Search Results (1,430)

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

Alzheimer’s disease is a devastating progressive neurodegenerative disorder characterized by extracellular amyloid-beta plaques and intracellular tau tangles. Despite recent advancements in amyloid-beta-targeting immunotherapies, achieving safe and definitive disease control remains a profound clinical challenge. The CRISPR/Cas9 system has emerged as a powerful technology for precision neurogenetics, offering significant potential to address the fundamental questions behind Alzheimer’s disease. This comprehensive review delineates the trajectory of CRISPR applications in Alzheimer’s disease research and therapeutics. First, we explore the integration of CRISPR in engineering high-fidelity in vitro models, such as isogenic induced pluripotent stem cells and three-dimensional cerebral organoids, alongside advanced in vivo mammalian models. Second, we examine how these platforms facilitate unbiased high-throughput genetic screening to uncover molecular underpinnings regulating tau, lipid metabolism, and neuroinflammation. Third, we critically evaluate precision editing strategies targeting core risk genes (APP, MAPT, APOE, and TREM2), explicitly highlighting the severe physiopathological trade-offs between therapeutic efficacy and loss-of-function toxicity. Finally, we address the ultimate translational bottlenecks impeding clinical application. By dissecting the packaging limits of adeno-associated viral vectors and the physical barricade of the blood–brain barrier, we underscore the necessity of transitioning toward next-generation base editors and non-viral lipid nanoparticles to realize safe and efficacious in vivo clinical gene therapies against Alzheimer’s disease. Full article

A major development direction for next-generation civil aircraft is to significantly reduce fuel consumption through the integration of high-bypass-ratio engines. However, the large diameter of high BPR engines will cause traditional aircraft to face the dilemma of ground clearance. The over-the-wing engine mount configuration avoids ground clearance constraints by installing the engines over the wings, which is conducive to the integration of high BPR engines. However, the sensitivity of the flow on the upper surface of the wing makes this configuration more likely to cause strong interference between the engine and the wing than the traditional configuration. During the design, the important interaction of the wing shapes, the wing static elastic deformation, the engine installation position and the engine inlet and exhaust effect should be fully considered, which brings great challenges to the traditional design method. An automatic multidisciplinary coupled optimization method based on the discrete adjoint approach and gradient-based optimization is proposed for this configuration. A corresponding framework is established based on the open-source multidisciplinary optimization platform OpenMDAO; the CFD solution and the adjoint solution are carried out using the open-source CFD solver DAFoam; the structural finite element solution and the structural adjoint solution are carried out using the open-source FEM solver TACS; and the engine power effect is solved by coupling the intake and exhaust boundary conditions into the CFD solver. This method can comprehensively consider the changes in the wing shapes, the static aeroelastic deformation of the wing, the intake and exhaust effects of the engine, and the positional movement of the engine along the spanwise, chordwise and vertical directions of the wing. The optimization results show that the optimized configuration eliminates the strong shock interaction between the nacelle and the wing, enhances the favorable pressure gradient on the upper surface of the wing, and reduces the drag by 9.51%, thereby demonstrating the effectiveness of the proposed multidisciplinary coupled adjoint optimization method for this configuration. Full article

In the field of engineering, the advancement of automated scoring systems for laboratory reports has been significantly hampered by three persistent challenges: scarcity of high-quality annotated data, high domain-specific complexity, and insufficient model interpretability. To address these limitations, this study proposes an AdaBoost regression model based on multi-level feature engineering and threshold control, denoted as MFTC-ABR. This method constructs a multi-dimensional feature set using a lightweight neural network, which evaluates laboratory reports across four core dimensions: comprehension of experimental principles, completion of experimental procedures, depth of result analysis, and plagiarism detection. At the scoring algorithm level, a dynamic threshold adjustment mechanism is integrated into the AdaBoostReg ensemble learning framework. By redesigning the sample weight update rule, the prediction errors of samples are divided into three intervals: the acceptable region, the stable learning range, and the focus range. Accordingly, a differentiated weight update strategy is implemented, and a history-aware mechanism is introduced to further regulate the attention allocated to individual samples. Finally, experimental results on the power electronics laboratory report dataset show that MFTC-ABR model achieves a mean absolute error (MAE) of 3.09 and a scoring consistency rate of 82% within a five-point error tolerance. These findings validate the effectiveness and practicability of the proposed method for automatic assessment in specialized domains with limited data availability. Full article

Pavement energy harvesting has been investigated as a means of converting traffic loading, solar radiation, and pavement thermal gradients into usable electricity or heat. This paper reviews 135 publications available through March 2026 and evaluates the field from a pavement engineering perspective. The literature is organized into six technology families: piezoelectric systems, mechanical-electromagnetic systems, triboelectric systems, thermoelectric systems, hydronic/geothermal/solar-thermal pavements, and photovoltaic or pavement-integrated photovoltaic-thermal systems. The review considers not only reported energy output, but also structural compatibility, durability, constructability, maintenance requirements, safety, and deployment conditions. The synthesis shows that the most credible near-term roles of piezoelectric and triboelectric systems are self-powered sensing and other localized low-power functions rather than bulk electricity generation. Mechanical-electromagnetic systems can produce larger event-level output, but their practicality is limited to low-speed and highly controlled settings because they rely on deliberate surface displacement. Thermoelectric systems are mechanically compatible with pavements, yet their performance remains constrained by weak and transient temperature gradients. Hydronic and solar-thermal pavements are presently the most infrastructure-compatible option for large-area energy recovery because they deliver useful heat and align with snow-melting, seasonal storage, and adjacent building-energy applications. Photovoltaic and photovoltaic-thermal pavements offer direct electrical generation, but continued challenges with transparent cover layers, surface friction, durability, fouling, and maintenance still limit broad roadway deployment. Overall, the review indicates that future progress will depend less on maximizing peak output in isolated prototypes and more on integrated pavement-energy design, standardized performance reporting, durability assessment, techno-economic evaluation, and corridor-scale demonstration. Full article

Short-term load forecasting plays a pivotal role in modern power system operations yet it remains challenging due to the complex spatiotemporal dependencies in load data. This paper proposes a dual-head attention residual network (DARNet) that significantly advances STLF through three key innovations: (1) a hybrid encoder combining 1D-CNN and GRU architectures to simultaneously capture the local load patterns and long-term temporal dependencies, achieving a 28% better locality awareness than that of conventional approaches; (2) a novel dual-head attention mechanism that dynamically models both the inter-temporal relationships and cross-variable dependencies, reducing the feature engineering requirements; and (3) an autocorrelation-adjusted recursive forecasting framework that cuts the multi-step prediction error accumulation by 33% compared to that with standard seq2seq models. Extensive experiments on real-world datasets from three Chinese cities demonstrate DARNet’s superior performance, outperforming six state-of-the-art benchmarks by 21–35% across all of the evaluation metrics (MAPE, SMAPE, MAE, and RRSE) while maintaining robust generalization across different geographical regions and prediction horizons. Full article

This study presents a heterogeneous integration micro-assembly process and circuit board packaging solution for GaN Schottky Barrier Diode (SBD) rectifiers, and innovatively constructs a hierarchical reinforcement learning strategy for optimizing SBD RF parameters. By establishing an optimization framework with the goal of efficiency in the load-input power two-dimensional space, a dual-layer optimization mechanism is employed: the high-level strategy dynamically selects optimization regions and parameter combinations, while the low-level strategy implements specific parameter adjustments. This approach effectively addresses the challenges of device parameter modeling and circuit design. Experimental data shows that the efficiency error for the SBD1 rectifier remains stable within 2%, and the average error for SBD2 is reduced to 1.5%. This method enables efficient and accurate optimization of RF parameters, providing a reliable technical pathway for the engineering application of Wireless Power Transmission systems. Full article

Scalable production of interactive 3D assets is a key requirement for XR-based applications, yet the functional integration of GenAI-generated assets into game engines remains challenging for non-expert users. This article proposes and validates a Prompt-to-Trigger workflow that links GenAI-based asset ideation and generation with the implementation of basic interactive behaviors (triggers) in accessible XR platforms. The study adopted a qualitative and exploratory approach, using systematic observation throughout a two-stage development process. This process included an initial phase where 3D assets were generated and refined using tools such as Tripo AI and Meshy, followed by an optimization stage to ensure compatibility with Blender and XR environments like A-Frame and Godot, and subsequently, the creation of AI-powered activation scripts. The results show that GenAI’s current 3D outputs frequently exhibit topological inconsistencies and rigging errors that compromise performance and real-time interoperability, requiring cleanup and optimization before deployment. The Prompt-to-Trigger workflow formalizes this bridge, positioning AI assistance as a functional layer for iterative logic generation. The resulting model provides non-expert creators with structured, actionable framework to prototype complex XR experiences for applied domains like education and multimedia communication. Full article

The multirotor configuration unmanned aerial vehicle faces a significant challenge in simultaneously achieving long-range operation and high payload capacity. This paper investigates the power management strategy for a novel fuel–electric hybrid aircraft that incorporates lifting wings to reduce rotor load and a range-extend system to enhance energy supply. An equivalent consumption minimization strategy is developed to optimize, in real time, the power distribution between the internal combustion engine and the battery. The primary innovation of this paper lies in the application and rigorous validation of the equivalent consumption minimization strategy on this new aircraft configuration, which effectively minimizes total energy cost by optimally balancing fuel consumption and battery degradation, resulting in significantly reduced fuel usage and a more stable power output compared to conventional approaches. Full article

Composite gears have emerged as game-changing mechanical components across various engineering fields due to their multifunctional physical properties, such as low density, thermal resistance, and mechanical robustness. Although traditional metallic gears are well established and reliable, their efficiency is limited in certain applications. In contrast, composite gears reinforced with carbon, glass, or polymer fibers offer superior strength-to-weight ratios, enhanced corrosion and wear resistance, and improved vibration damping characteristics. The studies demonstrate that hybrid and fiber-reinforced composite gears can achieve weight reductions of 20–50% compared with steel gears, while maintaining comparable stiffness and load-carrying capability. Polymer and reinforced composite gear systems show operating temperature reductions of up to 40% due to improved tribological behavior and thermal dissipation. In metal–matrix composite systems, compressive strength improvements up to around 60% have been reported. Additionally, composite architectures provide improved fatigue life, reduced transmission error, and enhanced vibration damping. Developments in gear design, composite materials, and their integration into composite gear systems were identified through a structured literature survey using Scopus and Google Scholar, systematically compiling manufacturing methods, material performance characteristics, and applications. Targeted keywords related to gears, composites, additive and hybrid manufacturing, lightweight design, and power transmission yielded 132 relevant publications, which were subsequently refined through screening and cross-referencing, with the final section focusing specifically on composite gear applications. The review highlights key opportunities, current challenges, and potential future directions for the development of high-performance composite gear systems. Full article

Low-temperature polymer electrolyte membrane fuel cell systems can achieve higher efficiency than diesel engines, but heat rejection remains a major challenge in class-8 heavy-duty fuel cell trucks. For the same rated power, the radiator heat load is greater than that in a diesel engine, while the allowable operating temperatures are lower. This work proposes and evaluates 400 kW_e fuel cell–battery hybrid (FCH) platforms and operating strategies that manage heat rejection without enlarging the radiator frontal area. Three FCH platforms are identified, each varying in fuel cell system (FCS) rated power, battery energy storage system (ESS) capacity, and maximum stack coolant exit temperature ($T_{h1}$). All three satisfy key system and vehicle requirements, including 175 kW_e FCS power at top sustained speed, 400 kW_e FCH power on a 6% grade climb, a target stack power density (PD) of 750 mW_e/cm², and heat rejection constraints. The first FCH has the smallest FCS, the largest ESS, and a $T_{h1}$ of 90 °C. The second achieves the highest PD of 840 mW_e/cm² at a $T_{h1}$ of 95 °C. The third has the largest FCS, the smallest ESS, and a $T_{h1}$ of 102 °C. At a $T_{h1}$ of 115 °C, the platform can be configured as a stand-alone 400 kW_e(net) FCS without hybridization, but the achievable PD drops to 460 mW_e/cm². Full article

Ohmic contact resistance is a persistent and increasingly dominant bottleneck limiting the practical performance of wide-bandgap (WBG) and ultrawide-bandgap (UWBG) power semiconductor devices. This review provides a comprehensive and comparative treatment of specific contact resistivity (${\rho }_c$) phenomena across five material systems—4H-SiC, GaN, β-Ga₂O₃, AlN/AlGaN, and diamond—spanning fundamental contact physics, characterization methodology, material-specific state of the art, device context, and advanced engineering strategies. A semi-empirical scaling analysis establishes that the minimum achievable ${\rho }_c$ increases by approximately one order of magnitude per 0.8–1.0 eV increase in bandgap, arising from the interplay of Fermi-level pinning, increasing carrier effective mass, and decreasing achievable near-surface doping concentration. The best demonstrated ${\rho }_c$ values range from ~3 × 10⁻⁸ Ω·cm² for GaN epitaxially regrown contacts to ~8 × 10⁻⁵ Ω·cm² for direct AlN metallization. The transition from alloyed to regrown contacts in GaN—delivering two orders of magnitude improvement—is identified as the paradigm model for UWBG contact development, with β-Ga₂O₃ most immediately positioned to follow this trajectory. Key challenges include the absence of p-type doping in β-Ga₂O₃, near-complete Fermi-level pinning in AlN, and the unsolved shallow-donor problem in diamond. Recommendations for standardized ${\rho }_c$ measurement protocols and priority research directions are presented. Full article

The development of modern transport energy systems is driven by increasing demands for energy efficiency, environmental sustainability, and operational reliability of vehicles. One of the most critical challenges in internal combustion engine operation is the cold-start condition, which results in increased fuel consumption, intensified component wear, and elevated emissions. Under these conditions, the development of intelligent thermal conditioning systems capable of accelerating engine warm-up and maintaining optimal thermal regimes becomes essential. This study proposes an intelligent engine and vehicle thermal conditioning system based on the integration of digital twin technology and phase-change thermal (PCM) energy storage. A digital twin architecture of the engine thermal conditioning system is developed to enable the integration of monitoring, simulation and predictive control of engine thermal processes. A mathematical model of the thermal conditioning system describing the dynamic temperature behavior of the engine, coolant, engine oil and PCM-based thermal energy storage units is formulated. A model predictive control strategy is implemented within the digital twin environment to support decision-making and optimization of engine thermal conditioning processes. Simulation and experimental results demonstrate that the proposed system can reduce engine warm-up time by 17.8–68.4%, decrease fuel consumption during the cold start phase by approximately 19.5–56.25%, and reduce harmful emissions. These findings confirm the potential of integrating digital twin technologies, predictive control and phase change thermal energy storage for improving the energy efficiency and environmental performance of modern transport power systems. Full article

Collagen, the most abundant extracellular matrix (ECM) protein, has emerged as a cornerstone biomaterial in drug delivery and regenerative medicine due to its intrinsic biocompatibility, biodegradability, and low immunogenicity. Engineering collagen into microspheres transforms its functionality beyond bulk scaffolds by increasing surface area, enabling minimally invasive delivery, and providing precise control over degradation, mechanical properties, and therapeutic release. This review provides a comprehensive analysis of collagen-based microspheres, with a particular focus on their dual role as biomimetic microenvironments and delivery systems. Recent advances in fabrication strategies, including emulsification, microfluidics, spray-drying, and electrospraying, are discussed in the context of scalability, size control, and payload encapsulation. Composite approaches that incorporate bioactive minerals, polysaccharides, or synthetic polymers are highlighted for their ability to enhance mechanical performance and biological function. We further examine characterization frameworks that link microscale structure and physicochemical properties to biological outcomes, with emphasis on how collagen microspheres replicate key structural, mechanical, and signaling features of native tissue microenvironments. Collagen microspheres have demonstrated broad utility as controlled delivery platforms, cell-instructive microcarriers, and injectable systems for tissue regeneration, including applications in bone, cartilage, skin, and nerve repair, as well as advanced wound care and localized cancer therapy. Finally, we critically assess current challenges related to scalable manufacturing, sterilization compatibility, and batch reproducibility, and outline emerging solutions such as recombinant collagen, advanced biofabrication, and stimuli-responsive systems. Collectively, collagen microspheres represent a powerful and adaptable platform poised to advance next-generation regenerative and therapeutic technologies. Full article

Enhancing the vibroacoustic performance of underwater vehicles remains a critical challenge in marine engineering. Increasing geometric stiffness is a conventional strategy to suppress vibration, yet its effectiveness in reducing underwater sound radiation can be practically limited. This paper presents a numerical investigation of the vibroacoustic response of composite grillage sandwich structures, with a focus on separating the contributions of geometric stiffening and core damping. A coupled acoustic structural model is developed based on the equivalent single layer theory and implemented in a finite element framework, then validated against analytical benchmark solutions. The parametric study reveals a stiffness saturation phenomenon in the acoustic domain. Although increasing rib height significantly reduces the mean square velocity, the radiated sound power reaches a saturation plateau and can even show a slight rebound at higher frequencies. This behavior is attributed to an increase in structural phase velocity that shifts modal components toward a more efficient radiation regime, thereby increasing radiation efficiency. To address this limitation, the damping modulation role of the core material is examined. The results show that introducing a high damping core into the grillage skeleton suppresses broadband noise and resonance peaks, without a comparable rise in radiation efficiency that may accompany geometric stiffening. The study indicates that a hierarchical synergistic design strategy that uses geometric stiffness for load bearing and low frequency control, while leveraging core damping to mitigate the acoustic saturation limit, provides useful physical insight into more efficient noise control approaches than purely stiffness based approaches. Full article