Search Results (3,665)

DC–DC converters have become a key component in the structure of renewable energy systems, where an interface to increase and regulate the output voltage is required. This paper presents a modular non-isolated topology that achieves high voltage gain through interconnected switched-inductor cells. For the proposed converter, the design rules for sizing the energy storage elements for n number of cells are obtained, considering continuous, discontinuous, and boundary operation modes. Therefore, design equations are provided to support the precise selection of passive components according to voltage and power specifications. A nonlinear dynamic model is developed, and a model-based control scheme with inner current and outer voltage loops ensures robust regulation and fast transient response. Experimental validation on a 200 W prototype confirms theoretical predictions under steady-state and real-life dynamic conditions. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

AC battery systems (ACBSs) based on multilevel converters (MLCs) have gained considerable attention in recent times for the provision of grid services due to high-power (HP) and high-energy (HE) capabilities. In a hybrid ACBS, multiple low-voltage ports provide DC interfaces for battery modules from the same or different chemistries, enabling flexible operation across a wide range of grid services. However, the design complexity increases substantially, due to (i) higher electrothermal coupling between heterogeneous battery modules and power electronic (PE) switches, (ii) grid compliance constraints and (iii) power quality requirements, which often leads to conservative oversizing and, consequently, increased total cost of ownership (TCO). To address these challenges, this paper proposes a co-design optimization framework for the sizing and selection of battery modules, PE components, and MLC architecture. A multi-fidelity modeling approach is presented to co-simulate the battery modules and MLC. The model captures electrochemical behavior, degradation dynamics, and power losses to enable accurate estimation of system-level energy efficiency. The framework then leverages a multi-objective nondominated sorting genetic algorithm (NSGA-II) to perform optimal cell-to-module sizing across different chemistries and MLC levels, while incorporating the inter-module balancing and AC power quality constraints. Comparative simulation studies show that the proposed co-design framework achieves life-cycle TCO reduction of 3.5%, 4.5% and 20% relative to non-hybrid (single chemistry) configurations based on LFP, NMC and LTO chemistries, respectively. The test results validate the effectiveness of the proposed co-design methodology for the optimal design of grid-tied AC battery systems. Full article

Smart healthcare is rapidly emerging as a transformative paradigm, enabling simultaneous health monitoring, therapeutic intervention, and early prediction of disease onset. In this context, electrochemical monitoring systems have attracted growing interest due to their cost-effectiveness, ease of operation, miniaturization and compatibility with wearable platforms. Accordingly, conductive hydrogel-based electrochemical (bio)sensors have gained significant attention for health monitoring owing to their soft mechanical properties, high water content, excellent biocompatibility, and ability to form intimate, conformal interfaces with biological tissues. Their three-dimensional polymeric networks facilitate efficient ion transport and mechanical flexibility, making them particularly suitable for wearable and noninvasive sensing and monitoring applications. However, the intrinsically limited conductivity and catalytic activity of pristine hydrogels often constrain their electrochemical performance. To overcome these limitations, functional nanomaterials such as metal–organic frameworks (MOFs) and MXene (MX) nanosheets have been increasingly integrated into hydrogel matrices to enhance conductivity and electrochemical activity. This review provides a comprehensive and critical comparison of recent advances in MOF- and MX-integrated conductive hydrogels for electrochemical health monitoring. In addition to material design strategies and sensing performance, emerging trends in data-driven sensing aimed at improving signal interpretation and multi-analyte discrimination are systematically discussed. Key challenges related to long-term stability, biocompatibility, scalability, and intelligent system integration are critically assessed, and the future potential of these platforms within closed-loop architectures is highlighted, paving the way for next-generation conductive hydrogel-based electrochemical sensors in smart healthcare applications. Full article

Rising concentrations of organic carbon (OC), phosphorus, and nitrogen in liquid waste from urban, industrial, and agricultural sources pose persistent challenges for environmental protection and resource recovery. Despite extensive application of microfiltration (MF) and ultrafiltration (UF) in wastewater treatment, their role in selective organic carbon and nutrient fractionation remains insufficiently clear-cut and is often interpreted solely through nominal pore size. This review was guided by the hypothesis that the reported limitations of MF and UF for nutrient separation are not intrinsic to the technologies but arise from simplified interpretations of separation mechanisms. A unified analytical framework was developed by synthesizing recent studies, linking membrane surface charge, pore structure, solute speciation, fouling-induced secondary layers, and operating conditions to the observed separation behavior. The analysis shows that MF fractionates particulate OC and suspended solids, whereas UF extends separation to macromolecular OC and phosphorus mainly via indirect retention mechanisms. Dissolved nitrogen species largely permeate both membranes unless they are transformed into retainable forms. Performance differences between MF and UF are conditional and system-dependent, with enhanced selectivity emerging through process integration. MF and UF can thus be repositioned as strategic fractionation interfaces within integrated treatment systems supporting circular economy–oriented wastewater management. Full article

High salinity and hardness in flowback fluids from tight reservoirs severely degrade the performance of conventional fracturing fluids, leading to formation damage and imposing major constraints on water recycling. An innovative in situ molecular interface regulation strategy that bypasses the need for costly pretreatment was proposed. A novel zwitterionic polymer was synthesized by grafting trimethylamine N-oxide (TMAO) onto hydrolyzed polyacrylamide. This hydrolyzed polyacrylamide grafted with trimethylamine N-oxide polymer (HPAMT) leverages zwitterionic TMAO groups to form a robust hydration layer approximately 0.25 nm thick on the polymer chains. Each TMAO group can immobilize up to 22.2 water molecules, effectively shielding the polymer from the detrimental effects of ions like Ca²⁺ and Na⁺, thereby preventing chain curling and preserving cross-linking sites. Experimental results demonstrate that HPAMT fracturing fluid prepared with untreated flowback fluids retains over 70% of its initial viscosity. The HPAMT fracturing fluid exhibits superior thermal and shear stability, maintaining more than 90% viscosity after exposure to 90 °C and the shear rate of 170 s⁻¹ for 60 min. Furthermore, HPAMT provides excellent proppant suspension, exceeding 60 min of static settling time. The broken gel viscosity remains below 5 mPa·s, enabling the direct reuse of flowback water. This technology overcomes the critical compatibility issue between traditional polymers and challenging brine chemistry, significantly reducing freshwater consumption and operational costs, thus presenting a viable and innovative solution for enhancing the environmental sustainability of unconventional resource development. Full article

In this study, we present the design and development of a mobile, multi-modal electroencephalography and functional near-infrared spectroscopy (EEG/fNIRS) device for wireless neurophysiological monitoring. The system was engineered to achieve high signal fidelity, low power consumption, and a fully untethered operation suitable for ambulatory brain research. The device integrates four Texas Instruments ADS1299 24-bit biopotential amplifiers, providing up to 32 simultaneous acquisition channels. Signal control, processing, and local storage via an SD card are managed by an STM32H7 microcontroller, while an ESP32-S2 module handles Wi-Fi communication. Dual-wavelength light-emitting diodes and OPT101 photodiodes form the optical front-end, driven by digitally controlled constant-current sources for stable illumination. The design employs galvanic isolation, multi-rail power management, and a four-layer PCB layout to minimise interference between analogue, power, and digital domains. Data are captured by a deterministic, clock-driven STM32 acquisition loop and forwarded to the ESP32, which operates under an RTOS and streams packets over Wi-Fi for collection on a mobile phone or PC using the Lab Streaming Layer (LSL) framework. The STM32H7 architecture was chosen for its capability to support future embedded edge-machine-learning functions, enabling on-device signal quality assessment and artefact rejection. Validation demonstrations include 32-channel synchronised acquisition using the ADS1299 internal test signal, eyes-open/eyes-closed alpha modulation visualised in EEGLAB, a forehead fNIRS breath-hold response with physiological spectral content, and real-time ECG/optical pulse streaming via LSL. The resulting system provides a compact platform with explicitly defined acquisition and data interfaces for synchronised EEG/fNIRS acquisition, enabling scalable, low-cost mobile neuroimaging research. Full article

High-power clutches operating under high-frequency engagement–disengagement cycles demand piston ring seals with exceptional leakage control and tribological reliability. Conventional architectures often experience lubrication failure and severe adhesive wear during transient pressure fluctuations. This research proposes an autonomous intelligent sealing strategy leveraging fluid pressure-induced morphological evolution. By strategically integrating periodic macroscopic structural relief features on the non-sealing surface, the sealing interface transforms into a micron-scale wavy topography in response to hydraulic loading. This structurally embedded intelligence significantly improves fluid pressure distribution, facilitating a transition toward a more favorable lubrication regime. Furthermore, a “self-healing and positional stagnation” logic is elucidated: upon pressure dissipation, the induced waviness elastically recovers to a planar state to ensure sealing integrity, while the ring maintains its axial position due to the predominant frictional resistance of the secondary seal. This synergistic mechanism effectively precludes deleterious dry friction during the clutch disengagement phase. High-fidelity numerical investigations, benchmarked against established experimental data, identify the rectangular groove configuration as the optimal geometry for maximizing waviness amplitude (≈1.5 µm). This research provides a robust framework for developing responsive, zero-wear intelligent seals in advanced power transmissions. Full article

Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive wear at the wheel–rail interface. Although limited research has explored the estimation of adhesion forces using data-driven algorithms, most existing approaches lack self-reliance and fail to adequately capture low adhesion levels, which are critical to identify. Moreover, obtaining labelled experimental data remains a significant challenge in adopting data-driven solutions for domain-specific problems. This study implements self-reliant deep learning (DL) models as perception modules for intelligent railway systems, enabling low adhesion identification by training on raw time sequences. In the second phase, to address the challenge of label acquisition, a semi-supervised generative adversarial network (SGAN) is developed. Compared to the supervised algorithms, the SGAN achieved superior performance, with 98.38% accuracy, 98.42% precision, and 98.28% F1-score in identifying seven different adhesion conditions. In contrast, the MLP and 1D-CNN models achieved accuracy of 91% and 93.88%, respectively. These findings demonstrate the potential of SGAN-based data-driven perception for enhancing autonomy, adaptability, and fault diagnosis in intelligent rail and robotic mobility systems. The proposed approach offers an efficient and scalable solution for real-time railway condition monitoring and fault identification, eliminating the overhead associated with manual data labelling. Full article

The Support System with Vertical Retractable Mechanism (SSVRS) is an advancement in telescopic technology that replaces continuous threaded or fluid-dependent interfaces with an internal stepped mechanism based on geometric mechanical interference. This coaxial design uses an integrated pin that engages with discrete grooves, enabling rapid height adjustments and positioning speeds that are significantly faster than those of traditional mechanisms. Unlike friction-based systems that are prone to slipping under dynamic loads, the SSVRS provides millimeter-level precision and exceptional stability, even in vibrational environments. The SSVRS’s versatility stems from its parametric modular design, which scales from lightweight domestic fixtures to heavy-duty industrial machinery by customizing material selection—ranging from high-strength steel to glass fiber-reinforced nylon—and slot configuration. Specifically, vertical slot arrangements facilitate rapid movement, and spiral geometries allow for high-precision alignment. Furthermore, the SSVRS optimizes long-term operational efficiency and sustainability through low maintenance requirements, minimal moving parts, and the use of recyclable materials. By combining high-speed positioning, robust structural integrity, and adaptive modularity, the SSVRS provides a high-performance, concrete alternative to current mainstream linear modules and traditional support structures. Full article

The inherently high-voltage-length product (V_πL) of thin-film lithium niobate (TFLN) modulators in the O-, C-, and L-telecom bands restricts further scaling of photonic integrated circuits’ bandwidth density, driving their migration toward shorter operating wavelengths. Nevertheless, the corresponding grating couplers, as critical optical input/outputs (optical I/Os) interfaces, remain largely undeveloped. Here, we demonstrate an 850 nm TFLN grating coupler designed based on topological unidirectional guided resonance (UGR). By breaking C₂ symmetry of the unit cell and precisely tailoring its geometry, we achieve unidirectional upward radiation with a 63.7 dB up/down intensity ratio. Subsequent apodization of groove widths and periods enables precise control of the electrical field distribution in both real and momentum spaces. This yields a vertical-cavity surface-emitting laser (VCSEL)-matched, highly fabrication-tolerant TFLN grating coupler that attains, to the best of our knowledge, the highest simulated coupling efficiency of −0.6 dB without mirrors or hybrid materials. This work delivers a high-efficiency, layout-flexible, and complementary metal oxide semiconductor (CMOS)-compatible optical I/Os solution for short-wavelength TFLN modulators with low V_πL. It offers substantial engineering value and broad applicability for on-chip light source integration and high-bandwidth-density short-reach optical interconnects. Full article

The rollout of 5G Standalone networks introduces unprecedented flexibility and performance through service-based architecture (SBA), virtualization, open APIs, and network slicing, while simultaneously expanding the attack surface across control, user, and cross-plane interfaces. This article provides a systematic, vulnerability-prioritized, selective characterization of the current state of weaknesses specific to the 5G control and user planes and transparent risk scoring. Using a PRISMA-aligned methodology, vulnerabilities are mapped explicitly to 3GPP network functions and interfaces (e.g., AMF, SMF, UPF; N2, N4, SBA APIs) and categorized by operational evidence level ranging from theoretical analysis to documented live-network exploitation. A normalized criticality scoring model integrates likelihood, impact, exploitability, and CVSS-derived severity. The analysis shows that control-plane signaling floods, PFCP misuse, and container escapes stand out as the most pressing risks. It also exposes how little attention has been given to securing the user plane and strengthening slice isolation. The paper wraps up with clear, evidence-based hardening priorities for each plane, along with research areas that matter for today’s 5G networks and the shift toward 6G. Full article

Flatland metasurfaces provide a fundamentally distinct approach to optical gas sensing by confining light–matter interaction to planar, subwavelength interfaces, where resonant energy storage and near-field enhancement replace extended optical path lengths. This review presents a physics-driven perspective on metasurface-enabled gas sensing, focusing on how gaseous analytes perturb the complex eigenmodes of engineered planar resonators. Diverse sensing modalities, including enhanced molecular absorption, refractive index-induced resonance shifts, loss modulation, polarization conversion, and chemo-optical transduction, are unified within a common perturbative framework that links sensitivity to mode confinement, quality factor, and analyte overlap. The analysis highlights fundamental trade-offs imposed by material dispersion, intrinsic loss, and radiation balance across plasmonic, dielectric, polaritonic, and hybrid metasurface platforms operating from the visible to the terahertz regime. Attention is given to the limits of chemical selectivity in flatland architectures and to the role of functional materials, multimodal transduction, and computational inference in addressing these constraints. System-level considerations, including thermal stability, fabrication tolerance, and integration with detectors and electronics, are identified as critical determinants of real-world performance. By consolidating disparate approaches within a unified flatland framework, this review provides physical insight and design guidance for the development of compact, integrable, and application-specific optical gas sensing systems. Full article

The role of power converters in modern electrical infrastructure (such as electric vehicle charging stations, battery energy storage systems and photovoltaic energy systems) has become critical. Given the high reliability required by these converters, continuous condition monitoring for predictive maintenance is mandatory. Traditional SCADA and HMI systems often face scalability bottlenecks and lack the flexibility in data aggregation and storage scalability required for long-term predictive maintenance. This paper proposes a scalable, containerized microservices-based architecture for degradation tracking and State-of-Health (SoH) monitoring in power conversion systems. The architecture features a decoupled four-layer structure, utilizing dedicated UDP servers for low-latency data ingestion, RabbitMQ (AMQP) for robust message routing, and a NoSQL (MongoDB) storage layer with a FastAPI interface. The proposed system was validated using a Hardware-in-the-Loop (HiL) setup with a Typhoon HIL606 simulator monitoring an Active Neutral Point Clamped (ANPC) power converter. Experimental stress tests demonstrated a Packet Delivery Ratio (PDR) of 1.0 at ingestion rates up to 100 messages per second (msgs/s) per node. The system exhibits transmission and processing overheads consistently below 5 ms, ensuring timely data availability for tracking thermal dynamics and parametric aging trends. This operational performance significantly exceeds the nominal requirement of 2 msgs/s for condition monitoring, ensuring robust data integrity. Finally, this modular approach provides the horizontal scalability necessary for Industry 4.0 integration, offering a high-performance framework for long-term health monitoring in modern power electronics. Full article

5G systems have delivered on their promise of seamless connectivity and efficiency improvements since their global rollout began in 2020. However, maintaining subscriber identity privacy on the network remains a critical challenge. The 3GPP specifications define numerous identifiers associated with the subscriber and their activity, all of which are critical to the operations of cellular networks. While the introduction of the Subscription Concealed Identifier (SUCI) protects users across the air interface, the 5G Core Network (CN) continues to operate largely on the basis of the Subscription Permanent Identifier (SUPI)—the 5G-equivalent to the IMSI from prior generations—for functions such as authentication, billing, session management, emergency services, and lawful interception. Furthermore, the SUPI relies solely on the transport layer’s encryption for protection from malicious observation and tracking of the SUPI across activities. The crucial role of the largely unprotected SUPI and other closely related identifiers creates a high-value target for insider threats, malware campaigns, and data exfiltration, effectively rendering the Mobile Network Operator (MNO) a single point of failure for identity privacy. In this paper, we analyze the architectural vulnerabilities of identity persistence within the CN, challenging the legacy “honest-but-curious” trust model. To quantify the extent of subscriber identities being utilized and exchange within various API calls in the CN, we conducted a study of the occurrence of SUPI as a parameter throughout the collection of 5G SBI (Service-Based Interface) Core VNF (Virtual Network Function) API (Application Programming Interface) schemas. Our extensive analysis of the 3GPP specifications for 3GPP Release 18 revealed a total of 4284 distinct parameter names being used across all API calls, with a total of 171,466 occurrences across the API schema. More importantly, it revealed a highly skewed distribution in which subscriber identity plays a pivotal role. Specifically, the “supi” parameter ranks 57th with 397 occurrences. We found that SUPI occurs both as a direct parameter (“supi”) and within 72 other parameter names that contain subscriber identifiers as defined in 3GPP TS 23.003. For these 73 parameter names, we identified a total of 8757 occurrences. At over 5.11% of all parameter occurrences, this constitutes a disproportionately large share of total references. We also detail scenarios where subscriber privacy can be compromised by internal actors and review future privacy-preserving frameworks that aim to decouple subscriber identity from network operations. By suggesting a shift towards a zero-trust model for CN architecture and providing subscribers with greater control over their identity management, this work also offers a potential roadmap for mitigating insider threats in current deployments and influencing specific standardization and regulatory requirements for future 6G and Beyond-6G networks. Full article