Search Results (25,552)

The cathode material design is a persistent challenge in the development of next-generation rechargeable batteries. The cathode performance is critically influenced by certain key parameters, i.e., composition, crystal structures, ion transport, and degradation behaviour. Moreover, techno-economic and sustainable considerations also play a pivotal role in the viable cathode material design. In recent years, the integration of static machine learning models with conventional experimental techniques has significantly enhanced the cathode material design. However, the sequential nature of cathode discovery has not been fully captured by these techniques as they do not update their decision strategy based on prior outcomes. In this review, reinforcement learning (RL) as a decision making technique for cathode material design has been evaluated. Firstly, cathode design space, including major cathode families, optimisation objectives, and key material variables have been explored. Afterwards, cathode discovery has been presented in terms of RL states, actions, rewards, policies, environments, constraints, and feedback. The key focus of this review is to analyse how RL can support the composition selection, dopant, and crystal structure optimisation. The review also discusses the current limitations of RL based cathode design including data scarcity, dataset bias, limited cathode specific benchmarks, reward function design, physical validity, and experimental validations. The future directions have been proposed for physics informed and experimentally validated RL infrastructure that integrates the density functional theory, molecular dynamics, artificial intelligence and human expertise. Full article

Rice is a globally important staple crop and a major dietary source of inorganic arsenic (As). Compared with upland crops, flooded rice cultivation profoundly alters soil redox conditions, making paddy soils one of the most active agricultural interfaces for As mobilization, transformation, and food-chain transfer. While previous research has primarily focused on total As and inorganic As [As(III)/As(V)], methylated and thiolated As species also carry critical agronomic and health implications. Dimethylarsinic acid (DMA) can accumulate in grain and induce straighthead disease, whereas dimethylmonothioarsenate (DMMTA) shows substantially higher toxicity and uptake potential; DMMTA root uptake can be approximately 10 times higher than DMA, and its straighthead-inducing potency can exceed DMA by more than fivefold. This review synthesizes the sources, biogeochemical transformations, plant uptake, grain accumulation, safety assessment, and management implications of As along the paddy soil–rice–grain continuum. Particular emphasis is placed on how water regimes, redox potential, Fe/Mn/Al oxides, sulfur cycling, dissolved organic matter (DOM), microbial functional genes, and crop genotypes regulate diverse As species. Quantitative evidence indicates that alternate wetting and drying (AWD) can reduce grain total As and inorganic As by medians of 32% and 22%, respectively, but may increase grain cadmium (Cd) by a median of 58%; meanwhile, DMA and DMMTA can account for approximately 10–90% and 1–21% of total grain As, respectively, emphasizing that grain-As risk cannot be evaluated using inorganic As alone. Future research should establish speciation-based monitoring systems for inorganic, methylated, and thiolated As; develop process models linking water regime, Fe/S cycling, microbial transformations, and plant transport; and translate these mechanisms into field decision tools that balance As–Cd risk reduction, crop yield, and rice safety under changing environmental conditions. Full article

Solute Carrier Family 13 Member 5 (SLC13A5) encodes the sodium-dependent citrate cotransporter NaCT, which mediates citrate transport across cell membranes. Pathogenic variants in SLC13A5 cause developmental and epileptic encephalopathy 25 with amelogenesis imperfecta, DEE25; OMIM #615905, a debilitating autosomal recessive disorder. To better define the role of NaCT in ameloblast function and enamel mineralization, we used CRISPR/Cas9 genome editing to generate Slc13a5^R337* knock-in mice that terminate NaCT translation at the Arg337 codon, which is homologous to the human SLC13A5^R333* variant associated with DEE25. We compared enamel phenotypes among wild-type, Slc13a5^+/+; heterozygous, Slc13a5^+/R337*; and homozygous, Slc13a5^R337*/R337* mice using light microscopy, in situ hybridization, immunohistochemistry, backscattered scanning electron microscopy (bSEM); and focused ion beam–scanning electron microscopy (FIB-SEM) with quantitative imaging of organelles and matrix. Citrate bioassays were performed on serum, long bones, such as the femur and tibia, and developing mouse first molars, including enamel organ epithelium, mineralized tooth matrix, and pulp mesenchyme, to assess citrate levels during the presecretory, secretory, and maturation stages of enamel formation. In addition, first molars collected at postnatal days 0, 3, 5, and 12 were analyzed to characterize glycolytic and TCA cycle-related metabolic signatures. Homozygous Slc13a5^R337*/R337* mice exhibited severe defects during the secretory and maturation stages of amelogenesis. Most notably, Slc13a5^R337*/R337* ameloblasts failed to develop a Tomes’ process, detached from the enamel matrix surface, and produced a thin, poorly mineralized crust on the dentin surface rather than organized enamel ribbons. Despite the absence of normal enamel deposition, ameloblasts initially appeared viable and did not become dysplastic until the late secretory stage. Cellular and subcellular analyses revealed increased secondary lysosomes and intracellular accumulation of enamel matrix proteins, consistent with impaired matrix processing or secretion. Citrate concentrations were elevated in serum and long bones at both 7 and 35 weeks of age. Citrate was elevated in secretory-stage Slc13a5^R337*/R337* molars at days 0 and 3, the enamel organ epithelium (including ameloblasts), the pulp mesenchyme (including odontoblasts), and mineralizing dentin and enamel matrices. These levels gradually declined at day 5 and into the enamel maturation stage (day 12). GC-MS-based analysis of central carbon metabolites revealed increased intracellular accumulation of citrate, malate, and pyruvate, suggesting altered energy metabolism and reduced metabolic efficiency in Slc13a5^R337*/R337* mice. Together, these findings indicate that loss of NaCT function in the ameloblasts causes citrate accumulation, which impairs hydroxyapatite formation. Consequently, only a thin, structurally defective mineral crust forms on the dentin surface, while mineral nodules develop ectopically within the maturation-stage enamel organ epithelium. We conclude that regulating citrate concentration is essential for proper appositional growth of enamel. Full article

Background and Objectives: Physical activity may relate to mental health differently depending on whether it is performed at work, for transport, or during leisure. Evidence regarding this distinction in Romanian community samples remains limited, and it is unclear whether these associations persist after accounting for perceived stress. We examined domain-specific physical activity, sedentary time and perceived stress in relation to anxiety and somatic symptom burden. Materials and Methods: This cross-sectional online survey included 307 Romanian adults aged 18–65 years. Physical activity was measured with the Global Physical Activity Questionnaire, perceived stress with the 14-item Perceived Stress Scale, anxiety with the Beck Anxiety Inventory, and somatic symptoms with the Patient Health Questionnaire-15. Spearman correlations were adjusted for multiple testing with the Benjamini–Hochberg false-discovery-rate procedure. Multivariable linear models used HC3 robust standard errors and included sex, age group, education, body mass index and religious affiliation as covariates. Scores for each physical activity domain were log(1 + x)-transformed and standardized. Sensitivity analyses considered participation versus non-participation, symptom severity categories, total activity and exclusion of an extreme activity value. Results: Median scores were 24 for perceived stress, 10 for anxiety and 7 for somatic symptoms. Perceived stress correlated with both anxiety (rho = 0.486) and somatic symptoms (rho = 0.428; both false-discovery-rate-adjusted p < 0.001). Vigorous leisure-time activity was inversely correlated with somatic symptoms (rho = −0.205; adjusted p = 0.002), whereas total physical activity was not associated with either outcome. In the fully adjusted models, a one-standard-deviation-higher PSS-14 score was associated with 5.57 more BAI points (95% CI 4.50–6.63) and 2.12 more PHQ-15 points (95% CI 1.62–2.63). Vigorous leisure-time activity remained associated with a lower PHQ-15 score (−0.84 points per standard deviation; 95% CI −1.35 to −0.32), but its association with BAI was no longer statistically significant after adjustment for perceived stress. Conclusions: Perceived stress showed the strongest association with both anxiety and somatic symptom burden. The use of a single total activity score concealed a more specific inverse association between vigorous leisure-time activity and somatic symptoms. Longitudinal studies using more representative sampling, objective measures of activity and broader confounder assessment are needed to clarify directionality and determine the potential relevance of vigorous leisure-time activity to somatic symptom burden. Full article

Liquid metals are enabling working fluids for several advanced nuclear systems, including fast reactors, accelerator-driven systems, and fusion blankets. System thermal-hydraulic (STH) codes require thermodynamically consistent property tables over pressure-temperature domains, whereas most liquid-metal correlations are available only as functions of temperature at a reference pressure. This paper presents the formulation and the code verification of four liquid-metal working fluids in RELAP5/Mod3.3: lead (Pb), lead-bismuth eutectic (LBE, denoted PbBi), lead-lithium alloy (PbLi, here Pb-17Li at.%), and sodium (Na). The liquid branch is reconstructed from reference correlations for specific volume, sound speed, and isobaric specific heat through a linearized pressure model, whose correction remains below 0.2% for the heavy liquid metals and below 1% for sodium over the whole tabulated pressure range. The reference pressure is set to the saturation pressure at the maximum tabulated temperature, which maximizes the admissible liquid domain, and a van der Waals equation of state closes the vapor branch. Liquid transport properties and selectable low-Prandtl-number heat-transfer correlations are implemented in the Fortran source code. Verification comprises property comparisons and two non-regression tests, a U-tube manometer, and a natural-circulation loop. The vapor model is a table-completion closure and must not be used for boiling-dominated transients. Full article

The increasing demand for lightweight and corrosion-resistant structures has accelerated the use of AA5083 aluminium alloy in automotive, aerospace, marine and transportation industries owing to its excellent corrosion resistance, weldability and favourable strength-to-weight ratio. However, the fabrication of complex AA5083 components remains challenging because of limited formability, localised thinning, fracture and springback associated with conventional forming processes. Robotic incremental sheet forming (RISF) has emerged as a promising dieless manufacturing technology capable of producing complex and customised components with reduced tooling requirements. Recent developments in assisted RISF, particularly heating-assisted and hydro-assisted approaches, have further enhanced process capability. The reviewed literature consistently demonstrates that heating-assisted RISF improves formability by reducing flow stress and fracture tendency, whereas hydro-assisted RISF provides superior thickness distribution, deformation stability and dimensional accuracy. Despite these advances, significant challenges remain, including the lack of standardised processing conditions, limited comparative studies between cold and assisted RISF, insufficient understanding of hydro-assisted RISF for AA5083, and the absence of comprehensive process–structure–performance correlations. This review critically summarises the principles of ISF, RISF and assisted RISF technologies, evaluates their technical developments, industrial potential and economic considerations, and identifies the major research gaps limiting industrial implementation. Future research should focus on standardised processing methodologies, predictive modelling, integrated process optimisation and comprehensive material characterisation to facilitate the wider adoption of assisted RISF for manufacturing advanced lightweight AA5083 components. Full article

Background: Artificial intelligence (AI) and machine-learning (ML) prognostic models are increasingly developed for inflammatory bowel disease (IBD), yet their reported performance and clinical readiness remain inadequately appraised. Methods: Following PRISMA 2020 and a registered protocol, we searched PubMed, Web of Science, IEEE Xplore, and arXiv (January 2012–January 2026) for studies developing or validating prognostic models in Crohn’s disease or ulcerative colitis. Two reviewers independently screened the studies, extracted data, and assessed risk of bias using PROBAST + AI; discrimination was summarized by area under the curve (AUC) and stratified by validation type. Results: Of the 3050 records, 111 studies were included. Treatment response was the most common target; laboratory data and electronic health records were the most frequent modalities. Across 83 studies, the median AUC was 0.850; externally validated models reached 0.870 versus 0.845 for internal-only and 0.790 for cross-validation-only. External validation was reported in 29.7%, calibration in 14.4% and analysis code in 3.6%; the analysis domain was the leading source of bias. Conclusions: The evidence base maps reported discrimination rather than demonstrated clinical readiness. Until calibration, decision-curve utility, and transportability are reported alongside external validation, clinical deployment remains premature. Full article

This study examines the structural drivers of carbon emission changes across 30 Chinese sectors from 2002 to 2023, employing a nested input–output structural decomposition analysis model grounded in both producer and consumer principles. We further construct a carbon inequality-adjusted network decoupling index to eliminate the systematic carbon transfer bias inherent to the conventional Tapio decoupling indicator. The core empirical findings are as follows: declining carbon intensity has served as the primary driver of emission reductions over the past two decades; however, its effect has been persistently offset by economic expansion. Upstream sectors, such as electricity generation, transfer substantial emissions downstream through sectoral chains, leading to a systematic overestimation of their decoupling performance, whereas the emission reductions in downstream manufacturing sectors are underestimated owing to embodied carbon imports. Inter-industry carbon inequality underwent a structural transformation following the launch of supply-side structural reforms in 2015, which substantially narrowed the arbitrage space for cross-sector carbon shifting. Cluster analysis further reveals that most industries continue to face considerable emission growth pressure. This study offers novel analytical perspectives and empirical evidence for designing carbon allowance allocation and differentiated emission reduction pathways that reconcile economic growth with environmental sustainability. This study offers a new analytical perspective and empirical evidence. It focuses on differentiated emission pathways and allowance allocations. The goal is to balance growth and sustainability. The findings also highlight a key point. Carbon markets must correct for sectoral chain carbon transfers. This study focuses on carbon emissions from 30 broadly defined sectors covering agriculture, mining, manufacturing, energy production and supply, construction, transportation, and commercial services. The accounting scope does not include direct fuel combustion emissions from residential consumption. Full article

Assessing ecosystem services, identifying their driving factors, and optimizing their spatial patterns are essential for coordinating ecological conservation and socioeconomic development in mountainous regions. Taking the Anning River Basin in southwestern China as the study area, this study quantitatively assessed ecosystem services from 2010 to 2024, integrated trade-off intensity into the Integrated Ecosystem Services Index, applied a Bayesian network model to identify the driving factors of ecosystem services, and proposed strategies for spatial pattern optimization. The results showed that: (1) the mean values of water conservation and soil conservation in the Anning River Basin were 94.45 mm and 1179.64 t ha⁻¹, respectively, both showing substantial interannual fluctuations. The habitat quality index was 0.83, and the mean carbon storage was 132.68 t ha⁻¹; both habitat quality and carbon storage declined slightly. The mean food production was 0.46 t ha⁻¹, indicating an improvement in the food production function. (2) The trade-off intensity among ecosystem services was 0.33, and the Integrated Ecosystem Services Index was 0.51, remaining generally stable overall. Ecosystem services were mainly influenced by land use, precipitation, population count, and fractional vegetation cover. Their spatial heterogeneity was pronounced, with relatively low values in the Anning River Plain, the Jinsha River dry-hot valley, and the Yanyuan Basin. (3) The identified priority conservation areas for ecosystem services covered 8307 km² and were mainly distributed within ecological conservation redline areas and regulated zones. The general functional areas for ecosystem services covered 149 km² and were concentrated in urban construction areas and along major transportation corridors in the basin. Targeted strategies were further proposed to enhance the synergistic improvement of ecosystem service supply. This study provides theoretical and practical support for ecosystem management in the Anning River Basin and other mountainous watersheds. Full article

Buchwaldoboletus xylophilus is a rare saprotrophic edible mushroom in the Boletaceae and is the second bolete species to have been successfully cultivated artificially, yet its developmental molecular regulation remains unexplored. We integrated RNA-seq transcriptomics and untargeted LC-MS/MS metabolomics across five developmental stages (A–E), applying differential expression, alternative splicing, ceRNA network construction, WGCNA, machine learning, consensus clustering, and O2PLS joint modelling. Transcriptomic analysis revealed that the mycelium-to-primordium transition (stage A to C) showed the most drastic reprogramming, with 2233 differentially expressed genes, while metabolomic comparison between stage A and E revealed overwhelmingly upregulated metabolites (184 up vs. 10 down), indicating asymmetric regulation between the two omics layers. Notably, mutually exclusive exon splicing events exhibited high functional significance, and all top ceRNA hubs were lncRNAs. Unsupervised consensus clustering validated the molecular distinctness of the sampled stages. Metabolite enrichment highlighted antioxidant functions and ABC transporter pathways, and O2PLS integrative modelling confirmed a strong shared developmental signal between transcriptome and metabolome. This study provides the first multidimensional molecular atlas for B. xylophilus, revealing a non-linear developmental programme, transcriptome–metabolome decoupling, and a central role of antioxidant defence throughout development. Our findings offer mechanistic clues regarding the evolutionary transition from ectomycorrhizal to saprotrophic lifestyles in the Boletaceae and establish a multi-omics foundation for the molecular breeding of this emerging edible and medicinal mushroom. Full article

Accurate traffic flow prediction is a core task in intelligent transportation systems because urban traffic observations are spatially distributed, temporally dynamic, and commonly held by different regional management entities. Existing centralized and local models remain limited when traffic data are non-independent and identically distributed across regions, and when raw data cannot be directly exchanged because of privacy, ownership, and communication constraints. To address these challenges, this study proposes a personalized similarity-aware federated spatiotemporal learning framework for multi-regional traffic flow prediction. The framework integrates three mechanisms: client-specific adaptation for regional distributional heterogeneity, adaptive delayed graph learning for dynamic congestion propagation, and similarity-aware federated aggregation for information-quality-based cross-client collaboration. Spatial dependency, temporal evolution, traffic-flow-theory-informed variables, road attributes, and temporal contextual features are jointly modeled without sharing raw client data. Experiments on controlled synthetic data and the Q-Traffic real-world dataset demonstrate that the proposed method consistently outperforms independent training, FedAvg, FedProx, FedSTN-inspired, and FedAGCN-inspired baselines. On the Q-Traffic grid-level setting, the proposed adaptive graph version reduces MSE by 35.3% compared with FedAvg, while the CNN version reduces MSE by 27.5%. Under the cluster-level setting, the adaptive graph version reduces MSE by 26.3% compared with FedAvg. Ablation, sensitivity, communication-cost, differential-privacy, and client-dropout analyses further show that the proposed framework improves predictive accuracy, cross-client stability, and robustness under heterogeneous federated traffic scenarios. Full article

Decarbonizing heavy-duty transport in the mining sector requires a deep understanding of the interplay between specific energy consumption, payload dynamics, and ambient thermal stressors. This study presents an integrated, physics-informed machine learning framework to compare the energy intensity of battery-electric (EV) and diesel internal combustion engine (ICE) tippers on a real quarry route in Poland. We develop a bidirectional, route-aware model using physical force balance and high-resolution elevation data to estimate net energy consumption and regenerative braking potential over a complete closed-loop cycle. Furthermore, an Artificial Intelligence analysis utilizing a Random Forest regressor is implemented to simulate and quantify the non-linear impacts of ambient temperature, haul road rolling resistance, and payload mass on the specific energy intensity ($E_{spec}$). Results indicate that while EV energy demand surges in sub-zero climates due to parasitic battery thermal management loads, electric powertrains exhibit a profound thermodynamic advantage during loaded downhill segments, acting as net energy generators via recuperation. The proposed multi-factor approach provides a robust predictive tool for optimizing fleet deployment, infrastructure positioning, and decarbonization pathways in transitionary mining environments. Full article

Accurate short-term travel time prediction is foundational for sustainable urban mobility and intelligent transportation systems on urban arterial corridors, where travel conditions are shaped by interacting traffic, weather, and public transport factors. This study proposes a multi-source machine learning framework for segment-direction-level prediction in the Denizli city center. Floating car data (FCD), Traffic Control Center (TCC) inductive loop detector measurements, historical weather, and public transport indicators were integrated into a 15 min time-segment structure. The final dataset includes 60 segment-direction targets. Performance was evaluated using Linear Regression, Random Forest, LightGBM, and LSTM under a chronological train-validation-test design. Tree-based ensemble models produced the most stable overall performance, with LightGBM and Random Forest yielding similarly low pooled test errors. Segment-level analyses revealed clear spatial and temporal heterogeneity, showing no single model is universally superior across all links. By providing reliable traffic-state information, the framework enables efficient traffic management and may indirectly reduce delay, fuel use, and emissions; these environmental effects were not quantified. SHAP-based interpretation showed that temporal and traffic-state variables dominate predictions, while weather and public transport provide complementary value. Full article

High classification performance in financial-risk early-warning research may reflect recovery of a constructed contemporaneous rating rule rather than prediction of an independent future outcome. This study distinguishes these two forms of evidence through a controlled synthetic-panel experiment situated in the context of higher education institutions (HEIs). Task 1 is a contemporaneous positive-control audit in which the four components defining a deterministic three-class rating are supplied to the classifiers. Task 2 uses predictors measured at year $t-1$ to classify the rating at year t through strict rolling-origin evaluation under prespecified Weak, Moderate, and Strong temporal-persistence conditions. In Task 1, the four fitted classifiers achieved held-out Macro-F1 values of 0.9968–1.0000, demonstrating near-complete recovery of the disclosed rating rule but providing no prospective forecasting evidence. In Task 2, the best mean annual Macro-F1/Macro-AUC increased from 0.480/0.696 under Weak persistence to 0.549/0.747 under Moderate persistence and 0.633/0.824 under Strong persistence. The Weak–Moderate–Strong ordering was observed for both primary metrics across all four fitted classifiers, while higher model complexity provided no consistent advantage within the disclosed synthetic mechanism. These findings confirm contemporaneous rule recoverability and sensitivity to deliberately embedded temporal persistence only within the controlled experiment. They do not establish predictive validity, transportability, or decision benefit in real HEIs. The study contributes a reproducible task-to-claim approach that aligns target construction, predictor–target overlap, information timing, rolling-origin evaluation, probability quality, and model performance with the inferences that the resulting evidence can legitimately support. Real-world validation would require source-indexed longitudinal HEI data, independently adjudicated post-origin outcomes, verified information-availability dates, external testing, and prospective decision evaluation. Full article

Kelp is a high-moisture, flexible, sheet-like marine biomass whose drying behavior is strongly affected by the coupled effects of radiative heating, convective vapor removal, internal moisture migration, tissue shrinkage, curling, and material overlap. Traditional sun drying and hot-air drying remain widely used but are limited by long processing cycles, environmental dependence, high energy consumption, and inconsistent product quality. With the development of infrared heating, heat-pump dehumidification, Internet of Things (IoT)-enabled sensing, fifth-generation (5G) mobile communication, multiphysics simulation, and digital control, kelp drying is progressively shifting toward monitored, model-assisted, and intelligent processing. This review critically summarizes recent advances in kelp and related seaweed drying, with particular emphasis on infrared-assisted heat and mass transfer, drying kinetics, coupled computational fluid dynamics–finite element method (CFD–FEM) simulation, quality evaluation, and intelligent control. Representative published studies demonstrate the engineering potential of these approaches. In a suspended infrared-array kelp drying system, an infrared power density of 1.2 kW m−2 combined with an air velocity of 3 m s−1 maintained the drying temperature at approximately 55–62 °C, while relative humidity decreased from about 80% to 20–30%. Under these conditions, the Page model achieved R² = 0.987 and RMSE = 0.019, the rehydration ratio exceeded 94%, and the total color difference remained below ΔE = 6.5. A recent CFD–FEM–MATLAB workflow further reported a composite operating-condition index of J = 0.4535, with mapped mean and maximum kelp surface temperatures of 62.23 and 63.57 °C, respectively. These quantitative results indicate that the key challenge in infrared kelp drying is not simply to increase heat input, but to coordinate radiation distribution, airflow organization, internal moisture transport, structural response, and quality preservation. Future research should therefore focus on experimentally validated heat–mass-transfer models, adaptive sensing and control, multi-objective optimization, and pilot-scale verification under realistic production conditions. Full article