Search Results (3,301)

Power load forecasting is a core technical component for achieving safe, stable, and economic operation in smart grids. This paper proposes a hybrid BiLSTM–Transformer forecasting method based on a Dynamic Adaptive Fusion (DAF) module. The core of this method involves utilizing the DAF module to adaptively weight different feature channels to highlight key influencing factors, while simultaneously employing a temporal attention mechanism to capture the contributions of various time steps. Building on this, the model effectively combines the strengths of BiLSTM networks in capturing bidirectional dependencies with the capability of Transformer models to extract global contextual features, thereby achieving a multi-level dynamic fusion of load characteristics. Experiments on real-world grid datasets demonstrate that the proposed method achieves a significant performance improvement over traditional models, particularly in terms of load peak prediction accuracy and stability. This provides effective technical support for the refined scheduling of power systems. Full article

The decarbonisation of the transport sector is a cornerstone of the European Green Deal, necessitating a transition toward integrated, digital, and sustainable mobility solutions such as Mobility-as-a-Service (MaaS). While early MaaS implementations were characterised by local bottom-up experiments, recent state-level actions mark a shift toward large-scale systemic deployment. This paper investigates the evolving role of universities within this transition, using MaaS4Italy initiative as a primary case study. Through a qualitative analysis of 11 pilot projects, conducted between January and July 2025, the research examines how academic institutions have been integrated into the national governance framework, transitioning from traditional living labs for technical testing to pivotal institutional anchors and governance buffers. The findings reveal a dual role for universities: as scientific partners and as neutral mediators. However, a relevant paradox is highlighted as well: while the institutionalisation of universities de-risks public investment and fosters data-sharing trust, it may simultaneously limit their potential as high-density operational testbeds for innovative Corporate MaaS (CMaaS) solutions. Present research supports a broader understanding for policymakers, thus underscoring the importance of formalising the role of intermediary institutions to ensure the long-term sustainability and scalability of smart mobility ecosystems. These insights prove to be pivotal towards the implementation of multi-level environmental governance mechanisms and the strategic use of recovery funds to catalyse the transition toward climate neutrality. Full article

Given the global aging of the population and the rising retirement age, the development of cross-generational technologies is crucial for a sustainable workforce supply. While AI-powered smart glasses can provide continuous cognitive support, current industrial solutions often prioritize work efficiency at the expense of the physical, cognitive, and socio-emotional needs of older workers. This study employed a mixed-methods approach grounded in embodied cognition. First, semi-structured interviews with ten participants were analyzed using grounded theory to develop a four-dimensional model of embodied experience: Perceived Pressure, Action Feedback, Collaboration Embedding, and Belonging. Subsequently, four interaction strategies—Rhythm Control, Transparent Feedback, Non-intrusive Assistance, and Legible Privacy & Social Signaling—were formulated and implemented. A high-fidelity prototype was developed to embody these strategies. Finally, a team of eight multidisciplinary experts evaluated the device using the System Usability Scale (SUS) and a proprietary twelve-item questionnaire. The results showed that the device’s overall usability was borderline acceptable (SUS = 68.13 ± 8.94). While the devices received stronger ratings for Control & Safety, the ratings for dignity and social acceptance were comparatively low. These findings contribute to the development of wearable device operation strategies suitable for users of different generations, and underline the importance of social and emotional compatibility as a prerequisite for future practice tests. Full article

Vehicle detection is a core task in smart city perception management and an important technical support for sustainable urban development and intelligent transportation optimization. In high-resolution unmanned aerial vehicle (UAV) remote sensing images, it faces challenges such as variable target scales, severe occlusion, and difficulty in modeling long-range dependencies. To address these issues, this study proposes the MCViM-YOLO algorithm, which integrates the local perception advantage of convolution with the global modeling capability of the state space model (Mamba). Based on YOLOv12, the algorithm reconstructs the neck network: it introduces the Mix-Mamba module (parallel multi-scale convolution and selective state space model) to simultaneously capture local details and global spatial dependencies, adopts the dual-factor calibration fusion module (DCFM) to adaptively fuse heterogeneous features, and employs a dual-branch attention detection head (DADH) to optimize the prediction of difficult samples (e.g., occluded, small-scale vehicles). Experiments on the VEBAI dataset demonstrate that our proposed model achieves an mAP@0.5 of 92.391% and a recall rate of 86.070%, with a computational complexity of 10.41 GFLOPs. The results show that the proposed method effectively improves the accuracy and efficiency of vehicle detection in complex remote sensing scenarios, provides technical support for traffic flow monitoring, low-carbon urban planning, and other sustainable applications, and offers an innovative paradigm for the deep integration of CNN and state space models with both theoretical research value and engineering application prospects. Full article

Non-intrusive load monitoring (NILM) is a key enabler for smart-grid applications, yet practical deployment is often hindered by limited appliance-level labels and severe distribution shifts across households and operating conditions. As a result, many deep learning approaches become unreliable in small-sample and out-of-distribution (OOD) settings. In this paper, we propose CR-MAT, a causality-driven representation learning framework for few-shot NILM classification. Instead of relying on large-scale training or heavy data augmentation, CR-MAT injects causal representation learning into multi-appliance task modeling, encouraging the network to learn appliance-discriminative features that are stable across environments while suppressing spurious, domain-specific correlations. We conduct extensive experiments under multiple OOD scenarios and consistently observe improved classification robustness compared with deep NILM baselines. Further analysis indicates that causal representation learning enhances resilience to non-stationary consumption patterns and improves generalization under OOD scenarios. The proposed framework provides a practical route toward reliable NILM classification and supports downstream smart-grid applications such as flexible load control and demand response. Full article

Conductive thread is an integral aspect of smart textiles in the domain of electronic textiles (e-textiles). This study unveils the development of twelve distinct variants of conductive threads using the twisting method: the fusion of copper filament with cotton and polyester threads. The threads are coated with a carbon paste solution enriched with dissolved sea salt. The carbon paste is obtained from non-functional dry cell batteries, conventionally categorized as hazardous electronic waste (e-waste), which underscores an economically viable and environmentally sustainable approach. Experiments proved that each variant demonstrates minimal electrical resistance. The lowest resistance, 0.0164 ± 0.0001 Ω/cm, was achieved by Carbon-Coated Cotton Twisted Copper Thread-II. Comparative evaluation with commercially available conductive threads, including Bekaert Bekinox^® VN type (12/1x275/100z), indicated comparable or moderately lower resistance values for the developed copper-based threads. Mechanical–electrical stability under bending, twisting, and wash–dry cycles confirmed consistent conductive performance with minimal resistance variation. Practical demonstrations further validated the integration of the threads into fabric-based flexible circuits and wearable electronic systems. These findings demonstrate that twisted copper-based conductive threads derived from sustainable coating materials provide a promising alternative for smart textile and wearable electronic applications. Future research should focus on scalable fabrication, enhanced coating fixation, and long-term durability assessment. Full article

China is experiencing rapid population aging and is actively promoting smart home–based eldercare. Smart homes offer a promising means of supporting older adults in aging in place. However, low adoption and limited sustained use constrain their potential benefits, thereby exacerbating social, economic, and healthcare burdens. This study examined factors influencing older adults’ continuance intention to use smart homes in Shandong Province, China, by integrating the Expectation–Confirmation Model of Information Systems and the Technology Acceptance Model and incorporating China-specific contextual antecedents, including government policy, intergenerational technical support, compatibility, and cost. Data were collected using an online questionnaire survey of older adults aged 60 years and older with prior smart home experience (n = 421) and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results showed that perceived usefulness, perceived ease of use, satisfaction, and cost directly affected continuance intention, whereas government policy, compatibility, and intergenerational technical support influenced continuance intention through perceived usefulness, perceived ease of use, and confirmation. Based on these results, this study proposes a conceptual framework for understanding older adults’ continuance intention toward smart homes. The findings provide implications for inclusive policy, user-centered design, and family-supported digital aging in rapidly aging societies. Full article

Global agricultural production is severely threatened by the intensification of crop diseases and pests. Traditional pesticide application methods, characterized by inefficiency and frequent phytotoxicity, necessitate the urgent development of smart plant protection technologies that feature precision, dosage reduction, and high efficiency. This study focuses on the core component of intelligent decision-making, systematically delineating the technological trajectory of the field through a three-tier analytical framework: “model evolution–system integration–application form.” Analysis reveals that decision-making models have transitioned from rule-driven and data-driven approaches to fusion-driven paradigms. This evolution marks a shift from the codification of empirical experience to data learning, culminating in the synergistic integration of multi-source information and domain knowledge. At the system application level, the core technical architecture—comprising multi-dimensional information sensing, real-time edge computing, and precise control execution—has facilitated the translation of intelligent pesticide application from laboratory settings to field deployment. Future decision-making systems are projected to evolve towards causal understanding, cluster collaboration, and ubiquitous service, providing critical technical support for the green transformation and sustainable development of agriculture. Full article

The global market for production organizations is becoming increasingly dynamic and complex. To address this development, production organizations develop strategies to become smarter in their operations, aiming at more flexibility and just-in-time mechanisms. These strategies imply advanced levels of digitization of the tactical decision making for and operational control of the primary production processes, both within individual organizations and at the level of supply networks. To embody this digitization, a spectrum of advanced digital technologies is available, such as artificial intelligence, blockchain, end-to-end process control, internet-of-things, and augmented reality. The realization of the strategies through digitization with these technologies is, however, severely hindered by two typical problems. Firstly, a large gap often exists between the high-level, abstract intentions formulated in strategies and the detailed, concrete embodiment in digital technology. Secondly, classes of digital technologies often are considered in isolation in projects resulting from a digital strategy, making synergies between technologies almost impossible and thus heavily reducing the added value of digitization efforts. This short commentary paper argues that the effective use of digital architecture in smart production system design is the path to effectively addressing these two problems. The observations and suggested architecture approach are illustrated by experiences with large international research and development projects in the smart production domain. Full article

The global maritime industry, a critical pillar of international trade, continues to face persistent challenges in ensuring the integrity, security, and transparency of containerized cargo data, particularly during ocean transport. Traditional container tracking systems at sea often lack the reliability and resilience required to prevent data tampering, cyber threats, and operational inefficiencies. As supply chains become more complex and interconnected, the demand for robust, end-to-end data security solutions becomes more pressing. A promising technological advancement in this area is the convergence of smart containers, equipped with Internet of Things (IoT) sensors for real-time condition monitoring, and blockchain technology (BCT) for secure data validation. These IoT devices facilitate continuous tracking of critical parameters such as location, temperature, humidity, tilt, and the like. However, the data they generate remains vulnerable to cyberattacks, signal disruptions, and unauthorized alterations. Blockchain’s decentralized and tamper-evident architecture addresses these vulnerabilities by enabling secure data immutability, transparent audit trails, and enhanced stakeholder trust. Despite its potential, the practical integration of blockchain with smart container systems in maritime logistics remains largely underexplored. To bridge this gap, this paper proposes a blockchain-enabled smart container monitoring system that combines container real-time data with secure physical tracking. Furthermore, to ensure scalability and efficient in data storage, hybrid on/off-chain architecture is introduced, balancing blockchain integrity with performance and resource optimization. Full article

Public transport systems in rapidly urbanizing Gulf cities confront the simultaneous challenge of decreasing emissions while guaranteeing equal access for riders, many of whom rely on transit for economic reasons. Sustainable smart city development necessitates bus services that are both efficient and sensitive to rider needs in adverse weather conditions. This study develops and evaluates a wellbeing-focused planning framework for Dubai’s bus network, filling gaps in prior research that primarily focuses on temperate, choice-based transport environments. The study uses Generalized Structured Component Analysis (GSCA) to analyze how Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance (SOA) impact overall journey wellbeing, based on a cross-sectional survey of 491 riders collected from July–August 2024. Data were collected during peak summer conditions, and the analysis followed a structured workflow that operationalized the proposed constructs into measurable indicators and estimated both the measurement and structural components of the GSCA model to find planning relevant wellbeing drivers. The model shows a strong fit (FIT = 0.684; GFI = 0.991; SRMR = 0.056), with SEA (β = 0.504) having the greatest influence on wellbeing, followed by SOA (β = 0.344) and PEPC (β = 0.070). Affordability and information quality are key SEA metrics, highlighting the necessity of economic access and multilingual, real-time communication. Overall, the findings indicate that wellbeing is most strongly shaped by accessibility-oriented service experience attributes particularly affordability and information quality followed by operational assurance, while comfort-related conditions remain significant under high heat exposure during waiting and transfers. On the other hand, the research indicates that operational reliability helps mitigate environmental discomfort in hyper-arid areas. The report suggests focusing on equal prices, digital information accessibility, dependable operations, and climate-adaptive infrastructure to promote sustainable mobility and long-term public transport use. Full article

Wireless sensor network (WSN) coverage optimization is a critical factor in improving network service quality, yet it faces challenges such as deployment uniformity, high-dimensional optimization, and the balance between exploration and exploitation under limited node resources. To address the shortcomings of the cultural historical optimization algorithm (CHOA), including insufficient global exploration, lack of dynamic regulation, and limited local exploitation accuracy, this paper proposes a film and television strategy-based multi-strategy cultural historical optimization algorithm (FTSCHOA). The proposed algorithm enhances performance through three synergistic mechanisms: a DE-style evolutionary operator that strengthens global exploration and population diversity; a film-and-television strategy that balances exploration and exploitation via random perturbations and adaptive parameter regulation; and a memory-based neighborhood local search that performs refined exploitation around high-quality solution sets to improve local optimization accuracy. Extensive experiments conducted on the CEC2017 and CEC2022 benchmark suites with dimensions of 10, 20, 30, and 50 demonstrate that FTSCHOA outperforms comparative algorithms in terms of optimization accuracy, convergence speed, and stability. The Friedman mean rank test indicates that FTSCHOA consistently achieves the best average ranking, while the Wilcoxon rank-sum test confirms that its performance differences with respect to competing algorithms are statistically significant ($p<0.05$). When applied to WSN coverage optimization in a $100\hspace{0.17em}\mathrm{m}\times 100\hspace{0.17em}\mathrm{m}$ monitoring region, FTSCHOA achieves coverage rates of 0.9351 and 0.9738 with 25 and 30 sensor nodes, respectively, which are significantly higher than those obtained by PSO, GWO, CHOA, and other algorithms. Moreover, the resulting node deployments exhibit greater uniformity, fewer coverage holes, and lower redundancy. The experimental results demonstrate that FTSCHOA effectively overcomes the limitations of traditional algorithms and provides an efficient and practical solution for WSN node deployment optimization, with strong potential for application in real-world scenarios such as environmental monitoring and smart agriculture. Full article

Accurate and reliable vehicle detection, tracking, and counting across different surveillance platforms are fundamental requirements for developing smart Traffic Management Systems (TMS) and promoting sustainable urban mobility. Recent advances in both ground-level surveillance and remote sensing using deep learning have opened new opportunities for extracting detailed vehicular information from high-resolution aerial and surveillance video data. Our research reported here aims to present a unified, real-time vehicle analysis framework that integrates lightweight deep learning–based detection, robust multi-object tracking, and trajectory-driven counting within a single modular pipeline. The proposed framework employs a “You Only Look Once” system, YOLOv10-S as the detection backbone and enhances its robustness through supervision-level knowledge distillation without introducing any architectural modifications. Temporal consistency is enforced using an observation-centric multi-object tracking algorithm (OC-SORT), enabling stable identity preservation under camera motion and dense traffic conditions. Vehicle counting is performed using a trajectory-based virtual gate strategy, reducing duplicate counts and improving counting reliability. Comprehensive experiments conducted on the UA-DETRAC and VisDrone benchmarks show that the proposed framework effectively balances detection performance, tracking robustness, counting accuracy, and real-time efficiency in both ground-based and aerial surveillance settings. Furthermore, cross-dataset evaluations under direct train–test transfer highlight the inherent challenges of domain shift while showing that knowledge distillation consistently improves robustness in detection, tracking identity consistency, and vehicle counting. Overall, this framework enables effective real-world traffic monitoring by adopting a scalable and practical system design, where reliability is prioritized over architectural complexity. Full article

Short auditory cues in enclosed built environments (such as elevator calls, access control, navigation, and heating, ventilation, and air conditioning (HVAC) notifications) influence not only usability but also stress and perceptions of well-being in daily indoor life. However, acoustic research remains largely focused on physical properties, and the psychophysiological impact of such short auditory cues remains under-quantified. To address this gap, a neuroscience-based evaluation approach, the Acoustic User Experience and Emotion (AUEX) model, is proposed. This model integrates functional near-infrared spectroscopy (fNIRS), electrodermal activity (EDA), and the User Experience Questionnaire (UEQ). With 33 in-cabin prompt sounds as a controlled typology of short auditory cues in an enclosed setting, we set up a simulated interaction experiment with 20 participants in a driving simulator vehicle cabin to investigate the relationship between acoustic properties and cognitive load, arousal, and user experience. The results show that timbre is the key factor, which was correlated positively with overall UX (r = 0.414) and negatively with prefrontal ΔHbO (CH3: r = −0.368; l-DLPFC: r = −0.449), indicating a decrease in cognitive load and a relaxed affective state. Conversely, high-frequency signals improved pragmatic quality but increased physiological arousal, which negatively affected hedonic assessment. To facilitate the translation of evaluation results into practice, we also completed a design phase that converted the AUEX results into scenario-based parameter targets and prototype designs for functional, warning, and brand/affective cues, illustrating how evidence-based relationships can be translated into design-ready outputs for enclosed built environments. These results confirm the AUEX approach as a transferable method for designing short auditory cues for well-being and provide parameter-level implications for therapeutic and human-centered sound design in smart buildings, intelligent vehicles, and other enclosed built environments. Overall, the AUEX approach provides a transferable evaluation-to-design workflow for short auditory cues in enclosed interactive contexts; however, direct generalization from a single controlled vehicle cabin setting to real-world building environments should be validated through future field studies. Accordingly, the present findings are positioned as evidence from a controlled enclosed case rather than universal conclusions for all enclosed spaces. Full article

To address the critical need for accurate human thermal comfort prediction in winter heating environments, this study established a comprehensive thermal comfort dataset containing 2089 valid samples through experiments. On this basis, thermal comfort prediction models were constructed using three multi-class machine learning algorithms: Support Vector Classification, K-Nearest Neighbors, and Random Forest. The predictive performance of 63 different feature combinations was systematically evaluated. The results indicate that the feature subset comprising indoor air temperature, forehead temperature, cheek temperature, dorsal hand temperature, heart rate, and systolic blood pressure yields the optimal prediction performance. Among the evaluated models, the Random Forest model demonstrated superior overall performance, achieving an accuracy exceeding 90% and an AUC ranging from 96% to 99%, significantly outperforming the SVC and KNN models. Compared with the traditional Predicted Mean Vote (PMV) model, the machine learning models developed in this study showed a substantial improvement in prediction accuracy under identical conditions; notably, the Random Forest model improved accuracy by approximately 40% over the PMV model. Based on these findings, a smart heating system framework integrating environmental sensors, wearable devices, and intelligent control valves is proposed, providing a theoretical basis and technical approach for realizing personalized and energy-efficient heating control. Full article