Search Results (320)

As substantial incorporation of variable renewable generation technologies, particularly wind and photovoltaic systems, becomes more common, the complexities of power supply and demand characteristics are increasing, making it essential to conduct a detailed power and energy balance analysis. Aiming at regional power systems with multi-source structures and internal transmission interface constraints, this paper proposes a power and energy balance analysis method that considers demand-side carbon emissions. First, a closed-loop mechanism of “carbon signal–load response–balance optimization” based on nodal carbon potential (NCP) is constructed. In this framework, NCP is utilized to generate carbon signals that guide the active response of flexible loads, which are subsequently integrated into the coordinated optimization of power and energy balance. Second, a power and energy balance optimization model adapted to multi-source structures is established, where transmission power limits between zones are directly embedded into the constraint system, overcoming the defects of traditional heuristic methods that require repeated iterations to correct interfaces. Finally, an improved hybrid solution strategy for large-scale balance analysis is designed, significantly reducing the variable scale through the aggregation of similar units within zones. Case studies show that this method can effectively guide the load to shift toward low-carbon periods and nodes, significantly reducing total system carbon emissions and improving renewable energy consumption while ensuring power and energy balance. Full article

Esophageal squamous cell carcinoma (ESCC) represents a major therapeutic challenge due to the rapid development of resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Recent evidence highlights that this resistance is driven not only by genetic mutations but also by spatial heterogeneity of tumor microenvironments and compensatory signaling mechanisms. In this review, we propose a “spatial-signaling-intervention” framework with a particular focus on the NTRK2/MAPK signaling axis, which plays dual roles in signaling compensation and immune evasion. By integrating spatial multi-omics, proteomics, and AI-assisted topological modeling, three resistant niches are identified: (1) cancer stemness-enriched zones, (2) MAPK hyperactive islands, and (3) immune-cold regions. Based on this atlas, we design precision nanotherapeutic platforms, including responsive, dual-target, and feedback-loop nanocarriers, to selectively modulate resistant spatial niches. Preclinical validation in patient-derived xenografts and organoid models further demonstrates the translational potential of these strategies. This work provides a conceptual and technological roadmap for overcoming EGFR-TKI resistance in ESCC. Atlas-guided nanocarrier systems offer a promising avenue for spatially targeted and feedback-responsive therapy, highlighting the role of pharmaceutics in advancing precision oncology. Full article

Industrial PLC programming faces persistent difficulties: lengthy development cycles, low fault tolerance, and cross-platform incompatibility among vendors. While LLMs show promise for automated code generation, their direct application is hindered by the gap between ambiguous natural language and the strict determinism required by control logic. This paper proposes MPC-Coder, a dual-knowledge enhanced multi-agent system that addresses this gap. The system combines a structured knowledge graph that imposes hard constraints on process parameters and equipment specifications with a vector database that offers implementation references such as code templates and function blocks. These two knowledge sources form a symmetric complementary architecture. A closed-loop “generation–verification–repair” mechanism leverages formal verification tools to iteratively refine the generated code. Experiments demonstrate that MPC-Coder achieves 100% syntactic correctness and 78% functional consistency, significantly outperforming general-purpose LLMs. The results indicate that the complementary fusion of domain knowledge and closed-loop verification effectively enhances the reliability of code generation, offering a viable technical pathway for the reliable application of LLMs in industrial control systems. Full article

This study draws on socio-technical transition theory to examine how multi-actor dynamics among producers, consumers, and the media within an experimental niche—Korea’s regulatory sandbox—shape policy responsiveness and the regulatory speed of governmental responses to emerging technologies, thereby influencing socio-technical transitions. We construct a longitudinal dataset of 2136 sandbox approvals between 2019 and 2025 and 1374 cases in which related legal or administrative adjustments have been completed. Changes in actor couplings before and after sandbox approval are first assessed using Pearson correlation analysis, while temporal lead–lag relationships are identified via vector autoregression (VAR) and Granger causality tests. Building on these dynamic analyses, the study subsequently investigates the determinants of regulatory response speed using ordered logistic regression, incorporating government policy orientation (progressive vs. conservative) as a moderating variable. The results show, first, that the strong producer–consumer coupling observed prior to sandbox approval weakens afterwards, whereas the consumer–media linkage becomes substantially stronger. Second, the time-series analysis of technologies within the regulatory sandbox reveals a typical technology-push pattern and a self-reinforcing feedback loop. Specifically, producer activity initiates the signal sequence, preceding consumer reactions; subsequently, media coverage significantly drives consumer engagement, and the resulting increase in consumer attention, in turn, stimulates further media coverage. Third, in the ordered logit model, media activity accelerates legal and regulatory reform, whereas consumer activity acts as a delaying factor, with producer activity showing no significant direct effect. Finally, government policy orientation systematically moderates the magnitude and direction of these effects. Overall, the study proposes an actor-centered mechanism in which learning generated in the sandbox is externalized through consumer–media channels and translated into regulatory pacing. Based on these findings, we derive practical implications for firms and regulators regarding proactive media engagement, transparent use of evidence, institutionalized channels for consumer input, and robust feedback standards that support sustainable commercialization of emerging technologies. Full article

In recent years, the rapid development of commercial satellite projects, such as low-Earth orbit (LEO) communication and remote sensing constellations, has driven the satellite industry toward low-cost, rapid development, and large-scale deployment. Commercial off-the-shelf (COTS) components have been widely adopted across various commercial satellite platforms due to their advantages of low cost, high performance, and plug-and-play availability. However, the space environment is complex and hostile. COTS components were not originally designed for such conditions, and they often lack systematically flight-verified protective frameworks, making their reliability issues a core bottleneck limiting their extensive application in critical missions. This paper focuses on COTS solid-state drives (SSDs) onboard the Jilin-1 KF satellite and presents a full-lifecycle reliability practice covering component selection, system design, on-orbit operation, and failure feedback. The core contribution lies in proposing a full-lifecycle methodology that integrates proactive design—including multi-module redundancy architecture and targeted environmental stress screening—with on-orbit data monitoring and failure cause analysis. Through fault tree analysis, on-orbit data mining, and statistical analysis, it was found that SSD failures show a significant correlation with high-energy particle radiation in the South Atlantic Anomaly region. Building on this key spatial correlation, the on-orbit failure mode was successfully reproduced via proton irradiation experiments, confirming the mechanism of radiation-induced SSD damage and providing a basis for subsequent model development and management decisions. The study demonstrates that although individual COTS SSDs exhibit a certain failure rate, reasonable design, protection, and testing can enhance the on-orbit survivability of storage systems using COTS components. More broadly, by providing a validated closed-loop paradigm—encompassing design, flight verification and feedback, and iterative improvement—we enable the reliable use of COTS components in future cost-sensitive, high-performance satellite missions, adopting system-level solutions to balance cost and reliability without being confined to expensive radiation-hardened products. Full article

To address the challenge of imprecise detection and control of fertilizer application rates caused by high granular flow during fertilization operations, a parallel diversion detection method with real-time application rate regulation is proposed. The mechanism of uniform distribution of discrete particles formed by high-throughput aggregated granular fertilizer was elucidated. Key components including the uniform fertilizer tube, sensor detection structure, six-channel diversion cone disc, and fertilizer convergence tube underwent parametric design, culminating in the innovative development of a six-channel parallel diversion detection device. A multi-channel parallel signal detection method was studied, and a synchronous multi-channel signal acquisition system was designed. Through calibration tests, relationship models were established between the measured flow rate of granular fertilizer and voltage, as well as between the actual flow rate and the rotational speed of the fertilizer discharge shaft. A fuzzy PID control model was constructed in MATLAB2023/Simulink. Using overshoot, response time, and stability as evaluation metrics, the control performance of traditional PID and fuzzy PID was compared and analyzed. To validate the control system’s precision, device performance tests were conducted. Results demonstrated that fuzzy PID control reduced the time required to reach steady state by 66.87% compared to traditional PID, while overshoot decreased from 7.38 g·s⁻¹ to 1.49 g·s⁻¹. Divergence uniformity tests revealed that at particle generation rates of 10, 20, 30, and 40 g·s⁻¹, the coefficient of variation for channel divergence consistency gradually increased with rising tilt angles. During field operations at 0–5.0° tilt, the coefficient of variation for channel divergence consistency remained below 7.72%. Bench tests revealed that the fuzzy PID control system achieved an average accuracy improvement of 3.64% compared to traditional PID control, with a maximum response time of 0.9 s. Field trials demonstrated detection accuracy no less than 92.64% at normal field operation speeds of 3.0–6.0 km·h⁻¹. This system enables real-time, precise detection of fertilizer application rates and closed-loop regulation. Full article

Hardware-in-the-loop (HIL) simulation is an essential tool for rapid and cost-effective development and validation of power-electronic systems. The primary objective of this work is to validate and fine-tune a PLECS-based HIL model of a single dual active bridge (DAB) DC-DC converter, thereby laying the foundation for building more complex models (e.g., multiple converters connected in series or parallel). To this end, the converter is experimentally characterized, and the HIL model is validated across a wide range of operating conditions by varying the PWM phase-shift angle, voltage gain, switching frequency, and leakage inductance. Power transfer and efficiency are analyzed to quantify the influence of these parameters on converter performance. These experimental trends provide insight into the optimal modulation range and the dominant loss mechanisms of the DAB under single phase shift (SPS) control. A detailed comparison between HIL simulations and hardware measurements, based on transferred power and efficiency, shows close agreement across all the tested operating points. These results confirm the accuracy and robustness of the proposed HIL model, demonstrate the suitability of the PLECS platform for DAB development and control validation, and support its use as a scalable basis for more complex multi-converter studies, reducing design time and prototyping risk. Full article

Planar multi-loop fractionated kinematic chains (FKCs)—kinematic chains that can be decomposed into two or more coupled subchains by separating joints or links—are widely used in heavy-duty manipulators, yet their large design space makes automatic synthesis and application-oriented screening challenging. The novelty of this paper is a general automated synthesis-and-screening framework for planar fractionated kinematic chains, regardless of whether multiple joints are present; multiple-joint chains are handled via an equivalent transformation to single-joint models, enabling the construction of a deduplicated topological graph atlas. In the mine scaler manipulator case study, an 18-link, 5-DOF (N18_M5) FKC with two multiple joints is taken as the target and converted into a single-joint equivalent N20_M7 model consisting of three subchains (KC1–KC3). Atlases of the required non-fractionated kinematic chains (NFKCs) for KC1 and KC3 are generated according to their link counts and DOFs. The subchains are then combined as building blocks under joint-fractionation (A-mode) and link-fractionation (B-mode) to enumerate fractionated candidates, and a WL-hash-based procedure is employed for isomorphism discrimination to obtain a non-isomorphic N20_M7 atlas. Finally, a connectivity-calculation-based screening is performed under task-driven structural and functional constraints, yielding 249 feasible configurations for the overall manipulator arm. The proposed pipeline provides standardized representations and reproducible outputs, offering a practical and transferable route from large-scale enumeration to engineering-feasible configuration sets for planar multi-loop FKCs, including those with multiple joints. Full article

The use of physical vehicles and environments during vehicle research and development is highly resource-intensive, particularly for autonomous vehicles. Recently, digital models are therefore increasingly used instead, which require high levels of fidelity and validity. While the two aforementioned qualities are often lacking, an absence of versatility for multi-purpose use is even more prevalent in current digital models. In response to these challenges, this work presents a novel real-time multi-physics digital twin of an offroad vehicle with high levels of fidelity and validity, both regarding the vehicle dynamics and hydraulics, as well as regarding the visual representation of the environment and the exteroceptive sensor emulation. The versatility of the digital twin enables its usage for vehicle development tasks concerning mechanical components and driveline, as well as for visual machine learning tasks, such as generation of auto-annotated visual training data. Development of control algorithms leveraging both visual input and mechanical systems is also enabled. Furthermore, the real-time capability allows for Hardware-in-the-Loop and Vehicle-in-the-Loop simulation. The modeling, calibration, and real-world validation of the digital twin is presented, with an emphasis on the vehicle dynamics and hydraulics. The shown validity enables advancements in the development of autonomous offroad vehicles. Full article

Effectively fusing the rich semantic information from camera images with the high-precision geometric measurements provided by LiDAR point clouds is a key challenge in mobile robot environmental perception. To address this problem, this paper proposes a highly extensible instance-aware fusion framework designed to achieve efficient alignment and unified modeling of heterogeneous sensory data. The proposed approach adopts a modular processing pipeline. First, semantic instance masks are extracted from RGB images using an instance segmentation network, and a projection mechanism is employed to establish spatial correspondences between image pixels and LiDAR point cloud measurements. Subsequently, three-dimensional bounding boxes are reconstructed through point cloud clustering and geometric fitting, and a reprojection-based validation mechanism is introduced to ensure consistency across modalities. Building upon this representation, the system integrates a data association module with a Kalman filter-based state estimator to form a closed-loop multi-object tracking framework. Experimental results on the KITTI dataset demonstrate that the proposed system achieves strong 2D and 3D detection performance across different difficulty levels. In multi-object tracking evaluation, the method attains a MOTA score of 47.8 and an IDF1 score of 71.93, validating the stability of the association strategy and the continuity of object trajectories in complex scenes. Furthermore, real-world experiments on a mobile computing platform show an average end-to-end latency of only 173.9 ms, while ablation studies further confirm the effectiveness of individual system components. Overall, the proposed framework exhibits strong performance in terms of geometric reconstruction accuracy and tracking robustness, and its lightweight design and low latency satisfy the stringent requirements of practical robotic deployment. Full article

Texture mapping of weft-knitted fabrics plays a crucial role in virtual try-on and digital textile design due to its computational efficiency and real-time performance. However, traditional texture mapping techniques typically adapt pre-generated textures to deformed surfaces through geometric transformations. These methods overlook the complex variations in yarn length, thickness, and loop morphology during stretching, often resulting in visual distortions. To overcome these limitations, we propose Knit-Pix2Pix, a dedicated framework for generating realistic weft-knitted fabric textures directly from knitted unit mesh maps. These maps provide grid-based representations where each cell corresponds to a physical loop region, capturing its deformation state. Knit-Pix2Pix is an integrated architecture that combines a multi-scale feature extraction module, a grid-guided attention mechanism, and a multi-scale discriminator. Together, these components address the multi-scale and deformation-aware requirements of this task. To validate our approach, we constructed a dataset of over 2000 pairs of fabric stretching images and corresponding knitted unit mesh maps, with further testing using spring-mass fabric simulation. Experiments show that, compared with traditional texture mapping methods, SSIM increased by 21.8%, PSNR by 20.9%, and LPIPS decreased by 24.3%. This integrated approach provides a practical solution for meeting the requirements of digital textile design. Full article

Polycystic Ovary Syndrome (PCOS) is characterized by a self-perpetuating vicious cycle between insulin resistance (IR) and hyperandrogenism (HA). While lifestyle management remains the internationally recommended first-line treatment, current clinical management, primarily relying on combined oral contraceptives and metformin, offers symptomatic relief or “masking” of the phenotype but fails to adequately disrupt this core pathophysiological loop, while also carrying potential intergenerational safety concerns. This review systematically evaluates the paradigm shift toward mechanism-based precision medicine. First, we analyze emerging precision-targeted therapies that intervene in specific pathological nodes: (1) metabolic regulators (e.g., GLP-1RAs, SGLT2i, and brown adipose tissue (BAT) activators) that target systemic glucotoxicity and the novel “BAT-Ovarian axis”; (2) neuroendocrine modulators (e.g., NK3R antagonists) that act as negative modulators of the hyperactive GnRH pulse generator; and (3) innovative androgen synthesis inhibitors (e.g., Artemisinins) that utilize a degradation-at-source mechanism. Complementing these, we explore the strategic value of Natural Products through the lens of “Network Pharmacology”, highlighting their ability to restore systemic homeostasis via multi-target modulation. Finally, we address critical translational challenges, specifically the need to establish long-term reproductive and offspring safety, providing a roadmap for developing true disease-modifying treatments for PCOS. Distinct from reviews limited to isolated therapeutic modalities, this article uniquely bridges current clinical management with emerging organ-specific precision targets and natural product networks. Full article

In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to enhance resilience, compliance, and trust in complex data environments. Drawing on the convergence of existing data governance models, best practice risk management (DAMA-DMBOK, NIST, and ISO 31000), and real-world enterprise experience, this framework provides a modular, expandable approach to dynamically aligning governance strategy with evolving contextual factors and threats in data management. The contribution is in the form of a multi-layered paradigm combining static policy with dynamic risk indicator through application of data sensitivity categorization, contextual risk scoring, and use of feedback loops to continuously adapt. The technical contribution is in the governance-risk matrix formulated, mapping data lifecycle stages (acquisition, storage, use, sharing, and archival) to corresponding risk mitigation mechanisms. This is embedded through a semi-automated rules-based engine capable of modifying governance controls based on predetermined thresholds and evolving data contexts. Validation was obtained through simulation-based training in cross-border data sharing, regulatory adherence, and cloud-based data management. Findings indicate that DRADG enhances governance responsiveness, reduces exposure to compliance risks, and provides a basis for sustainable data accountability. The research concludes by providing guidelines for implementation and avenues for future research in AI-driven governance automation and policy learning. DRADG sets a precedent for imbuing intelligence and responsiveness at the heart of data governance operations of modern-day digital enterprises. Full article

Multi-UAV (unmanned aerial vehicle) target enclosing control is one of the key technologies for achieving cooperative tasks. It faces limitations in communication resources and task framework separation. To address this, a distributed cooperative control strategy is proposed based on dynamic time-varying formation description and event-triggering mechanism. Firstly, a formation description method based on a geometric transformation parameter set is established to uniformly describe the translation, rotation, and scaling movements of the formation, providing a foundation for time-varying formation control. Secondly, a cooperative architecture for adaptive target enclosing tasks is designed. This architecture achieves an organic combination of formation control and target enclosing in a unified framework, thereby meeting flexible transitions between multiple formation patterns such as equidistant surrounding and variable-distance enclosing. Thirdly, a distributed dynamic event-triggered cooperative enclosing controller is developed. This strategy achieves online adjustment of communication thresholds through internal dynamic variables, significantly reducing communication while strictly ensuring system performance. By constructing a Lyapunov function, the stability and Zeno free behavior of the closed-loop system are proven. The simulation results verify this strategy, showing that this strategy can significantly reduce communication frequency while ensuring enclosing accuracy and formation consistency and effectively adapt to uniform and maneuvering target scenarios. Full article

Three-dimensional (3D) printing has transformed the development of microneedle arrays (MNAs) by enabling exceptional control over their geometry, distribution, materials, and functionality in a single-step, customizable process. This review represents a design-centric framework that organizes recent advancements in four interconnected levers: (i) individual microneedle (MN) geometry and size; (ii) patch-level MN distribution and multi-array architectures; (iii) computer-aided design (CAD), finite element analysis (FEA), computational fluid dynamics (CFD), and artificial intelligence/machine learning (AI/ML)-driven optimization; and (iv) manufacturing constraints and emerging solutions for scalability and reproducibility. Outcomes show that small changes in the radius of the MN’s tip, the MN’s aspect ratio, the MN’s internal lattice architecture, and the spacing of the array can dramatically influence their insertion force, mechanical reliability, payload capacity, and therapeutic coverage. Now, digital tools can bridge the design and experimental outcomes, while novel morphologies, hybrid materials, and theranostic integrations are expanding the clinical potential of MNs. The remaining challenges, resolution-versus-throughput trade-offs, biocompatibility, batch-to-batch consistency, and lack of testing standardization are examined alongside promising directions in high-throughput 3D printing, stimuli-responsive materials, and closed-loop systems. Finally, rational, model-guided design strategies are positioning 3D-printed MNAs as versatile platforms for painless, patient-specific drug delivery, diagnostics, and personalized medicine. Full article