Search Results (58,772)

Canton (present-day Guangzhou, China) has a long history as a trading port and serves as a window for studying the history of Sino-Western cultural exchanges. Canton was a city built under Confucian orders, leading to significant differences (when compared to Christian cities) in urban functional zoning, layout, urban landscape, and methods for shaping spatial order. Therefore, the churches constructed by Christian missionary societies in Canton merit particular attention in missionary history research and urban planning history. Based on local gazetteers, historical maps, export paintings, Western travelogues, and archives at that time, from a cultural landscape perspective, this article compares and analyzes the spatial expansion of Christian churches and their influences on the urban landscape in Canton in two stages. In the late Ming and early Qing dynasties, the spatial layout of the churches indicated an active integration into Canton City. After the Opium War, churches were not only used for religious purposes but also served as symbols asserting the presence of Christians and Western powers (which made the situation more complicated). Missionary societies attracted believers through the construction of public facilities, building Christian communities centered around churches, thereby competing with authorities for spatial power and influencing the urban functional system and spatial layout controlled by the authorities. Comparatively, the Roman Catholic Cathedral has profoundly changed the traditional landscape order in Canton, while the Protestant Dongshan Church interacted more closely with the city. Full article

One of the primary global objectives is to decrease building energy consumption to promote energy efficiency and environmental sustainability. The large-scale food retail trade sector accounts for over 15% of total primary energy consumption in Europe, posing a significant challenge to the transition towards green energy. This study proposes a simple method for energy efficiency, environmental sustainability, and cost-saving assessment and improvement in large-scale food retail trade buildings. It aims to analyze the energy and environmental performance of building–plant systems, establishing an interactive network to assess intervention potential for the energy transition. The investigation focuses on the proper selection and analysis of the benefits of retrofit solution implementation, emphasizing potential energy savings in current and future climate change scenarios. Dynamic simulation with the Building Energy Model (BEM) was used to evaluate the impacts of building–plant system retrofit solutions, such as high thermal insulation, photovoltaic (PV) panels, Light Emitting Diode (LED) installation, waste heat recovery, and improvement in refrigeration units. The results show a reduction in annual energy consumption for the PV panel installation by up to 29% and lighting systems with high-quality LED to 60%. Additionally, CO₂ emissions can be decreased by up to 41% by combining these two strategies. Full article

Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged. Full article

The very high cycle fatigue (VHCF) strength of welded joints made of high-strength structural materials is generally poor, which poses a serious threat to the long life and reliability of the structural components. The purpose of this work is to establish an ultrasonic vibration fatigue testing system under biaxial loading and to analyze the biaxial fatigue failure mechanism of the welded joints. The results revealed that under uniaxial loading conditions, the propensity for fatigue failure in plate specimens was predominantly observed at the specimen surface. Regardless of whether under uniaxial or biaxial loading, the initiation of fatigue cracks in cruciform joints was consistently traced back to unfused flaws, which were primarily located at the interface between the solder and the base material. Concurrently, it was noted that the fatigue strength of cruciform joints under biaxial loading was merely 44.4% of that under uniaxial loading. The geometric peculiarities of the unfused defects led to severe stress concentrations, which significantly reduced the fatigue life of the material under biaxial loading conditions. Full article

Analysis of unintended compromising emissions from Video Display Units (VDUs) is an important topic in research communities. This paper examines the feasibility of recovering the information displayed on the monitor from reconstructed video frames. The study holds particular significance for our understanding of security vulnerabilities associated with the electromagnetic radiation of digital displays. Considering the amount of noise that reconstructed TEMPEST video frames have, the work in this paper focuses on two different approaches to de-noising images for efficient optical character recognition. First, an Adaptive Wiener Filter (AWF) with adaptive window size implemented in the spatial domain was tested, and then a Convolutional Neural Network (CNN) with an encoder–decoder structure that follows both classical auto-encoder model architecture and U-Net architecture (auto-encoder with skip connections). These two techniques resulted in an improvement of more than two times on the Structural Similarity Index Metric (SSIM) for AWF and up to four times for the SSIM for the Deep Learning (DL) approach. In addition, to validate the results, the possibility of text recovery from processed noisy frames was studied using a state-of-the-art Tesseract Optical Character Recognition (OCR) engine. The present work aims to bring to attention the security importance of this topic and the non-negligible character of VDU information leakages. Full article

In linear elastic fracture mechanics, the stress intensity factor describes the magnitude of the stress singularity near a crack tip caused by remote stress and is related to the rate of fatigue crack growth. The literature lacks SIF solutions for cracks emanating from a three-dimensional semi-ellipsoidal pit. This study undertakes a comprehensive parametric investigation of the Mode I stress intensity factor ${(K}_I)$ concerning cracks originating from a semi-ellipsoidal pit in a plate. This work utilizes finite element analysis, controlled by Python scripts, to conduct an extensive study on the effect of various pit dimensions and crack lengths on $K_I$. Two cracks in the shape of a circular arc are introduced at the pit mouth perpendicular to the loading direction. The $K_I$ values are calculated using the displacement extrapolation method. The effect of normalized geometric parameters pit-depth-to-pit-width $(a/2c$), pit-depth-to-plate-thickness $(a/t)$, and crack-radius-to-pit-depth $(R/a)$ are investigated. The crack-radius-to-pit-depth $(R/a)$ is found to be the dominating parameter based on correlation analysis. The data obtained from 216 FEA simulations are incorporated into a predictive model using a k-dimensional (k-d) tree and k-Nearest Neighbour (k-NN) algorithm. Full article

Early fire detection is the key to saving lives and limiting property damage. Advanced technology can detect fires in high-risk zones with minimal human presence before they escalate beyond control. This study focuses on providing a more advanced model structure based on the YOLOv8 architecture to enhance early recognition of fire. Although YOLOv8 is excellent at real-time object detection, it can still be better adjusted to the nuances of fire detection. We achieved this advancement by incorporating an additional context-to-flow layer, enabling the YOLOv8 model to more effectively capture both local and global contextual information. The context-to-flow layer enhances the model’s ability to recognize complex patterns like smoke and flames, leading to more effective feature extraction. This extra layer helps the model better detect fires and smoke by improving its ability to focus on fine-grained details and minor variation, which is crucial in challenging environments with low visibility, dynamic fire behavior, and complex backgrounds. Our proposed model achieved a 2.9% greater precision rate, 4.7% more recall rate, and 4% more F1-score in comparison to the YOLOv8 default model. This study discovered that the architecture modification increases information flow and improves fire detection at all fire sizes, from tiny sparks to massive flames. We also included explainable AI strategies to explain the model’s decision-making, thus adding more transparency and improving trust in its predictions. Ultimately, this enhanced system demonstrates remarkable efficacy and accuracy, which allows additional improvements in autonomous fire detection systems. Full article

Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity. Full article

The effect of urban green spaces on traffic noise reduction has been extensively studied at the level of single vegetation, hedges, etc., but there is a lack of corresponding studies at the scale of spatial patterns of urban green spaces. Therefore, this study aims to analyze the relationship between the spatial pattern of urban green space and the change in green space’s noise reduction capacity. Through the morphology spatial pattern analysis method, this analysis divides the urban green space in the Fuzhou high-tech zone into seven types of elements with different ecological definitions and simulates the noise condition of the urban environment with the presence of green space as well as without the presence of green space by computer simulation, calculates the distribution map of the noise reduction produced by the urban green space, and analyzes the correlation between the seven types of green space elements and the noise reduction with the geographically weighted regression modeling analysis. The study finds that (1) Urban green space patterns can significantly affect the net noise reduction of green space. Areas with high green coverage can produce a stronger green space noise reduction effect. (2) More complex green space shapes and more fragmented urban green space can produce higher noise reduction. (3) The green space close to the source of noise can exert a stronger noise reduction effect. Therefore, in the process of planning and design, from the perspective of improving the urban acoustic environment, the configuration of high-quality green spaces in areas with higher levels of noise pollution should be given priority, which may have better noise reduction effects. Full article

The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It is highly relevant in industries like textiles, where speed and accuracy are critical. In the textile industry, accurate fabric type detection and classification are essential for improving quality control, optimizing inventory management, and enhancing customer satisfaction. This paper proposes a new approach using the YOLOv10 model, which offers enhanced detection accuracy, processing speed, and detection on the torn path of each type of fabric. We developed and utilized a specialized, annotated dataset featuring diverse textile samples, including cotton, hanbok, cotton yarn-dyed, and cotton blend plain fabrics, to detect the torn path in fabric. The YOLOv10 model was selected for its superior performance, leveraging advancements in deep learning architecture and applying data augmentation techniques to improve adaptability and generalization to the various textile patterns and textures. Through comprehensive experiments, we demonstrate the effectiveness of YOLOv10, which achieved an accuracy of 85.6% and outperformed previous YOLO variants in both precision and processing speed. Specifically, YOLOv10 showed a 2.4% improvement over YOLOv9, 1.8% over YOLOv8, 6.8% over YOLOv7, 5.6% over YOLOv6, and 6.2% over YOLOv5. These results underscore the significant potential of YOLOv10 in automating fabric detection processes, thereby enhancing operational efficiency and productivity in textile manufacturing and retail. Full article

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively. Full article

This article is a critical analysis of the conservation of a historic earth building: the Saint Bartholomew’s Church in Nigeria. It presents the conservation actions carried out through the application of conservation principles adapted to local context and contributes to building knowledge regarding building conservation in Africa. The conservation actions consisted of diagnostics, technical interventions and developing guidance for future maintenance of the building. The conservation was carried out between August 2021 and April 2023. A dualistic approach that combines local resources and internationally acceptable conservation practices was employed in the conservation of the church. This approach ensured that the appropriate interventions were carried out on the church building fabric simultaneously with training and knowledge exchange between experts from Nigeria and the UNESCO World Heritage site in Djenne, Bamako, Mali. This article highlights the challenges of conserving earthen architectural conservation in the 21st century and how these challenges can be mitigated through repair and documentation. Full article

Using survey data from three dwelling types in Jakarta, we examine how dwelling type, socioeconomic identity, and commuting distance affect women’s travel-mode choices and motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) model with two machine-learning classifiers, random forest (RF) and XGBoost, using Shapley additive explanations (SHAP) for interpretation. The models’ efficacy varies across different datasets, with XGBoost mostly outperforming other models. The women’s preferred commuting modes varied by dwelling type and trip purpose, but their motives for choosing the nearest activity were similar. Over half of the women rely on private motorized vehicles, with women living in the gated community heavily relying on private cars. For nearby shopping trips, low income and young age discourage women in urban villages (kampungs) and apartment complexes from walking. Women living in gated communities often choose private cars to fulfill household responsibilities, enabling them to access distant options. For nearby leisure, longer commutes discourage walking except for residents of apartment complexes. Car ownership and household responsibilities increase private car use for distant options. SHAP analysis offers practitioners insights into identifying key variables affecting travel-mode choice to design effective targeted interventions that address women’s mobility needs. Full article

This paper introduces a new mathematical modelling method of a thermoelastic and electromagnetic half-space in the context of four different thermoelastic theorems: Green–Naghdi type-I, and type-III; Lord–Shulman; and Moore–Gibson–Thompson. The bunding plane of the half-space surface is subjected to ramp-type heat and traction-free. We consider that Maxwell’s time-fractional equations have been under Caputo’s fractional derivative definition, which is the novelty of this work. Laplace transform techniques are utilized to obtain solutions using the state-space approach. Laplace transform’s inversions were calculated using Tzou’s iteration method. The temperature increment, strain, displacement, stress, induced electric field, and induced magnetic field distributions were obtained numerically and are illustrated in figures. The time-fraction parameter of Maxwell’s equations had a major impact on all the studied functions. The time-fractional parameter of Maxwell’s equations worked as resistant to the changing of temperature, particle movement, and induced magnetic field, while it acted as a catalyst to the induced electric field through the material. Moreover, all the studied functions have different values in the context of the four studied theorems. Full article

The growing emphasis on carbon considerations and the pursuit of net-zero emissions have brought about a paradigm shift in project management. To successfully facilitate the transition towards net-zero emissions, major projects must not only adapt existing systems but also embed carbon targets into their core strategies. While several studies have investigated carbon integration during the procurement phase, limited attention has been given to the construction project manager (PM) perspective. This study aims to bridge this research gap by exploring the challenges and barriers faced by construction PMs when integrating carbon targets and metrics into major Architectural, Engineering and Construction (AEC) projects, as well as evaluating the readiness of project teams to deliver on these. This study deployed a qualitative exploratory research design, where semi-structured interviews were conducted with 17 AEC project professionals actively engaged in the planning and execution of major projects in the UK. Thematic analysis of the data revealed a range of challenges and barriers faced by PM teams delivering these projects. The research findings contribute to the field of construction major projects and project management by enhancing the understanding of the challenges faced by PMs when planning and delivering major AEC projects within the context of the net-zero transition. This study uncovers a series of challenges and prevalent practices that have the potential to impede progress towards net zero. A conceptual model is also proposed, offering a synthesis of the different PM perspectives on carbon integration. Full article