Search Results (488)

The possibility of using pyrolysis for the valorisation of leather and textile wastes constituting post-consumer clothes is analysed in this paper. The effect of gas type was investigated on the physico-chemical properties, composition, structure, and formation of the specific surfaces of carbonised materials produced by the pyrolysis process. The differences in the elemental composition of the carbonised materials derived from textile and leather wastes may be due to the specific chemical compositions. Both textile and leather wastes are rich in organic compounds, but their structural and compositional differences significantly influence the element content of carbonised materials. The characteristic feature of carbonised material made from leather waste is a relatively high nitrogen content (approx. 9 wt. %). In turn, in the case of carbonised material made from textile waste, a high carbon content is characteristic (75–80 wt. %). Moreover, G- and D-bands were detected in all the analysed carbonised materials. The presence of these bands confirms the transformation of leather and textile wastes into carbon materials. It was found that maintaining a high degree of order in the structure (calculated as I_D/I_G ratios based on the D and G peak intensities) of carbonised materials is advantageous to conducting the pyrolysis process on textile materials in N₂ and on leather materials in CO₂. The carbonised materials produced using these gases are characterised by an I_D/I_G ratio at a level of 0.05. Pyrolysis carried out in these gases also has a positive effect on the size of the BET surface area. However, it was shown that the carbonised products from textile materials are characterised by a higher BET surface area than that of carbonised products from leather materials regardless of the type of gas used during the pyrolysis process. Furthermore, all the carbonised materials are characterised by a high percentage content of mesopores in the carbon structure. These types of carbon materials have widespread application potential. The presented studies contribute data about the pyrolytic processing of post-consumer clothes (such as leather and textile waste) into carbonised materials to reuse, according to the circular economy model. Full article

The adaptive reuse of historic buildings into contemporary office spaces prompts intriguing inquiries regarding its impact on employee satisfaction and workplace culture. This study explores the potential of adaptive reuse to transform historic buildings into functional, sustainable offices, using Erbil Citadel houses as a base for the study. Through this research study, user preferences and perceptions of the integration of historical features into the modern work environment were examined. Quantitative data were extracted from 60 survey respondents and analyzed in terms of medians, modes and the analysis of key themes, such as historical aesthetics, employee creativity, work satisfaction and environmental factors, including natural light and airflow. The findings emphasize the equilibrium between safeguarding the cultural heritage of the historical structure and the requirements of contemporary office environments. The findings underscore the need for sustainable practices and technological integration to enhance workplace functionality and team well-being, particularly in shared spaces. This paper highlights the importance of decision makers’ perspectives on heritage conservation, stressing the necessity for a culturally attuned and sustainable reuse strategy that addresses community requirements. This study offers a methodological framework for reconciling historical narratives with modern office requirements while also addressing broader discussions on adaptive reuse and the potential for enhancing workplace quality. Full article

In response to the challenges of detecting rice pests and diseases at different scales and the difficulties associated with deploying and running models on embedded devices with limited computational resources, this study proposes a multi-scale rice pest and disease recognition model (RGC-YOLO). Based on the YOLOv8n network, which includes an SPPF layer, the model introduces a structural reparameterization module (RepGhost) to achieve implicit feature reuse through reparameterization. GhostConv layers replace some standard convolutions, reducing the model’s computational cost and improving inference speed. A Hybrid Attention Module (CBAM) is incorporated into the backbone network to enhance the model’s ability to extract important features. The RGC-YOLO model is evaluated for accuracy and inference time on a multi-scale rice pest and disease dataset, including bacterial blight, rice blast, brown spot, and rice planthopper. Experimental results show that RGC-YOLO achieves a precision (P) of 86.2%, a recall (R) of 90.8%, and a mean average precision at Intersection over Union 0.5(mAP50) of 93.2%. In terms of model size, the parameters are reduced by 33.2%, and GFLOPs decrease by 29.27% compared to the base YOLOv8n model. Finally, the RGC-YOLO model is deployed on an embedded Jetson Nano device, where the inference time per image is reduced by 21.3% compared to the base YOLOv8n model, reaching 170 milliseconds. This study develops a multi-scale rice pest and disease recognition model, which is successfully deployed on embedded field devices, achieving high-accuracy real-time monitoring and providing valuable reference for intelligent equipment in unmanned farms. Full article

Traditional Chinese villages and architectural cultural resources are abundant. Against the backdrop of rapid development in contemporary socioeconomic and urbanization processes, rural construction is facing multiple challenges such as imbalanced urban–rural development, gradually fading cultural traditions, and disharmonious living environments. The cultural elements of rural architecture urgently need more systematic and effective protection, integration, and reuse. Therefore, the precise extraction of traditional architectural features and their translation applications in modern contexts are gradually becoming key issues in current research and practice fields. This study takes traditional architecture of the Goulou Cluster of Yue Dialects in Guangxi, China, as an example. Through field investigations and mathematical and GIS spatial analysis, architectural samples were identified and extracted typologically, and a database of traditional architecture was constructed, delineating architectural cultural zones and summarizing type characteristics to create a genealogy map. Based on the results of the architectural genealogy study, modern translation pathways for traditional architecture were proposed through spatial modeling, technical analysis, and iterative optimization. Modern translation experiments were conducted on selected typical villages and their traditional buildings, exploring the application model system of traditional architecture in modern contexts. This study not only deepens the scientific understanding of the genealogy zoning characteristics of traditional architecture in the Goulou Cluster of Yue Dialects in Guangxi but also provides a reference for the modern translation and optimization path of traditional architecture, providing important theoretical basis and application guidance for promoting the inheritance and innovation of rural culture, and realizing the protection and updating of rural architectural style. Full article

With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offers a way to analyze drone firmware lacking source code. This paper proposes DEGNN, a novel graph neural network for binary code similarity analysis. It uses call-enhanced control graphs and attention mechanisms to generate dual embeddings of functions and predict similarity based on graph structures and node features. DEGNN is effective in cross-architecture tasks. Experimental results show that in the cross-architecture binary function search, DEGNN’s mean reciprocal rank and recall@1 surpass the state of the art by 12% and 28.6%, respectively. In the cross-architecture real-world vulnerability search, specifically targeting drone systems, it has a 33.3% performance improvement over the SOTA model, indicating its great potential in enhancing drone cyber security. Full article

Convolutional Neural Networks (CNNs) have demonstrated high accuracy in applications such as object detection, classification, and image processing. However, convolutional layers account for the majority of computations within CNNs. Typically, these layers are executed on GPUs, resulting in higher-power consumption and hindering lightweight deployment. This paper presents a design that deploys convolutional layers on FPGAs with adjustable parameters. In this FPGA deployment, a 4 × 4 3D sliding window is used to traverse the data, reducing bandwidth requirements and facilitating seamless integration with subsequent processing stages. A three-dimensional plane buffer design is proposed, which implements data reuse. Compared to directly inputting the feature map and performing the computation, it reduces the on-chip memory bandwidth requirement by 75%. Additionally, a new addressing strategy is introduced to map 3D feature maps to RAM addresses, eliminating addressing time. Due to the resource-intensive nature of high-level synthesis (HLS) technology, HDL design is used for the convolutional layers. This design achieves an inference speed of 121.36 GOPS at a 16-bit width, providing a 39.10 times increase in performance compared to CPU implementations. Full article

In the adaptive re-use of buildings, the physicality of buildings—the way they are designed, planned, constructed and maintained—has fallen out of fashion in favour of socio-economic conceptualisations and critical urban interpretations of the redevelopment process. However, the materiality of buildings plays a key part in how locations are re-produced in response to socio-economic circumstances—in this case, the creation and sustaining of cultural and creative clusters. In response, this paper adopts a forensic approach to the characteristics of physical buildings in order to develop an original taxonomy of lo-fi adaptive features and interventions that enable the authors to infer which types and aspects of industrial buildings lend themselves to sustaining cultural and creative clusters. The focus on lo-fi interventions is an original contribution to the adaptive re-use literature where attention tends to focus on more formal and traditional design-based interactions with existing buildings. In doing so, the research utilises a comparative case study approach of several former industrial buildings associated with the contemporary independent food and drink industry in the Ouseburn Valley cultural and creative quarter of Newcastle upon-Tyne in England. The research finds that it is the functional tolerance and malleability of the case study buildings—their inherent adaptive capacity, that in part helps to sustain the cultural and creative cluster in this location. Full article

The aim of infrared and visible image fusion is to produce a composite image that can highlight the infrared targets and maintain plentiful detailed textures simultaneously. Despite the promising fusion performance of current deep-learning-based algorithms, most fusion algorithms highly depend on convolution operations, which limits their capability to represent long-range contextual information. To overcome this challenge, we design a novel infrared and visible image fusion network based on Res2Net and multiscale Transformer, called RMTFuse. Specifically, we devise a local feature extraction module based on Res2Net (LFE-RN) in which dense connections are adopted to reuse the information that might be lost in convolution operation and a global feature extraction module based on multiscale Transformer (GFE-MT) which is composed of a Transformer module and a global feature integration module (GFIM). The Transformer module extracts the coarse-to-fine semantic features of the source images, while GFIM is used to further aggregate the hierarchical features to strengthen contextual feature representations. Furthermore, we employ the pre-trained VGG-16 network to compute the loss of features with different depths. Massive experiments on mainstream datasets indicate that RMTFuse is superior to the state-of-the-art methods in both subjective and objective assessments. Full article

The KREIS-Haus, an inhabited living lab in Switzerland, serves as a demonstrator of the implementation of sustainable and circular building practices. Addressing the environmental impacts associated with construction, operation, and deconstruction, this study presents an innovative systematic design concept that synthesizes principles of the circular economy, Cradle-to-Cradle design, and ecological engineering. The design process was applied to the KREIS-Haus as a lighthouse project, combining theoretical frameworks with real-word application to derive actionable insights. The novelty of the KREIS-Haus lies in the holistic integration of circular and sustainable concepts within a compact footprint, realized in a real-life, publicly accessible living lab. Its design maximizes resource efficiency by incorporating locally sourced materials, modular construction techniques, and flexible interior features, which allow for easy disassembly and reuse. At the heart of its circular design is the multifunctional conservatory, which provides heat and sound insulation, generates solar power, and expands the living space. Additionally, it supports plant cultivation and enables the reuse of treated wastewater and nutrients, as part of the off-grid water and nutrient management system to reduce reliance on external resources. The principles of solar architecture further minimize the building’s energy demands. Key insights from the design and construction process highlight the challenges of navigating conflicting goals, the importance of partner alignment, and considerations for scaling these concepts to larger developments. While technical challenges may arise, addressing systemic barriers will be essential for advancing sustainable and circular building practices on a broader scale. Full article

Urban villages (UVs) are the most typical urban informal settlements in China, and the study of an effective identification method for UVs can help to provide a reference for the development of locally adapted UV transformation policies. In order to reduce the cost of labeling and enhance transferability, this study integrates remote sensing and social sensing data and applies sample migration from a labeled area to a less labeled area based on the theory of transfer learning. There are two main results of this study: (1) This study constructed a feature system for UV identification based on multi-feature extraction using a block as a unit, and experiments based on Tianhe District achieved an overall accuracy of 90% and a kappa coefficient of 0.76. (2) Using Tianhe District as the source domain and Jiangan District as the target domain, samples from the source domain were reused based on the KMM, TCA, and CORAL algorithms. The CORAL+RF algorithm showed the best performance, where its overall accuracy reached 97.06% and its kappa coefficient reached 0.89, and its overall accuracy reached 91.17% and its kappa coefficient reached 0.67 in the case of no target domain labeling. To sum up, the identification method for UVs proposed in the present study provides theoretical references for identification methods for UVs in different geographical areas. Full article

Casting process design is crucial in manufacturing; however, traditional design workflows are time-consuming and seriously reliant on the experience and expertise of designers. To overcome these challenges, database technology has emerged as a promising solution to optimize the design process and enhance efficiency. However, conventional database storing process cases often lack adequate parametric information, limiting their ability to support intelligent and automated design. Thus, this study has developed a casting process database based on parametric case modeling, enabling the rapid design of casting processes for new parts using case-based reasoning (CBR). The database framework was designed to organize process cases into four distinct information modules, allowing for structured and separated storage. Data unit associations were established between these modules to ensure the completeness and scalability of process information. The database stores sufficient parametric information to describe key process characteristics and multidimensional elements. This includes the detailed structural parameters of parts, calculated based on the accurate analysis of their structural features. A process cost estimation model was incorporated to calculate and record direct process costs, enabling the effective comparison and ranking of various process plans for the same part. Additionally, the parametric model of the gating system is stored to support the transfer of processes between similar parts. The functionality and effectiveness of the proposed database were visually validated through a case study on the process design of an actual casting part. The results indicate that the database significantly improves efficiency and ensures the accuracy of CBR-based process design while optimizing the reuse of design knowledge and expertise. The developed database achieved a 90% reduction in design time compared to conventional methods. Full article

Smart car parking management systems (SPMSs) have gained an ever-increasing popularity for the digital management of car parking processes. While various techniques and technologies have been proposed for SPMSs, the literature lacks in any generic software architecture design that can be reused systematically for the specifications of quality SPMS architectures. To bridge this gap, we propose a reference architecture (RA) for the SPMS product family after performing a comprehensive domain analysis. Our RA design offers a feature model that consists of the common and varying features for SPMSs. We offer multiple viewpoints for our RA, including context, module, component and connector, and allocation. The context viewpoint focuses on the stakeholders, the module viewpoint focuses on the software units, the component and connector viewpoint focuses on the layered architecture of SPMSs, and the allocation viewpoint focuses on mapping software units into the physical components. Each viewpoint can be re-used for specifying the application architecture of any SPMSs. We validated our RA with a real SPMS scenario specification and prototype development, where the former measures the reusability of RA and the latter measures the development performance. The RA design for SPMSs is expected to be useful for several stakeholders who research, develop, and sell SPMS solutions. Full article

Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO’s search capabilities. The original PLO’s particle collision strategy is replaced with DE’s mutation and crossover operators, enabling a more effective global exploration and using a dynamic crossover rate to improve convergence. Furthermore, a cryptobiosis mechanism records and reuses historically successful solutions, thereby improving the greedy selection process. The experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE’s superior performance compared to eight classical optimization algorithms, with higher average ranks and faster convergence. Moreover, CPLODE achieved competitive results in feature selection on ten real-world datasets, outperforming several well-known binary metaheuristic algorithms in classification accuracy and feature reduction. These results highlight CPLODE’s effectiveness for both global optimization and feature selection. Full article

Vertical Federated Learning (VFL) is a promising category of Federated Learning that enables collaborative model training among distributed parties with data privacy protection. Due to its unique training architecture, a key challenge of VFL is high communication cost due to transmitting intermediate results between the Active Party and Passive Parties. Current communication-efficient VFL methods rely on using stale results without meticulous selection, which can impair model accuracy, particularly in noisy data environments. To address these limitations, this work proposes VFL-Cafe, a new VFL training method that leverages dynamic caching and feature selection to boost communication efficiency and model accuracy. In each communication round, the employed caching scheme allows multiple batches of intermediate results to be cached and strategically reused by different parties, reducing the communication overhead while maintaining model accuracy. Additionally, to eliminate the negative impact of noisy features that may undermine the effectiveness of using stale results to reduce communication rounds and incur significant model degradation, a feature selection strategy is integrated into each round of local updates. Theoretical analysis is then conducted to provide guidance on cache configuration, optimizing performance. Finally, extensive experimental results validate VFL-Cafe’s efficacy, demonstrating remarkable improvements in communication efficiency and model accuracy. Full article

Linear antenna arrays exhibit radiation patterns that are restricted to a half-space and feature axial radiation, which can be a significant drawback for applications that require omnidirectional coverage. To address this limitation, the synthesis method utilizing the Taguchi approach, originally designed for linear arrays, can be effectively extended to two-dimensional or planar antenna arrays. In the context of a linear array, the synthesis process primarily involves determining the feeding law and/or the spatial distribution of the elements along a single axis. Conversely, for a planar array, the synthesis becomes more complex, as it requires the identification of the complex weighting of the feed and/or the spatial distribution of sources across a two-dimensional plane. This adaptation to planar arrays is facilitated by substituting the direction $\theta$ with the pair of directions $(\theta ,\varphi )$, allowing for a more comprehensive coverage of the angular domain. This article focuses on exploring various configurations of planar arrays, aiming to enhance their performance. The primary objective of these configurations is often to minimize the levels of secondary lobes and/or array lobes while enabling a full sweep of the angular space. Secondary lobes can significantly impede system performance, particularly in multibeam applications, where they restrict the minimum distance for frequency channel reuse. This restriction is critical, as it affects the overall efficiency and effectiveness of communication systems that rely on precise beamforming and frequency allocation. By investigating alternative planar array designs and their synthesis methods, this research seeks to provide solutions that improve coverage, reduce interference from secondary lobes, and ultimately enhance the functionality of antennas in diverse applications, including telecommunications, radar systems, and wireless communication. Full article