Search Results (969)

China’s High-Speed Railway (HSR), the world’s largest HSR system and a core component of national transportation, exhibits vulnerability in operational robustness to uncertain events such as natural disasters. Existing hypernetwork-based studies on HSR robustness often assume full connections among nodes within hyperedges, an assumption that deviates from reality. Using China’s HSR line and station data, this paper constructs a real-world HSR hypernetwork and three reconstructed hypernetworks with distinct internal hyperedge structures. It also proposes a cascading failure model accounting for internal hyperedge structures and quantifies economic feasibility through an optimization cost index. Experimental results show the real-world HSR hypernetwork has scale-free properties. Sub-line density within hyperedges shows a positive correlation with robustness, where denser sub-lines enhance robustness. Prioritizing sub-line deployment around hub stations offers the most economical solution. This paper is the first to provide an HSR hypernetwork robustness optimization scheme from the perspective of internal hyperedge structures, offering theoretical reference for research on transportation networks with similar topological characteristics. Full article

Assessing the performance of the smart-grid system (SGS) under uncertainty is essential for ensuring a reliable energy supply from the perspective of the grid operator. In this study, a multistate smart-grid network (MSGN) is developed to evaluate the delivery capability of the SGS. An MSGN consists of multiple interconnected facilities, where nodes represent energy sources or converters and arcs denote feeders. The output of each facility in the MSGN is modeled as multistate, as maintenance activities and partial failures can result in multiple possible output levels. During power delivery, transmission losses may arise due to heat dissipation and feeder aging, potentially resulting in insufficient power supply at the demand side. From a smart-grid management perspective, delivery reliability, defined as the probability that the MSGN can successfully deliver sufficient power from energy sources to the destination under transmission loss, is adopted as a performance index for evaluating SGS capability. To compute delivery reliability, a minimal-path-based algorithm is developed. A practical SGS is presented to demonstrate the applicability of the proposed model and to provide managerial insights into smart-grid performance and operational decision-making. Full article

Blockchain oracles, as data intermediaries between on-chain and off-chain environments, have opened up a wide range of application scenarios for blockchain technology. The dependability of a blockchain oracle system will affect the dependability of blockchain systems. However, the dynamic and heterogeneous nature of blockchain oracle systems poses challenges to assessing their dependability. Furthermore, how to comprehensively analyze the dependability of blockchain oracle systems from multiple dimensions of transient availability, steady-state availability, and reliability is also a challenge. In order to solve these challenges, this paper proposes three models based on a semi-Markov process (SMP): (1) the SMP model for steady-state availability analysis; (2) the hierarchical model for transient analysis; and (3) the SMP model with absorption states for reliability analysis. Then, we derive the formulas for calculating the dependability metrics, which can be used to evaluate the dependability of blockchain oracle systems composed of any number of oracle nodes. Finally, based on the comparative experiments to verify the approximate accuracy of the proposed model and formulas, we analyze the impact of system parameters and the number of oracle nodes on the dependability metrics. The experimental results reveal that the key factor affecting availability is the failure time and recovery time of the threshold oracle, while the key factor affecting MTTF is the failure time of the threshold oracle. Full article

Agricultural intensification in greenhouse systems leads to a substantial accumulation of pesticides, yet its role in reshaping soil microbial interactions and their network stability remains poorly understood. This study reveals a critical ecological paradox: contrary to classical theory, greenhouse soils under chronic pesticide contamination exhibit significantly enhanced network stability (quantified as the robustness of network global efficiency under targeted node removal simulations) despite a concurrent sharp decline in bacterial diversity. We investigated this counter-intuitive phenomenon by integrating 16S rRNA sequencing, motif-based network analysis, and resilience modeling. Our findings suggest that this enhanced stability is not explained by species richness but, rather, coincides with a fundamental restructuring of the network’s local interaction architecture. Pesticide residues, acting as a strong deterministic selection pressure, shaped the microbial community into a “low-aggregation, high-redundancy” network topology. This was characterized by a decrease in highly clustered, “brittle” interaction motifs (e.g., M3-2) and an increase in sparse triangular anti-motifs (e.g., M3-1). This new architecture mitigates the risk of cascading failures, thereby elevating the network’s collapse threshold. Triazole fungicides (e.g., Tricyclazole and Hexaconazole) were significantly associated with this structural shift. Our study establishes a novel mechanistic link from pesticide stress to motif-level restructuring and enhanced system stability, offering new insights for assessing the health of highly stressed agricultural ecosystems. Full article

Background: Cardiac implantable electronic devices (CIEDs) have become indispensable tools in the management of bradyarrhythmia and heart failure, prompting a surge in research activity. To characterize the evolving research landscape, we conducted a bibliometric analysis focused on institutional contributions, author networks, journal trends, funding patterns, and emerging thematic hotspots in the field of cardiac devices to highlight keywords and identify knowledge development timelines and emerging trends, providing a comprehensive overview of the current state of research in this area. Methods: We conducted a bibliometric analysis of cardiac devices using the Web of Science Core Collection (WOSCC) on 27 November 2024, with search terms “ST (cardiac defibrillator) OR (pacemaker)”. Data from 1 January 2019 to 1 January 2024 resulted in 3753 articles, refined to 1000 after excluding non-English and methodologically inappropriate papers. VosViewer, Excel, and Drawio facilitated data visualization, creating networks where node size indicates frequency, line thickness shows association strength, and colors denote clusters. This approach helped identify key research trends and collaborations in the field. Results: The United States led in publication volume (362 papers) and citations (7198), with Emory University emerging as the most prolific institution. Heart Rhythm was the most productive journal, while Europace was the most co-cited. Kurt Stromberg was the leading author by publications and citations. Funding was predominantly from U.S. agencies, with the NIH and HHS each supporting 127 studies. Co-citation and keyword analyses revealed three dominant research clusters: (1) leadless pacemakers; (2) permanent pacemaker implantation following transcatheter aortic valve replacement (TAVR); and (3) development of self-powered pacing technologies, including piezoelectric and bioresorbable systems. Conclusions: This study offers a comprehensive overview of recent trends and intellectual structures in cardiac device research. By identifying key contributors, collaborative networks, and thematic evolutions, it provides a valuable reference for researchers, clinicians, and innovators seeking to navigate or shape the rapidly advancing field of cardiac electrophysiology and device therapy. Full article

Bilateral elective nodal irradiation (ENI) remains standard for treating most head and neck squamous cell carcinomas (HNSCC) but is associated with significant toxicity. Advances in lymphatic mapping, particularly with SPECT/CT-guided sentinel lymph node (SLN) identification, have enabled more personalized radiotherapy strategies. This systematic review evaluates the efficacy and quality-of-life impact of ENI strategies using SPECT/CT-guided SLN mapping. This systematic review, conducted according to PRISMA guidelines, included ten studies published between January 2014 and March 2024, including prospective, retrospective studies, randomized trials, and systematic reviews, examining oncologic outcomes and toxicity in patients undergoing SPECT/CT-guided SLN mapping or individualized ENI. Findings show that in well-lateralized, early stage carcinomas, SPECT/CT-guided ENI safely allows for unilateral treatment in up to 82% of patients, with a low contralateral regional failure rate. This approach significantly reduces radiation exposure to organs at risk and rates of xerostomia, dysphagia, and hypothyroidism, leading to improved quality of life. However, its applicability to advanced or midline tumors remains limited. SPECT/CT-guided SLN mapping and individualized ENI offer a promising, less toxic alternative for selected patients. Further prospective, multicenter, and randomized studies are needed to confirm these benefits and support broader clinical adoption. Full article

Random walks with stochastic resetting, where walkers periodically return to a designated node, have emerged as an important framework for understanding transport processes in complex networks. While resetting is known to optimize search times, its effects on extreme events—defined as exceedances of walker flux above a critical threshold—remain largely unexplored. Such events model critical network phenomena, including traffic congestion, server overloads, and infrastructure failures. In this work, we systematically investigate how stochastic resetting influences both the probability and magnitude of extreme events in complex networks. Through analytical derivation of the stationary occupation probabilities and comprehensive numerical simulations, we demonstrate that resetting significantly reduces the occurrence of extreme events while concentrating event-size fluctuations. Our results reveal a universal suppression effect: increasing the resetting rate $\gamma$ monotonically decreases extreme event probabilities across all nodes, with complete elimination at $\gamma =1$. Notably, this suppression is most pronounced for vulnerable low-degree nodes and nodes distant from the resetting node, which experience the largest reduction in both event probability and fluctuation magnitude. These findings provide theoretical foundations for using resetting as a control mechanism to mitigate extreme events in networked systems. Full article

Semiconductor wafer fabrication is one of the most complex and demanding processes in industry. The process involves numerous sequential steps, including photolithography, deposition, etching, and chemical–mechanical polishing (CMP). At advanced process nodes below 5 nanometers, even angstrom-level deviations in parameters such as oxide thickness or critical dimension (CD) can lead to yield degradation or device failure. Traditional single-factor experimental methods are insufficient to capture the inherent multivariate interactions within plasma, thermal, and chemical processes. This review introduces the application of Design of Experiments (DOE) in wafer fabrication and demonstrates that it provides a statistically rigorous framework for addressing these challenges. It enables the simultaneous analysis of multiple variables, quantifying main effects and interactions, and developing predictive models with fewer runs. DOE can accelerate process development, reduce wafer consumption, enhance process robustness, and support applications in processes such as photolithography, CMP, and deposition. Beyond process optimization, DOE, combined with virtual metrology, machine learning, and digital twin technologies, provides a balanced dataset for predictive analytics and real-time control. Its functions encompass proactive monitoring, adaptive formulation optimization, and eco-efficient manufacturing aligned with sustainability goals. As wafer fabs adopt AI-assisted, simulation-driven environments, experimental design remains the foundation for knowledge-intensive, data-driven decision-making. This ensures continuous improvement in yield, manufacturability, and competitiveness in future semiconductor miniaturization processes. Full article

Inference of large machine learning models can quickly exceed the capabilities of edge devices in terms of performance, memory or energy consumption. When offloading computations to a cloud server is not possible or feasible, for instance, due to data sovereignty concerns or latency constraints, a solution can be to distribute the inference load across multiple devices in a local edge network. We propose an approach which is capable of orchestrating multi-stage inference tasks in a mobile ad-hoc network consisting of heterogeneous devices in a self-organized and fully distributed manner. As individual edge devices may be battery-powered and volatile, the framework ensures a high degree of reliability even in dynamic environments. In particular, new nodes are automatically and seamlessly integrated into the ensemble, rendering the approach highly scalable. Moreover, resilience against spontaneous node dropouts or connection failures is implemented through adaptive task rerouting. Finally, by enabling complex inference tasks to be processed in small segments on the most suitable hardware available in the network, the ensemble is able to attain considerable pipelining performance and energy efficiency. Full article

Background: Indocyanine green (ICG)-guided surgery is an emerging technique to enhance intraoperative visualization of nodes and tumor location. However, there is no uniform protocol regarding the optimal timing, dosage, or injection site for ICG in colorectal cancer surgery. We assess the feasibility of ICG injection at the anorectal junction immediately before surgery to safely identify the inferior mesenteric artery (IMA). Methods: This was a prospective study involving robotic left hemicolectomy or anterior resection of the rectum for primary colorectal cancer in 2024 in a single center. A total of 10–20 mg was injected into the anorectal submucosa at four quadrants circumferentially using an anoscope immediately before robot docking. Results: In this first study, ICG allowed us to identify the IMA in 84.6% of 26 patients (mean age 66.5 years; BMI 26.7 kg/m²), without intraoperative medical and surgical complications. Elevated BMI correlated with failure of IMA detection (r = −0.77, p < 0.001), despite high ICG doses trending toward improved vascular visualization (p = 0.097). A mean of 22 lymph nodes was harvested after ICG injection, with yields unaffected by the quality of IMA visualization. Conclusions: Submucosal injection of ICG is a feasible and easily adoptable option for early identification of the IMA, thereby preventing major vascular injuries, particularly in patients with challenging anatomy. A standardized protocol was implemented to improve reliability. Full article

The construction sector faces growing pressure to reduce its environmental impact, particularly in regions with limited access to conventional materials and urgent housing needs. Bamboo, a fast-growing and renewable resource with favorable mechanical properties, offers a sustainable alternative for structural applications. This study aims to enhance the efficiency of bamboo–concrete composites by investigating shear connection methods for composite floor systems. Different connection configurations were examined: (i) notch-type, (ii) dowel-type, and (iii) combined systems. Symmetric push-out tests were conducted to evaluate the load transfer mechanisms between bamboo logs and concrete layers. The mechanical behavior of each configuration was characterized through load–slip responses, failure modes, stiffness, strength, and deformation capacity. The results show that notch-type connections with longer grooves provided the highest stiffness and strength. In contrast, dowel-type connections exhibited superior ductility but lower stiffness and strength. The combined configuration delivered a balanced performance, integrating favorable aspects of both systems. A predictive model for each connection type was developed and validated against the experimental data, demonstrating satisfactory accuracy and reliable prediction of failure modes. These findings highlight the potential of optimized shear connections to advance sustainable bamboo–concrete composite construction, while also revealing the significant influence of bamboo’s natural variability, such as differences in diameter, node geometry, straightness, and material properties, on structural performance. Full article

To enable remote and automatic monitoring of the farmland soil information, this paper has developed a soil monitoring system based on the Internet of Things (IoT), which mainly involves the development of a gateway server node, wireless sensor nodes, a remote monitoring platform, and photovoltaic (PV) modules. The Raspberry Pi 5-based gateway server periodically sends data acquisition commands to wireless sensor nodes via LoRa, receives soil data returned by sensor nodes, and stores them in a MySQL database. Using a remote monitoring platform, Internet users can monitor real-time and historical soil data stored in the database. The STM32F103C8T6-based wireless sensor node receives data acquisition commands from the gateway server, uses soil temperature and humidity sensors as well as a pH sensor to collect soil status, and then sends sensor data back to the gateway server via LoRa. The system is powered by both PV energy and batteries, which enhances the endurance capability. Experimental results show that the designed system works well in remotely monitoring soil information. Using the proposed query attempt dynamic adjustment (QADA) method, the wireless sensor node dynamically adjusts the number of query attempts, which reduces the data acquisition failure rate from 21–25% to no more than 0.33%. Using the obtained qualitative relationship that the data acquisition delay varies inversely with the LoRa transfer rate, the data acquisition delay can be reduced to less than 67 ms. Full article

As a core factor in advancing urban agglomeration development and new urbanization, the structural resilience of multi-modal transportation networks in metropolitan areas directly determines their disturbance resistance during emergencies and their sustainable development. To address the prevalent “core–peripheral” connectivity imbalance in medium-sized metropolitan areas, this study takes the Jinan Metropolitan Area as an empirical case to systematically explore its multi-modal transportation network’s structural resilience. A three-dimensional evaluation framework of “absorbing capacity–buffering capacity–recovery capacity” was built based on complex network theory. Network efficiency was used to measure absorbing capacity, the average number of independent paths was used to characterize buffering capacity, and structural entropy was used to determine recovery capacity. The entropy weight method was used to calculate integrated multi-dimensional resilience values, and a sequential node failure simulation was used to analyze network invulnerability. The main findings are as follows: (1) The Jinan Metropolitan Area’s multi-modal transportation network has “small-world characteristics” but low density, with trunk line coverage gaps. (2) Sub-networks differ significantly. The railway sub-network performs best, the highway sub-network is the weakest, and the composite network achieves resilience balance through multi-modal collaboration. (3) Node failure analysis reveals that “core hubs are resilience pillars, while secondary highway nodes are weaknesses.” The proposed “three-dimensional evaluation framework” provides a methodological reference for resilience quantification in similar metropolitan areas, and the “trunk line densification + peripheral connection” strategy supports the implementation of metropolitan planning policies to promote modern metropolitan transportation systems with efficient commuting and robust disturbance resistance. Full article

This study develops a systematic kinematic upper-bound framework to evaluate the ultimate bearing capacity and failure mechanisms of prefabricated cast-in-place slab–wall joints in overlapped metro stations. Recognizing the complex shear–compression interaction in these critical structural nodes, a novel three-dimensional short-block shear failure model is established based on the principle of energy balance. The analysis employs a modified Mohr–Coulomb strength criterion incorporating a finite tensile strength cut-off, enabling more accurate representation of cracking and tensile resistance effects. Analytical solutions are derived to predict the ultimate capacity and critical failure angle, followed by a comprehensive parametric analysis. The results reveal that cross-sectional dimensions dominate the bearing capacity, while the internal friction angle and tensile-to-compressive strength ratio significantly influence both the magnitude and mode of failure. A narrower load distribution width enhances capacity and reduces the optimal failure angle. Overall, the proposed 3D model provides a rigorous and efficient theoretical tool for the design optimization and safety assessment of prefabricated underground structures. Full article

The Internet of Things (IoT) plays an important role in the development of smart cities. IoT forms a large network, and optimal controller placement plays a crucial role in ensuring network performance and resilience. This paper proposes a hybrid optimization approach that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to strategically place controllers. Kaunas (Lithuania) was selected as a real-world smart city model. A large-scale Narrowband Internet of Things (NB-IoT) network with 2000 nodes was simulated, and 10 controllers were optimally placed in the network to minimize latency, balance load, enhance energy efficiency, and redundancy. The performance of the proposed hybrid GA-PSO algorithm was compared with random and K-Means clustering placements under three scenarios: normal operation, node failures, and traffic spikes. Simulation results demonstrate that the hybrid approach outperforms the other two methods in terms of load balancing, packet loss, energy efficiency, scalability, and redundancy. These findings highlight the robustness and effectiveness of the proposed hybrid algorithm in optimizing controller placement for smart city environments. Full article