Search Results (328)

In the context of the digital transition, office environments are increasingly shaped by flexibility, technological integration, and occupant-centered design. These transformations influence not only building operations but also the social dynamics and well-being of workers, thereby intersecting with the broader goals of socially sustainable design. To address this complexity, Building Management Systems (BMS) and Digital Twins must evolve from static automation to adaptive frameworks that recognize and respond to diverse workplace activities and social interactions. This study proposes a standardized taxonomy of office activities as a foundation for activity recognition and environment adaptation. A systematic literature review identified key activity categories and defining attributes, which were refined and validated through direct observations, diary logs, and semi-structured interviews in small, shared offices with open-plan workspaces. The resulting taxonomy comprises four main classes—Focused Work, Meetings, Shallow Work, and Resting—each defined by contextual attributes such as plannability, social interaction, number of participants, posture, modality, location, and duration. The framework supports the development of human-centric, situationally aware BMS capable of dynamically adjusting environmental conditions to promote comfort, well-being, and energy efficiency. By integrating user agendas and feedback, this approach contributes to more inclusive and socially sustainable work environments, aligning with the emerging paradigm of adaptive, human-oriented architecture. Full article

Productive landscapes are an important part of intangible cultural heritage, and their protection and inheritance are of great significance to the prosperity and sustainable development of national culture. It not only reflects the wisdom accumulated through the long-term interaction between human production activities and the natural environment, but also carries a strong symbolic meaning of rural culture. However, current research and investigation on productive landscapes still rely mainly on field surveys and manual records conducted by experts and scholars. This process is time-consuming and costly, and it is difficult to achieve efficient and systematic analysis and comparison, especially when dealing with large-scale and diverse types of landscapes. To address this problem, this study takes the Inner Mongolia region as the main research area and builds a productive landscape feature data framework that reflects the diversity of rural production activities and cultural landscapes. The framework covers four major types of landscapes: agriculture, animal husbandry, fishery and hunting, and sideline production and processing. Based on artificial intelligence and deep learning technologies, this study conducts comparative experiments on several convolutional neural network models to evaluate their classification performance and adaptability in complex rural environments. The results show that the improved CEM-ResNet50 model performs better than the other models in terms of accuracy, stability, and feature recognition ability, demonstrating stronger generalization and robustness. Through a semantic clustering approach in image classification, the model’s recognition process is visually interpreted, revealing the clustering patterns and possible sources of confusion among different landscape elements in the semantic space. This study reduces the time and economic cost of traditional field investigations and achieves efficient and intelligent recognition of rural productive landscapes. It also provides a new technical approach for the digital protection and cultural heritage transmission of productive landscapes, offering valuable references for future research in related fields. Full article

Accurate recognition of complex human activities from wearable sensors plays a critical role in sports analytics and human performance monitoring. However, the high dimensionality and redundancy of raw inertial data can hinder model performance and interpretability. This study proposes a hybrid feature selection framework that combines Minimum Redundancy Maximum Relevance (MRMR) and Regularized Neighborhood Component Analysis (RNCA) to improve classification accuracy while reducing computational complexity. Multi-sensor inertial data were collected from field hockey players performing six activity types. Time- and frequency-domain features were extracted from four body-mounted inertial measurement units (IMUs), resulting in 432 initial features. MRMR, combined with Pearson correlation filtering (|ρ| > 0.7), eliminated redundant features, and RNCA further refined the subset by learning supervised feature weights. The final model achieved a test accuracy of 92.82% and F1-score of 86.91% using only 83 features, surpassing the MRMR-only configuration and slightly outperforming the full feature set. This performance was supported by reduced training time, improved confusion matrix profiles, and enhanced class separability in PCA and t-SNE visualizations. These results demonstrate the effectiveness of the proposed two-stage feature selection method in optimizing classification performance while enhancing model efficiency and interpretability for real-time human activity recognition systems. Full article

A series of dimethylpyridine-3-carboxamide derivatives was designed as potential, selective, non-zinc chelating inhibitors of matrix metalloproteinase 13 (MMP-13), and subsequently synthesized. The identity of the obtained compounds was confirmed by FT-IR, ¹H/¹³C NMR, and HR-MS methods. Fluorescence spectroscopy was applied to study the interaction of synthesized compounds with human serum albumin, providing insight into their potential transport properties in plasma. In parallel, the electronic properties and reactivity parameters relevant to enzyme binding of the designed molecules were analyzed using density functional theory. Molecular docking and molecular dynamics simulations revealed the compounds to interact preferentially and stably within the S₁ pocket of MMP-13 via hydrogen bonds and π-stacking interactions. The calculated binding free energy confirmed the stability and persistence of the complexes during simulation, indicating a strong and specific recognition pattern. On the other hand, their affinity towards MMP-8 was considerably weaker, which is consistent with the predicted selectivity profile. In addition, the biological evaluation confirmed MMP-13 inhibition. Finally, in vitro tests revealed their cytotoxic activity against cancer cell lines. Full article

Human Activity Recognition (HAR) using wearable sensor data plays a vital role in health monitoring, context-aware computing, and smart environments. Many existing deep learning models for HAR incorporate MaxPooling layers after convolutional operations to reduce dimensionality and computational load. While this approach is effective in image-based tasks, it is less suitable for the sensor signals used in HAR. MaxPooling introduces a form of temporal downsampling that can discard subtle yet crucial temporal information. Also, traditional CNNs often struggle to capture long-range dependencies within each window due to their limited receptive fields, and they lack effective mechanisms to aggregate information across multiple windows without stacking multiple layers, which increases computational cost. In this study, we introduce Retentive-HAR, a model designed to enhance feature learning by capturing dependencies both within and across sliding windows. The proposed model intentionally omits the MaxPooling layer, thereby preserving the full temporal resolution throughout the network. The model begins with parallel dilated convolutions, which capture long-range dependencies within each window. Feature outputs from these convolutional layers are then concatenated along the feature dimension and transposed, allowing the Retentive Module to analyze dependencies across both window and feature dimensions. Additional 1D-CNN layers are then applied to the transposed feature maps to capture complex interactions across concatenated window representations before including Bi-LSTM layers. Experiments on PAMAP2, HAPT, and WISDM datasets achieve a performance of 96.40%, 94.70%, and 96.16%, respectively, which outperforms the existing methods with minimal computational cost. Full article

Human activity recognition (HAR) based on wearable sensors is a key technology in the fields of smart sensing and health monitoring. With the rapid development of deep learning, its powerful feature extraction capabilities have significantly enhanced recognition performance and reduced reliance on traditional handcrafted feature engineering. However, current deep learning models still face challenges in effectively capturing complex temporal dependencies in long-term time-series sensor data and addressing information redundancy, which affect model recognition accuracy and generalization ability. To address these issues, this paper proposes an innovative CNN-BiGRU–Transformer hybrid deep learning model aimed at improving the accuracy and robustness of human activity recognition. The proposed model integrates a multi-branch Convolutional Neural Network (CNN) to effectively extract multi-scale local spatial features, and combines a Bidirectional Gated Recurrent Unit (BiGRU) with a Transformer hybrid module for modeling temporal dependencies and extracting temporal features in long-term time-series data. Additionally, an attention mechanism is incorporated to dynamically allocate weights, suppress redundant information, and enhance key features, further improving recognition performance. To demonstrate the capability of the proposed model, evaluations are performed on three public datasets: WISDM, PAMAP2, and UCI-HAR. The model achieved recognition accuracies of 98.41%, 95.62%, and 96.74% on the three datasets, respectively, outperforming several state-of-the-art methods. These results confirm that the proposed approach effectively addresses feature extraction and redundancy challenges in long-term sensor time-series data and provides a robust solution for wearable sensor-based human activity recognition. Full article

Given the increasing threat of lightning to modern electronic systems and human activities, the accurate identification and classification of lightning electromagnetic pulses has become a critical research focus, prompting the present study. A dataset was established by collecting lightning electromagnetic signals generated by various types of lightning under diverse environmental conditions via the lightning location system of the Institute of Electrical Engineering, Chinese Academy of Sciences. Subsequently, A deep learning model integrating a convolutional neural network was developed for feature extraction and pattern recognition using the multi-station data. Experimental results demonstrate that the proposed model significantly improves LEMP identification accuracy (exceeding 97%) compared to existing single-station methods. Moreover, it effectively uncovers complex hidden features within the data, outperforming conventional approaches in both accuracy and robustness. In conclusion, the proposed deep learning model offers a reliable technical foundation for lightning monitoring and localization based on LEMP signals. Full article

Brucellosis is a severe zoonotic disease caused by Brucella infection, which remains prevalent in several regions worldwide and poses a significant public health challenge. The host deploys complex immune mechanisms to combat the pathogen, including the recognition of pathogenic signals, secretion of inflammatory factors, and activation of innate and adaptive immune responses. Brucella, as a facultative intracellular pathogen, replicates within host cells and establishes chronic infections through diverse immune evasion strategies. These include subversion of Toll-like receptor (TLR) signaling, disruption of antigen presentation, and interference with T cell responses. This review focuses on Brucella species with significant human infectivity, such as B. melitensis and B. abortus, summarizing their interactions with the host immune system. Recent studies have highlighted TLR pathway inhibition, antigen presentation impairment, and T cell dysregulation as key mechanisms of immune evasion. Understanding these processes is crucial for elucidating Brucella pathogenesis and developing novel therapeutic and vaccine strategies against brucellosis. Full article

Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors’ data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70–30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70–30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires $\times 57.1$ and $\times 31.3$ more Multiply and ACcumulate (MAC) and $\times 100.8$ and $\times 28.3$ more Multiplication and Addition (MA) operations, which measure the complexity of the network’s inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH. Full article

Heavy metal ions (HMIs) threaten ecosystems and human health due to their carcinogenicity, bioaccumulativity, and persistence, demanding highly sensitive, low-cost real-time detection. Electrochemical sensing technology has gained significant attention owing to its rapid response, high sensitivity, and low cost. Molybdenum disulfide (MoS₂), with its layered structure, tunable bandgap, and abundant edge active sites, demonstrates significant potential in the electrochemical detection of heavy metals. This review systematically summarizes the crystal structure characteristics of MoS₂, various preparation strategies, and their mechanisms for regulating electrochemical sensing performance. It particularly explores the cooperative effects of MoS₂ composites with other materials, which effectively enhance the sensitivity, selectivity, and detection limits of electrochemical sensors. Although MoS₂-based materials have made significant progress in theoretical and applied research, practical challenges remain, including fabrication process optimization, interference from complex-matrix ions, slow trace-metal enrichment kinetics, and stability issues in flexible devices. Future work should focus on developing efficient, low-cost synthesis methods, enhancing interference resistance through microfluidic and biomimetic recognition technologies, optimizing composite designs, resolving interfacial reaction dynamics via in situ characterization, and establishing structure–property relationship models using machine learning, ultimately promoting practical applications in environmental monitoring, food safety, and biomedical fields. Full article

Background/Objectives: Naegleria fowleri is a free-living protozoan that causes primary amoebic meningoencephalitis, a rapidly progressing central nervous system infection with high mortality rates and limited treatment options. Targeting virulence-associated proteins is essential for effective drug development. Fowlerpain-1 (FWP1), a papain-like cysteine protease (CP) implicated in extracellular matrix degradation and host–cell cytotoxicity, has been investigated as a therapeutic target. This study aimed to evaluate the FWP1 pocket geometry and stefin binding using an integrated in silico structural biology approach. Methods: A computational pipeline was used, including AlphaFold2-Multimer modeling of FWP1–stefin complexes, 20-ns molecular dynamics simulations under NPT conditions for conformational sampling, and molecular mechanics Poisson–Boltzmann surface area free energy calculations. Three natural CP inhibitors (stefins) were investigated. Structural stability was assessed using root mean square deviations, and binding profiles were characterized using protein–protein interaction analysis. Results: Stable FWP1–stefin interaction interfaces were predicted, with human stefin A showing favorable binding free energy. Two conserved motifs (PG and QVVAG) were identified as critical mediators of active-site recognition. Druggability analysis revealed a concave pocket with both hydrophobic and polar characteristics, consistent with a high-affinity ligand-binding site. Conclusions: This computational study supports a structural hypothesis for selective FWP1 inhibition and identifies stefins as promising scaffolds for developing structure-guided protease-targeted therapeutics against N. fowleri. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article

Human cytomegalovirus (HCMV) establishes lifelong latency following primary infection, residing within myeloid progenitor cells and monocytes. To achieve this, the virus employs multiple immune evasion strategies. It suppresses innate immune signaling by inhibiting Toll-like receptor and cGAS-STING pathways. In addition, the virus suppresses major histocompatibility complex (MHC)-dependent antigen presentation to evade T cell recognition. As the downregulation of MHC molecules may trigger NK cell activation, the virus compensates for this by expressing proteins such as UL40 and IL-10, which engage inhibitory NK cell receptors and block activating signals, thereby suppressing NK cell immune surveillance. Viral proteins like UL36 and UL37 block host cell apoptosis and necroptosis, allowing HCMV to persist undetected and avoid clearance. In settings of profound immunosuppression, such as after allogeneic hematopoietic stem cell transplantation (allo-HSCT) or solid organ transplantation, slow immune reconstitution creates a window for viral reactivation. Likewise, immunosenescence and chronic low-grade inflammation during aging increases the risk of reactivation. Once reactivated, HCMV triggers programmed cell death, releasing viral PAMPs (pathogen-associated molecular patterns) and host-derived DAMPs (damage-associated molecular patterns). This release fuels a potent inflammatory response, promoting further viral reactivation and exacerbating tissue damage, creating a vicious cycle. This cycle of inflammation and reactivation contributes to both transplant-related complications and the decline of antiviral immunity in the elderly. Therefore, understanding the immune regulatory mechanisms that govern the switch from latency to reactivation is critical, especially within the unique immune landscapes of transplantation and aging. Elucidating these pathways is essential for developing strategies to prevent and treat HCMV-related disease in these high-risk populations. 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

Mobile crowd computing (MCdC) leverages the collective computational resources of nearby mobile devices to execute complex tasks without relying on remote cloud infrastructure. However, existing MCdC systems struggle with device heterogeneity and complex application dependencies, often leading to inefficient resource utilization and poor scalability. This paper presents Honeybee-Tx, a novel dependency-aware work stealing framework designed for heterogeneous mobile device clusters. The framework introduces three key contributions: (1) capability-aware job selection that matches computational tasks to device capabilities through lightweight profiling and dynamic scoring, (2) static dependency-aware work stealing that respects predefined task dependencies while maintaining decentralized execution, and (3) staged result transfers that minimize communication overhead by selectively transmitting intermediate results. We evaluate Honeybee-Tx using two applications: Human Activity Recognition (HAR) for sensor analytics and multi-camera video processing for compute-intensive workflows. The experimental results on five heterogeneous Android devices (OnePlus 5T, Pixel 6 Pro, and Pixel 7) demonstrate performance improvements over monolithic execution. For HAR workloads, Honeybee-Tx achieves up to 4.72× speed-up while reducing per-device energy consumption by 63% (from 1.5% to 0.56% battery usage). For video processing tasks, the framework delivers 2.06× speed-up compared to monolithic execution, with 51.4% energy reduction and 71.6% memory savings, while generating 42% less network traffic than non-dependency-aware approaches. These results demonstrate that Honeybee-Tx successfully addresses key challenges in heterogeneous MCdC environments, enabling efficient execution of dependency-aware applications across diverse mobile device capabilities. The framework provides a practical foundation for collaborative mobile computing applications in scenarios where cloud connectivity is limited or unavailable. Full article