Search Results (2,251)

The integrity and stability of railway fasteners are of vital importance to railway safety. To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this paper proposes an unsupervised anomaly detection method using a knowledge-distilled generative adversarial network. First, the proposed method employs collaborative teacher–student learning to model normal sample distributions, where the student network reconstructs input images as normal outputs while a discriminator identifies anomalies by comparing input and reconstructed images. Second, a multi-scale attention-coupling feature-enhancement mechanism is proposed, effectively integrating hierarchical semantic information with spatial-channel attention to achieve both precise target localization and robust background suppression in the teacher network. Third, an enhanced anomaly discriminator is designed to incorporate an enhanced pyramid upsampling module, through which fine-grained details are preserved via multi-level feature map aggregation, resulting in significantly improved sensitivity for small-sized anomaly detection. Finally, the proposed method achieved an AUC of 94.0%, an ACC of 92.5%, and an F1 score of 91.6% on the MNIST dataset, and an AUC of 94.7%, an ACC of 90.1%, and an F1 score of 87.8% on the railway fastener dataset, which proves the superior anomaly detection ability of this method. Full article

By aggregating intelligence on emerging threats, attack techniques, and vulnerabilities, organizations can establish a more comprehensive threat landscape awareness and proactively identify potential risks. However, in the process of sharing threat intelligence, companies often hesitate due to concerns over information leakage, which reduces their willingness to collaborate. Furthermore, the lack of transparency and credibility in intelligence sources has negatively impacted the quality and trustworthiness of shared data. To address these issues, authors aim to leverage blockchain technology, utilizing its decentralized and tamper-proof properties to ensure corporate privacy and the reliability of intelligence sources. Additionally, a dual blockchain architecture is implemented to enhance operational efficiency and reduce storage burdens. However, with the advent of large-scale quantum computing, traditional cryptographic mechanisms used in blockchain systems face potential vulnerabilities due to Shor’s algorithm, which threatens widely adopted public key cryptographic schemes. To ensure long-term security and resilience in a quantum-enabled threat landscape, quantum-resistant cryptographic technologies, including SPHINCS+ and CRYSTALS-KYBER, are integrated to facilitate quantum-safe migration in blockchain applications, ensuring system security and resilience in future environments of quantum computing. Full article

Domain shifts pose significant challenges for cross-domain semantic segmentation in high-resolution remote sensing imagery. Inspired by the cognitive mechanisms of the human brain, we propose a Brain-Inspired Style Transfer and Semantic Segmentation Collaborative Adversarial Framework (SAF), which mimics neural processes such as hierarchical perception, memory retrieval, and multimodal integration to enhance cross-domain feature alignment and segmentation performance. To achieve the joint optimization of style transfer and semantic segmentation networks, we introduce three key components: a Semantic-Aware Transformer Module (SATM) that dynamically captures and preserves essential semantic features during style transfer; a Semantic-Driven Multi-feature Memory Module (SMM) that stores and retrieves historical style and semantic information to improve domain adaptability; a Domain-Invariant Style-Semantic Center Space (DSCS) that aligns style and semantic features within a shared representation space, mitigating discrepancies between style and semantic domains. Extensive experiments across multiple tasks demonstrate that SAF effectively reduces distortions and semantic inconsistencies by achieving deep style–semantic alignment. Compared to leading approaches, SAF achieves a superior balance between style adaptation and semantic preservation, significantly improving model generalization in remote sensing applications. Full article

This review addresses the growing demand for sustainable alternatives to conventional synthetic nets used in marine and agricultural applications, which are often persistent, poorly degradable, and difficult to manage at end of life. It examines recent developments in biodegradable polymers—particularly polylactic acid (PLA), polyhydroxyalkanoates (PHAs), and poly(butylene adipate-co-terephthalate) (PBAT)—alongside reinforced blends and nanocomposites designed to improve mechanical performance and degradation behavior under real-world conditions. Strategies based on the regeneration of discarded nets, especially those made from polyamide 6 (PA6), are also considered for their potential to close material loops and reduce environmental leakage. A critical analysis of current testing protocols and regulatory frameworks is provided to assess their suitability for novel materials. In addition, this study highlights the emergence of multifunctional nets capable of providing environmental sensing or biological support, marking a transition toward adaptive and ecosystem-responsive designs. Finally, a survey of ongoing European and international projects illustrates scalable pathways for implementing biodegradable and recyclable netting systems, integrating material innovation with circular economy strategies. These findings emphasize the need for harmonized standards, targeted environmental testing, and cross-sectoral collaboration to enable the large-scale adoption of sustainable net technologies. Full article

As an important part of the Internet of Things, the Internet of Vehicles (IoV) has achieved efficient interconnection and collaboration between vehicles and road infrastructure, and between vehicles through advanced information and communication technologies. However, the high-speed movement of vehicles has generated a large number of cross-domain behaviors, which has greatly increased the number of authentications. Existing authentication protocols face challenges such as high cost, high computational overhead, and easy eavesdropping, interception, or tampering. To this end, this paper proposes an ECC-based IoV secure and efficient handover authentication protocol. The protocol adopts a “non-full key escrow” mechanism. The private key of the vehicle is jointly generated by the Trusted Authority (TA) and the vehicle. The TA only holds part of the private key. Even if the TA is malicious, the security of the vehicle’s private key can be ensured. At the same time, the proposed protocol uses the time tree technology in trusted computing to share part of the vehicle’s private data, which not only ensures the security of authentication, but also improves the efficiency of authentication, and solves the high-latency problem caused by the use of blockchain in previous protocols. When the vehicle moves across domains, there is no need to re-register and authenticate, which reduces the authentication overhead. Compared with existing protocols, this protocol is lightweight in both computational and communication overheads, effectively solving the problem of excessive cost. Full article

The internal response mechanism of vegetation change in fragile high-altitude ecosystems is pivotal for ecological stability. This study focuses on the Lhasa River Basin (LRB) on the Tibetan Plateau (TP), a typical high-altitude fragile ecosystem where vegetation dynamics are highly sensitive to climate change and human activities. Utilizing MODIS surface reflectance data (MOD09Q1), a general regression neural network (GRNN) was applied to create a 250 m resolution fractional vegetation cover (FVC) dataset from 2001 to 2022, whose accuracy was verified with field survey data. Through methods like the Theil–Sen Median trend analysis, Mann–Kendall significance test, Hurst exponent, and geographical detector, the collaborative mechanism of 14 driving factors was systematically explored. Key conclusions are as follows: (1) The FVC in the LRB evolved in stages, first decreasing and then increasing, with 46.71% of the basin area expected to show an improvement trend in the future. (2) Among natural factors, elevation (q = 0.480), annual mean potential evapotranspiration (q = 0.362), and annual mean temperature (q = 0.361) are the main determinants of FVC spatiotemporal variation. (3) In terms of human activities, land use type has the highest explanatory power (q = 0.365) for FVC. (4) The interaction of two factors on FVC is stronger than that of a single factor, with the elevation–land use interaction being the most significant (q = 0.558). These results deepen our understanding of the interactions among vegetation, climate, and humans in fragile high-altitude ecosystems and provide a scientific basis for formulating zoned restoration strategies on the TP. Full article

With the increasing demand for drag reduction, energy consumption reduction, and low weight in civil aircraft, high-precision microgroove preparation technology is being developed internationally to reduce wall friction resistance and save energy. Compared to mechanical processing, chemical etching, roll forming, and ultrafast laser processing, nanosecond lasers offer processing precision, high efficiency, and controllable thermal effects, enabling low-cost and high-quality preparation of microgrooves. However, the impact of nanosecond laser etching on the fatigue performance of substrate materials remains unclear, leading to controversy over whether high-precision shape control and fatigue performance enhancement in microgrooves can be achieved simultaneously. This has become a bottleneck issue that urgently needs to be addressed. This paper focuses on the current research status of nanosecond laser processing quality control for microgrooves and the research status of laser effects on enhancing the fatigue performance of substrate materials. It identifies the main existing issues: (1) how to induce surface residual compressive stress through the thermo-mechanical coupling effect of nanosecond lasers to suppress micro-defects while ensuring high-precision shape control of fixed microgrooves; and (2) how to quantify the regulation of nanosecond laser process parameters on residual stress distribution and fatigue performance in the microgroove area. To address these issues, this paper proposes a collaborative strategy for high-quality shape control and surface strengthening in fixed microgrooves, an analysis of multi-dimensional fatigue regulation mechanisms, and a new method for multi-objective process optimization. The aim is to control the geometric accuracy error of the prepared surface microgrooves within 5% and to enhance the fatigue life of the substrate by more than 20%, breaking through the technical bottleneck of separating “drag reduction design” from “fatigue resistance manufacturing”, and providing theoretical support for the integrated manufacturing of “drag reduction-fatigue resistance” in aircraft skins. Full article

Congenital heart disease (CHD), the most common congenital anomaly, remains a significant lifelong burden despite advancements in medical and surgical interventions. Induced pluripotent stem cells (iPSCs) have emerged as a groundbreaking platform in CHD research, offering patient-specific models to investigate the genetic, epigenetic, and molecular mechanisms driving the disease. Utilizing technologies such as CRISPR/Cas9 gene editing, cardiac organoids, and high-throughput screening, iPSCs enable innovative strategies in disease modeling, precision drug discovery, and regenerative therapies. However, clinical translation faces challenges related to immaturity, differentiation variability, large-scale feasibility, and tumorigenicity. Addressing these barriers will require standardized protocols, bioengineering solutions, and interdisciplinary collaboration. This review examines the critical role of iPSCs in advancing CHD research and care, demonstrating their potential to revolutionize treatment through patient-specific, regenerative approaches. By addressing current limitations and advancing iPSC technology, the field is positioned to pave the way for precision-based CHD therapies for this lifelong condition. Full article

Precise detection of potato diseases is critical for food security, yet traditional image segmentation methods struggle with challenges including uneven illumination, background noise, and the gradual color transitions of lesions under complex field conditions. Therefore, a collaborative segmentation framework of Otsu and Sobel edge detection based on the beluga whale optimization algorithm with a danger sensing mechanism (DSBWO) is proposed. The method introduces an S-shaped control parameter, a danger sensing mechanism, a dynamic foraging strategy, and an improved whale fall model to enhance global search ability, prevent premature convergence, and improve solution quality. DSBWO demonstrates superior optimization performance on the CEC2017 benchmark, with faster convergence and higher accuracy than other algorithms. Experiments on the Berkeley Segmentation Dataset and potato early/late blight images show that DSBWO achieves excellent segmentation performance across multiple evaluation metrics. Specifically, it reaches a maximum IoU of 0.8797, outperforming JSBWO (0.8482) and PSOSHO (0.8503), while maintaining competitive PSNR and SSIM values. Even under different Gaussian noise levels, DSBWO maintains stable segmentation accuracy and low CPU time, confirming its robustness. These findings suggest that DSBWO provides a reliable and efficient solution for automatic crop disease monitoring and can be extended to other smart agriculture applications. Full article

Regenerative agriculture has emerged as an innovative approach to food production, offering the potential to achieve reduced or even positive environmental and social outcomes compared to the soil degradation and greenhouse gas emissions of conventional agriculture. Simultaneously, a sophisticated dual-use system combining solar energy generation from photovoltaics with agricultural production, called agrivoltaics, is rapidly expanding. Combining these approaches into regenerative agrivoltaics offers a promising solution to the challenges regarding food in a rapidly warming world. This review theoretically examines the compatibility and mutual benefits of combining agrivoltaics and regenerative agriculture while also identifying the challenges, opportunities, and pathways for implementing this system. A foundation for advancing regenerative agrivoltaics is made by identifying areas for research, which include the following: (1) carbon sequestration, (2) soil health and fertility, (3) soil moisture, (4) soil microbial activity, (5) soil nutrients, (6) crop performance, (7) water-use efficiency, and (8) economics. By addressing the intersection of agriculture, renewable energy, and sustainability, regenerative agrivoltaics emphasizes the transformative potential of integrated systems in reshaping land use and resource management. This evaluation underscores the importance of policy and industry collaboration in facilitating the adoption of regenerative agrivoltaics, advocating for tailored support mechanisms to enable widespread implementation of low-cost, zero-carbon, resilient food systems. Full article

Sepsis and cancer, though distinct in their clinical manifestations, share profound pathophysiological overlaps that underscore their interconnectedness in disease progression and outcomes. Here we discuss the intricate biological mechanisms linking these two conditions, focusing on the roles of inflammation, immune dysregulation, and metabolic alterations. In sepsis, an uncontrolled immune response to infection leads to a cytokine storm, tissue damage, and immune paralysis, while cancer exploits chronic inflammation and immunosuppressive pathways to promote tumor growth and metastasis. Both conditions exhibit metabolic reprogramming, such as the Warburg effect in cancer and glycolysis-driven immune cell activation in sepsis, which fuels disease progression and complicates treatment. Sepsis can exacerbate cancer progression by inducing genomic instability, epigenetic modifications, and a pro-tumorigenic microenvironment, while cancer increases susceptibility to sepsis through immunosuppression and treatment-related complications. The shared pathways between sepsis and cancer present unique opportunities for therapeutic intervention, including anti-inflammatory agents, immune checkpoint inhibitors, and metabolic modulators. Anti-inflammatory therapies, such as IL-6 and TNF-α inhibitors, show promise in mitigating inflammation, while immune checkpoint inhibitors like anti-PD-1 and anti-CTLA-4 antibodies are being explored to restore immune function in sepsis and enhance antitumor immunity in cancer. Metabolic modulators, including glycolysis and glutaminolysis inhibitors, target the metabolic reprogramming common to both conditions, though their dual roles in normal and pathological processes necessitate careful consideration. Additionally, antimicrobial peptides (AMPs) represent a versatile therapeutic option with their dual antimicrobial and antitumor properties. In this review, we also highlight the critical need for integrated approaches to understanding and managing the complex interactions between sepsis and cancer. By bridging the gap between sepsis and cancer research, this work aims to inspire interdisciplinary collaboration and advance the development of targeted therapies that address the shared mechanisms driving these devastating diseases. Ultimately, these insights may pave the way for novel diagnostic tools and therapeutic strategies to improve outcomes for patients affected by both conditions. Full article

Objectives: The widespread adoption of three-dimensional (3D)-printed resins in restorative dentistry has introduced significant challenges in establishing strong and lasting bonds with resin-based cements. Despite the development of numerous surface treatment techniques designed to improve adhesion, a clear consensus on the most effective approach remains elusive. This systematic review and meta-analysis critically examined the impact of various surface treatment protocols on the bond strength of 3D-printed resins. By comparing treated versus untreated surfaces, the study aimed to determine the most reliable strategies for enhancing adhesion, ultimately offering evidence-based guidance to inform clinical decision-making. Methods: This review identified relevant studies through a comprehensive search of MEDLINE via PubMed, Web of Science, Scielo, Scopus, and EMBASE databases, supplemented by manual reference checks, to identify in vitro studies published up to February 2025. Studies assessing the bonding of 3D-printed resins following various surface treatments and bonding protocols were included. Data on bond strength outcomes, such as shear bond strength, microtensile bond strength, and microshear bond strength, were extracted. Data extraction included study details, type of 3D-printed resin and printing technology, surface treatment protocols, bond strength testing methods, storage conditions, and results. The quality of included studies was assessed using the ROBDEMat tool. Meta-analyses were performed using the Review Manager Software (version 5.4, The Cochrane Collaboration, Copenhagen, Denmark), with statistical significance set at p < 0.05. Statistical heterogeneity among studies was evaluated using the Cochran Q test and the I² inconsistency test. Results: Nine studies met the criteria for qualitative analysis, with eight included in the meta-analysis. The findings revealed that surface treatment protocols significantly enhanced the immediate bond strength to 3D-printed resins (p = 0.01), with only sandblasting and silane demonstrating a statistically significant effect (p < 0.007). Similarly, after aging, surface treatments continued to improve bond strength (p = 0.01), with sandblasting and hydrofluoric acid being the only methods to produce a significant increase in bond strength values (p < 0.001). Conclusions: This meta-analysis underscores the importance of combining mechanical and chemical surface treatments, especially sandblasting and silane application, to achieve reliable and durable bonding to 3D-printed resins. Full article

The rapid emergence of the platform economy has accelerated the practice of embedded innovation, with ecosystem partner selection serving as a critical first step in platform enterprises’ innovation collaborations and playing a key role in enhancing innovation efficiency and outcomes. Based on the theory of embedded innovation, this study identifies the core innovation demands of platform enterprises at distinct stages. It then employs QFD to quantify decision indicator weights for ecosystem partner selection. By integrating Prospect Theory with Field Theory, this study develops both a decision evaluation model and an optimization model to achieve the optimal screening of ecosystem partners. Specifically, this study contributes in the following ways: (1) It constructs an embedded innovation direction selection model to uncover the evolving innovation demands at each stage. Within the QFD framework, we map these demands onto selection evaluation indicators, assess their importance via the maximum entropy principle, and determine indicator weights through a correlation matrix. (2) It proposes a Prospect Theory-based TOPSIS evaluation model, incorporating decision-makers’ psychological preferences to mitigate bias arising from singular or excessive risk attitudes. This model ranks potential partners according to their closeness to an ideal solution. Finally, (3) it designs a Field Theory-based optimization model that accounts for the platform enterprise’s perspective, partner-matching rationality, and continuity of interaction. This model emphasizes the complementarity and synergy of innovation resources to enhance cooperation fit and strategic alignment between the platform and its partners. Finally, this study conducts an empirical analysis on platform enterprise XM and validates the model’s feasibility and stability through sensitivity testing and comparative analyses. This study enriches the understanding of ecosystem partner selection within platform ecosystems by advancing methods for quantifying partner demands and refining the selection of evaluation indicators. It also deepens the depiction of non-rational characteristics in behavioral decision-making and elucidates the mechanisms underlying the ongoing interactions between platform enterprises and their ecosystem partners. These theoretical contributions not only extend the scope of research on platform ecosystems and embedded innovation but also provide feasible approaches for platform enterprises to improve partner governance and foster collaborative innovation in dynamic and complex environments. Ultimately, the findings offer strong support for enhancing innovation performance and building sustainable competitive advantages. Full article

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article

Mobile robots are extensively utilized across various fields, with path planning consistently representing a core and pivotal area of research. Path planning is essential for enabling the efficient navigation of robots within complex environments. In reality, the terrain on which the robot operates is non-uniform, resulting in varying costs associated with different areas due to differing terrains and materials. Practical tasks often necessitate traversing a series of landmark points to fulfill specific requirements. Furthermore, considerations related to control and dynamics frequently require setting minimum line segment lengths between curves and maximum curve curvatures to ensure the successful execution of the path. The objective of this paper is to find a low-cost path with continuous curvature on a map with an assigned cost, which passes through all the given landmark points while avoiding obstacles, and satisfies the minimum length of the line segments between the curves and the maximum curvature constraints of the curves. We propose an innovative path planning method that solves the limitations of traditional algorithms by considering map cost, curvature continuity, and other factors by establishing a collaborative mechanism between global coarse search and local fine-tuning. The method is divided into two stages: In the first stage, the graph structure is constructed by generating points on the map, and uses Dijkstra’s Algorithm to obtain the connection order of the landmark points. In the second stage, which builds on the previous stage and processes landmark points sequentially, the key points of the path are generated using our proposed Smooth Beacon Reconnection (SBR) algorithm. A low-cost path meeting the requirements is then obtained through fine-tuning. The smooth path generated by this method is verified on multiple maps and demonstrates superior performance compared to traditional methods. Full article