Search Results (14,531)

This study investigates the optimization of traffic scheduling in autonomous vehicle networks using time-sensitive networking (TSN), a type of deterministic Ethernet. Ethernet has high bandwidth and compatibility to support various protocols, and its application range is expanding from office environments to smart factories, aerospace, and automobiles. TSN is a representative technology of deterministic Ethernet and is composed of various standards such as time synchronization, stream reservation, seamless redundancy, frame preemption, and scheduled traffic, which are sub-standards of IEEE 802.1 Ethernet established by the IEEE TSN task group. In order to ensure real-time transmission by minimizing end-to-end delay in a TSN network environment, it is necessary to schedule transmission timing in all links transmitting ST (Scheduled Traffic). This paper proposes network performance metrics and methods for applying machine learning (ML) techniques to optimize traffic scheduling. This study demonstrates that the traffic scheduling problem, which has NP-hard complexity, can be optimized using ML algorithms. The performance of each algorithm is compared and analyzed to identify the scheduling algorithm that best meets the network requirements. Reinforcement learning algorithms, specifically DQN (Deep Q Network) and A2C (Advantage Actor-Critic) were used, and normalized performance metrics (E2E delay, jitter, and guard band bandwidth usage) along with an evaluation function based on their weighted sum were proposed. The performance of each algorithm was evaluated using the topology of a real autonomous vehicle network, and their strengths and weaknesses were compared. The results confirm that artificial intelligence-based algorithms are effective for optimizing TSN traffic scheduling. This study suggests that further theoretical and practical research is needed to enhance the feasibility of applying deterministic Ethernet to autonomous vehicle networks, focusing on time synchronization and schedule optimization. Full article

The discussion of factors driving young people’s involvement in serious violence continues to be well documented across policy, news media, and academic research. The government response to riots taking place across the UK in 2011 set a precedent for an increasingly punitive discourse surrounding young people’s involvement in criminal lifestyles, as well as the Criminal Justice System's response to the overall issue. In order to develop a greater understanding of the complex breadth of driving factors behind serious youth violence and their discoursal representation, this article presents findings of a multifaceted investigation through the interpretivist paradigm, merging macro-level policy with micro-level community insights. The article commences with an argumentative discourse analysis of a selection of Government and Youth Violence Commission policy documents before drawing on three semi-structured interviews with community-level practitioners in England working within policing and youth work organisations. The findings reveal a complex interplay of socio-environmental factors, poverty, domestic trauma, cultural dimensions, and street-based exploitation positioned alongside constructs of social exclusion and masculinity. The study uncovers a broad issue of systemic marginalisation and reduction in community resources, exacerbating conditions of social exclusion that create a greater propensity for involvement in serious youth violence. The findings support calls for the framing of serious youth violence as an issue of ‘public health’, encouraging deeper investigation into underlying socio-economic, cultural, and political conditions. Full article

Due to the intricate and volatile nature of the service environment surrounding prestressing anchoring materials, stress corrosion poses a significant challenge to the sustained stability of underground reinforcement systems. Consequently, it is imperative to identify effective countermeasures against stress corrosion failure in cable bolts within deep underground environments, thereby ensuring the safety of deep resource extraction processes. In this study, the influence of various coatings on the stress corrosion resistance of cable bolts was meticulously examined and evaluated using specifically designed stress-corrosion-testing systems. The specimens were subjected to loading using four-point bending frames and exposed to simulated underground corrosive environments. A detailed analysis and comparison of the failure patterns and mechanisms of specimens coated with different materials were conducted through the meticulous observation of fractographic features. The results revealed stark differences in the stress corrosion behavior of coated and uncoated bolts. Notably, epoxy coatings and chlorinated rubber coatings exhibited superior anti-corrosion capabilities. Conversely, galvanized layers demonstrated the weakest effect due to their sacrificial anti-corrosion mechanism. Furthermore, the effectiveness of the coatings was found to be closely linked to the curing agent and additives used. The findings provide valuable insights for the design and selection of coatings that can enhance the durability and reliability of cable bolts in deep underground environments. Full article

To investigate the anchorage loss mechanism of externally prestressed CFRP tendons in concrete beams, this study introduces a novel theoretical calculation system (TCS) developed through both the finite element method (FEM) and experimental validation. Firstly, the FEM and the proposed TCS were employed based on the mechanism of anchorage loss to compute the deformation of each part of the prestressed tendon–main beam connection system, ensuring result accuracy through mutual validation. Subsequently, field tests, designed according to FEM guidelines, measured the anchorage loss in externally prestressed CFRP tendons, with long-term monitoring included. Finally, experimental data were then used to refine the TCS. The results indicate that deformation at the connecting screw and the front end of the steel reaction frame constitutes approximately 95% of the total deformation, with theoretical calculations aligning closely with the FEM results. The field tests revealed that the anchorage loss of a 12 m long CFRP tendon under 950 MPa prestress accounted for about 35% of the total prestress loss. The discrepancy in deformation compared with the theoretical results was due to a gap of approximately 0.4 mm between the two threaded connections, which can be minimized by improving construction techniques. After correction, the calculation error was reduced to about 5%. Control variable studies confirmed that anchorage loss is influenced by the prestress level, the dimensions of the steel reaction frame front end, the connecting screw length, and the number of thread gaps. This study provides a comprehensive approach for accurately predicting and mitigating anchorage loss in externally prestressed CFRP tendons, with significant implications for future engineering applications. Full article

In his groundbreaking 1905 paper on special relativity, Einstein distinguished between local and global time in inertial systems, introducing his famous definition of distant simultaneity to give physical content to the notion of global time. Over the following decade, Einstein attempted to generalize this analysis of relativistic time to include accelerated frames of reference, which, according to the principle of equivalence, should also account for time in the presence of gravity. Characteristically, Einstein’s methodology during this period focused on simple, intuitively accessible physical situations, exhibiting a high degree of symmetry. However, in the final general theory of relativity, the a priori existence of such global symmetries cannot be assumed. Despite this, Einstein repeated some of his early reasoning patterns even in his 1916 review paper on general relativity and in later writings. Modern commentators have criticized these arguments as confused, invalid, and inconsistent. Here, we defend Einstein in the specific context of his use of global time and his derivations of the gravitational redshift formula. We argue that a detailed examination of Einstein’s early work clarifies his later reasoning and demonstrates its consistency and validity. Full article

Loquat leaves exhibiting obvious yellowing, blistering, mosaic, leaf upward cupping, crinkle, and leaf narrowing were identified in Panzhihua City, Sichuan Province, China. High-throughput sequencing (HTS) with the ribo-depleted cDNA library was employed to identify the virome in the loquat samples; only tomato mosaic virus (ToMV) and citrus exocortis viroid (CEVd) were identified in the transcriptome data. The complete genome sequence of ToMV and CEVd were obtained from the loquat leaves. The full-length genome of the ToMV-loquat is 6376 nt and comprises four open reading frames (ORFs) encoding 183 kDa protein, RNA-dependent RNA polymerase (RdRp), movement protein (MP), and coat protein (CP), respectively. A pairwise identity analysis showed that the complete sequence of the ToMV-loquat had a nucleotide identity between 98.5 and 99.3% with other ToMV isolates. A phylogenetic analysis indicated that ToMV-loquat was more closely related to ToMV-IFA9 (GenBank No. ON156781). A CEVd sequence with 361 nt in length was amplified based on the HTS contigs, sequence alignment indicated CEVd-loquat had the highest identity with the strain of CEVd-Balad (GenBank No. PP869624), phylogenetic analysis showed that CEVd-loquat was more closely related to CEVd-lettuce (GenBank No. ON993891). This significant discovery marks the first documentation and characterization of ToMV and CEVd infecting loquat plants, shedding light on potential threats to loquat cultivation and providing insights for disease management strategies. Full article

The inspection of overhead power transmission lines is of the utmost importance to ensure the power network’s uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project “ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)” has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically. Full article

A lightweight forest fire detection model based on YOLOv8 is proposed in this paper in response to the problems existing in traditional sensors for forest fire detection. The performance of traditional sensors is easily constrained by hardware computing power, and their adaptability in different environments needs improvement. To balance the accuracy and speed of fire detection, the GhostNetV2 lightweight network is adopted to replace the backbone network for feature extraction of YOLOv8. The Ghost module is utilized to replace traditional convolution operations, conducting feature extraction independently in different dimensional channels, significantly reducing the complexity of the model while maintaining excellent performance. Additionally, an improved CPDCA channel priority attention mechanism is proposed, which extracts spatial features through dilated convolution, thereby reducing computational overhead and enabling the model to focus more on fire targets, achieving more accurate detection. In response to the problem of small targets in fire detection, the Inner IoU loss function is introduced. By adjusting the size of the auxiliary bounding boxes, this function effectively enhances the convergence effect of small target detection, further reducing missed detections, and improving overall detection accuracy. Experimental results indicate that, compared with traditional methods, the algorithm proposed in this paper significantly improves the average precision and FPS of fire detection while maintaining a smaller model size. Through experimental analysis, compared with YOLOv3-tiny, the average precision increased by 5.9% and the frame rate reached 285.3 FPS when the model size was only 4.9 M; compared with Shufflenet, the average precision increased by 2.9%, and the inference speed tripled. Additionally, the algorithm effectively addresses false positives, such as cloud and reflective light, further enhancing the detection of small targets and reducing missed detections. Full article

(1) Background: Studies on women with disabilities (WwD) are rare and tend to reveal certain representation patterns. This study aims to understand how and to what extent WwD and chronic diseases are deemed newsworthy in the Portuguese public news TV station RTP1 primetime news program. Feminist disability, standpoint, agenda-setting, and framing theories are used alongside the concept of intersectionality. (2) Methods: A mixed-method approach is adopted, combining quantitative content analysis of all broadcasted news in January 2020 (n = 704), and qualitative discourse analysis of news items on PwD (n = 5). (3) Results: The results reveal that disability is a reduced issue in Telejornal’s agenda. PwD, in general, are often portrayed in secondary roles and without a voice. The protagonists of news stories about disability or persons with disabilities are predominantly women without disabilities, occupying traditional roles as caregivers (mothers, nurses), while men are more often portrayed as public agents. Discourse analysis deepens understanding by uncovering the prevalence of negative news values and a problem-centred framing, often associated with negativity, rather than presenting solutions. (4) Conclusions: Consequently, WwD were found to be deprived of news representation with a more positive or ‘normal’ focus and an intersectional approach reveals a lack of inclusion, with the few existing news tending to focus on exclusion issues, portraying only white Portuguese women. This study underscores the urgent need for a more equitable approach in media representation, recognising the diversity and positive contributions of WwD to promote an inclusive narrative. Full article

Vibrating flip-flow screens (VFFSs) provide an effective solution for deeply screening moist and fine-grained minerals, and an accurate dynamic model of VFFSs is critical for its dynamic analysis and optimization, thereby improving the vibration stability and symmetry of VFFSs. In this paper, uniaxial tension, uniaxial compression, plane tension, and shear stress relaxation experiments were conducted on screen panel samples to illustrate that the third-order Ogden model and the generalized Maxwell model can accurately describe the hyperelasticity and viscoelasticity of screen panels. Then, the coupling method of finite element and discrete element was adopted to establish the simulation model of the screen panel and material group coupling system, and the dynamics of the coupling system under different loading conditions were explored. Finally, the dynamic model of the coupling system of VFFSs mass, screen panel, and material group was proposed, and the non-dominated sorting genetic algorithm II was applied to optimize the system’s dynamic response. The results reveal that the use of optimized shear springs can reduce the relative amplitude change rate of the main and floating screen frame by 44.30% while maintaining the periodic motion of the VFFSs under operation conditions, greatly enhancing the stability of the VFFSs system. Full article

Overlay accuracy, one of the three fundamental indicators of lithography, is directly influenced by alignment precision. During the alignment process based on the Moiré fringe method, a slight angular misalignment between the mask and wafer will cause the Moiré fringes to tilt, thereby affecting the alignment accuracy. This paper proposes a leveling strategy based on the DQN (Deep Q-Network) algorithm. This strategy involves using four consecutive frames of wafer tilt images as the input values for a convolutional neural network (CNN), which serves as the environment model. The environment model is divided into two groups: the horizontal plane tilt environment model and the vertical plane tilt environment model. After convolution through the CNN and training with the pooling operation, the Q-value consisting of n discrete actions is output. In the DQN algorithm, the main contributions of this paper lie in three points: the adaptive application of environmental model input, parameter optimization of the loss function, and the possibility of application in the actual environment to provide some ideas. The environment model input interface can be applied to different tilt models and more complex scenes. The optimization of the loss function can match the leveling of different tilt models. Considering the application of this strategy in actual scenarios, motion calibration and detection between the mask and the wafer provide some ideas. To verify the reliability of the algorithm, simulations were conducted to generate tilted Moiré fringes resulting from tilt angles of the wafer plate, and the phase of the tilted Moiré fringes was subsequently calculated. The angle of the wafer was automatically adjusted using the DQN algorithm, and then various angles were measured. Repeated measurements were also conducted at the same angle. The angle deviation accuracy of the horizontal plane tilt environment model reached 0.0011 degrees, and the accuracy of repeated measurements reached 0.00025 degrees. The angle deviation accuracy of the vertical plane tilt environment model reached 0.0043 degrees, and repeated measurements achieved a precision of 0.00027 degrees. Moreover, in practical applications, it also provides corresponding ideas to ensure the determination of the relative position between the mask and wafer and the detection of movement, offering the potential for its application in the industry. Full article

A robust AR-DSNet (Augmented Reality method based on DSST and SiamFC networks) tracking registration method in complex scenarios is proposed to improve the ability of AR (Augmented Reality) tracking registration to distinguish target foreground and semantic interference background, and to address the issue of registration failure caused by similar target drift when obtaining scale information based on predicted target positions. Firstly, the pre-trained network in SiamFC (Siamese Fully-Convolutional) is utilized to obtain the response map of a larger search area and set a threshold to filter out the initial possible positions of the target; Then, combining the advantage of the DSST (Discriminative Scale Space Tracking) filter tracker to update the template online, a new scale filter is trained after collecting multi-scale images at the initial possible position of target to reason the target scale change. And linear interpolation is used to update the correlation coefficient to determine the final position of target tracking based on the difference between two frames. Finally, ORB (Oriented FAST and Rotated BRIEF) feature detection and matching are performed on the accurate target position image, and the registration matrix is calculated through matching relationships to overlay the virtual model onto the real scene, achieving enhancement of the real world. Simulation experiments show that in complex scenarios such as similar interference, target occlusion, and local deformation, the proposed AR-DSNet method can complete the registration of the target in AR 3D tracking, ensuring real-time performance while improving the robustness of the AR tracking registration algorithm. Full article

Although “frail warrior” appears a contradiction in terms, the epithet captures how Robert Louis Stevenson’s admirers sought to reconcile a late-nineteenth-century ideal of physical manliness with the reality of the adventure writer’s debilitating illness. This construction of the writer’s public image is evident in accounts of his stay at the Saranac Lake, NY, tuberculosis sanatorium during the frigid winter of 1887–1888. The institution’s distinctive wilderness setting for medical treatment enabled a heroic model of disabled masculinity, one that is framed by American national identity. This archetype informs the author’s posthumous reputation and shows how gender and nationality shape metaphoric thinking about illness and authorship. Full article

In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas emissions and combat climate change. The precise prediction of solar power generation holds a critical role in the seamless integration and effective management of renewable energy systems within microgrids. This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting solar power generation in microgrid applications. The study meticulously evaluates these models’ accuracy, reliability, training times, and memory usage, providing detailed experimental insights into optimizing solar energy utilization and driving environmental sustainability forward. The comparison between the LGBM and KNN models reveals significant performance differences. The LGBM model demonstrates superior accuracy with an R-squared of 0.84 compared to KNN’s 0.77, along with lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) and Mean Absolute Error (MAE: 3.93 vs. 4.34). However, the LGBM model requires longer training times (120 s vs. 90 s) and higher memory usage (500 MB vs. 300 MB). Despite these computational differences, the LGBM model exhibits stability across diverse time frames and seasons, showing robustness in handling outliers. These findings underscore its suitability for microgrid applications, offering enhanced energy management strategies crucial for advancing environmental sustainability. This research provides essential insights into sustainable practices and lays the foundation for a cleaner energy future, emphasizing the importance of accurate solar power forecasting in microgrid planning and operation. Full article

Significant inelastic deformations induced in structural systems lead to structures possibly possessing some degree of permanent lateral deformation following major seismic events. These permanent deformations have led to considerable research being conducted over the past 20 years into developing structural systems that exhibit self-centring behaviour. For a structural system such as the concentrically braced frame (CBF), for which the dissipating mechanism is the tensile yielding and compressive buckling of the diagonal steel tubular members, these residual deformations present a problem when considering the structure’s overall resilience to the seismic loading both during and after an event. This paper describes the numerical modelling of a novel self-centring, concentrically braced frame (SC-CBF) system that combines a conventional CBF with a self-centring arrangement to produce a structure that possesses the desirable lateral load-resisting capacity of the CBF but which also re-centres when subjected to many cycles of large inelastic brace deformation. First, an experimental test programme for the SC-CBF is briefly described, followed by a numerical model to capture the SC-CBF’s characteristics during cyclic loading. This numerical model is validated using the experimental test data, showing that the experimental and numerical simulation data match rather well. This development presents a platform upon which further research through experimental testing and numerical simulation can be conducted. The proposed SC-CBF system can then be developed into a viable lateral load-resisting system that will provide a more resilient system than the current conventional CBF. Full article