Search Results (2,935)

Cephalopods and crustaceans are known to bioaccumulate heavy metals, potentially posing both non-carcinogenic and carcinogenic health risks to consumers. This study was conducted to determine heavy metal concentrations and assess associated health risks in the edible tissues of 84 cephalopod and crustacean samples. Heavy metal concentrations and assess associated health risks in the edible tissues of 84 cephalopod and crustacean samples collected from selected wholesale markets and major fish landing ports throughout Peninsular Malaysia. The analysis focused on nine heavy metals: selenium (Se), cadmium (Cd), lead (Pb), copper (Cu), zinc (Zn), antimony (Sb), tin (Sn), chromium (Cr), and manganese (Mn). The samples were digested using a microwave digestion system, and heavy metal concentrations were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Results showed that Mn was the most abundant metal, followed by Cr and Zn. Octopus (C. indicus) had the highest Mn concentration (5.01 mg/kg WW), while Rainbow shrimp (P. sculptilis) had the highest overall metal concentration (91.02 mg/kg WW). Significant differences were observed between cephalopods and crustaceans, with Cd and Sn concentrations being notably higher in cephalopods (p < 0.001). However, no significant associations were observed between heavy metal concentrations and sample weight or length, indicating a greater influence of environmental factors. Principal Component Analysis (PCA) explained 80.4% of the variance, with Cd, Sn, Pb, Cu, Zn, Cr, and Mn accounting for the majority of the variance. Estimated weekly intake (EWI) values ranged from 0.002 to 26.30 µg/kg bw/week for cephalopods and 8.02 × 10⁻⁶ to 243.175 µg/kg bw/week for crustaceans. All metal levels were below the permissible limits set by the Food and Agriculture Organisation of the United Nations/World Health Organisation (FAO/WHO). Hazard Index values were <1, indicating low non-carcinogenic risk, and Total Carcinogenic Risk values for Pb and Cr were below 1 × 10⁻⁴, suggesting negligible carcinogenic risk. Full article

In complex maritime environments and scenarios with severe signal interference, unmanned surface vehicle (USV) swarms face dual challenges: unreliable GNSS signals due to interference and difficulties in accurately calibrating multi-sensor installation errors. These issues severely constrain the capability for high-precision cooperative formation operations. To address these problems, this paper proposes a cooperative localization and all-source online calibration algorithm based on a unified factor graph optimization framework. First, a tightly coupled all-source graph framework is established, integrating navigation radar, electro-optical systems (EOSs) with laser rangefinders, IMU, and GNSS into a sliding window. By leveraging high-precision mutual observations among the swarm, strong geometric constraints are constructed to mitigate the drift of individual inertial navigation systems. Second, an adaptive GNSS weighting mechanism based on signal quality and a degradation detection strategy based on eigenvalue analysis of the Fisher Information Matrix (FIM) are designed. These mechanisms enable online identification and robust estimation of extrinsic parameters, effectively resolving calibration divergence under weak excitation conditions such as straight-line sailing. Finally, the proposed algorithm is validated using field data from three USVs combined with simulated interference experiments. Results demonstrate that the algorithm can rapidly converge to high-precision calibration parameters without artificial targets (radar translation error < 0.2 m, EOS rotation error < 0.05°). During periods of simulated GNSS interference, the cooperative localization root mean square error (RMSE) is reduced to 2.85 m, representing an accuracy improvement of approximately 84.5% compared to traditional methods. This study achieves a “more accurate as it runs” cooperative navigation effect, providing reliable technical support for USV swarm applications in GNSS-denied environments. Full article

Capacitive Power Transfer (CPT) technology, as an emerging wireless power supply solution, exhibits great potential in areas such as electric vehicle charging, underwater equipment power supply, biomedical implants, and consumer electronics due to its advantages of low cost, light weight, insensitivity to metals, and potential high power density. However, the coupling capacitance is susceptible to the influence of transmission distance, misalignment, and changes in environmental media, leading to fluctuations in system output characteristics and becoming a key challenge restricting its application. This report aims to systematically review the key technological advancements proposed in recent years to achieve constant voltage/current/power output and enhance system robustness. Firstly, this study categorically reviews the CPT system topologies for constant voltage output, constant current output, and constant power output, analyzing the principles, advantages, and disadvantages of achieving load-independent or coupling-independent output. Secondly, it sorts out various active and passive control strategies, including frequency regulation, impedance matching, adaptive parameter switching, and pulse modulation, which are used to manage dynamic changes. Next, it summarizes innovative design and optimization methods for couplers tailored to specific application scenarios, such as large-gap electric vehicle charging, underwater, and rotating mechanisms. Finally, based on existing research, this review describes the challenges that CPT technology still faces in achieving efficient, high-power, and highly robust constant output, and looks forward to future research directions. Full article

High-performance speed control of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicle (EV) applications faces significant challenges due to inherent nonlinearities, parameter variations, and signal non-idealities such as sensor noise and measurement latency. To address these issues, this paper proposes a robust PI-based Fractional-Order PSO-Fuzzy Weight Controller (PI-FOPSOFWC). The proposed strategy integrates a fractional-order PI (FOPI) core to ensure iso-damping robustness, a fuzzy inference mechanism for online gain scheduling against nonlinear load dynamics, and a novel Fractional-Order Particle Swarm Optimization (FOPSO) algorithm for optimal parameter tuning. A key contribution of this study is the validation of the control strategy within a high-fidelity co-simulation framework coupling MATLAB/Simulink with CarSim 2023, which incorporates realistic vehicle dynamics and time-varying road loads unavailable in conventional simplified simulations. Co-simulation results demonstrate that the proposed controller effectively eliminates overshoot in step responses and maintains stability under significant parameter mismatches ($2.0\times$ inertia). Furthermore, under the EPA urban driving cycle, the proposed method reduces the speed tracking Root Mean Square Error (RMSE) by 75.0% compared to the standard PI controller. Computational complexity analysis further confirms the feasibility of the proposed algorithm for real-time implementation in commercial EV traction drives. Full article

This paper investigates deflection deformation and premature bearing failure in deep-hole machining spindles with high length-to-diameter ratios under eccentric loading. A contact stiffness model for angular contact ball bearings was developed based on Hertz contact theory. Combined with the finite element method (FEM), a comprehensive mechanical analysis model of the spindle was established. The results show that spindles with high length-to-diameter ratios exhibit significant cantilever behavior, leading to considerable front-end deflection under eccentric loading. This deflection causes the inner and outer rings to incline, resulting in localized stress concentrations, which are the primary contributors to spindle fatigue failure. To improve the spindle’s stress distribution and dynamic performance, an optimized design replacing the metal housing with carbon fiber composite material is proposed. Static and modal analyses were performed using Abaqus and Romax. The analysis results demonstrate that the carbon fiber shell reduces self-weight deformation by 35.8%, decreases coupled deformation under self-weight and grinding loads by 28.6%, and increases modal fundamental frequencies by 20.88% to 47.41%. These improvements significantly enhance structural stiffness and dynamic stability. Experimental vibration monitoring during machine testing validated the accuracy of the modeling and simulation. Full article

Urban sustainability is increasingly challenged in rapidly transforming coastal regions. This study operationalizes urban green development capacity as a measurable proxy for urban sustainability and constructs a multidimensional assessment framework integrating economic development, technological innovation, green transformation performance, and green coordination capacity. Using the entropy weight–TOPSIS method, we conduct a longitudinal analysis of Qingdao (2014–2023) and a cross-sectional comparison of 24 coastal cities in eastern China in 2023. Qingdao’s composite score increased steadily from 0.25 in 2014 to 0.81 in 2023, with post-2020 growth accelerating markedly following structural and policy adjustments. In contrast, inter-city disparities remain substantial: the leading cities exhibit sustainability capacity levels nearly twofold higher than those at the lower end of the distribution. Cities with stronger technological innovation intensity and institutional coordination consistently outperform others, highlighting the importance of governance–technology coupling. These results suggest that urban sustainability is associated with a coupled interaction pattern among capital, technology, performance, and institutional coordination rather than linear economic expansion. The study provides a quantitative tool for measuring and benchmarking urban sustainability capacity and offers empirical support for differentiated sustainability transition pathways in coastal and transition-economy cities. Full article

Coastal marine sediments and beach sands receive microbial and genetic inputs from wastewater discharge, urban runoff, aquaculture, wildlife, and recreational activity, yet their role as coupled microbial–genetic interfaces linking environmental processes and human exposure remains incompletely synthesized. This review integrates quantitative evidence from culture-based studies, qPCR surveys, metagenomic analyses, and multi-year monitoring investigations focused on coastal sediments and sands. Reported antibiotic resistance gene (ARG) concentrations in coastal sediments reach 2.2 × 10⁹ copies g⁻¹ (wet weight) for sul1 in wastewater-impacted systems, with total ARG abundances commonly ranging from 1.59 × 10⁷ to 2.88 × 10⁸ copies g⁻¹ in effluent-receiving zones and tetM reported at 1.43 × 10⁷ copies g⁻¹. Beach sands contain measurable resistance markers, including intI1 at 9–3823 copies g⁻¹ and blaTEM up to 14 copies g⁻¹ in wet sand. Viable fecal indicator bacteria and pathogens have been cultured directly from sands, including Staphylococcus aureus at 0–8710 CFU g⁻¹ and methicillin-resistant S. aureus at 0–605 CFU g⁻¹. Collectively, the evidence indicates that coastal sediments and sands function as structured microbial and genetic reservoirs requiring integrated assessment of benthic retention, hydrodynamic redistribution, and exposure-relevant interpretation. Full article

Secure and bandwidth-conscious transmission of model updates is a central bottleneck in distributed machine learning. Existing secure aggregation and homomorphic encryption pipelines either reveal more than the task requires or incur prohibitive computation and communication costs. We introduce a verifiable functional encryption (VFE) framework that releases only the intended linear functions of client gradients while providing end-to-end integrity and privacy guarantees under standard lattice assumptions. Our instantiation, FlowAgg-FE, combines two novel components. First, KS-IPFE, a key-splittable inner-product FE scheme, supports per-round weighted aggregation, vector packing, and on-the-fly function changes without client re-encryption; function keys are distributed across two non-colluding helpers, eliminating a single point of trust and enabling lightweight, homomorphically verifiable tags on decrypted outputs. Second, PaS-Stream is a rate-adaptive encryption-and-compression pipeline that couples sketch-based gradient compression with batched FE ciphertext streaming, ensuring unbiased aggregation in the presence of stragglers and dropouts. We further bind client-side clipping to zero-knowledge range proofs and offer an optional differentially private release layer that composes with FE to yield $(\epsilon ,\delta )$-privacy. A prototype based on LWE demonstrates practicality across cross-device and cross-silo training: client uplink is reduced by 1.9–3.4× and server CPU time by $1.6\times$ versus state-of-practice encrypted secure aggregation, with accuracy within $0.3\%$ of plaintext baselines and correctness preserved under up to $30\%$ client dropout. These results show that verifiable FE can make secure, communication-efficient gradient transmission viable, as appropriate for theme of security and privacy in distributed machine learning of the Special Issue. Full article

As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to capture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a multi-head attention mechanism and DQN (Deep Q Network) to adaptively adjust attention weights optimizing its performance in intrusion detection tasks. After extensive experiments using the UNSW NB15 and CIC-IDS2017 dataset, the proposed GCN-DQN outperformed the baseline model in classification accuracy. We also applied LIME and SHAP techniques to provide explainability to our proposed intrusion detection model. Full article

Rapid urbanization increasingly threatens urban cultural heritage. While buffer zones are crucial for mitigating external pressures, conventional delineation relies on value-based or geometric rules, overlooking parcel-scale heterogeneous externalities. This study addresses this gap by proposing a parcel-based, risk–value coupling framework that delineates heritage buffer zones and supports differentiated land-use regulations. In this study, “negative-impact risk” is operationalized as a composite proxy of cumulative urban development pressures that may increase the likelihood and potential severity of adverse externalities on heritage settings, rather than a full hazard–exposure–vulnerability risk model. And we construct a multi-source indicator system with 12 parcel-level indicators to characterize negative impact risk and heritage value, and adopt a hybrid weighting strategy integrating an AHP, entropy weighting, and game-theoretic combination to reconcile expert judgement and data-driven heterogeneity. To address uncertainty in multi-criteria evaluation, a cloud model maps indicator sets into discrete management levels. The framework is applied to the Pingjiang Historic District in Suzhou, China, using 121 land parcels as decision units. Results show that the approach identifies spatial risk–value patterns and delineates an operational buffer prioritizing parcels with elevated coupled scores. Compared with a fixed-distance buffer, it achieves greater coverage of high-risk parcels while maintaining a smaller regulatory scope. The parcel classification is then translated into tiered planning controls, including development intensity limits, land-use rules, and monitoring priorities. The framework integrates risk management and heritage conservation to support uncertainty-aware, proactive, and transferable zoning decisions. Full article

Optimizing the coupling effect between irrigation and N fertilizer to balance mung bean (Vigna radiata L.) production and the effective utilization of water and fertilizer resources is an important challenge for sustainable agricultural production. In this study, a field drip irrigation experiment was conducted on Mollisols in Northeast China, and twelve treatments were performed: four levels of soil water content (W1, 80~100% of field capacity; W2, 70~90% of field capacity; W3, 60~80% of field capacity; W4, rainfed condition) and three N application treatments (40 (N1), 80 (N2), and 120 (N3) kg/ha). We analyzed the coupling effects of water and N levels on mung bean growth, yield and yield components, water consumption, water use efficiency (WUE) and N partial factor productivity (PFP) in 2021 and 2022 and screened the optimal water and N regulation by the TOPSIS method. The results showed that the amount of N application dominated the regulation of water and N. In the first year, plant height, stem diameter, number of seeds per pod, 100-seeds weight, yield, aboveground dry matter accumulation, WUE, and PFP in mung bean decreased with increasing N applications at the same irrigation treatment. Furthermore, except for WUE, all results of the W3N1 treatment reached the highest levels, at 79.14 cm, 13 mm, 12.4, 6.2 g, 1430.45 kg/ha, 79.27 g (the drumming stage), and 35.76 kg/kg, respectively. The second year, plant height, stem diameter, yield and WUE had an increasing trend with increasing N applications at the W1. Based on the TOPSIS method, the W3N1 treatment could obtain the optimal comprehensive benefits of yield, WUE and PFP. This study can provide a most suitable water and N regulation model for guiding mung bean cultivation in the Mollisols region of Northeast China. Full article

Seed viability fundamentally determines crop establishment, stress resilience, and yield stability in pepper (Capsicum annuum L.), yet conventional assessment remains destructive, labor-intensive, and poorly scalable, while existing spectral learning approaches largely lack physiological interpretability, limiting their reliability for industrial seed quality management. Here, we present ViaNet, a lightweight, interpretable deep hyperspectral classification framework for 1038 naturally aged pepper seeds labeled via standardized 14-day germination tests. ROI-averaged hyperspectral reflectance vectors are modeled as a binary classification task, and ViaNet integrates Successive Projections Algorithm (SPA)-based wavelength sparsification with Efficient Channel Attention (ECA)-driven spectral weighting within a compact 1D-CNN architecture, enabling physiologically grounded feature learning under strict computational constraints. The model achieves recall for germinable seeds (79.75%) and outperforms classical machine learning methods. In addition, ViaNet consistently highlights reproducible spectral bands associated with natural-aging-related biochemical changes as reported in the literature (e.g., carotenoid-related absorption features in the near-UV region). By coupling spectral feature selection with attention-guided wavelength focusing, ViaNet establishes a closed analytical chain from spectral compression to physiologically interpretable inference. This framework balances predictive accuracy, interpretability, and deployability and provides a scalable, non-destructive, and biologically informed paradigm for hyperspectral seed viability assessment. Full article

Effective collective motion hinges on the seamless transfer of local information, yet vision-based mechanisms, while potent for generating rapid consensus, are inherently fragile. Visual links can be severed instantly by occlusions, leading to a phenomenon characterized as “sensory amnesia.” Seeking to fortify this vulnerability, Pheromone-Modulated Body Orientation Change (PM-BOC) is introduced as a dual-channel framework that fuses transient visual cues with a persistent environmental memory. Rather than treating these inputs in isolation, motion salience is quantified via BOC and mapped onto a decaying virtual pheromone field, dynamically modulating interaction weights by coupling instantaneous visual projections with local pheromone concentrations. This strategy effectively constructs a temporal buffer, bridging the informational voids left by blind spots. Validation, spanning from systematic physics simulations to high-fidelity simulations with a swarm of 50 UUVs, reveals that PM-BOC sustains superior cohesion in obstacle-laden environments where baseline visual models falter. Notably, this coupling suppresses high-frequency sensory noise while inducing resilient, scale-free velocity correlations that scale linearly with system size. By reconciling the trade-off between the immediacy of visual responsiveness and the robustness of environmental memory, this study offers a scalable paradigm for engineering resilient swarm systems capable of navigating the uncertainties of perception-limited environments. Full article

Against the backdrop of significant climate change, resource constraints, and industrial upgrading, optimizing the coupling and coordination of the Water–Energy–Food (WEF) system in Northeast China is crucial for ensuring regional security and sustainable development. Existing research lacks long-term continuous analysis and inter-provincial comparisons. This article utilizes data from 2005 to 2023 to evaluate the development of the three provinces of Northeast China using a framework of 24 indicators covering safety, coordination, and resilience. Methodologies employed include the entropy weight method, the coupling coordination model, and the constraint model. The results show that: (1) The overall development level fluctuates with an overall upward trend, reaching a medium-coordinated level, and there are notable differences between provinces. (2) The coordination levels among provinces initially diverged but later converged, evolving from near dysfunction to a state of moderate coordination. Additionally, a bidirectional reinforcement mechanism has formed between system security and coupling coordination. (3) The key obstacles are deep-rooted in the system’s structure and have cross-provincial implications due to interconnected infrastructure, among which energy self-sufficiency and water-use efficiency are the primary constraints. (4) Resilience serves as a key mediating variable in regulating the relationship between security and coordination within the WEF system. In order to achieve a high level of coordination between WEF systems, it is necessary to formulate tailor-made subsystem governance policies, enhance the technological empowerment of water and energy conservation and efficiency improvement, and promote the development of resilient infrastructure. This integrated approach could systematically resolve resource competition conflicts, thus enhancing the overall resilience and sustainability of regional development. Full article

Scale variations pose a significant challenge in hyperspectral target tracking. To address this challenge, we propose a method that leverages spatial–spectral attention mechanisms combined with depth estimation to enhance the capabilities of the tracker. First and foremost, the method processes raw hyperspectral video inputs through spatial–spectral attention weight variance gradient, utilizing variance gradient for effective dimensionality reduction and obtaining fused spatial–spectral attention weights for subsequent tracking. Moreover, our method integrates a dual-path preprocessing module for handling template and search regions, coupled with a Vision Transformer encoder that incorporates depth contrast enhancement. Last but not least, the proposed tracker is enhanced by the weight adaptive mixed fusion that optimizes the fusion of the fused spatial–spectral attention weights with enhanced depth contrast. The key advantage of our proposed method lies in depth-aware geometric constraints and the use of spectral–spatial information, which enables robust appearance modeling that intrinsically adapts to target scale variations. Extensive experiments on hyperspectral video sequences demonstrate that our method achieves state-of-the-art performance, with an AUC of 0.6704 and a DP@20 of 0.9455, outperforming existing state-of-the-art methods by 3.1% in robustness to scale variations. Full article