Search Results (2,125)

Phase-shifting transformers (PST) are widely used to control power flows. However, conventional designs can vary only the phase angle, leaving the voltage magnitude unaffected or requiring structurally complex devices. This study proposes a compact PST topology that realizes simultaneous, decoupled control of both voltage magnitude and phase angle through two coordinated sets of windings. Closed-form equations are derived to link the phase-shifting and voltage regulation windings turn ratios to any target magnitude ratio and phase-shift angle, providing a unified design framework that guarantees the full practical operating range. Steady-state tests verify that the device can change the phase or adjust the magnitude independently without cross-coupling. Dynamic tests demonstrate that, when a tap command is issued, the line currents and active power converge to new set-points within a few fundamental periods and with minimal oscillation. Furthermore, the PST’s application to loop closing operations in 220 kV networks is investigated, where simulation results show it can suppress loop closing currents by over 90% under adverse voltage mismatch conditions. These results confirm that the proposed PST offers a fast, economical alternative to Flexible AC Transmission Systems (FACTS) equipment for real-time power flow balancing, renewable integration and inter-area exchange in modern transmission networks. Full article

Optically pumped magnetometers (OPMs) have brought a transformative advancement to magnetoencephalography (MEG), enabling flexible, noncryogenic, and wearable neuroimaging systems. In particular, the development of triaxial OPM sensors allows for simultaneous measurement of full magnetic field vectors, including both radial and additional tangential components. Previous studies have shown that incorporating tangential components helps enhance the separation between neural signals and external interference, but their optimal configurations remain unclear. This study systematically investigated the impact of tangential component configurations on array sensitivity and the lead field correlation coefficient ($R_{12}$) in triaxial OPM-MEG sensor arrays, considering tangential component rotations, relative orientations of sensor and source, source depths, and head model types. Based on the above analysis, we proposed an optimization strategy aimed at minimizing $R_{12}$, referred to as $R_{12}$-minimization array optimization (RMAO), to explore the optimal configuration of tangential components. The simulation results showed that the proposed method significantly enhanced sensitivity to cortical sources and effectively suppressed external interference, enabling more accurate source localization. This study highlights the critical role of tangential components in improving system performance and provides theoretical foundation and methodological guidance for the design of triaxial OPM-MEG sensor arrays. Full article

Toward the innovative design of tunable structures for energy generation, this paper presents an extended Floating Frame of Reference (FFR) formulation capable of modeling slope discontinuities in flexible multibody systems—overcoming a key limitation of conventional FFR methods that assume slope continuity. The model is validated using a spatial double-pendulum structure composed of circular beam elements, representative of out-of-plane energy harvesting systems. To investigate the influence of boundary constraints on dynamic behavior, three electromagnetic clamping configurations—Fixed–Free–Free (XFF), Fixed–Free–Fixed (XFX), and Free–Fixed–Free (FXF)—are implemented. Tri-axial accelerometer measurements are analyzed via Fast Fourier Transform (FFT), revealing natural frequencies spanning from 38.87 Hz (lower frequency range) to 149.01 Hz (higher frequency range). For the lower frequency range, the FFR results (38.76 Hz) show a close match with the experimental prediction (38.87 Hz) and ANSYS simulation (36.49 Hz), yielding 0.28% error between FFR and experiments and 5.85% between FFR and ANSYS. For the higher frequency range, the FFR model (148.17 Hz) achieves 0.56% error with experiments (149.01 Hz) and 0.85% with ANSYS (146.91 Hz). These high correlation percentages validate the robustness and accuracy of the proposed FFR formulation. The study further shows that altering boundary conditions enables effective frequency tuning in discontinuous structures—an essential feature for the optimization of application-specific systems such as wave energy converters. This validated framework offers a versatile and reliable tool for the design of vibration-sensitive devices with geometric discontinuities. Full article

The Unit Inverse Maxwell–Boltzmann (UIMB) distribution is introduced as a novel single-parameter model for data constrained within the unit interval ($0,1$), derived through an exponential transformation of the Inverse Maxwell–Boltzmann distribution. Designed to address the limitations of traditional unit-interval distributions, the UIMB model exhibits flexible density shapes and hazard rate behaviors, including right-skewed, left-skewed, unimodal, and bathtub-shaped patterns, making it suitable for applications in reliability engineering, environmental science, and health studies. This study derives the statistical properties of the UIMB distribution, including moments, quantiles, survival, and hazard functions, as well as stochastic ordering, entropy measures, and the moment-generating function, and evaluates its performance through simulation studies and real-data applications. Various estimation methods, including maximum likelihood, Anderson–Darling, maximum product spacing, least-squares, and Cramér–von Mises, are assessed, with maximum likelihood demonstrating superior accuracy. Simulation studies confirm the model’s robustness under normal and outlier-contaminated scenarios, with MLE showing resilience across varying skewness levels. Applications to manufacturing and environmental datasets reveal the UIMB distribution’s exceptional fit compared to competing models, as evidenced by lower information criteria and goodness-of-fit statistics. The UIMB distribution’s computational efficiency and adaptability position it as a robust tool for modeling complex unit-interval data, with potential for further extensions in diverse domains. Full article

Energy storage will play an important role in the new power system with a high penetration of renewable energy due to its flexibility. Large-scale energy storage can participate in electricity market clearing, and knowing how to make more profits through bidding strategies in various types of electricity markets is crucial for encouraging its market participation. This paper considers differentiated bidding parameters for energy storage in a two-stage market with wind power integration, and transforms the market clearing process, which is represented by a two-stage bi-level model, into a single-level model using Karush–Kuhn–Tucker conditions. Nonlinear terms are addressed using binary expansion and the big-M method to facilitate the model solution. Numerical verification is conducted on the modified IEEE RTS-24 and 118-bus systems. The results show that compared to bidding as a price-taker and with marginal cost, the proposed mothod can bring a 16.73% and 13.02% increase in total market revenue, respectively. The case studies of systems with different scales verify the effectiveness and scalability of the proposed method. Full article

This study focuses on the adaptive tracking control issue for nonlinear multi-agent systems (MASs) under the influence of asymmetric input constraints and external disturbances. Firstly, an auxiliary system is proposed, which can ensure flexible prescribed performance under input saturation conditions. Meanwhile, by introducing a transformation function, the distributed errors are freed from initial constraints. Employing the backstepping method, the adaptive technique, and a neural network approximation technology, a finite-time prescribed performance adaptive tracking control algorithm is designed, enabling the tracking errors to stably converge within the prescribed performance bounds. Secondly, a composite disturbance observer is developed to estimate and mitigate the combined disturbances, which include external perturbations and approximation errors from radial basis function neural networks (RBF NNs). It not only achieves effective disturbance compensation but also further suppresses the approximation errors of RBF NNs. Finally, stability analysis using the Lyapunov function demonstrates that all closed-loop signals remain uniformly ultimately bounded (UUB), with adaptive tracking errors converging to a compact region within a finite time. Simulation results and comparative studies confirm the proposed method’s effectiveness and advantages, providing a basis for its practical use in distributed control applications. Full article

Background: The genomic era has transformed not only the tools of medicine but the very logic by which we understand health and disease. Whole Exome Sequencing (WES), Clinical Exome Sequencing (CES), and Whole Genome Sequencing (WGS) have catalyzed a shift from Mendelian simplicity to polygenic complexity, from genetic determinism to probabilistic interpretation. This epistemological evolution calls into question long-standing notions of causality, certainty, and identity in clinical genomics. Yet, as the promise of precision medicine grows, so too do the tensions it generates: fragmented data, interpretative opacity, and the ethical puzzles of Variants of Uncertain Significance (VUSs) and unsolicited secondary findings. Results: Despite technological refinement, the diagnostic yield of Next-Generation Sequencing (NGS) remains inconsistent, hindered by the inherent intricacy of gene–environment interactions and constrained by rigid classificatory systems like OMIM and HPO. VUSs (neither definitively benign nor pathogenic) occupy a liminal space that resists closure, burdening both patients and clinicians with uncertainty. Meanwhile, secondary findings, though potentially life-altering, challenge the boundaries of consent, privacy, and responsibility. In both adult and pediatric contexts, genomic knowledge reshapes notions of autonomy, risk, and even personhood. Conclusions: Genomic medicine has to develop into a flexible, morally sensitive paradigm that neither celebrates certainty nor ignores ambiguity. Open infrastructures, dynamic variant reclassification, and a renewed focus on interdisciplinary and humanistic approaches are essential. Only by embracing the uncertainty intrinsic to our biology can precision medicine fulfill its promise, not as a deterministic science, but as a nuanced dialogue between genes, environments, and lived experience. Full article

Limited depth of field in modern optical imaging systems often results in partially focused images. Multi-focus image fusion (MFF) addresses this by synthesizing an all-in-focus image from multiple source images captured at different focal planes. While deep learning-based MFF methods have shown promising results, existing approaches face significant challenges. Convolutional Neural Networks (CNNs) often struggle to capture long-range dependencies effectively, while Transformer and Mamba-based architectures, despite their strengths, suffer from high computational costs and rigid input size constraints, frequently necessitating patch-wise fusion during inference—a compromise that undermines the realization of a true global receptive field. To overcome these limitations, we propose MLP-MFF, a novel lightweight, end-to-end MFF network built upon the Pyramid Fusion Multi-Layer Perceptron (PFMLP) architecture. MLP-MFF is specifically designed to handle flexible input scales, efficiently learn multi-scale feature representations, and capture critical long-range dependencies. Furthermore, we introduce a Dual-Path Adaptive Multi-scale Feature-Fusion Module based on Hybrid Attention (DAMFFM-HA), which adaptively integrates hybrid attention mechanisms and allocates weights to optimally fuse multi-scale features, thereby significantly enhancing fusion performance. Extensive experiments on public multi-focus image datasets demonstrate that our proposed MLP-MFF achieves competitive, and often superior, fusion quality compared to current state-of-the-art MFF methods, all while maintaining a lightweight and efficient architecture. Full article

As global e-commerce expands, efficient cross-border logistics services have become essential. To support the evaluation of logistics service providers (LSPs), we propose HD-CBDTOPSIS (Technique for Order Preference by Similarity to Ideal Solution with heterogeneous data and cloud Bhattacharyya distance), a hybrid multi-criteria group decision-making (MCGDM) model designed to handle complex, uncertain data. Our criteria system integrates traditional supplier evaluation with cross-border e-commerce characteristics, using heterogeneous data types—including exact numbers, intervals, digital datasets, multi-granularity linguistic terms, and linguistic expressions. These are unified using normal cloud models (NCMs), ensuring uncertainty is consistently represented. A novel algorithm, improved multi-step backward cloud transformation with sampling replacement (IMBCT-SR), is developed for converting dataset-type indicators into cloud models. We also introduce a new similarity measure, the Cloud Bhattacharyya Distance (CBD), which shows superior discrimination ability compared to traditional distances. Using the coefficient of variation (CV) based on CBD, we objectively determine criteria weights. A cloud-based TOPSIS approach is then applied to rank alternative LSPs, with all variables modeled using NCMs to ensure consistent uncertainty representation. An application case and comparative experiments demonstrate that HD-CBDTOPSIS is an effective, flexible, and robust tool for evaluating cross-border LSPs under uncertain and multi-dimensional conditions. Full article

This study introduces a novel family of probability distributions, termed the Pi-Power Logistic-G family, constructed through the application of the Pi-power transformation technique. By employing the Weibull distribution as the baseline generator, a new and flexible model, the Pi-Power Logistic Weibull distribution, is formulated. Particular emphasis is given to this specific member of the family, which demonstrates a rich variety of hazard rate shapes, including J-shaped, reverse J-shaped, and monotonic increasing patterns, thereby highlighting its adaptability in modeling diverse types of lifetime data. The paper examines the fundamental properties of this distribution and applies the method of maximum likelihood estimation (MLE) to determine its parameters. A Monte Carlo simulation was performed to assess the performance of the estimation method, demonstrating that both Bias and mean square error decline as the sample size increases. The utility of the proposed distribution is further highlighted through its application to real-world engineering datasets. Using model selection metrics and goodness-of-fit tests, the results demonstrate that the proposed model outperforms existing alternatives. In addition, a Bayesian approach was used to estimate the parameters of both datasets, further extending the model’s applicability. The findings of this study have significant implications for the fields of reliability modeling, survival analysis, and distribution theory, enhancing methodologies and offering valuable theoretical insights. Full article

This study explores the application of modular design in the regeneration of community public spaces within rapidly transforming urban environments, using Haikou as a case study. The objective is to improve spatial quality and community sustainability while preserving cultural identity and community engagement. Through a mixed-methods approach involving questionnaires, GIS-based spatial analysis, and case studies, the research identifies key challenges such as fragmented layouts, limited accessibility, and insufficient green space. In response, a “policy–design–community” integration mechanism is proposed to guide bottom-up and top-down coordination. A multidimensional evaluation framework is developed to assess the effectiveness of modular interventions across functional, spatial, and cultural dimensions. The findings suggest that modular design—owing to its standardization and flexibility—enhances spatial adaptability and construction efficiency, and strengthens cultural identity and community engagement. This research provides a replicable and data-informed strategy for the renewal of public spaces in Chinese urban environments. Full article

Recently, plastic and agricultural waste have gained attention as sustainable alternatives. Despite efforts to recycle these materials, much still ends up in landfills, raising environmental concerns. To optimize their potential, these wastes ought to be transformed into value-added products for diverse industrial applications. This work focused on producing thin composite material films using olive oil solid waste called JEFT and recycled plastic bottles. JEFT was cleaned, dried, and processed mechanically via ball milling to produce nano- and micron-sized particles. Composite films were produced via melt compounding and compression molding with a rapid cooling process for controlled crystallinity and enhanced flexibility. Their density, water absorption, tensile strength, thermal stability, water permeability, functional groups, and biodegradation were comprehensively analyzed. Results indicated that 50% JEFT in recycled plastic accelerated thermal deterioration (42.7%) and biodegradation (13.4% over 60 days), highlighting JEFT’s role in decomposition. Peak tensile strength (≈32 MPa) occurred at 5% JEFT, decreasing at higher concentrations due to agglomeration. Water absorption and permeability slightly increased with JEFT content, with only a 1% rise in water permeability for 50% JEFT composites after 60 days. JEFT maintained the recycled plastic’s surface chemistry, ensuring stability. The findings of this study suggest that JEFT/r-HDPE films show potential as greenhouse coverings, enhancing crop production and water efficiency while improving plastic biodegradation, offering a sustainable waste management solution. Full article

The objective was to propose a human–robot bidirectional trust-triggered cyber–physical–human (CPH) system framework for human–robot collaborative assembly in flexible manufacturing and investigate the impact of modulating communications in the CPH system on system performance and human–robot interactions (HRIs). As the research method, we developed a one human–one robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operation of different manufacturing components in a flexible manufacturing setup. We configured the human–robot collaborative system in three interconnected components of a CPH system: (i) cyber system, (ii) physical system, and (iii) human system. We divided the functions of the CPH system into three interconnected modules: (i) communication, (ii) computing or computation, and (iii) control. We derived a model to compute the human and robot’s bidirectional trust in each other in real time. We implemented the trust-triggered CPH framework on the human–robot collaborative assembly setup and modulated the communication methods among the cyber, physical, and human components of the CPH system in different innovative ways in three separate experiments. The research results show that modulating the communication methods triggered by bidirectional trust impacts on the effectiveness of the CPH system in terms of human–robot interactions, and task performance (efficiency and quality) differently. The results show that communication methods with an appropriate combination of a higher number of communication modes (cues) produces better HRIs and task performance. Based on a comparative study, it was concluded that the results prove the efficacy and superiority of configuring the HRC system in the form of a modular CPH system over using conventional HRC systems in terms of HRI and task performance. Configuring human–robot collaborative systems in the form of a CPH system can transform the design, development, analysis, and control of the systems and enhance their scope, ease, and effectiveness for various applications, such as industrial manufacturing, construction, transport and logistics, forestry, etc. Full article

Temperate forest ecosystems in Korea are currently undergoing a successional transition from Pinus densiflora Siebold & Zucc. (evergreen conifer) communities to Quercus mongolica Fisch. ex Ledeb. (deciduous broadleaf) communities. This study aimed to assess interspecific differences in ecological responses to climate change [Representative Concentration Pathway (RCP) 4.5] by evaluating changes in ecological niche characteristics and species distribution. Controlled-environment experiments, principal component analysis (PCA), and MaxEnt species distribution modeling were employed to quantify and predict ecological shifts in the two dominant species under climate change scenarios. Both species exhibited increases in niche breadth and interspecific overlap under climate change conditions. However, Q. mongolica showed a more pronounced increase in niche breadth compared to P. densiflora, indicating greater ecological flexibility and adaptive potential to warming conditions. According to the MaxEnt model projections, climate change is expected to result in an approximate 30% reduction in suitable habitat for P. densiflora in lowland areas. In contrast, Q. mongolica is projected to expand its suitable habitat by over 80%, notably in both low-elevation (below 800 m) and high-elevation (above 1400 m) zones, without being restricted to any specific altitudinal range. Our findings suggest that climate change may increase ecological similarity between P. densiflora and Q. mongolica, thereby raising the potential for interspecific competition. This convergence in niche traits could contribute to an accelerated successional transition, although actual competitive interactions in natural ecosystems require further empirical validation. Consequently, Korean forests are likely to transform into predominantly deciduous forest ecosystems under future climate conditions. Full article

The design of automatic control systems is critical for ensuring safety in gas field surface engineering production. However, over-reliance on standardized design approaches within the context of automation technology can compromise system flexibility and neglect individualized cost-effectiveness considerations. This paper identifies a comprehensive evaluation method as the preferred approach for assessing station control systems by comparing the advantages and disadvantages of various common evaluation techniques. We propose an integrated semi-quantitative and quantitative evaluation method designed to comprehensively and accurately assess the effectiveness of station automatic control systems. For the semi-quantitative framework, we first establish a specific indicator system for the control system and employ the Analytic Hierarchy Process (AHP) to determine indicator weights tailored to different station types, achieving a scientific quantification of evaluation criteria. Additionally, we utilize quantitative calculation methods, specifically reliability and availability analyses, to evaluate the station’s automatic control system. Differential research is conducted to customize the evaluation based on the distinct process characteristics of various gas field stations. Differential design calculations and analyses were performed for a single station, improving the economy and adaptability of the automatic control system design. The proposed comprehensive evaluation method ensures the safe and stable operation of control system designs and provides a new approach for the automation and intelligent transformation of gas field surface engineering. Full article