Search Results (15,364)

New power systems with penetration of inverter-based resources (IBRs) exhibit symmetry breaking in post-disturbance frequency, as nodal trajectories depend on disturbance location, network coupling, and heterogeneous frequency channels across synchronous generators (SGs), grid-forming (GFM) converters, and grid-following (GFL) converters with phase-locked loops (PLLs). As a consequence, relying only on aggregated center-of-inertia/center-of-frequency (COI) metrics can underestimate asymmetric local risks, including worst-node rate of change of frequency (RoCoF), worst-node nadir, and nodal frequency split. This paper proposes a disturbance location-aware coordination framework that explicitly models and balances heterogeneous active-power frequency support across the network using an electromechanical-scale state-space formulation. First, a heterogeneous nodal frequency response (HNFR) model yields an explicit state-space input–output mapping from location-specific active power disturbances to nodal frequency outputs for both electromechanical and PLL-estimated channels. Second, a reproducible signal processing protocol computes nodal RoCoF/nadir/split indices and enables large-scale location sweeping via atlas-ready matrices that are naturally parallelizable for high-performance computing. Third, a constrained allocation layer schedules heterogeneous fast frequency response subject to converter limits and finite energy constraints, supporting an atlas-based gain scheduling implementation. Case studies demonstrate that the proposed symmetry-aware design improves worst-node security and suppresses frequency split while maintaining comparable COI behavior. Under budget-matched conditions on the modified IEEE 39-bus system, the proposed allocation reduces worst-node RoCoF by 32.2% and maximum nodal frequency split by 17.8% relative to the COI-based benchmark. Full article

To address the limitations of zero-shot generalization in Vision-Language Navigation (VLN), this paper proposes a novel knowledge graph-driven reinforcement learning approach. Our method constructs a hierarchical, dynamically updated knowledge graph online during the agent’s real-time interaction with the environment, seamlessly aligning external semantic priors with continuous visual perception. By leveraging a Chain-of-Thought (CoT) prompting mechanism, the agent performs multi-hop reasoning to precisely locate target objects. Furthermore, we design an end-to-end optimized reinforcement learning framework that fuses multi-modal features and employs a task-oriented composite reward function. Extensive experiments in the AI2-THOR simulation environment demonstrate that the proposed method significantly improves navigation success rates in zero-shot settings. The results validate its robust generalization capabilities, particularly for unseen object categories and complex scene layouts. Full article

To address the challenge of balancing regenerative braking efficiency and braking safety in rear-wheel-drive electric motorcycles, this study proposes a genetic algorithm-based electromechanical hybrid fuzzy braking control strategy. First, a three-dimensional fuzzy controller is designed with braking force, motorcycle speed, and battery state of charge (SOC) as input variables to adjust the regenerative braking ratio in real-time. To further improve the fuzzy logic, which typically relies on engineering experience, a genetic algorithm (GA) is employed to optimize the controller’s parameter space. Co-simulation results using BikeSim 2013.1 and MATLAB/Simulink R2022a demonstrate that, under WMTC and NEDC standard driving cycles, the proposed GA-optimized fuzzy control system increases energy recovery rates by 6.59% and 11.65%, respectively, compared with the unoptimized fuzzy control strategy. Full article

Outdoor thermal comfort in university courtyards is a key factor influencing students’ environmental experience and the usability of outdoor spaces in hot-arid climates. Courtyard design may also affect the environmental conditions of adjacent classrooms by modifying solar exposure, shading, air movement, and surface heat gain. Accordingly, this study aims to develop optimized design scenarios for improving outdoor thermal comfort in university courtyards through hybrid passive strategies, including vegetation, shading systems, and cool pavements. To achieve this goal, the research adopted a combined field-based and simulation-based methodology. Field measurements and student questionnaires for 292 students were conducted in courtyards and classrooms of three university buildings in Luxor, Egypt. These buildings represent different urban morphologies, courtyard aspect ratios, geometric configurations, and student densities. In parallel, simulation models were developed using ENVI-met V5.6.1 and Rhinoceros V8 with Grasshopper, to test and compare various design scenarios. Field monitoring revealed that wider courtyards with low aspect ratios (0.28–0.38), lacking trees and finished with concrete paving, recorded lower CO₂ concentrations (around 800 ppm), but experienced higher surface and air temperatures. These elevated temperatures negatively affected outdoor thermal comfort and increased heat gain in classrooms overlooking the courtyards. In contrast, courtyards with higher aspect ratios (0.63–0.82) demonstrated better microclimatic moderation and improved comfort conditions. Simulation results indicate that integrating a belt vegetation pattern of Cassia leptophylla, combined with textile shading and cool pavements with an albedo of 0.5, can reduce the Universal Thermal Climate Index (UTCI) by up to 14.7 °C, shifting conditions toward moderate heat stress. The findings provide practical design guidance for upgrading existing university courtyards and designing future educational buildings in hot-arid climates to enhance student comfort and environmental performance. Full article

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given to how users progressively adapt to AI systems. This paper introduces the concept of algorithmic habituation, defined as the gradual accommodation of users to the regularities and predictive patterns of AI systems. The objective is to provide a neurocognitive and systems-based framework that explains this phenomenon. Methods: The study develops a conceptual and integrative framework grounded in classical theories of habituation, neuroplasticity, predictive processing, and systems theory. Building on these foundations, we propose a mechanistic model of human–AI co-adaptation, conceptualized as a recursive feedback loop involving repeated interaction, pattern recognition, expectation stabilization, and cognitive economy. In addition, a typology of algorithmic habituation is advanced, alongside proposed empirical pathways for future validation, including scale development, experimental paradigms, and longitudinal designs. Results: The proposed framework suggests that repeated interaction with AI systems leads to stabilization of cognitive expectations, reduced cognitive effort, and increased behavioral standardization. This process extends beyond perceptual habituation into higher-order domains, including decision-making, creativity, and moral judgment. The typology identifies four primary forms of algorithmic habituation: cognitive, decisional, creative, and moral. The model predicts both adaptive outcomes (efficiency, reduced cognitive load) and maladaptive consequences (reduced reflexivity, automation bias, and potential erosion of critical thinking). Conclusions: Algorithmic habituation represents a novel construct at the intersection of neuroscience, cognitive psychology, and human–AI interaction. By framing user adaptation as a form of neurocognitively grounded habituation within recursive systems, this paper contributes a new perspective to understanding AI integration in human cognition. The framework has implications for digital wellbeing, education, and AI ethics, and opens multiple avenues for empirical research. Full article

Power electronic systems are often engineered through a sequential–iterative workflow in which hardware parameters are initially sized from steady-state, ripple, thermal, and electromagnetic-compatibility constraints, and controllers are subsequently tuned to satisfy dynamic and closed-loop performance requirements. While converters are inherently designed for closed-loop operation, increasing power density, uncertainty, and distributed interaction make the underlying design process resemble a strategic interplay among multiple decision-makers, including hardware designers, control algorithms, loads, disturbances, and manufacturing constraints. This paper develops a unifying game-theoretic perspective on the optimal design and control of power electronic systems. Classical concepts—such as robust control, worst-case design, droop-based load sharing, and tolerance allocation—are reinterpreted as equilibrium solutions of zero-sum, Stackelberg, non-cooperative, or cooperative games. Beyond a conceptual taxonomy, two illustrative simulation case studies are provided: (i) a Stackelberg hardware–controller co-design of a buck converter, demonstrating simultaneous passive-component reduction and improved transient performance relative to a conservative sequential design; and (ii) a droop-controlled parallel-converter example contrasting Nash and cooperative equilibria, explicitly quantifying trade-offs between bus-voltage regulation, current-sharing fairness, and conduction losses. By framing power electronic design and control as interacting strategic processes rather than isolated optimization stages, the paper aims to show that game theory can serve as a structured and practically interpretable framework for distributed and uncertainty-aware power electronic systems. Full article

Continuous monitoring of vehicle engine vibration is a key enabler of real-time diagnostics, yet conventional accelerometers require an external power supply and fit poorly into the distributed sensor networks envisioned for next-generation vehicles. Triboelectric nanogenerators offer an attractive self-powered alternative, but their direct application to the vibration of a running passenger vehicle engine, and the explicit link between sensor design parameters and individual engine operating states, remains largely unexplored. Here, we address this gap by co-tuning the air gap and the substrate rigidity of a contact-separation triboelectric vibration sensor to the vibration spectrum of an automotive engine. A systematic 3 × 3 design sweep across three gap distances and three substrate types identifies a single configuration that simultaneously resolves the low-frequency idle band and the higher-frequency acceleration band of a four-cylinder gasoline engine. A frequency-amplitude response map confirms that the real engine operating points fall within the sensitive region of the optimized device, and an on-vehicle test demonstrates clean discrimination of all seven operating states, from ready to shut-down, without any external power. The results establish design guidelines for source-matched triboelectric vibration sensors and outline a practical path toward self-powered, wireless-ready engine health monitoring in future vehicles. Full article

Laminar co-flow in microchannels typically results in stratified streams with diffusion-limited mixing. This work presents an additively manufactured microfluidic platform that integrates a pulse tank and a transverse injection nozzle into an otherwise straight channel, enabling pulse-driven mixing and droplet generation using air-pressure actuation alone. In Device A, transverse pulsed injection disrupted the stratified interface and significantly enhanced mixing compared with the no-pulse case, as confirmed by an entropy-based mixing index. In Device B, pulsed injection into a continuous oil phase enabled stable droplet-on-demand generation with pressure-tunable droplet diameter in a straight circular channel. The devices operated in a laminar regime, with representative Reynolds, Péclet, and capillary numbers confirming diffusion-limited baseline mixing and stable dripping-type droplet formation. The results demonstrate that pulse-driven injections in a simple, additively manufactured geometry provide an effective, low-complexity approach to mixing enhancement and droplet generation without external fields or complex channel designs. Full article

Distributed hybrid flow-shop scheduling problems (DHFSPs) are widely encountered in manufacturing systems. Their complexity increases significantly when multiple overhead cranes are used for material handling. This paper investigates a distributed hybrid flow-shop scheduling problem with multiple overhead crane transportation (DHFSP-MCT), aiming to simultaneously minimize makespan and total energy consumption (including machining and transport). A hierarchical reinforcement learning-based bi-population collaborative metaheuristic algorithm (HRL-BCMA) is proposed. In HRL-BCMA, an iterated greedy strategy is first adopted to generate an initial population. Then, a two-level reinforcement learning framework is designed: a high-level agent decides when to release jobs to the shop floor, while a low-level agent based on a graph isomorphism network selects improvement operators. Furthermore, a bi-population co-evolutionary framework and a knowledge-informed strategy are introduced to enhance solution quality and diversity. Experimental evaluations on both randomly generated instances and a real-world-inspired aluminum manufacturing case show that HRL-BCMA reduces makespan by 8.6% and total energy consumption by 12.3% on average compared to the best existing algorithm (CBMA) while achieving superior Pareto front coverage. These results demonstrate the effectiveness of the proposed method for green scheduling problems with crane transport constraints. Full article

Smart insoles equipped with pressure sensor matrices are increasingly used for gait analysis, yet high-density arrays compromise battery life and data throughput. This study aims to identify the optimal sparse sensor layout required to accurately estimate the Center of Pressure (CoP) by analyzing the trade-off between sensor number, spatial placement, and reconstruction error. Plantar pressure data were collected from twelve healthy participants walking at a self-selected speed using 16-sensor connected insoles. A combinatorial algorithm evaluated all $2^{16}-1$ possible sensor combinations to minimize the Root Mean Square Error (RMSE) in the antero-posterior, medio-lateral, and global Euclidean directions. Results reveal a non-linear convergence of accuracy that depends on the spatial axis. For longitudinal and global progression, a clear inflection point achieving sub-centimetric accuracy (RMSE < 5 mm) is reached at seven sensors. In contrast, medio-lateral tracking shows its largest discrete error reduction at five sensors, followed by gradual improvements at higher densities. Anatomical frequency analysis highlights distinct spatial requirements: the posterior heel is consistently selected for medio-lateral accuracy, while the lateral arch and metatarsal regions are critical for longitudinal progression. These findings suggest that while a minimum of seven strategically placed sensors enables robust CoP tracking across all spatial axes, optimal hardware design should remain task-specific. This work provides a data-driven framework for the development of energy-efficient wearable gait monitoring systems. Full article

Biodegradable metals represent a paradigm shift in orthopedic fixation by providing temporary mechanical support synchronized with bone healing while eliminating long-term complications associated with permanent implants. Conventional bioinert alloys, including stainless steels, Ti-based alloys, and Co-Cr alloys, exhibit high elastic moduli that induce stress shielding and often require secondary removal surgeries. In response, resorbable metallic systems based on Mg, Zn, and Fe have emerged as promising alternatives. Among these, Fe-Mn-C alloys stand out for load-bearing applications due to their exceptional strength-ductility balance governed by twinning-induced plasticity mechanisms, tunable degradation behavior, and intrinsic magnetic resonance imaging compatibility through austenitic phase stabilization. Focusing on Fe-Mn-C alloys, this review critically examines the metallurgical design principles underlying stacking fault energy optimization, phase stability, and Mn-controlled electrochemical behavior. Processing innovations, such as additive manufacturing, are discussed as tools to architecture porosity, refine microstructure, and accelerate degradation by graded designs while preserving mechanical structural support during healing. Hybrid metallic-bioactive systems, surface functionalization strategies, and functionally graded porous architectures were evaluated as advanced approaches to enhance osteointegration and modulate degradability. Despite these advances, significant barriers remain for clinical translation. Persistent discrepancies between in vitro and in vivo degradation rates, often attributed to biological encapsulation and degradation product accumulation, complicate lifetime prediction. Localized corrosion at microstructural heterogeneities such as twin boundaries and phase interfaces can undermine structural reliability under load-bearing conditions. Moreover, predictive multi-physics modeling frameworks capable of coupling electrochemical kinetics, mechanical loading, microstructural evolution, and bone remodeling remain underdeveloped, limiting reliable safety-margin estimation. Regulatory progress is further hindered by the absence of standardized testing protocols specifically tailored to Fe-based biodegradable alloys, including harmonized degradation rate windows, validated corrosion-mechanics coupling methodologies, and clinically defined Mn ion release thresholds. This review aims to discuss whether Fe-based alloys, especially Fe-Mn-C alloys, can transition from promising laboratory materials to clinically viable next-generation orthopedic implants capable of delivering patient-specific, mechanically compatible, and biologically synchronized temporary fixation. Full article

The construction sector consumes significant amounts of natural resources and contributes substantially to global CO₂ emissions, making it necessary to develop materials with a reduced environmental impact. In this context, the valorization of reusable industrial waste as secondary raw materials represents a strategic direction for applying circular economy principles and for decarbonizing the construction materials industry. The scientific problem addressed in this review is the urgent need to develop construction materials with a reduced environmental footprint, given that the construction sector is a major consumer of natural resources and a significant contributor to global CO₂ emissions. This challenge requires the identification and critical evaluation of sustainable solutions that support decarbonization and the transition toward a circular economy. The main findings indicate that the valorization of industrial waste offers high decarbonization potential: supplementary cementitious materials (SCMs), such as ground granulated blast furnace slag and fly ash, can reduce CO₂ emissions by approximately 20–50%, while alkali-activated binders and geopolymers achieve reductions of 40–80% compared to Portland cement. These materials also enhance durability, extending service life by 10–20% in aggressive environments, although early-age strength may decrease by 10–30%; recycled aggregates derived from construction and demolition waste (CDW) can substitute up to 100% of natural aggregates, while rubber fibers can increase impact resistance by 30–50% and reduce density by 10–20%. However, key limitations relate to waste variability, heavy metal leaching risks (requiring immobilization efficiencies > 90%), and the relatively low technological maturity of many solutions (TRL < 7), leading to the TRL–CO₂ paradox and highlighting the need for standardization and performance-based regulatory frameworks. The synthesized results indicate that the appropriate integration of industrial waste enables a significant reduction in clinker content, lowers associated CO₂ emissions, and decreases primary energy consumption while maintaining physical–mechanical properties and durability characteristics comparable to or in some cases superior to those of traditional materials, if mix design is based on clear performance criteria, stratified according to the type of waste, dosage used, curing regime, binder chemistry, and the target application. Full article

This study investigates the valorization of dredged sludge as a sustainable subgrade fill material through stabilization with a nano-calcium carbonate–cement composite. Unconfined compressive strength (UCS) tests were systematically conducted to determine the optimal dosage of nano-CaCO₃ as a supplementary additive at a fixed cement content of 8% by dry soil mass. Scanning electron microscopy (SEM), X-ray diffraction (XRD), and quantitative pore structure analysis were employed to elucidate the underlying solidification mechanisms. The results demonstrate that the addition of 2% nano-CaCO₃ yields the highest 28-day UCS of 721 kPa, representing a statistically significant 21% improvement over the cement-only reference (596 kPa) and a more than threefold increase relative to untreated sludge (213 kPa). Conversely, increasing the nano-CaCO₃ dosage to 2.5% leads to a significant strength reduction, attributed to nanoparticle agglomeration and hindered cement hydration. Microstructural characterization reveals that the optimal nano-CaCO₃ dosage accelerates early-age hydration through a nucleation effect, promotes the consumption of portlandite, and enhances the formation of calcium silicate hydrate (C–S–H) gel. Semi-quantitative XRD analysis further confirms the conversion of less stable monosulfate (AFm-SO₄) into stable monocarboaluminate (AFm-CO₃) phases. These synergistic mechanisms—nucleation, physical pore filling, and chemical reaction—result in a densified matrix with a refined pore structure, reduced total porosity, and a more homogeneous pore-size distribution. The findings provide a robust theoretical basis for the resource-oriented utilization of dredged sludge and the design of low-carbon composite stabilizers for soft soil treatment. Full article

Deep learning has transformed protein design from a field long dominated by explicit energy-function optimization into one dominated by probabilistic generative modeling. In this review, we summarize the protein representation algorithmic basis for this transition, from sequence-centered encodings to geometric graph representations and, more recently, SE(3)-equivariant structural manifolds that directly respect three-dimensional symmetry. We classify current approaches into three methodological paradigms according to how sequence and structure are related during design: sequence–structure decoupled design, hybrid approaches, and sequence–structure co-design. For decoupled workflows, we discuss hallucination, backbone generation, and backbone-conditioned sequence design. For hybrid approaches, we examine integrated two-stage architectures and predictor-driven iterative co-refinement. For co-design, we review explicit joint generative formulations in which sequence and structure are treated as a coupled design state throughout generation. Additionally, we summarize evaluation principles for assessing the design results, such as physical validity, folding consistency, and design coverage, and then introduce some important applications in several fields. Taken together, these developments indicate that generative protein design is making progress from structure generation toward the programmable engineering of complex biological function. Full article

A rapid accumulation of plastic waste has created an urgent need for efficient and sustainable recycling technologies. Among various approaches, pyrolysis stands out as promising method of thermochemical recycling of plastic waste; however, the process needs optimization and further research to make it more energy-efficient and sustainable. The conventional approaches for optimization focus on the enhancement of yield, only overlooking efficiency and system-level sustainability. In this study, a machine learning-enabled surrogate-assisted multi-objective artificial intelligence (AI) optimization framework is developed for plastic pyrolysis to maximize product recovery and minimize energy consumption. The model integrates energy return on investment (EROI) and higher heating value (HHV) into process design. A curated dataset of 312 experimental cases covering polyolefins, PET, nylon, and mixed plastics was used to train multiple machine learning algorithms, such as polynomial regression, Gaussian process regression, and Random Forest models. The Random Forest algorithm demonstrated superior predictive robustness across oil yield, HHV, char formation, and EROI. Pareto front analysis using NSGA-II revealed that moderate reaction severities (400–450 °C, 40–70 min) maximize net energy performance while minimizing solid residues. The conditional variational autoencoder as a GenAI model was incorporated to work as a generative proposal engine, which enhances the exploration of chemically feasible operating regions under uncertainty-aware active learning. The integration of techno-economic and life-cycle assessment demonstrates that energy-positive configurations outperform high-yield scenarios, achieving IRR > 15%, energy intensity < 10 MJ kg⁻¹, and CO₂ reductions up to 47% relative to incineration. The proposed framework establishes a data-driven methodology for aligning polymer pyrolysis optimization with circular economy and energy sustainability objectives. Full article