Search Results (2,570)

South Africa’s public hospital system faces mounting pressure from ageing infrastructure, rising patient demand, and constrained maintenance budgets. While significant investment has been directed toward the construction of new healthcare facilities, the diffusion and adoption of advanced technologies within operations and maintenance (O&M) remain uneven and underdeveloped. This misalignment limits the long-term performance, safety, and sustainability of hospital assets. This study investigates technological diffusion within the O&M environment of a newly commissioned 500-bed regional hospital in Durban, KwaZulu-Natal. A qualitative single-case study approach was adopted, drawing on semi-structured interviews with 14 stakeholders across project delivery and facility management functions. Data were analysed thematically to identify systemic patterns and operational constraints. Findings reveal a persistent reliance on manual, reactive maintenance practices, with minimal integration of digital tools, including building management systems, predictive maintenance technologies, and real-time monitoring platforms. Key barriers include unclear institutional roles, inadequate handover processes, limited technical capacity, and the absence of strategic leadership to drive innovation. A critical disconnect was also identified between managerial expectations and operational realities. The study argues that technological adoption in hospital O&M is not merely a technical challenge but an institutional one. It recommends targeted capacity development, structured transition frameworks, and stronger governance mechanisms to enable sustainable digital integration. Full article

This study examines the extent to which Chile’s 18 state universities contribute to sustainability and the 2030 Agenda, with a specific focus on the 17 Sustainable Development Goals (SDGs). To this end, scientific publications, technological developments, innovation initiatives, and funded research projects carried out between 2022 and 2023 were analyzed using a combination of bibliometric analysis and document review. Data were collected from Scopus, Web of Science, and national databases, and classified using a structured keyword strategy aligned with each SDG. A PRISMA-inspired screening and selection workflow was employed to ensure consistency and transparency in the selection of the results. The analysis reveals a clear institutional focus on SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities), which together account for the majority of outputs analyzed. In contrast, SDG 14 (Life Below Water) and SDG 15 (Life on Land) exhibit comparatively lower levels of representation. Differences were also observed among universities and across geographical macro-zones. The integrated analysis revealed important thematic asymmetries, territorial specialization patterns, and differentiated institutional sustainability profiles across the Chilean public university system. These findings highlight both the strengths and the current gaps in institutional alignment with the SDGs. The paper concludes by proposing concrete measures to improve coordination and information systems with the aim of reinforcing the strategic role of public universities in advancing sustainable development at both the national and regional levels. Full article

Universities serve as catalysts for knowledge creation across territories, promoting innovation and economic development through different channels. This paper investigates the role of university graduates as a location determinant of new green-tech-based firms (NGTBFs) across Italian NUTS-3 regions over the period 2011–2017. We examine whether universities, as providers of high-skilled human capital, affect the spatial distribution of new green ventures. Adopting a patent-based definition of NGTBFs and an econometric framework accounting for regional heterogeneity, we analyse the impact of university graduates on green firm creation. The results show that higher education fosters green entrepreneurship primarily through the channel of producing doctoral and STEM-oriented graduates, who serve as key drivers of NGTBF formation. Interestingly, the analysis reveals marked spatial heterogeneity across Italy’s North–South divide, with stronger associations of PhD and STEM graduates in Southern regions, where specialised human capital appears to compensate for weaker innovation systems. These findings deliver clear policy implications, suggesting that strategies aimed at promoting green entrepreneurship should prioritise advanced, STEM-oriented human capital and explicitly account for regional contexts, rather than relying on uniform higher education expansion approaches. Full article

In complex low-altitude environments, unmanned aerial vehicles (UAVs) require reliable positioning, yet Global Navigation Satellite System (GNSS) signals are vulnerable to occlusion and interference. Pseudolite-augmented cooperative localization, which combines ground base-station signals with inter-UAV relative observations, can complement GNSS in such environments. However, two practical issues remain in real-world deployment: UAV-to-base-station (U-B) and UAV-to-UAV (U-U) observations have markedly different error statistics that a unified noise adjustment cannot handle, and the conservative covariance estimates produced by Covariance Intersection (CI) fusion bias the innovation-based adaptive noise estimation in distributed architectures. To address these issues, this paper proposes a Distributed Group Covariance Compensation Adaptive Kalman Filter (DGCC-AKF) for collaborative enhancement of UAV regional localization. DGCC-AKF establishes a group adaptive mechanism that independently adjusts the noise covariance matrices of U-B and U-U observations, enabling observation-type-level adaptive weighting that suppresses anomalous U-B or U-U measurements at the group level. In addition, a bounded covariance compensation factor is incorporated to alleviate the CI-induced conservatism in the adaptive noise estimation. The proposed method is evaluated on a 2800 km² semi-physical testbed based on the Ground-based High-precision Local Positioning System (GH-LPS) pseudolite network using measured U-B observations and high-dynamic (>300 km/h) flight trajectories collected from a fixed-wing platform across three independent flight sessions. Results demonstrate that under observation fault periods, the proposed method improves 3D positioning accuracy by up to about 75% over single-UAV extended Kalman filter (EKF). Compared with two advanced algorithms in this field, variational Bayesian adaptive Kalman filter (VBAKF) and maximum correntropy criterion Kalman filter (MCC-EKF), it is the only scheme that remains accurate and stable across all UAVs and fault types. The framework provides a practical step toward field deployment for resilient multi-UAV cooperative navigation in pseudolite-augmented GNSS-denied regions. Full article

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range. Full article

Environmental information disclosure is a critical pathway for manufacturing enterprises to advance the modern environmental governance system. Using the New-Generation Artificial Intelligence Innovation and Development Pilot Zones (NAIDP) as a quasi-natural experiment, this study employs panel data of Chinese A-share listed manufacturing firms from 2011 to 2023 to investigate the effect and underlying mechanism of the NAIDP on corporate environmental information disclosure. The results indicate that the NAIDP significantly enhances enterprises’ environmental information disclosure, and this positive effect is more salient for non-state-owned, superior digital infrastructure, and non-heavy-pollution enterprises. Mechanism tests demonstrate that the NAIDP functions by mitigating information asymmetry and enhancing internal control. Further analysis of the moderating effect suggests that management’s environmental awareness and regional environmental regulation intensity positively strengthen the promotional effect of the NAIDP. This study not only supplements micro-level empirical evidence for the environmental governance effect of artificial intelligence applications but also provides practical insights for policy optimization to facilitate the green transformation of the manufacturing industry. Full article

Against the backdrop of the “Dual Carbon” strategy and global shipping digitalization, data elements have emerged as the key enabling factor and predictive correlate of coastal port smartness. Using panel data for seven coastal provinces/municipalities and eight coastal ports in China from 2017 to 2024, this paper constructs a “base-supply-flow-use” data element development index (DEDI) and a “WSR” coastal port smartness index (CPSI), employing VHSD-EM dynamic model, random forest algorithm, and partial effect model to examine the association patterns, nonlinear responses, and differentiated enhancement pathways between data elements and port smartness. Findings reveal: (1) CPSI and DEDI exhibit a high positive correlation with narrowing regional disparities; (2) CPSI shows stepwise spatial differentiation, with Shanghai and Ningbo-Zhoushan Ports leading, while Guangdong demonstrates “data advancement but smartness lag”; (3) in the random forest model, the predictive contribution of DEDI to CPSI is 13.586%, which ranks behind digital inclusive finance and openness level but is higher than regional economic strength and innovation output. The combined predictive contribution of the DEDI main effect and its interaction terms reaches 32.567%; (4) the univariate partial effect of DEDI on predicted CPSI followed a three-stage nonlinear pattern of initial accumulation, accelerated improvement around a threshold of DEDI ≈ 0.215, and diminishing marginal effects at higher levels; and (5) the joint partial effects of DEDI with digital inclusive finance, economic development, fiscal transportation expenditure, and innovation output showed clear dimensional and regional heterogeneity. Accordingly, four policy pathways are proposed: constructing a full-chain data element system, enabling synergistic empowerment of data and supporting elements, formulating regionally differentiated catch-up strategies, and strengthening the dual-wheel support of digital inclusive finance and opening-up—all aimed at advancing the development of world-class ports. Full article

The study is driven by its importance in modern material processing and precision engineering, where understanding and controlling interfacial stability is crucial in maintaining reliable performance across various operating conditions. The interplay between the tangential magnetic field and temporal periodicity generates additional mechanisms of mode coupling and amplifies instability. These observations address critical shortcomings in nonlinear stability theory and suggest practical uses in flow regulation and the control of conductive fluids. The fluids are assumed as Eyring–Powell non-Newtonian and flow with uniform velocities through porous media. The analysis is conducted using a non-perturbative method that relies mainly on He’s frequency formulation. To facilitate the mathematical treatment, viscous potential theory is adopted. The governing linear partial differential equations describing the flow are then solved under nonlinear boundary conditions, resulting in a nonlinear characteristic equation that represents the displacement of the interface. A non-dimensional procedure is then applied to extract the key dimensionless physical parameters influencing the system behavior. A set of graphical results is provided to demonstrate how the system’s stability behavior is influenced by changes in the key dimensionless physical parameters. The validation of the innovative process is achieved using Mathematica Software. The study considers both uniform and periodically varying magnetic fields, and the associated stability conditions are evaluated for each case, where the impacts of various non-dimensional attributes are assessed. As density ratio increases, it stabilizes periodic magnetic fields while destabilizing uniform ones. A stronger MF enhances magnetic damping, reducing instability regions and promoting stable periodic interfacial motion. Enhanced conductivity improves Magnetohydrodynamic interactions, resulting in greater energy dissipation and stability. Full article

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R² = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article

Global climate governance is undergoing a rapid transformation, and energy systems are increasingly shifting toward low-carbon development. Against this background, improving manufacturing firms’ green total factor productivity (MFGTFP) is essential for achieving sustainable industrial development. China has introduced two major policy instruments: new energy demonstration cities (NEDCs) and low-carbon city pilots (LCCPs). NEDCs focus on optimizing the energy supply structure, whereas LCCPs seek to reduce carbon emissions through demand-side regulatory constraints. This study treated the joint implementation of NEDCs and LCCPs as a quasi-natural experiment and employed panel data from Chinese A-share listed manufacturing firms from 2007 to 2024. Using a multi-period difference-in-differences model and mechanism tests, we examined the effect of the joint implementation of these policies on MFGTFP. The empirical results show that the joint implementation of NEDCs and LCCPs significantly improves MFGTFP. This effect is more pronounced when NEDCs are introduced prior to LCCPs, particularly in cities with a higher government ecological governance capacity (GEGC) and in regions characterized by a lower carbon emission intensity (CEI). Mechanism analysis revealed that the joint effects of NEDCs and LCCPs operate through supply-side innovation and partially through demand-side cost-pressure channels. On the supply side, NEDCs promote green innovation (GI), thereby enhancing firms’ supply innovation. On the demand side, the evidence mainly reflects financing constraint (FC) alleviation rather than a positive capacity utilization (CU) channel. Together, these findings suggest that improvements in MFGTFP are driven by supply-side innovation incentives and partially by demand-side cost-pressure effects through FC alleviation. These findings provide firm-level evidence on how the joint implementation of energy and carbon policies promotes green productivity improvement. Full article

The authentication of Algerian honey represents a critical challenge for the valuation of national biological patrimony. The present review provides a comprehensive synthesis of existing literature regarding Algerian honeys, emphasizing their diverse botanical origins and complex chemical profiles across seven distinct biogeographical regions, while proposing an innovative Foodomics and AI-driven roadmap to secure geographic authenticity and sustainable rural development. Such evidence underscores the necessity of transitioning from this classical analytical framework toward the emerging ‘Foodomics’ paradigm. By integrating advanced technologies like DNA metabarcoding and molecular fingerprinting, the establishment of a proposed ‘digital passport’ is proposed as a strategic solution to secure Protected Geographical Indications (PGI). Beyond technical innovation, this evolution is presented as a vital socio-economic necessity to ensure the sustainability of rural beekeeping and the international competitiveness of the industry. Ultimately, bridging established data with a molecular roadmap ensures that the biological prestige of this natural heritage is preserved for future generations. Beyond chemical and botanical analyses, this roadmap also incorporates Chemometric Modeling as a cognitive system. By applying techniques such as self-organizing maps (SOMs) and principal component analysis (PCA). This combination ensures highly accurate classification and supports the implementation of a sustainable digital passport system for the local honey industry. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

With the increasing penetration of distributed renewable energy, distribution networks face severe operational challenges during grid faults, where rapid power restoration and system stability are crucial. Traditional fault restoration strategies often rely on static dynamic zoning or simple power balancing, neglecting the critical electrical interactions among nodes. To address these limitations, this paper innovatively proposes a hierarchical coordinated control framework for distribution network fault recovery, combining dynamic zoning and coordinated load shedding. The core novelty of this research lies in integrating the node electrical correlation degree into the load grading process to assist in coordinating dynamic network dynamic zoning. By comprehensively evaluating real-time power flow, the regulation capabilities of distributed resources, and intra-region electrical correlations, the proposed framework adaptively optimizes both the zoning structure and the load shedding sequence. Simulation results demonstrate that, compared with conventional static or uncoordinated methods, the proposed approach significantly minimizes load loss while improving grid recovery efficiency and voltage stability. Ultimately, this coordinated control strategy effectively enhances the resilience and operational safety of high-penetration renewable distribution networks, providing robust support for distribution network operations under a high proportion of renewable energy integration. Full article

Artificial intelligence (AI), which is advancing rapidly, offers a novel and important tool for driving sustainable energy transition, although the spatiotemporal correlation between the two is complex. Taking China’s 30 provinces as the study subjects, this research constructs an evaluation index system from the perspective of energy transition outcomes to assess the level of China’s energy transition. It evaluates the level of AI development based on the foundation of AI development, AI technological innovation, and AI application, and analyzes its spatiotemporal evolution characteristics. Pearson correlation analysis and bivariate local spatial autocorrelation are employed to investigate the spatiotemporal associations between energy transition and AI. In addition, the dynamic mechanisms linking the two are further investigated using a geographically and temporally weighted regression (GTWR) model. The results indicate that, first, innovation and application in AI were on the rise, while regional disparities were widening and a polarization phenomenon was emerging; AI development was concentrated in the eastern regions, with a decreasing trend toward the northwestern inland areas. Second, the overall level of China’s energy transition continued to rise, with a box-shaped clustering pattern observed across regions; Beijing, Inner Mongolia, Jiangsu, and Shandong had achieved a relatively high level of energy transition. Third, the development of AI did not always correlate positively with the energy transition. There was a significant positive correlation between AI technological innovation and application and the energy transition. There were significant differences in the spatial patterns linking AI development and the energy transition. The positive correlation between the two was significant and widespread, concentrated in the central and eastern provinces. Full article

This paper aims to scientifically evaluate the sustainable development level of China’s pilot free trade zones (FTZs) and provide empirical support for policy optimization. Taking 22 Chinese FTZs from 2013 to 2022 as research samples, we construct a six-dimensional evaluation system, including environmental optimization, economic development, opening-up, radiation-driven capacity, business environment, and scientific and technological innovation, and use principal component analysis (PCA) and analytic hierarchy process (AHP) for comprehensive measurement. The results show the following: (1) The overall sustainable development level of FTZs presents an upward trend with significant regional differences—coastal FTZs grow rapidly, inland FTZs grow steadily, and border FTZs grow slowly. (2) The 2022 comprehensive scores show a gradient distribution, and regional development imbalance is prominent. (3) Economic development and opening-up are the core driving dimensions, while environmental optimization and radiation-driven capacity have relatively low weights and weak contributions. The marginal contribution of this paper is the construction of a multi-dimensional standardized evaluation system for FTZ sustainable development and clarification of regional differentiation characteristics and driving mechanisms. Based on this, this paper puts forward targeted policy suggestions: implementing differentiated empowerment according to coastal, inland and border FTZ positioning, promoting cross-regional experience sharing, and establishing a dynamic monitoring mechanism to narrow the development gap and achieve high-quality coordinated development. Full article