Search Results (925)

Multi-day travel diaries are often associated with high rates of partial completion, limiting their value for activity-based demand modeling. This paper develops a probabilistic framework that encodes daily activity sequences, clusters them with a Gaussian Mixture Model (GMM) to obtain soft (probabilistic) memberships, and predicts missing days through a constrained Lagrangian regression that guarantees valid probability distributions. Applied to the New York City Citywide Mobility Survey for 2019 and 2022, the soft-clustering approach achieves an RMSE as low as 0.17—substantially outperforming hard-clustering baselines (16–36% accuracy)—and reconstructs population-level time-use profiles with approximately 5–6% mean absolute error. Results show that post-pandemic activity patterns are more home-anchored and less varied, with pronounced socioeconomic divergence in recovery trajectories. Full article

Meeting the physiological and psychological needs of shelter cats through appropriate care is critical to reducing stress and disease risk, as well as enabling positive homing outcomes. Shelter organisations across the British Isles provide care for many cats; however, little is known about the types of housing and husbandry approaches applied. This study, therefore, aimed to quantify current approaches to cat housing, husbandry, and general care practices, in addition to providing information relevant to local site capacity, considering reported practices against sector minimum standards where applicable. Nine hundred and sixty-one shelter organisations and/or sites caring for cats were identified and invited to complete an online survey including predominantly multiple-choice questions. A total of 393 unique responses were collected from employees and volunteers, and quantitative data were summarised descriptively. In most cases, the results provided evidence of majority alignment with sector standards, although substantial variations in reported practices were also consistently captured. While most responses described approaches supportive of meeting cats’ basic physiological needs (e.g., access to veterinary care and basic resources), psychological needs were addressed less consistently (e.g., general housing and husbandry approaches), potentially leading to poor welfare outcomes. Identified opportunities to better meet cats’ needs include more cat-friendly, low-stress approaches to pen cleaning and cat handling; greater and more consistent provisioning of within-pen resources; and improved approaches to multi-cat housing and associated decision-making. Additional opportunities to enhance both cat and human wellbeing include more structured intake and assessment processes and capacity management to support optimal cat-to-staff ratios, staff working hours, cat lengths of stay and more consistent access to isolation and emergency intake facilities. Full article

Background: RIDInet-Reading Trainer 2 (RT-2) is a web-platform for the remote treatment of developmental dyslexia (DD) which has been shown to improve reading performance. However, no previous studies have investigated stability in reading improvement after RT-2 training and the influence of a previous diagnosis of developmental language disorder (DLD) and of participants’ age on stability. Objectives: In a sample of 52 Italian-speaking children with DD who participated in a 3-month home-based treatment with RT-2, we aimed (1) to assess the stability in reading improvement after RT-2 training at a 3-month follow-up and the potential moderating role of DLD and age; and (2) to evaluate the impact of RT-2 training in reading comprehension. Results: By implementing linear mixed model analysis, our findings confirmed reading improvement after RT-2 training in word and text reading in DD. Moreover, we observed an overall stability in single-word and text reading speed performances after three months, regardless of the diagnosis of DLD and the age of the participants. Conversely, accuracy showed an overall stability for single-word reading, while it was significantly stable only in the younger participants in text reading. The improvement was educationally relevant as it impacted reading comprehension. Conclusions: The current study supports the use of remotely delivered DD interventions among school-aged children. Full article

Students with disabilities, particularly those needing additional support or intervention to manage emotions and behaviors, build healthy relationships, and navigate social and academic demands, face heightened risks of high school pushout that can be traced back to their transition into high school. Project Elevate (PE) is a multi-component intervention that strategically invests in early coordinated student, family, and school supports to prevent barriers associated with high school pushout, such as a lack of continuity of effective services across school sites. This mixed-methods pilot study examined the implementation of PE with three 8th-grade students and their parents during their last term in middle school. This study includes quantitative pre–post descriptive analyses of multi-informant reports of students’ social, emotional, and behavioral skills, as well as descriptive analyses of weekly teacher- and parent-reported behavior and student attendance. Qualitative analysis using the Framework Method was applied to student and parent interviews and open-ended responses on a satisfaction questionnaire to understand their experience receiving PE support. Session case notes were also used as contextual data to describe implementation processes and contextualize findings. Results indicated improvements in student attendance and reductions in home-based behavioral concerns, with mixed findings across school-based outcomes. Students and parents reported high satisfaction with the intervention, highlighting the value of individualized support, goal setting, and strengthened communication with schools. Findings from this intervention development pilot study provide preliminary evidence regarding the implementation and perceived value of PE. Results also highlight the importance of culturally responsive, relationship-centered practices that affirm student strengths and support access to educational opportunities. Further investigation of PE in larger studies is warranted. Full article

Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

The increasing integration of renewable energy sources, smart appliances, and distributed energy technologies has significantly increased the complexity of residential energy systems, necessitating advanced Home Energy Management Systems (HEMS). Optimization techniques play a critical role in achieving key objectives, including energy cost reduction, load balancing, minimizing the peak-to-average ratio, and enhancing user comfort. This paper presents a comprehensive review and critical analysis of optimization techniques employed in HEMS, including mathematical methods, metaheuristic algorithms, artificial intelligence (AI)-based approaches, and rule-based strategies. These techniques are systematically classified and compared based on scalability, computational complexity, uncertainty handling, and real-time applicability. The analysis reveals that while conventional methods provide reliable solutions for structured problems, AI-based techniques offer superior adaptability and performance in dynamic and data-driven environments. Furthermore, key research gaps are identified, including limited multi-objective optimization, inadequate consideration of uncertainty and electric vehicle integration, and the lack of real-world implementation. Finally, future research directions are outlined, emphasizing hybrid optimization frameworks and intelligent, IoT-enabled energy management systems. Full article

Human activity recognition (HAR) using Wi-Fi channel state information (CSI) offers a privacy-preserving and contactless sensing modality suitable for smart homes, healthcare monitoring, and pervasive mobile Internet of Things (IoT) environments. However, existing CSI-based HAR approaches often suffer from computational inefficiency, high latency, and limited feasibility on resource-constrained embedded platforms. This work presents STAR (Sensing Technology for Activity Recognition), an edge AI-optimized framework that integrates lightweight temporal modeling, adaptive signal processing, and hardware-aware co-optimization to enable real-time, energy-efficient HAR on low-power embedded devices. STAR employs a streamlined three-layer Gated Recurrent Unit (GRU) architecture that reduces model parameters by 33% compared to conventional Long Short-Term Memory (LSTM) designs while maintaining strong temporal modeling capability. To enhance signal quality, STAR incorporates a multi-stage pre-processing pipeline consisting of median filtering, an eighth-order Butterworth low-pass filtering, and empirical mode decomposition (EMD) to denoise CSI amplitude measurements and extract stable spatial-temporal features. For on-device deployment, the system is implemented on a Rockchip RV1126 processor equipped with an embedded Neural Processing Unit (NPU) and interfaced with an ESP32-S3 CSI acquisition module. Experimental results demonstrate a mean recognition accuracy of 93.52% across seven activity classes and 99.11% for human-presence detection using a compact 97.6k-parameter model. INT8-quantized inference achieves a processing throughput of 33 MHz with only 8% CPU utilization, achieving a six-fold improvement in inference speed over CPU-based execution. With sub-second response latency and low power consumption, the system ensures real-time, privacy-preserving HAR, offering a practical, scalable solution for mobile and pervasive computing environments. Full article

The exponential growth of cyber threats in modern digital infrastructure demands advanced detection systems that adapt to evolving attack patterns. Traditional cybersecurity approaches struggle with dynamic threats, requiring extensive labeled datasets and retraining for each new category. This paper presents a comprehensive transfer learning framework for cybersecurity threat detection, leveraging the CICIoMT dataset as a benchmark to enhance detection capabilities across heterogeneous cybersecurity environments. We propose a machine learning (ML)-enabled framework that employs systematic feature alignment, hybrid class balancing, and multi-algorithm evaluation using machine learning models, including Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting, and XGBoost. The proposed approach addresses the critical challenges of data scarcity and domain heterogeneity in cybersecurity by enhancing feature engineering with cybersecurity-specific features, statistical aggregations, and PCA embeddings. Extensive experimental evaluation across two target datasets (CICIoT and IoT-23) demonstrates both the exceptional successes and critical limitations of cross-domain transfer learning in cybersecurity. The framework achieved outstanding performance on domain-compatible datasets, with RF reaching 99.0% accuracy on CICIoT, Gradient Boosting achieving 98.9%, and XGBoost delivering 98.4%, demonstrating exceptional knowledge transfer from medical IoT to smart home IoT environments. However, transfer learning to IoT-23 was unsuccessful (50% accuracy, equivalent to random guessing), revealing that feature domain difference, where identical attack labels encode fundamentally different behavioral patterns, prevents effective knowledge transfer despite nominal class overlap. This research makes significant advances in adaptive cybersecurity systems by providing a rigorous evaluation of both the successes and limitations of transfer learning. This work demonstrates that ensemble methods (RF, XGBoost, and Gradient Boosting) achieve superior cross-domain performance compared with neural networks on compatible domains, while also revealing fundamental challenges when the source and target domains differ in their feature spaces. Full article

With the large-scale deployment of Internet of Things (IoT) devices in smart homes, smart healthcare, and industrial internet scenarios, network attacks against IoT environments have become increasingly sophisticated, making reliable intrusion detection increasingly important. Focusing on tabular traffic statistical features, this study systematically evaluates an FT-Transformer-based IoT network attack detection framework across primary-dataset classification, feature-aligned external validation, feature-union validation, standardized NetFlow external validation, domain-aligned training, multi-class classification, and SHAP-based interpretability analysis. CICIoT2023 is used as the primary dataset for binary and multi-class attack detection, while CICIoMT2024 is used for feature-aligned external validation. In addition, NF-ToN-IoT and NF-BoT-IoT are introduced as standardized NetFlow IoT datasets to provide an additional external validation scenario. Random Forest, XGBoost, MLP, TabNet, and a TabTransformer-style numerical Transformer baseline are included for comparison. Experimental results show that FT-Transformer achieves competitive performance on the CICIoT2023 binary classification task, with an accuracy of 0.980675, an attack-class F1-score of 0.980366, a ROC-AUC of 0.995014, and a PR-AUC of 0.996261. Under the controlled 38-feature aligned CICIoT2023-to-CICIoMT2024 validation setting, FT-Transformer shows better ROC-AUC stability than Random Forest and XGBoost. However, the feature-union validation experiment reveals that this advantage does not necessarily extend to less constrained feature-space settings, indicating that FT-Transformer should not be interpreted as a universal cross-dataset generalization solution. In the additional standardized NetFlow NF-ToN-IoT-to-NF-BoT-IoT validation experiment, FT-Transformer achieves the strongest external validation performance among the compared models. Furthermore, the domain-aligned FT-Transformer-CORAL experiment shows that explicit source-target representation alignment can improve external validation performance, especially in terms of ROC-AUC. For the multi-class task, FT-Transformer achieves an accuracy of 0.809554, a Macro-F1 score of 0.724006, and a Weighted-F1 score of 0.806680. SHAP analysis further indicates that key features such as Number, HTTPS, Header_Length, Rate, and AVG have meaningful correspondence with known attack behaviors. Overall, this study provides a systematic and reproducible empirical evaluation of FT-Transformer for tabular IoT network attack detection. The results suggest that meaningful cross-dataset robustness requires not only suitable tabular model architectures but also carefully designed validation protocols and representation-learning strategies. Full article

Cell replacement therapy is a promising investigational approach for Parkinson’s disease (PD), a neurodegenerative disorder characterized by progressive loss of dopaminergic neurons in the substantia nigra. Although current PD therapies provide symptomatic relief, none halt or reverse disease progression. Early transplantation studies using fetal dopaminergic neurons provided proof of concept for PD cell replacement, with recent efforts focusing on pluripotent stem cell-derived dopaminergic progenitors that are now entering clinical testing. These strategies face challenges, however, including immune compatibility, tumorigenic risk, and the need for controlled differentiation and functional integration. Multi-lineage differentiating stress-enduring (Muse) cells are endogenous, non-tumorigenic pluripotent-like stem cells that home to sites of tissue injury and differentiate in response to the host microenvironment. A targeted literature search of PubMed and Scopus, however, did not identify prior reviews specifically addressing Muse cells in the context of PD, highlighting a gap in the literature. Here, we examine current limitations of established cell-replacement approaches and consider whether Muse cells may represent a mechanistically distinct cell source. Early clinical studies of Muse cell therapy in stroke and amyotrophic lateral sclerosis suggest an encouraging safety profile and preliminary signals of potential therapeutic benefit, although these findings are based on small, early-stage trials and require confirmation. The evidence supporting Muse cell therapy in PD is currently limited to a single preclinical animal study, supported by mechanistic in vitro findings and indirect evidence from other neurologic disease models; therefore, its relevance to PD remains to be established, and current evidence is insufficient to support conclusions regarding clinical efficacy. Together, these observations provide a rationale for further targeted preclinical investigation and support the systematic evaluation of Muse cells as a mechanistically distinct candidate for regenerative therapy in PD. Full article

Background/Objectives: We describe a case series of two patients with non-specific chronic low back pain (CLBP) and measurable decreased quality of life, who showed improvements after a specific multi-modal conservative spine and postural therapy regimen. CLBP is the leading cause of years lived with disability and disability-adjusted life years. This case series adds observational data to the medical literature on conservative treatment of CLBP and potentially improves diagnostic and treatment understanding of how conservative therapies can benefit patients suffering with CLBP. Methods: Two patients (Patient A: 58-year-old female; Patient B: 43-year-old male) presented with severe CLBP who did not find relief with prior traditional chiropractic manipulation. The patients sought treatment at a spine rehabilitation facility closest to their remote locations in Wyoming, USA. The conservative rehabilitation treatment program consisted of multi-modal therapies to strengthen postural muscles, postural spinal manipulation, and specific Mirror Image^® traction. After 36 treatments over 12 weeks in office and home rehabilitation exercises, baseline tests and outcome measures were repeated. Results: Patient-reported objective outcomes, disability indices, and radiographic analysis demonstrated changes at the conclusion of treatment that were maintained at long-term follow-up re-examination. Lumbar lordosis initially changed from −21.8° L1–L5 lordosis to post-treatment −33.6° for patient A and from −22.6° to −42.4° for patient B. Long-term follow-up demonstrated continued resolution of initial symptoms and maintained spine alignment. Conclusions: In these two patients, the described multimodal conservative program was associated with sustained improvements in symptoms, function, and radiographic parameters. This case series adds to prior biomedical literature regarding potential conservative interventions for treating CLBP and abnormal posture. Larger randomized controlled studies are required to evaluate generalizability and relative effectiveness. Full article

The growing demand for elderly and bedridden patient care in hospitals, nursing homes, and long-term care facilities has increased the need for safe and efficient repositioning methods. Repositioning immobile patients is essential for preventing pressure injuries and other complications associated with prolonged immobility. However, this task is still commonly performed manually using bed sheets, pillows, and similar support aids, making it physically demanding and increasing the risk of musculoskeletal injury among caregivers. This paper presents a two-stage soft growing robot for safe and assisted patient repositioning from a supine posture to a side-lying position. The proposed mechanism consists of two soft pneumatic chambers with distinct roles. The first chamber enables pressure-driven eversion, allowing the robot to deploy smoothly beneath the patient with minimal friction. The second chamber is then pressurized to generate the lifting and rolling motion required for repositioning. A first-principles dynamic model of the pressure-driven vine robot is developed by integrating pneumatic supply dynamics, internal pressure evolution, and tip-extension mechanics within a Lagrangian framework. Based on this model, a robust multi-stage nonlinear model predictive control strategy is formulated to regulate deployment beneath the patient under parameter uncertainty. The rolling dynamics of the second stage are also analyzed to determine the minimum pressure required for repositioning as a function of patient weight and roll angle. Simulation results show that the proposed controller achieves smooth and accurate deployment while satisfying input and state constraints under uncertainty. The rolling analysis further indicates that the required pressure increases with patient weight and decreases with roll angle. These findings demonstrate the potential of the proposed mechanism to reduce caregiver effort and enable safe, controlled patient repositioning. Full article

This study investigates how future climate change will alter heating and cooling demand in UK social housing, addressing wider concerns about fuel poverty risks and long-term climate resilience. Using a two-bedroom, offsite manufactured home in Northwest England as a case study, the research applies dynamic building performance modeling to assess energy use. This output then uses a multi-criteria decision-making (MCDM) tool to inform the choice of the weather file in the simulation process. The simulation weather files represent the 2030, 2050, and 2080 climate scenarios. The results show a clear shift from heating-led demand toward increasing summer cooling requirements. These changes have implications for grid capacity, occupant wellbeing, and the affordability of energy services. The analysis demonstrates that weather file selection significantly affects predicted performance outcomes. The study concludes that accurate weather file choice is essential for reliable Building Performance Simulation (BPS). It underscores the need to integrate environmental, social, and economic considerations into future housing design. Full article

Shoes are increasingly being bought online without being put on in person as internet shopping gains popularity. As a result, returns have increased significantly, which has had negative effects on the economy and the environment. Numerous technologies are available to measure foot size precisely at home or in-store in order to address this problem. People can identify their perfect shoe size and avoid needless returns by taking accurate foot measurements. A single image should be enough to measure the foot in order to make the system as easy as feasible for the user. This is accomplished by using point clouds from one side of the foot, which are produced by capturing a depth image. In order to optimise the reconstruction of partial data, this study investigates the impact of the acquisition position of a single partial foot scan on reconstruction quality and measurement accuracy when a state-of-the-art network is employed for completion. To this end, task-specific partial foot datasets were created with varying camera positions and foot orientations to determine the optimal conditions for depth map acquisition. Utilising the foot dataset that has been introduced for the purposes of training and evaluation, the network was able to generate accurate reconstructions. These reconstructions allowed for the estimation of shoe size in accordance with the European sizing system. The method is accurate enough in all tested positions to reconstruct a foot with sufficient precision. However, we also identified position 5 in our multi-view setup, which is viewed from a lower angle, as the position that leads to the best reconstruction results. Additionally, advantages were found with input data that show more of the forefoot than the heel area. Therefore, the forefoot provides more information on the overall geometry and should be the focus of single-shot procedures. Full article