Search Results (3,626)

The development of digital learning technologies has introduced innovative tools to enhance science and chemistry education, including PhET simulations. This study aims to evaluate the effectiveness of PhET simulations on students’ learning outcomes through a systematic literature review following the PRISMA 2020 guidelines. A systematic search of Scopus and Crossref databases was conducted (last search: January 2026) using predefined keywords. Eligible studies were empirical research published between 2020 and 2026 that investigated PhET simulations in science-related education and reported learning outcomes, while non-empirical studies and non-Scopus-indexed articles were excluded. Risk of bias was assessed using an adapted Joanna Briggs Institute critical appraisal tool. Due to heterogeneity in study designs and outcome measures, the results were synthesized using a narrative approach. A total of 14 studies across elementary to higher education levels were included. The findings indicate that PhET simulations consistently improve learning outcomes, particularly academic achievement and conceptual understanding, with effects generally favoring simulation-based instruction over traditional methods. However, higher-order skills and affective outcomes such as motivation and attitude remain less frequently investigated. The evidence is limited by variability in study designs, incomplete reporting of non-cognitive outcomes, and the absence of quantitative synthesis. Overall, PhET simulations demonstrate strong potential as an effective interactive learning medium, although their impact depends on instructional design, teacher facilitation, and technological accessibility. Full article

Against the backdrop of frequent global shocks and increasingly complex supply chain networks, supply chain disruption risk exposure has become a major challenge affecting firms’ operational stability and sustainable competitive advantage. Meanwhile, generative artificial intelligence is being increasingly embedded in business operations and has demonstrated strong application potential in information processing, risk identification, and decision support. Based on data from Chinese A-share listed firms from 2017 to 2024 and using text measures based on Management Discussion and Analysis (MD&A) disclosures of Generative AI application and supply chain disruption risk exposure, this study examines the relationship between Generative AI application and corporate supply chain disruption risk exposure, and further explores the channels through which this relationship may operate from the perspective of risk governance transformation. The results show that Generative AI application is significantly associated with lower corporate supply chain disruption risk exposure, and this relationship remains robust across a series of robustness checks and supplementary endogeneity analyses. Channel analyses suggest that this relationship may be related to firms’ risk governance transformation, mainly reflected in enhanced risk identification capability, improved resource allocation capability, and strengthened collaborative response capability. Heterogeneity analyses show that this association is more pronounced among firms facing higher environmental uncertainty, manufacturing firms, and firms located in cities with lower entrepreneurial vitality. This study provides text-based firm-level evidence for understanding the relationship between Generative AI application and supply chain risk governance, and offers managerial implications for firms seeking to promote scenario-based Generative AI application and enhance supply chain resilience and risk governance capability. Full article

Software quality assessment in agile projects remains fragmented. Technical, process-related, and team-related indicators are often evaluated through separate models, tools, and reports. This fragmentation limits cross-project comparability and weakens evidence-based decisions for software quality improvement. To address this problem, this study proposes the Overall Software Quality Index (OSQI), a multidimensional decision-support framework for software quality assessment in agile projects. OSQI integrates code quality, process quality, and team quality into a single project-level assessment model. The framework was initially grounded in ISO/IEC 25010:2011 and is discussed in relation to the ISO/IEC 25010:2023 revision, particularly its explicit inclusion of Safety as a product quality characteristic. Since the industrial datasets used in this study were not collected from safety-critical systems, Safety was not modeled as a separate OSQI dimension in the current version; instead, it is addressed as a scope limitation and future extension. The measurement structure was defined using the Goal–Question–Metric (GQM) approach. An initial set of 49 candidate metrics was reduced to 15 core indicators. This reduction was performed using dimension-specific strategies: Random Forest-based feature importance for code quality, Delphi and Analytic Hierarchy Process (AHP) for process quality, and thematic consolidation for team quality. The selected indicators were normalized and integrated through entropy-based weighting. This process generates an interpretable composite quality score. The main contribution of OSQI is not the isolated use of these methods, but their integration into a reproducible and tool-supported framework. The framework converts heterogeneous software engineering signals into a unified decision-support index. OSQI was evaluated using industrial agile project data. The data included static code analysis outputs, issue-tracking records, team assessment results, and product outcome indicators. In an exploratory validation across five industrial projects, OSQI showed a strong positive association with Net Promoter Score ($r=0.97, p=0.0076$) and a strong negative association with churn rate ($r=-0.97, p=0.0061$). A supporting software tool was also developed to automate data integration, score calculation, visualization, and project-level comparison. The findings suggest that OSQI can support quality monitoring, project benchmarking, and evidence-based improvement decisions in agile software engineering contexts. Full article

Heat transport in heterogeneous materials can deviate markedly from classical Fourier behavior when microstructural disorder, trapping effects, nonlinear mobility, and long-range temporal correlations interact across multiple spatial and temporal scales. These mechanisms may produce delayed relaxation, persistent thermal footprints, front deformation, and non-classical spreading patterns that are not adequately represented by conventional integer-order diffusion models. In this study, a modeling and simulation framework is developed for anomalous heat transport in heterogeneous media using a two-dimensional time-fractional porous medium equation. The model combines a Caputo fractional time derivative, which represents thermal memory, with nonlinear degenerate porous-medium diffusion, spatially heterogeneous conductivity, localized volumetric heating, and Robin-type convective boundary exchange. A conservative fully discrete numerical scheme is constructed using flux-based finite differences for the heterogeneous nonlinear diffusion operator and an $L1$ approximation for the Caputo derivative. The nonlinear algebraic system at each time level is solved using an under-relaxed Picard frozen-coefficient iteration with non-negativity enforcement and sparse direct solution of the resulting linear systems. The numerical implementation is verified through a manufactured-solution convergence study, and additional analyses are performed to examine computational cost, Picard iteration behavior, coefficient-regularization sensitivity, strong-source effects, heterogeneous conductivity structures, and long-time thermal-footprint persistence. The results show that heterogeneous conductivity mainly redirects heat through preferential pathways and enlarges the spatial footprint while producing negligible changes in global heat content. Stronger fractional memory, represented by smaller fractional order, increases the persistence and spatial reach of moderate heating, whereas larger porous-medium exponents confine heat near the source and preserve higher local peaks. Source amplitude increases the thermal burden and footprint monotonically over the tested range, including strong forcing, without producing an abrupt localization-spreading transition. Boundary exchange remains secondary in the short-time interior-heating regime considered. These findings demonstrate that the proposed two-dimensional time-fractional porous medium framework provides a verified and physically interpretable model for non-Fourier heat transport in heterogeneous materials, where local intensity, global heat retention, and spatial thermal exposure must be assessed jointly. Full article

Life cycle assessment is increasingly applied in healthcare, yet the healthcare-specific standardisation landscape and its relation to current practice remain unclear. This study maps existing frameworks and analyses their alignment with published healthcare LCA to identify standardisation gaps. Healthcare-specific standards and product category rules were identified through grey literature searches. Published healthcare LCA studies were quantitatively analysed and compared with the identified frameworks to assess methodological convergence and divergence. Six healthcare-specific frameworks were identified: five address medical products, one addresses services, and none cover organisational assessment. Product-level applications showed strong alignment in structural modelling elements including system boundaries and life cycle stages, while substantial heterogeneity persisted in functional unit definitions and impact assessment approaches. Service and organisational assessments showed broader variability in modelling approaches, functional units, and system boundary conceptualisations, indicating distinct modelling logics of healthcare delivery across assessment levels. Healthcare LCA practice is consistent with ISO-based principles but lacks a shared conceptual modelling logic for healthcare delivery systems. Rather than reflecting a single methodological paradigm, healthcare LCA combines product-, intervention-, pathway-, and organisational-oriented approaches. Standardisation efforts should therefore focus not only on harmonising calculation methods but also on developing healthcare-specific modelling conventions for products, services, and organisational structures. Full article

Neuroblastoma is characterized by frequent involvement of bone marrow (BM) as a site of cell dissemination and spread. In this study, single-cell RNA sequencing (scRNA-seq) was used to analyze the cellular heterogeneity of a subset of metastatic BM samples collected at initial diagnosis. Comparison of the single-cell data with bulk RNA sequencing further refined the analysis. An enrichment of regulatory T cells relative to a healthy control and activation of the CD24, CD47, and CD200 “don’t eat me” signals were documented. Computational analyses highlighted communication between neuroblastoma and myeloid cells via the amyloid precursor protein (APP) and midkine (MK) signaling networks. Within neuroblastoma cells, mutually exclusive adrenergic and transitory cell states were identified, and ten sub-clusters were denoted. In addition, common and unique tumor cell antigens were investigated. CNTFR and CHRNA3, as high-ranking candidates, were validated, confirming their strong selectivity for neuroblastoma cells. Taken together, these findings support the existence of a significant tumor-dependent modulation of the BM ecosystem, which should be considered when introducing immunotherapy. Furthermore, they highlight the potential to investigate new antigens at the single-cell resolution. Full article

In the context of global climate change and China’s dual-carbon strategy, this analysis examines how land-use transition is associated with land-use carbon effects in China’s resource-based cities. From the perspective of urban development stages, an analytical framework is built by linking development stage, land-use structure, and carbon source–sink structure. Using 262 resource-based cities from 2011 to 2023, we estimate land-use-related carbon emissions, carbon sequestration, and net land-use carbon effects with the carbon emission coefficient method and analyze their spatiotemporal patterns and driving factors using GeoDetector. The results show clear differences among city types. Mature cities form the largest group. Growth cities show the fastest expansion of impervious surfaces, while regenerative cities present signs of ecological recovery. This suggests that land-use transition is not simply the expansion of impervious surfaces, but a stage-dependent process of structural change. Land-use carbon effects also differ across stages. Mature cities maintain high and stable carbon-source effects. Growth cities exhibit increasing carbon-source effects, declining cities show reduced emissions but limited improvement in the carbon source–sink structure, and regenerative cities show improved carbon-sink capacity under ecological restoration. Overall, net land-use carbon effects follow a rise–decline–rebound pattern and show clear spatial heterogeneity and visually apparent clustering patterns. Population size has strong explanatory power, while interactions between socioeconomic and land-use factors further shape spatial differences. These results support stage-specific low-carbon transition strategies. Full article

Calcrete-type uranium prospectivity prediction is challenged by the strong heterogeneity of multi-source geoscientific raster datasets, weak anomaly responses, and the lack of explicit heterogeneous information organization in conventional deep learning models. In this study, the Yilgarn Craton of Western Australia was selected as the study area, and a geology-guided fixed-group fusion ResUNet model (GGF-ResUNet) was developed based on 12-channel multi-source geoscientific raster datasets. At the input stage, the evidence layers were divided into four fixed geoscientific proxy groups according to their data modality and geological interpretation, namely gravity, aeromagnetic, radiometric, and geochemical groups, and intra-group channel weighting together with inter-group gating was introduced to enhance the hierarchical representation and adaptive fusion of heterogeneous information. Ablation results showed that GGF-ResUNet achieved better performance than the baseline ResUNet, with AUC increasing from 0.9340 to 0.9740 and F1-score improving from 0.7264 to 0.8356. Further comparative experiments with Attention U-Net, U-Net, SegNet, and FCN showed that GGF-ResUNet achieved comparatively better quantitative performance and more spatially coherent prediction results under the current experimental setting. Without substantially increasing model complexity, the proposed method improves the representation and integration of heterogeneous geoscientific information and provides a feasible technical pathway for calcrete-type uranium prospectivity prediction under weak-anomaly conditions. Full article

The adoption of large language models (LLMs) is reshaping how organizations approach automation, decision-making, and user engagement across sectors. This study investigates the trends, theoretical frameworks, and adoption factors influencing the integration of LLMs in five key domains: education, commerce, banking, healthcare, and service. By employing a systematic literature review and meta-analysis, this paper synthesizes research published between 2022 and early 2026, corresponding to the period when LLMs became widely accessible for public and enterprise use, to evaluate both conceptual and empirical dimensions of LLM adoption. The review identifies the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, including its extensions, as the most frequently applied frameworks. It also highlights the growing incorporation of complementary models such as the diffusion of innovation, the information system success model, and self-determination theory. The meta-analysis examines 59 pairwise relationships drawn from 154 studies with a cumulative sample size of 88,886 participants. Using correlation coefficients, $I^2$ statistics, and Egger’s test, the analysis reveals strong, consistent associations between behavioral intention and both use behavior and actual use, while also identifying high heterogeneity across contexts. Constructs such as trust, hedonic motivation, and personal innovativeness emerged as influential but were underrepresented in the theoretical modeling. The study underscores the importance of facilitating conditions, infrastructure, and organizational readiness for enabling sustained use while also drawing attention to gaps in addressing perceived risks, privacy concerns, and ethical implications. Full article

Biomass valorization plays a vital role in achieving carbon neutrality and circular economy frameworks. Owing to its carbon-rich structure, biomass represents a promising feedstock to produce bio-based hydrocarbons via biological and thermochemical pathways. While biological conversion routes have been extensively studied, their deployment at commercial scale is constrained by high capital costs and low product yields. In contrast, thermochemical conversion technologies are increasingly being explored as viable large-scale biomass valorization routes. This review presents a comprehensive assessment of thermochemical pathways, with particular emphasis on hydrothermal liquefaction (HTL). The review identifies hydrothermal liquefaction (HTL) as a strategically advantageous route for wet and heterogeneous biomass valorization, due to simultaneous yields of liquid biocrude, and solid hydrochar. The review emphasizes the application of biocrude upgradation processes like hydrodeoxygenation under biphasic solvent systems using sulfided NiMo and CoMo catalysts. Further, the review also establishes hydrochar as a tunable functional material rather than a mere byproduct for applications in fields of energy production, soil amendment, and heterogeneous catalysis. The review article examines technology readiness levels of different biomass valorization techniques, and suggests that while combustion, anaerobic digestion, torrefaction, and transesterification are commercially mature, HTL and carbon capture utilization and storage (CCUS)-integrated fuel synthesis pathways remain at intermediate readiness. Additionally, the review carries out an in-depth study on artificial intelligence and machine learning (AI and ML) applications in biomass valorization, where it observes that Tree-based ensemble models, particularly Random Forest and XGBoost, show strong performance for several HTL prediction tasks, while Gaussian Process Regression and neural network–Bayesian optimization approaches provide additional advantages for uncertainty estimation and process-level optimization. Finally, the future research opportunities in biomass valorization and AI/ML application in HTL-process optimization have been identified for improving the bio-based fuel production techniques. Full article

To address the insufficient accuracy of short-term load forecasting caused by the strong randomness of distributed energy output, variable electricity consumption patterns, and complex meteorological factors, this study proposes a load forecasting method that integrates K-means scene clustering and a Transformer–BiGRU–Attention (CTBA) hybrid deep learning architecture. Different from conventional Transformer–BiGRU hybrid forecasters that train a single global predictor across all operating conditions, the proposed CTBA framework first partitions daily load curves into representative scenes and then routes each sample to a scene-specific Transformer–BiGRU–Attention predictor, thereby reducing distributional heterogeneity before temporal modeling. First, the K-means algorithm is used to perform scene clustering on historical daily load curves, and the optimal number of clusters is selected according to the silhouette coefficient and downstream prediction performance. Subsequently, the CTBA model is trained separately for each clustering subset. The Transformer encoder captures the long-range global dependencies of load sequences through the self-attention mechanism, the BiGRU module extracts local bidirectional temporal fluctuation features, and the Attention mechanism further focuses on key time nodes such as morning and evening peaks while fusing multi-source data including historical load, day-ahead electricity price, and multi-dimensional meteorological factors. Experimental results based on the German ENTSO-E power dataset show that the coefficient of determination R² of the proposed model reaches 0.9893, with MAE, RMSE, and MAPE as low as 0.0141, 0.0187, and 3.92%, respectively, which are significantly improved compared to benchmark models such as SVR, LSTM, CNN, and TCN-BiGRU. Ablation experiments further demonstrate that removing the clustering, Transformer, BiGRU, or attention layer will degrade performance, thus verifying the effectiveness and superiority of the method in short-term load forecasting and providing an accurate solution for the short-term load forecasting of power systems. Full article

The green transition in agriculture is a key issue for achieving sustainable development. Based on panel data from 30 Chinese provinces covering the period from 2011 to 2022, this paper examines the relationship between environmental enforcement and agricultural green total factor productivity (AGTFP), with a focus on analyzing the moderating effects of land transfer and agricultural insurance, as well as their synergistic threshold characteristics. The study employs two-way fixed-effects models, moderating effect models, and Hansen threshold regression methods for empirical analysis. The baseline regression results show a significant positive association between environmental enforcement and AGTFP. This conclusion remains robust after various tests, including truncation, replacement of core explanatory variables, difference GMM, and instrumental variables. The decomposition test shows that this positive correlation is mainly reflected through the channel of technological progress, rather than the improvement in technical efficiency. Heterogeneity analysis indicates that the positive association is more pronounced in regions with high GDP, strong law enforcement capacity, and in northern regions. Moderation analysis reveals that both the land transfer rate and insurance depth positively moderate the relationship between environmental enforcement and AGTFP, and the two exhibit a synergistic effect. However, this synergistic effect exhibits nonlinear characteristics and may weaken or even reverse at extreme value intervals. A threshold model further reveals an asymmetric complementary relationship between the two institutional conditions. The moderating effect of land transfer is activated only after insurance depth crosses a threshold value, while the moderating effect of insurance depth is most effective during the small-scale farming stage. These findings suggest that environmental regulation policies should be advanced in coordination with land transfer and agricultural insurance systems, with a focus on institutional alignment and coordination. Full article

Tuberculosis (TB) remains a major public health burden in China. Although meteorological and environmental factors are recognized to influence TB transmission, their non-linear effects and spatiotemporal heterogeneity have not been fully elucidated. Based on monthly TB incidence data from 31 provinces in China during 2005–2020, this study systematically investigated these effects by integrating nine meteorological and air pollution variables within a combined machine learning and spatial statistical modeling framework. The results indicated that the Extreme Gradient Boosting (XGBoost) model effectively captured the complex non-linear relationships between environmental exposure and TB incidence. SHAP interpretability analysis identified surface pressure (SP), vegetation coverage, and PM_2.5 as the key drivers and revealed pronounced nonlinear response patterns and threshold effects. In particular, the promoting effect of PM_2.5 on TB incidence increased sharply at medium-to-high concentration levels. To further investigate spatial and temporal non-stationarity, Geographically and Temporally Weighted Regression (GTWR) was applied. The results demonstrated strong spatiotemporal heterogeneity in driver effects across provinces. The influence of PM_2.5 showed a consistently positive association with TB incidence and exhibited a distinct temporal evolution characterized by an initial strengthening before 2015 followed by a weakening thereafter, closely aligning with China’s air pollution control process. These findings provide new insights into the nonlinear and spatiotemporally heterogeneous effects of meteorological and environmental factors on TB incidence and support the development of more targeted, region-specific TB prevention strategies. Full article

Ice recrystallization inhibition (IRI) activity of peptides is influenced by both peptide length and secondary structure; however, whether combinations of peptides with different lengths exhibit cooperative or antagonistic effects remains poorly understood. Using molecular dynamics simulations, this study investigated how secondary structure and chain-length heterogeneity jointly affect the IRI activity of peptide-pair mixtures. For systems containing only β-sheet-rich peptides, mixtures of different chain lengths consistently reduced ice content relative to the corresponding single-peptide systems, suggesting cooperative enhancement of IRI activity. In contrast, individual α-helical peptides showed strong inhibition of ice growth, but this effect was diminished after they were mixed into peptide pairs. Structural analyses suggested that the improved performance of β-sheet mixtures was associated less with the simple preservation of native β-sheet structure than with mixing-induced changes in peptide–peptide coupling and surface exposure. By contrast, helix-containing mixtures retained more of their original local structure in some cases, but this structural retention was not accompanied by improved ice-growth suppression after mixing. Together, these findings suggest that peptide length effects on IRI are not universally synergistic but depend strongly on secondary-structure compatibility. Full article

The thyroid gland is particularly vulnerable to the effects of environmental pollutants, due to its high vascularization, dependence on iodine uptake, and intrinsic oxidative environment required for hormone biosynthesis. Therefore, environmental exposure to heavy metals (HMs) and endocrine-disrupting chemicals (EDCs) has emerged as a potential contributor to thyroid dysfunction and carcinogenesis. Despite increasing interest, the clinical relevance of these exposures remains incompletely defined. Available epidemiological data suggest heterogeneous associations across EDCs and HMs classes. While evidence is more consistent for some pollutants, for other compounds it remains limited. Furthermore, while experimental studies provide strong mechanistic support for the key pathways linking environmental exposure to thyroid dysfunction and carcinogenesis, the clinical interpretation of epidemiological data is constrained by important methodological limitations. This narrative review aims to integrate current epidemiological and experimental evidence on the role of HMs and EDCs in thyroid diseases, including both non-neoplastic disorders and thyroid cancer, examining their environmental distribution, exposure pathways, and biological effects. Full article