Search Results (2,468)

Bridge health monitoring is critical for infrastructure safety, especially with the growing deployment of IoT sensors. This work addresses the challenge of securely storing large volumes of sensor data and extracting actionable insights for timely damage detection. We propose BIONIB, a novel framework that combines an unsupervised machine learning approach called the Novelty Index (NI) with a scalable blockchain platform (EOSIO) for secure, real-time monitoring of bridges. BIONIB leverages EOSIO’s smart contracts for efficient, programmable, and secure data management across distributed sensor nodes. Experiments on real-world bridge sensor data under varying loads, climatic conditions, and health states demonstrate BIONIB’s practical effectiveness. Key findings include CPU utilization below 40% across scenarios, a twofold increase in storage efficiency, and acceptable latency degradation, which is not critical in this domain. Our comparative analysis suggests that BIONIB fills a unique niche by coupling NI-based detection with a decentralized architecture, offering real-time alerts and transparent, verifiable records across sensor nodes. Full article

This paper investigates physical layer security (PHY-security) for multi-hop transmission in underlay cognitive radio networks under various eavesdropping attacks. To enhance secrecy performance, we propose two opportunistic scheduling schemes. The first scheme, called the minimal node selection (MNS) scheme, selects the node in each cluster that minimizes the eavesdropper’s channel capacity. The second scheme, named the optimal node selection (ONS) scheme, chooses the node that maximizes secrecy capacity by using both the main and eavesdropper channel information. To reveal the relationship between network parameters and secrecy performance, we derive closed-form expressions for the secrecy outage probability (SOP) under different scheduling schemes and eavesdropping scenarios. Numerical results show that the ONS scheme provides the most robust secrecy performance among the considered schemes. Furthermore, we analyze the impact of key network parameters on secrecy performance. In detail, although the proposed ONS scheme requires more channel information than the MNS scheme, under a 20 dB interference threshold, the secrecy performance of the ONS scheme is 15% more robust than that of the MNS scheme. Full article

The main objective of this work is to provide a comprehensive explanation of the Register Transfer Level (RTL) to Graphic Data System II (GDSII) flow for designing custom Field-Programmable Gate Array (FPGA) architectures at the 130 nm technology node using the SKY130 Process Design Kit (PDK). By leveraging open-source tools—specifically OpenLane and OpenFPGA—this study details the methodology and implementation steps required to generate a GDSII layout of a custom FPGA. OpenLane offers an integrated RTL-to-GDSII flow by combining multiple Electronic Design Automation (EDA) tools, while OpenFPGA enables the construction of flexible and customizable FPGA architectures. The article covers key aspects of the RTL-to-GDSII workflow, including RTL file configuration, the utilization of configuration variables for physical design, hierarchical chip design, macro and core implementation, chip-level integration, and gate-level simulation. Experimental results validate the proposed workflow, showcasing the successful transformation from RTL to GDSII. The findings of this research provide valuable insights for researchers and engineers in the FPGA design field, advancing the state of the art in FPGA architecture development. Full article

Sales competition and recycling rivalry are critical factors affecting the operation of closed-loop supply (CLSC). The existing research on competitive CLSCs primarily analyzes the impact of competition between two sales entities and/or two recycling entities on management decisions. To make the study more realistic, this study constructs a Stackelberg game model with the manufacturer as a leader, and analyzes the impacts of competition among $n$ retailers ($\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} n\ge 2$) and rivalry among $m$ third-party recyclers $(\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} m\ge 2)$ on the decision-making and profits of both node enterprises and the supply chain system, and proposes a linear transfer-payment contract to coordinate the CLSC from an economic perspective. Numerical analyses are conducted to visualize the effects of competition on the decisions and profits. The key findings are as follows: (1) In the centralized system, inter-retailer competition reduces optimal order quantities but does not affect optimal retail prices. In the decentralized system, however, it decreases both optimal order quantities and retail prices. (2) Rivalry among recyclers reduces their optimal recycling volumes but does not affect their optimal recycling prices in the centralized system. In the decentralized system, however, such rivalry not only decreases recycling volumes but also increases optimal recycling prices. (3) The manufacturer’s product wholesale price and used product recycling price remain independent of competitive interactions among retailers and recyclers in the decentralized system. (4) Competition among retailers and recyclers positively affects the profits of the CLSC and the manufacturer, but negatively impacts those of retailers and recyclers. (5) When the reward–penalty factors for product order and used product recycling fall within a specific range, the linear transfer-payment contract can coordinate the CLSC in the presence of competition in both retail and recycling. (6) All enterprises’ profits are sensitive to the penalty–reward factor, but this sensitivities also gradually decrease as the number of retailers and (or) recyclers increases. Full article

Background/Objectives: Taraxacum officinale F.H.Wigg. (Asteraceae), an edible plant and commonly used Chinese herbal medicine, has significant anti-inflammatory and antioxidant effects in the form of its root water extract (TRWE). Therefore, this study was designed to elucidate the principal pharmacological effects and underlying mechanisms of water extract from Taraxacum roots (TRWE) against cisplatin-induced nephrotoxicity through an integrated approach combining network pharmacology and experimental validation. Methods: Mechanistic prediction was performed using network pharmacology, molecular docking, and GeneMANIA-based functional analysis, followed by experimental validation via H&E staining, TUNEL, biochemical assays (blood urea nitrogen, BUN; creatinine, CRE; malondialdehyde, MDA; superoxide dismutase, SOD; and catalase, CAT), and Western blotting. Results: Network pharmacology identified 52 kidney injury-associated targets of Taraxacum. Functional enrichment analysis indicated their roles in apoptosis and endoplasmic reticulum stress, particularly through the PERK-mediated UPR pathway, suggesting the PERK/eIF2α/ATF4 axis as a potential key regulatory node. Animal experiments suggested that 100, 200, and 400 mg/kg inhibited cisplatin-induced increases in BUN, CRE, and MDA; restored SOD/CAT levels; and alleviated kidney apoptosis and endoplasmic reticulum stress via the PERK/eIF2α/ATF4 pathway. Molecular docking suggested strong binding of phytochemicals (caftaric acid, CTA; chlorogenic acid, CGA; caffeic acid, CA; and cichoric acid, CCA) to PERK, eIF2α, and ATF4. Conclusions: This study predicts that the PERK/eIF2α/ATF4 signaling pathway may be a critical mediator of TRWE’s potential renoprotective effects against cisplatin-induced acute kidney injury, offering a potential theoretical basis for further mechanistic exploration. Full article

Background: Breast cancer remains the most prevalent malignancy in women worldwide, characterized by remarkable genetic, molecular, and clinical heterogeneity. Traditional preclinical models have significantly advanced our understanding of tumor biology, yet consistently fall short in recapitulating the complexity of the human tumor microenvironment (TME), immune, and metastatic behavior. In recent years, breast cancer-on-a-chip (BCOC) have emerged as powerful microengineered systems that integrate patient-derived cells, stromal and immune components, and physiological stimuli such as perfusion, hypoxia, and acidic milieu within controlled three-dimensional microenvironments. Aim: To comprehensively review the BCOC development and application, encompassing fabrication materials, biological modeling of key subtypes (DCIS, luminal A, triple-negative), dynamic tumor–stroma–immune crosstalk, and organotropic metastasis to bone, liver, brain, lungs, and lymph nodes. Methods: We selected papers from academic trusted databases (PubMed, Web of Science, Google Scholar) by using Breast Cancer, Microfluidic System, and Breast Cancer on a Chip as the main search terms. Results: We critically discuss and highlight how microfluidic systems replicate essential features of disease progression—such as epithelial-to-mesenchymal transition, vascular invasion, immune evasion, and therapy resistance—with unprecedented physiological relevance. Special attention has been paid to the integration of liquid biopsy technologies within microfluidic platforms for non-invasive, real-time analysis of circulating tumor cells, cell-free nucleic acids, and exosomes. Conclusions: In light of regulatory momentum toward reducing animal use in drug development, BCOC platforms stand at the forefront of a new era in precision oncology. By bridging biological fidelity with engineering innovation, these systems hold immense potential to transform cancer research, therapy screening, and personalized medicine. Full article

Carbon-fiber-reinforced-polymer/aluminum (CFRP/Al) double-sided countersunk riveted joint is a key joining technology for lightweight and high-performance aircraft structures. Advanced numerical simulation techniques are helpful in predicting riveting damage evolution and the optimization of the joining process. In this study, a discrete crack modeling (DCM) method based on the floating node method (FNM) was employed to investigate the initial riveting damage behavior and interference characteristics during the electromagnetic riveting (EMR) process with five cases of rivet-hole clearances. The results were compared with those obtained from the conventional smeared crack method (SCM). The findings show that the interference distribution along the axial direction of the joint is non-uniform, and increasing the rivet-hole clearance helps alleviate the initial riveting damage. The FNM accurately modeled the initiation and propagation of matrix cracks and delamination, albeit at the cost of some computational efficiency. Full article

The role of regional lymph nodes in melanoma metastasis has long been recognized. This review will detail the evolving role of sentinel lymph node biopsy (SLNB) in melanoma management in the era of adjuvant therapies. We analyze key themes and findings from recent publications, highlighting both areas of consensus and ongoing controversies. While the landscape of melanoma management continues to evolve with the advent of novel therapeutic combinations, SLNB remains a valuable tool for staging and potentially provides therapeutic benefit. However, optimal patient selection for SLNB requires careful consideration of individual risk factors and benefits of adjuvant therapy. Full article

Due to the complexity of shipyards’ operating scenes and the inconsistency of ship parts’ type and size, current sorting operations for ship parts mainly rely on laborers, resulting in weak control over the production process and key nodes. With the gradual advancement of intelligent manufacturing technology in the shipbuilding process, the trend of machines replacing humans is obvious. In order to promote the automation of the sorting process, intelligent scene recognition and route planning algorithms are needed. In this work, we introduce a localization method based on a laser line profile sensor and ship parts layout analysis algorithm, aiming at obtaining the information needed for sorting route planning. In addition, a heuristic-based route planning algorithm is proposed to solve the built mathematical model of the ship part sorting process. The proposed method can optimize the sorting order of parts, realize stable stacking, shorten sorting distance (taking about 490 m for 43 parts), and thereby improve operation efficiency. These results show that the proposed approach can make intelligent and comprehensible sorting route planning for the ship parts layout. Full article

In order to enhance leakage detection accuracy in water distribution networks (WDNs) while reducing sensor deployment costs, an intelligent algorithm for the optimal deployment of water network monitoring sensors based on the automatic labelling and graph neural network (ALGN) was proposed for the optimal deployment of WDN monitoring sensors. The research aims to develop a data-driven, topology-aware sensor deployment strategy that achieves high leakage detection performance with minimal hardware requirements. The methodology consisted of three main steps: first, the dung beetle optimization algorithm (DBO) was employed to automatically determine optimal parameters for the DBSCAN clustering algorithm, which generated initial cluster labels; second, a customized graph neural network architecture was used to perform topology-aware node clustering, integrating network structure information; finally, optimal pressure sensor locations were selected based on minimum distance criteria within identified clusters. The key innovation lies in the integration of metaheuristic optimization with graph-based learning to fully automate the sensor placement process while explicitly incorporating the hydraulic network topology. The proposed approach was validated on real-world WDN infrastructure, demonstrating superior performance with 93% node coverage and 99.77% leakage detection accuracy, surpassing state-of-the-art methods by 2% and 0.7%, respectively. These results indicate that the ALGN framework provides municipal water utilities with a robust, automated solution for designing efficient pressure monitoring systems that balance detection performance with implementation cost. Full article

Sedum alfredii is a cadmium (Cd) hyperaccumulator, but the regulatory mechanisms linking phytohormones and redox balance to Cd transporter expression remain unclear. In this study, we omitted external cadmium (Cd) stress to isolate and examine the interplay between phytohormone and reactive oxygen species (ROS) signaling. Exogenous treatments with abscisic acid (ABA), indole-3-acetic acid (IAA), gibberellic acid (GA₃), trans-zeatin (t-Z), and H₂O₂ were combined with analyses of hormone levels, antioxidant enzyme activities, and transporter gene expression. Correlation and PLS-SEM analyses identified the CAT–H₂O₂ module as a key node: ABA and IAA enhanced CAT activity and alleviated ROS-mediated repression of transporters, while GA₃ and t-Z exerted opposite effects. Functional validation using an H₂O₂ scavenger revealed that the regulation of HMA3 and Nramp5 by ABA and t-Z is H₂O₂-dependent. In contrast, IAA modulates Nramp5 through a ROS-independent pathway, while the regulatory effects of GA₃ were negligible. Functional validation under Cd exposure suggests a model wherein HMA3 and Nramp5 act in a complementary manner to sequester and redistribute Cd in leaves, thereby supporting hyperaccumulation. These findings highlight hormone-specific ROS pathways as central to transporter regulation and provide mechanistic insights to improve phytoremediation efficiency. Full article

Wireless sensor networks (WSNs) are key enablers of efficient communication in the Internet of Things (IoT) ecosystem. These networks comprise numerous sensor nodes that collaboratively collect and transmit data, requiring adaptive and energy-efficient management. However, high node density and resource limitations introduce challenges such as control overhead, packet collisions, interference, and energy inefficiency. To mitigate these issues, this paper adopts the Hybrid Wireless Mesh Protocol (HWMP), standardized under IEEE 802.11s for wireless mesh networks (WMNs), as the routing protocol in WSNs. HWMP’s hybrid design combining reactive and proactive routing is well-suited for dynamic and mobile environments, making it applicable to WSNs operating under similar conditions. Building on this foundation, we propose a community-aware topology control mechanism that constructs a Connected Dominating Set (CDS) to serve as the network’s energy-efficient backbone. Node selection is guided by centrality metrics and detected community structures to enhance routing efficiency and network longevity. The mechanism is evaluated across six mobility scenarios characterized by realistic movement patterns. Comparative results show that incorporating community structure significantly improves routing performance and reduces energy consumption, validating the approach’s effectiveness in real-world WSN deployments. Full article

Optimizing the layout of major productive forces is key in the advancement of high-quality economic development and will inevitably drive significant changes in the spatial pattern of China’s iron and steel enterprises. This study selects 2005, 2010, 2014, 2020, and 2023 as time nodes during the period from the Tenth to the Fourteenth Five-Year Plan, analyzing the spatial evolution pattern and agglomeration characteristics from multiple scales of China’s iron and steel industry over the past 20 years by adopting various mathematical and theoretical methods. The results show that the distribution characteristics of “gradient” are reduced in the east, middle, and west from the perspective of the belt scale. There are notable differences in the spatial agglomeration of different types of iron and steel member units, except for the trade-type iron and steel member units; for example, on the national scale, iron and steel member units as a whole show a spatial distribution trend of “Northeast–Southwest”. There are a large number of production-type enterprise units displaying obvious relative concentrations and geographies; the movement trend of the regional centre of gravity can first be found in the southwest, moving then to the northeast and finally to the southwest. Based on this study, coastal and existing production bases should further improve environmental regulations, increase structural adjustment, and better play the role of demonstration and drive. Full article

This study investigates how preferences for sad or happy music influence the network structures linking depressive symptoms and meaning in life. Analyzing data from 1681 college students, results indicate that individuals who listen to sad music display a denser network structure with stronger connections between depressive symptoms and meaning in life, while those favoring happy music exhibit a more dispersed network with weaker connections. The Sad Music Group showed higher global strength, suggesting a tightly knit network, whereas the Happy Music Group had lower global strength, implying greater flexibility among nodes. These findings highlight distinct network configurations between the two groups, offering insights into the interplay between music engagement and psychological well-being. By identifying key nodes and connectivity, we can develop more targeted therapy interventions. However, it is crucial to consider individual differences and contextual factors that influence how music affects psychological well-being. Full article

Community detection is a fundamental task for understanding network structures, crucial for identifying groups of nodes with close connections. However, existing methods generally treat all connections in networks as equally important, overlooking the inherent inequality of connection strengths in social networks, and often require large quantities of labeled data. To address these challenges, we propose a few-shot community detection framework, Strong Triadic Closure Community Detection with Prompt (STC-CDP), which combines the Strong Triadic Closure (STC) principle, Graph Neural Networks, and prompt learning. The STC principle, derived from social network theory, states that if two nodes share strong connections with a third node, they are likely to be connected with each other. By incorporating STC constraints during the pre-training phase, STC-CDP can differentiate between strong and weak connections in networks, thereby more accurately capturing community structures. We design an innovative prompt learning mechanism that enables the model to extract key features from a small number of labeled communities and transfer them to the identification of unlabeled communities. Experiments on multiple real-world datasets demonstrate that STC-CDP significantly outperforms existing state-of-the-art methods under few-shot conditions, achieving higher F1 scores and Jaccard similarity particularly on Facebook, Amazon, and DBLP datasets. Our approach not only improves the precision of community detection but also provides new insights into understanding connection inequality in social networks. Full article