Search Results (4,762)

Introduction: Caring for a dependent individual, particularly over an extended period, places significant strain on family caregivers, often leading to adverse physical, mental, emotional, social, and economic outcomes for both caregivers and those they care for. Common challenges include anxiety, depression, loneliness, and diminished overall well-being. E-health applications have emerged as effective tools to support family caregivers by promoting positive mental health through online interventions, enhancing problem-solving skills, autonomy, interpersonal relationships, self-control, and a prosocial attitude. Methods: This study aimed to translate and culturally adapt the Spanish “Program to Promote Positive Mental Health through the Cuidadoras Crónicos Manual” into the Portuguese context, supporting its implementation as a smartphone application. The process involved translation, back-translation by two native experts, and refinement through a focus group with eight participants. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. Results: The translation and back-translation processes identified several adjustments, which informed discussions in the focus group. Three key themes emerged: (i) conceptual and semantic equivalence, (ii) optimisation of content, and (iii) relevance and timeliness of the manual. Conclusions: The Spanish manual for promoting positive mental health among family caregivers was successfully translated and culturally adapted into European Portuguese. Validated through expert input, this marks the first version of the manual tailored to Portuguese caregivers, using Positive Mental Health models to support caregivers of individuals with chronic conditions. Full article

Environmental monitoring systems integrated with IoT networks have rapidly evolved, enabling the collection of vast amounts of data accessible to facility managers and authorized users via smartphone apps. This paper presents a system developed to monitor environmental parameters across multiple buildings at the University of Pisa, with a focus on its potential for improving energy efficiency. Efficient energy management has become increasingly important, especially following the COVID-19 pandemic, which introduced legal requirements for mechanical ventilation. These measures have significantly increased energy consumption during both winter and summer seasons. Our system, built using low-cost components and a secure IoT network, demonstrates how CO₂ monitoring and smart controls can reduce energy waste in buildings. In a case study conducted on selected buildings, the system achieved up to 34% energy savings. The paper highlights both the benefits and the limitations of current technology in this context, emphasizing the role of IoT in enhancing sustainability while ensuring safety and security within academic institutions. Full article

Objectives: To summarize the impact of various telerehabilitation interventions on motor function, balance, gait, activities of daily living (ADLs), and quality of life (QoL) among patients with stroke and to determine the existing telerehabilitation interventions for delivering physiotherapy sessions in clinical practice. Methods: Six electronic databases were searched to identify relevant quantitative systematic reviews (SRs). Due to substantial heterogeneity, the data were analysed narratively. Results: A total of 28 systematic reviews (n = 245 primary studies) were included that examined various telerehabilitation interventions after stroke. Motor function was the most studied outcome domain across the reviews (20 SRs), followed by ADL (18 SRs), and balance (14 SRs) domains. For primary outcomes, our findings highlight moderate- to high-quality evidence showing either a significant effect or no significant difference between telerehabilitation and other interventions. There was insufficient evidence to draw a conclusion regarding feasibility outcomes, including participant satisfaction, adherence to treatment, and cost. Most reviews under this umbrella included patients with stroke in the subacute or chronic phase (12 SRs). Simple and complex telerehabilitation interventions such as telephone calls, videoconferencing, smartphone- or tablet-based mobile health applications, messaging, virtual reality, robot-assisted devices, and 3D animation videos, either alone or in combination with other interventions, were included across reviews. Conclusions: Various telerehabilitation interventions have shown either a significant effect or no significant difference compared to other interventions in improving upper and lower limb motor function, balance, gait, ADLs, and QoL, regardless of whether simple or complex approaches were used. Further research is needed to support the delivery of rehabilitation services through telerehabilitation intervention following a stroke. Full article

Background/Objectives: This international multicenter study aimed to assess the effectiveness of the Pivot-Shift Meter (PSM) mobile application in diagnosing and classifying anterior cruciate ligament (ACL) injuries, emphasizing the need for standardization to improve diagnostic precision and treatment outcomes. Methods: ACL evaluations were conducted by eight experienced orthopedic surgeons across five Latin American countries (Bolivia, Chile, Colombia, Ecuador, and Mexico). The PSM app utilized smartphone gyroscopes and accelerometers to standardize the pivot-shift test. Data analysis from 224 control tests and 399 standardized tests included non-parametric statistical methods, such as the Mann–Whitney U test for group comparisons and chi-square tests for categorical associations, alongside neural network modeling for injury grade classification. Results: Statistical analysis demonstrated significant differences between standardized and control tests, confirming the effectiveness of the standardization. The neural network model achieved high classification accuracy (94.7%), with precision, recall, and F1 scores exceeding 90%. Receiver Operating Characteristic (ROC) analysis yielded an area under the curve of 0.80, indicating reliable diagnostic accuracy. Conclusions: The PSM mobile application, combined with standardized pivot-shift techniques, is a reliable tool for diagnosing and classifying ACL injuries. Its high performance in predicting injury grades makes it a valuable addition to clinical practice for enhancing diagnostic precision and informing treatment planning. Full article

The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection. Our machine learning models consist of a novel and comprehensive dataset collected from a network of strategically placed sensors, capturing not only electromagnetic field readings but also additional urban features, including population density, levels of urbanization, and specific building characteristics. This sensor-driven approach, coupled with explainable AI, enables us to identify key factors influencing electromagnetic exposure more accurately. The integration of IoT sensor data with machine learning opens the potential for creating highly detailed and dynamic electromagnetic pollution maps. These maps are not merely static snapshots; they offer researchers the ability to track trends over time, assess the effectiveness of mitigation efforts, and gain a deeper understanding of electromagnetic field distribution in urban environments. Through the extensive dataset, our models can yield highly accurate and dynamic electric field strength maps. For this study, we performed a comprehensive analysis involving 566 machine learning models across eight French cities: Lyon, Saint-Étienne, Clermont-Ferrand, Dijon, Nantes, Rouen, Lille, and Paris. The analysis incorporated six core approaches: k-Nearest Neighbors, XGBoost, Random Forest, Neural Networks, Decision Trees, and Linear Regression. The findings underscore the superior predictive capabilities of ensemble methods such as Random Forests and XGBoost, which outperform individual models. Simpler approaches like Decision Trees and k-NN offer effective yet slightly less precise alternatives. Neural Networks, despite their complexity, highlight the potential for further refinement in this application. In addition, our results show that the machine learning models significantly outperform the linear regression baseline, demonstrating the added value of more complex techniques in this domain. Our SHAP analysis reveals that the feature importance rankings in tree-based machine learning models differ significantly from those in k-NN, neural network, and linear regression models. Full article

Lithium-ion batteries (LIBs) are fundamental to modern technology, powering everything from portable electronics to electric vehicles and large-scale energy storage systems. As their use expands across various industries, ensuring the reliability and safety of these batteries becomes paramount. This review explores the multifaceted aspects of LIB reliability, highlighting recent advancements and ongoing challenges. The importance of safety has been underscored by numerous incidents, such as the well-known smartphone battery explosions and more than 10,000 fires a year at facilities throughout Australia, both linked to LIB failures. These events emphasize the need for robust reliability and safety measures to ensure consistent performance and longevity. Factors like battery chemistry, design, manufacturing, and operating conditions can all influence the reliability of LIBs. Despite their widespread use, the mechanisms of failure, failure rates, and consequences of LIB failures are still not well understood, raising significant safety concerns. Current reliability assessment techniques include experimental methods, computational models, and data-driven approaches. Emerging trends, such as advanced characterization techniques and standardized testing protocols, advocate for improved practices to enhance the reliability and safety of LIBs across all applications. Full article

In the rapidly advancing realm of mobile technology, under-display camera (UDC) systems have emerged as a promising solution for achieving seamless full-screen displays. Despite their innovative potential, UDC systems face significant challenges, including low light transmittance and pronounced diffraction effects that degrade image quality. This study aims to address these issues by examining degradation phenomena through optical simulation and employing a deep neural network model incorporating hybrid frequency–spatial domain learning. To effectively train the model, we generated a substantial synthetic dataset that virtually simulates the unique image degradation characteristics of UDC systems, utilizing the angular spectrum method for optical simulation. This approach enabled the creation of a diverse and comprehensive dataset of virtual degraded images by accurately replicating the degradation process from pristine images. The augmented virtual data were combined with actual degraded images as training data, compensating for the limitations of real data availability. Through our proposed methods, we achieved a marked improvement in image quality, with the average structural similarity index measure (SSIM) value increasing from 0.8047 to 0.9608 and the peak signal-to-noise ratio (PSNR) improving from 26.383 dB to 36.046 dB on an experimentally degraded image dataset. These results highlight the potential of our integrated optics and AI-based methodology in addressing image restoration challenges within UDC systems and advancing the quality of display technology in smartphones. Full article

Smartphone push notifications aim to provide time-sensitive information to their users. However, notifications are often transmitted in ill-timed situations, causing users to be interrupted, annoyed, and stressed. This ultimately affects the overall notification experience as it does not consider the external contexts the users are situated in. This study aims to shed light on how users manage notifications in their daily lives and how they perceive the experience as a whole. A total of 28 participants took part in a 5-day mixed-method diary study, which logged a total of 135 entries. Based on this, six types of characteristics emerged. These characteristics were formed from the combination of three main categories: notification related, day related, and user related. The findings of this study highlight implementing different strategies for each type of characteristic to mitigate the adverse effects notifications have on users. Full article

Federated learning (FL) has emerged as a decentralized, cutting-edge framework for training models across distributed devices, such as smartphones, IoT devices, and local servers while preserving data privacy and security. FL allows devices to collaboratively learn from shared models without exchanging sensitive data, significantly reducing privacy risks. With these benefits, the deployment of FL over wireless communication systems has gained substantial attention in recent years. However, implementing FL in wireless environments poses significant challenges due to the unpredictable and fluctuating nature of wireless channels. In particular, the limited energy resources of mobile and IoT devices, many of which operate on constrained battery power, make energy management a critical concern. Optimizing energy efficiency is therefore crucial for the successful deployment of FL in wireless networks. However, existing reviews on FL predominantly focus on framework design, wireless communication, and security/privacy concerns, while paying limited attention to the system’s energy consumption. To bridge this gap, this article delves into the foundational principles of FL and highlights energy-efficient strategies tailored for various wireless architectures. It provides a comprehensive overview of FL principles and introduces energy-efficient designs, including resource allocation techniques and communication architectures, tailored to address the unique challenges of wireless communications. Furthermore, we explore emerging technologies aimed at enhancing energy efficiency and discuss future challenges and opportunities for continued research in this field. Full article

Background/Objectives: This study assessed the feasibility and security of remote surgical wound monitoring using the RedScar© smartphone app, which employs automated diagnosis for early visual detection of infections without direct healthcare personnel involvement. Additionally, patient satisfaction with telematic care was evaluated as a secondary aim. Surgical site infection (SSI) is the second leading cause of healthcare-associated infections (HAIs), leading to prolonged hospital stays, heightened patient distress, and increased healthcare costs. Methods: The study employed a prospective paired-cohort and single-blinded design, with a sample size of 47 adult patients undergoing abdominal surgery. RedScar© was used for remote telematic monitoring, evaluating the feasibility and security of this approach. A satisfaction questionnaire assessed patient experience. The study protocol was registered at ClinicalTrials.gov under the identifier NCT05485233. Results: Out of 47 patients, 41 successfully completed both remote and in-person follow-ups. RedScar© demonstrated a sensitivity of 100% in detecting SSIs, with a specificity of 83.13%. The kappa coefficient of 0.8171 indicated substantial agreement between the application’s results and human observers. Patient satisfaction with telemonitoring was high: 97.6% believed telemonitoring reduces costs, 90.47% perceived it prevents work/school absenteeism, and 80.9% found telemonitoring comfortable. Conclusions: This is the first study to evaluate an automatic smartphone application on real patients for diagnosing postoperative wound infections. It establishes the safety and feasibility of telematic follow-up using the RedScar© application for surgical wound assessment. The high sensitivity suggests its utility in identifying true cases of infection, highlighting its potential role in clinical practice. Future studies are needed to address limitations and validate the efficacy of RedScar© in diverse patient populations. Full article

Novel low-cost portable spectrophotometers could be an alternative to traditional spectrophotometers and calibrated RGB cameras by offering lower prices and convenient measurements but retaining high colorimetric accuracy. This study evaluated the colorimetric accuracy of low-cost, portable spectrophotometers on the established color calibration target—RAL Design System Plus (RAL+). Four spectrophotometers with a listed price between USD 100–1200 (Nix Spectro 2, Spectro 1 Pro, ColorReader, and Pico) and a smartphone RGB camera were tested on a representative subset of 183 RAL+ colors. Key performance metrics included the devices’ ability to match and measure RAL+ colors in the CIELAB color space using the color difference CIEDE2000 ΔE. The results showed that Nix Spectro 2 had the best performance, matching 99% of RAL+ colors with an estimated ΔE of 0.5–1.05. Spectro 1 Pro and ColorReader matched approximately 85% of colors with ΔE values between 1.07 and 1.39, while Pico and the Asus 8 smartphone matched 54–77% of colors, with ΔE of around 1.85. Our findings showed that low-cost, portable spectrophotometers offered excellent colorimetric measurements. They mostly outperformed existing RGB camera-based colorimetric systems, making them valuable tools in science and industry. Full article

In seedling cultivation of hybrid rice, fast estimation of seedling density is of great significance for classifying seedling cultivation. This research presents an improved YOLOv8 model for estimating seedling density at the needle leaf stage. Firstly, the auxiliary frame technology was used to address the problem of locating the detection area of seedlings. Secondly, the Standard Convolution (SConv) layers in the neck network were replaced by the Group Shuffle Convolution (GSConv) layer to lightweight the model. A dynamic head module was added to the head network to enhance the capability of the model to identify seedlings. The CIoU loss function was replaced by the EIoU loss function, enhancing the convergence speed of the model. The results showed that the improved model achieved an average precision of 96.4%; the parameters and floating-point computations (FLOPs) were 7.2 M and 2.4 G. In contrast with the original model, the parameters and FLOPs were reduced by 0.9 M and 0.6 G, and the average precision was improved by 1.9%. Compared with state-of-the-art models such as YOLOv7 et al., the improved YOLOv8 achieved preferred comprehensive performance. Finally, a fast estimation system for hybrid rice seedling density was developed using a smartphone and the improved YOLOv8. The average inference time for each image was 8.5 ms, and the average relative error of detection was 4.98%. The fast estimation system realized portable real-time detection of seedling density, providing technical support for classifying seedling cultivation of hybrid rice. Full article

Background/Objectives: In recent years, the use of smartphones has significantly increased among populations of almost every age. The aim of our work is to analyze the impact of an application (app) that follows up with the progress of a patient who underwent a thoracic surgery procedure in the first 30 days after discharge. Methods: We prospectively analyzed all the patients included in the pilot study from March 2023 to September 2023. The Care4Today™ app was downloaded and activated by the patient preoperatively. From the day of discharge, the app sent questions related to pain perception, breathing capacity, general clinical conditions, problems with surgical wound and quality of life. In the case of negative responses, clinical staff received an email with an orange (medium problem) or red (serious problem) alert. Results: Among the 96 patients who were included, 82 eventually downloaded and used the app. The mean age of the patients was 60.7 years (range 19–80), and 43 (52.4%) were female. Minimally invasive techniques (VATS or RATS) were used in 76 cases (92.7%). The mean length of in-hospital stay was 5.3 days. Malignancy was the reason for surgery in 66 cases (80.5%). The answer rate was 75.8%. A total of 698 orange alerts and 52 red alerts were sent by the app. Re-hospitalization was needed in two cases (only one case related to our surgical procedure). The app was globally judged as useful in the management of convalescence (with an average rating of 7.4 out of 10). Age was not related to the completion rate of answers. Conclusions: The use of the app Care4Today could prevent unexpected re-hospitalization and possible complications. The patients appreciated the use of this tool, and they found it useful for safer postoperative recovery. No difference according to the patients’ age was found regarding the use of the app. Full article

Hyperspectral imagery and advanced classification techniques can significantly enhance remote sensing’s role in forest monitoring. Thanks to recent missions, such as the Italian Space Agency’s PRISMA (PRecursore IperSpettrale della Missione Applicativa—Hyperspectral PRecursor of the Application Mission), hyperspectral data in narrow bands spanning visible/near infrared to shortwave infrared are now available. In this study, hyperspectral data from PRISMA were used with the aim of testing the applicability of PRISMA with different band sizes to classify tree species in highly biodiverse forest environments. The Serre Regional Park in southern Italy was used as a case study. The classification focused on forest category classes based on the predominant tree species in sample plots. Ground truth data were collected using a global positioning system together with a smartphone application to test its contribution to facilitating field data collection. The final result, measured on a test dataset, showed an F1 greater than 0.75 for four classes: fir (0.81), pine (0.77), beech (0.90), and holm oak (0.82). Beech forests showed the highest accuracy (0.92), while chestnut forests (0.68) and a mixed class of hygrophilous species (0.69) showed lower accuracy. These results demonstrate the potential of hyperspectral spaceborne data for identifying trends in spectral signatures for forest tree classification. Full article

The use of smartphones is widespread among adolescents and can affect various cognitive processes. However, the effects of smartphone use on sensory processing, particularly among individuals with attention deficit hyperactivity disorder (ADHD), remain largely unknown. The present study investigated the relationship between smartphone use intensity and sensory processing in adolescents with typical development and those with ADHD. The sample included 184 adolescents aged 14 to 18 years (M = 16.56; SD = ±1.87), with 92 diagnosed with ADHD and 92 with typical development, matched for age, gender, and IQ. Participants completed a self-report questionnaire to measure smartphone use intensity, while sensory processing was assessed using the Adolescent Sensory Profile (ASP). The results revealed a significant association between the intensity of smartphone use and heightened sensory responses in adolescents with typical development. However, this relationship was not observed in participants with ADHD. These preliminary findings suggest that smartphone use may influence sensory processing differently depending on neurotypical development or the presence of ADHD, potentially contributing to the promotion or mitigation of sensory dysfunctions. Future studies are needed to further explore the mechanisms underlying these differences and to better understand the impact of digital technologies on sensory functioning. Full article