Search Results (6,933)

To address the unclear vibration failure mechanism and the lack of system-level reliability evaluation methods for multirotor transport UAVs under complex operating conditions, this paper proposes a comprehensive analysis method that combines fluid–structure interaction dynamics with dynamic reliability theory. First, the study analyzes rotor dynamics and vibration characteristics under bidirectional fluid–structure coupling and obtains vibration displacement data. Then, it builds a dynamic reliability model using the Second-Order Reliability Method (SORM) and the Laplace method. The model explores reliability evolution in a dynamic airflow coupling environment. Finally, it establishes a multi-rotor UAV system reliability evaluation method and analyzes the impact of rotor number and layout on system reliability. The results provide a theoretical basis for structural optimization, reliability assurance, and fault tolerance improvement of multi-rotor UAVs under complex conditions. Full article

A new generation of compact radon detectors with high sensitivity and fine spatial resolution (1–2 cm scale) was used to investigate indoor radon distribution and identify potential entry pathways. Solid-state nuclear track detectors (Kodak-Pathe LR-115 type II, Dosirad, France), combined with activated carbon fabric (ACC-5092-10), enabled sensitive, spatially resolved radon measurements. Two case studies were conducted: Case 1 involves a room with elevated radon levels suspected to originate from the floor. Case 2 involves a house with persistently high indoor radon concentrations despite active basement ventilation. In the first case, radon emission from the floor was found to be highly inhomogeneous, with concentrations varying by more than a factor of four. In the second, unexpectedly high radon levels were detected at electrical switches and outlets on walls in the living space, suggesting radon transport through wall voids and entry via non-hermetic electrical fittings. These novel detectors facilitate fine-scale mapping of indoor radon concentrations, revealing ingress routes that were previously undetectable. Their use can significantly enhance radon diagnostics and support the development of more effective mitigation strategies. Full article

Multiple uncertainties in traffic demand fluctuations and infrastructure vulnerability during seismic events pose significant challenges for the resilience assessment of highway transportation networks (HTNs). While Monte Carlo simulation remains the dominant approach for uncertainty propagation, its high computational cost limits its scalability, particularly in metropolitan-scale networks. This study proposes an EQResNet framework for accelerated post-earthquake resilience assessment of HTNs. The model integrates network topology, interregional traffic demand, and roadway characteristics into a streamlined deep neural network architecture. A comprehensive surrogate modeling strategy is developed to replace conventional traffic simulation modules, including highway status realization, shortest path computation, and traffic flow assignment. Combined with seismic fragility models and recovery functions for regional bridges, the framework captures the dynamic evolution of HTN functionality following seismic events. A multi-dimensional resilience evaluation system is also established to quantify network performance from emergency response and recovery perspectives. A case study on the Sioux Falls network under probabilistic earthquake scenarios demonstrates the effectiveness of the proposed method, achieving 95% prediction accuracy while reducing computational time by 90% compared to traditional numerical simulations. The results highlight the framework’s potential as a scalable, efficient, and reliable tool for large-scale post-disaster transportation system analysis. Full article

Background: Drug–drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women’s healthcare. Methods: A DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein–protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. Results: This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25%, respectively). Some were expected to improve current therapies (n = 23), while others would cause harmful effects (n = 11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Conclusions: These results show the importance of DDI studies aimed at identifying those that might compromise or improve their efficacy, which could lead to personalizing female reproductive therapies. Full article

The original Modbus specification for RS-485 and RS-232 buses supported broadcast transmission. As the protocol evolved into Modbus-TCP, to use the TCP transport, this useful feature was lost, likely due to the point-to-point nature of TCP connections. Later proposals did not restore the broadcast transmission capability, although they used UDP as transport and UDP, by itself, would have supported it. Moreover, they did not address the inherent lack of reliable delivery of UDP, leaving datagram loss detection and recovery to the application layer. This paper describes a novel redesign of Modbus-UDP that addresses the aforementioned shortcomings. It achieves a mean round-trip time of only 38% with respect to Modbus-TCP and seamlessly supports a previously published protocol based on Modbus broadcast. In addition, the built-in retransmission of Modbus-UDP reacts more efficiently than the equivalent Modbus-TCP mechanism, exhibiting 50% of its round-trip standard deviation when subject to a 1% two-way IP datagram loss probability. Combined with the lower overhead of UDP versus TCP, this makes the redesigned Modbus-UDP protocol better suited for a variety of Industrial Internet of Things systems with limited computing and communication resources. Full article

Eurasian aspen (Populus tremula L.) is a tree species with recognised ecological and economic importance for both natural and plantation forests. For the fast cloning of selected aspen genotypes, the method of plant propagation through in vitro culture (micropropagation) is often recommended. The efficiency of this method is related to the use of shoot-inducing chemical growth regulators, among which cytokinins, a type of plant hormone, dominate. Although cytokinins can inhibit rooting, this effect is avoided by using cytokinin-free media. This study sought to identify concentrations and combinations of growth regulators that would stimulate one type of P. tremula organogenesis (either shoot or root formation) without inhibiting the other. The investigated growth regulators included cytokinin 6-benzylaminopurine (BAP), auxin transport inhibitor 2,3,5-triiodobenzoic acid (TIBA), auxins indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA), gibberellin biosynthesis inhibitor paclobutrazol (PBZ), and a gibberellin mixture (GA₄/₇). Both BAP and TIBA increased shoot number per P. tremula explant and decreased the number of adventitious roots, but TIBA, in contrast to BAP, did not inhibit lateral root formation. However, for the maintenance of both adventitious shoot and root formation above the control level, the combination of PBZ and GA_4/7 was shown to be especially promising. Full article

The Shahejie Formation in Nanpu Sag is a crucial region for deep-layer hydrocarbon exploration in the Bohai Bay Basin. To address the impact of faults on sublacustrine fan formation and spatial distribution within the study area, this study integrated well logging, laboratory analysis, and 3D seismic data to systematically analyze sedimentary characteristics of sandbodies from the first member of the Shahejie Formation (Es₁) sublacustrine fans, clarifying their planar and cross-sectional distributions. Further research indicates that Gaoliu Fault activity during Es₁ deposition played a significant role in fan development through two mechanisms: (1) vertical displacement between hanging wall and footwall reshaped local paleogeomorphology; (2) tectonic stresses generated by fault movement affected slope stability, triggering gravitational mass transport processes that remobilized fan delta sediments into the central depression zone as sublacustrine fans through slumping and collapse mechanisms. Core observations reveal soft-sediment deformation features, including slump structures, flame structures, and shale rip-up clasts. Seismic profiles show lens-shaped geometries with thick centers thinning laterally, exhibiting lateral pinch-out terminations. Inverse fault-step architectures formed by underlying faults control sandbody distribution patterns, restricting primary deposition locations for sublacustrine fan development. The study demonstrates that sublacustrine fans in the study area are formed by gravity flow processes. A new model was established, illustrating the combined control of the Gaoliu Fault and reverse stepover faults on fan development. These findings provide valuable insights for gravity flow exploration and reservoir prediction in the Nanpu Sag, offering important implications for hydrocarbon exploration in similar lacustrine rift basins. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

Background Plasma membrane proteins (PMPs) play key roles in cell signalling, adhesion, and trafficking, and are attractive therapeutic targets in cancer due to their surface accessibility. However, their typically low abundance limits detection by conventional proteomic approaches. Methods: To improve PMP detection, we employed a surface proteomics workflow combining cell surface biotinylation and affinity purification prior to LC-MS/MS analysis in cervical (SiHa) and bladder (UMUC3) cancer cell lines cultured under normoxic (21% O₂) or hypoxic (0.1% O₂) conditions. Results: In SiHa cells, 43 hypoxia-upregulated proteins were identified exclusively in the biotin-enriched fraction, including ITGB2, ITGA7, AXL, MET, JAG2, and CAV1/CAV2. In UMUC3 cells, 32 unique upregulated PMPs were detected, including CD55, ADGRB1, SLC9A1, NECTIN3, and ACTG1. These proteins were not observed in corresponding whole-cell lysates and are associated with extracellular matrix remodelling, immune modulation, and ion transport. Biotinylation enhanced the detection of membrane-associated pathways such as ECM organisation, integrin signalling, and PI3K–Akt activation. Protein–protein interaction analysis revealed links between membrane receptors and intracellular stress regulators, including mitochondrial proteins. Conclusions: These findings demonstrate that surface biotinylation improves the sensitivity and selectivity of plasma membrane proteomics under hypoxia, revealing hypoxia-responsive proteins and pathways not captured by standard whole-cell analysis. Full article

According to the Transportation Safety Board of Canada, between 2013 and 2023, 62% of aviation accidents occurred during the approach, landing, and post-impact phases of flight. Hence, this study targets factors contributing to increased accident rates during the final stages of flight. It will review how pilot experience influences decision-making and identifies mitigation strategies, focusing on go-arounds to prevent accidents during these critical phases. Surveys and roundtable discussions were conducted to identify factors influencing pilot performance during approach, landing, and go-around manoeuvres. By using a mixed-methods approach that combined thematic and statistical analyses, key safety factors were identified, including situational awareness, decision-making, and operational complexity. The study also examined the relationship between experience and decision-making, highlighting areas for targeted interventions to improve safety. The research emphasises the importance of integrating decision-making considerations into training programmes and connecting these to human factors. Through identifying areas for improvement, this study offers a safety-driven framework to address decision-making challenges during approach, landing, and go-around phases, with the objective of reducing accident and incident rates in general aviation. Full article

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM. Full article

Emerging transportation challenges necessitate cutting-edge technologies for real-time infrastructure and traffic monitoring. To create a dynamic digital twin for intersection monitoring, data gathering, performance assessment, and calibration of microsimulation software, this study presents a state-of-the-art platform that combines high-resolution LiDAR sensor data with VISSIM simulation software. Intending to track traffic flow and evaluate important factors, including congestion, delays, and lane configurations, the platform gathers and analyzes real-time data. The technology allows proactive actions to improve safety and reduce interruptions by utilizing the comprehensive information that LiDAR provides, such as vehicle trajectories, speed profiles, and lane changes. The digital twin technique offers unparalleled precision in traffic and infrastructure state monitoring by fusing real data streams with simulation-based performance analysis. The results show how the platform can transform real-time monitoring and open the door to data-driven decision-making, safer intersections, and more intelligent traffic data collection methods. Using the proposed platform, this study calibrated a VISSIM simulation network to optimize the driving behavior parameters in the software. This study addresses current issues in urban traffic management with real-time solutions, demonstrating the revolutionary impact of emerging technology in intelligent infrastructure monitoring. Full article

The global transition towards a low-carbon energy system urgently demands efficient and safe energy storage solutions. Aqueous zinc-ion batteries (AZIBs) are considered a promising alternative to lithium-ion batteries due to their inherent safety and environmental friendliness. However, conventional manufacturing methods are costly and labor-intensive, hindering their large-scale production. Recent advances in 3D printing technology offer innovative pathways to address these challenges. By combining design flexibility with material optimization, 3D printing holds the potential to enhance battery performance and enable customized structures. This review systematically examines the application of 3D printing technology in fabricating key AZIB components, including electrodes, electrolytes, and integrated battery designs. We critically compare the advantages and disadvantages of different 3D printing techniques for these components, discuss the potential and mechanisms by which 3D-printed structures enhance ion transport and electrochemical stability, highlight critical existing scientific questions and research gaps, and explore potential strategies for optimizing the manufacturing process. Full article

This study presents an integrated geospatial framework for assessing the risk to high-speed railway (HSR) infrastructure, combining a persistent scatterer interferometric synthetic aperture radar (PS-InSAR) analysis with multi-criteria decision-making in a geographic information system (GIS) environment. Focusing on the Honam HSR corridor in South Korea, the model incorporates both maximum ground deformation and subsidence velocity to construct a dynamic hazard index. Social vulnerability is quantified using five demographic and infrastructural indicators, and a two-stage analytic hierarchy process (AHP) is applied with dependency correction to mitigate inter-variable redundancy. The resulting high-resolution risk maps highlight spatial mismatches between geotechnical hazards and social exposure, revealing vulnerable segments in Gongju and Iksan that require prioritized maintenance and mitigation. The framework also addresses data limitations by interpolating groundwater levels and estimating train speed using spatial techniques. Designed to be scalable and transferable, this methodology offers a practical decision-support tool for infrastructure managers and policymakers aiming to enhance the resilience of linear transport systems. Full article

Cadmium (Cd) is a toxic environmental heavy metal that exerts harmful effects on multiple tissues, including the kidney, liver, lung, and bone, and is also associated with the development of anemia. However, the precise molecular mechanisms underlying Cd-induced toxicity remain incompletely understood. In this paper, we review the recent molecular mechanisms of Cd-induced toxicity and its modification, with a particular emphasis on our recent findings. Using a combination of DNA microarray analysis, protein–DNA binding assays, and siRNA-mediated gene silencing, we identified several transcription factors, YY1, FOXF1, ARNT, and MEF2A, as novel molecular targets of Cd. The downregulation of their downstream genes, including UBE2D2, UBE2D4, BIRC3, and SLC2A4, was directly associated with the expression of cytotoxicity. In addition, PPARδ plays a pivotal role in modulating cellular susceptibility to Cd-induced renal toxicity, potentially by regulating apoptosis-related signaling pathways. In addition to apoptosis pathways, Cd toxicity through ROS generation, ferroptosis and pyroptosis were summarized. Furthermore, it has been revealed that Cd suppresses the expression of iron transport-related genes in duodenal epithelial cells leading to impaired intestinal iron absorption as well as decreased hepatic iron levels. These findings provide a mechanistic basis for Cd-induced iron deficiency anemia, implicating disrupted iron homeostasis as a contributing factor. Full article