Search Results (1,570)

Flexible acceleration sensors demonstrate revolutionary potential in healthcare, structural vibration monitoring, and consumer electronics owing to their unique conformal adhesion capability and mechanical adaptability. However, current academic research presents two distinct paradigms for realizing flexibility: one is the hybridly flexible sensor, which incorporates traditional micro-electro-mechanical System (MEMS) acceleration sensor chips with flexible packaging/substrates; the other is the intrinsically flexible sensor, whose sensing unit and substrate are entirely composed of flexible materials enabled by microstructural design. This review first analyzes the fundamental differences and design challenges between these two flexible architectures. It then systematically elucidates five core sensing mechanisms—capacitive, piezoresistive, triboelectric, piezoelectric, and electromagnetic—comparing their working principles, material systems, structural designs, and performance metrics. Among these, piezoelectric and triboelectric types exhibit distinctive advantages in self-powering capability, whereas resistive and capacitive approaches offer greater ease of integration. Furthermore, the applications of intrinsically flexible acceleration sensors in structural health monitoring, wearable devices, automotive safety, and other fields are discussed, with particular emphasis on their unique strengths in real-time vibration monitoring. Finally, the review summarizes existing challenges, such as the trade-off between sensitivity and flexibility, and provides theoretical insights to guide future innovations in intrinsically flexible acceleration sensor technology. Full article

This paper proposes an advanced wind energy conversion and management framework for improving grid integration and mitigating frequency and power fluctuations caused by wind intermittency. The studied system combines a permanent magnet synchronous generator (PMSG), a unidirectional Vienna rectifier on the machine side, a Li-ion battery energy storage system, and a bidirectional Vienna rectifier on the grid side. The main scientific challenge addressed in this work is to ensure efficient wind power extraction, secure battery charging/discharging operation, and stable power exchange with the grid under variable operating conditions. To this end, a comprehensive nonlinear state-space model of the overall system is first established. Then, nonlinear controllers based on integral sliding mode principles are developed to guarantee rotor-speed tracking, DC-bus voltage regulation, battery charging current limitation, and active/reactive power control. In addition, an adaptive observer is designed to estimate the battery open-circuit voltage and support the supervision of the state of charge. An energy management strategy is further proposed to coordinate the operating modes according to grid conditions and battery constraints. Simulation results demonstrate that the proposed approach effectively smooths wind power fluctuations, improves grid support capability, and enhances the overall dynamic performance of the wind energy conversion system. Full article

Wicked problems spanning systemic educational inequities, economic disparities, and environmental sustainability resist most traditional change efforts. This theory-building article advances a systems explanation that introduces complex conceptual systems theory which models collective conceptualizations as complex adaptive systems composed of densely interconnected ideas. These systems stabilize around attractor states that generate emergent potentials for what becomes sayable, seeable, doable, and valuable, thereby constraining the very practices needed for transformation. The article defines core constructs and articulates operational principles for diagnosis and intervention in complex social and socio-technical systems. It then specifies a first-generation analytical workflow, complex conceptual systems analysis (CCSA), that integrates qualitative coding with network-based modeling to map conceptual architectures, identify attractor states, and locate leverage points where sustained pressure can catalyze system reorganization. Empirical grounding is provided through a synthesis of a decade-long research program reported in prior publications across multiple domains, rather than through a single new empirical dataset. Accordingly, the manuscript is organized as a theory-development and methodology contribution, moving from conceptual architecture to operational principles, analytic workflow, and cross-domain exemplars. The theory offers systems science a pragmatic, justice-attentive approach for anticipatory, intervention-oriented change in entrenched wicked problems. Full article

The Variational Quantum Eigensolver (VQE), as one of the most promising quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era, exhibits unique advantages in quantum chemistry simulations. It provides a novel approach to solving molecular electronic structure problems that are difficult to handle with classical computing. However, the performance of the VQE algorithm in quantum chemistry simulation is jointly affected by multiple factors, and its application in practical scenarios still faces numerous challenges. This paper first outlines the basic principles of the VQE algorithm and its core application scenarios in quantum chemistry simulation. Subsequently, it systematically analyzes the mechanism of the influencing factors, such as molecular system characteristics and algorithm parameter design, focusing on exploring how each factor specifically influences the results. Finally, the current research status and limitations in the optimization of influencing factors are summarized, and future research directions are proposed. This work aims to provide theoretical reference and technical support for improving the performance of quantum chemistry simulation based on the VQE algorithm and promoting its practical application. Full article

This critical review examines the evolution of mathematical modeling approaches for aerobic digestion processes in food industry waste management, highlighting their role in operational optimization and dynamic prediction. In recent years, increasing pressure for sustainable waste management, circular bioeconomy strategies, and process intensification in the food industry has accelerated the development of mathematical tools for describing complex biological treatment systems, making a critical synthesis of available modeling approaches particularly timely. Starting from mass conservation principles, simple kinetic models such as first-order and Monod models are analyzed. These models assume homogeneity and perfect mixing but fail to capture the heterogeneity of effluents rich in variable carbohydrates, proteins, and lipids. Structural limitations, including numerical rigidity, parametric non-identifiability, and idealized assumptions that underestimate spatial gradients and stochastic fluctuations, are examined. In continuous systems, coupled substrate–biomass–oxygen dynamics, washout phenomena, and extensions toward partial differential equations for representing real heterogeneity are explored. Structured models such as Activated Sludge Models (ASMs) incorporate multicomponent fractions but face parameterization challenges exacerbated by limited industrial data availability, as less than 25% of treatment plants currently employ formal modeling frameworks. Emerging paradigms include hybrid mechanistic–machine learning approaches for prediction under perturbations, multiscale modeling, and spatially explicit modeling. Unlike previous reviews that focus primarily on technological aspects of waste treatment, this study provides a critical comparison of modeling frameworks and their applicability to different food waste matrices. A classification table distributes approaches by food matrix, revealing the dominance of simple kinetics in composting and ASMs in activated sludge systems. Finally, a progressive model selection framework based on operational objectives is proposed, balancing model complexity with predictive robustness and experimental validation to support sustainable industrial adoption. Full article

Reconfigurable antennas play a central role in next-generation wireless communication systems by enabling dynamic adaptation of operating frequency, radiation pattern, and polarization. Tunable metasurfaces have emerged as a powerful and compact approach to antenna reconfiguration, allowing electromagnetic wave manipulation through engineered, planar structures whose properties can be dynamically controlled. By embedding active devices or tunable materials within metasurface unit cells, antenna characteristics can be modified without altering the antenna geometry. This review provides a comprehensive overview of reconfigurable antennas enabled by tunable metasurfaces. We adopt a functionality-based classification that focuses on operating frequency, radiation pattern, polarization, and multifunction reconfiguration. An overview of major tunability technologies, including PIN diodes, varactors, MEMS, graphene and two-dimensional materials, and liquid crystal (LC) or phase-change materials, is first presented. Subsequently, metasurface-based reconfiguration strategies are discussed and compared for each antenna functionality, highlighting design principles, practical trade-offs, and limitations. The review concludes with an assessment of challenges and future research directions relevant to next-generation wireless communications and beyond. Full article

The impact of skin pigmentation on the accuracy of optical biomedical devices has gained increased attention since the COVID-19 pandemic, particularly following evidence of oximetry measurement bias in dark-skinned individuals. Meanwhile, many computational models utilising the Monte Carlo (MC) technique have been developed as a cost-effective and scalable method for investigating these effects. Hence, this review explores the application of the MC technique in modelling skin pigmentation, focusing specifically on how melanin in the epidermis is represented across different studies. First, the biological mechanisms of pigmentation and current stratification methods are outlined to contextualise the variability in skin tone, followed by the principles of MC modelling, including photon scattering, absorption, reflection, and detection. Following a screening and exclusion process, 50 studies were evaluated in terms of how melanin concentration and distribution are incorporated into MC models and their applications, revealing a range of approaches that include analytical equations, experimental optical property measurements, or hybrid methods. The benefits and limitations of each approach is discussed, in addition to emerging advancements such as heterogeneous melanin distribution and the relation between optical properties and skin colour classification scales. Overall, the review outlines the current methodological approaches utilised for skin pigmentation modelling and offers a reference framework for researchers seeking to improve the representation of skin pigmentation in MC-based optical simulations. Full article

The rising popularity of sports practiced without adequate preparation has increased the incidence of anterior cruciate ligament (ACL) injuries, particularly among young individuals. Because the ACL has a very limited intrinsic healing capacity, surgical reconstruction—most often using autologous grafts—remains the standard of care. However, current techniques frequently lead to donor-site morbidity and do not consistently restore long-term joint stability, contributing to early post-traumatic osteoarthritis in active patients. Over the past decades, tissue engineering (TE) has opened promising avenues for developing biological substitutes capable of overcoming these limitations. Despite substantial progress, no strategy has yet demonstrated reliable and clinically validated functional regeneration of the human ACL. Meanwhile, artificial intelligence is emerging as a complementary tool for diagnosis, surgical planning, biomechanical assessment, and personalized reconstruction strategies. This review aims to provide a comprehensive overview of current TE-based approaches for ACL repair and reconstruction, analyzes their biological and biomechanical limitations, and discusses emerging concepts that may enhance future clinical outcomes. We first summarize the fundamental principles of tissue engineering, then examine the major strategies proposed for ACL regeneration—highlighting their respective strengths and shortcomings—and finally outline perspectives for a novel approach currently under development. Full article

Photocatalysts have emerged as a promising approach for the treatment of contaminated water, particularly for the removal of dyes and pharmaceutical residues that pose risks to human health. In addition, they can be employed for the generation of chemical fuels such as H₂ and oxidizers such as H₂O₂, which have been proposed as sustainable energy carriers to reduce reliance on fossil fuels. The first part of this brief review provides a detailed overview of the fundamental concepts of photocatalysis, including reaction pathways and reported mechanisms. The second part explores the main design strategies for enhancing photocatalytic performance, including morphology control and structural modification. Then, the third section highlights the benefits of theoretical modeling, including first-principles calculations and molecular simulations. The document culminates with a section on challenges and future perspectives, highlighting major issues in photocatalyst development such as large-scale synthesis, material stability, and reusability. This brief review is intended to provide young researchers with a concise understanding of the most effective strategies for enhancing photocatalytic performance, as well as the mechanisms influencing morphology and structural parameters. This work presents an integrated framework linking synthesis strategies, particle growth mechanisms, multidimensional nanostructures, in situ and operando characterization, and computational modeling to guide the rational design of next-generation photocatalysts. Full article

The study presents a comprehensive first-principles investigation of the structural, electronic, and magnetic properties of rocksalt scandium nitride (ScN) and its Cr- and Mn-doped derivatives using spin-polarized density-functional theory within the GGA + U (U_Cr = 3.5 eV, U_Mn = 2.7 eV) and HSE06 frameworks. Pristine ScN crystallizes in the cubic Fm$\bar{3}$m structure and exhibits narrow-gap semiconducting behavior, with an indirect band gap of 0.82 eV obtained from hybrid-functional calculations, in excellent agreement with reported theoretical values. Substitutional doping with Cr and Mn introduces localized 3d states near the Fermi level, driving a transition toward spin-polarized metallic or half-metallic behavior accompanied by robust ferromagnetism. Density-of-states and band-structure analyses reveal that magnetism and charge transport in the doped systems are dominated by exchange-split transition-metal 3d states hybridized with N-2p orbitals. Total energy calculations confirm ferromagnetic ground states for both Cr- and Mn-doped ScN, with Mn substitution yielding stronger exchange stabilization and higher magnetic moments. Magnetocrystalline anisotropy energies, evaluated using the force-theorem approach, are found to be negligibly small, indicating weak anisotropy consistent with the moderate spin–orbit coupling strength in ScN-based nitrides. Nevertheless, symmetry breaking around dopant sites gives rise to a finite Dzyaloshinskii–Moriya interaction, leading to weak spin canting and non-collinear magnetic tendencies. The interplay between magnetic exchange coupling, spin–orbit interaction, and local inversion symmetry breaking positions of Cr- and Mn-doped ScN as promising dilute magnetic semiconductors with tunable spin polarization and chiral magnetic interactions, offering a viable platform for nitride-based spintronic and magneto-electronic applications. Full article

Antibiotic residues in aquatic products pose a serious food safety concern, whereas conventional laboratory methods often fail to meet the demand for on-site rapid screening. This study systematically reviews the research progress from 2021 to 2025 on both the risks of antibiotic residues in aquatic products and the development of rapid on-site detection technologies. First, based on a literature survey covering major aquatic products (e.g., fish, shrimp, and shellfish), the widespread occurrence of multiple antibiotics at high concentrations was documented, with quinolones and sulfonamides identified as the most frequently detected classes. To address the need for on-site testing, this review focuses on six rapid detection techniques: fluorescent sensor (FRS), lateral flow immunoassay (LFIA), surface-enhanced Raman scattering (SERS), enzyme-linked immunosorbent assay (ELISA), electrochemical sensor (ECRS), and colorimetric sensor (CRS). The core principles, technical advantages, recent application cases (e.g., integration with smartphones and novel nanomaterials), and development trends for each method are analyzed. Finally, it discusses the current challenges faced by existing on-site detection approaches and their potential solutions. Technology selection strategies tailored to different application scenarios (e.g., aquaculture farms, distribution channels, and consumer-level use) are also proposed. Full article

Micro air vehicles (MAVs) operating at low Reynolds numbers face aerodynamic and structural challenges that differ significantly from those encountered by conventional aircrafts. Nature provides effective solutions to these constraints, as insects, birds, and bats demonstrate highly efficient flight through integrated interactions between morphology, kinematics, and unsteady aerodynamic mechanisms. This review examines how biological flight principles can inform the design of next-generation MAVs. The study first analyzes biological flight strategies across insects, birds, and bats, with emphasis on scaling laws and physiological adaptations relevant to small-scale flight. It then reviews key unsteady aerodynamic phenomena governing low-Reynolds-number flight, including leading-edge vortex stability, wing–wake interactions, tandem-wing effects, and ground influence, as well as current modeling approaches ranging from quasi-steady methods to high-fidelity Navier–Stokes simulations. Building on these principles, the paper discusses biomimetic design strategies for MAV wings, structural–aerodynamic coupling, and actuation technologies used to replicate flapping flight. Existing MAV demonstrators inspired by biological flyers are analyzed, including concepts relevant to planetary exploration environments. Finally, the review identifies current technological limitations and research gaps in materials, actuation, aerodynamic modeling, and system integration. By synthesizing insights from biology and engineering, this work highlights key directions for the development of efficient, adaptable biomimetic MAV platforms capable of operating in complex environments. Full article

Elevated temperatures are frequently encountered in numerous occupational settings such as iron and steel foundries, non-ferrous metal foundries, brick and ceramic manufacturing plants, glass production facilities, rubber factories, electrical power plants, bakeries, laundries, chemical processing sites, mining operations, smelting plants, and steam tunnels. Employees working in these environments are at risk of developing various health issues and injuries, including ocular complications, due to prolonged exposure to heat and the physical demands of handling heavy materials. This study focuses on examining the pressure within the eye’s anterior chamber, referred to as Intraocular Pressure (IOP), and its association with the cornea’s biomechanical characteristics, with particular attention to corneal temperature. Our methodology is grounded in the principles of the first law of thermodynamics. The findings reveal a link between the temperature of the eye’s anterior chamber and the biomechanical behaviour of the cornea. Specifically, IOP serves as an indicator of the cornea’s elasticity and its optical properties as influenced by temperature variations. We investigated how the cornea’s elastic energy, or the work it performs, varies with temperature changes. The results show that an increase in temperature corresponds to a reduction in the work exerted by the cornea. The corneal temperature is affected by both the ambient environment and the temperature of the aqueous humour within the anterior chamber. This indicates a relationship between the mechanical work done by the cornea and the pressure exerted by the fluid in the eye’s front segment. Furthermore, our study identified a correlation between corneal thickness and IOP, which our modelling approach successfully quantifies. Utilizing the first law of thermodynamics, we calculated the work performed by the anterior chamber against the cornea’s internal surface. Temperature fluctuations influence the secretion, drainage, and flow characteristics of the aqueous humour, thereby impacting IOP and associated ocular conditions. These insights are valuable for devising strategies aimed at preventing eye injuries among workers exposed to high-temperature environments. Full article

Axions and axion-like particles are compelling candidates for ultralight bosonic dark matter, forming coherent oscillating fields that can be probed by experiments known as haloscopes. A broad range of haloscope concepts has been developed, including resonant cavity haloscopes, lumped-element circuit detectors, and spin-based experiments, each sensitive to different axion couplings and mass ranges. Rather than attempting an exhaustive survey of all existing approaches, this comparative review provides a unified framework for the major haloscope classes, establishing a common language for the descriptions of signal generation, noise properties, analytical methodologies, and scanning strategies. Key properties of ultralight bosonic dark matter relevant for detection are summarized first, including coherence time, spectral linewidth, and stochasticity under the standard halo model. The discussion then compares cavity, Earth-scale, lumped-element, and spin haloscopes, focusing on expected signal shapes, dominant noise sources, and statistical frameworks for axion searches. Particular emphasis is placed on consistent definitions of signal-to-noise ratio and on how detector bandwidth, axion coherence, and noise characteristics determine optimal scan strategies. By systematically comparing operating principles and performance metrics across these detector families, this framework clarifies shared concepts as well as the essential differences that govern sensitivity in different mass and coupling regimes. The resulting perspective synthesizes current search methodologies and offers guidance for optimizing future haloscope experiments. Full article

Polymers are widely used in applications ranging from flexible electronics and thermal interface materials to structural composites and textile fabrics. Their inherently low $\kappa$, strongly governed by molecular structure and morphology, makes polymers a challenging yet scientifically rich class of materials for thermal transport studies. Over the past two decades, modeling and simulation have played a central role in elucidating heat transport mechanisms in polymers and in guiding the rational design of polymer systems with enhanced or tunable thermal properties. This review provides a comprehensive overview of the theoretical frameworks and computational approaches used to model thermal transport in polymers. We discuss atomistic methods including density functional theory, molecular dynamics, and first-principles Boltzmann transport equation approaches, as well as emerging data-driven and machine learning-based techniques. Special attention is devoted to the effects of chain conformation, crystallinity, orientation, interchain coupling, interfaces, and nanocomposite architectures. Current challenges and future research directions are highlighted, with particular emphasis on multiscale modeling, method integration, and predictive materials design. Full article