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BackgroundTime-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. ObjectiveWe aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. MethodsWe applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. ResultsIn the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. ConclusionsIn a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

Diamond/Cu–Sn–Ti composites as potential super-hard abrasive products were prepared in this work by spark plasma sintering at 1223 K. The effects of Ti content on the wettability, interfacial reaction layer thickness, and wear resistance of the bulk composite changes were systematically studied. The results revealed that when the Ti content was below 5 wt%, completely uncoated and partially coated diamond grains were still observed, indicating insufficient wettability, and the interfacial reaction layer appeared to be thin. When the Ti content was 5 wt%, all diamond grains were completely coated, and a continuous and stable interfacial reaction layer composed of TiC has been generated, in which the thickness reached a peak value of 1.72 μm. However, when the Ti content exceeded 5 wt%, the formation of brittle Cu 41 Sn 11 phases was observed, which led to inferior wear resistance. The minimum weight loss was obtained at 5 wt% Ti content. Meanwhile, through FIB-TEM, confirmed that the interfacial reaction layer was mainly composed of continuous TiC layer adjacent to diamond and discontinuous TiC layer in the filler metal, and constitute the formation of a high strength metallurgical interfacial bonding layer. [ABSTRACT FROM AUTHOR]

With the intelligent development of magnetic particle inspection, the quality of magnetic indications formed at cracks is closely related to the accuracy of magnetic particle inspection image analysis results. The concentration of magnetic suspension is a key process parameter affecting the quality of magnetic indication formation. Hence, this study presents an online detection method based on machine vision for measuring magnetic suspension concentration. The method initially enhances the contrast of images of the pear-shaped measuring tube containing magnetic suspension and then extracts scale lines through feature analysis and morphological processing. A method for extracting the magnetic particle sedimentation area of magnetic suspension based on a dual-threshold segmentation algorithm is proposed. The contour filtering algorithm and pixel calibration method are used to obtain the magnetic particle concentration of the non-estimation and estimation areas based on scale line extraction, ultimately forming an online accurate detection method for magnetic suspension concentration values. Experiments were conducted to validate the method against different concentrations, turbidity levels, tilting angles of the pear-shaped measuring tube, and ambient brightness. The results show that the error in magnetic suspension concentration detection based on this method is within 5%. This has certain reference value for the stable control of magnetic suspension concentration and for enhancing the reliability of intelligent decision-making results in magnetic particle inspection. [ABSTRACT FROM AUTHOR]

Compressive performances of composite structures can be significantly decreased due to the lightning strike damage, so the structures with lightning frighten must be considered lightning protection design. In this study, compressive experiments after lightning strikes were conducted on non‐protected, 12‐layer interlaminar carbon nanotube film (CNF) protected, and traditional surface silver coating (TSSC)‐protected composite laminates. A numerical analysis procedure was established, incorporating a lightning strike ablation damage simulation (LSADS) module and compressive residual strength calculation (CRSC) module. The procedure's effectiveness was verified by the experiment results. Based on this procedure, the compressive performances and the possible failure mechanism of laminates after lightning strikes were analyzed. The results show that the laminates with TSSC protection and 12‐layer interlaminar CNF protection can increase compressive residual strength compared with non‐protected laminates. The predominant compressive damages of the laminates after lightning strikes are fiber‐matrix shear damage and fiber fracture damage. Optimized one‐layer interlaminar CNF protection with a thickness of 0.36 mm can increase the laminate compressive strength but decrease the structural weight. This study offers a reference and basis for the lightning strike protection design of composite structures. Highlights: CNF in lightning strike protection of composite structures.Compressive failure mechanism of composite laminates after lightning strikes.A numerical procedure to evaluate lightning damage and compressive performances.Design of interlaminar CNF protection. [ABSTRACT FROM AUTHOR]

This study investigates the well-being of primary caregivers responsible for orphaned and vulnerable children. Well-being is defined as overall wellness, happiness, and satisfaction. Through mixed methods case studies and purposive sampling, we analyzed data from the Ziway Food for the Hungry Ethiopia program in 2017. Our explanatory analytic approach highlighted issues including resource constraints, chronic illnesses, and community challenges faced by the respondents. Nonetheless, spiritual well-being emerged as a crucial factor for their coping mechanisms. The findings underscore that critical well-being deficiencies require immediate attention. Strategies should prioritize financial and emotional support, emphasizing community capital to enhance the well-being of primary caregivers. [ABSTRACT FROM AUTHOR]

A set of polymer composite bolted T-joints with a novel configuration consisting of an internal skeleton and external skin was fabricated using a prepreg-RTM co-curing molding process. Experiments were conducted to study their mechanical properties under a bending load. A finite element model with a polymer resin area between the skin and skeleton was established and verified by the experimental results. Then, the damage propagation process and failure mechanism of the joint and the influence of three factors related to the layer characteristics of the skin and skeleton were investigated by the validated models. The results show that the bending stiffness and the yield limit load of the novel composite T-joint are 0.81 times and 1.65 times that of the 2A12 aluminum T-joint, respectively, while at only 55.4% of its weight. The damage of the joint is initiated within the resin area and leads to the degradation of the joint's bending performance. The preferred stacking sequence of the skeleton is [0/+45/90/−45]ns when primarily subjected to bending loads. The decrease in the bending performance is within 5% of the inclining angle of the skeleton, less than 12 degrees. The more 90° layers in the skin, the better the bending performance of the joints, while the more 0° layers, the poorer the bending performance. [ABSTRACT FROM AUTHOR]

One of the key elements influencing carbon emissions is changes in land use, which affects the roles of carbon sources and sinks. We calculated the carbon emissions from land use in Xinjiang, constructed a Geographically and Temporally Weighted Regression model (GTWR), and investigated the spatio-temporal evolutionary trajectory and heterogeneity of carbon emissions based on the land use data of three periods from 2000 to 2020 and the socio-economic and energy data of the same period. The results show the following: ① in Xinjiang, the area of water, forests, grasslands, and idle land declined between 2000 and 2020, while the area of construction and agricultural land increased; ② Xinjiang's land-use-related carbon emissions are rising annually. The primary driver of this expansion is the region's growing area of construction land; ③ the degree of economic development, the amount of land used for building, and the degree of urbanization are the primary factors influencing carbon emissions in Xinjiang; and ④ In Xinjiang, there is a clear spatial heterogeneity in the factors that influence carbon emissions. Additionally, different influencing factors have different effects on emissions, depending on the region and stage of development, indicating that the level of resources and the region's economic center of gravity have different effects on emissions. [ABSTRACT FROM AUTHOR]