Search Results (154)

Magnetic gears (MGs) are attracting increasing attention in power transmission systems due to their contactless operation principles, low frictional losses, and high efficiency. However, the broad application potential of these technologies requires a comprehensive evaluation of engineering parameters, such as material selection, energy efficiency, and structural design. This review focuses solely on solid-core magnetic gear systems designed using laminated electrical steels, soft magnetic composites (SMCs), and high-saturation alloys. This review systematically examines the topological diversity, torque transmission principles, and the impact of various core materials, such as electrical steels, soft magnetic composites (SMCs), and cobalt-based alloys, on the performance of magnetic gear systems. Literature-based comparative analyses are structured around topological classifications, evaluation of material properties, and performance analyses based on losses. Additionally, the study highlights that aligning material properties with appropriate manufacturing methods, such as powder metallurgy, wire electrical discharge machining (EDM), and precision casting, is essential for the practical scalability of magnetic gear systems. The findings reveal that coaxial magnetic gears (CMGs) offer a favorable balance between high torque density and compactness, while soft magnetic composites provide significant advantages in loss reduction, particularly at high frequencies. Additionally, application trends in fields such as renewable energy, electric vehicles (EVs), aerospace, and robotics are highlighted. Full article

Group IV element-based topological semimetals (TSMs) are pivotal for next-generation quantum devices due to their ultra-high carrier mobility and low-energy consumption. However, germanium (Ge)-based TSMs remain underexplored despite their compatibility with existing semiconductor technologies. Here, we propose a novel I229-Ge₄₈ allotrope constructed via bottom-up cluster assembly that exhibits a unique porous spherical Fermi surface and strain-tunable topological robustness. First-principles calculations reveal that I229-Ge₄₈ is a topological nodal line semimetal with exceptional mechanical anisotropy (Young’s modulus ratio: 2.27) and ductility (B/G = 2.21, ν = 0.30). Remarkably, the topological property persists under spin-orbit coupling (SOC) and tensile strain, while compressive strain induces a semiconductor transition (bandgap: 0.29 eV). Furthermore, I229-Ge₄₈ demonstrates strong visible-light absorption (10⁵ cm⁻¹) and a strong strain-modulated infrared response, surpassing conventional Ge allotropes. These findings establish I229-Ge₄₈ as a multifunctional platform for strain-engineered nanoelectronics and optoelectronic devices. Full article

Memristors, as the fourth fundamental circuit element, have attracted significant interest for their potential in analog signal processing, computing, and memory storage technologies. However, physical memristor implementations still face challenges in reproducibility, scalability, and integration with standard CMOS processes. Memristor emulator circuits, implemented using analog, digital, and mixed components, have emerged as practical alternatives, offering tunability, cost effectiveness, and compatibility with existing fabrication technologies for research and prototyping. This review paper provides a comprehensive analysis of recent advancements in memristor emulator design methodologies, including active and passive analog circuits, digital implementations, and hybrid approaches. A critical evaluation of these emulation techniques is conducted based on several performance metrics, including maximum operational frequency range, power consumption, and circuit topology. Additional parameters are also taken into account to ensure a comprehensive assessment. Furthermore, the paper examines promising healthcare applications of memristor and memristor emulators, focusing on their integration into biomedical systems. Finally, key challenges and promising directions for future research in memristor emulator development are outlined. Overall, the research presented highlights the promising future of memristor emulator technology in bridging the gap between theoretical memristor models and practical circuit implementations. Full article

Water Supply and Distribution Networks (WSDNs) offer underexplored potential for energy recovery. While many studies confirm their technical feasibility, few assess the long-term operational compatibility and economic viability of such solutions. This study evaluates the energy recovery potential of the Brussels Capital Region’s WSDN using four years (2019–2022) of operational data. Rather than focusing on available technologies, the analysis examines whether the real behavior of the network supports sustainable energy extraction. The approach includes network topology identification, theoretical power modeling, and detailed flow and pressure analysis. The Brussels system, composed of a Water Supply Network (WSN) and a Water Distribution Network (WDN), reveals strong disparities: the WSN offers localized opportunities for energy recovery, while the WDN presents significant operational constraints that limit economic viability. Our findings suggest that day-ahead electricity markets provide more suitable valorization pathways than flexibility markets. Most importantly, the study highlights the necessity of long-term behavioral analysis to avoid misleading conclusions based on short-term data and to support informed investment decisions in the urban water–energy nexus. Full article

In this study, we present the design and electromagnetic performance optimization of a micro-electromechanical system (MEMS) miniature outer-rotor permanent magnet motor. With increased attention towards higher torque density and lower torque pulsations in MEMS micromotor designs, an adaptation of an external rotor can be highly attractive. However, with the design complexity involved in such high-performance MEMS outer-rotor motor designs, the ultra-miniature 3D coil structures and the thin-film topology surrounding the air gap have been one of the main challenges. In this study, an ultra-thin outer-rotor motor with 3D MEMS silicon-based coils and a MEMS-compatible manufacturing method for the 3D coils is presented. Additionally, finite element simulations are conducted for the thin-film topology around the air gap to optimize performance characteristics such as torque developed, torque pulsations, and back electromotive force amplitude. Ultimately, the average magnetic flux density increased by 37.1%, from 0.361 T to 0.495 T. The root mean square (RMS) value of the back EMF per phase rises by 14.4%. Notably, the average torque is improved by 11.3%, while the torque ripple is significantly reduced from 1.281 mNm to 0.74 mNm, corresponding to a reduction of 49.9% in torque ripple percentage. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

This paper introduces a framework of hypercompositional algebra on fuzzy graphs by defining and analyzing fuzzy path-based hyperoperations. Building on the notion of strongest strong paths (paths that are both strength-optimal and composed exclusively of strong edges, where each edge achieves maximum connection strength between its endpoints), we define two operations: a vertex-based fuzzy path hyperoperation and an edge-based variant. These operations generalize classical graph hyperoperations to the fuzzy setting while maintaining compatibility with the underlying topology. We prove that the vertex fuzzy path hyperoperation is associative, forming a fuzzy hypersemigroup, and establish additional properties such as reflexivity and monotonicity with respect to $\alpha$-cuts. Structural features such as fuzzy strong cut vertices and edges are examined, and a fuzzy distance function is introduced to quantify directional connectivity strength. We define an equivalence relation based on mutual full-strength reachability and construct a quotient fuzzy graph that reflects maximal closed substructures under the vertex fuzzy path hyperoperation. Applications are discussed in domains such as trust networks, biological systems, and uncertainty-aware communications. This work aims to lay the algebraic foundations for further exploration of fuzzy hyperstructures that support modeling, analysis, and decision-making in systems governed by partial and asymmetric relationships. Full article

We study knowable informational dependence between empirical questions, modeled as continuous functional dependence between variables in a topological setting. We also investigate epistemic independence in topological terms and show that it is compatible with functional (but non-continuous) dependence. We then proceed to study a stronger notion of knowability based on uniformly continuous dependence. On the technical logical side, we determine the complete logics of languages that combine general functional dependence, continuous dependence, and uniformly continuous dependence. Full article

This review is different from previous studies focusing on polypyrrole (PPy) in universal fields such as sensors and supercapacitors. It is the first TO systematically review the specific applications of PPy-based electrospun nanofiber composites in the biomedical field, focusing on its biocompatibility regulation mechanism and tissue repair function. Although PPy exhibits exceptional electrical conductivity, redox activity, and biocompatibility, its clinical translation is hindered by processing challenges and poor degradability. These limitations can be significantly mitigated through composite strategies with degradable nanomaterials, enhancing both process compatibility and biofunctionality. Leveraging the morphological similarity between electrospun nanofibers and the natural extracellular matrix (ECM), this work comprehensively analyzes the topological characteristics of three composite fiber architectures—randomly distributed, aligned, and core–shell structures—and elucidates their application mechanisms in nerve regeneration, skin repair, bone mineralization, and myocardial tissue reconstruction (e.g., facilitating oriented cell migration and regulating differentiation through specific signaling pathway activation). The study further highlights critical challenges in the field, including PPy’s poor solubility, limited spinnability, insufficient mechanical strength, and scalability limitations. Future efforts should prioritize the development of multifunctional gradient composites, intelligent dynamic-responsive scaffolds, and standardized biosafety evaluation systems to accelerate the substantive translation of these materials into clinical applications. Full article

Plastic waste accumulation presents critical environmental challenges demanding innovative circular economy solutions. This study developed a comprehensive machine learning framework to systematically identify optimal enzyme combinations for polyester depolymerization. We integrated kinetic parameters from the BRENDA database with sequence-derived features and network topology metrics to train ensemble classifiers predicting enzyme-substrate relationships. A multi-objective optimization algorithm evaluated enzyme combinations across four criteria: prediction confidence, substrate coverage, operational compatibility, and functional diversity. The ensemble classifier achieved 86.3% accuracy across six polymer families, significantly outperforming individual models. Network analysis revealed a modular organization with hub enzymes exhibiting broad substrate specificity. Multi-objective optimization identified 156 Pareto-optimal enzyme combinations, with top-ranked pairs achieving composite scores exceeding 0.89. The Cutinase–PETase combination demonstrated exceptional complementarity (score: $0.875\pm 0.008$), combining complete substrate coverage with high catalytic efficiency. Validation against experimental benchmarks confirmed enhanced depolymerization rates for recommended enzyme cocktails. This framework provides a systematic approach for enzyme prioritization in plastic valorization, advancing biological recycling technologies through data-driven biocatalyst selection while identifying key economic barriers requiring technological innovation. Full article

Gait is considered a valuable biometric feature, and it is essential for uncovering the latent information embedded within gait patterns. Gait recognition methods are expected to serve as significant components in numerous applications. However, existing gait recognition methods exhibit limitations in complex scenarios. To address these, we construct a dual-Kinect V2 system that focuses more on gait skeleton joint data and related acoustic signals. This setup lays a solid foundation for subsequent methods and updating strategies. The core framework consists of enhanced ensemble learning methods and Dempster–Shafer Evidence Theory (D-SET). Our recognition methods serve as the foundation, and the decision support mechanism is used to evaluate the compatibility of various modules within our system. On this basis, our main contributions are as follows: (1) an improved gait skeleton joint AdaBoost recognition method based on Circle Chaotic Mapping and Gramian Angular Field (GAF) representations; (2) a data-adaptive gait-related acoustic signal AdaBoost recognition method based on GAF and a Parallel Convolutional Neural Network (PCNN); and (3) an amalgamation of the Triangulation Topology Aggregation Optimizer (TTAO) and D-SET, providing a robust and innovative decision support mechanism. These collaborations improve the overall recognition accuracy and demonstrate their considerable application values. Full article

The present work proposes a novel fractional-order multifunction filter topology in current-mode (CM), which is designed based on the Modified Current Feedback Operational Amplifier (MCFOA). The proposed design simultaneously generates fractional-order low-pass (FO-LPF), high-pass (FO-HPF), and band-pass (FO-BPF) outputs while utilizing an optimized set of essential active and passive elements, thereby ensuring simplicity, cost efficiency, and compatibility with integrated circuits (ICs). The fractional-order feature allows precise control over the transition slope between the passband and the stopband, enhancing design flexibility. PSpice simulations validated the filter’s theoretical performance, confirming a 1 kHz cut-off frequency, making it suitable for VLF applications such as military communication and submarine navigation. Monte Carlo analyses demonstrate robustness against parameter variations, while a low THD, a wide dynamic range, and low power consumption highlight its efficiency for high-precision, low-power applications. This work offers a practical and adaptable approach to fractional-order circuit design, with significant potential in communication, control, and biomedical systems. Full article

Terahertz reconfigurable reflectarray antennas (RRAs) hold significant promise for next-generation wireless communication systems by enabling dynamic beam control to mitigate severe path loss at high frequencies. This work presents a Complementary Metal-Oxide-Semiconductor (CMOS)-based RRA for terahertz amplitude control using tunable split-ring resonators. First, a terahertz switch in standard 65 nm CMOS process is designed, tested, and calibrated on the chip to extract the equivalent impedance, enabling precise RRA element design. Next, a reconfigurable element architecture is presented through the co-design of a split-ring radiator, control line, and a single switch. Experimental characterization demonstrates that the fabricated RRA achieves 3 dB amplitude variation at 0.290 THz with <8.5 dB element loss under 0.8 V gate bias. The measured results validate that the proposed single-switch topology effectively balances reconfigurability and loss performance in the terahertz regime. The demonstrated CMOS-compatible RRA provides a scalable solution for real-time beamforming in terahertz communication systems. Full article

Direct methanol fuel cells (DMFCs) represent a promising pathway for energy conversion, yet their reliance on platinum-group metal (PGM)-based anode catalysts poses critical sustainability challenges, which stem from finite mineral reserves, environmentally detrimental extraction processes, and prohibitive lifecycle costs. Current anode catalysts for DMFCs are dominated by platinum materials; therefore, this review systematically evaluates the following three emerging eco-efficient design paradigms using platinum materials as a starting point: (1) the atomic-level optimization of low-Pt alloy surfaces to maximize catalytic efficiency per metal atom, (2) Earth-abundant transition metal compounds (e.g., nitrides and sulfides) and coordination-tunable metal–organic frameworks as viable PGM-free alternatives, and (3) mechanically robust carbon architectures with engineered topological defects that enhance catalyst stability through covalent metal–carbon interactions. Through comparative analysis with pure Pt benchmarks, we critically examine how these strategic material innovations collectively mitigate CO intermediate poisoning risks and improve electrochemical durability. Such fundamental advances in catalyst design not only address immediate technical barriers, but also establish essential material foundations for the development of DMFC technologies compatible with circular economy frameworks and United Nations Sustainable Development Goal 7 targets. Full article

This work presents a current-controlled CMOS ring oscillator (CCRO) optimized for ultra-low-voltage applications in next-generation energy-constrained systems. Leveraging bulk voltage tuning in 22 nm FDSOI differential inverter stages, the topology enables frequency adjustment while operating MOSFETs in the subthreshold region—critical for minimizing power in sub-1 V environments. Simulations at 0.4 V supply demonstrate robust performance: a three-stage oscillator achieves a 537–800 MHz tuning range with bias current (I_BIAS) modulation from 30–130 nA, while a four-stage configuration spans 388–587 MHz. At 70 nA I_BIAS, the three-stage design delivers a nominal frequency of 666.8 MHz with just 10.23 µW power dissipation, underscoring its suitability for ultra-low-power IoT and biomedical applications. The oscillator’s linear frequency sensitivity (2.63 MHz/nA) allows precise, dynamic control over performance–power tradeoffs. To address diverse application needs, the design integrates three tunability mechanisms: programmable capacitor arrays for coarse frequency adjustments, configurable stage counts (three- or four-stage topologies), and supply voltage scaling. This multi-modal approach extends the operational range to 1 MHz–1 GHz, ensuring compatibility with low-speed sensor interfaces and high-speed edge-computing tasks. The CCRO’s subthreshold operation at 0.4 V—coupled with nanoampere-level current consumption—makes it uniquely suited for battery-less systems, wearable health monitors, and implantable medical devices where energy efficiency and adaptive clocking are paramount. By eliminating traditional voltage-controlled oscillators’ complexity, this topology offers a compact, scalable solution for emerging ultra-low-power technologies. Full article