Search Results (279)

At a time when China confronts the dual challenges of intensifying international competition and urgent industrial transformation, enhancing enterprises’ new quality productivity (NQP) has become a critical pathway to strengthening market competitiveness. This study constructs a comprehensive micro-level NQP index system for enterprises, encompassing three core dimensions: revolutionary breakthroughs in science and technology, deep transformation and upgrading of industrial systems, and innovative allocation of production factors. Using panel data from listed enterprises in China’s three major eastern urban agglomerations (Beijing–Tianjin–Hebei, Yangtze River Delta, and Guangdong–Hong Kong–Macao Greater Bay Area), we systematically examine the spatiotemporal evolution patterns and market expansion effects of enterprise NQP. The results reveal that while enterprises’ NQP has shown a generally upward trend, significant regional disparities and pronounced polarization persist across the three urban agglomerations. Development is notably path-dependent and spatially correlated, being easily influenced by neighboring cities. More importantly, empirical evidence from benchmark regression and spatial Durbin models indicates that enhancing NQP significantly boosts enterprises’ market potential, with substantial positive spatial spillover effects. This study contributes to the literature by developing a novel micro-level measurement framework for new quality productivity and providing robust evidence that NQP serves as a powerful driver for expanding market potential in an era of technological and industrial transformation. Full article

Background/Objectives: Accurate land cover mapping from multispectral Sentinel-2 imagery is fundamental for environmental monitoring, efficient natural resource management, and spatial planning. While deep learning has become the dominant approach for semantic segmentation, the combined impact of spectral input selection and network architecture on cross-regional robustness remains insufficiently explored. This study systematically investigates multispectral land cover segmentation in Serbia and evaluates its transferability to Western Balkan regions using a structured experimental framework. Methods: A comprehensive band-combination ablation analysis (3–10 spectral bands and index-only inputs) was first conducted using Attention U-Net, followed by a comparative evaluation of representative convolutional and transformer-based architectures, including ResNet-UNet-50, ConvNeXt-UNet, DeepLabV3+ (ResNet-50), and DINOv2-S/14. Model performance is evaluated on an internal Serbian test split (Test SR), an external Serbian dataset (Ext SR), and a cross-regional Balkan dataset (Ext WB). Results: The results demonstrate that compact multispectral configurations (6–9 bands) provide the most stable performance, achieving mIoU values of approximately 0.72–0.74 under in-domain evaluation and remaining robust under external testing. The inclusion of near-infrared and shortwave infrared bands proved critical for effective land cover discrimination, whereas increasing spectral dimensionality beyond this range did not yield systematic improvements in external robustness. Notably, the magnitude of performance degradation under pronounced geographic domain shift exceeds the performance differences observed between architectures under in-domain conditions, indicating that distribution shift exerts a stronger influence on segmentation accuracy than model choice alone. Class-wise analysis revealed agricultural areas as the most domain-sensitive category, while Shapley-based explainability analysis provides additional insight into class-specific spectral dependencies and their role in generalization behavior. Conclusions: Although transformer-based models demonstrated competitive robustness, attention-enhanced convolutional architectures achieved comparable stability across evaluation scenarios. Overall, the findings emphasize the importance of balanced spectral design, class-aware robustness analysis, and explicit out-of-domain evaluation for developing transferable land cover segmentation models in remote sensing applications. Full article

In this article, we develop a goodness-of-fit test for the Kumaraswamy distribution based on energy statistics. Due to the availability of its quantile (inverse) function, the Kumaraswamy distribution has been shown to be the preferred alternative to the Beta distribution, since both have bounded support in the $(0,1)$ interval. The proposed test procedure is simple and more powerful against general alternatives. Under different settings, simulations show that the proposed test is capable of being well controlled for any given significance (nominal) levels. In terms of power comparisons, the proposed test outperforms other existing methods in different settings. We then apply the proposed test to two real datasets (electricity access data and underground economy index) to demonstrate its competitiveness and usefulness. Full article

[Objective] This study aims to reveal the spatiotemporal evolution and transition patterns of green utilization efficiency of cultivated land (GUECL) across Chinese provinces and to identify multidimensional configurational pathways for improving efficiency. [Method] Carbon emissions and total carbon sinks were incorporated into the evaluation index system of GUECL. The super-efficiency SBM model was used to measure GUECL. A three-dimensional analytical framework of “driving forces–external foundations–internal conditions” was then constructed. Exploratory Spatio-Temporal Data Analysis and the fsQCA method were combined to examine the spatiotemporal evolution characteristics and multiple configurational pathways. [Results] (1) From 2013 to 2023, GUECL showed a fluctuating upward trend, with the mean value increasing from 0.550 to 0.835. Spatially, it presented a pattern of high efficiency in Northeast China and low efficiency in Southwest China. (2) The local spatial structure of GUECL was generally stable, although its spatiotemporal transition paths fluctuated to some extent. The cooperative effects in northeastern and western provinces were stronger than the competitive effects. The spatiotemporal evolution showed strong path dependence and lock-in effects, and the spatial association pattern was mainly positive, indicating a high degree of spatial integration. (3) Efficiency improvement was driven by the coupling of multiple factors. Four specific configurations were identified and further summarized into three typical pathways: a socially driven and economic-foundation-led pathway assisted by resource conditions; an economic- and technological-foundation-led pathway dominated by resource conditions and assisted by policy support; and a multi-factor synergistic pathway. [Conclusion] GUECL is driven by the combined and synergistic effects of driving forces, external foundations, and internal conditions. Therefore, differentiated regional strategies should be adopted to promote the precise matching and coordinated governance of multiple factors, thereby supporting the green and high-quality development of agriculture. Full article

Predicting student performance has emerged as an essential element of contemporary learning assessment, allowing educational organizations to determine problematic students and offer early intellectual assistance. Many machine learning (ML) methodologies prioritize predicted accuracy at the expense of interpretability and practical insights. This paper provides a framework for predicting student performance that is both risk aware and explainable utilizing a chaotic educational competition optimizer (ECO) in conjunction with a support vector classifier (SVC) to overcome existing challenges. The ECO serves as a metaheuristic feature selection technique for selecting the most significant features from a multivariate educational dataset consisting of 1195 students and 29 behavioral, demographic, and academic characteristics. Experimental findings demonstrate that ECO effectively condenses the feature space to 11 essential indications and improves generalization of model while maintaining classification robustness. Utilizing the chosen features, the ECO–SVC model attains a complete classification accuracy of 87.03%, with F1-scores of 0.92, 0.69, and 0.82 for high-, medium-, and low-performance student categories, respectively, surpassing other benchmark ML methods. The proposed framework incorporates explainable artificial intelligence (XAI) to improve transparency by utilizing local explanations and permutation-driven feature significance. The XAI research verifies that institutional support, learner engagement, and previous academic success are the most important contributing factors to predictive results. Notably the ECO functions as a classifier-independent feature selection mechanism; however, the support vector classifier (SVC) is adopted in this study due to its strong generalization capability and effectiveness in exploiting the optimized feature space. The findings are analyzed using a semiotic-linguistic framework, wherein certain qualities are correlated with symbolic, indexical, and temporal educational signs, converting numerical significance into substantive pedagogical insights. Furthermore, an initial academic risk profile strategy is established by utilizing SVC decision confidence and elucidating feature contributors. The consequent risk ratings accurately categorize students into low-, medium-, and high-risk categories, facilitating the detection of at-risk learners beyond mere final score assessment. The proposed risk-aware and explainable ECO–SVC framework enhances learning outcomes assessment by integrating interpretability, high accuracy, and proactive academic reasoning, rendering it suitable for real-life educational decision-support systems. Full article

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy ($R^2$ up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

Background/Objectives: This systematic review examined whether oral vitamin D supplementation improves vitamin D status, health, and exercise outcomes in indoor-training aquatic athletes. Methods: We systematically reviewed randomized, placebo-controlled trials (>2 weeks) investigating vitamin D supplementation in competitive swimmers and divers. Six eligible trials (n = 246) were included and summarized descriptively. Results: Supplementation (2000–5000 IU/day for 12 weeks to 6 months) consistently increased serum 25(OH)D compared with placebo, with average increases up to 9.3 ng/mL. While higher doses occasionally improved muscle strength and lean mass, evidence showed no consistent benefits for swimming performance, immune function, or bone turnover. Additionally, higher body mass index (BMI) correlated with smaller 25(OH)D increases. Conclusions: Vitamin D effectively corrects deficiencies in aquatic athletes but lacks consistent ergogenic benefits. Therefore, in practice, supplementation should serve primarily as a targeted corrective measure for deficiency to support fundamental musculoskeletal health, rather than a generalized strategy for performance enhancement. Full article

Ethyl oleate (EO) is an emerging compound used in the food industry as a pre-treatment additive in preservation processes, such as drying, allowing the shelf-life to be extended while preserving the nutritional value of the treated food without compromising consumer safety. Currently, EO is mostly synthesised from edible oils, which raises concerns about competition with the food chain. As an alternative, we previously developed an EO product from a High-Oleic Waste (HOW) obtained from industrial distillation pipelines. Due to the potential application of EO as a food additive, the present study aimed to evaluate its stability throughout its shelf-life in comparison with two commercial benchmarks under accelerated conditions (40 °C, 75% relative humidity, 6 months). Colour parameters (Total Colour Difference and Yellow Index), structural properties by FTIR-ATR, thermal properties by DSC, compositional stability by GC-MS, formation of lipid oxidation products by UV-Vis and cytotoxicity in keratinocytes were evaluated at the beginning (T0) and at the end (T6) of the assay. In general, the synthesised EO showed no considerable changes in the parameters studied after storage, being comparable to the assayed benchmarks. In conclusion, the developed EO was found to be stable during the assayed shelf-life, confirming its potential suitability as an additive for the food industry. Future studies should perform validation in food matrices. Full article

To investigate the deterioration law of the mechanical properties of PVA-fiber-reinforced rubber concrete under the combined action of high-temperature and salt erosion, physical index tests, dynamic mechanical property experiments, and microstructural morphology observations were carried out on specimens subjected to different temperatures (ambient temperature, 100 °C, 300 °C) and various solution attacks (water, 5% NaCl, 5% Na₂SO₄, and 5% NaCl + 5% Na₂SO₄ mixture). The results show that, after exposure to 300 °C, the PVA fibers melt and the rubber pyrolyzes, since this temperature exceeds their melting points. A residual pore network is formed inside the matrix, and the damage degree of ultrasonic pulse velocity is about 2.3 times that of the 100 °C group. Although salt solution and its crystallization products can physically fill the pores and cause a partial recovery of pulse velocity, this change is mainly due to the alteration of the pore medium and does not represent a substantial restoration of the microstructure. The effects of different salt solutions on dynamic mechanical properties vary significantly: Sulfate erosion improves the dynamic performance significantly at ambient temperature by forming gypsum and ettringite to fill pores, but this strengthening effect disappears after 300 °C. Sodium chloride attack generates Friedel’s salt and consumes C₃A, leading to general strength deterioration. In composite salt erosion, the competitive and synergistic effects of Cl⁻ and SO₄²⁻ destabilize erosion products and weaken interfacial bonding, resulting in consistent decreases in dynamic compressive strength and elastic modulus under all temperatures and impact pressures. The strength reduction reaches 66.2% after 300 °C. Microscopic analysis confirms that composite salt erosion leads to the dissolution of ettringite and loose structure, which verifies the synergistic deterioration law of macroscopic properties. This study systematically reveals the damage evolution mechanism of PVA-fiber-reinforced rubber concrete under the coupled action of high-temperature and salt erosion, and provides a theoretical basis for the dynamic bearing capacity evaluation and durability design of concrete structures in such coupled environments. Full article

National competitiveness has become a central policy concern for resource-dependent economies pursuing structural transformation. Saudi Arabia’s Vision 2030 represents a comprehensive national strategy aimed at diversifying the economy, upgrading productivity, and strengthening institutional capacity. Despite extensive discussion of individual reforms, there remains a lack of integrated, theory-guided analysis that explains how these changes interact systemically at the national level. This study addresses this gap by applying Porter’s Diamond Model as a conceptual descriptive analytical framework to examine Saudi Arabia’s economic transformation. The analysis treats the Diamond determinants—factor conditions, demand conditions, related and supporting industries, and firm strategy, structure, and rivalry—as an interconnected system shaped by government intervention. Drawing on secondary data from official policy documents, international competitiveness indicators, (including the Global Innovation Index, IMD World Competitiveness Rankings, Logistics Performance Index, and Worldwide Governance Indicators), and institutional reports, the study maps key reform dynamics onto each determinant and examines their cross-determinant interactions and feedback loops. The findings suggest that Saudi Arabia has made substantial progress in upgrading factor conditions and generating sophisticated domestic demand, while systemic challenges remain in firm level rivalry and innovation ecosystem depth. The study highlights that sustainable national competitiveness depends on coordinated upgrading across all determinants rather than isolated reforms. By reframing Porter’s Diamond as a dynamic, systems-oriented analytical tool, this paper contributes to the literature on national competitiveness in transformation economies and provides policy relevant insights for advancing productivity driven growth under Vision 2030. Full article

Background: Strongman competitions impose extreme mechanical and metabolic stress on the shoulder girdle, yet quantitative neuromuscular responses under real competition conditions remain poorly characterized. Methods: Ten elite strongmen (Tier 4) and ten age-matched trained controls (Tier 2) completed an official Strongman Champions League competition protocol. Surface EMG was recorded from seven shoulder-girdle muscles during maximal voluntary contraction (MVC) trials performed immediately before and after competition. Normalized RMS amplitudes were expressed as a relative EMG index (% group peak) and analyzed using linear mixed-effects models with Benjamini–Hochberg false discovery rate (FDR) correction. Results: Within-group analyses revealed no generalized pre–post reductions in normalized EMG amplitude in either group after FDR correction. However, the control group demonstrated consistent negative pre–post trends with moderate-to-large effect sizes across several muscles, particularly for mean and median descriptors. In contrast, elite strongmen exhibited smaller and more variable changes without a systematic decline. Difference-in-differences analysis showed that temporal changes generally favored the elite group. After FDR adjustment, a significant interaction was identified for the median lower trapezius amplitude (ΔΔ = 33.76 ± 9.13, pFDR = 0.021), indicating relatively greater preservation of neuromuscular activation in elite strongmen compared with controls. No contrast demonstrated a greater decline in the elite group. Conclusions: Although most effects did not survive correction for multiple testing, the observed effect-size patterns and a significantly lower trapezius interaction suggest greater stability of neuromuscular activation in elite strongmen compared with trained, non-specialized controls. These findings support muscle- and metric-specific fatigue resistance associated with long-term strongman training. Full article

The state budget system in Vietnam functions within a cohesive structure that allocates financial resources between central and local governments; nevertheless, substantial disparities in socioeconomic conditions among provinces have resulted in increasing discrepancies in local budget revenue. This study, therefore, examines the impacts of fiscal decentralization policy, land utilization, urbanization, provincial competitiveness index, and human capital on local government revenue. The analysis utilizes quantitative panel-data techniques on a dataset encompassing all 63 Vietnamese provinces and municipalities from 2017 to 2022, totaling 378 observations. Econometric estimation employs pooled ordinary least squares, fixed-effects, random-effects, and viable generalized least squares models, along with diagnostic and robustness checks to mitigate unobserved heterogeneity and error dependence. The findings demonstrate statistically significant correlations between local budget revenue and five studied determinants. However, fiscal decentralization policy exerts the most significant influence on the revenue of the local government budget. The results suggest that enhancing municipal fiscal performance needs more than merely modifying revenue-sharing ratios, with significant ramifications. Full article

This paper addresses the growing sustainability fatigue in advanced economies. By analyzing Amazon’s artificial intelligence (AI) agent strategy as a model for “Rational Sustainability”, the study identifies a self-propagating growth trajectory that reconciles economic rationality with value creation. It provides a theoretical and empirical framework to overcome technological saturation and strategic homogenization in the generative AI era. To ensure methodological transparency, the analysis was conducted through two distinct stages: (i) Techno-econometric analysis (macro-level): Using an empirical dataset of 160 countries (40 advanced, 70 emerging, and 50 developing) from 2014 to 2024, the study utilized regression models to quantify the correlations and elasticities between three key proxies: GDP per capita (Y); the Human Capital Index (HCI), representing Institutional Capacity Building (ICB); and the E-Government Development Index (EGI), representing Endogenous Institutional Evolution (EIE). (ii) Hybrid AI analysis (case study): Utilizing process-tracing research, the paper examines Amazon’s R&D structure and AI agent strategy. This qualitative and structural analysis identifies how Amazon co-evolves EIE and ICB to conceptualize tacit knowledge and operationalize it into a competitive advantage. The findings reveal a marked disruption of the co-evolutionary mechanism in advanced economies, where the elasticity of EGI to GDP has declined since 2019, leading to a withdrawal state. In contrast, Amazon’s model demonstrates that the co-evolution of EIE and ICB creates a self-propagating growth engine. This research concludes that “Rational Sustainability”—grounded in evidence, economic rationality, and clear trade-offs—offers a viable pathway for revitalizing sustainability strategies in mature digital economies. Full article

Face recognition is increasingly being adopted in industries such as education, security, and personalized services. This research introduces a face recognition system that leverages the embedding capabilities of the CLIP model. The model is trained on multimodal data, such as images and text and it generates high-dimensional features, which are then stored in a vector index for further queries. The system is designed to facilitate accurate real-time identification, with potential applications in areas such as attendance tracking and security screening. Specific use cases include event check-ins, implementation of advanced security systems, and more. The process involves encoding known faces into high-dimensional vectors, indexing them using a vector index FAISS, and comparing them to unknown images based on L2 (euclidean) distance. Experimental results demonstrate a high accuracy that exceeds 90% and prove efficient scalability and good performance efficiency even in datasets with a high volume of entries. Notably, the system exhibits superior computational efficiency compared to traditional deep convolutional neural networks (CNNs), significantly reducing CPU load and memory consumption while maintaining competitive inference speeds. In the first iteration of experiments, the system achieved over 90% accuracy on live video feeds where each identity had a single reference video for both training and validation; however, when tested on a more challenging dataset with many low-quality classes, accuracy dropped to approximately 73%, highlighting the impact of dataset quality and variability on performance. Full article

Traditional electronic phased array radars are constrained by electronic bottlenecks, resulting in inherent limitations including large form factor, fixed operational parameters, and narrow instantaneous bandwidth, which fail to meet the stringent requirements of next-generation high-performance radar systems. Optical true time delay (OTTD) technology based on integrated optical waveguides emerges as a core solution for realizing broadband, compact optically controlled beamforming systems. Traditional silicon-based waveguides suffer from severe mode competition (delay jitter > ±0.05 ps), energy leakage (transmission loss > 0.5 dB/cm) and large beamforming angle fluctuation (>0.3°) in OTTD systems, failing to meet the picosecond-level delay accuracy and broadband beam squint-free requirements of next-generation phased array radars. Thus, a customized mode-selective waveguide design for OTTD systems is urgently required. To address these critical challenges, this study proposes an OTTD-customized mode-selective integrated optical waveguide design tailored for OTTD systems, with three distinct innovations: (1) A systematic OTTD-oriented mode classification and selection methodology is established—instead of a conventional single-mode design, the fundamental TE₀ mode is identified as the optimal operating mode through Finite-Difference Time-Domain (FDTD) simulation, (95% TE polarization fraction and 2.0553 effective refractive index at 1548.39 nm, which cannot be achieved by other guided modes for OTTD applications). (2) The wavelength-dependent transmission characteristics of the TE₀ mode are quantitatively characterized, revealing a linear correlation between the effective refractive index (2.05–2.10) and wavelength (1500–1550 nm), alongside a controllable group delay range of 1.4315–1.4395 ps—this precise linear model fills the gap of lacking OTTD-specialized delay calibration theory in conventional waveguide research. (3) An OTTD-optimized practical mode selection criterion for OTTD applications is proposed by modifying the standard guided-vs-leaky condition for asymmetric waveguides: the effective refractive index of the operating mode must exceed the substrate refractive index with a fabrication tolerance margin (n_eff > 1.44 ± 0.02 for SiO₂ substrate) to mitigate leakage and adapt to OTTD picosecond-level delay precision. This criterion is validated through system-level beamforming experiments (rather than only device-level simulation), and the designed waveguide achieves a mode suppression ratio (MSR) of >30 dB for leakage modes and a transmission loss of <0.2 dB/cm, which is significantly superior to conventional single-mode waveguides in OTTD systems. Experimental results indicate that the angle fluctuation of the beamforming system is less than 0.08°, which is significantly superior to the 0.3° fluctuation observed in traditional silicon waveguide OTTD systems. This work provides a technical solution for improving the performance of optical phased array radar and laser radar and has broad engineering application prospects in microwave photonics and optical communication fields. Full article