding thermal efficiency on the geometric characteristics of the weld pool is systematically analyzed via numerical simulation. On this basis, finite element models considering different welding sequences are established for single and multiple T-rib components, and appropriate welding process parameters are determined according to the influence laws of heat source parameters. The thermo-elastic–plastic finite element method is then adopted to analyze the effects of welding sequences on the welding residual stress and deformation of T-rib and top-plate joints in steel box girders. By comparing different welding schemes, optimized welding strategies for single and multi-rib welding are proposed. The results show that for single T-ribs, simultaneous welding in the same direction produces the minimum residual stress and deformation with almost no distortion, followed by sequential bilateral welding in the same direction. For multi-rib welding with a spacing of 300 mm, synchronous welding yields the smallest deformation, followed by symmetric double-pass synchronous welding from inside to outside. For continuous single-pass welding, an inside-to-outside skip welding sequence is recommended to effectively control residual stress and deformation. Full article

Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications. Full article

Background: Gastric cancer (GC) is characterized by aggressive invasion and early peritoneal dissemination, which are strongly driven by chronic inflammation and epithelial–mesenchymal transition (EMT). c-Jun N-terminal kinase (JNK), a stress-responsive serine/threonine kinase within the mitogen-activated protein kinase (MAPK) family, integrates inflammatory cues to promote EMT and metastasis. Huangjin Shuangshen granules (HJSS) is a multi-component traditional Chinese medicine (TCM) formula derived from Simiao Yong’an Decoction and clinically used as an adjuvant therapy for GC. However, whether HJSS restrains inflammation-driven metastasis through modulation of JNK-associated EMT signaling remains unclear. Methods: The anti-metastatic efficacy of HJSS was evaluated using integrated in vivo and in vitro models, combined with transcriptomics, network pharmacology and molecular validation. Results: HJSS markedly attenuated LPS-induced metastatic behavior and inflammatory activation. Multilevel analyses converged on MAPK8/JNK as a central regulatory node. HJSS reversed EMT progression and inhibited nuclear phosphorylation of JNK without affecting its upstream kinases. Thermal-shift assays and molecular docking supported potential target engagement of HJSS-derived constituents, including possible interactions with JNK-related signaling targets. Pharmacologic reactivation of JNK partially abrogated the inhibitory effects of HJSS, confirming JNK-dependent action. Conclusions: HJSS suppresses inflammation-driven GC metastasis primarily by attenuating JNK-associated EMT, potentially through modulation of JNK activation by its bioactive constituents. These findings provide mechanistic insight into HJSS as a low-toxicity anti-metastatic strategy and support further exploration of its active constituents. Full article

Chronic oxidative stress and lipid imbalance drive metabolic disorders such as obesity and non-alcoholic fatty liver disease, yet few therapies target the upstream redox imbalance in key tissues. Human carbonic anhydrase III (hCA III), a redox-associated enzyme enriched in liver and adipose tissue, has long remained pharmacologically elusive due to its low catalytic activity and lack of modulators. Here, we identify fragment-like nicotinic acid derivatives as non-sulfonamide hCA III modulators and evaluate their associated cellular effects. Using an esterase activity assay, we screened 25 analogues and identified two fragment-like hits, compound 17 (2-thioethyl) and compound 22 (6-morpholino), with IC₅₀ values of 487 and 361 µM, respectively. Orthogonal thermal shift analysis supported compound-protein interaction, and selected hits were subsequently evaluated in HepG2 cells. Both compounds were associated with reduced CA3 mRNA expression after treatment at 1 µM, while their cellular phenotypes diverged, with compound 22 increasing ROS under oxidative stress conditions and compound 17 affecting mitochondrial membrane potential. Taken together, these findings identify tractable nicotinic acid-derived fragment hits and associated cellular phenotypes that warrant further mechanistic investigation. These fragment-like hits provide a practical starting point for studying the redox-linked biology of hCA III. Full article

To address the vulnerability of renewable-dominated power grids to cascading failures under extreme conditions and the limitations of existing methods in jointly handling vulnerability identification, energy storage allocation, and online control, this paper proposes an energy-storage-assisted coordinated defense strategy. First, a source-load uncertainty model is constructed and seven typical extreme operating scenarios are identified. Second, a cascading-failure evolution model that accounts for thermal accumulation is established to identify critical vulnerable branches. Third, for areas prone to local disconnection and weak terminal voltages, a coordinated ESS allocation model is developed by jointly considering active power, energy capacity, and reactive power support to determine candidate deployment locations and capacities. Finally, a graph neural network (GNN) is used to extract time-varying topological and electrical-state features, and proximal policy optimization (PPO) is employed to generate coordinated control commands for multiple ESSs, thereby linking overload suppression with voltage support. The results for the modified IEEE 39-bus system show that the proposed method identifies high-risk branches more accurately and forms an integrated defense chain covering identification, allocation, and control. The method reduces thermal stress in critical sections during the early stage of a fault, mitigates load shedding, and enhances system survivability. Full article

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article