Search Results (339)

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

Red bricks serve as an important material for load-bearing or enclosing structures in traditional architecture and are widely used in construction projects both domestically and internationally. Fujian red bricks, due to geographical, trade, and immigration-related factors, have spread to Taiwan and various regions in Southeast Asia, giving rise to distinctive red brick architectural complexes. To further investigate the types of damage, such as cracking and missing bricks, that occur in traditional red brick buildings due to multiple factors, including climate and human activities, this study takes Fujian red brick buildings as its research subject. It employs the YOLOv12 rapid detection method to conduct technical support research on structural assessment, type detection, and damage localization of surface damage in red brick building materials. The experimental model was conducted through the following procedures: on-site photo collection, slice marking, creation of an image training set, establishment of an iterative model training, accuracy analysis, and experimental result verification. Based on this, the causes of damage types and corresponding countermeasures were analyzed. The objective of this study is to attempt to utilize computer vision image recognition technology to provide practical, automated detection and efficient identification methods for damage types in red brick building brick structures, particularly those involving physical and mechanical structural damage that severely threaten the overall structural safety of the building. This research model will reduce the complex manual processes typically involved, thereby improving work efficiency. This enables the development of customized intervention strategies with minimal impact and enhanced timeliness for the maintenance, repair, and preservation of red brick buildings, further advancing the practical application of intelligent protection for architectural heritage. Full article

Surface defect detection serves as one of the crucial auxiliary components in the quality control of varistors, and it faces real challenges such as the scarcity of defect samples, high labelling cost, and insufficient a priori knowledge, which makes unsupervised deep learning-based detection methods attract attention. However, existing unsupervised models have problems such as inaccurate defect localisation and a low recognition rate of subtle defects in the detection results. To solve the above problems, an unsupervised detection method (Var-MNDR) is proposed to reconstruct the normal distribution of surface defects of varistors under mask constraints. Firstly, on the basis of colour space as well as morphology, an image preprocessing method is proposed to extract the main body image of the varistor, and a mask-constrained main body pseudo-anomaly generation strategy is adopted so that the model focuses on the texture distribution of the main body region of the image, reduces the model’s focus on the background region, and improves the defect localisation capability of the model. Secondly, Kolmogorov–Arnold Networks (KANs) are combined with the U-Network (U-Net) to construct a segmentation sub-network, and the Gaussian radial basis function is introduced as the learnable activation function of the KAN to improve the model’s ability to express the image features, so as to realise more accurate defect detection. Finally, by comparing the four unsupervised defect detection methods, the experimental results prove the superiority and generalisation of the proposed method. Full article

The Niokolo-Koba National Park (NKNP) was inscribed on the World Heritage List in 1981 for its exceptional biodiversity and unique ecosystem. However, due to poaching, livestock grazing, and dam construction projects in the Sambangalou area, the site was added to the List of World Heritage in Danger in 2007. Through regional and international cooperation, enhanced monitoring, and community engagement in conservation efforts, the site was removed from the List of World Heritage in Danger in 2024. As a typical case of the entire process from inscription on to removal from the World Heritage List in Danger, the NKNP’s threats and successful removal experience profoundly reveal complex internal and external challenges and governance needs in heritage conservation. Its successful experience can provide valuable lessons for World Heritage sites around the world facing similar threats. As part of our qualitative research, we reviewed the literature from UNESCO and IUCN, which annually assessed the state of conservation of the NKNP between 2007 and 2024. In 2024, a field mission assessed on-site conservation progress and discussed challenges and responses to the NKNP management with 30 stakeholders. Our results highlight the lengthy and potentially costly process of removal, such as Senegal’s EUR 4.57 million Emergency Plan, the threats to the park’s integrity by the State itself, and the value placed on World Heritage status, further emphasizing the need for long-term investment from both the national government and international partners. Therefore, ensuring returns on such investment, whether through increased ecotourism, international recognition, or strengthened ecosystem services, is essential for sustainable conservation financing. The case of the NKNP also illustrates the positive impact of improved national governance and partnerships involving international and local NGOs, as well as the private sector, on conservation efforts. It also highlights the importance of a new collaborative governance paradigm for heritage sites facing severe human interference (poaching, illegal development) and governance challenges, particularly in ecologically fragile or socio-economically pressured regions, by strengthening national responsibility, leveraging international mechanisms, and activating local participation. Full article

Macau’s Ching Chau Hill, as a composite entity of modern industrial heritage and natural cultural landscape, faces the dual challenges of conservation and regeneration. This study takes Ching Chau Hill as a case study, integrating structural equation modeling (SEM) with Geographic Information System (GIS) technology and combining the theory of the creative class, to construct an evaluation model of “industrial heritage-creative perception-cultural innovation.” Through questionnaire surveys, data from the creative class were collected, and SEM was employed for path analysis and hypothesis testing, while GIS was used for spatial analysis and visualization. This study systematically explores the creative perception pathways of industrial heritage value from the perspective of the creative class and its driving mechanisms for cultural inheritance and innovation. This study found that the retention rate of industrial structures (73%) and the “sacred-industrial” axis formed by the integrity of the spatial sequence (β = 0.58) together constitute the core of the material attachment path, and there is a significant threshold for the site identity effect: when the material authenticity score exceeds the 3.5 critical point, the identity value jumps by 37.8%, which provides a quantitative basis for the precise protection of “ruin aesthetics”. In the process of transforming cultural inheritance into innovative practice, the participation in creative activities showed a mediating effect of 72.1%, and the driving efficiency of co-creation activities was ten times higher than that of ceremonial guided tours, confirming the core position of “learning by doing” in heritage revitalization. The results show the following: (1) the creative class’s perception of the aesthetic uniqueness and historical memory of Ching Chau Hill’s industrial heritage significantly and positively influences their recognition of its creative value; (2) spatial accessibility and environmental atmosphere are key geographical factors affecting creative perception; (3) recognition of creative value effectively drives the innovative transformation of cultural heritage by stimulating participation willingness and innovative ideas. This study provides a strategy basis with both theoretical depth and practical guidance value for the revitalization and utilization of industrial heritage in post-industrial urban renewal. Full article

Radio frequency (RF)-based human activity sensing is an essential component of ubiquitous computing, with WiFi sensing providing a practical and low-cost solution for gesture and activity recognition. However, challenges such as manual data collection, multipath interference, and poor cross-domain generalization hinder real-world deployment. Existing data augmentation approaches often neglect the biomechanical structure underlying RF signals. To address these limitations, we present CM-GR, a cross-modal gesture recognition framework that integrates semantic learning with generative modeling. CM-GR leverages 3D skeletal points extracted from vision data as semantic priors to guide the synthesis of realistic WiFi signals, thereby incorporating biomechanical constraints without requiring extensive manual labeling. In addition, dynamic conditional vectors are constructed from inter-subject skeletal differences, enabling user-specific WiFi data generation without the need for dedicated data collection and annotation for each new user. Extensive experiments on the public MM-Fi dataset and our SelfSet dataset demonstrate that CM-GR substantially improves the cross-subject gesture recognition accuracy, achieving gains of up to 10.26% and 9.5%, respectively. These results confirm the effectiveness of CM-GR in synthesizing personalized WiFi data and highlight its potential for robust and scalable gesture recognition in practical settings. Full article

With the widespread application of wearable sensors in health monitoring and human–computer interaction, deep learning-based human activity recognition (HAR) research faces challenges such as the effective extraction of multi-scale temporal features and the enhancement of robustness against noise in multi-source data. This study proposes the TGA-HAR (TCN-GRU-Attention-HAR) model. The TGA-HAR model integrates Temporal Convolutional Neural Networks and Recurrent Neural Networks by constructing a hierarchical feature abstraction architecture through cascading Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU) layers for complex activity recognition. This study utilizes TCN layers with dilated convolution kernels to extract multi-order temporal features. This study utilizes BiGRU layers to capture bidirectional temporal contextual correlation information. To further optimize feature representation, the TGA-HAR model introduces residual connections to enhance the stability of gradient propagation and employs an adaptive weighted attention mechanism to strengthen feature representation. The experimental results of this study demonstrate that the model achieved test accuracies of 99.37% on the WISDM dataset, 95.36% on the USC-HAD dataset, and 96.96% on the PAMAP2 dataset. Furthermore, we conducted tests on datasets collected in real-world scenarios. This method provides a highly robust solution for complex human activity recognition tasks. Full article

The sustainable development of traditional villages faces a core challenge stemming from the disconnect between public perception and spatial planning. To address this issue, this study, taking Baishe Village—a national-level traditional village—as a case study, constructs and applies a “Digital Humanities + Spatial Analysis” research paradigm that integrates text mining, sentiment analysis, visual coding, and spatial analysis based on multimodal social media data (Sina Weibo and Xiaohongshu) from 2013 to 2023. It aims to conduct an in-depth analysis of tourists’ rural image perception structure, emotional tendencies, and their spatial differentiation characteristics, and subsequently propose spatial optimization pathways that promote the revitalization of its cultural landscape and sustainable land use. The main findings reveal the following: (1) In terms of cognitive structure, the rural image presents a ‘settlement-dominated’ four-dimensional structure, with settlement elements such as pit kilns (accounting for more than 70%) as the absolute core. (2) In terms of emotional tendencies, a cognitive tension is formed between the high recognition of architectural heritage value (positive sentiment: 57.44%) and significant dissatisfaction with service facilities. (3) In terms of spatial patterns, a “dual-core-driven” pattern of perceived hotspots emerges, with 83% of tourist activities concentrated in the central–southern main road area, revealing a “revitalization gap” in village spatial utilization. The contribution of this study lies in translating abstract public perceptions into quantifiable spatial insights, thereby constructing and validating a “Digital Humanities + Spatial Analysis” paradigm that fuses multimodal data and links abstract perception with concrete space. This provides a crucial theoretical basis and practical guidance for the living conservation of cultural landscapes, the enhancement of land use efficiency, and refined spatial governance. Full article

Accurately decoding human locomotion intention from physiological signals remains a significant hurdle for the seamless control of advanced rehabilitation devices like exoskeletons and intelligent prosthetics. Conventional recognition methods often falter, exhibiting limited accuracy and struggling to capture the complex, nonlinear dynamics inherent in biological data streams. Addressing these critical limitations, this study introduces a novel framework for lower-limb motion intent recognition, integrating Kernel Principal Component Analysis (KPCA) with a Support Vector Machine (SVM) optimized via an Improved Sparrow Search Algorithm (ISSA). Our approach commences by constructing a comprehensive high-dimensional feature space from synchronized surface electromyography (sEMG) and inertial measurement unit (IMU) data—a potent combination reflecting both muscle activation and limb kinematics. Critically, KPCA is employed for nonlinear dimensionality reduction; leveraging the power of kernel functions, it transcends the linear constraints of traditional PCA to extract low-dimensional principal components that retain significantly more discriminative information. Furthermore, the Sparrow Search Algorithm (SSA) undergoes three strategic enhancements: chaotic opposition-based learning for superior population diversity, adaptive dynamic weighting to adeptly balance exploration and exploitation, and hybrid mutation strategies to effectively mitigate premature convergence. This enhanced ISSA meticulously optimizes the SVM hyperparameters, ensuring robust classification performance. Experimental validation, conducted on a challenging 13-class lower-limb motion dataset, compellingly demonstrates the superiority of the proposed KPCA-ISSA-SVM architecture. It achieves a remarkable recognition accuracy of 95.35% offline and 93.3% online, substantially outperforming conventional PCA-SVM (91.85%) and standalone SVM (89.76%) benchmarks. This work provides a robust and significantly more accurate solution for intention perception in human–machine systems, paving the way for more intuitive and effective rehabilitation technologies by adeptly handling the nonlinear coupling characteristics of sEMG-IMU data and complex motion patterns. Full article

The Arctic, characterized by extensive ice and snow cover with persistent low solar elevation angles and prolonged polar nights, poses significant challenges for conventional spectral threshold methods in cloud detection and cloud-top thermodynamic phase classification. The study addressed these limitations by combining active and passive remote sensing and developing a machine learning framework for cloud detection and cloud-top thermodynamic phase classification. Utilizing the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud product from 2021 as the truth reference, the model was trained with spatiotemporally collocated datasets from FY3D/MERSI-II (Medium Resolution Spectral Imager-II) and CALIOP. The AdaBoost (Adaptive Boosting) machine learning algorithm was employed to construct the model, with considerations for six distinct Arctic surface types to enhance its performance. The accuracy test results showed that the cloud detection model achieved an accuracy of 0.92, and the cloud recognition model achieved an accuracy of 0.93. The inversion performance of the final model was then rigorously evaluated using a completely independent dataset collected in July 2022. Our findings demonstrated that our model results align well with results from CALIOP, and the detection and identification outcomes across various surface scenarios show high consistency with the actual situations displayed in false-color images. Full article

Textile defect detection technology has become a core component of industrial quality control. With the advancement of artificial intelligence technologies, an increasing number of intelligent recognition methods are being actively researched and deployed in the textile defect detection. To further improve detection accuracy and quality, we propose a new lightweight process named WSF-RTDETR with reduced computational resources. Firstly, we integrated WTConv convolution with residual blocks to form a lightweight WTConv-Block module, which could enhance the capability of capturing detailed features of tiny defective targets while reducing computational overhead. Subsequently, a lightweight slimneck-SSFF feature fusion architecture was constructed to enhance the feature fusion performance. In addition, the Focaler–MPDIoU loss function was presented by incorporating dynamic weight adjustment and multi-scale perception mechanism, which could improve the detection accuracy and convergence speed for tiny defective targets. Finally, we conducted experiments on a textile defect dataset to further validate the effectiveness of the WSF-RTDETR model. The results demonstrate that the model improves mean average precision (mAP50) by 4.71% while reducing GFLOPs and the number of parameters by 24.39% and 31.11%, respectively. The improvements in both detection performance and computational efficiency would provide an effective and reliable solution for industrial textile defect detection. Full article

To address the challenges of dynamic coupling interference and time-frequency feature degradation in current approaches to Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) behavior recognition, this study proposes a novel behavior recognition method integrating multi-feature analysis with a dual-path residual network. The proposed method mitigates interference by using phase difference methods to eliminate signal errors and cross-correlation, as well as adaptive equalization algorithms to decouple interfering signals. To identify the target tags participating in behavioral interactions, we construct a three-dimensional feature space and apply an improved weighted isolated forest algorithm to detect active tags during interactions. Subsequently, Doppler shift analysis extracts behavioral features, and multiscale wavelet-packet decomposition generates time-frequency representations. The dual-path residual network then fuses global and local features from these time-frequency representations for behavioral classification, thereby identifying interaction behaviors such as ‘taking away’, ‘putting back’, and ‘hesitation’ (characterized by picking up, then putting back). Experimental results demonstrate that the proposed scheme achieves behavioral recognition accuracy of 94% in complex scenarios, significantly enhancing the overall robustness of interaction behavior recognition. Full article

Extracellular matrix (ECM) bioscaffolds have demonstrated therapeutic potential across a variety of clinical and preclinical applications for tissue repair and regeneration. In parallel, these scaffolds and their components have shown the capacity to modulate the immune response. Unlike synthetic implants, which are often associated with chronic inflammation or fibrotic encapsulation, ECM bioscaffolds interact dynamically with host cells, promoting constructive tissue remodeling. This effect is largely attributed to the preservation of structural and biochemical cues—such as degradation products and matrix-bound nanovesicles (MBV). These cues influence immune cell behavior and support the transition from inflammation to resolution and functional tissue regeneration. However, the immunomodulatory properties of ECM bioscaffolds are dependent on the source tissue and, critically, on the methods used for decellularization. Inadequate removal of cellular components or the presence of residual chemicals can shift the host response towards a pro-inflammatory, non-constructive phenotype, ultimately compromising therapeutic outcomes. This review synthesizes current basic concepts on the innate immune response to ECM bioscaffolds, with particular attention to the inflammatory, proliferative, and remodeling phases following implantation. We explore how specific ECM features shape these responses and distinguish between pro-remodeling and pro-inflammatory outcomes. Additionally, we examine the impact of manufacturing practices and quality control on the preservation of ECM bioactivity. These insights challenge the conventional classification of ECM bioscaffolds as medical devices and support their recognition as biologically active materials with distinct immunoregulatory potential. A deeper understanding of these properties is critical for optimizing clinical applications and guiding the development of updated regulatory frameworks in regenerative medicine. Full article

Polarization information is essential for material identification, stress mapping, biological imaging, and robust vision under strong illumination, yet conventional approaches rely on external polarization optics and active biasing, which are bulky, alignment-sensitive, and power-hungry. A more desirable route is to encode polarization at the pixel level and read it out at zero bias, enabling compact, low-noise, and polarization imaging. Low-symmetry layered semiconductors provide persistent in-plane anisotropy as a materials basis for polarization selectivity. Here, we construct an eight-terminal radial ‘star-shaped’ Au/ReS₂ metal-semiconductor junction array pixel that operates in a genuine photovoltaic mode under zero external bias based on the photothermoelectric effect. Based on this, electrical summation of phase-matched multi-junction channels increases the signal amplitude approximately linearly without sacrificing the two-lobed modulation depth, achieving ‘gain by stacking’ without external amplification. The device exhibits millisecond-scale transient response and robust cycling stability and, as a minimal pixel unit, realizes polarization-resolved imaging and pattern recognition. Treating linear combinations of channels as operators in the polarization domain, these results provide a general pixel-level foundation for compact, zero-bias, and scalable polarization cameras and on-pixel computational sensing. Full article

Pursuing quality and sustainability concerns in construction activities is not new. However, the construction sector continues to face criticism for the outcome of many interventions, and significant progress is still required to realise both objectives. This is particularly pressing in sectors essential for quality of life and wellbeing, such as housing, and in areas frequently neglected in research and practice, such as existing buildings. This paper provides insights into the assessment of quality and sustainability in existing buildings, clarifying these concerns, exploring their interrelationship, emphasising the critical role of the design phase, and synthesising relevant methodologies focused on each objective. Furthermore, a novel methodology is proposed to minimise the risk of poor quality in building rehabilitation processes. Methodologically, the paper includes a review of concepts associated with quality and sustainability in building rehabilitation, a synthesis of existing evaluation tools and methods, and the development of the proposed methodology. The main findings include a definition of construction quality, identification of strong correlations between quality and sustainability, and the recognition that using accessible, flexible, and collaborative tools during the design phase is crucial to achieving both objectives, especially in the context of existing buildings, where practical and operational outcomes remain limited. Full article