Search Results (5,339)

Continuous intraocular pressure (IOP) monitoring is crucial for glaucoma management. Currently, traditional static IOP measurements often fail to detect circadian fluctuations, leading to a clinical dilemma where “normal IOP” is observed despite persistent visual field deterioration. This study presents a wireless, passive localized surface plasmon resonance (LSPR) sensing platform integrated into flexible silicone hydrogel contact lenses. Gold nanoparticles (AuNPs), synthesized via the sodium citrate reduction method, were incorporated into the lens periphery using a “swelling-induced nano-doping” technique to transduce IOP-induced corneal strain into detectable spectral shifts. Ex vivo porcine eye investigations established a physical mapping model, confirming significant LSPR peak wavelength response trends in correlation with IOP variations (10–50 mmHg) and corneal curvature changes. Subsequent 21-day in vivo rabbit studies demonstrated excellent ocular surface biocompatibility; quantitative histopathological analysis (HE, PAS, and Ki67 staining) revealed no significant adverse alterations in corneal endothelial cell density or conjunctival goblet cell function compared to control groups (p > 0.05). Furthermore, the platform maintained high structural integrity and anterior segment tolerance under transient high-IOP conditions. While currently a proof-of-concept, these results indicate that the LSPR-active hybrid system effectively captures dynamic IOP fluctuation patterns as an optical response to acute interventions, providing a foundational engineering path for next-generation, battery-free wearable diagnostics in personalized glaucoma care without the need for built-in electronics. Full article

Within the framework of European Union-funded Clean Aviation and TheMa4HERA (Thermal Management for the Hybrid Electric Regional Aircraft) projects, a preliminary performance prediction and design tool for centrifugal compressors has been developed, targeting the turbomachinery components used in environmental control systems (ECS) in short/medium-range types of aircraft. This tool is an integral part of the objective to establish a complete optimization methodology for the performance assessment and sizing of air generation systems for next-generation aircraft. The methodology is based on mean-line analysis for the impeller, vaneless and vaned (including variable-vaned) diffusers, and volute, with a two-zone approach for the flow analysis in the vaned diffuser passage. The results of the model are validated against experimental data related to two different open-source compressor designs with both diffuser types. It is concluded from these cases that, for the purpose of the design tool, the model provides accurate results for the impeller and both diffuser types. Extreme conditions such as stall and choke remain difficult to accurately predict due to the complex three-dimensional nature of these phenomena. Future developments of the tool will include modeling capabilities for radial turbines and heat exchangers. Full article

Surface-enhanced Raman spectroscopy (SERS) has evolved from a fundamental optical phenomenon to a powerful, molecule-specific analytical technique capable of detecting ultra-trace-level species across biomedicine, catalysis, environmental monitoring, and national security applications. In this review, we summarize recent advances in SERS probe design and fabrication along three major directions: (i) engineering plasmonic hotspots with enhanced field confinement to achieve stronger and more uniform signals; (ii) analyte-directed strategies that precisely position and retain target molecules via tailored surface chemistries, nanoscale confinement, and on-surface reactions for single hotspot SERS; and (iii) hybrid architectures integrating plasmonic metals with functional materials, including high entropy materials, semiconductors, and graphene and other 2D materials, to synergistically couple electromagnetic and chemical enhancement mechanisms. Despite significant progress, key challenges remain for practical applications outside laboratories, including substrate reproducibility and stability, diverse analyte compatibility, unknown molecule identification and standardized quantitative performance in complex environments. We highlight emerging solutions, such as large-area nanomanufacturing for controlled nanoscale gaps, high-resolution Raman mapping for spatial–temporal characterization, density-functional-theory-guided molecular interpretation, and machine-learning-enabled spectral analysis. Advances in foundational AI models and data-driven discovery are positioning SERS to become an increasingly versatile platform, from decoding unknown molecular structures to analyzing complicated multi-component systems for environmental, biomedical, and national security applications with high sensitivity and selectivity. Full article

This study presents a mechanics based analytical framework for predicting the flexural–shear capacity of reinforced concrete (RC) beams strengthened with Engineered Cementitious Composites (ECCs) and a hybrid ECC–GFRP near surface mounted (NSM) system. Building upon previously reported experimental observations, the present work aims to establish rational prediction models capable of capturing the interaction between flexural and shear mechanisms in strengthened beams. The analytical approach integrates sectional analysis for flexural capacity with a modified truss analogy for shear resistance, explicitly incorporating the strain hardening tensile contribution of ECC and the tensile and confinement effects of GFRP reinforcement. An interaction based failure criterion is subsequently employed to identify the governing failure mode under combined flexural shear actions. The proposed model is validated against experimental results obtained from twenty seven beam specimens with varying flexural and shear reinforcement ratios and strengthening configurations. The predicted ultimate loads show good agreement with experimental values, with an average deviation within ±10%. The analytical framework accurately captures the transition between flexural dominated, combined flexural–shear, and diagonal tension failures observed experimentally. Results demonstrate that ECC significantly enhances ductility and shear crack control, while the hybrid ECC–GFRP system provides substantial strength enhancement with a controlled shift in failure mode. Overall, the developed analytical models offer a reliable and computationally efficient tool for predicting the flexural–shear capacity and failure behavior of ECC and hybrid ECC–GFRP-strengthened RC beams, supporting performance based design and practical strengthening applications. Full article

Tropical lignocellulosic residues are increasingly relevant feedstocks for lignin-containing nanocellulose composites, but their performance cannot be predicted from botanical origin or bulk lignin percentage alone. This review defines the interface as the geometrical boundary between phases and the interphase as the finite, compositionally graded region in which lignin distribution, nanocellulose morphology, adsorbed water, and the surrounding matrix jointly govern stress transfer and mass transport. Using an evidence-weighted framework, the literature is organized into the following categories: residual-lignin nanofibrils, redeposited-lignin systems, lignin nanoparticle assemblies, compatibilized thermoplastic hybrids, and all-lignocellulosic sheets. Representative quantitative observations show that controlled residual lignin can the increase water contact angle from approximately 35 degrees to 78 degrees and reduce oxygen permeability by up to 200-fold in nanopapers, while selected PLA/LCNF systems show tensile-strength and modulus increases of 37% and 61%, respectively; however, high or poorly distributed lignin can suppress fibrillation, lower viscosity, weaken gel networks, and reduce reproducibility. The most defensible near-term product windows are packaging layers, grease/oil barrier papers, coatings, paper-like multilayers, and selected porous media. Thermoplastic matrices remain process-sensitive, and biomedical, additive-manufacturing, nano-reactor, and energy-material claims require stronger validation of the extractables, rheology, humidity history, TEA/LCA metrics, and end-of-life behavior. This review, therefore, provides a critical, application-backward roadmap for tropical biorefineries in which interfacial function, wet handling, drying energy, and process integration are assessed together rather than treated as independent variables. The abbreviations used in the abstract are defined as follows: CNFs, cellulose nanofibrils; CNC, cellulose nanocrystals; LCNF, lignin-containing cellulose nanofibrils; LCNCs, lignin-containing cellulose nanocrystals; PLA, poly(lactic acid); PHB, polyhydroxybutyrate; PHAs, polyhydroxyalkanoates; PVA, poly(vinyl alcohol); DESs, deep eutectic solvents; TEA, techno-economic analysis; LCA, life-cycle assessment; ML, machine learning. Full article

The rapid advancement of photovoltaic (PV) technologies has transformed solar energy systems into intelligent, high-efficiency platforms. This review systematically examines next-generation PV materials, hybrid system architectures, and intelligent control strategies. Key technologies include perovskite-based tandem cells, N-type TOPCon, bifacial, heterojunction (HJT), and photovoltaic-thermal (PVT) systems. These innovations overcome the intrinsic limitations of conventional P-type silicon panels by reducing recombination losses, mitigating light- and temperature-induced degradation, and enhancing energy yield under real-world operating conditions. At the system level, AI-enabled inverters, adaptive maximum power point tracking (MPPT), predictive maintenance, and real-time grid interaction enable dynamic optimization under variable irradiance, thermal stress, and load fluctuations. A critical comparison across diverse deployment environments highlights current challenges, including manufacturing complexity, material stability, and AI data-quality limitations. Despite higher upfront costs and system complexity, these advanced PV systems offer superior long-term performance, improved reliability, and reduced levelized cost of electricity through lower degradation rates and enhanced operational resilience. Collectively, intelligent, material-optimized PV technologies represent a scalable, sustainable, and grid-compatible solution for solar energy deployment across diverse climates, supporting the global transition toward low-carbon energy infrastructures. Full article

Machine learning models increasingly rely on continuously generated sensor data for automated decision-making in Internet of Things (IoT) environments. The distributed and often insecure nature of IoT infrastructures introduces risks related to data manipulation, lack of traceability, and unverifiable model evolution. Existing solutions typically address isolated aspects such as data security or access control but do not provide end-to-end provenance across the machine learning lifecycle. This paper proposes a tamper-evident data and model provenance framework for IoT-based machine learning that integrates blockchain with off-chain storage. The framework records cryptographic hashes and metadata of data, preprocessing outputs, and trained models on-chain while maintaining large artifacts off-chain to ensure scalability. Smart contracts establish verifiable linkage among lifecycle artifacts and automate provenance registration. The framework is evaluated in a simulated IoT–ML pipeline under integrity attack scenarios including data manipulation, model tampering, and metadata modification. Experimental results demonstrate reliable detection of unauthorized modifications with low verification latency and constant on-chain storage per record under controlled conditions. These findings indicate the feasibility of hybrid blockchain architectures for tamper-evident provenance in IoT-based machine learning systems, while highlighting the need for further validation in real-world deployments. Full article

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision. Full article

Small-disturbance damping characteristics have become a critical concern in renewable-dominated power systems under low short circuit ratio (SCR) conditions. In heterogeneous systems composed of grid-following photovoltaic (GFL-PV) and grid-forming energy storage system (GFM-ESS) units, strong dynamic coupling may weaken the damping of critical oscillation modes, thereby complicating stability analysis and coordinated parameter tuning. This paper proposes a damping enhancement strategy for a low-SCR GFL-PV/GFM-ESS system. The main innovation is an integrated damping-oriented framework that links detailed small-disturbance modeling, dominant-mode identification, participation-factor analysis, parameter-sensitivity evaluation, and coordinated optimization. First, a dynamic model including GFL-PV, GFM-ESS, and their coupling is established, and the corresponding linearized model is verified. Then, eigenvalue, modal, participation-factor, and sensitivity analyses are performed to identify weakly damped modes, key state variables, and sensitive parameters. Furthermore, a Joint Opposite Selection-enhanced particle swarm optimization (JOS-PSO) strategy is proposed to tune multiple coupled parameters. Simulation results under different operating conditions show that the proposed method improves damping characteristics, small-disturbance stability, and dynamic performance. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

Conventional high-voltage direct current (HVDC) systems based on line-commutated converters (LCC) are prone to commutation failures and consume excessive reactive power during AC grid faults. The controllable line-commutated converter (CLCC) was developed to solve these problems. To further investigate CLCC’s practical application in the AC system, this paper proposes a fixed AC voltage control strategy for the inverter-side CLCC. A hybrid LCC-CLCC HVDC transmission system model is built in PSCAD. Simulations are performed under three-phase short-circuit faults and wind power fluctuation scenarios. The results show that, unlike traditional LCC, the CLCC under the proposed control can actively increase its firing angle over 160 degrees during disturbances. This action injects dynamic reactive power into the grid and significantly reduces the AC bus voltage drop. Especially in weak grid conditions, CLCC can greatly reduce reactive power consumption through wide-range active adjustment of the firing angle, thereby improving voltage stability. Full article

Conventional multi-criteria decision-making approaches for battery energy storage system (BESS) dispatch evaluation treat regulatory and policy conditions as compensable criteria within a single aggregate score. This becomes problematic when institutional admissibility functions as a prerequisite for deployment rather than a tradeable attribute. This study aims to develop and test a sequential gate framework. The methodological contribution lies in the evaluation architecture itself: the framework distinguishes sequential admissibility gating from conventional compensatory Multi-Criteria Decision-Making (MCDM). Deployment feasibility is conceptualized as a layered construct in which regulatory admissibility defines the feasible solution space and technical performance differentiates among admissible options. The framework integrates systematic literature screening, quantitative policy and regulatory assessment, and technical ranking using a hybrid Best-Worst Method, Entropy weighting, and TOPSIS approach. A Danish case study covering twelve dispatch strategies compares the proposed sequential design with two flat alternatives. The results show that the evaluation architecture materially affects outcomes: sequential gating excludes an institutionally incomplete strategy and reorders the upper tier by removing compensatory policy effects. Coordinated multi-BESS control at Electric Vehicle charging parks achieves the highest combined feasibility (closeness coefficient 0.891, ranked 1st), while mobile BESS is excluded by the admissibility gate. The sequential design reorders the upper tier relative to flat MCDM, with S4 and S6 rising and S2 and S10 falling once policy compensation is neutralized after the gate. The top-ranked strategy remains robust across sensitivity analysis, Monte Carlo simulation, score perturbation, and VIKOR cross-validation. The framework is presented as an analytical pre-simulation screening tool rather than a validated implementation instrument; external validation against real deployment outcomes is identified as a priority for future research. The framework provides a structured, decision-consistent approach for evaluating deployment feasibility in regulated energy systems. Full article

To address the insufficient frequency-support capability, the difficulty of multi-terminal power coordination, and the constraints on DC-voltage fluctuations in flexible DC transmission systems under weak-grid interconnection, this paper conducts a simulation-based control strategy study. First, based on the coupling relationship between AC frequency and DC voltage, an inertia-enhanced grid-forming/VSG control method is proposed, enabling converter stations to use DC-link capacitor energy to provide transient frequency support during the initial stage of a disturbance. Second, for multi-terminal flexible DC systems, an adaptive U-P-f dual-droop distributed control strategy is designed to coordinate unbalanced power sharing among multiple converter stations and to limit the DC-voltage deviation generated during frequency support. In this paper, a hybrid half-bridge/full-bridge MMC is adopted as a fixed-converter simulation platform, rather than being treated as an object of systematic topology optimization. Finally, a four-terminal MMC-HVDC simulation model is established in MATLAB/Simulink, and the proposed control strategy is evaluated under weak-grid step-load disturbances, different short-circuit-ratio conditions, and continuous pseudo-random load disturbance scenarios. Simulation results show that, under the tested operating conditions, the proposed method can reduce the maximum frequency deviation, suppress DC-voltage fluctuations, and improve the power-sharing process among multi-terminal converter stations compared with conventional VSG control and fixed-droop control. Full article

Plastic design enables efficient structural systems by exploiting controlled inelastic deformation and force redistribution. While mature in steel structures due to stable ductility and well-defined yielding, its extension to reinforced concrete (RC) remains challenging because cracking, stiffness degradation, confinement dependency, and progressive damage govern deformation capacity and collapse mechanisms. This paper presents a state-of-the-art review of optimal plastic design methodologies for RC structures by tracing the evolution from classical plasticity theory to modern damage-informed, reliability-oriented, and sustainability-driven formulations. A systematic and structured literature review of more than 90 peer-reviewed journal articles (1990–2025) was conducted using Scopus, Web of Science, and ScienceDirect. The selected studies are classified by structural system type, plastic analysis approach, constitutive modeling strategy, and strengthening technique, including CFRP and hybrid fiber systems, optimization framework, and uncertainty treatment. The review highlights how nonlinear elasto-plastic and damage–plasticity models improve the prediction of plastic hinge development, redistribution, and failure-mode transitions, and how metaheuristic optimization, topology optimization, surrogate modeling, and machine learning are increasingly used to manage discrete design variables and computational cost. Reliability-based methods (e.g., FORM/SORM and simulation) are shown to be essential for quantifying deformation-capacity uncertainty and ensuring consistent collapse-prevention performance. A comparative assessment of nine plastic design methodologies is also provided, identifying their core assumptions, limitations, and domains of applicability within a structured evaluative framework. Remaining challenges include robust deformation-capacity prediction, reproducible calibration of damage models, and integration of life-cycle sustainability criteria within reliability-constrained plastic optimization. Future research directions are proposed toward multi-objective reliability-based design, durability-informed plastic modeling, and hybrid physics-informed AI-assisted workflows. Full article

Due to the controlled characteristics of fault current in doubly fed wind farms and the distributed capacitance effects of transmission lines, traditional distance protection is prone to failure or maloperation during high resistance faults. To improve protection reliability, this paper proposes a novel time–domain distance protection scheme based on the hybrid-π model. First, the improved time–domain fault differential equations are formulated based on the hybrid-π model, incorporating ground capacitance and integrating electrical quantities at both ends of the line. Next, the composite weight matrix integrating transient mutation weights and fitting error weights is introduced and embedded within a nonlinear least-squares framework. This enables the algorithm to adaptively distinguish and suppress unreliable data, simultaneously achieving transient disturbance resistance and rapid steady-state convergence. Finally, a 220 kV double-fed wind power grid-connected system with a 100 km transmission line is built in MATLAB/Simulink for simulation. Different types of faults under various locations and transition resistances are simulated to verify the effectiveness of the proposed scheme. Full article