Search Results (23,998)

Magnetic microrobots have emerged as a promising platform for drug delivery in recent years. By enabling remotely controlled motion and precise navigation under external magnetic fields, these systems offer new solutions to overcome the limitations of traditional drug delivery nanocarriers, such as inadequate tissue penetration and heterogeneous biodistribution. Over the past few years, significant advancements have been made in the structural design of magnetic microrobots, as well as in drug loading techniques and stimuli-responsive drug release mechanisms, thereby demonstrating distinct advantages in enhancing therapeutic efficacy and targeting precision. This review provides a comprehensive overview of magnetic drug delivery microrobots, which are categorised into biomimetic structural, bio-templated and advanced material-based types, and introduces their differences in propulsion efficiency and biocompatibility. Additionally, drug loading and release strategies are summarised, including physical adsorption, covalent coupling, encapsulation, and multistimuli-responsive mechanisms such as pH, enzyme activity and thermal triggers. Overall, these advancements highlight the significant potential of magnetic microrobots in targeted drug delivery and emphasise the key challenges in their clinical translation, such as biological safety, large-scale production and precise targeted navigation within complex biological environments. Full article

Post-quantum cryptography (PQC) is rapidly being standardized, with key primitives such as Key Encapsulation Mechanisms (KEMs) and Digital Signature Algorithms (DSAs) moving into practical applications. While initial research focused on pure software and hardware implementations, the focus is shifting toward flexible, high-efficiency solutions suitable for widespread deployment. A system-on-chip is a viable option with the ability to coordinate between hardware and software flexibly. However, the main drawback of this system is the latency in exchanging data during computation. Currently, most SoCs are implemented on FPGAs, and there is a lack of SoCs realized on ASICs. This paper introduces a complete RISC-V SoC design in an ASIC for Module Lattice-based KEM. Our system features a RISC-V processor tightly integrated with a high-efficiency Number Theoretic Transform (NTT) accelerator. This accelerator leverages custom instructions to accelerate cryptographic operations. Our research has achieved the following results: (1) The accelerator provides a speedup of up to 14.51 × for NTT and 16.75 × for inverse NTT operations compared to other RISC-V platforms; (2) This leads to end-to-end performance improvements for ML-KEM of up to 56.5% for security level I, 50.9% for level III, and 45.4% for level V; (3) The ASIC design is fabricated using a 180 nm CMOS process at a maximum operating frequency of 118 MHz with an area overhead of 8.7%. The chip achieved a minimum power consumption of 5.913 $\mathsf{\mu }$W at 10 kHz and 0.9 V of supply voltage. Full article

Regional water systems face growing pressure from climate variability, water scarcity, and increasingly complex wastewater pollution. These challenges require governance models that integrate institutional coordination with effective technological solutions. This review is based on a structured analysis of peer-reviewed literature indexed in Scopus, Web of Science, and ScienceDirect, covering publications from approximately 2014 to 2025. The findings show that clearly defined institutional roles, basin-level coordination, stable financing mechanisms, and active stakeholder participation significantly improve governance outcomes. Technological advances such as membrane filtration, advanced oxidation processes, nature-based treatment systems, and digital monitoring platforms enhance treatment efficiency, resilience, and opportunities for resource recovery. Regions differ widely in their ability to adopt these solutions, mainly due to variations in governance coherence, investment capacity, and climate-adaptation readiness. The review highlights the need for policy frameworks that align institutional reforms with technological modernization, including the adoption of basin-based planning, digital decision-support systems, and circular water-economy principles. These measures provide actionable guidance for policymakers and regional authorities seeking to strengthen long-term water security and wastewater management performance. Full article

The automation of quality control in agricultural food processing, particularly the detection of incomplete peeling in taro, constitutes a critical frontier for ensuring food safety and optimizing production efficiency in the Industry 4.0 era. However, this domain is fraught with significant technical challenges, primarily stemming from the inherent visual characteristics of residual peel: extremely minute scales relative to the vegetable body, highly irregular morphological variations, and the dense occlusion of objects on industrial conveyor belts. To address these persistent impediments, this study introduces a comprehensive solution comprising a specialized dataset and a novel detection architecture. We established the Taro Peel Industrial Dataset (TPID), a rigorously annotated collection of 18,341 high-density instances reflecting real-world production conditions. Building upon this foundation, we propose MDB-YOLO, a lightweight, multi-dimensional bionic detection model evolved from the YOLOv8s architecture. The MDB-YOLO framework integrates a synergistic set of innovations designed to resolve specific detection bottlenecks. To mitigate the conflict between background texture interference and tiny target detection, we integrated the C2f_EMA module with a Wise-IoU (WIoU) loss function, a combination that significantly enhances feature response to low-contrast residues while reducing the penalty on low-quality anchor boxes through a dynamic non-monotonic focusing mechanism. To effectively manage irregular peel shapes, a dynamic feature processing chain was constructed utilizing DySample for morphology-aware upsampling, BiFPN_Concat2 for weighted multi-scale fusion, and ODConv2d for geometric preservation. Furthermore, to address the issue of missed detections caused by dense occlusion in industrial stacking scenarios, Soft-NMS was implemented to replace traditional greedy suppression mechanisms. Experimental validation demonstrates the superiority of the proposed framework. MDB-YOLO achieves a mean Average Precision (mAP50-95) of 69.7% and a Recall of 88.0%, significantly outperforming the baseline YOLOv8s and advanced transformer-based models like RT-DETR-L. Crucially, the model maintains high operational efficiency, achieving an inference speed of 1.1 ms on an NVIDIA A100 and reaching 27 FPS on an NVIDIA Jetson Xavier NX using INT8 quantization. These findings confirm that MDB-YOLO provides a robust, high-precision, and cost-effective solution for real-time quality control in agricultural food processing, marking a significant advancement in the application of computer vision to complex biological targets. Full article

A novel hydraulically actuated uniform clamping flange connection mechanism is proposed to address the long-standing challenges in high-pressure natural gas flowmeter calibration, including cumbersome bolt-by-bolt assembly/disassembly, high leakage risk, and severe non-uniform gasket contact pressure associated with conventional multi-bolt flanges. Unlike traditional discrete bolt loading, the proposed mechanism generates a continuous and actively adjustable circumferential clamping force via an integrated hydraulic annular piston, ensuring excellent sealing uniformity and rapid installation within minutes. A high-fidelity transient finite element model of the hydraulic clamping flange assembly is established, incorporating the nonlinear compression/rebound behavior of flexible graphite–stainless steel spiral-wound gaskets and one-way fluid–structure interaction under water hammer loading. Parametric studies reveal that reducing the effective clamping area to below 80% of the original design significantly intensifies stress concentration and compromises sealing integrity, while clamping force below 80% or above 120% of the nominal value leads to leakage or component overstress, respectively. Under steady 10 MPa pressurization, the flange exhibits a maximum stress of 150.57 MPa, a minimum gasket contact stress exceeding 30 MPa, and a rotation angle below 1°, demonstrating robust sealing performance. During a severe water hammer event induced by rapid valve closure, the peak flange stress remains acceptable at 140.41 MPa, while the minimum gasket contact stress stays above the critical sealing threshold (38.051 MPa). However, repeated water hammer cycles increase the risk of long-term gasket fatigue. This study introduces, for the first time, a hydraulic uniform-clamping flange solution that dramatically improves sealing reliability, installation efficiency, and operational safety in high-pressure flowmeter calibration and similar temporary high-integrity piping connections, providing crucial technical guidance for field applications. Full article

In this study, we develop a stochastic optimal control model for corporate carbon management that synergistically combines emission reduction initiatives with carbon trading mechanisms. The model incorporates two control variables: the autonomous emission reduction rate and initial carbon allowance purchases, while accounting for both deterministic and stochastic carbon pricing scenarios. The solution is obtained through a two-step optimization procedure that addresses each control variable sequentially. In the first step, the problem is transformed into a Hamilton–Jacobi–Bellman (HJB) equation in the sense of viscosity solution. A key aspect of the methodology is deriving the corresponding analytical solution based on this equation’s structure. The second-step optimization results are shown to depend on the relationship between the risk-free interest rate and carbon price dynamics. Furthermore, we employ daily closing prices from 16 July 2021, to 31 December 2024, as the sample dataset to calibrate the parameters governing carbon allowance price evolution. The marginal abatement cost (MAC) curve is calibrated using data derived from the Emissions Prediction and Policy Analysis (EPPA) model, enabling the estimation of the emission reduction efficiency parameter. Additional policy-related parameters are obtained from relevant regulatory documents. The numerical results demonstrate how enterprises can implement the model’s outputs to inform carbon emission reduction decisions in practice and offer enterprises a decision-support tool that integrates theoretical rigor and practical applicability for achieving emission targets in the carbon market. Full article

The typical microstructure and mechanical properties of twin-roll cast (TRC) 2139 aluminum alloy were investigated and compared with mold casting (MC) 2139 alloy. This work pioneers the application of TRC to produce 2139 Al-Cu-Mg alloy, a material that is challenging for rapid solidification. The TRC process resulted in a denser dendritic structure, with the composition of intermetallic compounds, primarily Al₂Cu and Al₂CuMg, remaining largely stable throughout the casting process. After solution treatment, the recrystallized grains in the MC sheets were uniformly distributed, while the TRC sheets exhibited a more localized and refined recrystallized microstructure, particularly within coarse second-phase regions. Following heat treatments, the TRC sheets showed a significant increase in the Ω phase after T6, with a slight growth in size and a uniform distribution, while the Ω phase in T8 showed an increased density and smaller size, which diffused evenly across the material. The TRC process uniquely refines the microstructure and enhances Ω phase precipitation, yielding a 10%+ improvement in strength and ductility over conventional casting. The mechanical properties of the TRC sheets improved significantly: tensile and yield strengths increased by over 10% after T6, compared to MC sheets, with elongation slightly higher in TRC. T8 treatment further enhanced the mechanical properties of the TRC sheets, achieving an improvement in strength with only a minor trade-off in elongation. This establishes TRC as a superior industrial route for high-performance aluminum sheets, offering a promising industrial route, delivering substantial improvements in both strength and ductility over conventional casting methods. Full article

To address the limitations of the traditional Balancing Composite Motion Optimization (BCMO) algorithm—namely weak directional global exploration, insufficient local exploitation accuracy, and a tendency to fall into local optima with reduced population diversity in feature selection tasks—this paper proposes a Multi-Strategy Enhanced Balancing Composite Motion Optimization algorithm (MEBCMO). From a symmetry perspective, MEBCMO exploits the symmetric and asymmetric relationships among candidate solutions in the search space to achieve a better balance between exploration and exploitation. The performance of MEBCMO is enhanced through three complementary strategies. First, an adaptive heat-conduction search mechanism is introduced to simulate thermal transmission behavior, where a Sigmoid function adjusts the heat-conduction coefficient α_T from 0.9 to 0.2 during iterations. By utilizing the symmetric fitness–distance relationship between the current solution and the global best, this mechanism improves the directionality and efficiency of global exploration. Second, a quadratic interpolation search strategy is designed. By constructing a quadratic model based on the current individual, a randomly selected individual, and the global best, the algorithm exploits local symmetric characteristics of the fitness landscape to strengthen local exploitation and alleviate performance degradation in high-dimensional spaces. Third, an elite population genetic strategy is incorporated, in which the top three individuals generate new candidates through symmetric linear combinations with non-elite individuals and Gaussian perturbations, preserving population diversity and preventing premature convergence. To evaluate MEBCMO, extensive global optimization experiments are conducted on the CEC2017 benchmark suite with dimensions of 30, 50, and 100, and comparisons are made with eight mainstream algorithms, including PSO, DE, and GWO. Experimental results demonstrate that MEBCMO achieves superior performance across unimodal, multimodal, hybrid, and composite functions. Furthermore, MEBCMO is combined with LightGBM to form the MEBCMO-LightGBM model for feature selection on 14 public datasets, yielding lower fitness values, higher classification accuracy, and fewer selected features. Statistical tests and convergence analyses confirm the effectiveness, stability, and rapid convergence of MEBCMO in symmetric and complex optimization landscapes. Full article

Frequent cloud cover severely limits the use of optical remote sensing for continuous ecological monitoring. Synthetic aperture radar (SAR) offers an all-weather alternative, but translating SAR data to optical equivalents is challenging, particularly in cloudy regions where paired training data are scarce. To address this, we developed an enhanced CycleGAN (denoted by SA-CycleGAN) to derive a high-fidelity, temporally continuous normalized difference vegetation index (NDVI) from SAR imagery. The SA-CycleGAN introduces a novel spatiotemporal attention generator that dynamically computes global and local feature relationships to capture long-range spatial dependencies across diverse landscapes. Furthermore, a structural similarity (SSIM) loss function is integrated into the SA-CycleGAN to preserve the structural and textural integrity of the synthesized images. The performance of the SA-CycleGAN and three unsupervised models (DualGAN, GP-UNIT, and DCLGAN) was evaluated by deriving NDVI time series from Sentinel-1 SAR images across four sites with different vegetation types. Ablation experiments were conducted to verify the contributions of the key components in the SA-CycleGAN model. The results demonstrate that the SA-CycleGAN significantly outperformed the comparison models across all four sites. Quantitatively, the proposed method achieved the lowest Root Mean Square Error (RMSE) of 0.0502 and the highest Coefficient of Determination (R²) of 0.88 at the Zhangbei and Xishuangbanna sites, respectively. The ablation experiments confirmed that the attention mechanism and SSIM loss function were crucial for capturing long-range features and maintaining spatial structure. The SA-CycleGAN proves to be a robust and effective solution for overcoming data gaps in optical time series. Full article

Accurate semantic segmentation of complex landforms in remote sensing imagery is hindered by pronounced intra-class heterogeneity, blurred boundaries, and irregular geomorphic structures. To overcome these challenges, this study presents LENet (Landforms Expert Segmentation Net), a novel segmentation network that combines axial semantic modeling with deformation-aware compensation. LENet follows an encoder–decoder framework, where the decoder integrates three key modules: the Expert Enhancement Block (EEBlock) for capturing long-range dependencies along axial directions; the Feature Expert Compensator (FEC) employing deformable convolutions with channel–spatial decoupled weights to emphasize ambiguous intra-class regions; and the Cross-Sparse Attention (CSA) mechanism that suppresses background noise via multi-rate sparsity masks and enhances intra-class consistency through cosine-similarity weighting. Experiments conducted on the PKLD plateau karst and GVLM landslide datasets demonstrate that LENet achieves IoU scores of 70.39% and 80.95% and Recall values of 83.33% and 91.38%, surpassing eight state-of-the-art methods. These results confirm that LENet effectively balances global contextual understanding and local detail refinement, providing a robust and accurate solution for complex landform segmentation in remote sensing imagery. Full article

To address the conflict between real-time performance and optimal resource allocation in large-scale digital array radars, this paper proposes a novel resource scheduling framework that integrates graph-theoretic modeling with an adaptive heuristic strategy. Unlike traditional methods, we formulate the multi-beam scheduling problem as a constrained connected subgraph optimization task. To solve this NP-hard problem, an Improved Ant Colony Optimization (I-ACO) algorithm is designed, incorporating pheromone boundary constraints and elite update strategies to effectively balance exploration and exploitation within complex solution spaces. Furthermore, a load-aware Adaptive Algorithm Switching (AAS) strategy is introduced. This mechanism dynamically transitions between the globally optimized I-ACO and a rapid, utility-guided greedy approach based on real-time system load, effectively resolving the trade-off between solution quality and response speed. Experimental results demonstrate that the proposed method reduces solution costs by up to 23.5% compared to greedy algorithms and increases the scheduling success rate to 99.2% under high-load conditions, while significantly improving long-term system load balancing by 41.5%. Full article

The growing global demand for food and energy requires land-use strategies that support agricultural production and renewable energy generation. Agrivoltaic (APV) systems allow farmland to be used for both agriculture and solar power generation. The aim of this study is to critically synthesize the interactions between the key dimensions of APV implementation—technical, agronomic, legal, and economic—in order to create a multidimensional framework for designing an APV optimization model. The analysis covers APV system topologies, appropriate types of photovoltaic modules, installation geometry, shading conditions, and micro-environmental impacts. The paper categorizes quantitative indicators and critical thresholds that define trade-offs between energy production and crop yields, including a discussion of shade-tolerant crops (such as lettuce, clover, grapevines, and hops) that are most compatible with APV. Quantitative aspects were integrated in detail through a review of mathematical approaches used to predict yields (including exponential-linear, logistic, Gompertz, and GENECROP models). These models are key to quantitatively assessing the impact of photovoltaic modules on the light balance, thus enabling the simultaneous estimation of energy efficiency and yields. Technical solutions that enhance synthesis, such as dynamic tracking systems, which can increase energy production by up to 25–30% while optimizing light availability for crops, are also discussed. Additionally, the study examines regional legal frameworks and the economic factors influencing APV deployment, highlighting key challenges such as land use classification, grid connection limitations, investment costs and the absence of harmonised APV policies in many countries. It has been shown that APV systems can increase water retention, mitigate wind erosion, strengthen crop resilience to extreme weather conditions, and reduce the levelized cost of electricity (LCOE) compared to small rooftop PV systems. A key contribution of the work is the creation of a coherent analytical design framework that integrates technical, agronomic, legal and economic requirements as the most important input parameters for the APV system optimization model. This indicates that wider implementation of APV requires clear regulatory definitions, standardized design criteria, and dedicated support mechanisms. Full article

During the vapor phase aluminizing process, protecting the joint regions of turbine blades remains a critical challenge, as the formation of the aluminide coating can significantly increase the brittleness of these areas. To address this issue, a novel double-layer anti-seepage coating was designed for the K4125 nickel-based superalloy. The coating employs a self-sealing mechanism, transforming from a porous structure into a dense NiAl/Al₂O₃ composite barrier at elevated temperatures, thereby suppressing aluminum penetration. Optimal anti-seepage performance is achieved at 1080 °C, reducing the transition zone width to 42 μm, which is a reduction of more than 70% compared to that of 880 °C. These results are attributed to the synergistic action of multiple mechanisms, including high-temperature densification, the formation of NiAl phase, and the growth of an oxide film on the substrate surface. Additionally, the thermal expansion mismatch enables easy mechanical removal of the coating after aluminizing without substrate damage. The coating system offers an effective and practical solution for high-temperature protection during vapor phase aluminizing in aerospace applications. Full article

Accurate pain and stress assessment remains a challenge in patients with limited communication ability. Current galvanic skin response (GSR) devices lack real-time feedback, wireless communication, and robustness against motion artifacts, limiting their clinical utility. This paper presents the design and development of a wearable internet-of-things (IoT) enabled GSR system incorporating Bluetooth Low Energy (BLE) communication, ergonomic mechanical housing, and artifact-filtering through a custom API. The system integrates finger-mounted electrodes, a custom amplifier and signal processor, an nRF52840 BLE microcontroller, and a rechargeable Li-ion battery in a compact 3D-printed wrist-mounted enclosure. Basic validation with two healthy subjects demonstrated reliable detection of stress-induced GSR fluctuations with reduced movement artifacts. Results indicate the feasibility of the proposed design as a low-cost, wireless, and ergonomic solution for objective pain and stress monitoring. Full article

Composite bonded anchors represent an innovative solution in the field of fastening technology, finding wide application in construction and civil engineering. This article presents a comprehensive review of the available scientific literature, a market analysis and a survey of patent databases related to this issue. Key aspects of the design, mechanical properties and durability of composite bonded anchors under various operating conditions are discussed. Special attention was paid to comparing composite solutions with traditional anchoring systems, highlighting their advantages and limitations. The results presented indicate a growing interest in this technology, which is due to both its high strength, corrosion resistance and applicability to lightweight structures. In conclusion, the article identifies key directions for further research and potential areas for the development of composite bonded anchors in the context of modern engineering challenges. Full article