Search Results (152)

Background/Objectives: Nutritional disorders are common conditions in older people. This study aimed to determine nutritional disorders in a Mediterranean cohort of nursing home residents without cognitive or functional impairment. Methods: A multicentre cross-sectional observational study was conducted in 10 Spanish geriatric centres. Socio-health, clinical, and laboratory data were recorded from the participants’ medical records. The Mini-Nutritional Assessment (MNA) and Global Leadership Initiative in Nutrition (GLIM) diagnostic criteria [weight loss and serum C-reactive protein (CRP)] were used. Frailty risk was assessed using the FRAIL questionnaire. Anthropometric parameters [body mass index, weight loss, triceps skinfold thickness (TSF), muscle mass circumference (MAMC), and calf-circumference] were evaluated. Body composition [hydration pattern, fat-free mass, muscle mass (MM), fat mass, and phase angle (PhA)] was measured by bioelectrical impedance analysis. Laboratory parameters, such as haemoglobin, total lymphocyte count, serum albumin, transferrin, and CRP, were recorded. Participants were classified into two groups: the disease-related malnutrition (DRM) group and the no-DRM group. Using multivariate regression analysis, predictive factors for nutritional status were tested. Results: Among 340 participants, 63.2% were over 85 years old, 28.2% were men, and the median length of stay was 24 months (range: 6–119). Nutritional risk or malnutrition, as assessed by the MNA, was present in 60.8% of the residents. DRM was diagnosed in 39.4%, and frailty risk was diagnosed in 57.6%. Older adults with DRM had significantly lower MAMC, calfcircumference, MM, and serum albumin, as well as higher CRP concentrations compared with their No-DRM counterparts (all, at least, p < 0.05). The frailty risk (OR = 3.317), MM (OR = 0.732), PhA (OR = 0.033), serum albumin (OR = 0.070), and EuroQol visual analogue scale (OR = 0.961) were risk predictors of DRM in nursing home residents. Conclusions: This study supports the importance of conducting comprehensive nutritional assessments to ensure the earliest recognition of nutrition disorders in nursing homes. Older adults with DRM had greater unintentional weight loss, inflammation, and a high risk of frailty, as well as reduced MM, compared to those without DRM. Subclinical low-grade systemic inflammation is a risk factor for DRE and functional decline in older adults living in nursing homes. The generalisation of the study results is limited to institutionalised older adults without cognitive impairment who are clinically stable and functionally independent. Full article

In the construction industry, occupational health and safety plays a critical role in preventing occupational accidents and increasing productivity. In recent years, computer vision and artificial intelligence-based systems have made significant contributions to improving these processes through automatic detection and tracking of objects. The aim of this study was to fine-tune object detection models and integrate them with Large Language Models for (i). accurate detection of personal protective equipment (PPE) by specifically focusing on helmets and (ii). providing real-time recommendations based on the detections for supporting the use of helmets in construction sites. For achieving the first objective of the study, large YOLOv8/v11/v12 models were trained using a helmet dataset consisting of 16,867 images. The dataset was divided into two classes: “Head (No Helmet)” and “Helmet”. The model, once trained, was able to analyze an image from a construction site and detect and count the people with and without helmets. A tool with the aim of providing advice to workers in real time was developed to fulfil the second objective of the study. The developed tool provides the counts of the people based on video feeds or analyzing a series of images and provides recommendations on occupational safety (based on the detections from the video feed and images) through an OpenAI GPT-3.5-turbo Large Language Model and with a Streamlit-based GUI. The use of YOLO enables quick and accurate detections; in addition, the use of the OpenAI model API serves the exact same purpose. The combination of the YOLO model and OpenAI model API enables near-real-time responses to the user over the web. The paper elaborates on the fine tuning of the detection model with the helmet dataset and the development of the real-time advisory tool. Full article

Alzheimer’s disease (AD) is the most common form of dementia, affecting over 50 million people globally. Since 1906, efforts to understand this neurodegenerative disease and to develop effective treatments have continued to this day. Recognizing docosahexaenoic acid (DHA, 22:6n-3) as a safe, inexpensive and vital nutrient for brain health and cognitive protection due to its key role in brain development and function, this study explores novel, sustainable non-fish sources as potential dietary supplements to prevent or mitigate AD, within a blue biotechnology framework. Forty 5×FAD male mice, five weeks old, were allocated to five body weight-matched dietary groups (n = 8) and fed isocaloric diets based on AIN-93M standard chow for 6 months. Each diet, except the control feed (non-supplemented group), enclosed a modified lipid fraction supplemented with 2% of the following: (1) linseed oil (LSO, rich in alpha-linolenic acid (ALA,18:3n-3)); (2) cod liver oil (fish oil, FO, rich in both DHA and eicosapentaenoic acid (EPA, 20:5n-3)); (3) Schizochytrium sp. microalga oil (Schizo) with 40% of DHA; and (4) commercial DHASCO oil (DHASCO) with 70% of DHA. The different diets did not affect (p > 0.05) growth performance criteria (e.g., final body weight, daily feed intake, and body weight gain) suggesting no effect on the overall caloric balance or mice growth, but n-3 long-chain polyunsaturated-fatty acid (n-3 LCPUFA) supplementation significantly reduced total cholesterol (p < 0.001) and total lipids (p < 0.001). No systemic inflammation was detected in 5×FAD mice. In parallel, a beneficial modulation of lipid metabolism by DHA-enriched diets was observed, with polyunsaturated fatty acid incorporation, particularly DHA, across key metabolic tissues, such as the liver (p < 0.001) and the brain (p < 0.001). No behavioural variations were detected using an open-field test after 6 months of diet (p > 0.05). While mice fed a standard diet or LSO diet showed cognitive deficit, the incorporation of FO, Schizo or DHASCO oils into dietary routine showed promising protective effects on the working memory (p < 0.05) and the last two diets also on the recognition memory (p < 0.05) Increased neuronal count (p < 0.05), reflecting neuronal survival, was clearly observed with the fish oil diet. In turn, the number of TAU-positive cells (p < 0.05) was reduced in the Schizo diet, while β-amyloid deposition (p < 0.01) and the neuroinflammatory marker, IBA1 (p < 0.05), were decreased across all DHA-enriched diets. These promising findings open new avenues for further studies focused on the protective effects of DHA derived from sustainable and underexploited Schizochytrium sp. microalga in the prevention of AD. Full article

The influence of social media personalities has grown significantly, especially among youth audiences who spend substantial time on platforms like TikTok. The emergence and popularity of different types of social media influencers accelerated during the COVID-19 pandemic in many countries, including South Africa. In turn, this period also saw a surge in youth audiences following these influencers. This rapid growth of influencer followings among young people is largely driven by specific types of content that resonate with them, thus encouraging continued engagement. However, the benefits that these young followers gain from engaging with various influencers and the factors driving their preferences for specific influencers remain underexplored, particularly within the context of South African students within higher education. Therefore, this study explores the types of social media influencers most followed by university students at a South African University and investigates the key factors that drive their preferences. A structured online questionnaire was distributed, gathering both multiple-choice and open-ended responses from students. The data were analyzed using categorical frequency counts and thematic analysis. The data highlight how students actively turn to influencers as emotional anchors, role models, and sources of practical guidance. Their engagement reflects a deep need for connection, inspiration, and identity formation in a challenging academic and social environment. These patterns show that influencer content is not just entertainment but plays a critical developmental role. Understanding these motivations helps educators, policymakers, and brands to align better with youth values. The significance of these results lies in how influencer content is now coming in to fill the emotional, cultural, and educational gaps left by traditional systems among the young South African university students in this modern era. Full article

When people overextend their step length, it leads to an increase in vertical movement and braking force. The overextension elevates landing impacts, which may increase pain in the knees or lower back. The objective of this study was to examine the effects of soft-landing walking techniques in a 90 min, instructor-led group class for older adults. To evaluate a landing impact, an accelerometer measurement system (Descente LTD., Tokyo, Japan) was used to measure a participant 10 meter (m) of walking. Assessment outcomes included the average number of steps, step length, upward acceleration which reflects the landing impact, and survey questions. A total of 223 older adults (31 men, 192 women, mean age 74.4 ± 5.7 years) completed the walking lesson. Following the lesson, participants decreased their step lengths and reduced upward acceleration, along with an increased step count. The number of steps increased, and a positive correlation (r = 0.73, p < 0.01) was observed between the rate of change in step length and upward acceleration. Over 95% of participants gave high marks for practicality and understanding the accelerometer measurements. The information derived from this study will provide valuable insight into the effectiveness of soft-landing techniques as a promotion of a healthy walking program for older adults. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

As people age, more careful health monitoring becomes increasingly important. The article presents the development and implementation of an integrated system for monitoring the health of elderly individuals using Internet of Things (IoT) technology and a wearable bracelet to continuously collect vital data. The device integrates MAX30100 sensors for heart rate monitoring and MPU-6050 for step counting and sleep quality analysis (deep and superficial sleep). The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. The system is connected to the Azure cloud infrastructure, ensuring secure data transmission, preprocessing, and the automatic generation of alerts for prompt medical interventions. Studies conducted over two years demonstrated a sensitivity of 100% and an accuracy of 98.5%, with a tendency to generate additional alerts to avoid overlooking critical events. The results outline the importance of personalizing the analysis, adapting algorithms to individual characteristics, and the system’s potential to prevent medical complications and improve the quality of life for elderly individuals. Full article

Background/Objectives: Technological approaches for the objective, quantitative assessment of motor functions have the potential to improve the medical management of people with Parkinson’s disease (PwPD), offering more precise, data-driven insights to enhance diagnosis, monitoring, and treatment. Markerless motion capture (MMC) is a promising approach for the integration of biomechanical analysis into clinical practice. The aims of this project were to evaluate a commercially available MMC system, develop and validate a custom MMC data processing algorithm, and evaluate the effectiveness of the algorithm in discriminating fine motor performance between PwPD and healthy controls (HCs). Methods: A total of 58 PwPD and 25 HCs completed finger-tapping assessments, administered and recorded by a self-worn augmented reality headset. Fine motor performance was evaluated using the headset’s built-in hand tracking software (Native-MMC) and a custom algorithm (CART-MMC). Outcomes from each were compared against a gold-standard motion capture system (Traditional-MC) to determine the equivalence. Known-group validity was evaluated using CART-MMC. Results: A total of 82 trials were analyzed for equivalence against the Traditional-MC, and 152 trials were analyzed for known-group validity. The CART-MMC outcomes were statistically equivalent to Traditional-MC (within 5%) for tap count, frequency, amplitude, and opening velocity metrics. The Native-MMC did not meet equivalence with the Traditional-MC, deviating by an average of 24% across all outcomes. The CART-MMC captured significant differences between PwPD and HCs for tapping amplitude, amplitude variability, frequency variability, finger opening and closing velocities, and their respective variabilities, and normalized path length. Conclusions: The biomechanical data gathered using a commercially available augmented reality device and analyzed via a custom algorithm accurately characterize fine motor performance in PwPD. Full article

This study presents a novel approach to passive human counting in indoor environments using Bluetooth Low Energy (BLE) signals and deep learning. The motivation behind this research is the need for non-intrusive, privacy-preserving occupancy monitoring in sensitive indoor settings, where traditional camera-based solutions may be unsuitable. Our method leverages the deformations that BLE signals undergo when interacting with the human body, enabling occupant detection and counting without requiring wearable devices or visual tracking. We evaluated three deep neural network models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN+LSTM architecture—under both classification and regression settings. Experimental results indicate that the hybrid CNN+LSTM model outperforms the others in terms of accuracy and mean absolute error. Notably, in the regression setup, the model can generalize to occupancy values not present in the fine-tuning dataset, requiring only a few minutes of calibration data to adapt to a new environment. We believe that this approach offers a valuable solution for real-time people counting in critical environments such as laboratories, clinics, or hospitals, where preserving privacy may limit the use of camera-based systems. Overall, our method demonstrates high adaptability and robustness, making it suitable for practical deployment in diverse indoor scenarios. Full article

Background/Objectives: People living with a spinal cord injury (PwSCI) present numerous complications at a systemic level that negatively impact their physical and mental health as well as their quality of life. The purpose of this study was to describe the clinical profile of PwSCI living in nursing homes (NHs), Complex Continuing Care Systems (CCCs), home care (HC), and inpatient mental health facilities (MHs) in nine Canadian provinces and territories. Methods: We analyzed data collected with the following assessment tools: Resident Assessment Instrument (RAI) Minimum Data Set (RAI-MDS 2.0), RAI–MH, RAI-HC, Cognitive Performance Scale, Activities of Daily Living (ADL) Hierarchy Scale and impairments in instrumental ADLs (IADLs), Pain Scale, Changes in Health, End-Stage Disease, Signs, and Symptoms (CHESS) Scale, Depression Rating Scale, and Deafblind Severity Index (DBSI). We reported counts (n) and percentages (%) and performed Chi-square tests with a Bonferroni correction to determine the statistical significance of the differences in frequencies within and between care settings. Results: We identified 13,136 PwSCI, predominantly males and younger than comparison groups. PwSCI presented fewer comorbidities but reported higher pain than comparison groups. Almost all of the PwSCI in NHs (99.4%) and CCCs (98.9%) needed assistance to perform ADLs. Conclusions: The prevalence of comorbidities and impairments following SCI varies based on the clinical setting. The present clinical profile of PwSCI will inform interventions to improve health of PwSCI across the continuum of care. Full article

Many people admire the Japanese language and culture, but mastering the language’s writing system, particularly handwritten kanji, presents a significant challenge. Furthermore, translating historical manuscripts containing archaic or rare kanji requires specialized expertise. To address this, we designed a new model for handwritten kanji recognition based on the concept of cross-language transfer learning using a Preact ResNet-18 architecture. The model was pretrained in a Chinese dataset and subsequently fine-tuned in a Japanese dataset. We also adapted and evaluated two fine-tuning strategies: unfreezing only the last layer and unfreezing all the layers during fine-tuning. During the implementation of our training algorithms, we trained a model with the CASIA-HWDB dataset with handwritten Chinese characters and used its weights to initialize models that were fine-tuned with a Kuzushiji-Kanji dataset that consists of Japanese handwritten kanji. We investigated the effectiveness of the developed model when solving a multiclass classification task for three subsets with the one hundred fifty, two hundred, and three hundred most-sampled classes and showed an improvement in the recognition accuracy and an enhancement in a number of recognizable kanji with the proposed model compared to those of the existing methods. Our best model achieved 97.94% accuracy for 150 kanji, exceeding the previous SOTA result by 1.51%, while our best model for 300 kanji achieved 97.62% accuracy (exceeding the 150-kanji SOTA accuracy by 1.19% while doubling the class count). This confirms the effectiveness of our proposed model and establishes new benchmarks in handwritten kanji recognition, both in terms of accuracy and the number of recognizable kanji. Full article

Poor PQ is a partial case of power system impact on society and the environment. Although the significance of good PQ is generally understood, the topic has not yet been sufficiently explored in the scientific literature. Firstly, this paper discusses the role of PQ in sustainable development by distinguishing economic, environmental, and social parts, including the existing PQ impact assessment methods. PQ problems must be studied through such prisms as financial losses of industrial companies, damage to end-use equipment, natural phenomena, interaction with animals, and social issues related to law, people’s well-being, health and safety. Secondly, this paper presents the results of the survey of Lithuanian industrial companies, which focuses on the assessment of industrial equipment immunity to both voltage sags and supply interruptions, as well as a unique methodology based on expert assessment, IEEE Std 1564-2014 and EN 50160:2010 voltage sag tables, matrix theory, a statistical hypothesis test, and convolution-based sample comparison that was developed for this purpose. The survey was carried out during the PQ monitoring campaign in the Lithuanian DSO grid, and is one of the few PQ surveys presented in the scientific literature. After counting the votes and introducing the rating system (with and without weights), the samples are compared both qualitatively and quantitatively in order to determine whether the PQ impact on various end-use equipment is similar or not. Full article

This pilot study aimed to evaluate the usability of a new, feedback-based dexterity training system in people with Parkinson’s disease (PwPD) and healthy adults. Seven PwPD and seven healthy adults participated in the study. The System Usability Scale (SUS) and the Post-Study System Usability Questionnaire Version 3 (PSSUQ) were used to assess usability. Additionally, the feedback shown as a counter, detected through newly developed algorithms, was evaluated by comparing the device-detected repetitions during six exercises to those counted by a supervisor. High median SUS scores of 92.5 were obtained in both PwPD (IQR = 81.25–98.75) and healthy adults (IQR = 87.5–93.75, maximum score 100, minimum score 0). Similarly, high PSSUQ median scores were achieved after the session (1.14, IQR = 1.00–1.33, PD; 1.08, IQR = 1.00–1.58, healthy adults, maximum score 1, minimum score 7). PwPD completed 648 repetitions, with 551 (85%) correctly recognized by the system. For healthy adults, 883 out of 913 (97%) repetitions were classified as right. The present study showed high usability and high perceived user satisfaction for the new training system in all study participants. The system effectively detects exercise repetition rates but requires further refinement to enhance accuracy for specific pinch grip exercises. Full article

Background: Schistosomiasis impacts over 230 million people globally, with 251.4 million needing treatment. The disease causes intestinal and urinary symptoms, such as hepatic fibrosis, hepatomegaly, splenomegaly, and bladder calcifications. While praziquantel (PZQ) is the primary treatment, its effectiveness against juvenile stages (schistosomula) is limited, highlighting the need for new therapeutic agents, repurposed drugs, or reformulated compounds. Existing microscopy methods for assessing schistosomula viability are labor-intensive, subjective, and time-consuming. Methods: An artificial intelligence (AI)-assisted culture system using YOLOv5 was developed to evaluate compounds against Schistosoma mansoni schistosomula. The AI model, based on object detection, was trained on 4390 images distinguishing between healthy and damaged schistosomula. The system was externally validated against human counters, and a small-scale assay was performed to demonstrate its potential for larger-scale assays in the future. Results: The AI model exhibited high accuracy, achieving a mean average precision (mAP) of 0.966 (96.6%) and effectively differentiating between healthy and damaged schistosomula. External validation demonstrated significantly improved accuracy and counting time compared to human counters. A small-scale assay was conducted to validate the system, identifying 28 potential compounds with schistosomicidal activity against schistosomula in vitro and providing their preliminary LC₅₀ values. Conclusions: This AI-powered method significantly improves accuracy and time efficiency compared to traditional microscopy. It enables the evaluation of compounds for potential schistosomiasis drugs without the need for dyes or specialized equipment, facilitating more efficient drug assessment. Full article

Background: Older people are more likely to have poor nutrition and low muscle mass, which leads to poor physical performance and anemia, resulting in a poor quality of life and risks to mobility and mortality. Furthermore, malnutrition may, in part, raise the level of inflammatory biomarkers as well as muscle catabolism. Moreover, a range of indices related to systemic inflammation, obtained from routine complete blood count (CBC) tests, have been applied to inflammation markers. However, these biomarkers remain insufficiently addressed in the evidence supporting the presence of sarcopenia and malnutrition. This study aimed to explore sarcopenia in terms of malnutrition, anemia, and inflammation among Thai community-dwelling older people. Methods: This study enrolled community-dwelling older people aged 60 years and above. All participants were requested to complete a questionnaire assessing for sarcopenia (SARC-F) and nutritional status using the mini nutritional assessment (MNA). In addition, blood samples were obtained for the CBC test. Logistic regression analysis explored the risk of sarcopenia, CBC, and malnutrition status. Results: Of 126 older people (aged 62–88 years) enrolled, 12 individuals (9.52%) had sarcopenia. Furthermore, 34.9% and 5.56% of the participants were demonstrated to have anemia and malnutrition, respectively. Nutrition status was positively associated with hemoglobin levels (r = 0.241, p = 0.007) and negatively related to SARC-F scores (r = −0.190, p = 0.034). Older people with anemia show an increased risk of malnutrition at an odds ratio (OR) of 3.375. Moreover, individuals with anemia were at a higher risk of developing sarcopenia (OR 4.982) than those with no anemia. However, individuals with a high level of inflammatory markers, e.g., a high systemic inflammatory response index (SIRI) and monocyte-to-lymphocyte ratio (MLR), had a higher risk of sarcopenia than those with low SIRI and MLR values. The systemic immune–inflammation index (SII) and platelet-to-lymphocyte ratio (PLR) were also positively associated with SARC-F scores. Conclusions: The association between sarcopenia, malnutrition status, and anemia might overlap in clinical manifestation. In addition, future research directions regarding the utility of routine CBC testing should focus on sarcopenia and malnutrition status. Full article