Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs. Full article

In this paper, an absorber with multi-band, tunable, high Q, and high sensitivity, based on terahertz periodic two-dimensional patterned graphene surface plasmon resonance (SPR), is proposed. The absorber consists of a bottom metal film separated by a periodically patterned graphene metamaterial structure and a SiO₂ dielectric layer, where the patterned graphene layer is etched by “+” and “L” shapes and circles. It has simple structural features that can greatly simplify the fabrication process. We have analyzed the optical properties of a graphene surface plasmon perfect metamaterial absorber based on graphene in the terahertz region using the finite-difference method in time domain (FDTD). The results show that the absorber device exhibits three perfect absorption peaks in the terahertz bands of f1 = 1.55 THz, f2 = 4.19 THz, and f3 = 6.92 THz, with absorption rates as high as 98.70%, 99.63%, and 99.42%, respectively. By discussing the effects of parameters such as the geometrical dimensions of patterned graphene metamaterial structure “+” width W1, “L” width W2, circular width R, and the thickness of the dielectric layer on the absorption performance of absorber, as well as investigating the chemical potential and relaxation time of patterned-layer graphene material, it was found that the amplitude of the absorption peaks and the frequency of resonance of absorber devices can be dynamically adjusted. Finally, we simulated the spectra as the surrounding refractive index n varied to better evaluate the sensing performance of the structure, yielding structural sensitivities up to 382 GHz/RIU. Based on this study, we find that the results of our research will open new doors for the use of multi-band, tunable, polarization-independent metamaterial absorbers that are insensitive to large-angle oblique incidence. Full article

As society continues to age, people are becoming more concerned about their health care. This has led to an increase in the demand for medical products in urban areas, emphasizing the need for regular and prompt deliveries. However, the existing logistics centers are located in the suburbs of Seoul, a metropolitan city, which makes it challenging to ensure timely delivery. To address this issue, this study aims to establish new logistics centers in urban areas, particularly in Seoul, while minimizing CO₂ emissions from delivery vehicles in alignment with sustainability efforts. The scientific gap addressed in and the novelty of this paper is that the input parameters are prepared based on actual data from a medical company in Korea to reflect reality, and the mathematical model-based optimization technique is applied to determine the optimal location of a new logistics center. The genetic algorithm is developed to solve the proposed mathematical model by deriving optimal or near-optimal solutions. Furthermore, the numerical experiment examined the impact of establishing a new logistics center in one of the candidate areas of local governments in Seoul by considering environmental impact. As a result, the new logistics network can reduce CO₂ emissions by approximately 66.74% compared to the existing logistics network. Full article

In-vehicle networks (IVNs) are networks that allow communication between different electronic components in a vehicle, such as infotainment systems, sensors, and control units. As these networks become more complex and interconnected, they become more vulnerable to cyber-attacks that can compromise safety and privacy. Anomaly detection is an important tool for detecting potential threats and preventing cyber-attacks in IVNs. The proposed machine learning-based anomaly detection technique uses deep learning and feature engineering to identify anomalous behavior in real-time. Feature engineering involves selecting and extracting relevant features from the data that are useful for detecting anomalies. Deep learning involves using neural networks to learn complex patterns and relationships in the data. Our experiments show that the proposed technique have achieved high accuracy in detecting anomalies and outperforms existing state-of-the-art methods. This technique can be used to enhance the security of IVNs and prevent cyber-attacks that can have serious consequences for drivers and passengers. Full article

Pea plants (Pisum sativum L.) can perceive the presence of potential supports in the environment and flexibly adapt their behavior to clasp them. How pea plants control and perfect this behavior during growth remains unexplored. Here, we attempt to fill this gap by studying the movement of the apex and the tendrils at different leaves using three-dimensional (3D) kinematical analysis. We hypothesized that plants accumulate information and resources through the circumnutation movements of each leaf. Information generates the kinematical coordinates for the final launch towards the potential support. Results suggest that developing a functional approach to grasp movement may involve an interactive trial and error process based on continuous cross-talk across leaves. This internal communication provides evidence that plants adopt plastic responses in a way that optimally corresponds to support search scenarios. Full article

Many communities are working to enhance the sustainability of their physical, economic, and social systems. While economic and physical systems are routinely measured (e.g., money and energy), psychological and behavioral elements of social systems (norms, attitudes, and individual behavior) are seldom tracked. The objective of this research was to evaluate a potentially scalable approach to measure the impact of sustainability initiatives on these variables in a community engaged in holistic sustainability programming. Online survey data were collected in 2012 (N = 155) and 2016 (N = 137), measuring pro-environmental thought and behavior in two towns in Ohio: Oberlin, a community engaged in holistic efforts to enhance environmental sustainability; and a similar community (Berea) used as a control. Survey links were distributed via recruitment letters mailed to randomly selected community residents from a purchased mailing list. We used two (town) by two (time) between subjects’ ANOVAs to evaluate whether Oberlin saw predicted increases in sustainable thought and behavior from 2012 to 2016, compared to the control community. Despite verifiable participation in and awareness of sustainability programs in Oberlin, our survey results did not provide strong evidence that programs resulted in the desired changes in attitudes, norms, and individual behaviors. Recycling attitudes and LED bulb installation were two exceptions. We conclude that assessing the psychological and behavioral dimensions of sustainability poses particular challenges. We encountered ceiling effects and inadequate statistical power. Possibly, norms and attitudes are not easily influenced even by a holistic community-wide effort. Full article

Background/Objectives: Obesity is currently considered a public health problem in both developed and developing countries. Gender- and age-specific body mass index (BMI) growth standards or references are particularly effective in monitoring the global obesity pandemic. This study aimed to report disparities in age-, gender- and ethnic-specific statistical estimates of overweight and obesity for 2–18 years aged Pakistani children and adolescents using the World Health Organization (WHO), the Center for Disease Control (CDC) 2000 references, the International Obesity Task Force (IOTF) and Pakistani references for BMI. Methods: The study used secondary data of 10,668 pediatric population, aged 2–18 years. Demographic information like age (years), gender, city and anthropometric examinations, i.e., height (cm) and weight (kg) were used in this study. The recommended age- and gender-specific BMI cut-offs of the WHO, CDC 2000 and the IOTF references were used to classify the children sampled as overweight and obese. For the Pakistani reference, overweight and obesity were defined as BMI-for-age ≥ 85th percentile and BMI-for-age ≥ 95th percentile, respectively. Cohen’s κ statistic was used to assess the agreement between the international references and local study population references in the classification of overweight/obesity. Results: The statistical estimates (%) of the participants for overweight and obesity varied according to the reference used: WHO (7.4% and 2.2%), CDC (4.9% and 2.1%), IOTF (5.2% and 2.0%) and Pakistan (8.8% and 6.0%), respectively; suggesting higher levels of overweight and obesity prevalence when local study references are used. The Kappa statistic shows a moderate to excellent agreement (κ ≥ 0.6) among three international references when classifying child overweight and obesity and poor agreement between local references and the WHO (0.45, 0.52), CDC (0.25, 0.50) and IOTF references (0.16, 0.31), for overweight and obesity, respectively. Conclusions: The results of the study showed a visible difference in the estimates of excess body weight after applying the WHO, CDC, IOTF and local BMI references to the study population. Based on the disparity results and poor agreement between international references and the local study reference, this study recommends using local BMI references in identifying children with overweight and obesity. Full article

Participation is important for children’s quality of life (QOL). This study aimed to identify participation factors that influence QOL among Japanese children with neurodevelopmental disorders. Ninety-two Japanese parents of children with neurodevelopmental disorders participated in this study. The parents completed the parent version of the Kid- and Kiddo-KINDL health-related QOL questionnaire and the Participation and Environment Measure for Children and Youth. The data were examined using the random forest algorithm to analyze the participation factors that affected the children’s QOL. The analyses revealed that school and community environmental factors that affected participation were the most important predictors of QOL among children. As school and community environments can significantly impact the QOL of children with neurodevelopmental disorders, greater focus should be placed on participation in environmental contexts. Full article

This paper analyzes how export margins responded to an intermediate input supply shock caused by the 2020 lockdown in China. We use regression decomposition with triple and quadruple difference-in-differences models to identify causal impacts and mitigate potential heterogeneity in transaction-level customs data from the Bangladesh apparel manufacturing industry. The triple difference estimate shows that the average export value per firm–product–destination combination declined by approximately 65%, leading to a decrease in overall exports of woven apparel from Bangladesh. The input supply shock also adversely affected the subgroups of firms across various firm-level characteristics along the intensive margin. Moreover, the export market share decomposition reveals that the shock significantly affected intensive margins by decreasing incumbents’ market allocation by 9%. An equivalent increase in extensive margins led to a readjustment in the market allocation, leading to fewer market leavers and slightly more new market entrants. Our results indicate that Bangladesh’s exports mostly decreased due to the smaller quantities of products exported rather than there being fewer firms, destinations, or products involved in export trade. There were significant market share reallocations that occurred after the Chinese input supply shock. An appropriate policy stance is required for sustainable export sector growth strategies, which will enhance the country’s defense against potential future shocks and foster the achievement of sustainable development goals (SDGs) in Bangladesh. Full article

Small extracellular vesicles (sEVs) have emerged as promising therapeutic agents and drug delivery vehicles. Targeted modification of sEVs and their contents using genetic modification strategies is one of the most popular methods. This study investigated the effects of p53 fusion with arrestin domain-containing protein 1 (ARRDC1) and CD63 on the generation of sEVs, p53 loading efficiency, and therapeutic efficacy. Overexpression of either ARRDC1–p53 (ARP) or CD63–p53 (CDP) significantly elevated p53 mRNA and protein levels. The incorporation of ARRDC1 and CD63 significantly enhanced HEK293T-sEV biogenesis, evidenced by significant increases in sEV-associated proteins TSG101 and LAMP1, resulting in a boost in sEV production. Importantly, fusion with ARRDC1 or CD63 substantially increased the efficiency of loading both p53 fusion proteins and its mRNA into sEVs. sEVs equipped with ARP or CDP significantly enhanced the enrichment of p53 fusion proteins and mRNA in p53-null H1299 cells, resulting in a marked increase in apoptosis and a reduction in cell proliferation, with ARP-sEVs demonstrating greater effectiveness than CDP-sEVs. These findings underscore the enhanced functionality of ARRDC1- and CD63-modified sEVs, emphasizing the potential of genetic modifications in sEV-based therapies for targeted cancer treatment. Full article

The present paper addresses a comprehensive historical assessment of water consumption, demand, and supply in Saudi Arabia, along with future projections regarding water balance, in terms of demand and supply by source in various sectors. Being an arid region, Saudi Arabia experiences scorching heat, low precipitation, a high rate of potential evaporation, and the absence of permanent water bodies over the territory. Groundwater contributes almost 61% of total available water, while the recharge rate is negligible. However, few widyan (ephemeral streams) systems exists to satisfy water demand, which could contribute to approximately one year of domestic water consumption if managed efficiently. The study also predicts water consumption scenarios for the next three consecutive development plans, i.e., the 10th plan (2015–2019), 11th plan (2020–2024), and 12th plan (2025–2029). The analysis shows that water consumption may decline significantly in the future, if the present rate of decline continues. Scenario I, if the current rate is assumed, provides a decrease in consumption of 14.36, 12.66, and 11.15 BCM for 10th, 11th, and 12th plans, respectively. Moreover, the domestic and industrial sectors will consume more water in the future. In the same way, scenarios II and III represent a decline in total water consumption, along with that of agriculture, while domestic and industrial water usage would increase, thus improving environmental sustainability. Full article

To achieve sustainable development goals and meet the demand for clean and efficient energy utilization, it is imperative to advance the penetration of renewable energy in various sectors. Energy storage systems can mitigate the intermittent issues of renewable energy and enhance the efficiency and economic viability of existing energy facilities. Among various energy storage technologies, thermocline heat storage (THS) has garnered widespread attention from researchers due to its stability and economic advantages. Currently, there are only a few review articles focusing on THS, and there is a gap in the literature regarding the optimization design of THS systems. Therefore, this paper provides a comprehensive review of the recent research progress in THS, elucidating its principles, thermal storage materials, applications, and optimization designs. The novelty of this work lies in the detailed classification and analysis of various optimization designs for THS, including tank shape, aspect ratio, inlet/outlet configuration, thermal energy storage materials arrangement, operating strategies, and numerical model optimization approaches. The limitations of existing research are also identified, and future perspectives are proposed, aiming to provide recommendations for THS research and contribute to the development and promotion of THS technology. Full article

As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing the evolutionary process of regional desertification. However, there are few reports on desertification monitoring based on KNDVI and feature space models. In this study, seven feature parameters, including the kernel normalized difference vegetation index (KNDVI) and Albedo, were introduced to construct different models for desertification remote-sensing monitoring. The optimal desertification remote-sensing monitoring index model was determined with the measured data; then, the spatiotemporal evolution pattern of desertification in Gulang County from 2013 to 2023 was analyzed and revealed. The main conclusions were as follows: (1) Compared with the NDVI and MSAVI, the KNDVI showed more advantages in the characterization of the desertification evolution process. (2) The point–line pattern KNDVI-Albedo remote-sensing index model had the highest monitoring accuracy, reaching 94.93%, while the point–line pattern NDVI-TGSI remote-sensing monitoring index had the lowest accuracy of 54.38%. (3) From 2013 to 2023, the overall desertification situation in Gulang County showed a trend of improvement with a pattern of “firstly aggravation and then alleviation.” Additionally, the gravity center of desertification in Gulang County first shifted to the southeast and then to the northeast, indicating that the northeast’s aggravating rate of desertification was higher than in the southwest during the period. (4) From 2013 to 2023, the area of stable desertification in Gulang County was the largest, followed by the slightly weakened zone, and the most significant transition area was that of extreme desertification to severe desertification. The research results provide important decision support for the precise monitoring and governance of regional desertification. Full article

In the present study, we have explored the involvement of Toll-like Receptor 4 (TLR4) in atrial fibrillation (AF), by using a meta-analysis of publicly available human transcriptomic data. The meta-analysis revealed 565 upregulated and 267 downregulated differentially expressed genes associated with AF. Pathway enrichment analysis highlighted a significant overrepresentation in immune-related pathways for the upregulated genes. A significant overlap between AF differentially expressed genes and TLR4-modulated genes was also identified, suggesting the potential role of TLR4 in AF-related transcriptional changes. Additionally, the analysis of other Toll-like receptors (TLRs) revealed a significant association with TLR2 and TLR3 in AF-related gene expression patterns. The examination of MYD88 and TICAM1, genes associated with TLR4 signalling pathways, indicated a significant yet nonspecific enrichment of AF differentially expressed genes. In summary, this study offers novel insights into the molecular aspects of AF, suggesting a pathophysiological role of TLR4 and other TLRs. By targeting these specific receptors, new treatments might be designed to better manage AF, offering hope for improved outcomes in affected patients. Full article

The use of deep convolutional neural networks has significantly improved the performance of super-resolution. Employing deeper networks to enhance the non-linear mapping capability from low-resolution (LR) to high-resolution (HR) images has inadvertently weakened the information flow and disrupted long-term memory. Moreover, overly deep networks are challenging to train, thus failing to exhibit the expressive capability commensurate with their depth. High-frequency and low-frequency features in images play different roles in image super-resolution. Networks based on CNNs, which should focus more on high-frequency features, treat these two types of features equally. This results in redundant computations when processing low-frequency features and causes complex and detailed parts of the reconstructed images to appear as smooth as the background. To maintain long-term memory and focus more on the restoration of image details in networks with strong representational capabilities, we propose the Frequency-Separated Attention Network (FSANet), where dense connections ensure the full utilization of multi-level features. In the Feature Extraction Module (FEM), the use of the Res ASPP Module expands the network’s receptive field without increasing its depth. To differentiate between high-frequency and low-frequency features within the network, we introduce the Feature-Separated Attention Block (FSAB). Furthermore, to enhance the quality of the restored images using heuristic features, we incorporate attention mechanisms into the Low-Frequency Attention Block (LFAB) and the High-Frequency Attention Block (HFAB) for processing low-frequency and high-frequency features, respectively. The proposed network outperforms the current state-of-the-art methods in tests on benchmark datasets. Full article

Peptide pools consist of short amino acid sequences and have proven to be versatile tools in various research areas in immunology and clinical applications. They are commercially available in many different compositions and variants. However, unlike other reagents that consist of only one or a few compounds, peptide pools are highly complex products which makes their quality control a major challenge. Quantitative peptide analysis usually requires sophisticated methods, in most cases isotope-labeled standards and reference materials. Usually, this would be prohibitively laborious and expensive. Therefore, an approach is needed to provide a practical and feasible method for quality control of peptide pools. With insufficient quality control, the use of such products could lead to incorrect experimental results, worsening the well-known reproducibility crisis in the biomedical sciences. Here we propose the use of ultra-high performance liquid chromatography (UHPLC) with two detectors, a standard UV detector at 214 nm for quantitative analysis and a high-resolution mass spectrometer (HRMS) for identity confirmation. To be cost-efficient and fast, quantification and identification are performed in one chromatographic run. An optimized protocol is shown, and different peak integration methods are compared and discussed. This work was performed using a peptide pool known as CEF advanced, which consists of 32 peptides derived from cytomegalovirus (CMV), Epstein–Barr virus (EBV) and influenza virus, ranging from 8 to 12 amino acids in length. Full article

Mounting evidence indicates a complex link between circulating saturated fatty acids (SFAs) and cardiovascular disease (CVD) risk factors, but research on erythrocyte membrane SFA associations with metabolic markers remains limited. Our study sought to investigate the correlations between erythrocyte membrane SFAs and key metabolic markers within glycemic and lipid metabolism in a Chinese population of 798 residents aged 41 to 71 from Guangzhou. Using gas chromatography–mass spectrometry, we assessed the erythrocyte membrane saturated fatty acid profile and performed multiple linear regression to evaluate the relationship between different SFA subtypes and metabolic markers. Our findings revealed that the odd-chain SFA group (C15:0 + C17:0) exhibited negative associations with fasting blood glucose (FBG), homeostatic model assessment for insulin resistance (HOMA-IR), and triglycerides (TG). Conversely, the very-long-chain SFA group (C20:0 + C22:0 + C23:0 + C24:0) exhibited positive associations with fasting insulins (FINS), HOMA-IR, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C). Furthermore, there was no evidence supporting an association between the even-chain group (C14:0 + C16:0+ C18:0) and metabolic markers. Our findings suggest that different subtypes of SFAs have diverse effects on glycemic and lipid metabolic markers, with odd-chain SFAs associated with a lower metabolic risk. However, the results concerning the correlations between even-chain SFAs and very-long-chain SFAs with markers of glycemic and lipid metabolism pathways are confusing, highlighting the necessity for further exploration and investigation. Full article

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system’s ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles’ operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model’s performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms. Full article

Astrocytes play a pivotal role in maintaining brain homeostasis. Recent research has highlighted the significance of palmitic acid (PA) in triggering pro-inflammatory pathways contributing to neurotoxicity. Furthermore, Genomic-scale metabolic models and control theory have revealed that metabolic switches (MSs) are metabolic pathway regulators by potentially exacerbating neurotoxicity, thereby offering promising therapeutic targets. Herein, we characterized these enzymatic MSs in silico as potential therapeutic targets, employing protein–protein and drug–protein interaction networks alongside structural characterization techniques. Our findings indicate that five MSs (P00558, P04406, Q08426, P09110, and O76062) were functionally linked to nervous system drug targets and may be indirectly regulated by specific neurological drugs, some of which exhibit polypharmacological potential (e.g., Trifluperidol, Trifluoperazine, Disulfiram, and Haloperidol). Furthermore, four MSs (P00558, P04406, Q08426, and P09110) feature ligand-binding or allosteric cavities with druggable potential. Our results advocate for a focused exploration of P00558 (phosphoglycerate kinase 1), P04406 (glyceraldehyde-3-phosphate dehydrogenase), Q08426 (peroxisomal bifunctional enzyme, enoyl-CoA hydratase, and 3-hydroxyacyl CoA dehydrogenase), P09110 (peroxisomal 3-ketoacyl-CoA thiolase), and O76062 (Delta(14)-sterol reductase) as promising targets for the development or repurposing of pharmacological compounds, which could have the potential to modulate lipotoxic-altered metabolic pathways, offering new avenues for the treatment of related human diseases such as neurological diseases. Full article

Environmental contamination by heavy metals (HMs) has emerged as a significant global issue in recent decades. Among natural substances, food-deriving polyphenols have found a valuable application in chelating therapy, partially limited by their low water solubility. Thus, three different hydroalcoholic extracts titrated in quercetin (QE), ellagic acid (EA), and curcumin (CUR) were formulated using maltodextrins as carriers, achieving a powder with a valuable water solubility (MQE 91.3 ± 1.2%, MEA 93.4 ± 2.1, and MCUR 89.3 ± 2%). Overcoming the problem of water solubility, such formulations were tested in an in vitro simulated gastrointestinal digestion experiment conducted on a water sample with standardized concentrations of the principal HMs. Our results indicate that regarding the nonessential HMs investigated (Pb, Cd, As, Sb, and Hg), MQE has been shown to be the most effective in increasing the HMs’ non-bioaccessible concentration, resulting in concentration increases in Cd of 68.3%, in As of 51.9%, in Hg of 58.9%, in Pb of 271.4, and in Sb of 111.2% (vs control, p < 0.001) in non-bioaccessible fractions. Regarding the essential HMs, MEA has shown the greatest capability to increase their intestinal bioaccessibility, resulting in +68.5%, +61.1, and +22.3% (vs control, p < 0.001) increases in Cu, Zn, and Fe, respectively. Finally, considering the strong relation between the antiradical and chelating activities, the radical scavenging potentials of the formulations was assayed in DPPH and ABTS assays. Full article

Gait analysis plays important roles in various applications such as exercise therapy, biometrics, and robot control. It can also be used to prevent and improve movement disorders and monitor health conditions. We implemented a wearable module equipped with an MPU-9250 IMU sensor, and Bluetooth modules were implemented on an Arduino Uno R3 board for gait analysis. Gait cycles were identified based on roll values measured by the accelerometer embedded in the IMU sensor. By superimposing the gait cycles that occurred during the walking period, they could be analyzed using statistical methods. We found that the subjects could be identified using the gait feature points extracted through the statistical modeling process. To validate the feasibility of feature-based gait pattern identification, we constructed various machine learning models and compared the accuracy of their gait pattern identification. Based on this, we also investigated whether there was a significant difference between the gait patterns of people who used cell phones while walking and those who did not. Full article

This work investigated the fermentation kinetics and characteristics of goat yogurt supplemented with bovine whey protein isolate (WPI) (0%, 2.5% and 5.0%) subjected to high shear dispersion (HSD) assisted by ultrasound (US). Protein supplementation and the physical processes increased the electronegativity of the zeta potential (≤60%), whereas particle size reduction was observed only with physical processes (≤42%). The addition of 2.5% WPI reduced yogurt fermentation time by 30 min. After 24 h of storage at 7 °C, lactic acid bacteria counts did not differ between samples (≥8 log CFU/mL), and the supplementation was sufficient to increase the apparent viscosity (≤5.65 times) and water-holding capacity (WHC) of the yogurt (≤35% increase). However, supplementation combined with physical processes promoted greater improvements in these parameters (6.41 times in apparent viscosity and 48% in WHC) (p < 0.05), as confirmed by the denser and better-organized protein clusters observed in microscopic evaluation. Thus, both approaches proved to be promising alternatives to improve goat yogurt quality. Therefore, the decision to adopt these strategies, either independently or in combination, should consider cost implications, the product quality, and market demand. Full article