Search Results (60)

Wind turbines are persistently threatened by both lightning overvoltage and switching overvoltage due to their ultra-high structure, dense power electronics, and harsh operational environments, which severely endanger the safe and stable operation of the units. This paper systematically reviews the generation mechanism, type characteristics, and hazards of overvoltages in wind turbines. An internal and collaborative overvoltage protection system based on lightning protection zones (LPZs) is described. Focusing on three core protective devices—metal oxide varistors (MOVs), gas discharge tubes (GDTs), and Transient Voltage Suppressors (TVSs)—the research progress in material modification, structural optimisation, and performance evolution laws is explored. Additionally, the development of series-parallel topological collaborative design for multiple devices and active-triggered intelligent protection technologies is analysed. It is highlighted that current wind turbine overvoltage protection still faces bottlenecks in standard applicability, device operating condition adaptability, and system-level collaborative design. Future research should focus on the application of a wide bandgap and nanomaterials, the improvement of test standards tailored for actual operating conditions, and the construction of multi-physics coupling simulation and active intelligent early warning protection systems, so as to provide theoretical and technical support for high-reliability overvoltage protection of large-capacity and offshore wind turbines. Full article

This concept paper advances stigma power as a central analytical mechanism for understanding how patriarchy, capitalism, white supremacy, and cis-heteronormativity operate with particular intensity against sex workers. Integrating Link and Phelan’s stigma power with Bourdieu’s symbolic violence and Foucauldian productive power, the framework theorises stigma as a mechanism institutionalised through law and enforced by institutions, which produces measurable consequences that include violence, exclusion, and health harms. Analysing the intersecting axes of gender, sexuality, race, migration, and class across three qualitative studies (SWMH, SEXHUM, VICSW), the article demonstrates why labour-rights reforms, including decriminalisation, are necessary but insufficient. Dismantling stigma requires not only removing sanctions but actively contesting the actors exercising stigma power and interrupting the stabilising mechanisms that reproduce it. This requires policy that acknowledges stigma’s existence whilst working to dismantle it, rather than eliding its reality through liberal mainstreaming or strengthening it through criminalisation or rescue frameworks. The framework explains why decriminalisation is associated with better access to rights and health; why all criminalisation including the so-called Swedish model correlates with increased violence; why stigma persists under optimal legal conditions; and how intersecting marginalisations produce differential vulnerability. Policy implications emphasise pairing decriminalisation with peer-led anti-stigma work, institutional reform, migrant rights, and funded support for sex worker self-organisation. Full article

This study proposes an innovative instructional approach to promote scientific literacy by integrating the Nature of Science and the Nature of Scientific Inquiry with experimental practice and the history of physics. The aim is to foster a deep understanding of how scientific knowledge is constructed and to promote informed trust in science. Using an explicit and reflective methodology, the intervention combines experimental activities with historical reflection. The core of the learning sequence is the experimental reconstruction of Galileo’s studies on falling bodies, based on the historical manuscript folio 116v, an original document that provides the empirical evidence for the law of falling bodies, illustrating the transition from raw experimental data to mathematical formalization. Through this activity, students engage with key epistemic aspects of scientific practice, including the management of uncertainty—distinguished into statistical/aleatory and structural/epistemic forms—the probabilistic nature of scientific knowledge, the predictive power of models and theories, and the underdetermination of scientific theories. Additional themes addressed include the role of thought experiments, the importance of communicating results for scrutiny and validation, the function of models as mediators between theory and phenomena, and the process of de-idealization. The study also challenges the persistent myth of a single, linear “scientific method,” highlighting instead the theory-laden character of scientific inquiry and the central role of the scientific community. This dimension is explored through the historical comparison between Galileo and Mersenne, which illustrates elements of the scientific ethos and the role of peer review as a mechanism for the correction and refinement of knowledge. The results obtained with pre-service teachers, with whom this instructional sequence was implemented, indicate that this contextualized approach facilitates the overcoming of a view of science as a set of absolute truths. Instead, it promotes a more mature understanding of science as a dynamic, provisional, and self-correcting human enterprise, while equipping future citizens with the critical tools necessary to navigate the challenges of the twenty-first century. Full article

Power system blackouts represent typical manifestations of instability in complex systems, whose evolution often exhibits non-stationarity, long-range correlations, and nonlinear scaling behavior. Most reliability assessment methods widely used in engineering practice are built on the core assumptions of event independence and light-tailed distribution, which will inevitably lead to systematic underestimation of extreme tail risks when blackouts actually present long-range memory and power-law heavy-tailed characteristics. Based on long-cycle historical blackout records of China’s power grid spanning 1981–2025, this paper develops an integrated framework combining Self-Organized Criticality (SOC) theory, Hurst exponent analysis, symbolic time-series methods, and Multifractal Detrended Fluctuation Analysis (MFDFA). This study systematically characterizes the evolution law and inherent dependence structure of blackout events from four dimensions: statistical scaling, temporal correlation, nonlinear structure, and multi-scale fractal spectrum. The results show that both the load-loss magnitudes and inter-event intervals of blackouts follow strict power-law distributions, with the system exhibiting scaling behavior consistent with SOC theory. The blackout event sequence presents significant long-range positive correlation and self-similarity, confirming a persistent long-term memory effect in the system evolution. Symbolic analysis further reveals the nonlinear fluctuation patterns and burst clustering behavior of the blackout process, reflecting the intermittency and complexity of blackout risks. MFDFA results verify that the blackout sequence has a broad-spectrum multifractal structure across different temporal scales, and Monte Carlo shuffle tests demonstrate that this multifractality mainly arises from intrinsic long-range temporal correlations, rather than being driven solely by heavy-tailed distribution. This study confirms that blackouts in China’s power grid are not random independent events, but present fractal statistical characteristics consistent with the self-organized critical mechanism. The findings provide a novel fractal perspective and quantitative framework for the statistical characterization, operational security assessment, and multi-scale early-warning modeling of blackout risks in China’s large-scale power systems. Full article

Canopy gaps are important footprints in understanding the forests’ disturbance regime and regeneration process, yet there is a need to employ multiple metrics along the gradient of time for a deeper understanding of the dynamics. In this study, aerial laser scanning-derived canopy gap data of three protected forests in the Carpathian Mountains, stratified by location and forest types, were examined at temporal and spatial scales. Multiple features were examined, such as gap size structure, gap area proportion, gap geometry, and the relationship between gap geometry and size and gap formation. The results indicated that the gap size frequency has a heavy-tailed right-skewed distribution and mostly maintains the same proportion across time, even after the reduction in gap numbers. Meanwhile, the probability distribution of the gap sizes is not exclusive to the power law; it also follows log-normal and exponential distributions. Gap counts and gap percentages decreased over time, but with the increasing size of gaps. Gap shape complexity was moderate around 2.0, but tended to have a complex shape as the gap size increased. The temporal gap dynamics were characterized by four gap types: recovered or closed gaps, persistent gaps, expanded gaps, or new openings, with the balance influenced by the severity of disturbance. These findings underscore the importance of collective gap metrics across temporal and spatial scales in elucidating gap dynamics of unmanaged forests. Full article

This study investigates the flow behavior of gyroid scaffolds using computational fluid dynamics (CFD) and three rheological models, Newtonian, Power-law, and Carreau, to assess the influence of pore size, inlet velocity, and scaffold size on wall shear stress (WSS) and permeability. The results show that non-Newtonian models yield substantially higher and broader WSS distributions than the Newtonian model, reflecting the importance of shear-dependent viscosity for physiologically realistic simulations. Larger pore size reduces the WSS and increases the permeability. Nevertheless, localized high-shear regions persist, particularly for the non-Newtonian fluids. Higher inlet velocities produce an increase in both WSS and permeability. However, this effect is lees remarkable for the Newtonian model. Comparisons between small and large scaffolds show lower wall shear stress levels in the larger geometry due to reduced local velocity gradients and a more evenly distributed flow field. Overall, rheological models influence the magnitude and heterogeneity of WSS. These findings highlight the need to incorporate non-Newtonian models when evaluating the scaffold performance in tissue engineering applications. Full article

Ball balancers are nonlinear, electromechanical, multivariable, open-loop unstable systems widely used in research laboratories, aerospace, military, and automotive industries to evaluate control mechanism effectiveness. The inherent difficulty in precisely managing ball position, combined with actuator saturation and system sensitivity to disturbances, makes trajectory tracking a persistent challenge. Conventional controllers often exhibit oscillatory responses with steady-state errors exceeding acceptable limits. Sliding mode control (SMC) offers robustness against model uncertainties; however, chattering finite-frequency, finite-amplitude oscillations near the sliding surface caused by switching imperfections, time delays, and actuator dynamics remain a significant limitation. This study addresses chattering through explicit vibration model compensation integrated into the SMC design for a 2-DOF ball balancer system using a visual servoing approach. A double-loop control architecture is implemented, where the inner loop handles servo angular position control and the outer loop manages ball position tracking through visual servoing feedback. The sliding mode controller is designed with a power rate reaching law, synthesizing two control laws: one with explicit vibration model compensation incorporating damping and stiffness terms, and one without. Experimental validation confirmed that SMC with compensation achieved significantly reduced steady-state error (0.034 mm vs. 0.386 mm) and lower overshoot (3.95% vs. 13.81%) compared to the uncompensated variant, with chattering amplitude reduced by approximately 72%. Full article

The large-scale rapid deployment of renewable generation and energy storage is transforming traditional power system dynamics through intermittency, reduced inertia, and pronounced long-range temporal dependence. Existing power system modeling frameworks are primarily based on short-memory assumptions and integer-order dynamics, which are unable to capture the persistence and oscillatory behavior of emerging renewable-dominant power systems. This structural mismatch leads to inaccurate system representation and degraded long-horizon prediction performance. Although fractional calculus has been applied to specific control and forecasting tasks in power systems, the joint system-level modeling of renewable generation and load demand using real-world data remains largely unexplored. In this paper, we develop a data-driven fractional-order dynamic modeling framework that explicitly incorporates long-memory effects into the governing equations through fractional differential equations based on the Caputo formulation. Using publicly available high-resolution datasets of load and renewable generation, empirical analysis reveals power-law decaying autocorrelations and dominant low-frequency spectral characteristics that motivate the use of fractional-order dynamics. Fractional orders and model parameters are jointly identified through prediction-error minimization to ensure consistency between modeled trajectories and observed persistence. The numerical results demonstrate that the proposed approach achieves a root–mean–square error of $3.12$, compared to $5.64$ and $4.98$ for integer-order and finite-memory models, respectively, and reduces the normalized root–mean–square error from $0.156$ and $0.132$ to $0.087$. Residual and spectral analyses further confirm that long-memory behavior is effectively captured by the proposed dynamics. The framework provides a scalable and physically interpretable foundation for the data-driven modeling of renewable-dominant power systems. Full article

Low-frequency acoustic fields—common in ventilation ducts, building façades, and industrial infrastructure—remain an underutilized source for ambient energy harvesting, particularly in humid environments where conventional contact-based or mechanically resonant harvesters may degrade over time. This study introduces a theoretical framework for converting acoustic pressure oscillations into electrical power through acoustic–electrokinetic coupling and proposes the Acoustic Baroionic Harvester (ABH) as a solid-state concept combining a Helmholtz resonator with a charged nanoporous membrane. The model is derived from coupled electrokinetic and fluid-mechanical governing relations, leading to closed-form expressions for the open-circuit voltage, internal electrokinetic resistance, and maximum deliverable power as functions of membrane surface charge, electrolyte properties, pore geometry, and resonance-induced pressure amplification. Numerical simulations are performed to validate the analytical scaling laws and to determine operating regimes that maximize power transfer to an external load. Under representative low-frequency acoustic excitation, the ABH predicts open-circuit voltages on the order of tens of millivolts and maximum power densities in the sub-microwatt-per-square-centimeter range. A compact CAD conceptual design tuned to approximately 120 Hz with a moderate resonance quality factor supports the feasibility of practical integration. The proposed approach enables micro-power generation from persistent low-frequency acoustic sources and provides a physically grounded pathway for self-powered sensing applications in built and industrial environments. Full article

This study evaluates the effectiveness of Jordan’s judiciary in overseeing electoral appeals within the framework of a constitutional monarchy. Adopting a mixed-methods approach, it combines doctrinal legal analysis of key constitutional provisions and Election Law No. 4 of 2022 with a comparative examination of electoral adjudication in Tunisia, Egypt, and Lebanon. The study is further strengthened by a structured content analysis of 120 appellate rulings issued between 2015 and 2023 and by qualitative insights drawn from anonymized interviews with judicial personnel engaged in electoral dispute resolution. Although Jordan’s legal framework formally empowers the judiciary to adjudicate electoral disputes, five structural limitations persist: narrow standing rules, rigid evidentiary thresholds, judicial reluctance to exercise investigatory powers, opaque reasoning in judgments, and the absence of specialized electoral courts. These constraints reflect systemic tensions between formal judicial independence and the realities of constrained discretion in hybrid regimes. An empirical analysis of 127 Jordanian electoral appeal cases from 2013 to 2020 reveals that a mere 7% of disputed electoral outcomes were overturned, whereas 73% of allegations were disregarded due to insufficient evidence. Furthermore, it is noteworthy that only 31% of rulings were publicly accessible, in stark contrast to the 89% accessibility rate observed in Tunisia. By identifying and addressing these systemic limitations, the study contributes to ongoing discourse on institutional reform and democratic resilience. In doing so, it underscores the importance of robust electoral justice mechanisms for sustaining public trust, rule of law, and inclusive governance—principles central to political and institutional sustainability as reflected in Sustainable Development Goal 16. Full article

Legal regulation and practical implementation of artificial intelligence (AI) in Kazakhstan’s criminal procedure are considered within the context of judicial digital transformation. Risks arise for fundamental procedural principles, including the presumption of innocence, adversarial process, and protection of individual rights and freedoms. Legislative mechanisms ensuring lawful and rights-based application of AI in criminal proceedings are required to maintain procedural balance. Comparative legal analysis, formal legal research, and a systemic approach reveal gaps in existing legislation: absence of clear definitions, insufficient regulation, and lack of accountability for AI use. Legal recognition of AI and the establishment of procedural safeguards are essential. The novelty of the study lies in the development of concrete approaches to the introduction of artificial intelligence technologies into criminal procedure, taking into account Kazakhstan’s practical experience with the digitalization of criminal case management. Unlike existing research, which examines AI in the legal profession primarily from a theoretical perspective, this work proposes detailed mechanisms for integrating models and algorithms into the processing of criminal cases. The implementation of AI in criminal justice enhances the efficiency, transparency, and accuracy of case handling by automating document preparation, data analysis, and monitoring compliance with procedural deadlines. At the same time, several constraints persist, including dependence on the quality of training datasets, the impossibility of fully replacing human legal judgment, and the need to uphold the principles of the presumption of innocence, the right to privacy, and algorithmic transparency. The findings of the study underscore the potential of AI, provided that procedural safeguards are strictly observed and competent authorities exercise appropriate oversight. Two potential approaches are outlined: selective amendments to the Criminal Procedure Code concerning rights protection, privacy, and judicial powers; or adoption of a separate provision on digital technologies and AI. Implementation of these measures would create a balanced legal framework that enables effective use of AI while preserving core procedural guarantees. Full article

Vector-borne infectious diseases transmitted by vector organisms (e.g., mosquitoes, rodents, and ticks) are recognized as key priorities in global public health. The construction of host–vector interaction frameworks within bipartite networks enables a clearer depiction of the transmission mechanisms underlying vector-borne infectious diseases. Compared with traditional models, the effective influence of historical information on vector-borne infectious diseases is more critical. In this study, the long-term memory behavior of infected populations during the recovery phase is regarded as a power-law tail distribution, a result that is consistent with fractional calculus. Thus, a fractional-order model for vector-borne diseases on bipartite networks is established.The basic reproduction number is derived about network topology and fractional order. With stability analysis, the conditions governing the global extinction and global persistence of vector-borne infectious diseases are determined. Furthermore, the validity of the proposed model is confirmed through numerical simulation results obtained from Barabási–Albert (BA) networks and Watts–Strogatz (WS) networks. Full article

Raindrop erosion of wind turbine blades’ leading edge is a critical degradation mechanism limiting wind turbine blade lifetime and aerodynamic efficiency. Protective coatings have been extensively studied to mitigate this damage. This review critically synthesises current knowledge on coating-based protection strategies against erosion, with emphasis on (i) the underlying mechanisms of erosion, (ii) advances in conventional and emerging coating technologies, and (iii) experimental approaches for testing and lifetime prediction. Across reported studies, nanofiller reinforcement (e.g., CNTs, graphene, CeO₂, Al₂O₃) enhances erosion resistance by 60–99%, primarily through improved toughness and stress-wave dissipation. Hybrid and multifunctional systems further combine mechanical durability with self-healing or anti-icing capabilities. Experimental results confirm that erosion rate follows a power-law dependence on impact velocity, with maximum damage occurring between 45° and 60° impact angles. Softer elastomeric coatings demonstrate longer incubation periods and superior viscoelastic recovery compared with rigid sol–gel systems. Persistent gaps include the lack of standardised testing, poor field–lab correlation, and limited long-term durability data. Future work should focus on coordinating multi-stressor testing with variable-frequency rain setups to replicate real field conditions and enable reliable lifetime prediction of next-generation erosion-resistant coatings. Full article

Bottle-brush polymers with aggrecan-like side chains represent a class of biomimetic macromolecules that replicate key structural and functional features of natural complexes of aggrecans with hyaluronic acid (HA) which are the major components of articular cartilage. In this study, we employ numerical self-consistent field (SCF) modeling combined with analytical theory to investigate the conformational properties of cylindrical molecular bottle-brushes composed of aggrecan-like double-comb side chains tethered to the main chain (the backbone of the bottle-brush). We demonstrate that the architecture of the brush-forming double-comb chains and, in particular, the distribution of polymer mass between the root and peripheral domains significantly influences the spatial distribution of primary side chain ends, leading to formation of a “dead” zone near the backbone of the bottle-brush and non-uniform density profiles. The axial stretching force imposed by grafted double-combs in the main chain, as well as normal force acting at the junction point between the bottle-brush backbone and the double-comb side chain are shown to depend strongly on the side-chain architecture. Furthermore, we analyze the induced bending rigidity and persistence length of the bottle-brush, revealing that while the overall scaling behavior follows established power laws, the internal structure can be finely tuned without altering the backbone stiffness. These theoretical findings provide valuable insights into relations between architecture and properties of bottle-brush-like supra-biomolecular structures, such as aggrecan-hyaluronan complexes. Full article

We report Al/ZnO/Al optoelectronic synaptic devices fabricated on c-plane and m-plane sapphire substrates using a sol–gel process. The devices exhibit essential synaptic behaviors such as excitatory postsynaptic current modulation, paired-pulse facilitation, and long-term learning–forgetting dynamics described by Wickelgren’s power law. Comparative analysis reveals that substrate orientation strongly influences memory performance: devices on m-plane consistently show higher EPSCs, slower decay rates, and superior retention compared to c-plane counterparts. These characteristics are attributed to crystallographic effects that enhance carrier trapping and persistent photoconductivity. To demonstrate their practical applicability, 3 × 3-pixel arrays of adjacent devices were constructed, where a “T”-shaped optical pattern was successfully encoded, learned, and retained across repeated stimulation cycles. These results highlight the critical role of substrate orientation in tailoring synaptic plasticity and memory retention, offering promising prospects for ZnO-based optoelectronic synaptic arrays in in-sensor neuromorphic computing and artificial visual memory systems. Full article