Search Results (2,376)

This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. Full article

In this paper, we investigate the generalized Hyers–Ulam stability of bi-homomorphisms, bi-derivations, and bi-isomorphisms in $C^{*}$-ternary algebras. The study of functional equations with a sufficient number of variables can be helpful in solving real-world problems such as artificial intelligence. In this paper, we build on previous research on functional equations with four variables to study functional equations with as many variables as desired. We introduce new bounds for the stability of mappings satisfying generalized bi-additive conditions and demonstrate the uniqueness of approximating bi-isomorphisms. The results contribute to the deeper understanding of ternary algebraic structures and related functional equations, relevant to both pure mathematics and quantum information science. Full article

A novel single-walled carbon nanotube (SWNT), silver (Ag) nanoparticle, and zinc benzene carboxylate (ZnBDC) metal–organic framework (MOF) composite was synthesised and systematically characterised to develop an efficient platform for mercury ion (Hg²⁺) detection. X-ray diffraction confirmed the successful incorporation of Ag nanoparticles and SWNTs without disrupting the crystalline structure of ZnBDC. Meanwhile, field-emission scanning electron microscopy and energy-dispersive spectroscopy mapping revealed a uniform elemental distribution. Thermogravimetric analysis indicated enhanced thermal stability. Electrochemical measurements (cyclic voltammetry and electrochemical impedance spectroscopy) demonstrated improved charge transfer properties. Electrochemical sensing investigations using differential pulse voltammetry revealed that the SWNTs/Ag@ZnBDC-modified glassy carbon electrode exhibited high selectivity toward Hg²⁺ ions over other metal ions (Cd²⁺, Co²⁺, Cr³⁺, Fe³⁺, and Zn²⁺), with optimal performance at pH 4. The sensor displayed a linear response in the concentration range of 0.1–1.0 nM (R² = 0.9908), with a calculated limit of detection of 0.102 nM, slightly close to the lowest tested point, confirming its high sensitivity for ultra-trace Hg²⁺ detection. The outstanding sensitivity, selectivity, and reproducibility underscore the potential of SWNTs/Ag@ZnBDC as a promising electrochemical platform for detecting trace levels of Hg2+ in environmental monitoring. Full article

Meiotic recombination is a key evolutionary process that generates novel allele combinations during prophase I of meiosis, promoting genetic diversity and enabling the selection of desirable traits in livestock breeding. Although its molecular mechanisms are well-characterised in model organisms such as humans and mice, studies in African indigenous cattle, particularly South African breeds, remain scarce. Key regulators of recombination, including PRDM9, SPO11, and DMC1, play essential roles in crossover formation and genome stability, with mutations in these genes often linked to fertility defects. Despite the Bonsmara and Nguni breeds’ exceptional adaptability to arid and resource-limited environments, little is known about how recombination contributes to their unique genetic architecture and adaptive traits. This review synthesises the current knowledge on the molecular basis of meiotic recombination, with a focus on prophase I events and associated structural proteins and enzymes. It also highlights the utility of genome-wide tools, particularly high-density single nucleotide polymorphism (SNP) markers for recombination mapping. By focusing on the underexplored recombination landscape in South African beef cattle, this review identifies key knowledge gaps. It outlines how recombination studies can inform breeding strategies aimed at enhancing genetic improvement, conservation, and the long-term sustainability of local beef production systems. Full article

Introduction: Liver transplantation remains the definitive treatment for end-stage liver disease but faces critical challenges including organ shortages and preservation difficulties, particularly with extended criteria donor (ECD) grafts. Hypothermic machine perfusion (HMP) represents a promising alternative to traditional static cold storage (SCS). Methods: This retrospective study analyzed outcomes from 62 liver transplant recipients between 2016 and 2025, comparing 8 grafts preserved by HMP using the Liver Assist^® system and 54 grafts preserved by SCS. Parameters assessed included postoperative complications, hemodynamic stability, ischemia times, and survival outcomes. Results: HMP significantly reduced surgical (0% vs. 75.9%, p = 0.01) and biliary complications (0% vs. 34.4%, p = 0.004), improved hemodynamic stability post-reperfusion (∆MAP%: 1 vs. 21, p = 0.006), and achieved superior one-year survival rates (100% vs. 84.4%). Despite longer ischemia periods, grafts treated with HMP exhibited fewer adverse effects from ischemia-reperfusion injury. Discussion: These findings highlight the substantial benefits of HMP, particularly in improving graft quality from marginal donors and reducing postoperative morbidity. Further adoption of this technology could significantly impact liver transplantation outcomes by expanding the viable donor pool. Conclusions: The study underscores the effectiveness of hypothermic machine perfusion (HMP) as a superior preservation method compared to traditional static cold storage (SCS), HMP appears to be associated with improved short-term outcomes in liver transplantation. By substantially reducing postoperative complications and enhancing graft viability, HMP emerges as a pivotal strategy for maximizing the use of marginal donor organs. Further research and broader clinical implementation are recommended to validate these promising results and to fully harness the potential of HMP in liver transplantation. Full article

Enhancing quadrotor control to improve both precision and responsiveness is essential for expanding their deployment in complex and dynamic environments. These aerial vehicles are widely used in applications, such as aerial mapping, delivery, disaster response, and defense, where maintaining stability and accuracy is critical, especially under external disturbances like wind. This paper makes three key contributions. First, it develops a nonlinear mathematical model of a quadrotor and designs a backstepping controller for trajectory tracking. Second, the controller’s parameters are optimized using three nature-inspired algorithms: Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the Flower Pollination Algorithm (FPA), enabling performance comparisons in terms of their tracking precision and control effort. Third, the robustness of the best-performing optimized controller is evaluated by applying wind disturbances at the simulation level, modeled as external forces acting along the x-axis and summed with the control input. The simulation results highlight the comparative efficiency of the optimization methods and demonstrate the robustness of the selected controller in maintaining stability and accuracy under wind-induced perturbations. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

The deep integration of smart grid and Internet of Things technologies has made high-speed power line carrier communication a key communication technology in energy management, industrial monitoring, and smart home applications, owing to its advantages of requiring no additional wiring and offering wide coverage. However, the inherent characteristics of power line channels, such as strong noise, multipath fading, and time-varying properties, pose challenges to traditional encryption algorithms, including low key distribution efficiency and weak anti-interference capabilities. These issues become particularly pronounced in high-speed transmission scenarios, where the conflict between data security and communication reliability is more acute. To address this problem, a secure transmission method for high-speed power line carrier communication based on generalized two-dimensional polynomial chaotic mapping is proposed. A high-speed power line carrier communication network is established using a power line carrier routing algorithm based on the minimal connected dominating set. The autoregressive moving average model is employed to determine the degree of transmission fluctuation deviation in the high-speed power line carrier communication network. Leveraging the complex dynamic behavior and anti-decoding capability of generalized two-dimensional polynomial chaotic mapping, combined with the deviation, the communication key is generated. This process yields encrypted high-speed power line carrier communication ciphertext that can resist power line noise interference and signal attenuation, thereby enhancing communication confidentiality and stability. By applying reference modulation differential chaotic shift keying and integrating the ciphertext of high-speed power line carrier communication, a secure transmission scheme is designed to achieve secure transmission in high-speed power line carrier communication. The experimental results demonstrate that this method can effectively establish a high-speed power line carrier communication network and encrypt information. The maximum error rate obtained by this method is 0.051, and the minimum error rate is 0.010, confirming its ability to ensure secure transmission in high-speed power line carrier communication while improving communication confidentiality. Full article

Vehicle detection in remote sensing images faces significant challenges due to small object sizes, scale variation, and cluttered backgrounds. To address these issues, we propose RSW-YOLO, an enhanced detection model built upon the YOLOv8n framework, designed to improve feature extraction and robustness against environmental noise. A Restormer module is incorporated into the backbone to model long-range dependencies via self-attention, enabling better handling of multi-scale features and complex scenes. A dedicated detection head is introduced for small objects, focusing on critical channels while suppressing irrelevant information. Additionally, the original CIoU loss is replaced with WIoU, which dynamically reweights predicted boxes based on their quality, enhancing localization accuracy and stability. Experimental results on the DJCAR dataset show mAP@0.5 and mAP@0.5:0.95 improvements of 5.4% and 6.2%, respectively, and corresponding gains of 4.3% and 2.6% on the VisDrone dataset. These results demonstrate that RSW-YOLO offers a robust and accurate solution for UAV-based vehicle detection, particularly in urban scenes with dense or small targets. Full article

Brucellosis continues to be a significant global zoonotic infection, with diagnosis largely relying on the detection of antibodies against the Brucella O-polysaccharide (O-PS) A and M antigens. In this study, computational methods, including homology modeling, molecular docking, and molecular dynamics simulations, were applied to investigate the interaction of the four murine monoclonal antibodies (mAbs) YsT9.1, YsT9.2, Bm10, and Bm28 with hexasaccharide fragments of the A and M epitopes. Through stringent stability criteria, based on interaction energies and mobility of the antigens, high-affinity binding of A antigen with YsT9.1 antibody and M antigen with Bm10 antibody was predicted. In both the complexes hydrophobic interactions dominate the antigen–antibody binding. These findings align well with experimental epitope mapping, indicating YsT9.1’s preference for internal sequences of the A epitope and Bm10’s preference for internal elements of the M epitope. Interestingly, no stable complexes were identified for YsT9.2 or Bm28 interacting with A or M antigen. This study provides valuable insights into the mechanism of molecular recognition of Brucella O-antigens that can be applied for the development of improved diagnostics, synthetic glycomimetics, and improved vaccine strategies. Full article

This study presents a cooperative unmanned aerial system (UAS) designed to enable precise autonomous landings in unstructured environments using low-cost onboard vision technology. This approach involves a carrier UAV with a stabilized RGB camera and a neural inference system, as well as a lightweight tailsitter payload UAV with an embedded grayscale vision module. The system relies on visually recognizable landing markers and does not require additional sensors. Field trials comprising full deployments achieved an 80% success rate in autonomous landings, with vertical touchdown occurring within a 1.5 m radius of the target. These results confirm that vision-based marker detection using compact neural models can effectively support autonomous UAV operations in constrained conditions. This architecture offers a scalable alternative to the high complexity of SLAM or terrain-mapping systems. Full article

This study investigates the influence of root cohesion spatial heterogeneity on rainfall-induced landslide distribution across the Loess Plateau, addressing limitations in existing methods that oversimplify root reinforcement. Leveraging Landsat and GaoFen satellite images, we developed a regional root cohesion inversion model that quantifies spatial heterogeneity using tree height (derived from time series Landsat imagery) and above-ground biomass (from 30 m resolution satellite products). This approach, integrated with land use-specific hydrological parameters and an infinite slope stability model, significantly improves landslide susceptibility predictions compared to models ignoring root cohesion or using uniform assignments. High-resolution pre- and post-rainfall GaoFen satellite imagery validated landslide inventories, revealing dynamic susceptibility patterns: farmland exhibited the highest risk, followed by artificial and secondary forests, with susceptibility escalating post-rainfall. This study underscores the critical role of remote sensing-driven root cohesion mapping in landslide risk assessment, offering actionable insights for land use planning and disaster mitigation on the Loess Plateau. Full article

Rapid identification of vessel height within the navigable space beneath bridges is crucial for ensuring bridge safety. To prevent bridge collisions caused by vessels exceeding their height limits, this article introduces a real-time warning framework for excessive vessel height based on video spatial transformation. The specific contributions include the following: (1) A spatial transformation-based method for locating vessel coordinates in the channel using buoys as control points, employing laser scanning to obtain their world coordinates from a broad channel range, and mapping the pixel coordinates of the buoys from side channel images to the world coordinates of the channel space, thus achieving pixel-level positioning of the vessel’s waterline intersection in the channel. (2) For video images, a key point recognition network for vessels based on attention mechanisms is developed to obtain pixel coordinates of the vessel’s waterline and top, and to capture the posture and position of multiple vessels in real time. (3) Analyzing the posture of vessels traveling in various directions within the channel, the method accounts for the pixel distance of spatial transformation control points and vessel height to determine vessel positioning coordinates, solve for the vessel’s height above water, and combine with real-time waterline height to enable over-height vessel collision warnings for downstream channel bridges. The method has been deployed in actual navigational scenarios beneath bridges, with the average error in vessel height estimation controlled within 10 cm and an error rate below 0.8%. The proposed approach enables real-time automatic estimation of vessel height in terms of computational speed, making it more suitable for practical engineering applications that demand both real-time performance and system stability. The system exhibits outstanding performance in terms of accuracy, stability, and engineering applicability, providing essential technical support for intelligent bridge safety management. Full article

Effective maintenance and management of road infrastructure are essential for community well-being, economic stability, and cost efficiency. Well-maintained roads reduce accident risks, improve safety, shorten travel times, lower vehicle repair costs, and facilitate the flow of goods, all of which positively contribute to GDP and economic development. Accurate intersection mapping forms the foundation of effective road asset management, yet traditional manual digitization methods remain time-consuming and prone to gaps and overlaps. This study presents an automated computational geometry solution for precise road intersection mapping that eliminates common digitization errors. Unlike conventional approaches that only detect intersection positions, our method systematically reconstructs complete intersection geometries while maintaining topological consistency. The technique combines plane surveying principles (including line-bearing analysis and curve detection) with spatial analytics to automatically identify intersections, characterize their connectivity patterns, and assign unique identifiers based on configurable parameters. When evaluated across multiple urban contexts using diverse data sources (manual digitization and OpenStreetMap), the method demonstrated consistent performance with mean Intersection over Union greater than 0.85 and F-scores more than 0.91. The high correctness and completeness metrics (both more than 0.9) confirm its ability to minimize both false positive and omission errors, even in complex roadway configurations. The approach consistently produced gap-free, overlap-free outputs, showing strength in handling interchange geometries. The solution enables transportation agencies to make data-driven maintenance decisions by providing reliable, standardized intersection inventories. Its adaptability to varying input data quality makes it particularly valuable for large-scale infrastructure monitoring and smart city applications. Full article

In this paper, a discrete element method (DEM) coupled with a finite element method (FEM) was used to elucidate the impact of packing structures and size ratios on the cold die compaction behavior of pure copper powders. HCP structure, SC structure, and three random packing structures with different particle size ratios (1:2, 1:3, and 1:4) were generated by the DEM, and then simulated by the FEM to analyze the average relative density, von Mises stress, and force chain structures of the compact. The results show that for HCP and SC structures with a regular stacking structure, the average relative densities of the compact were higher than those of random packing structures, which were 0.9823, 0.9693, 0.9456, 0.9502, and 0.9507, respectively. Compared with their initial packing density, it could be improved by up to 21.13%. For the bigger particle in HCP and SC structures, the stress concentration was located between the adjacent layers, while in the small particles, it was located between contacted particles. During the initial compaction phase, smaller particles tend to occupy the voids between larger particles. As the pressure increases, larger particles deform plastically in a notable way to create a stabilizing force chain. This action reduces the axial stress gradient and improves radial symmetry. The transition from a contact-dominated to a body-stress-dominated state is further demonstrated by stress distribution maps and contact force vector analysis, highlighting the interaction between particle rearrangement and plasticity. Full article