Search Results (932)

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

Informal settlements, urban areas with substandard housing conditions and inadequate infrastructure, are increasing in Africa’s sub-Saharan cities, fueled by rapid urbanization, economic challenges, and high housing prices. However, developers often ignore the green building (GB) concept when upgrading housing conditions for these communities. This study aims to investigate GB design strategies specifically for residential structures in Akabahizi to identify and propose practical strategies suitable for informal settlements such as Akabahizi and to develop sustainable housing solutions that enhance environmental quality and meet the needs of residents. Simulation software and combined qualitative and quantitative data collection techniques, including field surveys, interviews, and assessments of existing building conditions, constitute the methodology used in this study. The focus was on the influence of climatic factors, including temperature, precipitation, and wind, on design choices, particularly GB design and current residential buildings in Akabahizi. Based on the survey, 82.5% of residents support the GB concept, 87.4% recognize the importance of GB for community well-being, and 97.1% recognize the benefits of integrating energy-efficient technology for residents’ well-being. Questionnaire findings were considered in decision-making for the design of the new proposed structure to address challenges in the area. Optimized energy efficiency, daylight access, and thermal comfort resulting from courtyard design support GB design incorporating a courtyard as a robust and culturally relevant sustainable design framework tailored for Akabahizi. The courtyard provides green space that promotes social interaction, improves air quality, and delivers natural cooling elements that are essential for residential housing. The proposed new design, with green roof and renewable energy devices, improved material usage, and natural ventilation elements, outperformed the existing one in terms of lower levels of carbon emission for environmental protection. In conclusion, a collaborative effort is needed among various stakeholders, including architects, urban planners, and educational institutions, to promote and implement sustainable building practices. The study suggests that enhancing awareness, offering training opportunities, and empowering local professionals and residents alike can pave the way for improved living conditions and sustainable urban development in Akabahizi and similar informal settlements. Full article

A biomimetic adaptive façade applies natural principles to building design using shading devices that dynamically respond to environmental changes, enhancing daylight, thermal comfort, and energy efficiency. While motorised systems offer precision through sensors and mechanical actuation, they consume energy and are complex. In contrast, passively actuated systems use smart materials that respond to environmental stimuli, offering simpler and more sustainable operation, but often lack responsiveness to dynamic conditions. This study explores a sequential approach by initially developing motorised shading concepts before transitioning to a passive actuation strategy. In the first phase, nine mechanically actuated shading device concepts were designed, inspired by the opening and closing behaviour of plant stomata, and evaluated on structural robustness, actuation efficiency, ease of installation, and visual integration. One concept was selected for further development. In the second phase, a biocomposite made of polylactic acid (PLA) and regenerated cellulose fibres was used for Fused Deposition Modelling (FDM) to fabricate 3D-printed modules with passive, moisture-responsive actuation. The modules underwent environmental testing, demonstrating repeatable shape changes in response to heat and moisture. Moisture application increased the range of motion, and heating led to flap closure as water evaporated. Reinforcement and layering strategies were also explored to optimise movement and minimise unwanted deformation, highlighting the material’s potential for sustainable, responsive façade systems. Full article

Mirror therapy (MT) has been recognized for its potential to harness neuroplasticity and improve recovery in post-stroke patients. In MT, a mirror tricks the brain into thinking that the weak or paralyzed side of the body is moving when the healthy side moves, thereby helping to stimulate healing and relearn movement after a stroke or injury. However, MT is limited in addressing the sensory impairment and visual feedback ownership on the affected hand. A combination of MT and electrical muscle stimulation (EMS) is believed to enhance muscle strength and sensory perception, but lacks synchronization with the movement intention of the healthy hand. This study aims to advance MT to further promote neuroplasticity through movement synchronization in both hands. A stretch-sensor glove was used on the unaffected hand to capture finger movement kinematics, controlling the electrical intensity of an EMS device on the assumed affected hand. Thereby, a proportional control of electrical intensity and synchronous movement of both hands was achieved. This study compared four types of electrical intensities, spanning from baseline (no stimulation) to higher intensities (S0–S4). As a result, body representation perception showed an overall negative correlation with the level of comfort associated with the stimulus. Enhancements in body representation perception were significantly confirmed (p < 0.01) in stronger stimulus types, notably S4 and S4F of the spontaneous movement scheme, compared to the baseline stimulus S0 and the weak intensity S1. There may be a possibility of enhancing neuroplasticity by strategically using various electrical intensities. The proposed system shows promising performance by enhancing body representation through improved visual feedback ownership at higher electrical intensities. Full article

Triboelectric nanogenerators (TENG) are mechanical energy harvesters characterized by high sensitivity and simple structure and are currently being widely developed for use in human body motion sensing. Among them, fiber-based TENGs (FTENG) are particularly suitable for wearable human motion sensors due to their unique structure, which offers flexibility, high durability, and comfort. However, studies involving doping to further modify the electrical output characteristics of FTENGs are very limited. Here, we propose an innovative approach that combines graphite (GP) doping with fiber-based TENG fabrication, successfully developing a graphite-doped polyester fiber-based TENG (GP@PET-TENG). Proper graphite doping can increase the amount of transferred charge and thus improve the output electrical performance of TENG, but this method has rarely been explored in FTENG. With the incorporation of 3%wt graphite, the open-circuit voltage of the GP@PET-TENG increased from 103.3 V to 202.1 V, and the short-circuit current increased from 60.7 μA to 105.1 μA, compared to the pure polyester fiber based TENG (PET-TENG). The device achieved a maximum output power of 4.15 mW (2.59 W/m²), demonstrates the capability to charge various capacitors, and successfully lit up 200 LEDs. By attaching the GP@PET tribo-layer to human skin, a single-electrode mode TENG can be formed, which captures the subject’s motion signals through skin contact and separation, converting them into voltage outputs. In fist-clenching and wrist-bending tests, motion-induced voltage signals up to 0.6 V were recorded, demonstrating the potential applications in rehabilitation assistance and mechanical control. Full article

Central venous access devices (CVADs) play a pivotal role in managing stem cell recipients, providing reliable access for the administration of chemotherapy, blood products, progenitor infusion, parenteral nutrition, and other crucial treatments. This review critically evaluates the various types of CVADs commonly employed in transplant settings, examining their indications, complications, and best practices to enhance patient outcomes. Moreover, it emphasizes the significance of broadening the selection algorithm for vascular devices and incorporating patient expectations and comfort into routine clinical practice. Full article

Cardiovascular disease (CVD) remains the leading cause of global mortality, highlighting the need for continuous vital sign monitoring. Photoplethysmography (PPG) is well suited for wearable devices. Smart rings, benefiting from dense capillary distribution and minimal tissue interference, can capture high-quality PPG signals with comfort, making them a promising next-generation wearable. However, ring rotation relative to the finger alters the optical path, especially for multi-wavelength light, thus reducing accuracy. This paper proposes a rotation-robust PPG sensor for smart rings. Monte Carlo simulations analyze photon transmission under different LED–photodiode (PD) angles, showing that at ±60°, green, red, and infrared light achieve optimal penetration into the microcirculation layer. Considering non-ideal conditions, the green-light angle is adjusted to ±30°, and a symmetrical sensor design is adopted. A prototype smart ring is developed, capable of recording 4-channel PPG, 3-axis acceleration, and 4-channel temperature signals at 100, 25, and 0.2 Hz, respectively. The system achieves reliable PPG acquisition with only 0.59 mA average current consumption. In continuous testing, heart rate estimation reached mean absolute errors of 0.82, 0.79, and 0.78 bpm for green, red, and IR light. The results provide a reference for future smart ring development. Full article

Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible headband system designed for multi-modal physiological signal acquisition, incorporating dry electrodes, a six-axis inertial measurement unit (IMU), and a temperature sensor. The device supports eight EEG channels and enables wireless data transmission via Bluetooth, ensuring user convenience and reliable long-term monitoring in home environments. To rigorously evaluate the system’s performance, we conducted comprehensive assessments involving 13 subjects over two consecutive nights, comparing its outputs with conventional PSG. Experimental results demonstrate the system’s low power consumption, ultra-low input noise, and robust signal fidelity, confirming its viability for overnight sleep tracking. Further validation was performed using the self-collected HBSleep dataset (over 184 h recordings of the 13 subjects), where state-of-the-art sleep staging models (DeepSleepNet, TinySleepNet, and AttnSleepNet) were applied. The system achieved an overall accuracy exceeding 75%, with AttnSleepNet emerging as the top-performing model, highlighting its compatibility with advanced machine learning frameworks. These results underscore the system’s potential as a reliable, comfortable, and practical solution for accurate sleep monitoring in non-clinical settings. Full article

Background: This study compared the performance of a Head-mounted Virtual Reality Perimeter (HVRP) with the Humphrey Field Analyzer (HFA), the standard in automated perimetry. The HFA is the established standard for automated perimetry but is constrained by lengthy testing, bulky equipment, and limited patient comfort. Comparative data on newer head-mounted virtual reality perimeters are limited, leaving uncertainty about their clinical reliability and potential advantages. Aim: The aim was to evaluate parameters such as visual field outcomes, portability, patient comfort, eye tracking, and usability. Methods: Participants underwent testing with both devices, assessing metrics like mean deviation (MD), pattern standard deviation (PSD), and duration. Results: The HVRP demonstrated small but statistically significant differences in MD and PSD compared to the HFA, while maintaining a consistent trend across participants. MD values were slightly more negative for HFA than HVRP (average difference −0.60 dB, p = 0.0006), while pattern standard deviation was marginally higher with HFA (average difference 0.38 dB, p = 0.00018). Although statistically significant, these differences were small in magnitude and do not undermine the clinical utility or reproducibility of the device. Notably, HVRP showed markedly shorter testing times with HVRP (7.15 vs. 18.11 min, mean difference 10.96 min, p < 0.0001). Its lightweight, portable design allowed for bedside and home testing, enhancing accessibility for pediatric, geriatric, and mobility-impaired patients. Participants reported greater comfort due to the headset design, which eliminated the need for chin rests. The device also offers potential for AI integration and remote data analysis. Conclusions: The HVRP proved to be a reliable, user-friendly alternative to traditional perimetry. Its advantages in comfort, portability, and test efficiency support its use in both clinical settings and remote screening programs for visual field assessment. Its portability and user-friendly design support broader use in clinical practice and expand possibilities for bedside assessment, home monitoring, and remote screening, particularly in populations with limited access to conventional perimetry. Full article

Purpose: Despite significant attention being paid to compression garments (CG) in the sports field, there remains ongoing debate regarding their actual effectiveness in enhancing athletic performance and expediting post-exercise recovery. This article examines their various aspects, with a focus on CG design and the materials they are made of, aiming to analyze the importance of personalized compression strategies based on individual anthropometric measurements and non-linear compression designs. Methods: Using anthropometric analysis of 40 healthy participants, this study examines the morphological characteristics of the lower limb and their implications for CG design. Results: Measurements of limb length and circumferences revealed complex interactions among anatomical variables, emphasizing the need for customized and adaptable device design. Finite element simulations clarified the challenges in achieving uniform pressure gradients along the lower limb, highlighting the limitations of one-piece devices and suggesting tailored segmented designs for individual limb segments. Conclusion: The results demonstrate that one-piece devices often fail to provide optimal compression due to non-linear variations in limb dimensions. Conversely, segmented devices, particularly those with bilinear progression, exhibited superior performance in applying targeted compression across different limb segments. This more detailed approach to customization could significantly contribute to optimizing outcomes and user comfort. Full article

The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from digital twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R²) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model’s robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems. Full article

Background/Objectives: This narrative review addresses ongoing controversies and advancements concerning ultrasound-guided peripheral intravenous (IV) catheter insertion, and the impact of ultrasound guidance on success rate, procedural time, patient and staff experience, complications and costs, as well as requirements for its use. Methods: A literature review was conducted. Results: Growing evidence suggests that ultrasound-guided insertion of peripheral IV catheter represents a superior technique across various patient populations, particularly those presenting with difficult IV access (DIVA). Key findings highlight significant improvements in first-attempt success rates, reduction of procedural complications, and enhanced patient comfort. Ultrasound-guided insertion is also associated with an increase in catheter dwell time, a reduction in repeat procedures and in central line placements, leading to improved resource utilization and the potential for substantial long-term cost-effectiveness, despite the cost of initial investment and training. However, obtaining these improvements involves a critical importance for standardized training, adherence to rigorous aseptic techniques, and generalization of the transformative impact of ongoing technological advancements in ultrasound devices. Conclusions: The collective body of evidence supports the widespread adoption of ultrasound-guided peripheral IV cannulation as an evidence-based best practice in modern healthcare. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

This research is based on the acoustic package design of new energy vehicles, investigating the application of the hybrid Finite Element–Statistical Energy Analysis (FE-SEA) model in predicting the high-frequency dynamic response of automotive structures, with a focus on the modeling and correction methods for hybrid point connections. New energy vehicles face unique acoustic challenges due to the special nature of their power systems and operating conditions, such as high-frequency noise from electric motors and electronic devices, wind noise, and road noise at low speeds, which directly affect the vehicle’s ride comfort. Therefore, optimizing the acoustic package design of new energy vehicles to reduce in-cabin noise and improve acoustic quality is an important issue in automotive engineering. In this context, this study proposes an improved point connection correction factor by optimizing the division range of the decision circle. The factor corrects the dynamic stiffness of point connections based on wave characteristics, aiming to improve the analysis accuracy of the hybrid FE-SEA model and enhance its ability to model boundary effects. Simulation results show that the proposed method can effectively improve the model’s analysis accuracy, reduce the degrees of freedom in analysis, and increase efficiency, providing important theoretical support and reference for the acoustic package design and NVH performance optimization of new energy vehicles. Full article

Introduction: 3D-printing is an emerging technology in the field of prosthetics, offering advantages such as cost-effectiveness, ease of customization, and improved accessibility. While previous reviews have focused on limited aspects, the aim of this systematic review is to provide a comprehensive evaluation of the clinical outcomes of 3D-printed prostheses for both upper and lower limbs. Methods: A search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across six databases (PubMed, Web of Science, EBSCO, Scopus, Cochrane Library, and Sage). Studies on 3D-printed prostheses in human rehabilitation that focused on the clinical outcomes of the device were included, while studies lacking clinical data, 3D printing details, or focusing on traditional manufacturing methods were excluded. Finally, the risk of bias was assessed using the modified Downs & Black Checklist. Results: A total of 1420 studies were identified, with 11 meeting the inclusion criteria. The included studies assessed different 3D-printed prosthetic types and upper and lower limb prostheses. The main clinical outcomes analyzed were functional performance, design and material integrity, and overall effectiveness of 3D-printed prostheses. Studies on upper limb prostheses reported improved dexterity, range of motion (ROM), and user satisfaction, despite some durability limitations. Lower limb prostheses showed enhancements in comfort, gait parameters, and customization, particularly in amphibious and partial foot designs. Conclusions: 3D-printed prostheses show potential to improve functional performance, patient satisfaction, fit, and implementation feasibility compared to conventional methods. However, limitations such as small sample sizes, variability in assessment tools, and limited high-quality evidence highlight the need for further research to support broader clinical adoption. Full article