Search Results (3,782)

This article describes a non-isolated boost DC–DC configuration that uses a two-winding coupled inductor (CI) together with two synchronous power switches to acquire ultra-high voltage conversion at relatively low duty cycles. The proposed structure combines a quadratic gain stage with the coupled inductor to realize a substantial output voltage boost. The overall conversion ratio can be flexibly adjusted through two independent design factors: the duty cycle of the switches and the turns ratio of the coupled inductor providing additional degrees of freedom for optimization. The main merits of the converter are its very high voltage gain (VG), reduced voltage stress (VS) on the active switches, continuous input current, common ground between input and output, soft-switching operation for diodes D₃ and D₄, and the possibility of using a synchronized gate-drive scheme. The paper thoroughly examines the operating intervals, steady-state behavior, design procedure, and efficiency performance, and also develops a dynamic model for control-oriented analysis. To highlight its strengths, the proposed topology is systematically compared with several existing high-gain converters. Finally, experimental outcomes obtained from a 400-W laboratory prototype operating at 50 kHz confirm the feasibility and effectiveness of the proposed converter in achieving high voltage gain, reduced device voltage stress, and high efficiency under practical operating conditions. Full article

The aim of this study was to evaluate the effects of protein-based (zein) and polysaccharide-based (carboxymethylcellulose, CMC) edible coatings and citric acid (CA) applied prior to freeze-drying on the quality parameters of Jerusalem artichoke (Helianthus tuberosus L.) slices from ‘Albik’ and ‘Rubik’ cultivars. Freeze-drying increased inulin extraction efficiency (57–61 g 100 g⁻¹ vs. 44–45 g 100 g⁻¹ in fresh samples). In the ‘Albik’ cv., CMC and CA coatings significantly minimized L-ascorbic acid losses, with a 10–20% reduction vs. control. For the same cultivar, enhanced polyphenol retention was observed (up to 13%) when CA coating was applied, while the use of zein reduced vitamin C content in both cultivars. Sensory analysis (PCA, 92.4% variance) revealed that CMC improved appearance, texture, and overall acceptability, while zein imparted an off-taste, odor, and fragility. Image texture analysis showed elevated parameters (e.g., HMean) post freeze-drying, with CA inducing the greatest structural changes and zein yielding samples most similar to raw material. Machine learning classification (quadratic/linear SVM, 10-fold CV) achieved 91.5% (‘Albik’) and 81.9% (‘Rubik’) accuracy, perfectly distinguishing raw slices (100%). These findings demonstrate that CMC and CA coatings optimize bioactive retention, sensory quality, and textural differentiation in freeze-dried Jerusalem artichoke, supporting their application in functional food production. Full article

Doubly Fed Induction Generators (DFIGs) are widely employed in variable-speed wind turbine systems due to their high efficiency, enhanced controllability, and economic viability. This paper presents an intelligent neural-network-based control strategy aimed at maximizing wind energy extraction while ensuring accurate speed regulation of a DFIG by continuously tracking the maximum power point under fluctuating wind conditions. Two independent control schemes are developed for the decoupled regulation of active and reactive power in a grid-connected DFIG wind turbine. The first scheme is based on conventional field-oriented control using proportional integral regulators (FOC–PI), while the second employs an Artificial Neural Network Controller (ANNC). The effectiveness of both controllers is evaluated through MATLAB/Simulink 2020 Version simulations of a 1.5 MW DFIG-based wind energy conversion system and experimentally validated using a real wind profile implemented on an eZdsp TMS320F28335 digital signal processor. The proposed control approach achieves low output ripple, a steady-state error below 0.16%, total harmonic distortion of 0.38%, and a limited overshoot of 5%. The obtained results confirm the robustness and reliability of the implemented control strategies in enhancing power capture and improving overall system stability under variable wind conditions. Full article

Agricultural practices (APs) comprehensively regulate crop growth; however, comprehensive studies evaluating the effects of APs on crop yield, water use efficiency (WUE), and nitrogen use efficiency (NUE) remain scarce, particularly regarding determining optimal APs for winter wheat in wheat–maize rotation systems. Here, this study conducted a meta-analysis based on 305 studies globally (4009 pairs of observations), focusing on five APs: irrigation, fertilization, tillage, residue utilization, and mulching. And the results indicated that APs significantly increased winter wheat yield (31.1%), NUE (14.7%), and WUE (27.6%), with fertilization showing the most pronounced effects at 43.7%, 16.9%, and 44.7%, respectively. Specifically, compared to no fertilization, combined organic and mineral fertilizer produced the highest yield increase (141.5%); among conventional fertilization, biochar addition showed the best yield increase (19.1%). Slow-controlled/-release fertilizer and inhibitor addition increased NUE by 17.7% and 26.6%, respectively, and residue utilization and mulching improved WUE (by 17.3% and 33.2%). Moreover, in cold and arid regions (mean annual temperature [MAT] < 13 °C and total annual precipitation [TAP] < 550 mm), APs showed stronger promotion of wheat yield and WUE, while in warm and humid regions, the increase in NUE was more significant (15.3–16.1%). When experiment duration was ≥5 years, APs resulted in the highest yield increase (47.9%), while NUE and WUE increased in short-term experiments. Although APs with high nitrogen application rates resulted in a greater yield increase (51.5%), fertilization significantly reduced NUE above 198 kg N ha⁻¹. Structural equation modeling revealed that, among APs, climatic conditions, soil properties, and management factors, APs were the primary driver of changes in yield and WUE, while NUE was mainly regulated by management factors. Overall, these findings provided an empirical basis for optimizing agricultural practices in wheat–maize systems and offer guidance for developing site-specific policy design. Full article

This study systematically investigates the role of Ni in Co₂SiO₄ in a bimetallic (Co²⁺,Ni²⁺)₂SiO₄ olivine-type system and the materials’ catalytic efficiency in a model Heck–Mizoroki coupling reaction. Thus, a series of olivines with varying (Co²⁺,Ni²⁺)₂SiO₄ compositions (0–100% Ni) was synthesised and characterised by ICP-OES, FTIR/Raman, P-XRD and XPS analysis. Ideal mixing of metals was achieved with (49:51) Co:Ni. Catalytic testing revealed distinct conversion vs. time profiles, with the (69:31) Co:Ni olivine exhibiting the best overall performance, combining good reactivity with near-perfect selectivity (>99%) and improved stability. Mechanistic pathways were probed through product scope analysis, reactant–product temporal profiling, leaching and radical scavenging experiments. Results suggest a radical-assisted Heck–Mizoroki mechanism. Spectroscopic data correlated Co²⁺ and Ni²⁺ incorporation with M1 and M2 site occupancy, where Ni²⁺ M2 sites enhanced reactant activation and intermediate stability and Co²⁺ in the M1 site enhanced product release, though also homocoupling in Co₂SiO₄. Minimal leaching was observed for all bimetallic catalysts. These findings highlight the tunability of bimetallic olivines for C–C coupling reactions via controlled cation distribution. Full article

Polysaccharide–protein composite hydrogels have demonstrated remarkable potential in targeted gastrointestinal delivery owing to their excellent biocompatibility, adjustable physicochemical characteristics, and intelligent responsiveness. This review provides a comprehensive overview of the underlying mechanisms and diverse applications of these composite hydrogels in gastrointestinal targeted delivery, with a particular emphasis on their stimuli-responsive release behaviors triggered by internal and external factors such as pH, enzymes, magnetic fields. Special attention is also given to their advantages in protecting sensitive bioactive ingredients, including curcumin, EGCG, probiotics. Furthermore, this review highlights their capabilities in achieving high encapsulation efficiency, smart controlled release and targeted delivery, while also presenting current challenges associated with material stability, targeting precision, large-scale production, and clinical translation. Finally, future perspectives are discussed, focusing on the development of multi-response system design, innovative biomaterials, advanced manufacturing technology applications, and AI-assisted optimization. These directions aim to provide theoretical foundations and technical strategies for advanced research and practical applications of polysaccharide–protein composite hydrogels in a targeted gastrointestinal delivery system. Overall, this review underscores the significant promise of polysaccharide–protein composite hydrogels as intelligent gastrointestinal delivery platforms and provides a systematic reference for their rational design and future translational development. Full article

Flow tubes are key rescue devices used to respond to explosions and fires caused by blowouts. Improperly designed flow tubes can cause buckling failures, which can result in injuries or fatalities, particularly during high-speed blowouts, so optimizing the design based on the mechanism of high-speed blowout flow near the flow tube can improve rescue efficiency and reduce risk. This study investigated the flow control mechanism and analyzed the lift force of variable-diameter flow tubes. Simultaneously, the suction effect generated by the flow tubes was also quantified. The effect of flow tube structure and posture parameters on the flow field near a blowout well was numerically investigated using Fluent CDF software 2020R2, and the realizable k-ε turbulent model was used to account for turbulence. The inlet velocity was set to 300 m/s in order to simulate a high-speed blowout flow. The diameter ratio of the upper and lower parts of the flow tube changed from 1:1 to 1:2.4, and the ratio of the lower part to the total length changed from 1:10 to 3:10. The effects of the diameter ratio and length ratio on the distribution of the velocity and pressure in the flow tube were investigated. A strong negative pressure profile was observed in the equal-diameter flow tube. As the diameter ratio increased from 1:1.6 to 1:2.4, the negative pressure decreased from −1094 Pa to −214 Pa. In addition, the risk of personal suction due to negative pressure at the bottom of the flow tube was evaluated, and the effectiveness of drainage and the capability of flow control were analyzed. When the diameter ratio was increased by approximately 12.5%, the flow rate of entrainment decreased by 4% compared to the equal-diameter tube. Furthermore, the flow tube was subjected to significant upward lift forces during the snapping process, thereby increasing the risk of dislodgment. The effect of the changes in height and angle on the lift forces on the flow tube during buckling-up-installation was examined. It was found that the lift force decreases with height and is sensitive to the angle of inclination. Overall, it was concluded that the diameter ratio of the flow tube and the length of the lower section are key parameters for flow tube design. Full article

As an emerging physical energy storage technology, the magnetic suspension flywheel battery boasts prominent advantages such as high working efficiency, long service life, and short charging time. However, improving the energy conversion efficiency of magnetic suspension flywheel battery systems and reducing their overall energy loss have long been critical bottleneck technologies that urgently need to be addressed for practical applications. To promote China’s green and low-carbon energy transition and accelerate the achievement of the “double carbon” goals, this paper summarizes two core components of flywheel battery systems—magnetic bearings and flywheel motors—along with two key technologies: topological structure and control strategy, based on numerous cutting-edge studies. Subsequently, focusing on further reducing the energy consumption of flywheel energy storage systems, technical prospects are extended from aspects including system material selection and intelligent integrated control, aiming to provide research directions for the low-energy-consumption operation of flywheel battery systems. Full article

Multi-stack Polymer electrolyte Membrane Fuel Cell (PEMFC) systems are increasingly adopted in heavy-duty mobility to overcome the power limitations and thermal instability of single-stack configurations. However, the overall energy efficiency, hydrogen utilization, and thermal behavior of multi-stack fuel cell trucks are highly dependent on the applied Power Management System (PMS). In this study, high-fidelity, system-level dynamic model of multi-stack fuel cell truck was developed using Matlab/Simscape^TM, and three PMS approaches (rule-based control, state-machine control, and fuzzy logic control) were comparatively evaluated. The analysis includes coolant temperature regulation, hydrogen consumption, battery State of Charge (SoC) dynamics, and the parasitic power demand of Balance of Plant (BoP) components. Results show that the fuzzy logic PMS provides the most balanced operating profile by smoothing transient fuel cell loading and actively leveraging the battery during high-demand periods. In the thermal domain, the fuzzy logic PMS reduced temperature overshoot by up to 61.20%, demonstrating the most stable thermal control among the three strategies. Hydrogen consumption decreased by 3.08% and 0.89% compared with the rule-based and state-machine PMS, respectively, while parasitic power consumption decreased by 7.12% and 3.32%, confirming improvements in overall energy efficiency. TOPSIS-based multi-criteria decision analysis further showed that the fuzzy logic PMS achieved the highest closeness coefficient (0.9112), indicating superior system-level performance. These findings highlight the importance of PMS design for achieving energy-optimal and thermally stable operation of multi-stack PEMFC trucks and provide practical guidance for future control strategies, heavy-duty mobility applications, and next-generation hydrogen powertrain optimization. Full article

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed for quasi-steady conditions and therefore struggle to respond to fast variations in PV output and network states. This paper presents a digital twin (DT)-enabled framework for dynamic Volt/VAR optimization in large PV plants. A four-layer DT architecture is developed to achieve real-time cyber-physical synchronization through multi-source data acquisition, secure transmission, fusion, and quality control. To balance model fidelity and computational efficiency, a hybrid physics–data-driven model is constructed, and a local voltage stability L-index is incorporated as an explicit security constraint. A multi-objective optimization problem is formulated to minimize node voltage deviations and reactive power losses while maximizing the static voltage stability margin. The problem is solved using an adaptive parameter particle swarm optimization (AP-PSO) algorithm with dynamic inertia and learning coefficients. Case studies on modified IEEE 33-bus and 53-bus systems demonstrate that the proposed method reduces the voltage profile index by up to 68.9%, improves the static voltage stability margin by 76.5%, and shortens optimization time by up to 30.3% compared with conventional control and representative meta-heuristic or learning-based baselines. The framework further shows good scalability and robustness under practical uncertainties, including irradiance forecast errors and measurement noise. Overall, the proposed approach provides a feasible pathway to enhance operational security and efficiency of grid-connected PV plants under high-penetration scenarios. Full article

The western region of the Tarim Basin is a typical deep and ultra-deep oil and gas reservoir with complex geological conditions in China. This area includes a thick salt–gypsum layer, high-pressure brine layers, and other formations with high pressures and a complex pressure system. These geological features present challenges such as a high risk of drilling fluid contamination by formation fluids, the deep burial of subsalt reservoirs, high temperatures, and difficulty in designing drilling fluids. In this paper, by systematically screening and optimizing key additives, a diesel oil-based drilling and completion fluid system resistant to 220 °C ultra-high temperatures with a density of 2.60 g/cm³ was developed. The overall performance was evaluated. Utilizing an independently developed high-temperature emulsifier (BZ-PSE), an organically modified lithium silicate viscosity modifier (BZ-CHT), and compounded fluid loss reducers (BZ-OLG/BZ-OSL), the system maintained excellent rheological stability (yield point > 4.3 Pa) and filtration control capacity (HTHP fluid loss < 4.8 mL) even after aging at 220 °C. The system demonstrated a resistance to contamination by 30–50% composite brines, 15% salt–gypsum cuttings, and 10% cement, proving its capability to effectively handle extremely thick mud shale, salt–gypsum layers, and high-pressure brine. Field tests were conducted in wells GL 3C, DB X, Boz 13X, and Boz 3X. The results indicated that the high-temperature, high-density diesel oil-based drilling fluids and completion fluids can effectively address the technical challenges posed by wellbore instability in thick salt–gypsum layers, high-pressure brine invasion, and performance degradation under ultra-high temperature conditions, providing reliable technical support for the safe and efficient drilling of similar complex formations. Full article

The density power divergence (DPD) is a well-studied member of the Bregman divergence family and forms the basis of widely used minimum divergence estimators that balance efficiency and robustness. In this paper, we introduce and study a new sub-class of Bregman divergences, termed the exponentially weighted divergence (EWD), designed to generate competitive and practically interpretable inference procedures. The EWD is constructed so that its associated weight function remains bounded within the interval $[0, 1]$, which facilitates a transparent interpretation of robustness through controlled downweighting of low-density observations and avoids excessive influence from high-density points. We develop minimum EWD estimators (MEWDEs) within a general framework accommodating independent but non-homogeneous data, thereby extending classical minimum divergence theory beyond the i.i.d. setting. Under standard regularity conditions, we establish Fisher consistency and asymptotic normality, and we analyze robustness properties through influence function calculations. The EWD framework is further extended to parametric hypothesis testing, for which we derive the asymptotic null distribution of a Bregman divergence-based test statistic. Extensive simulation studies and real-data applications demonstrate that the proposed estimators perform comparably to, and often more robustly than, existing DPD-based procedures, particularly under moderate to heavy contamination, while retaining high efficiency under clean data. Overall, the EWD provides a tractable and interpretable alternative within the Bregman divergence class for robust parametric estimation and testing. Full article

The rapid growth of remote sensing data offers unprecedented opportunities for global environmental monitoring and resource assessment, yet poses significant challenges for efficient selection of large-scale image datasets. Traditional conditional retrieval methods often return extensive sets with substantial spatial redundancy, imposing heavy selection burdens on users. Existing automated selection methods struggle to balance coverage accuracy, redundancy control, and computational efficiency in large-scale scenarios, making efficient and accurate image selection a critical challenge for large-scale applications. To address this, we propose SCOPES (Spatially-Constrained Optimization for Efficient Image Selection), a novel spatial constraint optimization framework. SCOPES operates directly on actual image footprints in continuous space, thereby circumventing the limitations of traditional discretization-based modeling. We design a unit area cost function aimed at balancing image quality with spatial contribution. To ensure computational efficiency and solution optimization, SCOPES adopts a three-stage “preliminary selection-structural optimization-supplementary selection” strategy: employing lazy greedy for efficient initial selection, spatial Boolean overlay for redundancy control, and supplementary selection for coverage gap repair. Experiments conducted in four regions of different scales demonstrate that compared to baseline methods, SCOPES minimizes the number of selected images and maximizes coverage while achieving a near-universally minimal redundancy ratio. Meanwhile, the introduction of the lazy greedy algorithm significantly improves computational efficiency, achieving up to a 229-fold speedup in the large-scale East Asia region. Overall, SCOPES provides an efficient, accurate, and scalable solution for remote sensing data selection, substantially reducing the manual selection workload for platform users. Full article

Dielectric barrier discharge (DBD) plasma actuators (PAs) are devices used to control airflow. DBD actuators generate an electric field that accelerates ionized air particles, inducing localized flow modifications. Among other applications, they are particularly effective for enhancing cooling, for aerodynamic drag reduction, and for lift enhancement, therefore capable of improving stall characteristics. In addition, they offer several distinct advantages, such as rapid response time, low power consumption, and no moving parts. The present review paper aims to summarize the main governing equations associated with the most common phenomenological PA Computational Fluid Dynamics (CFD) models, Shyy and Suzen-Huang, as well as highlight the major applications to flat plates, wind turbine airfoils and entire wind turbines. The application of DBD plasma actuators on individual wind turbine blades, as well as dynamic horizontal and vertical axis wind turbines, is reviewed, drawing from key numerical and experimental investigations. The simulated performance of various configurations of single and multiple PAs on representative airfoils at different chordwise locations is discussed. The overall findings indicate that the chordwise location of the actuators on airfoils and their optimum spanwise placement on small and large wind turbine blades, along with the geometry and excitation parameters of the actuators, play a crucial role in their performance, affecting the boundary layer and the flow pattern. The reader shall obtain an overall idea of the most recent aerodynamic applications of PAs as well as their expected efficiency. Full article

Maritime operations are increasingly reliant on sensor data to drive efficiency and enhance decision-making. However, despite rapid advances in large language models, including expanded context windows and stronger generative capabilities, critical industrial settings still require secure, role-constrained access to enterprise data and explicit limitation of model context. Retrieval-Augmented Generation (RAG) remains essential to enforce data minimization, preserve privacy, support verifiability, and meet regulatory obligations by retrieving only permissioned, provenance-tracked slices of information at query time. However, current RAG solutions lack robust validation protocols for numerical accuracy for high-stakes industrial applications. This paper introduces Lighthouse Bot, a novel Agentic RAG system specifically designed to provide natural-language access to complex maritime sensor data, including time-series and relational sensor data. The system addresses a critical need for verifiable autonomous data analysis within the Artificial Intelligence of Things (AIoT) domain, which we explore through a case study on optimizing ferry operations. We present a detailed architecture that integrates a Large Language Model with a specialized database and coding agents to transform natural language into executable tasks, enabling core AIoT capabilities such as generating Python code for time-series analysis, executing complex SQL queries on relational sensor databases, and automating workflows, while keeping sensitive data outside the prompt and ensuring auditable, policy-aligned tool use. To evaluate performance, we designed a test suite of 24 questions with ground-truth answers, categorized by query complexity (simple, moderate, complex) and data interaction type (retrieval, aggregation, analysis). Our results show robust, controlled data access with high factual fidelity: the proprietary Claude 3.7 achieved close to 90% overall factual correctness, while the open-source Qwen 72B achieved 66% overall and 99% on simple retrieval and aggregation queries. These findings underscore the need for a secure limited-context RAG in maritime AIoT and the potential for cost-effective automation of routine exploratory analyses. Full article