Search Results (4,497)

Background: Childhood undernutrition and overnutrition continue to be major public health challenges in South Africa. There is limited evidence on how socio-economic factors and dietary behaviors influence nutritional outcomes among school-aged children, particularly in rural areas such as Thulamela Municipality. Objective: This study aimed to examine the socio-demographic determinants, dietary patterns, and nutritional status among school-aged children in Thulamela Municipality, Limpopo Province, South Africa. Methods: A cross-sectional survey was conducted with 347 children aged 8–12 years. Simple random sampling was used to select eight villages from a total of 227 within the municipality. A snowball sampling method was used to recruit eligible children. Data on socio-demographic characteristics, including the child’s sex, parental education level, marital status, and employment status, were collected. Additionally, their dietary habits and meal frequency patterns were collected using structured questionnaires. Anthropometric measurements including height, weight, and BMI-for-age were obtained following WHO growth standards. Associations between variables were assessed using chi-square tests, with p-values < 0.05 considered statistically significant. Results: The prevalence of severe and moderate stunting was 20.5% and 21.0%, respectively. Overweight conditions and obesity affected 32.6% and 16.2% of participants, respectively. Parental education (p = 0.027), marital status (p = 0.001), and household income (p = 0.043) showed significant associations with height-for-age and BMI-for-age Z-scores. Additionally, regular breakfast consumption and the frequent intake of vegetables and dairy products were positively associated with improved nutritional outcomes (p < 0.05). Conclusions: The nutritional profile of school-aged children in Thulamela Municipality reflects a double burden of malnutrition, with concurrent high rates of stunting, overweight conditions, and obesity. Interventions that promote balanced diets and address socio-economic disparities are crucial for improving child growth and overall health. Socio-economic factors, including parental education, marital status, and household income, were significantly associated with children’s height-for-age and BMI-for-age. Furthermore, the regular consumption of breakfast, vegetables, and dairy products was associated with better nutritional outcomes, highlighting the influence of both dietary behaviors and socio-demographic determinants on child growth and health. Implementing nutrition education programs within schools that emphasize the value of balanced diets and highlighting the significance of eating breakfast regularly and incorporating vegetables and dairy products into daily meals is important. These programs should include both children and their caregivers to support regular healthy eating behaviors at home and in school. Additionally, schools should carry out regular growth monitoring and nutritional assessments to identify early indications of undernutrition or overnutrition, enabling prompt referrals and interventions for children who may be at risk. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

The design of smart skin with lightweight requirements utilizes high-performance composite materials, resulting in thin structural characteristics. When subjected to complex aerodynamic loads, the smart skin structure experiences warping deformation, which significantly impacts both flight efficiency and structural integrity. However, this deformation behavior has been largely overlooked in current shape sensing methods embedded within the structural health monitoring (SHM) systems of smart skin, leading to insufficient monitoring capabilities. To address this issue, this paper proposes a novel shape sensing methodology for the real-time monitoring of warping deformation in smart skin. Initially, the structural displacement field of the smart skin and the warping function are mathematically defined, incorporating constitutive relations and considering the influence of material parameters on sectional strains. Subsequently, the inverse finite element method (iFEM) is employed to establish a shape sensing model. The interpolation function and the actual sectional strains, derived from discrete strain measurements, are calculated based on the current constitutive equations. Finally, to validate the accuracy of the proposed iFEM for monitoring warping deformation, numerical tests are conducted on curved skin structures. The results indicate that the proposed methodology enhances reconstruction capability, with a 10% improvement in accuracy compared to traditional iFEM methods. Consequently, the shape sensing algorithm can be seamlessly integrated into the SHM system of smart skin to ensure the predicted performance. Full article

Ancient walnut trees (Juglans regia L.), revered as “cultural heritage in motion,” have coexisted harmoniously with dense clusters of Tibetan traditional villages for centuries. However, accelerating climate change and expanding human activities along the middle reaches of the Yarlung Tsangpo River have increasingly threatened their survival. To quantitatively evaluate the health of these ancient trees and identify the underlying driving mechanisms, this study developed a remote sensing-enhanced Structural Equation Model (SEM) that integrated satellite-derived ecological indices, land-use intensity, and field-measured morphological and physiological indicators. A total of 135 ancient walnut trees from villages such as Gamai in Jiacha County, Tibet, were examined. Key findings: (1) The SEM demonstrated an excellent model–data fit (Minimum Discrepancy Divided by Degrees of Freedom (CMIN/DF) = 1.372, Root Mean Square Error of Approximation (RMSEA) = 0.053, Tucker–Lewis Index (TLI) = 0.956, and Comparative Fit Index (CFI) = 0.962), confirming its robustness. (2) Among the latent variables, overall condition exerted the strongest influence (weight = 0.360), whereas foliage condition contributed least (0.289). (3) Approximately 35.56% of trees were healthy or sub-healthy, while 61.48% showed varying levels of decline. (4) Tree health was jointly shaped by intrinsic and extrinsic factors, with intrinsic drivers exhibiting stronger explanatory power. Externally, human disturbance negatively affected health, whereas ecological quality was positively associated. These results highlight the effectiveness of integrating remote sensing and SEM for ancient tree assessment and underscore the urgent need for long-term monitoring and adaptive conservation strategies to enhance ecological resilience. Full article

This study presents a wide-linear-range flexible pressure sensor based on a gradient non-uniform porous structure. Through co-optimization of material composition and structural parameters, the sensor integrates high sensitivity, a broad linear response range, and excellent stability. The sensing layer is fabricated using a PVC/CNT composite slurry, with interdigital silver electrodes screen-printed on a PET substrate. A porous architecture is constructed via solution blending and a template method. Innovatively, orthogonal experiments were employed to optimize the conductive filler concentration and porosity. A mixed sugar template comprising particles of 50–75 μm and 125–150 μm was introduced to form a gradient non-uniform porous structure, effectively expanding the linear response range. Experimental results demonstrate that the sensor exhibits outstanding linearity (R² > 0.99) and high sensitivity (5.57 kPa⁻¹) over a broad pressure range of 0–120 kPa. It also shows a dynamic response speed of 50 ms, cyclic stability exceeding 500 cycles, and signal fluctuation of less than 5%. Scanning electron microscopy (SEM) analysis reveals the synergistic mechanism of the non-uniform pores, confirming the effectiveness of this design in reconciling the trade-off between sensitivity and linear range. This study offers new insights into the performance optimization of flexible pressure sensors and demonstrates significant potential for applications in health monitoring and electronic skin (E-skin). Full article

Crack type classification in concrete structures is essential for assessing structural integrity, yet traditional visual inspections and RA–AF parameter-based Acoustic Emission (AE) methods suffer from subjectivity and limited ability to capture temporal signal dependencies. This study proposes TemporalAE-Net, a self-attention-based machine learning framework designed to classify tensile and shear cracks while explicitly incorporating the temporal evolution of AE signals. AE data were collected from axial tension tests, shear-failure tests, and four-point bending tests on reinforced concrete beams, and a sliding-window reconstruction method was used to transform sequential AE signals into two-dimensional temporal matrices. TemporalAE-Net integrates one-dimensional convolution for local feature extraction and multi-head self-attention for global temporal correlation learning, followed by multilayer perceptron classification. The proposed model achieved an accuracy of 99.72%, outperforming both its ablated variants without convolutional or attention modules and conventional time-series architectures. Generalization tests on 12 unseen specimens yielded 100% correct classifications, and predictions for reinforced concrete beams closely matched established crack-evolution patterns, with shear cracks detected approximately 15 s prior to visual observation. These results demonstrate that TemporalAE-Net effectively captures temporal dependencies in AE signals. Moreover, it provides accurate and efficient tensile–shear crack identification, making it suitable for real-time structural health monitoring applications. Full article

Background: Rhythmic sensory stimulation, including structured musical interventions, has gained renewed interest as a non-pharmacological strategy that may modulate cortical excitability and network stability in focal epilepsy. Although several small studies have reported changes in seizure frequency or epileptiform activity during rhythmic or music exposure, the underlying mechanisms and translational relevance remain insufficiently synthesized. Objective: This narrative review summarizes clinical evidence on music-based and rhythmic sensory interventions in focal epilepsy, outlines plausible neurophysiological mechanisms related to neural entrainment and large-scale network regulation, and discusses emerging opportunities for digital delivery of rhythmic protocols in everyday self-management. Methods: A structured search of recent clinical, neurophysiological, and rehabilitation literature was performed with emphasis on rhythmic auditory, tactile, and multimodal stimulation in epilepsy or related conditions. Additional theoretical and translational sources addressing oscillatory dynamics, entrainment, timing networks, and patient-centered digital tools were reviewed to establish a mechanistic framework. Results: Existing studies—although limited by small cohorts and heterogeneous methodology—suggest that certain rhythmic structures, including specific musical compositions, may transiently modulate cortical synchronization, reduce epileptiform discharges, or alleviate seizure-related symptoms in selected patients. Evidence from neurologic music therapy and rhythmic stimulation in other neurological disorders further supports the concept that externally delivered rhythms can influence timing networks, attentional control, and interhemispheric coordination. Advances in mobile health platforms enable structured rhythmic exercises to be delivered and monitored in real-world settings. Conclusions: Music-based and rhythmic sensory interventions represent a promising but underexplored adjunctive approach for focal epilepsy. Their effectiveness likely depends on individual network characteristics and on the structure of the applied rhythm. Digital integration may enhance personalization and adherence. Rigorous clinical trials and mechanistic studies are required to define optimal parameters, identify responders, and clarify the role of rhythmic stimulation within modern epilepsy care. Full article

With the growing use of 3D-printed polymers in structural applications, understanding their damage response under impact is critical for reliability and safety. This study investigates the impact response and damage progression in Fused Deposition Modelling (FDM)-printed Polylactic Acid (PLA) plates with varying infill densities (40%, 60%, and 100%) using a combination of scanning laser Doppler vibrometry (SLDV) and electromechanical impedance (EMI) techniques. Progressive impacts were applied in four stages, and damage was evaluated through wave attenuation, impedance deviation, and phase distortion metrics. Results show that lower infill densities exhibit more severe degradation, with increased damping and poor wave transmission, while 100% infill demonstrates higher damage resistance and better detectability. The findings underscore the importance of infill design in optimizing mechanical performance and structural health monitoring in additively manufactured components. Full article

The health and operational continuity of emergency responders are fundamental pillars of sustainable and resilient disaster management systems. These personnel operate in high-risk environments, exposed to intense physical, environmental, and psychological stress. This makes it crucial to monitor their health to safeguard their well-being and performance. Traditional methods, which rely on intermittent, voice-based check-ins, are reactive and create a dangerous information gap regarding a responder’s real-time health and safety. To address this sustainability challenge, the convergence of the Internet of Things (IoT) and wearable biosensors presents a transformative opportunity to shift from reactive to proactive safety monitoring, enabling the continuous capture of high-resolution physiological and environmental data. However, realizing a field-deployable system is a complex “system-of-systems” challenge. This review contributes to the field of sustainable emergency management by analyzing the complete technological chain required to build such a solution, structured along the data workflow from acquisition to action. It examines: (1) foundational health sensing technologies for bioelectrical, biophysical, and biochemical signals; (2) powering strategies, including low-power design and self-powering systems via energy harvesting; (3) ad hoc communication networks (terrestrial, aerial, and space-based) essential for infrastructure-denied disaster zones; (4) data processing architectures, comparing edge, fog, and cloud computing for real-time analytics; and (5) visualization tools, such as augmented reality (AR) and heads-up displays (HUDs), for decision support. The review synthesizes these components by discussing their integrated application in scenarios like firefighting and urban search and rescue. It concludes that a robust system depends not on a single component but on the seamless integration of this entire technological chain, and highlights future research directions crucial for quantifying and maximizing its impact on sustainable development goals (SDGs 3, 9, and 11) related to health, sustainable cities, and resilient infrastructure. Full article

Monitoring fish skin health is essential in aquaculture, where scale loss serves as a critical indicator of fish health and welfare. However, automatic detection of scale loss regions remains challenging due to factors such as uneven underwater illumination, water turbidity, and complex background conditions. To address this issue, we constructed a scale loss dataset comprising approximately 2750 images captured under both clear above-water and complex underwater conditions, featuring over 7200 annotated targets. Various image enhancement techniques were evaluated, and the Clarity method was selected for preprocessing underwater samples to enhance feature representation. Based on the YOLOv8m architecture, we replaced the original FPN + PAN structure with a weighted bidirectional feature pyramid network to improve multi-scale feature fusion. A convolutional block attention module was incorporated into the output layers to highlight scale loss features in both channel and spatial dimensions. Additionally, a two-stage transfer learning strategy was employed, involving pretraining the model on above water data and subsequently fine-tuning it on a limited set of underwater samples to mitigate the effects of domain shift. Experimental results demonstrate that the proposed method achieves a mAP50 of 96.81%, a 5.98 percentage point improvement over the baseline YOLOv8m, with Precision and Recall increased by 10.14% and 8.70%, respectively. This approach reduces false positives and false negatives, showing excellent detection accuracy and robustness in complex underwater environments, offering a practical and effective approach for early fish disease monitoring in aquaculture. Full article

Gaseous emissions from livestock facilities pose environmental and health concerns. Monitoring pollutant gases is essential to mitigate impact and enhance the sustainability of livestock systems. Emerging Artificial Intelligence (AI) technologies—particularly Artificial Neural Networks (ANNs)—offer advanced tools to address these challenges by improving livestock monitoring and management. Following PRISMA guidelines, 18 studies published between 2007 and 2024 were selected from Web of Science^® and Scopus^®. Most research was conducted in Europe (55%), primarily focusing on cattle and swine. Among gases, ammonia (NH₃) was predicted in 50% of studies and methane (CH₄) in 35%. The most common ANN architecture was the Multilayer Perceptron (MLP), trained mainly with backpropagation algorithms and validated using the Root Mean Square Error (RMSE). The results show that ANN models consistently outperformed traditional statistical approaches, offering greater prediction accuracy. Future research should focus on identifying optimal ANN structures for precise emission prediction, accounting for environmental variability, reducing dataset bias, and combining ANN with statistical models to develop hybrid approaches that further improve livestock management and sustainability. Full article

Structural deterioration inevitably leads to defects in buildings. It is primarily caused by environmental exposure, material ageing, and long-term service conditions, whereas defects such as poor soil compaction arise from improper construction practices rather than deterioration mechanisms. Major concrete defects include missing portions such as cracking, corrosion, dents, blemishes, and spalling. Failure to identify minor issues can lead to serious problems, which become more expensive and difficult to repair, as well as poorer overall building performance. Traditional structural assessment methods, such as visual inspections and non-destructive testing are typically used for periodic condition evaluation, whereas SHM involves continuous or long-term monitoring using sensor-based systems. However, such approaches can be manual, costly, dangerous, and biased. In order to overcome these limitations, contemporary SHM systems combine traditional approaches with building information modelling (BIM) and artificial intelligence (AI). Different AI algorithms are used, including SVM, random forest, regression, and KNN for machine learning and decision trees; random forest, K-means clustering, CNN, U-Net, ResNet, FCN, VGG16, and DeepLabv3+ for deep learning. This review will survey both the traditional and novel approaches in the field of SHM and the recent advancements. Full article

In order to improve the accuracy of the deformation reconstruction method based on the Ko displacement theory, a beam deformation reconstruction method based on CEEMDAN-HT-GMM-KO is proposed in this study. The method uses the CEEMDAN method to decompose the original signal and the GMM method to identify the noise so as to complete the noise reduction of the original data. A three-dimensional (3D) laser scanner was used to verify the results of strain information reconstruction before and after noise reduction. The results show that the average relative error of strain information reconstruction results after noise reduction is 4.54%. This method can eliminate the noise in the strain information and verify the accuracy of the deformation reconstruction method based on the Ko displacement theory in the overhanging beam under the condition of pre-deformation, providing a new method for the health monitoring of large steel structures. Full article

Background: Body physique refers to body size, structure, and composition. PS is used to describe the profile of athletes in different sports. Aims: To determine body physique and body fat percentage in elite athletes using the Hattori chart and to identify the elite zone. Methods: Scoping review. The search was performed in PubMed, Google Scholar, Ovid Books, CAB eBooks, Clarivate InCites, MyiLibrary, Web of Science, Taylor & Francis Online, Core Collection, and Scopus. The search strategy was “body physique” OR “anthropometric” OR “body composition” AND “elite athlete” OR “athlete” OR “elite”. Results: Using indirect methods, elite athletes showed intermediate solid body physique (male) and lean intermediate body physique (female), and 13.6% ± 3.6% (male) and 22.3% ± 2.8 (female) body fat. Using doubly indirect methods, elite athletes showed lean intermediate body physique (male), and intermediate body physique (female), and a percentage of body fat of 13.7% ± 5.2% (male) and of 21.7% ± 4.3% (female) of body fat. Conclusions: Hattori’s chart facilitates the visualization of changes in body mass index, fat-free mass index, fat mass index, and percentage of body fat, helping personalize training, monitor composition changes, and guide nutrition programs to optimize performance and health. Full article

Type 2 diabetes mellitus (T2DM) is a growing public health challenge worldwide, disproportionately affecting populations in low- and middle-income countries (LMICs). Poor glycemic control contributes significantly to the global burden of non-communicable diseases (NCDs), increasing morbidity, mortality, and healthcare costs. Understanding behavioral and lifestyle determinants is critical for designing effective public health strategies, particularly in resource-limited settings such as Lesotho. A cross-sectional population-based study was conducted among 184 adults with T2DM attending the out-patient department of Maluti Adventist Hospital, Lesotho. Data was collected using a structured questionnaire and analyzed descriptively with SPSS 26 Variables assessed included sociodemographic, dietary practices, physical activity, behavioral risk factors and self-care knowledge. Participants were predominantly aged 45–69 years (65.2%), with an equal sex distribution. Hypertension was the most prevalent comorbidity (65.2%). Risk factor exposure was widespread, 100% consumed fewer than five daily servings of fruits/vegetables, 95.1% reported insufficient physical activity, and 88.0% had elevated blood pressure. Overall, 86.4% had three or more NCD risk factors. Knowledge levels were intermediate, with 33.2% scoring poor, 52.7% moderate, and only 14.1% good. Glycemic control was suboptimal, with 40.8% uncontrolled. This study highlights the urgent public health need to address lifestyle and behavioral determinants of poor glycemic control in Lesotho. Tailored interventions focusing on dietary education, physical activity promotion, and routine monitoring are essential to reduce NCD risks and improve outcomes. The findings have broader implications for achieving Sustainable Development Goal 3.4 on reducing premature NCD mortality in LMICs. Strengthening culturally sensitive health promotion, community-based interventions, and integrated chronic disease care models could significantly advance diabetes prevention and control in low-resource settings. Full article