Search Results (3,357)

This article analyzes how the OECD and UNESCO conceptualize and develop proposals linking systemic leadership with educational equity. These proposals aim to address the challenges of an increasingly complex global scenario in which management efficiency must be balanced with the need to strengthen the public value of educational policies. A qualitative descriptive-analytical design based on the analysis of 37 strategic documents from both institutions was used to study and compare their perspectives on equity, guidelines on systemic leadership, and proposed practices for implementing it. The results show that the OECD favors a vision of systemic leadership that focuses on improving management capacities, multiscale coordination, and the strategic use of data to enhance policy effectiveness. Meanwhile, UNESCO’s proposals lean toward a more transformative and social vision of systemic leadership, with a greater number and variety of references to inclusion, democratic participation, and social justice. The conclusions highlight the importance of linking research on educational leadership with research on educational policies. Systemic leadership can be a fundamental link in this regard if it balances visions of the purposes of education (the what) with strategic definitions for achieving them (the how). Full article

Offshore oil exploration and the volume of imported crude oil shipping have increased steadily, elevating the risk of oil spills. An advanced offshore oil film identification method is proposed to realize the accurate and robust recognition and segmentation of oil films from marine radar images in offshore oil spill detection. This method integrates feature engineering with an improved Beetle Antennae Search (BAS) optimization algorithm, aiming to address the key issues of low discrimination between oil films and complex marine backgrounds and insufficient spill boundary localization accuracy in radar image analysis. First, the raw radar image was transformed into the Cartesian coordinate system, and a filtering procedure was applied to attenuate interference. Subsequently, the gray distribution and local contrast of the denoised image was further improved. Afterwards, the complexity of the grayscale distribution within each feature map was quantified using Shannon entropy. The Top-K feature maps with the highest entropy values were subsequently used to construct an information-rich subset. The subset was then processed through a pixel-wise averaging strategy to generate a coupled feature image. Then, Otsu threshold was used to refine ocean wave regions. Finally, the oil films were segmented with an improved BAS optimization algorithm. The fitness function of the improved BAS algorithm was augmented through the integration of edge fitting accuracy, and a target-proximity penalization scheme. Through an adaptive step-length modulation paradigm and Perceptual Mechanism, it can achieve a marked improvement in search accuracy and achieving precise segmentation of oil slicks. The detection accuracy of the proposed method is significantly enhanced relative to the traditional BAS algorithm and existing marine radar oil spill detection methods. The IOU, Dice, recall and F1-score reached 81.2%, 89.6%, 85.2%, and 90.1% respectively. This method not only advances the methodological rigor of spill detection but also provides critical data support for the development of more effective control and remediation practices. Full article

Unidirectional multilevel back-to-back (BTB) converters are widely employed in renewable energy generation systems and in motor drives for coal mining operations. However, the current zero-crossing distortion (CZCD) on the grid side and the neutral-point potential (NPP) imbalance on the common DC bus all restrict its applicability, such as in grids with stringent low harmonic requirements and in medium to high power situations. This paper proposes a coordinated control strategy to simultaneously address these issues theoretically. The study focuses on topology comprising a Vienna rectifier structure on the grid side and a three-level NPC inverter structure on the load side. In the proposed strategy, the current distortion angle, the manifestation of CZCD, is first eliminated by reactive current compensation on the Vienna rectifier side. Furthermore, the coupling between CZCD and NPP imbalance is resolved by reconstructing the neutral-point current target function. Ultimately, the optimal zero-sequence voltage (ZSV) is obtained using an interpolation function and then injected into the three-phase reference voltages of the inverter side to balance the NPP on the DC bus. The strategy transforms the influence of the rectifier on the NPP from an unknown coupling factor into a known disturbance and enables the inverter to actively compensate for variations in the overall converter system. An experimental platform was independently developed to verify the effectiveness of the proposed control strategy. Full article

In the dynamic and service-intensive context of the tourism and hospitality industry, organizational innovation performance (OIP) is a critical determinant of competitiveness. This study investigates the systemic role of knowledge management capability (KMC) in driving OIP, with a focus on the mediating effects of intellectual capital (IC) and knowledge utilization (KU). Drawing on Dynamic Capabilities Theory and the Knowledge-Based View, KMC is conceptualized as a higher-order capability that facilitates the accumulation, coordination, and application of knowledge resources, thereby shaping both organizational knowledge assets and their practical enactment. Data were collected from senior managers across five-star hotels in Greater Cairo, Egypt, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that KMC positively influences OIP directly and indirectly through the development of IC and the effective utilization of knowledge. Both IC and KU are found to mediate the relationship between KMC and innovation performance, highlighting the importance of transforming knowledge resources into actionable and value-creating organizational capabilities. The study advances theoretical understanding by elucidating the systemic mechanisms linking knowledge management, intellectual capital, and knowledge utilization to innovation outcomes, and provides practical insights for hospitality managers seeking to leverage knowledge-driven strategies to enhance competitiveness and service excellence. Full article

Chronic liver injury is accompanied by coordinated disturbances in lipid trafficking and inflammatory–fibrogenic signaling. Transforming growth factor beta 1 (TGF-β1) signaling has been implicated in hepatic fibrogenesis and tumor-associated remodeling and may co-vary with disturbances in lipid trafficking. Lisosan G (LG), a fermented wheat-derived nutraceutical, has reported antioxidant and anti-inflammatory activity and may influence these interconnected pathways. This study evaluated whether dietary LG alters the lipid composition of plasma lipoprotein fractions and hepatic TGF-β1 levels across distinct liver contexts. Seventy-two female Wistar rats were randomized into nine groups (n = 8/group) defined by liver condition, consisting of healthy control (Control), non-neoplastic liver (PH), and neoplastic liver injury (HCC; PH followed by diethylnitrosamine, DEN), and diet (standard diet, SD + 2.5% LG, or SD + 5% LG). Plasma lipoproteins (VLDL, LDL, HDL₁, HDL₂) were isolated by stepwise KBr density-gradient ultracentrifugation, and cholesterol (TC), phospholipids (PL), and triacylglycerols (TG) were quantified in each fraction. Hepatic TGF-β1 was measured by ELISA and normalized to total protein. LG effects depended strongly on baseline liver status, with significant Condition × Diet interactions for most lipid endpoints and for hepatic TGF-β1. In healthy rats, LG produced fraction-selective remodeling rather than uniform lipid lowering, including increased VLDL-TG at both doses and non-linear changes in cholesterol distribution across LDL and HDL subfractions. After PH, LG broadened lipid remodeling, including reduced VLDL-PL, increased VLDL-TG (both doses), and an increase in LDL-TC at 5% LG, accompanied by marked changes in HDL₁/HDL₂ cholesterol partitioning. In HCC, LG induced pronounced, often dose-dependent increases in LDL-associated lipids (LDL-PL, LDL-TG, LDL-TC) and increased HDL₁-TC while decreasing HDL₂-TC. Hepatic TGF-β1 was elevated in PH and further increased in HCC versus controls; LG reduced hepatic TGF-β1 in a condition-dependent manner, with the strongest reduction at 5% LG in HCC. Dietary Lisosan G remodels circulating lipoprotein lipid composition in a liver-status-dependent manner and is associated with reduced hepatic TGF-β1 abundance in injured liver, most prominently in neoplastic injury. These findings are consistent with the notion that nutraceutical interventions may show stronger phenotypic effects under perturbed metabolic–fibrogenic states than under stable physiology, while highlighting the need for mechanistic work to distinguish altered lipoprotein secretion from changes in peripheral clearance and to assess pathway-level TGF-β signaling. Full article

In this study, barley biomass from the brewing industry was used to obtain fraction-rich arabinoxylans, polysaccharides that, due to their chemical and structural properties, can form films. The effect of adding three plasticizers at a concentration of 20% w/w on the mechanical, optical, and barrier properties of the thermoplasticized films was evaluated. Tensile strength (TS) and percent elongation (%E) tests were performed to determine the mechanical properties, water vapor transmission rate (WVTR) and water vapor permeability (WVP) were evaluated by gravimetric methods, the ΔE and color index (CI) were calculated with the chromatic coordinates of the CIE-L*a*b system, and structural morphology was determined by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR-ATR). The results show that plasticizers decrease the TS values and increase the %E, obtaining more flexible films compared to films made without plasticizers. The structural characteristics of plasticizers directly influence the CI of films. The values corresponding to %E and PVA were higher in the arabinoxylan films thermoplasticized with glycerol. Films’ stability was evaluated using electrochemical impedance spectroscopy. The results show that there are significant differences when the films are coated with polylactic acid. Full article

In the context of global decarbonization and digital transformation, this study investigates whether and how the adoption of artificial intelligence (AI) promotes corporate energy transition, as measured by firms’ total energy consumption, energy intensity, and carbon emission intensity. Drawing on the theories of general-purpose technology (GPT), the resource-based view (RBV), and dynamic capabilities, the paper conceptualizes AI as a production-embedded technological capability that enhances intelligent automation, energy monitoring, and resource coordination within firms. Using panel data on Chinese A-share listed firms from 2012 to 2024, and capturing AI adoption through observable changes in firms’ production-related capital intensity, the analysis employs firm- and year-fixed effects, instrumental variables, and a dynamic event-study design to address endogeneity and temporal dynamics. The results show that AI adoption reduces firms’ energy consumption by approximately 2.0%, energy intensity by 1.8%, and carbon emission intensity by 2.3% within two to three years after adoption. Mechanism tests indicate that green innovation, operational efficiency, and resource allocation efficiency mediate this effect. Heterogeneity analyses reveal more substantial effects among non-state, large-scale, and technology-intensive firms operating in highly marketized regions. The findings broaden understanding of AI as a strategic sustainability technology and provide actionable implications for policymakers to align digital and energy governance to achieve carbon neutrality goals. Full article

The socio-ecological transition requires not only technological innovation but also new ways of recognizing the social, environmental, and territorial value generated by collective action. Many of these positive externalities remain invisible in conventional assessment frameworks, limiting the legitimacy, financing, and scaling of local sustainability initiatives. This article presents a strategic framework designed to identify and structure positive externalities in collective self-consumption and other transformative projects. The method combines four components: (i) normative identification through the Sustainable Development Goals; (ii) balanced multi-stakeholder participation to surface diverse perspectives; (iii) perceptive mapping using an adapted Kano model; and (iv) strategic articulation. The framework was applied in two contrasting contexts: an energy community centered on shared renewable production, and a women’s empowerment program focused on capability-building and social innovation. These applications do not aim at empirical replication or the validation of results, but at examining how the framework supports collective recognition and strategic structuring in different organizational settings. Across these distinct settings, it led to the formulation of coherent and actionable strategic roadmaps, illustrating how positive externalities can inform governance choices, strengthen institutional legitimacy, and support long-term project consolidation. These results suggest that collective recognition enables externalities to structure strategic action beyond their original sector, demonstrating the potential transferability of the approach. Developed within a research program supported by the French Agency for Ecological Transition (ADEME) and the national urban-transition initiative (PUCA), the framework provides a practical decision architecture for structuring shared value within coordinated strategies. Full article

In the context of minimally invasive spine surgery, accurately estimating the 3D coordinates of the vertebrae from intraoperative 2D X-ray images is crucial for aligning preoperative data with the patient’s real-time posture. However, existing methods are hindered by the ill-posed nature of 2D-to-3D localization and the distinctive anatomical features of the spinal column, leading to ambiguities and reduced accuracy. In this paper, we introduce X2P-net, a novel prompt-guided and semantic context-enhanced 2D/3D vertebra detection framework. To achieve this, we design a novel Transformer architecture, referred to as BrickFormer, which can automatically extract the refined vertebral foreground context at low computational cost using a dual-attention mechanism. Comprehensive experiments were conducted to validate the proposed approach on two datasets: a large-scale synthetic dataset (BiSpineX) and a sheep spine dataset (SheepSpineX). Results obtained from these experiments demonstrate superior landmark localization performance of the proposed method compared to other state-of-the-art methods. Specifically, on the BiSpineX dataset, X2P-Net achieves percentages of 96.9% and 98.8% at 10 mm and 20 mm thresholds, respectively, a mean position error of 2.99 mm, and an $AUC$ of 0.9923. Similar superior performance was also observed when the proposed method was applied to the SheepSpineX dataset, with percentages of 98.4% and 100.0% at 10 mm and 20 mm thresholds, respectively, a mean position error of 1.08 mm, and an $AUC$ of 0.9972. Full article

Coastal wetlands are among the most ecologically valuable yet vulnerable ecosystems, particularly in regions experiencing rapid urban expansion. This study provides a four-decade assessment of land use and land cover (LULC) dynamics and their implications for ecosystem service value (ESV) in the Beidagang Wetland Nature Reserve (BWNR), located adjacent to the fast-developing Tianjin region in China. Using an integrated geospatial framework, combining multi-temporal remote sensing, supervised classification, and a modified benefit-transfer valuation approach, we analyzed LULC transitions and the associated variations in ecosystem service values (ESVs) across three critical phases: (i) a period of minimal anthropogenic pressure and climate influence (1984–1999); (ii) a phase of increased human activities (2000–2013); and (iii) an active ecological restoration period (2014–2023). Findings across the three phases show that the LULC changes are not in equilibrium, as indicated by the decrease in vegetation (−46.43%) and bare ground (−31.34%), while the water areas (+547.50%) and built-up areas (+14.40%) increased remarkably. This indicates an intensive human-induced environmental transformation; although some ecosystem service functions degraded, the total ecosystem service value (ESV) of BWNR continued to increase due to water area expansion. The variations in ecosystem service value (ESV) in response to LULC changes resulting from anthropogenic activities and climate change were estimated, and the results show that the total ESV of BWNR was approximately CNY 10,631.1 million in 1984, CNY 15,078.7 million in 2000, CNY 17,768.3 million in 2013, and CNY 19,365.4 million in 2023. Findings from this study will contribute to the theoretical understanding of coastal wetland vulnerability and provide empirical evidence for the coordinated management of wetland ecological conservation and economic development in the context of rapid urbanization in Tianjin’s coastal areas. Full article

The increasing need for accurate prediction of geochemical anomalies requires methods capable of capturing complex spatial patterns that traditional approaches often fail to represent adequately. For N datasets of the form (X_i,Y_i) representing the geographic coordinates of sampling points and C_i denoting the geochemical measurement, training multilayer perceptrons (MLPs) presents a challenge. The low informativeness of the input features and their weak correlation with the target variable result in excessively simplified predictions. Analysis of a baseline model trained only on geographic coordinates showed that, while the loss function converges rapidly, the resulting values become overly “compressed” and fail to reflect the actual concentration range. To address this, a preprocessing method based on anisotropy was developed to enhance the correlation between input and output variables. This approach constructs, for each prediction point, a structured informational model that incorporates the direction and magnitude of spatial variability through sectoral and radial partitioning of the nearest sampling data. The transformed features are then used in a dual-MLP architecture, where the first network produces sectoral estimates, and the second aggregates them into the final prediction. The results show that anisotropic feature transformation significantly improves neural network prediction capabilities in geochemical analysis. Full article

Measuring national progress toward green transformation remains challenging due to fragmented assessment frameworks. This study develops and validates a Green Transformation Index that captures the capacity for sustainability transitions by integrating resource efficiency, innovation systems, and policy instruments. Using OECD Green Growth Indicators covering 58 economies from 2017 to 2025, we construct a composite index from 47 standardized indicators organized into three theoretically grounded dimensions. The GTI measures transformation capacity through innovation investment and policy frameworks rather than environmental outcomes. Results reveal substantial heterogeneity in transformation capacity with a Gini coefficient of 0.283, indicating persistent global inequality. Temporal analysis identifies a three-phase trajectory: consolidation from 2017 to 2019, acceleration during 2021 to 2023 driven by green recovery investments, and marked reversal in 2024 to 2025, highlighting vulnerability to economic shocks. Cluster analysis identifies four distinct pathways: innovation-driven, balanced integration, resource-first, and policy-led approaches. Critical findings show only 19 percent of countries demonstrate strong coordination between innovation investments and policy instruments, revealing significant governance fragmentation. Validation tests confirm the index effectively measures innovation capacity but shows weak correlation with emissions outcomes, underscoring the distinction between transformation inputs and environmental performance. Full article

In the era of the digital economy, enhancing enterprise resilience has become a strategic imperative for sustainable manufacturing development. However, the micro-level mechanisms through which data element policies, specifically China’s National Big Data Comprehensive Pilot Zone, empower enterprise resilience remain insufficiently explored. To address this gap, this study leverages the policy rollout as a quasi-natural experiment and employs a multi-period difference-in-differences approach to analyze panel data of listed manufacturing firms. The results reveal that enterprises within pilot zones exhibit a 2.3% average increase in resilience compared to non-pilot counterparts. This effect is significantly amplified by enterprise digital transformation and regional innovation-entrepreneurship vitality. Mechanism analysis further identifies that the policy enhances resilience primarily by reducing supply chain coordination costs and improving relationship stability, with additional positive spillovers observed in adjacent cities. These findings highlight the disruptive potential of big data in reshaping corporate resilience paradigms and provide empirical support for scaling data-driven industrial policies to foster high-quality economic development. Full article

Due to the significant potential of crowd flow prediction in the domains of commercial activities and public management, numerous researchers have commenced investing in pertinent investigations. The majority of existing studies employ recurrent neural networks, long short-term memory, and similar models to achieve their objectives. Despite the advancements in predictive modeling, the objective of many existing studies remains in the minimization of distance errors. This focus, however, introduces three notable limitations in prediction outcomes: (1) the predicted location may represent an average of multiple points rather than a distinct target, (2) the results may fail to reflect actual user behavior patterns, and (3) the predictions may lack geographic plausibility. To address these challenges, we developed a Transformer-based model integrated with adversarial network architecture. The Transformer component has shown considerable effectiveness in forecasting individual movement trajectories, while the discriminator within the adversarial framework guides the generator in refining outputs to better reflect user habits and spatial rationality. Full article

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction. Full article