Search Results (14,400)

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, anatomical sites, calibration methods, and signal-processing approaches limit direct comparison of reported performance. In addition, challenges such as subject-specific calibration, motion artifacts, and limited clinical validation remain. Overall, this review highlights current progress and outlines key challenges for future development and translation of TENG-based cardiovascular monitoring systems. Full article

The increasing complexity of Cyber-Physical Energy Systems, driven by the high penetration of power electronics, advanced control, and digitalization, demands scalable, flexible real-time simulation platforms beyond traditional laboratory-based solutions. This paper investigates the feasibility of deploying open-source real-time power system simulation frameworks on cloud-based infrastructures while meeting real-time computational constraints. An open-source architecture based on DPsim and the VILLAS framework is implemented and evaluated across five computing environments using open-source tools: bare-metal, non-cloud virtual machines, private cloud Kubernetes clusters, public cloud virtual machines, and public cloud Kubernetes clusters. Each environment is carefully configured and tuned using real-time operating systems, CPU isolation, and affinity mechanisms to improve deterministic behavior. Performance and scalability are assessed through a benchmark based on replicated IEEE 9-bus systems, progressively increasing system size, and measuring simulation-timestep execution time. The results show that cloud and cloud-like infrastructures can support soft and, under controlled conditions, firm real-time simulation tasks, although achievable system scale decreases as additional abstraction layers are introduced. The study identifies practical performance limits for each infrastructure and discusses their suitability for different real-time simulation and co-simulation applications. These findings demonstrate that cloud-based real-time simulation can complement traditional digital real-time simulators, enabling scalable and cost-effective CPES experimentation. Full article

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance. Full article

Flexible and wearable electronics require soft sensing materials that balance mechanical compliance, stable signal transduction, and durability for human–machine interfaces (HMIs). To address the limitations of single-filler systems, we propose a poly(vinyl alcohol) (PVA)/aramid nanofiber (ANF)/MXene organogel (PAM) as a multifunctional soft platform. This design integrates a PVA physically crosslinked network with ANF for mechanical reinforcement and MXene for electrical functionality. The optimized PAM composite exhibits outstanding mechanical properties, including a fracture stress of 2931 kPa, a fracture strain of 676%, and a fracture toughness of 9.04 MJ m⁻³. Importantly, PAM serves as a single material platform configurable into three sensing modalities. The resistive strain sensor achieves a gauge factor of 3.1 over 10–100% strain and enables the reliable recognition of human joint movements and gestures. The capacitive pressure sensor delivers a sensitivity of 0.298 kPa⁻¹, rapid response/recovery times of 30/10 ms, and is integrated with a wireless module to control a smart car. Furthermore, the PAM-based triboelectric nanogenerator (TENG) delivers excellent electrical outputs (V_oc = 123 V, I_sc = 0.52 μA, Q_sc = 58 nC) and functions as a self-powered smart handwriting pad, achieving a machine-learning-based recognition accuracy of 97.6%. This work demonstrates the immense potential of the PAM organogel for advanced, self-powered HMIs. Full article

Background: The reliable co-loading of paclitaxel (PTX) and indocyanine green (ICG) into a single lipid nanoparticle (LNP) enables synergistic antitumor delivery but remains challenging due to their distinct physicochemical properties. Methods: This study integrated COSMO-RS calculations, molecular dynamics simulations, and in vitro assays to systematically investigate the effects of lipid composition, drug modification, particle size, and solvent environment on dual-drug loading. Results: This work indicate that DMPS lipid membranes featuring highly polar headgroups and ordered bilayer structures stably bind both ICG and PTX, achieving drug-loading efficiencies (DLEs) of 7.2% and 5.6%, respectively. Carboxylation of PTX enhanced hydrogen bonding with DMPS, while alkyl chain modifications improved membrane insertion, though excessive chain length (e.g., C12) reduced stability due to increased flexibility. Increasing the LNP size from 50 nm to 250 nm raised the DLE of PTX from 4.7% to 8.1%, while sizes beyond 500 nm led to membrane destabilization. The use of 20 vol% ethanol increased total drug loading by 51% by disrupting the hydration shell of ICG and suppressing PTX aggregation; however, ethanol concentrations exceeding 40 vol% intensified drug–solvent competition and weakened membrane binding. Conclusions: This study provides a comprehensive elucidation of the multifactorial regulatory mechanisms underlying dual-drug loading in LNPs, offering a theoretical basis for the rational design of efficient co-delivery systems. Full article

Efficient mixing is a persistent bottleneck in agricultural and agrochemical processing, where rapid and uniform mixing must be achieved under laminar flow with low energy input and gentle shear. Inspired by peristaltic transport in biological systems, this study investigates a bio-inspired flexible-wall squeezing mixer and establishes a two-dimensional computational framework to quantify how periodic wall deformation governs scalar homogenization in a flexible conduit. An Arbitrary Lagrangian–Eulerian dynamic mesh approach is implemented to resolve moving boundaries and to prescribe actuation, enabling the systematic evaluation of the separate and coupled effects of peak wall-normal velocity amplitude A and actuation frequency f on mixing performance. Mixing effectiveness is quantified using a variance-based mixing index MI and a sustained-threshold mixing time ts, and response surface methodology is employed to map the A–f design space and interpret the roles of time-dependent shear, interfacial stretching and folding, and vortex intensification. Relative to a non-actuated baseline, a peak wall-normal velocity amplitude of 3 × 10⁻³ m s⁻¹ at 2 Hz reduces t_s by 21.3%. At fixed f = 3 Hz, increasing A from 1 × 10⁻³ to 4 × 10⁻³ m s⁻¹ shortens t_s by 10.2%, while at fixed A = 3 × 10⁻³ m s⁻¹, raising f from 1 to 5 Hz further decreases t_s by 6.6% with diminishing gains at the lowest frequencies. The response surface identifies an operating optimum at A = 4 × 10⁻³ m s⁻¹ and f = 5 Hz, achieving a peak MI of 0.9557 and a minimum t_s of 7.81 s. A periodically squeezed physical mixing loop was further examined using fluorescence imaging to assess outlet homogeneity trends. The stabilized outlet coefficient of variation (CV) decreased from about 0.65 without squeezing to 0.60 at 1 Hz and 10 mm s⁻¹, 0.58 at 2 Hz and 10 mm s⁻¹, and 0.54 at 2 Hz and 30 mm s⁻¹, indicating that stronger and faster actuation improves outlet uniformity. The numerical and experimental results are therefore interpreted jointly as mechanistic and trend-level evidence, while a rigorous quantitative prediction for the cylindrical compliant device will require future three-dimensional, compliance-resolved simulations and broader experimental benchmarking. Full article

Servo electric cylinders have been widely adopted in high-performance linear drive applications such as aerospace systems, robotic servo systems, medical equipment, advanced manufacturing, precision testing, and high-end equipment due to their advantages, including high cleanliness, compact structure, high transmission efficiency, and ease of achieving precise control. However, under complex operating conditions, system performance is influenced not only by control strategies but also closely related to factors such as friction, clearance, transmission flexibility, structural vibrations, and modeling accuracy. This paper reviews mainstream control strategies and dynamic simulation methods for servo electric cylinders, providing structured analysis and systematic evaluation of representative research. In terms of control strategies, key approaches, including classical PID control, robust nonlinear control, intelligent and learning-based control, and active disturbance rejection control, are discussed, with comparative analysis of their characteristics and limitations in tracking accuracy, robustness, adaptability, and engineering feasibility. Regarding dynamic modeling and simulation, methods such as multibody dynamics, finite element analysis, rigid-flexible coupling, and multi-domain collaborative simulation are reviewed, examining their applicability in nonlinear mechanism characterization, local structural response assessment, and high-fidelity system modeling. Current research indicates that servo cylinder control is evolving from single-method improvements toward integrated and composite approaches, while dynamic modeling has progressed from low-order simplified analyses to system-level, multi-level, and high-fidelity descriptions. Existing studies still face challenges, including insufficient unified evaluation criteria, inadequate cross-method comparisons, and insufficient integration between control design and high-fidelity models. Future research should focus on enhancing control-model co-design, experimental validation under complex conditions, and system-level optimization oriented toward intelligent and high-reliability systems. Full article

Partial agricultural production loads exhibit significant temporality. The concentrated access of temporary loads can easily trigger operational challenges in distribution networks, such as heavy overload, terminal voltage violations, and increased network losses. To address these issues, this paper proposes a coordinated dispatch strategy for multiple flexible resources to cope with temporary loads. First, combining the operational characteristics of motor-pumped well loads, a refined model for motor-pumped well loads is constructed to fully exploit their regulation potential as flexible loads. Second, considering the supporting role of mobile energy storage systems (MESS) for heavy overload distribution networks, a spatiotemporal dispatch model for MESS is established. Then, aiming to minimize the total system operating cost, an economic dispatch model coordinating multiple flexible resources, including MESS, distributed generators (DG), and flexible loads, is developed. The original non-convex problem is transformed into a mixed-integer second-order cone programming problem using Second-Order Cone Relaxation (SOCR) method for efficient solution. Finally, the effectiveness of the proposed strategy is verified on an improved IEEE 33-bus system. Full article

In the age of remote communication and digital archiving, automated analysis of voice data has become increasingly important in various application areas. Despite significant advances in the field of Automatic Speech Recognition, integrating speaker recognition, textual sentiment analysis, and acoustic sentiment detection within a unified real-time processing pipeline remains a challenging task. Current approaches are often limited to monolithic designs or operate in batch processing modes, which restricts their scalability and real-time applicability. To address this gap, this work proposes a novel feature selection method called RAM, along with a hybrid decision-level merging approach combining Conv1D CNN and AutoML-based models. The proposed hybrid framework enables independent model training and integrates its probabilistic outputs through a weighted merging strategy for performance improvement. Furthermore, a scalable microservice-based software architecture has been developed to support real-time processing, feature selection, and model deployment. This design enhances system modularity, flexibility, and integration capability in practical applications. Experimental results show that when the proposed RAM method is used in conjunction with a hybrid AI model, it achieves over 97% accuracy in speaker recognition and over 82% accuracy in emotion classification, even with short audio samples. These findings demonstrate that the proposed approach provides a robust and efficient solution for real-time speech analysis tasks. Full article

Robotic milling offers flexibility and lower capital cost than conventional CNC machining but is limited by low, pose-dependent structural stiffness. This study experimentally investigates how pose, cutting orientation, and engagement conditions govern dynamic response and machining accuracy, benchmarked against a CNC machine under matched conditions. Tool-point frequency response functions show that the robot exhibits dominant low-frequency structural modes at 8–15 Hz with compliances on the order of 10⁻⁵ m/N, one to two orders of magnitude more flexible than higher-frequency tool–holder modes (~10⁻⁶ m/N). In contrast, the CNC system is dominated by a stiff mode near 600 Hz (~2 × 10⁻⁷ m/N) with negligible low-frequency compliance. During cutting, the response is not resonance-driven; instead, low-frequency compliance induces modulation of spindle-synchronous vibrations, resulting in broadband spectral spreading and cycle-to-cycle variability. Poincaré analysis captures this modulation, which increases with spindle speed and depth of cut. Orientation-dependent alignment with compliant directions amplifies vibration and cross-axis coupling. Regression analysis shows a significant association between Z-direction vibration and depth-of-cut deviation (R = 0.739 locally; R = 0.363 globally). The results establish a framework linking compliance, modulation, and machining performance in robotic milling. Full article

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article

The use of millimeter-wave spectrum in fifth-generation (5G) systems is increasing the need for accurate prediction of received power and coverage in real deployment scenarios. In this context, ray tracing (RT) is a promising approach for site-specific analysis, although its reliability depends on how accurately different tools reproduce measurements in complex urban environments. This work presents a comparative assessment at 27 GHz of three RT tools: in-house Exact tool based on Vertical Plane Launching (VPL), Matlab 5G and open-source Sionna RT based on Shooting and Bouncing Rays (SBR). The comparison relies on a large outdoor walk-test campaign, including about 14,725 measurement points collected in a real urban area around a 27 GHz mMIMO base station, using real operator-provided antenna radiation patterns. Measured and simulated power levels are compared using statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and a planning-oriented coverage-rate metric. The results show a reasonable agreement between simulations and measurements, with RMSE and MAE values around 10–12 dB, highlighting tool-specific behaviors related to boundary effects, interaction modeling, and high-power overestimation. This work confirms that RT is a flexible support for 5G preliminary network design, reducing the need for extensive drive tests. Full article

Energy efficiency improvement represents a central strategic objective of the European Union (EU), essential for mitigating climate change and facilitating the transition toward a sustainable energy system. In 2023, renewable energy sources generated approximately 46% of the electricity produced in the EU, becoming the dominant component of the regional energy mix. This progress has been supported by coherent public policies, dedicated investment programs, and regulatory mechanisms aimed at accelerating the adoption of sustainable technologies. However, the existing literature highlights a research gap regarding the relationship between the dynamics of the European energy transition, the operational challenges generated by the rapid increase in the share of renewable energy sources, and the potential for energy savings in the residential sector through non-technological interventions. This paper analyzes the structural transformations of the European energy mix, the limitations of energy systems in the context of accelerated renewable energy integration, and the role of behavioral interventions in supporting the stability of the energy system. The study examines the dynamics of residential energy consumption, behavioral determinants of energy use, and the effectiveness of instruments such as information campaigns, real-time feedback, dynamic pricing, and demand response programs. The results indicate that these interventions can reduce peak loads, increase consumption flexibility, and alleviate pressure on energy networks under conditions of variable renewable energy generation. The integration of energy storage systems and the implementation of low-cost behavioral measures can act as complementary instruments for maintaining the dynamic stability of the energy system and for achieving the EU’s sustainability and climate neutrality objectives. Full article

Kolmogorov–Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications. Full article

Optimal coordination of distance and directional overcurrent relays (DR–DOCR) aims to achieve a fast, selective, and reliable protection scheme for transmission and sub-transmission systems. However, it constitutes a complex, nonlinear, and highly constrained optimization problem. In particular, single-setting DOCR characteristics used in conventional DR-DOCR coordination introduce additional challenges in lowering relay operating times while satisfying the coordination time interval (CTI) constraint. To address this issue, this paper proposes a novel DR-DOCR coordination approach that leverages a two-level DOCR characteristic. The objective is to exploit this characteristic, which partitions the relay curve into primary and backup protection regions in a highly flexible manner, thereby enabling easier avoidance of CTI violations. In addition, an enhanced variant of the red-tailed hawk algorithm, called ERTH, has been newly developed to solve this challenging problem. The proposed method is validated on versions of the 8-bus and 33-kV portion of the 30-bus power networks that have been modified to include renewable energy sources. Results demonstrate that the proposed method achieves total relay operating times of 23.681 s and 70.742 s for the 8-bus and 30-bus power systems, respectively. These values correspond to an 80.4% and 81.2% reduction compared to the conventional coordination scheme optimized by the ERTH algorithm, which yields 120.702 s and 376.757 s, respectively. Moreover, the ERTH algorithm exhibits superior performance in attaining near-global optimal solutions compared to the original RTH and other competitive optimization algorithms. In particular, for the 30-bus system under the conventional coordination scheme, the second-best result after ERTH is obtained by the teaching-learning-based optimization algorithm with a total relay operating time of 415.885 s. This indicates a 9.4% improvement achieved by ERTH (376.757 s) and a significantly higher improvement of 83% (70.742 s) achieved by the proposed strategy integrating ERTH with the two-level DOCR-based coordination scheme. Full article