Search Results (275)

Locomotive syndrome (LS) predisposes older adults to falls and functional dependency. In older adults with LS, the validity of the Lower Quarter Y Balance Test (YBT-LQ)—a dynamic balance assessment tool—remains unclear. This cross-sectional study aimed to assess the convergent validity of the YBT-LQ with the Two-Step and Timed Up and Go (TUG) tests and compare YBT-LQ performance between individuals with and without LS. Sixty Thai community-dwelling older adults (≥60 years) were equally divided into LS and non-LS groups and performed the YBT-LQ, Two-Step test, and TUG test. Correlation analyses and independent t-tests assessed relationships and between-group comparisons, respectively. The YBT-LQ exhibited moderate positive correlations with the Two-Step test (r = 0.366, p = 0.004) and moderate negative correlations with the TUG test (r = −0.412, p = 0.001). The LS group exhibited significantly lower YBT-LQ scores across all reach directions than the non-LS group (p < 0.05), highlighting impaired balance in individuals with LS. The YBT-LQ is a valid and reliable tool for assessing dynamic balance and postural control, as well as identifying multidirectional stability deficits in older adults, particularly those with LS. Implementing the YBT-LQ in routine geriatric evaluations could enhance early detection and targeted interventions to reduce fall risk and improve mobility in aging populations. Full article

To conduct an environmental risk assessment for metals in the groundwater of a site in Jiujiang City, Jiangxi Province, we analyzed seven metals (Cr, Zn, Pb, Ni, Sb, Cu, and Tl) that exhibited higher detection rates among the elements we measured. For example, in our measurement data, the average concentration of the element cobalt (Co) is less than 2 × 10^–3 μg/L, and the average concentration of the element cadmium (Cd) is less than 5 × 10^–3 μg/L. The purpose of this environmental risk assessment was to provide a scientific basis for site remediation and subsequent construction. The risk assessment was carried out using the single-factor pollution index, the Nemerow comprehensive pollution index (Pn), and potential ecological hazard index methods. Principal component analysis and correlation analyses were used to investigate the sources of metal pollution in the groundwater. The results indicated the following: (1) The average concentrations of the seven metals in the groundwater of the study area did not exceed the Class IV groundwater quality standard limits. The highest average concentration was for Zn (38.08 μg/L), indicating that metal concentrations in the groundwater were relatively low. (2) The Pn for the seven metals was below 0.7, indicating that the study area was at a non-polluted level. (3) The correlation and principal component analyses of the metals indicate that the sources of these metals may be residues from material stored in the raw material warehouse of the former iron smelting plant at the site. The results show that the level of groundwater contamination at the site falls within an extremely low range; thus, the focus on groundwater pollution can be reduced in subsequent site remediation and construction activities. Full article

Surface electromyography (sEMG) is increasingly important for prevention, diagnosis, and rehabilitation in healthcare. The continuous monitoring of muscle electrical activity enables the detection of abnormal events, but existing sEMG systems often rely on disposable pre-gelled electrodes that can cause skin irritation and require precise placement by trained personnel. Wearable sEMG systems integrating textile electrodes have been proposed to improve usability; however, they often suffer from poor skin–electrode coupling, leading to higher impedance, motion artifacts, and reduced signal quality. To address these limitations, we propose a preliminary model of smart socks, integrating biocompatible hybrid polymer electrodes positioned over the target muscles. Compared with commercial Ag/AgCl electrodes, these hybrid electrodes ensure lower the skin–electrode impedance, enhancing signal acquisition (19.2 ± 3.1 kΩ vs. 27.8 ± 4.5 kΩ for Ag/AgCl electrodes). Moreover, to the best of our knowledge, this is the first wearable system incorporating hydrogel-based electrodes in a sock specifically designed for the analysis of lower limb muscles, which are crucial for evaluating conditions such as sarcopenia, fall risk, and gait anomalies. The system incorporates a lightweight, wireless commercial module for data pre-processing and transmission. sEMG signals from the Gastrocnemius and Tibialis muscles were analyzed, demonstrating a strong correlation (R = 0.87) between signals acquired with the smart socks and those obtained using commercial Ag/AgCl electrodes. Future studies will further validate its long-term performance under real-world conditions and with a larger dataset. Full article

Radiofrequency fields in the 1–28 GHz range are ubiquitous in the modern world, giving rise to numerous studies of potential health risks such as cancer, neurological conditions, reproductive risks and electromagnetic hypersensitivity. However, results are inconsistent due to a lack of precision in exposure conditions and vastly differing experimental models, whereas measured RF effects are often indirect and occur over many hours or even days. Here, we present a simplified RF exposure protocol providing a single 1.8 GHz carrier frequency to human HEK293 cell monolayer cultures. A custom-built exposure box and antenna maintained in a fully shielded anechoic chamber emits discrete RF signals which can be precisely characterized and modelled. The chosen amplitudes are non-thermal and fall within the range of modern telecommunication devices. A critical feature of the protocol is that cell cultures are exposed to only a single, short (15 min) RF exposure period, followed by detection of immediate, rapid changes in gene expression. In this way, we show that modulation of genes implicated in oxidative stress and ROS signaling is among the earliest cellular responses to RF exposure. Moreover, these genes respond in complex ways to varying RF signal amplitudes consistent with a hormetic, receptor-driven biological mechanism. We conclude that induction of mild cellular stress and reactive oxygen species (ROS) is a primary response of human cells to RF signals, and that these responses occur at RF signal amplitudes within the range of normal telecommunications devices. We suggest that this method may help provide a guideline for greater reliability and reproducibility of research results between labs, and thereby help resolve existing controversy on underlying mechanisms and outcomes of RF exposure in the general population. Full article

As one of the serious hazards in deep mining, rock instability will cause roof falls, rib spalling, rockburst, and other serious disasters. It will lead to significant casualties and property losses. Conducting risk assessments for rock instability is of significant importance. Firstly, an evaluation model called IAHP-SPA was proposed to address uncertainties in the weight determination process. Secondly, the Partial Connection Number (PCN) including the first-order PCN, the second-order PCN, the third-order PCN and the fourth-order PCN were introduced. Thus, a dynamic and comprehensive evaluation of rock instability in metal mines was obtained. Finally, the availability and reasonability of the proposed method were verified by comparing the results obtained with the number of microseismic events detected by the sensors in a metal mine. The proposed model provides a novel approach to dynamic risk assessment in mining, offering a reliable alternative for evaluating complex safety challenges. This method holds substantial potential for its practical application in the assessment and control of rock instability risks in deep metal mines, thereby improving safety and operational efficiency. Full article

Background/Objectives: Falls are the most common adverse in-hospital event, resulting in a considerable social and economic burden on individuals, their families, and the healthcare system. This study aims to develop and implement an automatic coding system using large language models (LLMs) to extract and categorize free-text information (including the location of the fall and any resulting injury) from in-hospital fall records. Methods: The study used the narrative description of the falls reported through the Incident Reporting system to the Risk Management Service of an Italian Local Health Authority in Italy (name not disclosed as per research agreement). The OpenAI application programming interface (API) was used to access the generative pre-trained transformers (GPT) models, extract data from the narrative description of the falls, and perform the classification task. The GPT-4-turbo models were used for the classification task. Two independent reviewers manually coded the information, representing the gold standard for the classification task. Sensitivity, specificity, and accuracy were calculated to evaluate the performance of the task. Results: The analysis included 187 fall records with free-text event descriptions detailing the location of the fall and 93 records providing information about the presence or absence of an injury. GPT-4-turbo showed excellent performance, with specificity, sensitivity, and accuracy values of at least 0.913 for detecting the location and 0.953 for detecting the injury. Conclusions: The GPT models effectively extracted and categorized the information, even though the text was not optimized for GPT-based analysis. This shows their potential for the use of LLMs in clinical risk management research. Full article

Chronic kidney disease (CKD) continues to pose a critical global health challenge, making ongoing monitoring vital for effective management and preventing its progression to end-stage renal disease. The urinary albumin-to-creatinine ratio (uACR) stands out as a reliable biomarker. MyACR was developed and validated as a novel point-of-care (POC) device for identifying and monitoring the progress of CKD. MyACR device operates using a colorimetric-based spectroscopy to quantify albumin and creatinine levels at 625 nm and 515 nm, respectively. Calculated uACR values were compared with results from the reference turbidimetry method using a dataset of 103 random urine samples from patients at high risk of advanced CKD. The device showed excellent performance in detecting severe nephropathy, with sensitivity, specificity, and accuracy of 100%, 100%, and 100%, respectively. The PPV (positive predictive value) was 100%, indicating perfect identification of patients with severe nephropathy (uACR > 300 mg/g creatinine). The NPV (negative predictive value) was 100%, suggesting a strong ability to rule out severe nephropathy, though a small risk of false negatives remained. Bland–Altman analysis confirmed a high level of agreement, with 96.11% (for all data) and 95.87% (for uACR > 300 mg/g creatinine) of MyACR measurements falling within the 95% confidence interval (−27 to +19). Correlation analysis revealed a significant alignment between MyACR and the reference method (r² 0.9720 to 0.9836). The ROC analysis suggested that combining uACR with the estimated glomerular filtration rate (eGFR) demonstrated strong predictive performance, yielding an area under the curve (AUC) of 0.933 (95% CI: 0.86–1.0). In conclusion, the MyACR device is a robust, affordable, and user-friendly tool for detecting nephropathy, showing performance comparable to the reference method. Its portability and cost-effectiveness make it particularly suitable for use in low-resource environments. Additionally, integrating uACR with eGFR enhances prognostic capabilities, offering a comprehensive approach to assessing kidney function and predicting CKD progression. Full article

Background/Objectives: Falls among the older adult population represent a significant public health concern, often leading to diminished quality of life and serious injuries that escalate healthcare costs, and they may even prove fatal. Accurate fall risk prediction is therefore crucial for implementing timely preventive measures. However, to date, there is no definitive metric to identify individuals with high risk of experiencing a fall. To address this, the present study proposes a novel approach that transforms biomechanical time-series data, derived from gait analysis, into visual representations to facilitate the application of deep learning (DL) methods for fall risk assessment. Methods: By leveraging convolutional neural networks (CNNs) and Siamese neural networks (SNNs), the proposed framework effectively addresses the challenges of limited datasets and delivers robust predictive capabilities. Results: Through the extraction of distinctive gait-related features and the generation of class-discriminative activation maps using Grad-CAM, the random forest (RF) machine learning (ML) model not only achieves commendable accuracy (83.29%) but also enhances explainability. Conclusions: Ultimately, this study underscores the potential of advanced computational tools and machine learning algorithms to improve fall risk prediction, reduce healthcare burdens, and promote greater independence and well-being among the older adults. Full article

Human gait is usually studied in clinical environments, but wearable devices have extended gait analysis beyond traditional assessments. Older adults tend to walk differently indoors and outdoors; however, most gait assessments are conducted on indoor surfaces. It is therefore important to evaluate gait in various outdoor environments. Insights gained from these assessments significantly enhance our understanding of the impact of environmental factors on gait performance and ensure that clinical evaluations are effectively aligned with everyday locomotion. A total of 100 participants with foot pain, 38 young (18–45 years) and 62 older adults (65–80 years), completed a 10-Metre Walk Test (10MWT) in three randomised conditions at their typical, comfortable walking pace, including (1) 10MWT of indoor walking, (2) 10MWT of outdoor walking on grass and (3) 10MWT of outdoor walking on a sidewalk. Wearable inertial sensors recorded gait data and the magnitudes of the following gait measures: gait speed, cadence, stride length, stride duration and asymmetry. A statistical analysis using ANOVA and post hoc comparisons revealed a significantly lower gait speed (p < 0.001), lower stride length (p < 0.001) and lower asymmetry (p < 0.001) indoors compared to outdoors, demonstrating that environmental factors significantly affect spatio-temporal gait parameters. Wearable sensor-based gait analysis performed in controlled clinical settings may underestimate real-life conditions. Some important spatio-temporal parameters, useful in detecting people with gait impairment and at risk of falling, are significantly affected by environment and individual postural ability more than demographic factors. Full article

Gait is a complex motor process that involves the coordination and synchronization of various body parts through continuous interaction with the environment. Monitoring gait is crucial for the early detection of abnormalities, such as toe walking, which is characterized by limited or absent heel contact with the floor during walking. Persistent toe walking can cause severe foot, ankle, and musculature conditions; poor balance; increased risk of falling or tripping; and can affect overall quality of life, making it difficult, for example, to participate in sports or social activities. This study proposes a new approach to detect toe walking using surface Electromyography (sEMG) on lower limbs. sEMG sensors, by measuring the electrical activity of muscles, can see signals before the movement corresponding to muscle activation, contributing to an early detection of a possible problem. The sEMG signal presents significant complexity due to its noisy nature and the challenge of extracting meaningful features for classification. To address this issue and enhance the model’s robustness across different devices and configurations, a Transfer Learning (TL) approach is introduced. This method leverages pre-trained models to effectively handle the variability of sEMG data and improve classification accuracy. In particular, Continuous Wavelet Transform (CWT) is applied to sEMG-filtered signals (with time windows of 1 s) to convert them into 2D images (scalograms). Preliminary tests were performed on a public dataset using some of the most well-known pre-trained architectures, obtaining an accuracy of about 95% on InceptionResNetV2. Full article

Reduced body movement and weakened musculoskeletal function as a result of aging increase the risk of falls and serious physical injuries requiring medical attention. To solve this problem, a fall prevention algorithm using an acceleration sensor has been developed, and research is being conducted to enable continuous monitoring using a Holter electrocardiograph. In this study, we implemented a multi-input model that can detect and classify movements, including falls, utilizing the baroreflex characteristics of the heart’s potential energy changes due to movement, measured with an electrocardiogram with a three-axis acceleration sensor and a Holter electrocardiograph. Patterns were identified from the various movement characteristics of acceleration sensor data using a deep learning model consisting of CNN-LSTM, and heart rate variability (HRV) data were analyzed using a wide learning model to provide additional weight values for fall classification. Finally, a multi-input model using wide and deep learning was proposed to enhance the accuracy of fall classification. The results show that the HRV increased in fall case except in two motion types, while it decreased when standing up from a chair, indicating the application of the baroreflex characteristics reflecting the heart’s potential energy. Compared to the classification model using conventional HRV and ACC, a higher accuracy was achieved in the multi-input model using ACC-HRV data, and a precision, recall, and F1 score of 0.91 was measured, indicating improved performance. This is expected to have a positive impact on fall prevention by improving the accuracy of fall classification in the elderly for 15 different movements. Full article

Impaired balance and mental stress are significant health concerns, particularly among older adults. This study investigated the relationship between the heart rate variability and fall risk during daily activities among individuals over 40 years old. This aimed to explore the potential of the heart rate variability as an indicator of stress and balance loss. Data were collected from 14 healthy participants who wore a Polar H10 heart rate monitor and performed Berg Balance Scale activities as part of an assessment of functional balance. Machine learning techniques applied to the collected data included two phases: unsupervised clustering and supervised classification. K-means clustering identified three distinct physiological states based on HRV features, such as the high-frequency band power and the root mean square of successive differences between normal heartbeats, suggesting patterns that may reflect stress levels. In the second phase, integrating the cluster labels obtained from the first phase together with HRV features into machine learning models for fall risk classification, we found that Gradient Boosting performed the best, achieving an accuracy of 95.45%, a precision of 93.10% and a recall of 85.71%. This study demonstrates the feasibility of using the heart rate variability and machine learning to monitor physiological responses associated with stress and fall risks. By highlighting this potential biomarker of autonomic health, the findings contribute to developing real-time monitoring systems that could support fall prevention efforts in everyday settings for older adults. Full article

The escalating complexity of cyber threats, coupled with the rapid evolution of digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores the transformative role of LLMs in addressing critical challenges in cybersecurity. With the rapid evolution of digital landscapes and the increasing sophistication of cyber threats, traditional security mechanisms often fall short in detecting, mitigating, and responding to complex risks. LLMs, such as GPT, BERT, and PaLM, demonstrate unparalleled capabilities in natural language processing, enabling them to parse vast datasets, identify vulnerabilities, and automate threat detection. Their applications extend to phishing detection, malware analysis, drafting security policies, and even incident response. By leveraging advanced features like context awareness and real-time adaptability, LLMs enhance organizational resilience against cyberattacks while also facilitating more informed decision-making. However, deploying LLMs in cybersecurity is not without challenges, including issues of interpretability, scalability, ethical concerns, and susceptibility to adversarial attacks. This review critically examines the foundational elements, real-world applications, and limitations of LLMs in cybersecurity while also highlighting key advancements in their integration into security frameworks. Through detailed analysis and case studies, this paper identifies emerging trends and proposes future research directions, such as improving robustness, addressing privacy concerns, and automating incident management. The study concludes by emphasizing the potential of LLMs to redefine cybersecurity, driving innovation and enhancing digital security ecosystems. Full article

Foot pain represents one of the most common symptoms in lower limb issues, especially in elderly individuals. This condition, often associated with other pathologies, increases the risk of falling. To better understand the risk of falls, it is essential to assess patients’ postural stability. In this pilot study, we aimed to set a protocol to prevent the falling risk. We propose the use of inertial sensors (IMUs) to detect even minimal body oscillations in a non-invasive, rapid, and cost-effective way. We have analyzed a sample of 35 patients (age = 58 ± 14 years, female = 20/male = 15) to investigate the total range of body sway in the anteroposterior (AP) and mediolateral (ML) directions during static balance in relation to their age and BMI. The analysis of the collected parameters (sway area, sway pathAP, and sway pathML) has showed a lower stability at t1, at the time of orthosis application, with respect to the previous condition, implied by the necessary period of adaptation to the new plantar device. In fact, the postural parameters have visibly improved at 30 days (t2). Comparing the results obtained in the different postural exercises, we have obtained significant differences between the natural standing position with eyes open and the others. According to these results, we can suppose that using inertial sensors associated to postural exercise is the best way to assess a patient’s postural stability and that the progressive improvements may be more marked over a longer period, such as six months (t3). Full article

In areas where there is high humidity and freezing rain, there is a tendency of blade icing on wind turbines. It results in energy dissipation and mechanical abrasion and also creates a safety concern due to the risk of having falling ice. Real-time online detection of icing is crucial in the enhancement of power generation efficiency and in the safety of wind turbines. The current methods of icing detection that use ultrasound, optics, vibration, and electromagnetics are already studied. But these methods have their drawbacks, including small detection ranges, low accuracy, large size, and challenges in distributed installation, making it hard to capture the real-time dynamics of the icing and de-icing processes on the wind turbine blades. To this end, this paper presents a new blade surface icing detection technique using microstrip lines. This approach uses the impact of icing state and thickness on the effective dielectric constant of the microstrip line surface. This paper presents the analysis of time-domain features of microwave signals, which facilitates the identification of both the icing state and the corresponding thickness. Simulation and experimental measurement of linear and S-shaped microstrip sensors are used in this research in order to compare the response of the sensors to the variation in the thickness of the icing layer. It is seen that for icing thickness ranging from 0 mm to 6 mm, the imaginary part of the S21 parameter of the S-shaped microstrip line has a more significant change than that of the linear microstrip line. The above experiments also confirm that the phase shift value of the S-shaped microstrip line is always higher than that of the linear microstrip line for the same variation of icing thickness, which proves that the S-shaped microstrip line is more sensitive than the linear one. Also, it was possible to establish the relationship between the phase shift values and icing thickness, which makes it possible to predict the icing thickness. The developed microwave microstrip detection technology is intended for usage in the wind turbine blade icing and similar surface detection areas. This method saves the size and thickness of icing sensors, which makes it possible to conduct measurements at various points. This is especially beneficial for usage in wind turbine blades and can be further applied in aerospace, automotive, and construction, especially the bridges. Full article