Search Results (35,008)

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

Accurate short-term wind speed forecasting is essential for the sustainable operation and planning of coastal wind farms. This study develops an improved BP-PSO hybrid model that integrates particle-swarm optimization, time-ordered walk-forward validation, and uncertainty quantification through block-bootstrap confidence intervals and Monte-Carlo dropout prediction intervals. Using multi-year and seasonal datasets from four coastal stations in China—from Bohai Bay (LHT, XCS, ZFD) to Zhejiang Province (SSN)—the proposed model achieves high predictive accuracy, with RMSE values between 1.09 and 1.54 m/s, MAE between 0.79 and 1.10 m/s, and R² exceeding 0.70 at most sites. The multi-year configuration provides the most stable and robust results, while autumn at ZFD yields the highest errors due to intensified turbulence. XCS and SSN exhibit the most consistent performance, confirming the model’s spatial adaptability across distinct climatic regions. Compared with the ARIMA and persistence baselines, BP-PSO reduces RMSE by over 50%, demonstrating improved efficiency and generalization. These results highlight the potential of intelligent data-driven forecasting frameworks to enhance renewable energy reliability and sustainability by enabling more accurate wind-power scheduling, grid stability, and coastal energy system resilience. Full article

The use of semi-transparent photovoltaic (Solar PV) glass in buildings is an effective strategy for integrating renewable energy generation, solar control, and thermal comfort. However, conventional simulation models rely on global optical properties, neglecting spectral radiation and its propagation within the material. This limits the accurate assessment of thermal comfort, light distribution, and performance in complex systems such as multi-layer glazing. This study presents the development, implementation, and experimental validation of a numerical model that reproduces the thermal, electrical, and optical behaviour of semi-transparent Solar PV glass, explicitly incorporating radiative transfer. The model simultaneously solves the conduction, convection, and electrical generation equations together with the radiative transfer equation, solved via the finite volume method across two spectral bands. The refractive index and extinction coefficient, derived from manufacturer-provided optical data, were used as inputs. Experimental validation employed 10% semi-transparent a-Si glass, comparing surface temperatures and electrical power generation. The model achieved average relative errors of 3.8% for temperature and 3.3% for electrical power. Comparisons with representative literature models yielded errors between 6% and 21%. Additionally, the proposed model estimated a solar factor of 0.32, closely matching the manufacturer’s 0.29. Full article

Antioxidants play a crucial role in preventing oxidative damage and are therefore integral to various sectors, including healthcare, food preservation, cosmetics, and industrial applications. Their capacity to enhance overall health and improve the quality and shelf life of products in these domains underscores their significance. Two powerful antioxidant dendrimers were synthesized using D-mannitol as the core and syringaldehyde as the antioxidant-producing phenolic unit. The generation 1 (G1) dendrimer features 12 syringic units on its surface, while the generation 2 (G2) dendrimer has 24. Antioxidant capacities were assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and the β-carotene bleaching assays. Based on IC50 values, the G2 (0.7 μM) and G1 (1.36 μM) dendrimers show 371- and 191-fold higher antioxidant activity, respectively, compared to the starting compound, syringaldehyde (260 μM). They are also 1,251- and 647-times more effective than butylated hydroxytoluene (BHT) (880 μM). Overall, G2 is twice as potent as G1. The dendrimers also provide strong protection against β-carotene bleaching. At concentrations between 3.75 and 60 μM, G2 preserves 75% to 88% of β-carotene after 16 h at 45 °C, while G1 maintains 51% to 84%. In comparison, syringaldehyde and BHT provide significantly less protection, with ranges of 21% to 47% and 22% to 36%, respectively. Their greatly enhanced antioxidant capabilities are due to the numerous free-radical-scavenging sites created by phenolic hydroxyl groups, which have electron-donating groups at the ortho and para positions. In cell viability assays using macrophages, G1 caused a decrease in cell viability at ≥31 µM. Conversely, G2 exhibited a gradual reduction in cell viability across the concentration range of 0.1 µM to 111 µM, with viability declining from 11.1% to 96.3%, indicating that the larger G2 is more cytotoxic than the smaller G1. Full article

Aluminum nitride (AlN), diamond, and β-phase gallium oxide (β-Ga₂O₃) belong to the family of ultra-wide bandgap (UWBG) semiconductors and exhibit remarkable properties for future power and optoelectronic applications. Compared to conventional wide bandgap (WBG) materials such as silicon carbide (SiC) and gallium nitride (GaN), they demonstrate clear advantages in terms of high-voltage, high-temperature, and high-frequency operation, as well as extremely high breakdown fields. In this work, numerical simulations are performed to evaluate and compare the radiative responses of AlN, diamond, and β-Ga₂O₃ when exposed to neutron irradiation covering the full atmospheric spectrum at sea level, from 1 meV to 10 GeV. The Geant4 simulation framework is used to model neutron interactions with the three materials, focusing on single-particle events that may be triggered. A detailed comparison is conducted, particularly concerning the generation of secondary charged particles and their distributions in energy, linear energy transfer (LET), and range given by SRIM. The contribution of the ¹⁴N(n,p)¹⁴C reaction in AlN is also specifically investigated. In addition, the study examines the consequences of these interactions in terms of electron-hole pair generation and charge deposition, and discusses the implications for the radiation sensitivity of these materials when exposed to atmospheric neutrons. Full article

Intelligent connected vehicles (ICVs) face challenges in handling intensive onboard computational tasks due to limited computing capacity. Vehicular edge computing networks (VECNs) offer a promising solution by enabling ICVs to offload tasks to mobile edge computing (MEC), alleviating computational load. As transportation systems are dynamic, vehicular tasks and MEC capacities vary over time, making efficient task offloading and resource allocation crucial. We explored a vehicle–road collaborative edge computing network and formulated the task offloading scheduling and resource allocation problem to minimize the sum of time and energy costs. To address the mixed nature of discrete and continuous decision variables and reduce computational complexity, we propose a hybrid hierarchical deep reinforcement learning (HHDRL) algorithm, structured in two layers. The upper layer of HHDRL enhances the double deep Q-network (DDQN) with a self-attention mechanism to improve feature correlation learning and generates discrete actions (communication decisions), while the lower layer employs deep deterministic policy gradient (DDPG) to produce continuous actions (power control, task offloading, and resource allocation decision). This hybrid design enables efficient decomposition of complex action spaces and improves adaptability in dynamic environments. Results from numerical simulations reveal that HHDRL achieves a significant reduction in total computational cost relative to current benchmark algorithms. Furthermore, the robustness of HHDRL to varying environmental conditions was confirmed by uniformly designing random numbers within a specified range for certain simulation parameters. Full article

Current photoconductive antennas (PCAs) fail to maximize the use of photogenerated carriers at the electrode edges. To address this limitation, we designed a novel PCA structure featuring a ring electrode and a disc electrode. The positive and negative electrodes are positioned on opposite sides of the substrate, and eight metal tips are incorporated into the ring electrode to enhance performance. The PCA-1 photoconductive antenna with both positive and negative electrodes on the same side of the substrate generates a peak current of about 18 μA, whereas under the same simulation parameters, the peak current generated by the PCA-1 and the conventional interdigitated photoconductive antenna are equal, and the PCA-2 photoconductive antenna with positive and negative electrodes on the top and bottom sides of the substrate generates a current nearly 1.45 times higher than that generated by the PCA-1. The PCA-3 photoconductive antenna with positive and negative electrodes on the top and bottom of the substrate and eight additional metal tips on the circular electrodes is nearly twice the peak current generated by the PCA-1, and the terahertz radiated power of the designed PCA-3 is four times that of the PCA-1, which suggests that the designed THz-PCA can improve the optical-terahertz conversion efficiency, and it has a great prospect of popularizing terahertz technology based on the THz-PCA. Full article

Against the backdrop of intelligent manufacturing, the ball mill, as a key energy-consuming piece of equipment, requires an accurate perception of its load state, which is crucial for optimizing production efficiency and ensuring operational safety. However, its vibration signals exhibit typical nonlinear and non-stationary characteristics, intertwined with complex noise, posing significant challenges to high-precision identification. A core contradiction exists in existing diagnostic methods: convolution network-based methods excel at capturing local features but overlook global trends, while Transformer-type models, although capable of capturing long-range dependencies, tend to “average out” critical local transient information during modeling. To address this dilemma, this paper proposes a new paradigm for multi-scale feature collaborative perception. This paradigm is implemented through an innovative deep learning architecture—the Residual Block-Swin Transformer Network (RB-SwinT). This architecture subtly achieves hierarchical and in-depth integration of the powerful global context modeling capability of Swin Transformer and the excellent local detail refinement capability of the residual module (ResBlock), enabling synchronous and efficient representation of both the macro trends and micro mutations of signals. On the experimental dataset covering nine types of fine operating conditions, the overall recognition accuracy of the proposed method reaches as high as 96.20%, which is significantly superior to a variety of mainstream models. To further verify the model’s generalization ability, this study was tested on the CWRU public bearing fault dataset, achieving a recognition accuracy of 99.36%, which outperforms various comparative methods such as SAVMD-CNN. This study not only provides a reliable new technical approach for ball mill load identification but also demonstrates its practical application value in indicating critical operating conditions and optimizing production operations through an in-depth analysis of the physical connotations of each load level. More importantly, its “global-local” collaborative modeling concept opens up a promising technical path for processing a broader range of complex industrial time-series data. Full article

Voltage-sensing phosphatases (VSPs) provide a conserved framework for dissecting the mechanics of voltage sensing and for engineering genetically encoded voltage indicators (GEVIs). To evaluate how natural sequence diversity shapes function, we compared VSP voltage-sensing domains (VSDs) from multiple species by replacing the phosphatase domain with a fluorescent protein to enable optical detection of VSD responses. Every construct that reached the plasma membrane produced a voltage-dependent optical signal, underscoring the deep conservation of voltage sensing across VSP orthologs. Yet lineage-specific substitutions generated strikingly different phenotypes. A plankton VSP ortholog from Eurytemora carolleeae and the Sea Hare (Aplysia californica) VSP exhibited left-shifted activation ranges, producing robust fluorescence transitions during modest depolarizations of the plasma membrane. The human VSD of hVSP2 yielded weak, sluggish responses with poor recovery, but reintroduction of a conserved arginine in S1 (G95R) partially restored reversibility, implicating lipid-facing residues in conformational stability. The Chinese hamster (Cricetulus griseus) VSD, with atypical S4 sensing charges (RWIR), generated a slow fluorescence increase during depolarization, while reverting to the consensus arginine (RRIR) inverted the polarity to a decrease. These contrasting behaviors show that single residue changes can reshape how VSD movements influence the fluorescent reporter, highlighting the molecular precision revealed by GEVI measurements. Together, these results show that voltage-dependent signaling is deeply conserved across VSPs but shaped by lineage-specific sequence variation, establishing VSPs as powerful models for probing voltage sensing and guiding GEVI design. Full article

The new availability of powerful Artificial Intelligence (AI) as an everyday copilot has instigated a new wave of attack techniques, especially in the area of Social Engineering (SE). The possibility of generating a multitude of different templates within seconds in order to carry out an SE-attack lowers the entry barrier for potential threat actors. Still, the question remains whether this can be done using openly available tools without specialized expert skill sets on the attacker side, and how these compare to each other. This paper conducts three experiments based on a blueprint from a real-world CFO fraud attack, which utilized two of the most used social engineering attacks, phishing and vishing, and investigates the success rate of these SE attacks based on utilizing different available LLMs. The third experiment centers around the training of an AI-powered chatbot to act as a social engineer and gather sensitive information from interacting users. As this work focuses on the offensive side of SE, all conducted experiments return promising results, proving not only the ability and effectiveness of AI technology to act unethically, but also the little to no implied restrictions. Based on a reflection on the findings and potential countermeasures available, this research provides a deeper understanding of the development and deployment of AI-enhanced SE attacks, further highlighting potential dangers, as well as mitigation methods against this “upgraded” type of threat. Full article

A general relativistic model of an astrophysical hypermassive extremely magnetized ultra-compact self-bound quark–gluon plasma (QGP: ALICE/LHC) object that is supported against its ultimate gravitational implosion by the simultaneous action of the vacuum polarization driven by nonlinear electrodynamics (NLED: ATLAS/LHC: light-by-light scattering)—the vacuum “awakening”—and the asymptotic freedom, a key feature of quantum chromodynamics (QCD), is presented. These QCD stars can be the final figures of the equilibrium of collapsing stellar cores permeated by magnetic fields with strengths well beyond the Schwinger threshold due to being self-bound, and for which post-supernova fallback material pushes the nascent remnant beyond its stability, forcing it to collapse into a hybrid hypermassive neutron star (HHMNS). Hypercritical accretion can drive its innermost core to spontaneously break away color confinement, powering a first-order hadron-to-quark phase transition to a sea of ever-freer quarks and gluons. This core is hydro-stabilized by the steady, endlessly compression-admitting asymptotic freedom state, possibly via gluon-mediated enduring exchange of color charge among bound states, e.g., the odderon: a glueball state of three gluons, or either quark-pairing (color superconductivity) or tetraquark/pentaquark states (LHCb Coll.). This fast—at the QGP speed of sound—but incremental quark–gluon deconfinement unbinds the HHMNS’s baryons so catastrophically that transforms it, turning it inside-out, into a neat self-bound QGP star. A solution to the nonlinear Tolman–Oppenheimer–Volkoff (TOV) equation is obtained—that clarifies the nonlinear effects of both NLED and QCD on the compact object’s structure—which clearly indicates the occurrence of hypermassive QGP/QCD stars with a wide mass spectrum ($0\lesssim {\mathrm{M}}_{\mathrm{Star}}^{\mathrm{QGP}}\lesssim$ 7 M_⊙ and beyond), for star radii ($0\lesssim R_{\mathrm{Star}}^{\mathrm{QGP}}\lesssim 24$ km and beyond) with B-fields (${10}^{14}\le {\mathrm{B}}_{\mathrm{Star}}^{\mathrm{QGP}}\le {10}^{16}$ G and beyond). This unexpected feature is described by a novel mass vs. radius relation derived within this scenario. Hence, endowed with these physical and astrophysical characteristics, such QCD stars can definitively emulate what the true (theoretical) black holes are supposed to gravitationally do in most astrophysical settings. This color quark star could be found through a search for its eternal “yo-yo” state gravitational-wave emission, or via lensing phenomena like a gravitational rainbow (quantum mechanics and gravity interaction), as in this scenario, it is expected that the light deflection angle—directly influenced by the larger effective mass/radius (${\mathrm{M}}_{\mathrm{Star}}^{\mathrm{QGP}}\left(B\right)$, ${\mathrm{R}}_{\mathrm{Star}}^{\mathrm{QGP}}\left(B\right)$) and magnetic field of the deflecting object—increases as the incidence angle decreases, in view of the lower values of the impact parameter. The gigantic—but not infinite—surface gravitational redshift, due to NLED photon acceleration, makes the object appear dark. Full article

Due to the large emissions of greenhouse gases from the burning of fossil fuels and people’s demand for green materials and energy, the development of environmentally friendly triboelectric nanogenerators (TENGs) is becoming increasingly significant. Silk fibroin (SF) is considered an ideal biopolymer candidate for fabricating green TENGs due to its biodegradability and renewability. However, its intrinsic brittleness and relatively weak triboelectric performance severely limit its practical applications. In this study, SF was physically blended with poly(ethylenimine) (PEI), a polymer rich in amino groups, to fabricate SF/PEI composite films. The resulting films were employed as tribopositive layers and paired with a poly(tetrafluoroethylene) (PTFE) tribonegative layer to assemble high-performance TENGs. Experimental results revealed that the incorporation of PEI markedly enhanced the flexibility and electron-donating capability of composite films. By optimizing the material composition, the SF/PEI-based TENG achieved an open-circuit voltage as high as 275 V and a short-circuit current of 850 nA, with a maximum output power density of 13.68 μW/cm². Application tests demonstrated that the device could serve as an efficient self-powered energy source, capable of lighting up 66 LEDs effortlessly through simple hand tapping and driving small electronic components such as timers. In addition, the device can function as a highly sensitive self-powered sensor, capable of generating rapid and distinguishable electrical responses to various human motions. This work not only provides an effective strategy to overcome the intrinsic limitations of SF-based materials but also opens up new avenues for the development of high-performance and environmentally friendly technologies for energy harvesting and sensing. Full article

The complex life cycle, high reproductive potential and ability to quickly develop resistance to insecticides are key factors contributing to the destructiveness of the peach–potato aphid (Myzus persicae Sulzer) among pest species. Monitoring its population dynamics at a large scale allows us to better understand M. persicae biology and take relevant measures for pest management. For this purpose, reliable molecular tools are needed. Based on the analysis of 128,362 microsatellite loci, we developed four multiplex assays including 49 comprehensively characterised SSR markers. Internal validation confirmed the species specificity and low genotyping error (e_a = 0.8%, e_l = 0.99%, e_obs = 22.7%) of the assays. A total of 194 alleles were identified (mean = 4 alleles per locus, range = 2–8 alleles per locus) within a group of 365 aphid accessions collected in the Vysočina region (Czechia). The studied aphid population showed the typical characteristics expected of the species with clonal or partially clonal reproduction (heterozygote excess, negative F_IS, moderate-to-high linkage disequilibrium (LD), and distortion of the H-W equilibrium for most of the loci), and did not exhibit any stratification on a spatiotemporal level. Owing to the high discriminatory power of the markers, we discovered that the population sample was founded upon a small number of fundatrices, as only five dominating lineages comprising over 70% of all accessions were identified. In conclusion, this study identified a significant number of new high-quality markers with the high discriminatory power necessary for revealing the population structure and dynamics of M. persicae, which holds considerable potential in both general biological and agricultural research. Full article

Grid-connected inverters play an indispensable and crucial role between new energy power generation devices and the power grid. As an important method for stability analysis of grid-connected inverters, impedance criteria have been widely applied. However, traditional impedance criteria have the problem of inaccurately representing system stability margins. Moreover, under weak grid conditions where grid impedance cannot be ignored, voltage disturbances at the point of common coupling will cause phase angle deviations in the output of the phase-locked loop, thereby affecting the stability of the control system. To address these issues, this paper establishes a stability analysis model based on the reshaping of impedance criteria and proposes a q-axis improved control compensation method, which expands the adaptability range of grid impedance for inverters and enhances the stability of grid-connected inverters. Finally, the correctness of the proposed algorithm is verified through experiments. Full article

Nearshoring in Mexico is expanding rapidly, yet chronic volatility in the national power grid threatens the reliability and cost-competitiveness of relocated manufacturing lines. To inform strategic mitigation, this study presents a hybrid Fuzzy–CES decision-support framework that embeds the Constant-Elasticity-of-Substitution (CES) production function within a Mamdani Fuzzy-Inference Engine, implemented in both Type-1 and Interval Type-2 variants, to evaluate and optimize production adaptability in energy-constrained environments. Using sector-wide data from Mexico’s automotive industry, key input variables (energy reliability, capital intensity, and labor availability) are objectively quantified and normalized to reflect the realities of regional plant operations. The system linguistically classifies each facility’s production elasticity as low, moderate, or high, and generates actionable recommendations for resource allocation, such as targeted investments in renewable microgrids or workforce strategies. Implemented in MATLAB, simulation results confirm that, while high capital and labor inputs are essential, energy reliability remains the primary bottleneck limiting adaptability; only states with all three strong factors achieve maximum resilience. The Type-2 fuzzy approach demonstrates superior robustness to input uncertainty, enhancing managerial decision-making under volatile grid conditions. In addition, a case study regarding the automotive industry is presented to illustrate how the proposed framework is implemented. The same structure can be used to deploy it in another industry. This research offers a transparent, data-driven tool to inform both firm-level investment and regional policy, directly supporting Mexico’s efforts to sustain competitiveness and resilience in the global shift toward nearshoring. Full article