Directional overcurrent relays (DOCRs) protection in modern microgrids is problematic, because of the massive inverter-based distributed generation penetration, the flow of power in both directions, lower fault currents, and an extremely high rate of grid-connected/islanded operational changes. Microgrids with a variety of operating cases, N-2 contingencies and relay characteristics that are user-defined cannot be coordinated using conventional methods. Also, the existing optimization methods have high computational complexity in the cases, when high number of contingencies and relay setting groups are taken into account, and they cannot be practically implemented. The purpose of this work is to create a computationally efficient and dynamic protection coordination model that can guarantee the effective DOCR coordination in microgrids dominated by inverters and preserve the security of coordination in varying conditions of operation. A hybrid optimization model that integrates reinforcement learning and metaheuristic search is presented, to jointly optimize time multiplier setting (TMS), plug setting (PS), and user-specified parameters of the relay curve of several groups of settings. A contingency reduction strategy that runs on hierarchical clustering of K-means augmented with BIRCH, is used to reduce the computational load of computing representative operating scenarios. The suggested approach is confirmed through altered IEEE 14-bus and IEEE 33-bus microgrid test systems with high substitutions of inverter-based distributed generation. The contingency reduction that is proposed reduced the operating scenarios to 124 as compared to 847 without sacrificing accuracy in coordination. This technique recorded the reduction of total operating time of the relay by 94.7, the success rate of coordination to 99.2% and an interval of coordination time that is more than 0.35s to fault resistance up to 40 ohms plus over 80% cut in computation time. The K-meansBIRCH approach was three times faster with lower operating time of 1.73s (OM1) and 5.80s (OM2) than the BRKGA-MILP method and two times quicker with better coordination performance and convergence rate, as compared to the BRKGA-MILP method. The suggested framework offers communication-independent, reliable, and quick protection coordination of microgrids based on inverters. The contingency reduction, clustering, and hybrid optimization approach allows the secure coordination of relays with high accuracy and with less computational effort, which makes the approach appropriate to a range of practical microgrid protection systems.

Comprehensive room acoustic characterization requires resolving reflection behavior across time, frequency, and space. The recently proposed eigenbeam–vMF-based analyzer provides a framework for this by modeling the reflection field as a time–frequency-dependent directional power distribution, estimated via spatial correlation of eigenbeams (ambisonics) and parameterized using von Mises–Fisher clustering. This formulation enables a unified and interpretable description of anisotropic early reflections, their transition into diffuse reverberation, and frequency-dependent acoustic behavior. Prior work showed that the analyzer reliably captures these features using higher-order ambisonics from a 32-channel spherical microphone array (SMA) and that constraining the same array to the first order still led to retaining the dominant features. This paper investigates whether this capability extends to first-order microphone arrays with sparser spatial sampling for more economical and practical deployment. A comparative study is conducted in a recording studio with variable wall panels (wood and felt), evaluating a four-channel first-order array against a 32-channel SMA. The results reveal distinct acoustic differences between panel settings, which are consistent across both arrays. While the SMA captures finer spatial detail and prolonged anisotropic reflections more effectively, the first-order array demonstrates potential for preliminary room acoustic assessments by identifying room mode frequencies, dominant reflection directions, and highly reflective surfaces.

Degradation of 2-(methylamino)benzoic acid and methyl anthranilate using a novel electrochemical trickle-fixed bed reactor without pH regulation

This paper presents an innovative adaptive frequency droop control technique for Voltage Source Converter (VSC)-based Multi-Terminal Direct Current (MTDC) systems. The primary objective is to enhance frequency stability and optimize power distribution in AC grids with significant wind power integration. Traditional fixed droop control methods are inadequate in adapting to changing grid conditions, leading to inadequate frequency responses, voltage instabilities, and reduced lifespan of the converter during disturbances. To address these issues, this work proposes an adaptive droop control strategy integrated with direct power control (DPC). This method dynamically adjusts droop coefficients based on the converters' current operational state. The proposed system enables healthy converters share the burden of power distribution more effectively based on their current state of operation during emergencies, thereby enhancing both frequency and voltage stability while preventing converter overload, malfunction, and subsequent load shedding. Furthermore, these adjustable droop controllers work in tandem with the primary frequency response of the wind farm (WF) that has significant level of penetration. The system's design is compatible with current wind farm control mechanisms, eliminating the need for further modifications or replacements of existing equipment.

As wind energy continues to play a significant role in the global energy mix, ensuring the efficiency, robustness, and stable operation of Wind Energy Conversion Systems (WECS) has become a critical concern. This highlights the need for advanced control strategies to enhance power regulation, improve dynamic performance, and ensure reliable grid integration under variable wind conditions. Among the proposed approaches, Direct Power Control (DPC) has attracted significant attention for its simplicity and fast dynamic response, making it suitable for real-time implementation in grid-connected applications. However, despite its advantages, conventional DPC suffers from several limitations, including high power ripples, increased Total Harmonic Distortion (THD) in currents and voltages, and variable switching frequency, which can significantly affect overall system performance and power quality. To address these limitations, this paper proposes an Advanced Predictive Direct Power Control (APDPC) strategy for grid-connected WECS based on a permanent magnet synchronous generator (PMSG). The PMSG is selected for its high efficiency, compact structure, and operational flexibility, enabling robust and coordinated control of both the Generator-Side Rectifier and the Grid-Side Inverter. The proposed APDPC is implemented for both converters within a unified control framework, ensuring seamless power flow regulation. Unlike the conventional DPC approach, at each sampling period, three voltage vectors are directly selected based on the predicted evolution of active and reactive powers, without the need for complex control loops, hysteresis controllers, switching table, or exhaustive evaluation of all candidate switching states. The corresponding application times of these vectors are subsequently optimized using a multi-objective cost function that simultaneously minimizes the instantaneous errors in active and reactive powers on both sides of the system. To assess its effectiveness, the proposed control strategy was validated through MATLAB/Simulink® simulations under both constant and variable wind speed conditions. The results clearly demonstrate a substantial enhancement in dynamic performance compared with conventional DPC, including a 37% faster response, complete elimination of overshoot and undershoot, and a 61% reduction in steady-state error. In addition to these dynamic improvements, the proposed APDPC significantly enhances power quality, achieving reductions of approximately 40-45% in torque and power ripples, along with up to a 50% reduction in the THD of the grid current and grid voltage. These findings are further reinforced by comparative analysis with recent control strategies, highlighting its superior tracking accuracy and overall performance. Finally, real-time implementation on the dSPACE DS1104 platform confirms the practical feasibility and effectiveness of the proposed approach under realistic operating conditions.

This paper presents a grid-connected photovoltaic system in which a two-level voltage source inverter operates as a shunt active power filter. A hybrid control strategy combining an Adaptive Linear Neuron (Adaline) with Direct Power Control (DPC) is proposed. In this control, Adaline extracts reference powers, while DPC generates the optimal switching signals through a switching table, without the need for a separate Pulse Width Modulation (PWM) or hysteresis modulation stage. The system performance is evaluated with MATLAB/Simulink simulations under various conditions, including linear and nonlinear loads as well as constant and variable irradiance. Simulation results confirm that the photovoltaic (PV) system effectively delivers active power to the grid while compensating for harmonic and reactive currents. The total harmonic distortion with the Adaline-DPC remains below 5% of the IEEE-519 standard limit. Although the Adaline-DPC has a higher computational burden, it outperforms the conventional hybrid DPC methods such as conventional DPC (CDPC), HSF instantaneous active and reactive power control with DPC (HSFDPC), SRF control with DPC (SRFDPC), and HSFSRF control with DPC (HSFSRF-DPC) by providing lower THD under both ideal and nonideal source and load conditions.

In an increasingly multipolar media environment, middle-power states are expanding the use of international broadcasting beyond conventional nation branding toward more structured forms of cultural diplomacy and regional coordination. This study examines Kazakhstan's Silk Way satellite channel and the inaugural Silk Way Star transnational music competition as a case of entertainment-centered international broadcasting across the Silk Road region. Drawing on a qualitative single-case study design, the analysis covers the completed 2025 broadcast season, including ten televised broadcasts (nine competitive rounds and a final episode), publicly available institutional materials, participant and jury composition, multilingual production features, scoring mechanisms, and distribution arrangements. The study integrates soft power theory, media governance scholarship, and research on international broadcasting to examine three analytical dimensions: institutional configuration, linguistic strategy, and format design. The findings indicate that Silk Way Star operates through a coordinated co-production framework involving twelve participating countries, a six-language broadcasting structure that combines accessibility with the visibility of the Kazakh language, and a hybrid jury–audience evaluation system that distributes cultural recognition across national participants. These structural features position Kazakhstan not solely as a content producer but as a coordinating actor within a transnational media architecture. Rather than demonstrating direct soft power outcomes, the study shows how entertainment formats may create institutional and symbolic conditions that support visibility and claims of cultural legitimacy within a regional media space. Limitations include reliance on publicly available materials and the absence of direct audience perception measures, suggesting the need for future work with audience analytics and comparative cases.

Direct Assessment of Total Cavopulmonary Connection Power Loss with Interventional CMR in Fontan Circulation.

High temporal resolution measurements of radiated power are essential for fusion plasma diagnostics, where key phenomena evolve on millisecond and sub-millisecond time scales. Fiber-optic bolometers (FOBs) offer attractive advantages for plasma radiation measurement, including immunity to electromagnetic interference, high temperature capability, and compact size; however, their thermal dynamics are dominated by heat diffusion along the silica fiber stub, making lumped-mass models unsuitable for direct power reconstruction. In this work, we present a detailed deconvolution-based numerical signal processing approach that treats the FOB as a linear time-invariant system, enabling recovery of incident radiation power from measured sensor temperature via inversion of the system impulse response. Compared with prior demonstrations limited by millisecond-scale impulse excitation and low-bandwidth wavelength-based interrogation, we achieve more than two orders-of-magnitude improvement in temporal resolution and demonstrate successful reconstruction of radiation pulses as narrow as 250 μs, establishing FOBs combined with deconvolution processing as a promising tool for resolving rapid radiation transients in fusion plasmas.

For the power control problem of a bidirectional voltage-source converter, because only one voltage vector is applied at each control period, the traditional model predictive direct power control strategy brings in high harmonic content. In this paper, a hybrid vector-modulation model predictive direct power control scheme is developed to address the high harmonic distortion issue under unbalanced grid conditions. Based on the instantaneous power theory, the expression for the power fluctuation is derived under unbalanced grid voltages. The new reference power under unbalanced grid voltages is represented by the α β components of the grid voltages and their orthogonal signals lagging by 9 0 ∘ . By means of the proposed strategy, neither the phase-locked loops nor the sequence extraction of the grid voltages are required. In addition, the optimal voltage vector is selected from eighteen alternative hybrid voltage vectors to further improve control performance. The effectiveness of the proposed strategy is verified through real-time simulations conducted on an RT-LAB hardware-in-the-loop platform.

The growing demand for distributed energy systems and wearable electronics has spurred strong interest in hydrovoltaic technologies. However, the vast majority of metal oxide-based hydrovoltaic devices, with Al2O3 being a prime example, are typically limited by low current output due to inefficient internal charge extraction and transport. In this work, we report a high-performance hydrovoltaic generator (HEG) fabricated by sequentially depositing zero-dimensional Al2O3 nanoparticles and two-dimensional MXene nanosheets onto a porous cotton fabric (CF) substrate. This hybrid architecture harnesses the electrokinetic charge generation of Al2O3 while utilizing the metallic conductivity of MXene as an efficient charge-collection and transport network, thereby significantly reducing internal resistance. The resulting MXene/Al2O3@CF hydrovoltaic generator (MAHEG) exhibits a three-order-of-magnitude enhancement in short-circuit current compared with a pure Al2O3 device. Under ambient conditions, the MAHEG delivers a maximum power density output of 47.72 μW cm−2 (∼0.39 V, ∼611.8 μA) and maintains excellent stability over repeated wet–dry cycles. Moreover, multiple units can be integrated with nearly linear scalability in voltage and current through series and parallel configurations, enabling direct powering of low-power electronic devices.

Rivers play a fundamental role in shaping the Earth’s surface and sustaining ecosystems. Accurate modeling and simulation of stream power are essential for understanding fluvial processes. As a measure of the energy exerted by overland flow, stream power possesses both magnitude and directional attributes. However, conventional simulation approaches based on scalar operations often neglect the directional component. In this study, a mathematical vector-based method is proposed to simulate stream power. This approach involves the vectorized representation of flow direction and the computation of the Stream Power Index (SPI) using vector operation rules. The method is validated in typical fluvial landscapes of the Chinese Loess Plateau and compared with conventional scalar-based approaches. Results indicate that the vector-based method effectively reduces the overestimation of stream power by accounting for its directional properties. For example, in Strahler-ordered stream networks, the average overestimation by scalar methods reaches 58.9% in first-order streams and up to 1091.1% in sixth-order streams. These findings demonstrate that the proposed vector-based approach aligns more closely with the actual dynamics of geographical processes. Furthermore, since directional power interactions are widespread in Earth surface processes, adopting a vector perspective in related simulations may significantly enhance the realism and accuracy of geographical modeling.

This paper proposes a single-stage three-phase AC-DC converter based on an LLC resonant topology utilizing a front-end matrix switch. Unlike traditional two-stage solutions, the proposed topology synthesizes a fluctuating equivalent DC voltage from the three-phase input, achieving direct power conversion with high efficiency. To maintain a stable DC output voltage against the time-varying input, a trajectory-based Pulse Frequency Modulation (PFM) control strategy is developed. By employing State-Plane Analysis (SPA), the operational trajectory is divided into four calculation segments, allowing precise derivation of the switching frequency and duty cycles for both boost and buck modes within a single line cycle. Furthermore, to improve power density and reduce parasitic parameters, a high-frequency planar inductor with interleaved windings and a planar transformer are designed for 500 kHz operation. A pipeline control architecture based on a single DSP is implemented to handle the complex real-time computations. A 500 W prototype is built and tested under 100 V input and 130 V output conditions. Experimental results demonstrate that the converter achieves a peak efficiency of 97%, a power factor of 0.99, and a grid current Total Harmonic Distortion (THD) of 3.95%, validating the effectiveness of the proposed topology and control scheme.

Quantum computers hold promise for solving problems intractable for classical computers, especially those with high time or space complexity. Practical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quantum advantage</i> can be said to exist for such problems when the end-to-end time for solving such a problem using a classical algorithm exceeds that required by a quantum algorithm. Reducing the power flow (PF) problem into a linear system of equations allows for the formulation of quantum PF (QPF) algorithms, which are based on solving methods for quantum linear systems such as the Harrow-Hassidim-Lloyd (HHL) algorithm. Speedup from using QPF algorithms is often claimed to be exponential when compared to classical PF solved by state-of-the-art algorithms. We investigate the potential for practical quantum advantage in solving QPF compared to classical methods on gate-based quantum computers. Notably, this paper does not present a new QPF solving algorithm but scrutinizes the end-to-end complexity of the QPF approach, providing a nuanced evaluation of the purported quantum speedup in this problem. Our analysis establishes a best-case bound for the HHL-based quantum power flow complexity, conclusively demonstrating that the HHL-based method has higher runtime complexity compared to the classical algorithm for solving the direct current power flow (DCPF) and fast decoupled load flow (FDLF) problem. Notably, our analysis and conclusions can be extended to any quantum linear system solver with rigorous performance guarantees, based on the known complexity lower bounds for this problem. Additionally, we establish that for potential practical quantum advantage (PQA) to exist it is necessary to consider DCPF-type problems with a very narrow range of condition number values and readout requirements.

Abstract Indonesia’s southern Java coast has long been recognized for its high wave-energy potential, yet localized assessments—especially along the Malang coastline—remain sparse. This lack of site-specific data poses a barrier to effective Wave Energy Converter (WEC) deployment, which depends on accurate resource mapping and siting strategies. This study addresses the data gap by conducting a high-resolution wave energy resource assessment and identifying optimal WEC deployment sites along the south coast of Malang Regency, East Java. We utilized the Delft3D-WAVE module incorporating the SWAN spectral model, forced with ERA5 for the full year of 2023. The model domain was downscaled to 200–500 m resolution to capture nearshore variability, and outputs included significant wave height, peak wave period, direction, and wave power density (WPD). A multi-criteria approach was applied to evaluate five candidate sites based on resource level, water depth, and distance from shore. The results reveal a strong seasonal signal, with southeast monsoon waves producing peak offshore WPD values exceeding 40 kW/m, while calmer northwest monsoon conditions generate WPD values of 5–10 kW/m. Spatially, nearshore WPD ranged from 12–18 kW/m, with Site 1 identified as the most technically and economically balanced option. Site 3 offers comparable resources but is very shallow, favoring shoreline or nearshore device types. This study provides a replicable framework for wave energy site selection in data-sparse coastal regions of Indonesia. It highlights the importance of integrating seasonal dynamics, local bathymetry, and engineering criteria to support the sustainable development of marine renewable energy.

Abstract To address the issues of fault localisation and recovery in medium-voltage direct current power systems for ships, this paper proposes a hybrid reconstruction algorithm that combines the entropy weighting method with grey relational analysis, and embeds them within a framework based on chaotic particle swarm optimisation. During initialisation, the algorithm employs chaotic mapping to enhance the population diversity. The entropy weighting method is used for adaptive weighting of the objective functions, while grey relational analysis is integrated to evaluate the particle fitness and determine optimal reconstruction paths, thereby accomplishing fault diagnosis and system restoration. Simulations and case studies demonstrate that the proposed method has advantages such as rapid convergence and strong ability to escape local optima. Compared with traditional methods, it achieves a 41% increase in the reconstruction success rate and a 4.7% improvement in the system restoration capability, effectively enhancing post-fault power supply capacity and overall reliability.

Rentier states, by relying on revenues derived from natural resources rather than citizen taxation, construct a distinctive rentier social contract, ideally exchanging welfare provisions and subsidies for political loyalty. In such systems, a patrimonial rather than democratic social contract emerges, reducing citizens to passive clients and undermining the fundamental principles of democratic governance, such as transparency and accountability. Where citizenship is degraded to clientelism and rights are treated as revocable privileges, civil society is not merely an amalgam of service-oriented organizations; it becomes a hegemonic battlefield in the Gramscian sense—potentially transcending its conventional intermediary role and emerging as a workshop for constructing alternatives. Therefore, this article, employing a qualitative approach, argues that the primary function of civil society in rentier states is not necessarily to engage in direct power struggles (a war of manoeuvre), but rather to conduct a protracted war of position aimed at eroding the ideological and/or cultural foundations of the rentier order. The findings prove that the success of civil society in such contexts is not measured by immediate political changes, but by its ability to reshape the societal “software”—transforming the citizen’s identity from a passive beneficiary to an active, rights-bearing agent. It is through this transformative function that civil societies lay the human and institutional foundations necessary for meaningful political transitions during critical junctures.

Numerous studies have explored the relationship between the four sources of self-efficacy—mastery experience (ME), vicarious experience (VE), verbal persuasion (VP), and emotional and physiological states (EPS)—in relation to an individual’s self-efficacy beliefs in English language learning. However, limited research has examined how these sources of self-efficacy interact to shape students’ self-efficacy in speaking English, particularly within specific cultural and educational contexts. This study aims to address this gap by investigating the dynamic interplay among these sources and their influence on the self-efficacy of 329 Indonesian high school students in speaking English. The survey included nine items related to self-efficacy in speaking English and twenty-four items pertaining to the sources of self-efficacy. Structural equation modelling (SEM) was used to validate the model, providing a robust framework for analysing the relationships among the sources of self-efficacy and their influence on speaking self-efficacy. The results revealed that ME and VP had directly influenced students’ self-efficacy in speaking English, while VE exerted an indirect effect mediated by ME, VP, and their combination. Notably, EPS did not demonstrate either direct or indirect predictive power on speaking self-efficacy. Further, all sources of self-efficacy, except EPS, were found to be interrelated, emphasising the complex interactions among these constructs. These results provide valuable insights for educators, highlighting the importance of leveraging students’ prior successes (ME), fostering supportive feedback (VP), and encouraging observational learning (VE) to enhance confidence and proficiency in English speaking.

ABSTRACT This article examines the role of devolution in shaping employment relations through a case study of the Living Wage in London. Drawing on a mixed methods approach it explores the rationale and methods by which the Greater London Authority has promoted fair pay despite possessing limited direct legislative power. The article identifies five key mechanisms; its role as an employer, soft regulation, procurement, institution‐building, and ideological leadership, and evaluates their effectiveness in promoting Living Wage adoption in the capital. Our findings suggest that that collaborative, multi‐actor strategies can reshape employment practices, highlighting the potential of ‘devolved without power’ governance to reconfigure labour standards.

ABSTRACT Wind energy's intermittency poses significant challenges for power grid stability. Existing forecasting methods exhibit notable limitations: traditional machine learning models struggle with long‐term temporal dependencies, while deep learning approaches often overlook spatial relationships among turbines. This paper proposes TSG‐Net, a novel framework integrating multiscale decomposition, graph neural networks, and attention mechanisms. TSG‐Net employs three innovative components: a multiscale decomposition linear network (MDLinear) with learnable decomposition weights for adaptive temporal feature extraction, a spatio‐temporal graph network (XTGN) that constructs dynamic adjacency matrices based on real‐time wind directions for wake effect modeling, and an adaptive attention fusion mechanism for scenario‐specific weight allocation. Experiments on the SDWPF dataset demonstrate that TSG‐Net achieves MAE of 36.53 kW and RMSE of 45.17 kW, representing improvements of 8.3% and 5.7%, respectively, compared with the best baseline TSB‐GNN (39.85‐kW MAE, 47.92‐kW RMSE), with particularly strong performance during wind direction changes, extreme weather, and power ramp events.