Search Results (1,060)

The development of fuel cell electric vehicles (FCEVs) remains challenged by complex system integration, powertrain design, and the limited availability of experimental data under realistic operating conditions, which constrains the validation of energy management systems (EMSs) and system-level performance assessment. To address this gap, this study presents a validated test bench platform for fuel cell powertrains that combines a hardware-based powertrain test bench with a simulation environment for EMS analysis. The platform enables the integration and testing of a fuel cell powertrain in an electric van under realistic operating conditions. Validation under the US06 driving cycle shows an equivalent hydrogen consumption deviation of only 5.1 g (2.4%) between the hardware and simulation environments, demonstrating high platform reliability. A comparative analysis of load-following and average load power strategies is conducted. Results indicate that the average load power strategy achieves higher energy efficiency, reducing equivalent hydrogen consumption by 1.8%, 3.8%, and 6.4% under city, rural, and highway conditions, respectively. The efficiency advantage becomes increasingly pronounced as power demand rises. The proposed platform provides a validated framework for system-level development, validation, and evaluation of fuel cell powertrain systems. Full article

Liriomyza trifolii is a global pest of vegetables and ornamental plants and a major invasive species in China. Long-term irrational use of chemical insecticides has reduced control efficacy, driving interest in plant-derived alternatives. Bitter gourd, Momordica charantia, extracts show potential against various pests, but their effects on L. trifolii are unknown. To address this, host suitability tests were first conducted and revealed that L. trifolii exhibited significantly higher adaptability to kidney bean than to bitter gourd in terms of both oviposition and feeding, and failed to complete its life cycle on bitter gourd. Further treatment with ethanol extracts of bitter gourd leaves demonstrated dose-dependent adulticidal activity; at the LC₅₀ concentration, the extract not only reduced feeding punctures and oviposition, but also significantly decreased egg hatching and larval survival, while showing no significant impact on pupation or emergence. To elucidate the underlying molecular mechanisms, integrative transcriptomic and metabolomic analyses were subsequently performed. Transcriptomics identified 254 differentially expressed genes (DEGs) enriched in ribosome, oxidative phosphorylation, and peroxisome pathways, and upregulated DEGs included a vitellin-degrading protease and cytochrome P450. Meanwhile, metabolomics detected 272 differential metabolites (DEMs): upregulated metabolites were linked to purine metabolism, while downregulated ones were associated with cysteine/methionine and arginine/proline metabolism. Integrated analysis revealed “Biosynthesis of amino acids” as the sole common pathway, with S-adenosyl-L-homocysteine, N-succinyl-LL-2,6-diaminoheptanedioate, and glutamine synthetase (Ltr05G008090) showing significant correlations. These findings suggest that reprogramming of amino acid and nitrogen metabolism may participate in the response and could serve as a candidate response pathway. Accordingly, this study may offer a theoretical basis for the future development of botanical insecticides based on bitter gourd leaf extract against L. trifolii. Full article

This study investigates the performance, energy flow, efficiency, and environmental impact of a C-segment battery electric vehicle (BEV). As BEVs are increasingly considered a sustainable alternative to conventional internal combustion engine vehicles, a detailed understanding of their energy utilization and operational emissions is essential. A MATLAB/Simulink-based vehicle model incorporating an 88.5 kWh battery pack, a 160 kW permanent magnet synchronous motor (PMSM), regenerative braking, and longitudinal vehicle dynamics was developed. The developed model was validated by comparing the simulated vehicle performance characteristics with the publicly available specifications and performance data of the reference TOGG T10F vehicle. The vehicle was evaluated under the WLTP Class 3 driving cycle, while the effects of aggressive and high-speed driving conditions were further investigated using the US06 and Artemis Motorway 150 cycles. The results indicate a net vehicle energy consumption of 136.4 Wh/km and a driving range of 623 km under WLTP conditions. The PMSM achieved average efficiencies of 93.4% in traction mode and 92.7% in regenerative braking mode, while the cumulative battery-to-wheel drivetrain efficiency reached 81.5%. In addition, approximately 19.9% of the consumed energy was recovered through regenerative braking. Vehicle emissions were also assessed using different electricity generation mixes based on the rated energy consumption, including charging losses, yielding operational emissions between 27.7 and 116.0 gCO₂e/km. The findings demonstrate that the developed model provides realistic performance predictions and confirm the potential of BEVs to achieve high efficiency and substantially lower emissions than conventional passenger vehicles. Full article

Background/Objectives: Mesoporous silica nanoparticles (MSNs) are widely investigated as nanocarriers for drug delivery, gene transfer, and bioimaging. However, the mechanisms underlying their size-dependent cytotoxicity at the metabolic level remain incompletely understood. This study aimed to determine whether different-sized MSNs induce distinct patterns of subcellular injury and metabolic reprogramming in lung epithelial cells. Methods: A549 cells were exposed to 80 nm and 600 nm MSNs at 50 and 200 μg/mL for 24 h. Ultrastructural changes were examined by transmission electron microscopy (TEM). Intracellular reactive oxygen species (ROS) and Ca²⁺ were measured by 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA) and Fluo-4 AM fluorescence, respectively. Inflammatory gene expression (IL1B, IL6, TNFA, HIF1A) was quantified by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Untargeted metabolomics were performed using combined gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) platforms, followed by principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and MetaboAnalyst-based pathway enrichment. Results: TEM revealed distinct size-dependent subcellular distributions: 80 nm MSNs were predominantly associated with mitochondrial abnormalities, including cristae disruption, swelling, and mitophagy-like features, whereas 600 nm MSNs accumulated in endocytic vesicles with membrane disruption. Metabolomic profiling showed that 80 nm MSNs were associated with TCA cycle blockade—characterized by the accumulation of early intermediates (citrate, oxaloacetate) and the depletion of distal intermediates (fumarate, malate)—with compensatory glycolytic activation (increased glyceraldehyde-3-phosphate and pyruvate) and reduced deoxynucleotide pools (dCDP, dUMP). By contrast, 600 nm MSNs triggered broad nucleotide triphosphate accumulation (ATP, CTP, dGTP, dCTP), amino acid depletion, and robust inflammatory activation, including a ~136-fold increase in IL1B expression and HIF1A transcriptional upregulation. PCA and PLS-DA confirmed distinct size-dependent metabolic phenotypes. Conclusions: MSN size strongly influences subcellular targeting—80 nm particles were predominantly associated with mitochondrial injury while 600 nm particles disrupted endocytic vesicles—driving qualitatively distinct patterns of metabolic reprogramming and inflammatory signaling. These findings establish a correlative mechanistic framework linking particle size to organelle-specific injury and provide candidate metabolic markers for nanotoxicological evaluation. Full article

Speed controllers for electric micromobility (EMM) drives are increasingly developed with Model-Based Design and deployed as automatically generated code, yet the cost that a given control law actually imposes on the target, and the mechanism by which competing laws differ once deployed, are seldom reported. This paper addresses both questions on an EMM-class test bench built around a 36 V, 250 W in-wheel BLDC motor. A proportional-integral (PI) regulator and a first-order Takagi-Sugeno (TS) fuzzy regulator are specified in Simulink, auto-coded to ANSI-C by Embedded Coder, and deployed unchanged on an STM32F446RE target driving a custom three-phase inverter through six-step Hall commutation. Over a six-step, 180 s duty cycle reaching 21.1 km/h, the two regulators are shown to occupy opposite ends of the speed-versus-damping trade-off. On the 30 to 100 RPM ascending step under load, the PI reaches the set-point in $0.4\pm 0.1$ s with $21.6\%$ overshoot and the TS in $2.7\pm 0.1$ s with $1.5\%$ overshoot, both quoted at the resolution of the 10 Hz acquisition, and over the complete duty cycle, a window that also contains segments on which neither regulator has control authority, the TS lowers the tracking RMSE by 9.4%. A structural analysis of the deployed firmware excludes the realisation form as the cause. The positional and incremental forms are algebraically equivalent while the command is unsaturated, which is the regime of the step above. Under saturation, the incremental accumulator of the TS is not clamped and winds up exactly as the positional PI integrator does. The two loops are also shown to share the same unfiltered speed feedback and the same command saturation limits. The difference is traced instead to the effective gains realised by the seven consequents. Far from the set-point, the TS applies an integral gain three to twelve times weaker than the PI for a comparable proportional gain. A fixed-gain PI in that range is predicted to reproduce the response for one eighth of the Flash. The embedded cost of both regulators is then quantified on the target from the linker map, the fuzzy controller occupying 2325 Bytes of Flash against 266 Bytes for the PI, a factor of 8.7, and 200 Bytes of stack against 32 Bytes, a factor of 6.3, rising to 248 Bytes against 32 Bytes when the complete call tree is counted, for 0.45% of the available Flash. The complete platform, comprising the inverter, the Hall front end, the auto-generated firmware, and a Python supervisory interface, is described together with its deployed timing, PWM, and saturation parameters. Full article

Main memory carries data outside the processor’s trust boundary, so commodity systems-on-chip (SoCs) increasingly encrypt it; yet, in-line memory encryption engine itself becomes a differential power analysis (DPA) target whose key, if recovered, unlocks all of dynamic random-access memory (DRAM). We present PACE, a page-adaptive, cache-anchored memory encryption engine for RISC-V that makes nth-order DPA resistance practical and keeps cryptographic latency off the cache eviction critical path. PACE inserts a TileLink adapter between the last-level cache and the memory port and applies, per physical page, one of four policies (plaintext/confidentiality/confidentiality+integrity/+masking-order-d) selected from RISC-V page table bits through a memory-mapped control plane. Confidentiality uses counter mode whose per-line keystream is precomputed during cache residency; integrity is tree-free at the embedded operating point via on-chip counters and tags, with a live split counter block-MAC Bonsai Merkle tree for scale-out. DPA resistance is layered: ISAP-style fresh re-keying caps the data complexity per key at $q\approx 1$, and domain-oriented masking (DOM, d + 1 shares) protects the sole key processing block to order d. We implement PACE in Chisel on a Rocket SoC (Chipyard) and evaluate it with open-source tooling. A deterministic TileLink-level harness proves ciphertext-in-memory and detects tamper/replay/splice, and the live Tier-B engine (DRAM counters and per-line message authentication codes (MACs) plus an on-chip-rooted block-MAC tree) is validated from end to end on full Rocket and BOOM SoCs and on the FPGA; the masked Ascon-p S-box is proven order-d secure (d = 1, 2) under a glitch- and transition-aware model by three independent formal tools (COCO, PROLEAD, and SILVER, the last also deciding the full composability lattice and confirming exact glitch-robust order-2 probing security), with COCO extending the exact verdict to the highest synthesized order d = 3 (secure at probing orders 1–3); a simulated trace correlation power analysis (CPA) recovers the full key from an unprotected core and is defeated by masking, with a mutual information analysis confirming the $N\propto {\sigma }^{2(d+1)}$ trace amplification law. We further realize PACE on field-programmable gate array (FPGA) silicon: the engine plus an on-chip ring oscillator power sensor is placed, routed, timing-closed at 100 MHz, and programmed on a Xilinx XC7Z020, and we drive a fixed-vs-random Test Vector Leakage Assessment (TVLA) campaign read back entirely over a JTAG (Joint Test Action Group). A multi-core configuration and a Linux control-plane driver are likewise validated. Across synthetic access patterns and named application kernels (AES, SHA-256, matrix multiplication, pointer chasing) on both in-order Rocket and out-of-order BOOM, application-level overhead is within measurement noise of plaintext for cache resident workloads (masking, in particular, is cycle-identical to plain confidentiality), and we characterize the cost of each policy, masking order, and re-keying interval, demonstrating side-channel-hardened memory encryption on open RISC-V hardware. Full article

Tourism is carbon-intensive, with transport and mobility driving much of its greenhouse-gas emissions, yet destination-level evidence on carbon hotspots and decarbonization from a supply-chain perspective remains scarce, especially in developing regions. This study examines low-carbon tourism supply chain management in seven purposively selected provinces of Northeastern Thailand (Isan) using a qualitative-dominant convergent mixed-methods design that integrates demand- and supply-side evidence. A visitor survey yielded 74 open-ended responses (60 complete questionnaires), alongside seven semi-structured key-informant interviews with tourism supply chain operators across the seven provinces. Quantitative data were analyzed with descriptive statistics, and qualitative data with thematic analysis using qualitative data analysis software, drawing on code-frequency and co-occurrence analysis; the two strands were triangulated in joint displays. Visitors reported moderate satisfaction (grand mean 3.91 on a five-point scale) but rated environmental management and safety lowest, engaging with sustainability through visible service cues rather than emissions. Key informants identified perceived carbon hotspots, carrying capacity, and seasonality as dominant concerns, attributing the destination’s footprint chiefly to transport dependence (informant-reported estimates ranging from approximately 80% to nearly 100% private-car arrivals, amid limited public transport) and accommodation energy; these hotspots reflect stakeholder perceptions rather than measured emissions, as no carbon accounting, environmentally extended input–output analysis, or life-cycle assessment was conducted. Integration revealed a demand–supply perception gap and five proposed, interdependent low-carbon pathways: coordinated mobility, accommodation energy efficiency, strengthened local procurement, carrying-capacity and waste management, and multi-stakeholder governance. The findings offer a developing-region, supply-chain-oriented basis for future tourism decarbonization research and practice. Full article

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking effective targeted therapies. The molecular drivers of its progression and metabolic reprogramming remain unclear. Here, we identify cell division cycle-associated 4 (CDCA4) as a novel oncogenic driver in TNBC. Analysis of the TCGA-BRCA dataset, including 1085 breast cancer tissues and 112 normal tissues, showed that CDCA4 expression was significantly upregulated in breast cancer tissues. Subgroup analysis of TCGA-BRCA samples further showed higher CDCA4 expression (fold change = 1.707) in TNBC than in non-TNBC samples [TNBC, n = 116; non-TNBC, n = 984]. Survival analysis demonstrated that high CDCA4 expression was associated with poorer overall survival, with a hazard ratio of 1.54 (log-rank p = 0.0053). Functional assays demonstrated that CDCA4 knockdown suppresses proliferation, migration, invasion, and tumor growth in vitro and in vivo, whereas overexpression exerts opposite effects. RNA-sequencing revealed that CDCA4-regulated genes are enriched in lipid metabolism and mTOR signaling pathways. Mechanistically, CDCA4 depletion reduces intracellular lipids and the expression of lipogenic enzymes (FASN, ACC1). We show that CDCA4 activates mTOR and increases the nuclear active form of SREBP1, enhancing its promoter occupancy. Pharmacological mTOR inhibition reverses CDCA4-induced malignancy and metabolic alterations. Furthermore, Our findings suggest that SESN2 may contribute to CDCA4-mediated activation of mTOR signalling. SESN2 knockdown attenuates mTOR signaling and negates the pro-tumorigenic effects of CDCA4 overexpression. Collectively, these findings demonstrate that CDCA4 drives TNBC progression and lipid reprogramming via the SESN2/mTOR/SREBP1 axis, positioning CDCA4 as a potential prognostic biomarker and therapeutic target. Full article

In this paper, we present LightBAL, an ultra-lightweight deep learning framework for real-time active cell balancing and onboard balancing control in electric vehicle (EV) battery management systems (BMSs). Although active cell balancing can improve battery utilisation and performance, applying deep learning-based balancing control strategies remains prohibitive in typical embeddable BMS platforms because of the computational complexity and inference latency of deep models. In response to this issue, we propose an AI-physics-informed controller that forecasts the voltage difference of a single cell, the SoC variation, and the optimal balancing current based on proportional feedback closed-loop (FCLL) control. The introduced framework exploits wavelet-based adaptive denoising, multi-scale hierarchical feature learning using a cooperative Principal Component Analysis (PCA) and autoencoder feature extraction technique, and a lightweight One-Dimensional Convolutional Neural Network (Conv1D) coupled with Bidirectional Long Short-Term Memory (BiLSTM) (Conv1D-BiLSTM). The implemented lightweight network is further trained by model compression methodologies such as knowledge distillation and 8-bit quantisation-aware training, aiming for efficient deployment on edge devices. Experimental validation on the multivariate battery time-series dataset demonstrates that LightBAL achieves an F1-score of 96.64%, a balancing efficiency of 94.30%, and a Mean Absolute Error (MAE) of 0.0379, outperforming methods based on conventional ANN, LSTM, and CNN. LightBAL without compression takes only 1.26 s to conclude on a PC workstation; the inference latency of the embedded light model is as low as 28.7 ms. In addition, hardware-in-the-loop (HIL) validation on the Raspberry Pi 4 platform indicates that the framework can fulfil real-time inference requirements under normal operating conditions, taking 28.7 ms per balancing process. Simulation shows that the proposed approach significantly decreases cumulative balancing energy loss by 12.4% across several driving cycle conditions. Full article

Low-grade thermal energy is widely available in industrial waste-heat streams and natural temperature fluctuations, yet its utilization remains limited because of weak thermodynamic driving forces and the complexity of conventional heat-engine technologies. This study presents a physics-based framework for modular thermoelastic energy harvesting using the reversible thermal expansion and contraction of structural materials. Analytical models are established to quantify thermoelastic work, structural constraints, thermodynamic and exergy efficiencies, and long-term energy production. Material selection and thermo-mechanical limitations are evaluated through parametric analysis and finite-element verification. The results indicate that extractable work is fundamentally constrained by yield strength, buckling resistance, temperature swing, and the limited exergy content of low-grade heat. Scaling laws show that annual energy generation scales approximately linearly with active structural mass while remaining strongly dependent on column diameter, thermal-cycle frequency, and material performance indices. Thermodynamic and exergy efficiencies remain well below the Carnot limit, highlighting the inherent limitations of solid-state thermoelastic conversion. A techno-economic assessment further indicates that economic viability depends primarily on multi-cycle operation and low-cost implementation. Although the achievable energy density remains modest compared with conventional renewable technologies, the proposed framework provides quantitative performance limits and practical design guidelines for evaluating thermoelastic energy harvesting from low-grade heat. Full article

Changes in lake area directly reflect the state of regional hydrological cycles and ecological balance. However, the lack of long-term monitoring data and the complexity of driving factors make it difficult to formulate effective management strategies. This study used landsat imagery and the random forest (RF) algorithm to construct a time series of the lake area of Chirui Lake from 1990 to 2024, revealing a counter-seasonal phenomenon in which the lake area during the low-flow period was significantly larger than that during the high-flow period. To clarify the driving mechanisms behind this phenomenon, a comprehensive analysis of climatic and hydrological factors and lake area was conducted using methods such as the Pettitt test, Morlet wavelet analysis, principal component analysis (PCA), structural equation modeling (SEM), and long short-term memory (LSTM) neural networks. The results indicate that, from the perspectives of climate and hydrology, sub surface runoff is likely to have played a significant role in the area changes of Chirui Lake, and that between 2025 and 2029, the lake’s surface area is likely to continue to fluctuate significantly between 0.4 km² and 0.6 km². This study explored the influence of climate and hydrology on the changes in the area of Chirui Lake and provides five-year forecast of lake surface area, however, given the inherent uncertainties in the forecast, these findings should be viewed as preliminary references rather than direct decision-making tools for lake management. Full article

Variable-speed induction motor drives spend most of their service life at partial load, where rated-flux field-oriented control (FOC) is inefficient and where loss-minimizing control (LMC) recovers a large part of the loss. LMC, however, is brittle in two ways that matter in industry: it is tuned isothermally, so as the windings heat, the rotor-resistance drift detunes the field orientation and corrupts torque; and it treats the loss-optimal flux as a quasi-static set-point, so an abrupt load rise from a light-load, low-flux condition forces a slow flux rebuild that throttles torque. This paper proposes a thermally adaptive economic model predictive controller (TA-EMPC) that retains the energy optimum of LMC while removing both weaknesses. A temperature-coupled total-loss model (machine copper and core loss plus inverter conduction and switching loss) is minimized over a finite horizon subject to a torque-delivery constraint; a reduced-order two-node thermal observer updates the loss-defining resistances online without a temperature sensor; and a load-demand-aware flux-reservation term pre-magnetizes the machine ahead of anticipated torque rises. In simulations on a representative 7.5 kW drive, TA-EMPC matched the energy of static LMC to within 0.3% across pump, conveyor, and fast-cycling duty profiles—both saving 1.4–2.3% of cycle energy relative to rated-flux FOC, and up to about 14.7 efficiency points at very light load—while, unlike LMC, holding the steady torque error below 0.5% when the winding temperature rose by about 95 °C, to a hot steady state near 115 °C (a stator-resistance increase of roughly 37%) (against an 8% error for the non-adaptive scheme) and reducing the torque undershoot during a light-to-heavy load step from about 23% to near zero. All quantitative results reported in this work are obtained entirely in simulation. A per-step operation-count analysis—not an on-target timing measurement—indicates that the condensed quadratic-program formulation with move blocking is executable within the 100 µs sampling interval on a production digital signal controller for the chosen control horizon; experimental validation on a loaded dynamometer bench, together with on-target timing measurement, is identified as future work. The contribution is thus energy-efficient operation delivered with the torque robustness that loss minimization alone does not provide. Full article

Electric-vehicle gearboxes remain key design elements because they determine how motor speed and torque are converted into wheel speed and tractive effort over a driving cycle. This design-oriented scoping review synthesizes EV gearbox architectures, gear ratio selection, efficiency losses, NVH, planetary and compound planetary systems, lubrication, thermal behavior, reliability, manufacturability, and cost within one evidence-informed architecture-selection perspective. A structured search of Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and MDPI identified 312 records; after removal of 71 duplicates, screening of 241 titles and abstracts, and full-text assessment of 61 articles, 40 sources formed the reproducible structured-search core. A gap-directed supplementary search then added 12 sources in underrepresented areas, producing a 52-source synthesis set. The revised analysis reports publication trends, evidence-level distributions, technical-focus frequencies, and a dimension-separated evidence-count table for ratio count, gear train topology, and integration level. The evidence indicates that single-speed reduction gearboxes remain the mature baseline for many passenger EVs, whereas two-speed, multi-speed, planetary, compound planetary, and integrated e-axle solutions require application-specific justification based on system-level benefits and risks. An illustrative screening calculation demonstrates the framework logic without being presented as production-level validation. The principal gaps are experimentally validated loss and NVH maps, coupled efficiency–thermal–lubrication–durability analysis, reliability-aware mission-profile validation, standardized benchmarks, and transparent comparison of compound planetary and integrated e-axle systems. Across heterogeneous study conditions, reported energy benefits range from 2.4% for fixed-ratio optimization to 15% for selected multi-speed comparisons; these results are not pooled because the vehicles, motor maps, drive cycles, loss models, and validation methods differ. Full article

Microarchitectural side-channels leak data through caches, branch predictors, store buffers, and speculative execution. Evaluating defenses at cycle-model fidelity has forced a choice between functional tools (Spike, QEMU) and gem5’s hours-long Linux boots. ARCHER is a cycle-model-relative RV64IMAFDC RISC-V emulator with pluggable superscalar out-of-order execution, coherent caches, simultaneous multithreading, 1 to 16 harts, and thirteen speculation policies: an unrestricted baseline plus five classical defenses and seven new defenses spanning issue-gate, predictive-redirect, and selective-cleanup mechanism families. It boots Linux 6.6 in under three minutes, runs $3.7\times$ faster (geometric mean) than gem5’s DerivO3CPU on head-to-head bare-metal microbench, matches gem5 within $\pm 20$% on 4 of 10 workloads, matches Chipyard’s fab-ready RTL to a 9.4% mean cycle-count deviation under two fitted match-configurations, and produces bit-identical architectural output to QEMU at a median $88\times$ host-throughput advantage over Chipyard’s Verilator flow. Every published Spectre-family defense drives leakage to zero at sub-0.3% instructions-per-cycle (IPC) loss on a full Linux boot, and each new policy is validated on the attack surface its mechanism defends. The Adaptive Memory Encryption Scheme (AMES) adds a bus-layer engine with four authenticated ciphers (each validated bit-exact against its published specification) and per-leaf mask-XOR for Differential Power Analysis (DPA) hardening; a first-order Correlation Power Analysis consistency check confirms the direction of the theoretical bound under the shipped leakage model. Together, ARCHER and AMES provide a single INI-configurable environment for cycle-model side-channel evaluation in minutes rather than hours. Full article