Search Results (10,100)

Technical and vocational education and training (TVET) colleges in South Africa continue to experience challenges related to staff commitment, organisational performance, and institutional effectiveness. These challenges highlight the need to better understand leadership approaches that sustain academic engagement and stability. This study examines the relationship between transformational leadership and organisational commitment among academic staff at a selected TVET college in Gauteng, South Africa. Grounded in the transformational leadership theory of Bass and Avolio, the study adopted a quantitative, cross-sectional survey design. Data were collected from 203 academic staff across six campuses using a structured self-administered questionnaire. Descriptive statistics and multiple regression analysis were performed using SPSS. The findings revealed low levels of organisational commitment among academic staff. While transformational leadership, as a composite construct, did not significantly predict organisational commitment, specific components—namely intellectual stimulation, inspirational motivation, and individualised consideration—showed significant positive relationships with organisational commitment. Theoretically, the study refines the application of transformational leadership theory within the TVET context by demonstrating that its components may operate differentially rather than as a unified construct in predicting organisational commitment. These findings challenge assumptions regarding the holistic predictive power of transformational leadership and extend leadership scholarship within under-researched TVET settings in developing-country contexts. Practically, the results provide evidence-based guidance for TVET management to design targeted leadership development interventions that emphasise specific transformational leadership behaviours to enhance academic staff commitment. Full article

The synergistic control of multiple pollutants is critically challenged by complex nonlinear interactions, strong spatiotemporal heterogeneity, and the difficulty of tracing causal drivers. Deep learning offers high predictive power but suffers from the “black-box” problem, limiting its acceptance in environmental decision-making. Explainable Deep Learning (XDL) integrates physical mechanisms with interpretable algorithms, achieving both prediction accuracy and explanatory transparency. This review systematically evaluates the effectiveness and limitations of XDL in analyzing multi-pollutant interactions, with a comparative focus on atmospheric and aquatic environments. Key techniques, including SHAP, attention mechanisms, and physics-informed neural networks, are examined for their roles in synergistic monitoring, source apportionment, and regulatory optimization. The main findings reveal that: (1) XDL, particularly the “tree model + SHAP” paradigm, has become a dominant tool for quantifying driving factors, yet most attributions remain correlational rather than causal; (2) physics-informed fusion (soft vs. hard constraints) improves physical consistency but faces unresolved conflicts between data and physical laws, with current models lacking a conflict detection mechanism; (3) cross-media comparison shows a unified technical logic of “physical mechanism guidance + post hoc feature attribution”, but atmospheric applications lead in embedding advection–diffusion constraints, while aquatic research excels in spatial topology modeling via graph neural networks; (4) critical bottlenecks include the lack of causal inference, uncertainty-unaware interpretations, and data scarcity. Future directions demand a shift from correlation-only to causal-aware attribution, from blind fusion to conflict-detecting systems, and from no evaluation standards to domain-specific validation benchmarks. XDL is poised to transform multi-pollutant governance from experience-driven to intelligence-driven approaches, provided that verifiable interpretability and physical consistency become core design principles. Full article

The inherent intermittency and uncertainty of wind power generation pose significant challenges to grid security and the integration of renewable energy. Accurate and reliable short-term wind power forecasting is crucial for enhancing wind energy usage and ensuring the safe operation of power systems. Current mainstream forecasting methods inadequately model spatial correlations among regional wind farms. Additionally, wind power generation is susceptible to sudden changes in weather conditions and environmental factors, limiting the robustness of existing forecasting methods when confronting dynamically changing prediction environments. This poses major challenges for accurate and reliable regional wind power forecasting. This paper employs Graph Convolutional Networks (GCN) to model spatial connections between wind farms while introducing a combined TCN-Transformer model for temporal feature extraction and dependency modeling. Furthermore, to enhance prediction accuracy and reliability, Deep Q-Network (DQN) is incorporated to dynamically correct model prediction errors. Experimental results demonstrate that the proposed short-term wind power forecasting method achieves an RMSE of 60.14 and an MAE of 45.98, showing significant improvement over predictions from models without DQN error correction and other comparative models. Future work may extend the forecasting horizon to provide more information support for grid supply security decisions. Full article

Let ${\mathcal{Z}}_2\left(s\right)$ denote the modified Mellin transforms of the modulus of the fourth power of the Riemann zeta-function. This paper is devoted to the probabilistic properties of generalized discrete shifts ${\mathcal{Z}}_2(s+i\psi \left(k\right))$, $k\in \mathbb{N}$, with a certain differentiable function $\psi \left(\tau \right)$ satisfying some estimate connected to the mean square of the function ${\mathcal{Z}}_2\left(s\right)$ and such that the sequence $\left\{\kappa \psi \right(k):k\in \mathbb{N}\}$ is uniformly distributed modulo 1 with every $\kappa \in \mathbb{R}\setminus \left\{0\right\}$. We propose the condition that ${\mathcal{Z}}_2(s+i\psi \left(k\right))$ in the space of analytic functions has a limit distribution concentrated at the point $g_0\left(s\right)\equiv 0$. Such a limit theorem is applied for the approximation of the function $g_0\left(s\right)$. Full article

Precision medicine is facing a critical transition driven by the growing complexity of biological data and the insufficient ability of current models to translate such data into clinically meaningful information. Linear, single-gene approaches are no longer adequate to explain the multifactorial nature of most modern diseases, whose phenotypes emerge from combinations of genetic, molecular, and environmental factors. Network-based precision medicine addresses this by providing a systemic framework capable of integrating heterogeneous omics data, interactomes, and clinical information to identify disease modules and novel therapeutic opportunities. The distinct novelty of this review is its focus on the potential of “network language” as the primary driver for realizing precision medicine through professional collaboration. We argue that networks are not merely tools that achieve precision “per se”; rather, their transformative power lies in their ability to serve as a shared and interpretable interface grounded in network theory. By offering this common conceptual ground, the paradigm bridges the deep cultural and methodological gaps between clinicians and data analysts, enabling effective cooperation between figures with fundamentally different, and often divergent, backgrounds. Practical tools—such as biological network analysis and Molecular Tumor Boards—demonstrate how computational modeling and clinical expertise can be successfully combined to generate actionable insights. Ultimately, network-based precision medicine represents a decisive step toward reconstructing the patient’s complexity and promoting a genuinely personalized clinical approach in which quantitative analysis and medical reasoning act synergistically through multidisciplinary integration. Full article

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is presented in this paper, which enhances situational awareness in smart distribution grids that supply dense urban loads and critical smart city services. The proposed approach targets various fault conditions, which include three-phase-to-ground, three-phase, two-phase-to-ground, two-phase, and single-phase-to-ground faults. The proposed method utilizes a wavelet-based signal processing technique to analyze the feeder’s current data captured by waveform measurement units (WMUs) and extracts features for fault analysis. As a result of these features, a multi-stage machine learning architecture incorporating deep learning components is developed to accurately determine the occurrence, type, and location of faults. To evaluate the performance of the proposed approach, simulations were conducted on a 16-bus distribution network. Results show a high level of accuracy in fault detection, classification, and localization. This indicates that the method can be a valuable tool for enhancing the resilience and intelligence of future power grids, as well as supporting self-healing and fast service restoration in smart city services. Full article

Urban heritage structures are most commonly understood as memorial artifacts, tourism assets, or redevelopment resources. While this common view acknowledges cultural and economic value, it overlooks a deeper function of heritage within the long evolution of human settlements. This paper advances a counter thesis: in addition to its historic contingencies and power relationships—which are real, but only part of the picture—urban heritage embodies valuable but often hidden intelligence that is highly relevant to contemporary urban challenges. Specifically, heritage environments encode useful structured information about spatial configurations that have gained adaptive value over time in a process known as stigmergy. Drawing on complexity science, network theory, the mathematics of symmetry, and theories of extended cognition, the paper argues that enduring urban forms persist not only for symbolic or historical reasons, but because they embed structural properties conducive to resilience, legibility, social interaction, climatic adaptation, and human well-being. Recurring characteristics include fine-grained network connectivity, fractal scaling hierarchies, organized symmetry, articulated thresholds, and biophilic integration. Evidence from environmental psychology, public health, and urban morphology suggests that such properties correlate with reduced stress, increased walkability, stronger social capital, and improved ecological performance. The paper proposes a methodological framework—what we call the Adaptive Patterns Model—for identifying, evaluating, and translating this embedded intelligence into contemporary regeneration practice. The Model is presented as a four-phase, conceptually synthesized framework—integrating insights from complexity science and stigmergy, urban morphological analysis, and pattern-language methodology—comprising documentation, pattern extraction, encoding, and performance correlation. It concludes by challenging a still-prevalent assumption that contemporary conditions invalidate accumulated spatial knowledge. Instead, urban heritage is understood as adaptive capital within an ongoing evolutionary process, offering a structurally grounded foundation for resilient urban transformation. Full article

Accurate forecasting of wind and photovoltaic power remains challenging due to the strong nonlinearity, nonstationarity, and seasonal heterogeneity of renewable generation series. To address this issue, this study proposes a hybrid forecasting framework integrating time–frequency joint analysis (TFAA), temporal convolutional networks (TCN), long short-term memory (LSTM), and multi-head self-attention (MHSA). Wavelet transform is used to extract frequency-domain representations, which are jointly encoded with the original time-domain sequence through a dual-branch architecture and adaptively fused. The fused features are then processed by a TCN-LSTM backbone to capture both long-range dependencies and short-term dynamics, while MHSA is introduced to enhance global contextual modeling. Experiments on wind-farm and photovoltaic datasets from China, together with external validation on the NREL WIND Toolkit and the GEFCom2014 Solar benchmark, show that the proposed model achieves the best overall seasonal performance and maintains competitive improvements on public benchmarks. Additional ablation studies, repeated-run statistical validation, persistence-based skill-score analysis, prediction-interval evaluation, ramp-event assessment, meteorological-driver enrichment, permutation-based driver attribution, regime-conditioned error diagnostics, and transferability evidence analysis further confirm the effectiveness, robustness, physical consistency, and practical applicability of the proposed framework. The results indicate that the proposed model provides a reliable and operationally relevant solution for short-term wind and photovoltaic power forecasting. These findings further support sustainable renewable-energy integration, smart-grid dispatch, and low-carbon power-system operation. Full article

To investigate the influence of train operating speed on the transient characteristics of the pantograph–catenary arc, this paper establishes an integrated simulation model encompassing the traction network, electric locomotive, and arc. In this model, the traction network adopts a chain circuit model based on multi-conductor transmission line theory. The electric locomotive model considers the train body and the on-board transformer. For the pantograph–catenary offline arc, an improved Habedank model is employed, which takes the train operating speed and arc current as variables. Based on this model, this paper systematically investigates the variation patterns of arc electrical parameters and transient currents in each line of the traction network with train operating speed under pantograph–catenary offline. The simulation results indicate that as train speed increases, both the steady-state arc voltage and the maximum voltage at arc ignition rise, and the arc extinction time at current zero-crossing is prolonged. The peak arc currents on the contact wire, feeder, protective wire, and rails decrease, while the transient current on the ground wire increases. This study can provide a reference for the electromagnetic compatibility design, insulation coordination optimization, and electromagnetic protection of high-speed railway traction power supply systems. Full article

Ensuring DC bus voltage stability is a key enabler for the sustainable development of photovoltaic-storage DC microgrids (PV–storage DC MGs), which are regarded as critical infrastructure for high-penetration renewable energy utilization. However, the inherent randomness of PV power generation seriously threatens this stability. This paper proposes a novel model predictive control (MPC) scheme for the energy storage system (ESS) to mitigate voltage fluctuations and enhance system stability. To improve the model precision, a forgetting-factor-augmented recursive least squares (RLS) algorithm is employed for online identification and correction of the estimated equivalent impedance between the ESS and the DC bus. Rigorous Lyapunov stability analysis is performed to obtain the sufficient stability conditions and quantitative tuning rules for the weighting coefficients, which transforms the qualitative parameter selection into a theoretical constrained optimization. The state of charge (SOC) of the ESS is set as a security constraint to avoid excessive charge/discharge and extend battery service life. A distinguished advantage of the proposed strategy is that it generates ESS power commands solely based on local measurements, eliminating the dependence on external communication and improving system reliability. Simulation results on MATLAB R2021b/Simulink and hardware-in-the-loop experiments based on RT-Lab and DSP demonstrate that the proposed MPC method significantly reduces the DC bus voltage deviation, accelerates the dynamic recovery process, and maintains stable ESS operation under both normal PV fluctuations and sudden PV outage conditions. Full article

Introduction: Hospital adverse events are a global patient safety problem that account for avoidable death, long-term disability, extended length of stay, and increased healthcare costs. Underreporting, wherein fewer than 10% of events are indeed recorded, is prevalent and is characterized primarily by cultural and organizational determinants. Artificial intelligence, in the form of machine learning and natural language processing, has been described as a potential tool for enhancing adverse events detection and prediction with the use of large-scale clinical data. Materials and Methods: PRISMA-DTA guidelines were followed in this systematic review. Scopus, PubMed, and Web of Science were searched employing keywords associated with adverse events, artificial intelligence methodologies (e.g., machine learning, deep learning, natural language processing), and healthcare settings. Inclusion criteria included original research on artificial intelligence-based solutions for the detection or prediction of adverse events such as medication errors, hospital-acquired infections, and complications during surgery. Reviews, meta-analyses, and non-artificial intelligence studies were excluded. Following screening, 15 studies were found to meet inclusion criteria. Results: The referenced studies show a shift from rule-based natural language processing models to advanced deep learning and Bidirectional Encoder Representations from Transformers models. Early approaches, i.e., Support Vector Machine classifiers, achieved AUC scores as high as 0.92, while later models (Random Forest, LightGBM, XGBoost) mirrored AUCs of over 0.93. Large language models achieved F1-scores of 0.84 for named entity recognition. Artificial intelligence models even identified unreported incidents. Discussion: Artificial intelligence-powered methods are transforming adverse events detection from retrospective to predictive, proactive monitoring. There remain some challenges, however, including limited external validation, class imbalance, and interpretability of complex models. Future studies must address explainable artificial intelligence, multicenter trials, and high-quality well-annotated datasets to offer secure clinical integration. Full article

This article presents a novel nonlinear control strategy for an active triple-bridge converter used in a direct current (DC) microgrid with multiple buses to interconnect its three constituent buses, which are comprised of generation systems and generalized loads. The proposed controller aims to regulate the voltage on two of the DC buses and manage the power exchanged between them in response to load and voltage changes. Unlike the existing literature, the control strategy is designed based on the generalized state-space averaged model of the converter, obtained from the delta equivalent circuit of the high-frequency transformer. The performance of the designed controller is validated through simulation results, demonstrating good performance under changes in generated power, load variations, and voltage fluctuations on both DC buses of the microgrid. Full article

A digital predistortion (DPD) scheme for concurrent multi-beam transmission in fully digital multiple-input, multiple-output (MIMO) systems, using Fast Fourier Transform (FFT) beamforming and so-called virtual-array processing, is proposed. In a MIMO array with nonlinear power amplifiers (PAs), transmitting multiple beams concurrently yields intermodulation products that end up in both user and non-user directions. In the setting with few users in a large array, the array dimension will typically be much larger than the number of generated intermodulation products. At the same time, linearization per PA is excessively costly for large arrays. This work shows that it is instead possible to linearize the system by producing predistorted user beams, and non-user intermodulation products, through DPD processing in a virtual array of a much smaller dimension than the physical array. Theoretical derivations and simulation examples show how this approach can lead to manyfold reductions in DPD complexity. Full article

Under the conditions of laser power of 1500 W, scanning speed of 5 mm/s, spot diameter of 3.5 mm, and powder feeding rate of 10 r/min, this study systematically investigated the influence of different tempering temperatures (200 °C and 600 °C) on the microstructure, friction and wear properties, and corrosion resistance of laser cladding Ni25 coatings, as well as the underlying mechanisms. The phase composition, microstructure, chemical composition, wear resistance, and corrosion resistance of the coatings were characterized and analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), pin-on-disk friction and wear tests, and electrochemical workstations. The results showed that the as-clad coating was composed of γ-Ni supersaturated solid solution and various metastable borides/carbides (such as Cr₃B₄), presenting fine-grained and non-equilibrium features. Tempering at 200 °C mainly achieved stress relaxation, enhancing and shifting the diffraction peaks to the left without changing the phase composition, while tempering at 600 °C drove significant diffusion-type phase transformation, leading to the decomposition of metastable Cr₃B₄ and the precipitation of stable phases such as Ni₂Si, accompanied by grain growth and microstructure coarsening. Friction tests indicated that the coating tempered at 600 °C exhibited the lowest average friction coefficient (0.679) and wear volume (0.0582 mm³) due to stable microstructure and hard phase strengthening, demonstrating the best wear resistance. However, electrochemical tests revealed a “trade-off” effect: the fine-grained microstructure of the as-clad coating, with its uniform composition, had the lowest corrosion current density (8.10 × 10⁻⁵ A/cm²) in 3.5% NaCl solution, showing the best resistance to uniform corrosion, while tempering, especially at 600 °C, caused grain growth, coarsening of the second phase, and micro-galvanic effects, slightly reducing the anodic dissolution resistance and increasing the corrosion current. This study clarified that heat treatment can significantly enhance the mechanical and tribological properties of Ni25 coatings by regulating their transformation from metastable to stable states, but at the potential cost of some corrosion resistance, providing a theoretical basis for optimizing post-treatment processes for different service conditions (wear resistance or corrosion resistance). Full article

This article examines how ancient Greek tragedy mobilizes landscape to reflect on the limits of civic order and the conditions of human dwelling. Rather than treating mountains, groves, meadows, and borderlands as neutral settings or as simple “nature/culture” oppositions, it argues that tragic landscapes are ethically charged spaces where human norms meet forces that exceed political regulation—divine presence, necessity, vulnerability, and finitude. Written for the polis yet unsettled by what lies beyond it, tragedy repeatedly turns to extra-civic spaces to test civic stability. Three case studies develop the argument. In Hippolytus, woodland and meadow sustain an ideal of purity grounded in withdrawal, an orientation incompatible with social life and culminating in catastrophic isolation. In Bacchae, Pentheus’ project of spatial control collapses as Dionysian forces traverse walls and institutions with ease, exposing the limits of civic rationality. In Oedipus Tyrannus and Oedipus at Colonus, the tragic trajectory moves from Mount Cithaeron, a site of abandonment and opaque necessity, to the sacred grove at Colonus, where prolonged suffering enables a transformed relation to place, law, and divine power. Taken together, these plays suggest that the polis is never fully self-sufficient: civic order endures only through engagement with what it cannot master or expel, and spatial orientation is inseparable from ethical choice. Full article