Search Results (11,449)

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being. Full article

 Background/Objectives: Identifying and classifying abnormal lung sounds is essential for diagnosing patients with respiratory disorders. In particular, the simultaneous recording of auscultation signals from multiple clinically relevant positions offers greater diagnostic potential compared to traditional single-channel measurements. This study aims to improve the accuracy of respiratory sound classification by leveraging multichannel signals and capturing positional characteristics from multiple sites in the same patient. Methods: We evaluated the performance of respiratory sound classification using multichannel lung sound data with a deep learning model that combines a convolutional neural network (CNN) and long short-term memory (LSTM), based on mel-frequency cepstral coefficients (MFCCs). We analyzed the impact of the number and placement of channels on classification performance. Results: The results demonstrated that using four-channel recordings improved accuracy, sensitivity, specificity, precision, and F1-score by approximately 1.11, 1.15, 1.05, 1.08, and 1.13 times, respectively, compared to using three, two, or single-channel recordings. Conclusion: This study confirms that multichannel data capture a richer set of features corresponding to various respiratory sound characteristics, leading to significantly improved classification performance. The proposed method holds promise for enhancing sound classification accuracy not only in clinical applications but also in broader domains such as speech and audio processing.  Full article

In this paper, we develop a geometric formulation of datasets. The key novel idea is to formulate a dataset to be a fuzzy topological measure space as a global object and equip the space with an atlas of local charts using graphs of fuzzy linear logical functions. We call such a space a logifold. In applications, the charts are constructed by machine learning with neural network models. We implement the logifold formulation to find fuzzy domains of a dataset and to improve accuracy in data classification problems. Full article

The frequency-domain free-surface Green’s function method is widely used in solving ship hydrodynamic problems, with its core challenge lying in the computation of the Green’s function and its partial derivatives. This study analyzes the relationship between the free-surface Green’s function and its derivatives, proposing a machine learning-based recursive prediction method termed the pulsating source recursive prediction method. The accuracy and efficiency of this method under various parameter settings are investigated, and its application to the hydrodynamic calculations of container ship S175 and a bulk carrier is demonstrated. Results show that the predicted Green’s function achieves an accuracy of 3–6 decimals, with computational efficiency surpassing numerical methods and matching analytical approaches. The hydrodynamic results are reliable, confirming the method’s practical value. Full article

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistent with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that our approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains. Full article

Large-scale wind power grid-connected systems can trigger the risk of power system instability. In order to enhance the stability margin of grid-connected systems, this paper accurately characterizes the topology of the global boundary of stability domain (BSD) of the grid-connected system based on BSD theory, using the method of combining the manifold topologies and singularities at infinity. On this basis, the effect of large-scale doubly fed induction generators (DFIGs) replacing synchronous units on the BSD of the system is analyzed. Simulation results based on the IEEE 39-bus system indicate that the negative impedance characteristics and low inertia of DFIGs lead to a contraction of the stability domain. The principle of singularity invariance (PSI) proposed in this paper can effectively expand the BSD by adjusting the inertia and damping, thereby increasing the critical clearing time by about 5.16% and decreasing the dynamic response time by about 6.22% (inertia increases by about 5.56%). PSI is superior and applicable compared to traditional energy functions, and can be used to study the power angle stability of power systems with a high proportion of renewable energy. Full article

Background: Lower phylogenetic species are known to rebuild cut-off caudal parts with regeneration of the central nervous system (CNS). In contrast, CNS regeneration in higher vertebrates is often attributed to immaturity, although this has never been conclusively demonstrated. The emergence of stem cells and their effective medical applications has intensified research into spinal cord regeneration. However, despite these advances, the impact of clinical trials involving spinal cord-injured (SCI) patients remains disappointingly low. Long-distance regeneration has yet to be proven. Methods: Our study involved a microsurgical dorsal myelotomy in fetal rats. The development of pioneering long primary afferent axons during early gestation was examined long after birth. Results: A single cut triggered the intrinsic ability of the dorsal root ganglion (DRG) neurons to reprogram. Susceptibility to hypoxia caused the axons to stop developing. However, the residual axonal outgrowth sheds light on the intriguing temporal and spatial events that reveal long-distance CNS regeneration. The altered phenotypes displayed axons of varying lengths and different features, which remained visible throughout life. The previously designed developmental blueprint was crucial for interpreting these enigmatic features. Conclusions: This research into immaturity enabled the exploration of the previously impenetrable domain of early life and the identification of a potential missing link in CNS regeneration research. Central axon regeneration appeared to occur much faster than is generally believed. The paradigm provides a challenging approach for exhaustive intrauterine reprogramming. When the results demonstrate pre-clinical effectiveness in CNS regeneration research, the transformational impact may ultimately lead to improved outcomes for patients with spinal cord injuries. Full article

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) system has shown substantial potential in distributed acoustic sensing applications. Accurate event classification is crucial for effective deployment of Φ-OTDR systems, and various methods have been proposed for event classification in Φ-OTDR systems. However, most existing methods typically rely on sufficient labeled signal data for model training, which poses a major bottleneck in applying these methods due to the expensive and laborious process of labeling extensive data. To address this limitation, we propose CLWTNet, a novel contrastive representation learning method enhanced with wavelet transform convolution for event classification in Φ-OTDR systems. CLWTNet learns robust and discriminative representations directly from unlabeled signal data by transforming time-domain signals into STFT images and employing contrastive learning to maximize inter-class separation while preserving intra-class similarity. Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. The experimental results demonstrate that CLWTNet achieves competitive performance with the supervised representation learning methods and superior performance to unsupervised representation learning methods, even when training with unlabeled signal data. These findings highlight the effectiveness of CLWTNet in extracting discriminative representations without relying on labeled data, thereby enhancing data efficiency and reducing the costs and effort involved in extensive data labeling in practical Φ-OTDR system applications. Full article

Recent advancements in data collection technologies, data science, and speech processing have fueled significant interest in the computational analysis of biological sounds. This enhanced analytical capability shows promise for improved understanding and detection of various pathological conditions, extending beyond traditional speech analysis to encompass other forms of acoustic data. A particularly promising and rapidly evolving area is the application of deep learning techniques for the detection and analysis of diverse pathologies, including respiratory, cardiac, and neurological disorders, through sound processing. This paper provides a comprehensive review of the current state-of-the-art in using deep learning for pathology detection via analysis of biological sounds. It highlights key successes achieved in the field, identifies existing challenges and limitations, and discusses potential future research directions. This review aims to serve as a valuable resource for researchers and clinicians working in this interdisciplinary domain. Full article

In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to be important components of the IoT system (WSN-IoT) to create smart applications and automate processes. As the number of connected IoT devices increases, privacy and security issues become more complicated due to their external working environments and limited resources. Hence, solutions need to be updated to ensure that data and user privacy are protected from threats and attacks. To support the safety and reliability of such systems, in this paper, security issues in the WSN-IoT are addressed and classified as identifying security challenges and requirements for different kinds of attacks in either WSNs or IoT systems. In addition, security solutions corresponding to different types of attacks are provided, analyzed, and evaluated. We provide different comparisons and classifications based on specific goals and applications that hopefully can suggest suitable solutions for specific purposes in practical. We also suggest some research directions to support new security mechanisms. Full article

Soft data, such as seismic imagery, plays a critical role in subsurface modeling by providing indirect constraints away from hard data locations. However, validating whether subsurface model realizations honor this type of data remains a challenge due to the lack of robust quantitative tools. This study introduces a machine learning-based workflow for soft data checking that uses an autoencoder (AE) to encode 2D seismic slices into a latent space. Subsurface model realizations are transformed into the same domain and projected into this latent space, enabling both visual and quantitative comparisons using principal component analysis and Euclidean distances. We demonstrate the workflow on rule-based models and their associated synthetic seismic data (soft data), showing that models with similar Markov chain parameters to the reference soft data score higher in proximity metrics. This approach provides a scalable, quantitative, and interpretable framework for evaluating the consistency between soft data and subsurface models, supporting better decision-making in reservoir characterization and other geoscience applications. Full article

Previous reviews have examined specific facets of Geographic Information Systems (GIS) in transportation planning, such as transit-focused applications and open source geospatial tools. However, this study offers the first systematic, PRISMA-guided longitudinal evaluation of GIS integration in transportation planning, spanning thematic domains, data models, methodologies, and outcomes from 2004 to 2024. This study addresses this gap through a longitudinal analysis of GIS-based transportation research from 2004 to 2024, adhering to PRISMA guidelines. By conducting a mixed-methods analysis of 241 peer-reviewed articles, this study delineates major trends, such as increased emphasis on sustainability, equity, stakeholder involvement, and the incorporation of advanced technologies. Prominent domains include land use–transportation coordination, accessibility, artificial intelligence, real-time monitoring, and policy evaluation. Expanded data sources, such as real-time sensor feeds and 3D models, alongside sophisticated modeling techniques, enable evidence-based, multifaceted decision-making. However, challenges like data limitations, ethical concerns, and the need for specialized expertise persist, particularly in developing regions. Future geospatial innovations should prioritize the responsible adoption of emerging technologies, inclusive capacity building, and environmental justice to foster equitable and efficient transportation systems. This review highlights GIS’s evolution from a supplementary tool to a cornerstone of data-driven, sustainable urban mobility planning, offering insights for researchers, practitioners, and policymakers to advance transportation strategies that align with equity and sustainability goals. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article