Search Results (294)

The electrical drive and the electrical motor share the same housing in Integrated Motor Drives (IMDs), which directly affects the reliability of failure-prone components in such a system. Existing studies are often conducted without considering system-level electrothermal reliability interactions in IMDs. This paper proposes a framework for electrothermal modeling for reliability analysis of IMDs during the early design phase. The framework is based on a back-to-back converter as an emulation platform adaptable to different high-speed electrical machines through software reconfiguration alone. It follows two stages: first, it converts the real-world mission profile, including high-speed operation, into load current commands and motor power loss. Secondly, the thermal network modeling accounts for thermal coupling between the components and the motor, which affects the junction temperature and the hot-spot temperature of the DC link capacitor. The parameters of the thermal network of the electrical motor can be the result of a multiphysics simulation or a real available motor. This framework enables reliability assessment considering motor thermal effects in the early design phase without requiring a physical motor prototype, while providing a fast and cost-effective approach for reliability evaluation. The experimental tests are performed to validate an electrothermal modeling framework capable of thermal modeling and reliability analysis. In addition, the reliability analysis of the power device is carried out by doing the simulation results using data from a real motor, selected for integrated power converter applications. The results demonstrate that the thermal interaction between the motor and the electrical drive causes an $11.5\%$ reduction in the predicted $B_{10}$ lifetime compared with the non-integrated configuration. The non-integrated configuration exhibits approximately 20,000$\mathrm{km}$ longer lifetime than the integrated configuration, highlighting the importance of considering motor-drive thermal coupling in IMD reliability assessment. Full article

This paper proposes a digital twin (DT)-enabled edge computing framework for healthcare task offloading in smart medical environments. The DT maintains a continuously synchronized virtual representation of the physical edge system, capturing queue states, resource utilization, and energy dynamics. Based on this virtual state, a deterministic optimization model is formulated to support real-time offloading and scheduling decisions under latency, energy, and fairness constraints. Unlike prediction-only approaches, the proposed DT operates in a closed-loop manner, where state estimation and synchronization directly influence scheduling feasibility and system performance. The offloading problem is formulated as a mixed-integer linear programming (MILP) model to jointly optimize task allocation, delay minimization, and energy efficiency. The simulated workload includes 10,000 heterogeneous healthcare applications. At each time slot, one to five tasks are generated and uniformly selected from ECG, video-processing, or medical-imaging workloads, with average task sizes of 90 KB, 280 KB, and 150 KB, respectively. This setup aims to emulate diverse real-world healthcare edge workloads with varying communication and computation demands. The proposed approach significantly reduces average latency by up to 57%, eliminates task drops in all evaluated scenarios, and improves load balancing compared with hospital-only and round-robin baselines. Although total energy consumption increases moderately, energy efficiency per completed task improves due to more effective scheduling by stability, reducing deadline violations and enhancing resource utilization. We further analyze the impact of DT freshness and updated frequency and show that outdated or misaligned DT updates can degrade performance by increasing delay and leading to suboptimal decisions, while overly frequent updates introduce additional coordination overhead. These results highlight the importance of jointly designing DT synchronization mechanisms and optimization-based scheduling strategies for reliable and cost-efficient healthcare edge systems. Full article

The accurate representation of frontal zones—characterized by sharp scalar gradients—remains a critical challenge in regional objective analysis and data assimilation, particularly when utilizing sparse or stochastically distributed observations. This study evaluates the efficacy of Multiquadric Radial Basis Functions (RBFs) as a high-order alternative to standard spatial mapping operators frequently used in machine learning atmospheric emulators. We contrast the performance of the regularized, $C^{\infty }$-continuous RBF approach against nearest neighbor and linear mesh interpolation schemes using both synthetic baroclinic wave profiles and an operational case study of the intense extratropical cyclone that impacted the East Coast of North America in mid-March 1993. To mitigate characteristic boundary artifacts and geometric clipping in bounded regional domains, we implement a targeted numerical stabilization framework combining localized boundary mirroring with four-corner domain anchoring. Our quantitative results demonstrate that the optimized RBF framework substantially improves gradient fidelity and reduces Root Mean Square Error across a wide range of observation densities. Furthermore, we evaluate the computational scalability of RBFs on high-performance computing architectures, demonstrating how Algebraic Multigrid solvers and Graphics Processing Unit acceleration mitigate the foundational $O\left(N^3\right)$ computational bottleneck. We conclude that RBF interpolation provides a physically consistent, analytically differentiable manifold that addresses the derivative discontinuities of traditional linear methods, offering a stable pre-processing framework for high-resolution meteorological analysis and machine learning optimization. Full article

Mechanistic simulation and machine learning are powerful but complementary tools: physics-based simulation is interpretable yet computationally expensive and blind to real-world context, whereas machine learning is fast but data-hungry and opaque. Biological systems resolve this tension elegantly, coupling physically grounded mechanoreceptor sensing with higher-level neural interpretation that places those signals in context. Inspired by this layered architecture, we present Mechanics-AI, an open-source framework that mirrors the same sensing-then-interpretation logic computationally. A first learning layer (ML1) emulates expensive finite-element, computational fluid dynamics and multiphysics simulations to produce interpretable physical metrics such as stress, strain, shear, and thermal and moisture fields, while a second layer (ML2) fuses these metrics with heterogeneous real-world metadata to predict categorical outcomes and design recommendations. Eight algorithms are benchmarked automatically, the most accurate is selected for each task, and Shapley additive explanations expose the dominant physical drivers to preserve interpretability. The framework is demonstrated across three independent domains using a single unchanged pipeline: forensic traumatic brain injury prediction, optimisation of a bio-inspired humanoid bioreactor for tissue engineering, and a zero-emission building (ZEBAI) framework that couples thermo-hygro-mechanical simulation with Sobol-sampled surrogate modelling to design sustainable, low-carbon envelopes from recycled aggregate concrete by balancing structural safety, energy and embodied carbon. Despite entirely different physics, data and objectives, the same architecture generalises across all three, showing that bio-inspired, layered coupling of mechanistic simulation and contextual learning offers a reusable, interpretable route to cross-domain engineering prediction and sustainable design. Full article

Soil water models are increasingly required to support irrigation, drought assessment and sustainable water management, yet physical, artificial intelligence (AI)-based and hybrid approaches differ in process representation, data demand and transferability. This structured narrative review critically compared these approaches and used auxiliary publication-record mapping in Web of Science, Scopus and OpenAlex for 2015–2026; quantitative comparisons were based on the complete years 2015–2025. Aggregated annual database records increased from 21,795 to 38,899 for physical models (1.78-fold), from 203 to 4088 for AI-based models (20.14-fold), and from 61 to 669 for hybrid models (10.97-fold); because records overlapped across databases, these values indicate relative trends rather than unique publications. Physical models remained essential for mechanistic interpretation but were constrained by hydraulic parameterisation, boundary conditions, heterogeneity and scale mismatch. AI-based models enabled flexible multi-source prediction and remote-sensing integration but remained vulnerable to domain shift, weak extrapolation and limited process interpretability. Hybrid strategies provided specific benefits through parameter estimation, emulation, residual correction, data assimilation, physics-informed learning and differentiable coupling, while potentially inheriting uncertainty from both components. No model class was universally superior. Model selection should therefore be problem-oriented and supported by independent validation, uncertainty quantification, domain assessment and evaluation at root-zone and management-relevant decision thresholds. Full article

Shallow-water underwater acoustic (UWA) communication is strongly affected by long-delay multipath propagation, time-varying Doppler distortion, and the computational burden of coherent demodulation. This paper presents a software-defined, quasi-real-time orthogonal chirp division multiplexing (OCDM) communication prototype implemented on a National Instruments compactRIO heterogeneous platform. The prototype maps previously developed multiplex resampling (MR) and Data Pick-Rake (DP-Rake) algorithms to a hardware–software processing chain, in which the FPGA executes sample-level physical-layer operations, including MR, DP-Rake windowing, OCDM demodulation, and equalization, while the ARM real-time controller performs frame-level control, FEC/CRC processing, and state-machine scheduling. The DMA-based RT–FPGA data exchange achieved a measured throughput of 80–100 MB/s. Under the maximum MR configuration, the FPGA physical-layer processing, DMA transfer, and ARM RT-layer processing required approximately 4.5 ms, 0.5 ms, and 5.0 ms per data block, respectively, resulting in a total receiver-side digital processing latency of approximately 10.0 ms. This corresponds to a 32.6 ms timing margin relative to the shortest acoustic data-block duration. The maximum FPGA resource utilization among the reported resource categories was 56.8%. The prototype was evaluated through replay-based channel emulation, Qiandao Lake mobility experiments, and Xiamen Outer Port sea trials. The replay-based results show that DP-Rake reception reduces the multipath-induced error floor under severe delay-spread conditions. In the Qiandao Lake experiments, dynamic MR branch selection achieved zero packet errors within the tested observation window while reducing the average number of active MR branches by 32.7% compared with the fixed-branch configuration. In the Xiamen Outer Port sea trials, an over 6 km shallow-water link with an approximately 12 ms delay spread was evaluated, and no packet errors were observed within the tested observation window when the measured SNR exceeded 7.9 dB. These results indicate that the implemented MR-DP-Rake OCDM prototype improves robustness over the OFDM-based baseline under the tested doubly spread UWA channel conditions. The main contribution of this work is the end-to-end software-defined implementation and field validation of MR-DP-Rake OCDM, rather than the proposal of new communication-theoretic algorithms. Full article

Tidal-flat changes during typhoon events are controlled by compound interactions among waves, tides, runoff, sediment transport, and vegetation resistance. However, rapid prediction remains challenging because high-resolution process-based morphodynamic models are computationally expensive. This study developed an observation-supported coastal morphodynamic emulator for rapid prediction of typhoon-induced tidal-flat erosion and deposition in the Jiuduansha Wetland, Yangtze Estuary. Multi-source field observations collected during Typhoons Bebinca and Pulasan in September 2024 were first used to validate a coupled MIKE21 FM model. The validated model was then applied to generate hydrodynamic and morphodynamic samples for emulator training and testing. Generalized Lagrangian mean velocity and bottom shear stress were selected as physically meaningful inputs. Current-timestep bed-level change was predicted using a UNet model enhanced with the Convolutional Block Attention Module (CBAM), hereafter referred to as CBAM-UNet. The numerical model reproduced the observed processes with acceptable accuracy, with Skill values of 0.98–0.99 for water level, 0.83–0.84 for wave height, and 0.82 for suspended sediment concentration. Compared with the conventional UNet, CBAM-UNet reduced the final cumulative RMSE from approximately 17.2 mm to 8.8 mm, corresponding to an error reduction of about 49%. Under prescribed wave, runoff, and tidal perturbations, the emulator reproduced the main erosion–deposition patterns, with final cumulative RMSE values of approximately 6.67 mm, 5.43 mm, and 10.68 mm, respectively. Across validation cases, replacing the morphodynamic module with the emulator reduced the average runtime by 42.50%. These results indicate that observation-supported morphodynamic emulation can support rapid tidal-flat assessment under compound typhoon forcing. Full article

The rapid growth of heterogeneous IoT devices in 5G environments has created stringent requirements for low-latency edge-based protocol processing. Existing static parsing frameworks lack adaptability to dynamic multi-protocol traffic, resulting in increased processing delays and quality-of-service (QoS) violations under bursty workloads. This paper presents HeteroEdge, a latency-aware adaptive protocol parsing framework for 5G Multi-access Edge Computing (MEC) environments. HeteroEdge integrates four tightly coupled components: (i) a lightweight machine-learning-based Heterogeneous Protocol Parsing Layer (HPPL) built on gradient-boosted decision trees (XGBoost); (ii) a Network Digital Twin (NDT) that maintains a compressed and continuously updated representation of IoT endpoint states; (iii) a Real-Time Inference Engine (RTIE) that dynamically reallocates parsing resources at 50 ms intervals; and (iv) a What-If Simulation (WIS) module that proactively evaluates resource-allocation strategies under hypothetical traffic scenarios. Experimental evaluation on a physical 5G MEC testbed comprising four Intel Xeon Silver 4316 edge nodes and 2000 emulated IoT endpoints spanning twelve protocol classes demonstrates the effectiveness of the proposed framework. HeteroEdge reduces median edge parsing latency (including parsing, classification, and queuing delays, but excluding the 5G radio component) by up to 44.7% compared with static MEC baselines, achieves a macro-averaged protocol classification accuracy of 97.8%, and sustains sub-7 ms edge parsing latency at a line-rate NIC injection throughput of 18 Gbps. Furthermore, latency spikes under bursty traffic are reduced by 39% at the 95th percentile, while SLA violation rates decrease by a factor of 3.9 relative to static resource allocation. These results demonstrate that HeteroEdge provides an effective and scalable solution for latency-critical IoT applications, including smart manufacturing, connected vehicles, and urban sensing. Full article

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work. Full article

Reproducing SM4 (GB/T 32907-2016) hardware-accelerator results on open-source RISC-V platforms is difficult, because most published designs depend on proprietary FPGA toolchains. This paper contributes an asymmetric dual-channel SM4 architecture together with a fully reproducible open-source verification framework; physical on-board acceleration is not claimed and is left as future work. The architecture exploits two algorithmic symmetries of SM4—encryption and decryption differ only in round-key order, and the round transform T shares the byte-wise S-box $\tau$ with the key-expansion transform $T^{\prime }$—but maps them onto an asymmetric workload. Bulk encryption is throughput-bound, whereas key expansion runs once per session. Accordingly, a 32-stage fully unrolled encryption pipeline (one 128-bit block per cycle in steady state) is paired with a single round function reused iteratively for the key schedule, and encryption and decryption share one datapath via round-key reversal. Because the TH1520 SoC on LicheePi 4A does not expose the Xuantie C910 RoCC port, we verify the design in three reproducible tiers on the board itself: (T1) RTL co-simulation of an sm4_rocc wrapper passes 1040/1040 vectors for both the standalone datapath and the full system. (T2) A pure-C reference model passes 10/10 GB/T 32907-2016 vectors on the real C910 at a measured 291.9 Mbps. (T3) A Linux illegal-instruction trap-and-emulate prototype confirms ISA and OS-level semantics. Open-source synthesis (Yosys + SkyWater Sky130) gives a measured area of 133 kGE and a switching-dominated post-synthesis power estimate of ≈0.28 W at 100 MHz (≈22 pJ/bit, ≈46 Gbps/W). At 100 MHz the unrolled pipeline reaches an RTL simulation-equivalent steady-state throughput of 12.8 Gbps, about 43.9× the software baseline. Every reported number is reproducible with open-source tools only (Icarus Verilog, GTKWave, GCC, Yosys, Sky130 PDK). Full article

Predicting thermal breakthrough and optimizing injection-production parameters are essential for sustainable geothermal development. Traditional hydrothermal coupled simulations in porous media entail substantial computational costs, which limits their use in dense multi-parameter screening. This study develops a physics-constrained surrogate workflow for the Qihe geothermal doublet system by using COMSOL to generate hydrothermal simulation data and a deep neural network (DNN) to emulate the simulator response within a predefined operating domain. The DNN was trained on physics-driven synthetic outputs rather than independent field observations, and a 2.0 °C decrease in production temperature was used as the thermal breakthrough criterion. Under scenario-wise validation, the surrogate model achieved a test-set R² of 0.9995 and an RMSE of 0.0351 °C, indicating accurate approximation of the deterministic simulator response within the bounded parameter space. The surrogate-based global scan identified a favorable operating region near a well spacing of 462 m, a reinjection temperature of 20 °C, and a reinjection rate of 150 m³/h. To evaluate whether this result was affected by sparse well-spacing sampling, additional COMSOL simulations were performed at 430, 440, 450, 460, 462, 470, 480, 490, and 500 m under the same reinjection temperature and rate. These simulator-based validation cases showed a continuous thermal response with increasing well spacing. The 2.0 °C thermal breakthrough time increased from 46 yr at 430 m to 61 yr at 500 m, while the 50-year cumulative heat extraction increased from 6594.2 to 6722.9 TJ. The 430 and 440 m cases experienced thermal breakthrough before the 50-year design life, whereas the 450 m case was close to the design boundary. The 460 and 462 m cases did not reach the 2.0 °C decline threshold within the 50-year design life and retained relatively high heat-extraction efficiency per unit well spacing. Therefore, the engineering recommendation is revised from a single precise optimum to a locally validated spacing interval of approximately 460–462 m under the present equivalent-porous-medium assumption. The proposed workflow does not replace hydrothermal simulation; instead, it provides a rapid screening tool that narrows the design space before targeted simulator verification and field calibration. Full article

Physical unclonable function (PUF) based on intrinsic device randomness has emerged as promising hardware security primitives, yet combining secure encryption with neuromorphic recognition within a single device platform remains challenging. Here, we demonstrate a photosensing PUF based on an intrinsically random SnTe memristor capable of both image encryption and memristive neural network recognition. The SnTe memristor, fabricated with an In₂O₃:SnO₂/SnTe/Nb:SrTiO₃ structure, exhibits stable resistive switching and stable retention exceeding 4000 s. Synaptic biomimetic behaviors including learning-experience emulation, short-term plasticity and long-term plasticity are also realized. Notably, the device displays pronounced optical sensitivity that produces stochastic photocurrent fluctuations originating from unavoidable device-to-device variations under illumination. By quantizing these random photocurrents, an encryption key stream is generated and utilized for image scrambling and diffusion. A memristive neural network is constructed to classify the encrypted images, achieving a recognition accuracy of 95.1% with a loss of 0.15 after 300 training epochs. This work establishes a viable pathway from intrinsic optical randomness to secure neuromorphic computing, highlighting the multifunctional potential of SnTe memristors in integrated hardware security and brain-inspired computation. Full article

This work investigates a hardware-aware, circuit-level emulation of BB84-like circuit primitives on noisy intermediate-scale quantum (NISQ) processors. The motivation is to evaluate whether BB84-like basis sifting and intercept–resend-induced QBER behavior remain observable when selected BB84 operations are mapped to parallel single-qubit circuits on gate-based devices. The proposed mapping represents Alice’s preparation, optional Eve intercept–resend emulation, and Bob’s measurement as processor-internal circuit layers; it is therefore an on-chip emulation and not an end-to-end optical QKD implementation. Experiments combine real IBM superconducting processors with Qiskit, Cirq, and Azure/Q# simulator-based or noise-modeled evaluations. Baseline QBER was first calibrated for each backend, and intercept–resend experiments then produced a clear QBER separation from the no-eavesdropper condition. The observed sifted-bit utilization was close to the expected 50% BB84 basis-matching reference, while the constant-depth circuit structure supported scalable raw/sifted-bit generation before any classical post-processing. These observations are treated as implementation-level consistency checks and backend-dependent experimental metrics, rather than as new BB84 protocol-level results. Finite-shot uncertainty, calibration drift, and backend-specific noise are treated as limitations of the proposed QBER-based evaluation rule rather than as deployment-level security guarantees. Because the study does not implement a physical quantum channel, authenticated classical communication, error correction, privacy amplification, finite-key security analysis, or general QKD attack models, the reported metrics should be interpreted as raw/sifted-bit experimental metrics and QBER-based disturbance evaluation for BB84-like NISQ emulation, not as secure key rates, secure throughput, or practical QKD deployment results. Full article

The Dynamic Economic Dispatch (DED) problem underpins the cost-efficient and reliable operation of modern power systems, yet valve-point loading, ramp-rate coupling, and the growing share of intermittent wind, photovoltaic, and pumped-storage hydro (PSH) resources render it highly non-convex. Metaheuristic methods typically require large computational budgets and hand-crafted constraint-handling rules, whereas deep reinforcement learning agents rarely guarantee the feasibility of the schedules they produce. To address both limitations, this paper proposes a Two-Stage PPO–RLMPA framework that couples data-driven policy learning with a biomimetic metaheuristic search inspired by marine predator–prey dynamics. In the first stage, a Proximal Policy Optimization (PPO) agent is trained on a Markov Decision Process reformulation of DED in which a deterministic Safety Layer projects every raw action onto the feasible set defined by capacity, ramp-rate, and power-balance constraints, so the policy only observes physically viable transitions. In the second stage, the PPO dispatch is refined by the RLMPA module, a Marine Predators Algorithm (MPA) whose exploration–exploitation balance, Lévy-flight foraging, and Fish Aggregating Devices (FADs) attraction mechanisms emulate strategies documented in marine ecosystems; its step-size factor and FADs probability are further adapted online by a Deep Q-Network. This biomimetics-informed refinement translates predator–prey foraging intelligence into economically efficient thermal dispatch under valve-point non-convexity. Across 30 independent runs on ten- and twenty-unit benchmark systems with wind, PV, and PSH integration, the framework attains best costs of USD 368,763 and USD 737,348 on Test Systems 1 and 2, corresponding to reductions of approximately $1.1\%$ and $4.4\%$ over the CFCEP baseline, with zero post-repair constraint violations in every run. Full article

There is a need for research to support the transition away from single-use plastic packaging towards a circular economy. This research developed simulated wear processes and assessment techniques that emulate aspects of a reuse system in order to evaluate different plastic food packaging types that are typically used for single-use applications. Two thermoformed polyethylene terephthalate materials (rPET and heat-resistant PET) for food packaging trays were tested. Researchers subjected both thermoformed packs to a range of simulated wear processes including wash cycles, simulated damage, surface scratching, and artificial fouling. Assessment techniques included using adenosine triphosphate (ATP) swabs to indicate cleanliness of the pack surface and 3D scan data to measure physical change. The findings show that scratch damage applied to packs, following fouling and wash cycles, produced promising readings under 30 relative light units (RLUs) on ATP swabs. The heat-resistant PET packs exhibited minimal deformation throughout repeated wash cycles. The assessment techniques developed to evaluate plastic materials have provided valuable insight into the cleaning, damage, and deformation of plastic packaging. These insights can support more complex decision making in the design and production of circular food-to-go plastic packaging solutions. Full article