This article presents a new input device for spastic patients and others with similar symptoms. The sensor consists of a disc used to determine positions and can replace standard joysticks in medical devices such as electric-powered wheelchairs. Using a standard joystick while operating a powered wheelchair can result in dangerous situations when spastic movements occur and extremities cramp making them uncontrollable. To avoid this, a disc was developed that can be controlled with any body part. By shifting weight (x- and y-axis) the disc can tilt in any direction creating a proportional output signal and can also be pushed down in the center (z-axis) to open a menu on a screen for example. When spasms occur it is impossible for users to get stuck on the input device because the disc is flat and can be mounted within a control panel. Body parts coming into contact with the disc would merely slide across the disc without triggering unintentional actions. The sensor presented here is also adaptive and can be adjusted to fit a user’s strength and range of motion. This proposal aims to develop an input device that enables spastic patients to operate sensitive systems safely.

The rational design of magnetic carbon composites, encompassing both composition and microstructure, holds significant potential for achieving exceptional electromagnetic wave absorbing materials (EAMs). In this study, FeCo@CM composites was efficiently fabricated through an advanced microwave plasma assisted reduction chemical vapor deposition (MPARCVD) technique, offering high efficiency, low cost, and energy-saving benefits. By depositing graphitized carbon microspheres, the dielectric properties were significantly enhanced, resulting in improved electromagnetic wave absorption performances through optimized impedance matching and a synergistic effect with magnetic loss. A systematic investigation revealed that the laminar-stacked structure of FeCo exhibited superior properties compared to its spherical counterpart, supplying a higher number of exposed edges and enhanced catalytic activity, which facilitated the deposition of uniform and low-defect graphitized carbon microspheres. Consequently, the dielectric loss performance of the FeCo@CM composites was dramatically improved due to increased electrical conductivity and the formation of abundant heterogeneous interfaces. At a 40 wt% filling amount and a frequency of 7.84 GHz, the FeCo@CM composites achieved a minimum reflection loss value of -58.2 dB with an effective absorption bandwidth (fE) of 5.13 GHz. This study presents an effective strategy for developing high-performance EAMs.

While some research supports utilizing plain radiography for measuring biomechanical alignment of the spine for prognosis and treatment, there are contrasting viewpoints regarding both the value and utilization of these procedures in conservative care. Evaluation of both conservative and non-conservative approaches to spinal care revealed vast differences in radiographic utilization and interpretation between orthopedic surgeons, primary care physicians, chiropractic physicians, and physical therapists, which may account for the different viewpoints and rationale in the literature. A research summary is provided regarding the various methods of spinal analysis utilized by these professions to help determine if there is any unique diagnostic or prognostic value associated with radiographic utilization that could assist in advancing conservative spinal assessment and treatment. Understanding any unique value provided through biomechanical assessment utilizing plain radiography of the spine established in the literature may help chiropractic physicians determine the appropriate use of radiographic procedures in clinical practice and how to coordinate efforts with other conservative and non-conservative professions to improve spinal healthcare.

The normal growth and development of skeletal muscle is essential for the health of the body. The regulation of skeletal muscle by intestinal microorganisms and their metabolites has been continuously revealed. Acetate is the predominant short-chain fatty acid synthesized by gut microbiota through the fermentation of dietary fiber, however, the underlying molecular mechanisms governing the interaction between acetate and skeletal muscle during rapid growth stage remain to be further elucidated; Here, specific pathogen-free (SPF) mice, germ-free (GF) mice, and germ-free mice supplemented with sodium acetate (GS) were used to evaluate the effects of acetate on skeletal muscle growth and development of young mice with gut microbiota deficiency; We found the concentration of serum acetate, body mass gain, succinate dehydrogenase activity and expression of myogenesis maker gene of skeletal muscle in the GS group was higher than GF group following sodium acetate supplement. Furthermore, the transcriptome analysis revealed that acetate activated biological processes regulating skeletal muscle growth and development in GF group that are inhibited due to gut microbiota deficiency. In vitro experiment, we proved that acetate up-regulated Gm16062 to promote skeletal muscle cell differentiation; Overall, our findings proved that acetate promote skeletal muscle growth and development in young mice via increase Gm16062 expression.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

A Wireless Body Area Sensor Network (WBASN) is combination of numerous sense nodes, positioned onto/close or inside a person body. Wireless Body Area Sensor Networks (WBASN) is a developing automation trend that exploits wireless sensor nodes to put instantaneous wearable well-being of ill person to improve individual’s existence. The sensor nodes might be used outwardly to observe abundant health parameters (like heart activity, blood pressure and cholesterol) of an ill person at a vital site within hospital. Hence the goal of WBASN is much crucial, enhancing the lifetime of nodes is compulsory to sustain many issues such as utility and efficiency. It is essential to evaluate time that when the first node will die it we want to refresh or change the battery reason is that loss of crucial information is not tolerable. The lifetime is termed as the time interval when a first node dies out due to battery exhaustion. In our proposed protocol life time of a network is the main concern as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: Body Mass Index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 11.90 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33) but increased risk in the BFP model (OR: 1.66). The BMI model demonstrates exceptional predictive ability (AUC=0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions:BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.

Abstract: This work for the first time investigated the densification of multi-doping zirconia ceramic body with organic coating powders for solid electrolyte of solid oxide fuel cells via online imaging technology. The densification results show the initial stage plays a key role in the sintering. It can be found the covered organic PVA (polyvinyl acetate) supplies a potential kinetics to the initial densification during sintering. As a result, a kinetic function of densification in the initial stage was suggested. Furthermore, a novel sintering model with six sub-stages is developed for polycrystalline zirconia ceramics. The findings would be a valuable reference for predicting final temperature of sintering, the equivalent strain during the sintering process, as well as optimizing the densification behavior.

Insulin-like Growth Factor 2 mRNA binding Protein 1 (IGF2BP1) is a promising candidate gene for influencing economic traits in livestock and poultry. The second intronic region of IGF2BP1 is closely related to growth traits, although the exact mechanism remains unclear. The preliminary resequencing results of goats in our laboratory demonstrated that the IGF2BP1 gene region was subject to strong selection, then we selected multiple single nucleotide polymorphisms (SNPs) to identify. Seven SNPs were located in the conserved region of the second intron, and their functions and relationship with growth traits require further clarification. This study selected 348 Nanjiang Yellow goats as experimental subjects, and chose Time of Flight Mass Spectrometer (MALDI-TOF) as the main method. Our results showed that six SNPs were significantly correlated with the growth traits of the Nanjiang yellow goat by association analysis. Among them, the rs652062749(A>G) was identified as the primary locus affecting the later stage of growth traits of Nanjiang yellow goat. Moreover, there was a strong linkage among three SNPs, and the rs638185407 (T>A) mutant type was observed to increase the luciferase activity in H293T cells extremely significantly. The TTAACT, TTCCCC, and ATCACT combination genotypes were dominant combination genotypes, which provided theoretical references for further genetic co-breeding. The results of our study demonstrate the potential role of the IGF2BP1 gene as a functional genetic marker in the breeding programs of Nanjiang Yellow goats.

The multi-stage, multi-level assembly job shop scheduling problem (MsMlAJSP) is commonly encountered in the manufacturing of complex customized products. Ensuring production efficiency while effectively improving energy utilization is a key focus in the industry. For the energy-efficient MsMlAJSP (EEMsMlAJSP), an improved imperialist competitive algorithm based on Q-learning (IICA-QL) is proposed to minimize the maximum completion time and total energy consumption. In IICA-QL, a decoding strategy with energy-efficient triggers based on problem characteristics is designed to ensure solution quality while effectively enhancing search efficiency. Additionally, an assimilation operation with operator parameter self-adaptation based on Q-learning is devised to overcome the challenge of balancing exploration and exploitation with fixed parameters, thus the convergence and diversity of the algorithmic search is enhanced. Finally, the effectiveness of the energy-efficient strategy decoding trigger mechanism and the operator parameter self-adaptation operation based on Q-learning is demonstrated through experimental results, and the effectiveness of IICA-QL for solving EEMsMlAJSP is verified by comparing with other algorithms.

Background/Objectives: Phenylketonuria is a hereditary metabolic disorder characterized by a deficiency of phenylalanine hydroxylase. Main treatment for PKU is a phenylalanine-restricted diet. Exclusion of protein rich natural foods and inclusion of low-Phe substitutes may give rise to an imbalanced diet increasing the risk of overweight and obesity in PKU is a cause of concern. We aimed to evaluate the body composition and nutritional biochemical biomarkers in adult PKU patients who are on Phe-restricted and essential amino acids supplemented nutrition therapy and to investigate the relationships, between these parameters and patient gender, adherence to dietary therapy and disease type defined as mild or classic PKU. Methods: The study group comprised 37 PKU patients and 26 healthy siblings as controls. The participants were assessed based on an analysis of anthropometric parameters, body composition and biochemical test results. Results: PKU patients do not have a higher incidence of overweight and obesity than healthy controls, and the proportion of energy derived from carbohydrates in their diets was below the recommended level, and the total energy intake was below the recommended daily allowance. It was remarkable that patients with a treatment adherence ratio of <50% displayed a higher prevalence of overweight and abdominal obesity in comparison to those with a more favorable treatment adherence ratio. Conclusions: In view of the growing prevalence of overweight in the general population, PKU patients should be kept under close long-term follow-up. Particularly in the group with low treatment compliance, more caution should be paid in terms of adverse outcomes.

Lean body mass (LBM) is correlated with powerlifting performance in athletes competing in different bodyweight classes. However, it remains unknown whether changes in LBM are correlated with performance changes in powerlifters preparing for a competition. Aim of the study was to investigate the changes in LBM and performance in powerlifters preparing for a competition. Eight male powerlifters (age 31.7±9.8years, height 1.77±0.06m, weight 99.2±14.6kg) and three female powerlifters (age 32.7±16.3years, height 1.54±0.06m, weight 66.6±20.9kg), participated in the study. Athletes followed individualized periodized training programs for 12 weeks aiming to maximize their performance for the national championship. Maximum strength (1-RM) in squat, bench press, and deadlift, body composition, grip strength, anaerobic power, quadriceps’ cross sectional area and vastus lateralis muscle architecture were measured before and after the training period. Significant increases were found after the training period in squat (5.8±7.0%, P

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

In previous papers we have derived equations of motion with radiation terms and proved the existence-uniqueness of periodic solution of the 3-body problem of classical electrodynamics. The number of equations of motion – 12 is more than the number of unknown functions – 9. It turns out that 9 equations are independent and the rest 3 ones are consequences of them. These three equations have a solution and they give the expressions for the energy of the moving particles. The main goal of the present paper is to estimate the energy of rounding about the nuclei particles and compare it with the values given by quantum mechanics.

Urinary tract diseases are common in cats, and often require surgical reconstruction. Here, to explore the possibility of urinary tract reconstruction in cats using in-vivo tissue architecture (iBTA), biosheets fabricated using iBTA technology were implanted into the feline bladder and the regeneration process was histologically evaluated. The biosheets were prepared by embedding molds into the dorsal subcutaneous pouches of six cats for 2 months. A section of the bladder wall was removed, and the biosheets were sutured to the excision site. After 1 and 3 months of implantation, the biosheets were harvested and evaluated histologically. Implantable biosheets were formed with a success rate of 67%. There were no major complications following implantation, including tissue rejection, severe inflammation, or infection. Urinary incontinence was also not observed. Histological evaluation revealed the bladder lumen was almost entirely covered by urothelium after 1 month, with myofibroblast infiltration into the biosheets. After 3 months, the urothelium became multilayered, and mature myocytes and nerve fibers were observed at the implantation site. In conclusion, this study showed that tissue reconstruction using iBTA can be applied to cats, and that biosheets have the potential to be useful in both the structural and functional regeneration of the feline urinary tract.

Laser Photoacoustic Spectrometers (LPAS), which provide photoacoustic spectra for detecting food frauds or adulteration, and fluorescence-based systems for recognition of contamination by heavy metal ions in water are two important families of sensors for health and environmental purposes. Both sensor families require one or more low-frequency Lock-In Amplifiers (LIAs) to extract the signal of interest from background noise. In the cited applications, the required LIA frequency is quite low (up to 1kHz), and this leads to a simplification of the hardware with consequent good results in portability, economy, reduced size, weight and low-cost characteristics. The present system, called ENEA DSP Box Due, is based on a very inexpensive microcontroller proto-board and can replace four commercial LIAs, resulting in significant savings in both cost and space. Furthermore, it incorporates a dual-channel oscilloscope and a sinusoidal function generator. This article outlines the architecture of the ENEA DSP Box Due, its electrical characterization and its applications within a project concerning laser techniques for food and water safety.

Faced with difficulties stemming from the complex interactions between tight gas sand bodies and fractures when describing and identifying reservoirs, a composite reservoir model was established. By setting the supply boundary to characterize the superposition characteristics of sand bodies, a mathematical model of unstable seepage in fractured vertical wells in tight sandstone gas reservoirs was constructed considering the factors such as stress sensitivity, fracture density and fracture symmetry. The seepage law and pressure response characteristics of gas well in tight sandstone discontinuous reservoir with stress sensitivity, semi-permeable supply boundary and complex fracture topology were determined, and the reliability of the model was verified. The research results more accurately depict the pressure characteristic curve of the superposed sand body complex fracture vertical well, and provide a more comprehensive model for tight gas production dynamic analysis and well test data analysis, which can more accurately guide the dynamic inversion of reservoir and fracture parameters.

This paper studies the maximum reliability of multi-hop relay UAV, in which UAV provides wireless services for remote users without direct communication link on the ground as a coded cooperative relay. In this paper, the analytical expressions of total power loss and total bit error rate are derived as reliability measures. Firstly, based on the environmental statistical parameters, a LOS probability model is proposed. Then the problem of minimizing the bit error rate of static and mobile UAVs is studied. The goal is to minimize the total bit error rate by jointly optimizing the height, elevation, power and path loss, and subject to the maximum allowable path loss con-straints, transmission power allocation constraints and UAV height and elevation constraints. At the same time the total path loss is minimized to achieve maximum ground communication cov-erage. However, the formulated joint optimization problem is non-convex and generally difficult to solve. Therefore, we decomposed the problem into two subproblems and proposed an effective joint optimization iteration algorithm. Finally, the simulation results are given, and the analysis shows that the optimal height of different reliability measures is slightly different, and using the mobility of UAVs can improve the reliability of communication performance.

Carotenoids are common and diverse organic compounds with various functional roles in ani-mals. Except for certain aphids, mites, and gall midges, all animals acquire necessary carotenoids only through their diet. The House fly (Musca domestica) is a cosmopolitan pest insect that populates diverse habitats. Its larvae feed on organic substrates that may vary in carotenoid composition according to their specific content. We hypothesized that the carotenoid composition in the adult House fly’s body would reflect its composition in the larval feed. We reared house fly larvae on substrates that differed in carotenoid composition and characterized the carotenoid composition of the emerging adults. Our results indicate that the carotenoid composition of adult House flies is related to the carotenoid composition in its natal substrate, but does not directly reflect it. We suggest that these findings may be developed to identify sources of House fly in-festations. We also recommend that care should be taken when considering the rearing substrates of House fly larvae used for animal feed.

Ferritin is an indirect marker for total body iron stores. Comparison of the ferritin concentrations in the meconium with the concentrations of proteins regulating iron metabolism could aid better understanding of principles behind the adjustment between the fetal iron pool and newborn body size. Ferritin, transferrin, haptoglobin, ceruloplasmin, lactoferrin, myeloperoxidase, neutrophil gelatinase-associated lipocalin (NGAL) and calprotectin concentrations were measured by ELISA in meconium samples from 122 neonates. The neonates were classified according to the meconium ferritin tertiles and the values were collated with the meconium concentrations of other proteins and newborn body weight and length measurements. Increasing ferritin concentrations in meconium in consecutive tertiles (p < 0.05) were parallel to increased NGAL concentrations (p < 0.05). The interrelationships between the meconium concentrations of transferrin, haptoglobin and NGAL as well as between myeloperoxidase, calprotectin and lactoferrin are maintained in all subsequent tertiles of ferritin concentrations, (p < 0.05). In contrast to ferritin, increasing meconium NGAL concentrations in consecutive tertiles were linked to increased concentrations of transferrin, haptoglobin, lactoferrin and myeloperoxidase (p < 0.05) and of calprotectin, ferritin and ceruloplasmin (p < 0.1). Meconium ceruloplasmin concentrations correlate exclusively with neutrophil granule proteins, i.e. myeloperoxidase, NGAL, lactoferrin and calprotectin (p < 0.05). Meconium ferritin concentrations negatively correlates with birth weight (r= -0.20) and length (r= -0.31) of neonates, whereas NGAL concentrations just with birth length (r= -0.26). The remaining proteins did not show such relationships with the size of the newborn. High meconium concentrations of both ferritin and NGAL may specifically characterize neonates of smaller size. High meconium concentrations of both ferritin and NGAL may specifically characterize neonates with lower body measurements. Correlations between meconium transferrin, haptoglobin and NGAL may confirm the interrelationships of these proteins for proper iron control and transport in the intrauterine environment. Meconium ceruloplasmin concentrations may reflect the control of neutrophil activity in utero.

Background: Bariatric surgery (BS) has a significant impact on body composition (BC) and con-sequently may affect established sarcopenic obesity (SO) in candidate patients. The aim of this study is to assess the utility of muscle ultrasound (MUS) of rectus femoris thickness (RFT) for the evalua-tion of BC and skeletal muscle function in patients undergoing BS compared to bioimpedance analysis (BIA), dual-energy X-ray absorptiometry (DEXA) and dynamometry. On the other hand, we aim to demonstrate how MUS of RFT correlates better with the quality of life (Qol) in this population, likely because it enables the detection of regional quadriceps muscle sarcopenia, in comparison to the other mentioned methods. Methods: This is a prospective pilot study including 77 subjects (64.9% female, mean age: 53.2 ± 8.67 years) who underwent BS. Handgrip strength was measured using a dynamometer, fat-free mass index (iFFM) was assessed by BIA, appendicular muscle index (AMI) was calculated using DEXA and RFT was measured by MUS. Moreover, Homeostasis Model Assessment of Insulin Resintance (HOMA-IR) was calculated. All these measurements were conducted 1 month prior to BS and at the 12-month follow-up. QoL was as-sessed using the Moorehead-Ardelt questionnaire. Results: The mean BMI decreased by 12.95 ± 3.56 kg/m2 (p=0.001). Firstly, we observed positive correlation pre-surgery between HOMA and: RFT (r=0.27, p=0.02), iFFM (r=0.36, p=0.001), AMI (r=0.31, p=0.01) and dynamometer (r=0.26, p=0.02). In addition, we found correlation between RFT and iFFM (pre-surgical: r=0.31, p=0.01; post-surgical: r=0.25, p=0.05) and between RFT and lower extremities AMI post-surgical (r=0.27, p=0.04). Secondly, we observed significant reductions in iFFM, AMI and RFT (p=0.001), but not with dynamometer (p=0.94). Finally, a tendency to a positive correlation between QoL question-naire and RFT post-surgery (r=0.23, p=0.079) was observed. Conclusions: Our results suggest that RFT measured by MUS for the evaluation of SO and the follow-up after BS of these patients. Moreover, RFT can provide relevant information about regional sarcopenia and probably has an accurate correlation with QoL in comparison with the other methods.

Background: The effect of COVID-19 infection, versus the indirect effect of the pandemic on body composition remains unclear. This study investigates long-term changes in body composition in COVID–19 survivors compared to a contemporary control group. Method: This is a prospective study involving adults who had a pre-pandemic whole-body DXA scan (DXA#1) performed between 2017 and 2019. Participants were asked to return for a repeat whole-body DXA scan (DXA#2) after the pandemic. Detailed data were collected including medical and COVID history. Inflammation markers and fasting lipids were measured. For those participants who had a COVID-19 infection between the 2 DXAs, DXA#2 was acquired at least one year after COVID-19 infection. Results: Overall, 160 adults were enrolled; 32.5% females, 51.8% non-white, with mean age of 43.2 years. Half (n=80) of the participants had documented COVID infection between their 2 DXA scans (COVID+ group), and the other half never had COVID. COVID-negative participants had an increase in annualized trunk fat (g) [922.5 vs. 159.7; P=0.01], total fat (g) [1564.3 vs. 199.9; P=0.2], and LBM (g) [974.9 vs. -64.5; P=0.0002], when compared to COVID+ group. However, among the COVID+ group, no differences were seen in annualized trunk fat, total fat mass, or LBM between those with PASC and without (p>0.05). Conclusion: During the pandemic, both COVID-19-survivors and COVID-negative groups exhibited increases in weight, and total and trunk fat, likely associated with pandemic-linked lifestyle modifications. However, only COVID-19-survivors had a decline in lean body mass over the same period, regardless of PASC symptoms.

The commercial aviation industry is constantly evolving, driven by technological innovation and the growing demand for more efficient and environmental friendly transportation solutions. In this context, the design and development of next-generation aircraft is a crucial task, with the goal of reducing environmental impact and improving flight efficiency while continuing to ensure passenger comfort and safety. This paper aims at evaluating the technical feasibility as well as economic and environmental viability of a narrow body aircraft powered by hydrogen for direct combustion. Notably, the paper offers innovative insights regarding the update of literature models for conceptual design, in order to consider the impact of hydrogen fuel on the sizing loop at aircraft level. Moreover, it also focuses on the evaluation of operating costs for the aforementioned aircraft class, highlighting the required model updates and considerations to properly match the effect on costs of the introduction of the innovative fuel. Ultimately, a preliminary analysis of upstream sustainability of the concept, considering fuel life cycle, is provided, in order to understand the overall competitiveness, not only in terms of monetary resources but also of environmental impact, beyond the pure operating life of the airplane.

Daily vitamin D supplementation using higher than normal dosing (up to the upper limit, UL) and intermittent (once or twice per week) dosing were studied in patients with increased risk of vitamin D deficiency. Using a PubMed database an adequate search for published randomized control trials and other studies was done and results were analyzed. This review provides overview of use of 7000 IU daily, 30,000 IU per week or twice weekly and 50,000 IU weekly of vitamin D on rapid restore of 25(OH)D concentrations to at least 30 ng/mL in different groups of patients at high risk of vitamin D deficiency. The above mention dosing should be considered in adults with obesity or multi-diseased patients on requiring multi-treatment with the use of several drugs, including drugs affecting vitamin D metabolism. The simple schedule for 7000 IU/day, 30,000 IU/week or twice weekly or 50,000 IU/week, all for 6-8 weeks of use by patients representing the increased risk of vitamin D deficiency were provided for consideration. It appeared that higher daily doses or intermittent doses suggested above are effective, safe and responsive for patient’s preferences.

Suicide is a public health problem influenced by a variety of biological, psychological, social, and environmental factors. Recognizing the warning signs at each stage is crucial for prevention and helping those at risk. In this context, body awareness, especially interoceptive body awareness, emerges as a potentially relevant and underexplored factor. In this study, binary logistic regression and linear regression analyses were used to examine the relationship between interoceptive body awareness and suicidal orientation in Colombian university students. In addition, the bootstrap technique was used to resample and estimate the distribution of the data. The results support the idea that greater interoceptive awareness may protect against suicidal orientation by improving emotional regulation. An inverse relationship was found between interoceptive awareness and suicidal ideation. These findings are consistent with previous literature on the importance of body awareness for emotional well-being. Further longitudinal research is needed to explore this relationship in depth.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

Hormone changes across women's menstrual cycle may lead to changes in perception of chemical signals and hormonal responses to these cues. The aim of the present study was to determine the role of menstrual cycle phase on the effects of extracts of male axillary secretions (EMAS) in women. We tested healthy reproductive age and premenopausal women (n=29). EMAS/control solution was applied once in two hours either in the follicular or luteal phase, while collecting saliva samples for LH and cortisol monitoring. LH and cortisol concentrations were analyzed using EIA technique. PANAS and VAS were used to assess the mood. We observed a significant increase in the number of LH peaks (p=0.0447) and their amplitude (p=0.0469) when EMAS was applied in the follicular phase. The same application in the luteal phase lowered the amplitude of LH peaks (p=0.0382). EMAS application increases salivary cortisol level in reproductive age women relative to premenopausal women (p=0.0032). PANAS revealed changes in positive and negative affect after EMAS application. Our data indicate the significance of the menstrual cycle phase for EMAS effects on LH secretion, mood, but not on cortisol secretion in women.

Despite the recognized influence of body mass on combat techniques, the relationship between body segment mass (BSM) and combat moves remains unexplored. This study aimed to examine the relationship between striking arm mass (SAM), kicking leg mass (KLM), and body mass (BM) with the dynamic forces of the direct punches (DP), palm strikes (PS), elbow strikes (ES), front kicks (FK), and countermovement jumps (CMJ) performance. Sixteen male military cadets (22.3 ± 1.8 years, 181.4 ± 7.0 cm, 82.1 ± 8.5 kg) performed combat techniques, with performance measured by using a force plate and body segment mass assessed by Dual-energy X-ray absorptiometry. Spearman's correlation analysis, the Wilcoxon test, and Cohen's d were applied. Results indicated the relationship between KLM or BM and FK impulse (r = 0.64, p = 0.01; r = 0.52, p = 0.04, respectively) and CMJ impact force (r = 0.80, p ≤ 0.01; r = 0.70, p ≤ 0.01, respectively). Moreover, FK peak and impact forces were moderately correlated with CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01). The highest relationship was found between KLM and FK impulse; however, the difference in variability explained by KLM versus body mass is only 12%. This suggests that knowledge of BSM did not provide a significantly better estimate of the dynamic forces of the punches and FK than BM.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

This purpose was to compare the ability of body shape index (ABSI) and body roundness index (BRI) with waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and body adiposity index (BAI) to predict metabolic risk. The cross-sectional study was conducted in 17,360 Chinese subjects (were aged 18-95 years old) who escaped cardiovascular disease (CVD) or diabetes. Biochemical and anthropometric variables were measured by trained staff. Receiver operating characteristic curve (ROC) and optimal cutoff values of obesity indices were recruited to compare the predictive ability for metabolic risk factors. The mean age of subjects was 53.7(13.1) years, 41.6 % were males. Within young group (<60 years), the areas under the curve (AUC) demonstrated that WC, BMI, WHR, WHtR and BRI were able to similarly predict high metabolic risk in males (0.74 vs. 0.74 vs. 0.73 vs. 0.73 vs. 0.73) and in females (0.73 vs. 0.73 vs. 0.71 vs. 0.73 vs. 0.73), while the approximate predictive ability were only acquired in males (0.73 vs. 0.73 vs. 0.70 vs. 0.73 vs. 0.73) within elder group (≥60 years). The optimal cut-off values of BRI for high metabolic risk were calculated in males (<60 y: 3.49 vs. ≥60 y: 3.46) and females (<60 y: 3.47 vs. ≥60 y: 3.60). Meanwhile, BRI displayed a strong prediction to elevated BP and elevated TG in males (AUC = 0.64; AUC = 0.70) and to elevated BP, elevated TG and elevated SUA in females (AUC = 0.67; AUC = 0.69; AUC =0.70). BRI was able to similarly predict high metabolic risk compared to WC, BMI, WHR, WHtR and BAI, while ABSI was not. Moreover, BRI revealed specific predictive ability for elevated BP, elevated TG and elevated SUA.

Training schedule is a factor that influences sports performance optimization. In a sport like soccer, there is often significant disparity in training schedules among different teams within the same club, without considering whether this may affect players' performance. The aim of this study was to describe differences in nutrient intake and body composition in elite youth soccer players from the Spanish league with different training schedules (morning and evening). A cross-sectional study was conducted to determine differences in anthropometric variables and dietary assessment in a sample of Spanish young soccer players. A total of 41 players participated in this study. After comparing the groups according to their training schedule, no differences were observed in body composition between both groups; the evening-night training group showed higher consumption of lipids and saturated fats. In summary, more experimental studies are needed to determine the effects on various health and performance parameters of different training schedules in young population.

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

The purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. Fifty athletes, 25 males and 25 females (age: 33.26 ± 6.81 years; body mass: 72.57 ± 12.17 kg; height: 169.55 ± 8.71 cm; BMI: 25.06 ± 2.31 kg·m−2) were recruited to participate and underwent BC analysis using dual-energy X-ray absorptiometry (DXA) and an all-out laboratory test on a cycle ergometer (Wingate) to determine their anaerobic performance. The results show a significant correlation between BC values and performance, ranging from moderate (r = -0.34, p = 0.015) to near-perfect (r = 0.96, p &lt; 0.01). Furthermore, the created performance prediction models exhibited predictive capacities ranging from 19% (p = 0.017) to 93% (p &lt; 0.001). All prediction models were created using total or segmental lean mass variables, excluding others. BC and performance variables found significant differences between males and females. The findings demonstrate that BC variables are crucial indicators of anaerobic performance in CF athletes. Professionals responsible for athlete performance should use this information to monitor athletes and design training programs.

Snow crab replaced groundfish fisheries as the primary focus of the Newfoundland and Labrador (NL) fishing industry in the mid-1990s. Over the past three decades, management philosophies in this male-only fishery have shifted from promoting industry expansion to promoting industry rationalization to implementing Precautionary Approach (PA) management. Until the mid-2010s, there were regionalized management strategies characterized by higher exploitation rates in northern than southern sub-stock units along the NL marine shelves. However, in the late 2010s, exploitation rates were permitted to elevate to high levels across the entire stock range in association with a large resource decline and evidence of biological harm through fishing emerged in chronically depleted areas. In 2019, when stock and fishery productivity were near historic lows, a multi-indicator PA system was informally introduced into management of the resource. This event coincided with anticipated improvements in stock and fishery performance. This paper examines the extent to which the multi-indicator PA management system, focused on promotion of both biological protection and maximization of fishing efficiency, may have contributed to recent improvements in stock and fishery status. A suite of indicators from areas implementing PA guidance into management in 2019 prior to formal adoption of the PA system in 2023 suggest the system has been highly beneficial in promoting rapid recovery, improving stock and fishery performance metrics to levels matching or exceeding historic levels under similar conditions. We discuss capacity of the system to better safeguard biological aspects of resource and fisheries productivity moving forward under a scenario of an expectant resource decline.

Climbing is an Olympic discipline in full development and multidisciplinary in nature, in which the influence of body composition and nutritional status on performance has not yet been clarified despite the quest for low weight in anti-gravity disciplines such as climbing. The present study aimed to do a nutritional (3-day dietary diaries) and body composition (ISAK profile) assessment of sport climbing athletes of different genders and sport levels. The mean age of the forty-six Spanish climbers (22 men and 24 women) was 30 years (SD: 9) with 7.66 years of experience (SD: 6.63). The mean somatotype of the athletes was classified as balanced mesomorph. Negative correlations were observed between fat mass variables and climbing level (p<0.01), and positive correlations with forearm circumference (p<0.05). Mean energy availability (EA) was 33.01 kcal-kg FFM1-d1 (SD: 9.02), with 55.6% of athletes in suboptimal EA status and 35.6% in low energy availability (LEA) status. Carbohydrate and protein intakes were below recommendations in 57.8% and 31.1% of athletes, respectively. There were deficient intakes of all micronutrients except phosphorus in males. These findings suggest that climbing athletes are at high risk of developing low energy availability states and concomitant problems. Optimal nutritional monitoring may be advisable in this type of athlete to try to reduce the risk of LEA.

The anthropometric features offer an overall image of the nutritional status. The investigated groups were aged 19 and 21. The anthropometric measurements of height and weight were recorded following standard procedures. The objectives of this study were to evaluate the harmonious body development, the nutrition status and the body composition. The evolution of the stature increases until 20 years after which stagnation follows. The body mass index of subjects was found to be normal. The body fat may vary over time in subjects depending on age and gender. Body water and body muscle mass content increased with age. The harmony of body development was assessed by means of the Amar index. Regarding the constitutional index, the women subjects belong to the leptosome type and the male subjects belong to the picnic type. The status of the investigated subjects is within the average stature.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

Objective: To analyse the association between maternal risk factors such as age, body mass index (BMI) and cigarette smoking, with perinatal outcomes. Materials and Methods: This a retrospective analysis from prospectively collected data performed at Hospital Universitario de Torrejón (Madrid, Spain), between September 2017 and December 2019. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination at 11+0 to 13+6 weeks' gestation were invited to participate. Association between preeclampsia, preterm birth, gestational diabetes mellitus (GDM), small for gestational age (SGA) or fetal growth restricted (FGR) neonates and type of delivery according to maternal age, BMI, and cigarrete smoking was studied. Logistic mixed models were used to analyze the data. Results: 1921 patients were included in the analysis. Women of 40 years old had a significantly higher risk of having GDM (Odds Ratio (OR) 1.61, 95% Confidence Interval (CI) 1.08 to 2.36), and SGA neonates (OR 1.54, 95% CI 1.00 to 2.37). Women with a BMI &lt;18 had an increased rate of birth SGA and FGR neonates (OR 3.28, 95% CI 1.51 to 7.05, and OR 3.73, 95% CI 1.54 to 8.37, respectively), whereas women with a BMI ≥35 had a higher risk of GDM (OR 3.10, 95% CI 1.95 to 4.89). Smoking increased the risk of having SGA and FGR neonates (OR 1.83, 95% CI 1.36 to 2.46, and OR 1.91, 95% CI 1.29 to 2.78). Conclusions: Advanced maternal age, low or high BMI, and smoking status are significant risk factors for pregnancy complications. Both clinicians and society should concentrate their efforts on addressing these factors to enhance reproductive health.

With the large-scale application of Internet of Things (IoT) in the medical field, eHealthcare has attracted wide attention. With the help of IoT devices, eHealthcare allows doctors to monitor patients’ health conditions and make recommendations accordingly. The addition of cloud computing greatly saves Healthcare Center’s (HC) computing resources. In order to provide privacy and fine-grained access control, Attribute-b ased encryption (ABE) is widely favored. HC usually operates multiple medical institutions in different regions, and each medical institution can be regarded as a attribute authority. Therefor, multi-authority (MA) ABE is usually concerned to encrypt the patient data. However for existing MA-ABE algorithms, the ciphertext is long and the efficiency of encryption and decryption is low. These problems will greatly increase the computing burden of patients and seriously affect the efficiency of data transmission. Hence we propose a decentralized key-policy MA-ABE in eHealthcare. The proposed algorithm can achieve decentralization, that is, it does not need central authority. Meanwhile, compared with existing MA-ABE algorithms, the length of ciphertext generated by our algorithm is shorter, and the efficiency of encryption and decryption is significantly improved. With the help of Java Pairing Based Cryptography, we carried out the experiments which show that our algorithm can effectively reduce the time required for encryption and decryption.

Major (exo)planetary and satellite bodies seem to concentrate at intermediate areas of the radial 1 distributions of all the objects orbiting in each (sub)system. We show that angular-momentum transport during secular evolution of (exo)planets and satellites necessarily results in the observed intermediate accumulation of the massive objects. We quantify the ‘middle’ as the mean of mean motions (orbital angular velocities) when three or more massive objects are involved. Radial evolution of the orbits is expected to be halted when the survivors settle near mean-motion resonances and angular-momentum transfer between them ceases (gravitational Landau damping). This dynamical behavior is opposite in direction to what has been theorized for viscous and magnetized accretion disks, in which gas spreads out and away from either side of any conceivable intermediate area. We present angular momentum transfer simulations in few-body systems, and we calculate the tidal dissipation timescales and the physical properties of the mean tidal field in planetary and satellite (sub)systems.

Farmers are continually looking for new reliable, objective and non-invasive methods for evaluation of ewe condition. Live weight (LW) and body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Body composition is an important aspect of monitoring animal condition. The body composition is the amount of fat, lean and bone; knowing the amount of each is important because the information can be used for better strategic management interventions. Experiments were conducted to establish the relationship between body composition and body parameters, at key life’s stages (weaning and pre-mating), using measurements automatically determined by an image processing ap-plication at Lincoln University sheep farm for 88 Coopworth ewes. Computerized Tomography technology was used to develop relationship with body parameters and a subset was used to validate the predicted model. Multivariate linear regression (MLR), artificial neural network (ANNs) and regression tree (RT) statistical analysis methods were evaluated to determine their efficacy to predict body fat, lean and bone. The results showed a correlation between fat, lean and bone determined by CT and the fat, lean, bone weight estimated by live weight and body parameters calculated using the image processing application with R2 values of 0.90 for fat, 0.72 for lean and 0.50 for bone using ANNs statistical model. From these results, farmers can utilize accurate measures of fat which will enhance nutritional and management practices.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

Assessing the quality of corn seeds necessitates evaluating their content of water, fat, protein, and starch. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using both the original spectra and the extracted characteristic wavelengths, four content detection models were constructed: PLSR, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Least Squares Support Vector Machine (LSSVM). The experimental results demonstrate that the DT-CARS-LSSVM model exhibits exceptional accuracy and stability in predicting these four key components, with determination coefficients of 0.9877, 0.9344, 0.9827, and 0.9592, respectively. This study not only provides a scientific basis for evaluating the quality of corn seeds but also opens up new avenues for the development of non-invasive detection technology in related fields.

The state of the grinding wheel in the grinding process directly affects the surface quality of the workpiece. The monitoring of the wear state of the grinding wheel can effectively identify the wear information of the grinding wheel and timely and effectively repair the grinding wheel. In this paper, a multi-sensor fusion precision grinding wheel wear intelligent monitoring system is established for typical difficult-to-machine aerospace material titanium alloy, and a multi-eigenvalue fusion algorithm based on improved support vector machine (SVM) is proposed. The extracted sound emission signal wavelet packet decomposition coefficient mean square value, force signal grinding force ratio, and vibration signal effective value are used as the input of the improved support vector machine, and the identification strategy is adjusted by the entropy weight evaluation method. The results show that the monitoring system established in this paper can effectively identify the wear state of the grinding wheel, and the monitoring accuracy can reach more than 92%.

The epidemic of obesity has increased worldwide and is associated with comorbidities as diabetes as cardiovascular disease. Strategies that modulate body weight and improves glycemic metabolism has increased, and the bariatric surgeries as Sleeve Gastrectomy (SG) has been highlighted in obesity treatment. However, the mechanism by which SG reduces body weight and improves glycemic control are unknow. C57BL mice were submitted to a 10 weeks high fat-diet protocol and submitted to SG. Food intake, fed/fasted glycemia and hypothalamic anorexigenic and orexigenic gene expression were evaluated. SG reduces body weight, fasting glycemia, insulinemia and leptinemia in obese mice. Moreover, SG reduces food and total calorie intake with no alterations in the expression of anorexigenic and orexigenic gene expression. These data indicates that SG improves obesity associated alterations, at least in part, by reduction on food intake. This effect is not associated with the classic food intake pathway in hypothalamus, indicating the involvement of supplementary pathways in this process.

The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification increases. In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution. The proposed method consisting of an L0-norm constrained reconstruction scheme and an enhanced residual back-projection network, integrating the flexibility of the variational model-based method and the feature learning capacity of the deep learning-based method. To verify the effectiveness of the proposed algorithm, extensive experiments with both simulated and real world sequences were implemented. The experimental results show that the proposed method yields superior performance in both objective and perceptual quality measurements, compared to the baseline super-resolution algorithms in the cascade model. In addition, the experiment indicates that this cascade model can be robustly applied to different SFSR and MFSR methods.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach.

Bigeye grunt Brachydeuterus auritus is an important economic species in the waters off Sierra Leone in West Africa, with great ecological value. Based on the survey data of bottom trawl nets from 6 voyages from 2019 to 2021, the body weight-length relationship (WLR), as well as their relative fatness, were studied in depth using the method of body length and its corresponding mean body weight. This study fitted the WLR of bigeye grunt by gender, calculated its fullness, and analyzed the influence of environmental factors. The results showed that month and gender had a significant impact on body length and weight distribution (P&lt;0.01). There was a highly significant negative correlation (P&lt;0.01) between the condition factor (a) and the allometric factor (b) in the WLR, and salinity significantly affects b (P&lt;0.05). The (lga)/b ratio was less affected by environmental factors which may be related to the body size and density of the fish. The difference in b between male and female populations was extremely significant (P&lt;0.01). The relative fatness had significant seasonal variation characteristics, with highly significant effects of sex and body length (P&lt;0.01). With increasing body length, the relative fatness showed two types of changes, continuously increasing and first increasing then decreasing, with salinity and b significantly affecting the type of change (P&lt;0.05) and depth having a highly significant effect (P&lt;0.01). The bigeye grunt off the coast Sierra Leone exhibited significant seasonal growth characteristics, with significant differences in growth characteristics between male and female individuals. This study suggested that salinity may be the main factor affecting its growth and development. In addition, this study hypothesised that the peak breeding season may occur during the dry season, beginning in December and ending before April of the following year. The study provides updated and more comprehensive insights into the biology and ecology of the bigeye grunt offshore Sierra Leone, West Africa, which can provide scientific basis for its resource assessment, development and utilization, and resource management.

The arsRBC operon encodes a 3-protein arsenic resistance system. ArsR regulates the transcription of the operon, while ArsB and ArsC are involved in exporting trivalent arsenic and reducing pentavalent arsenic respectively. Previous research in Agrobacterium tumefaciens 5A demonstrated ArsR has regulatory control over a wide range of metal related proteins and metabolic pathways. We hypothesized that ArsR has broad regulatory control in other Gram-negative bacteria and set out to test this. Here, we use differential proteomics to investigate changes caused by the presence of the arsR gene in human microbiome-relevant Escherichia coli during arsenite (AsIII) exposure. We show that ArsR has broad-ranging impacts such as the expression of TCA cycle enzymes during AsIII stress. Additionally, we found that the Isc [Fe-S] cluster and molybdenum cofactor assembly proteins are upregulated regardless of the presence of ArsR under these same conditions. An important finding from this differential proteomics analysis was the identification of response mechanisms that were strain, ArsR, and arsenic specific, providing new clarity to this complex regulon. Given the widespread occurrence of the arsRBC operon, these findings should have broad applicability across microbial genera, including sensitive environments such as the human gastrointestinal tract.

Despite the recognized influence of maximum or acceleration strength on combat techniques and countermovement jump (CMJ) performance, the relationship between body segment weights and combat techniques remains unexplored. This study aimed to examine the impact of body segment weights, including striking arm weight (SAW), kicking leg weight (KLW), and trunk weight (TW), on the dynamics of direct punches (DP), palm strikes (PS), elbow strikes (ES), front kicks (FK), and CMJ performance. Sixteen male military cadets (22.3±1.8 years, 181.4±7.0 cm, 83.0±8.1 kg) performed combat techniques, with performance measured by using a force plate and body segment weights assessed by Dual-energy X-ray absorptiometry. Spearman's correlation analysis was applied. Results indicated a strong correlation between KLW and FK impulse (r = 0.64, p ≤ 0.01) and CMJ impact force (r = 0.80, p ≤ 0.01). SAW showed moderate correlations with DP impulse (r = 0.53, p ≤ 0.05) and ES impulse (r = 0.54, p ≤ 0.05). Additionally, FK peak and impact forces were strongly correlated with CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01), and FK peak force moderately correlated with CMJ peak velocity (r = 0.63, p ≤ 0.05). These findings suggest targeted arm strength training could enhance striking abilities, while improvements in leg acceleration strength may directly boost kicking performance and vertical jump capabilities, which are crucial in combat scenarios.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

The incidence of obesity which leads to insulin resistance (IR) and metabolic disorder increases in developing countries including Indonesia. Male adult has higher risk to have abdominal obesity than female which is associated with cardiometabolic disorders. Several anthropometric measurements have been proposed to predict IR. The aim of this study was to investigate whether body mass, body mass index (BMI), waist circumference (WC), body fat percentage (BF) or visceral fat percentage (VF) could become a better predictor of IR in healthy young male adult. Total of 140 healthy young male adults ranging from 18-25 years were recruited in the study. Insulin resistance was measured by calculating Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). Subjects with HOMA-IR value >75th percentile with cut off 3.75 were defined as IR. Anthropometric measurements included body weight, BMI, WC were performed whereas BF and VC were measured by bioelectrical impedance analysis (BIA). IR had significant strong correlation with body weight, BMI, WC, BF and VF. The area under curve of body mass, BF, VF were greater than WC and BMI. Anthropometric measurements correlated strongly with IR but body weight, BF, VF have stronger correlation than WC and BMI in healthy young male adult.

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes 1 from a single image has long been a challenging task. While deep learning networks enable us to 2 approximate the shape of unseen sides, capturing the texture details of the non-visible side remains 3 difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) 4 to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles 5 on the model’s surface. However, we have identified challenges with existing normal prediction 6 networks when dealing with complex scenes, such as a lack of focus on local features and insufficient 7 modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced 8 Multi-scale Attention-driven Single Image 3D Human Reconstruction. This approach incorporates 9 a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate 10 features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention 11 mechanisms by adaptively adjusting the weights between features, enabling the network to more 12 effectively leverage information across various scales.Furthermore, we have improved the feature 13 fusion method to better integrate representations from different scales. This enhanced feature fusion 14 allows the network to more comprehensively understand both fine details and global structures 15 within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention 16 mechanism and feature fusion method, optimizing the network’s training process and enhancing the 17 quality of reconstruction results.Our network demonstrates significant improvements in performance 18 for 3D human model reconstruction. Experimental results show that our method exhibits greater 19 robustness to challenging poses compared to traditional single-scale approaches.

Although the field of geomatics has seen multiple technological advances in the recent years which enabled new applications and simplified the consolidated ones, some tasks remain challenging, inefficient, and time- and cost-consuming. This is the case of accurate tridimensional surveys of narrow spaces. Static laser scanning is an accurate and reliable approach but impractical for extensive tunnel environments; portable laser scanning is on the other hand time-effective and efficient but not very reliable without ground control constraints. This paper describes the development process of a novel image-based multi-camera system meant to solve this specific problem: delivering accurate, reliable, and efficient results. The development is illustrated from the system conceptualization and initial investigations to the design choices and requirements for accuracy. The resulted working prototype has been put to the test to verify effectiveness of the proposed approach.

Vaccines decrease morbidity and mortality. Nevertheless, their benefits depend on public response. During COVID-19, vaccine hesitancy and refusal were rampant, threatening public health. A thorough understand-ing of opponents' arguments is required to address the diffusion of unreliable information on social media and prevent vaccine hesitancy from developing into vaccine refusal. Accordingly, the article examines vac-cine opponents' justifications on social media. Textual content analysis of reader comments on three health-related Israeli Facebook pages was conducted. Data collection encompassed the Israeli COVID-19 vaccination period from October 2020 to May 2022. The comments were analyzed according to the health beliefs model (HBM). We found that vaccine opponents were characterized by low perceptions of the severity of the disease combined with high perceptions of the damages of the vaccine; low perceived benefits of vac-cine compliance; vaccine hesitancy and fear along with public distrust as barriers to change; and call for ac-tion to resist the vaccine and spread related anti-establishment views on the web. As vaccination hesitancy was found to develop into public distrust in the state systems and escalate into conspiracy beliefs and an-ti-vaccination activism, its early detection may prevent future vaccine resistance.

This study aims to investigate the impact of body heat loss on the thermal and aerodynamic conditions in a whole-body cryotherapy chamber. The underlying hypothesis is that the heat generated by the human body alters the thermal and aerodynamic environment inside the cabin. A numerical study was conducted to test this hypothesis to analyze the thermodynamic exchanges between the human body and the cabin during a 3-minute whole-body cryotherapy session. The computational fluid dynamics (CFD) approach was used to study the unsteady heat transfer between the human body and the interior of the cryotherapy cabin. A thermal boundary condition, based on a mathematical model developed from experimental data, was applied to simulate skin cooling kinetics over time. The post-processing of the 3D results, including temperature, velocity fields, and thermal flux maps at the body surface, provided insight into the thermo-convective mechanisms involved in a whole-body cryotherapy session. The study found that body heat loss significantly affects the temperature fields inside the cabin, leading to global modifications of the aeraulic and thermal conditions. These findings suggest that cryotherapy protocols may need to be adjusted or the cabin set temperature optimized to enhance the therapeutic benefits.

This paper investigates the periodic and quasi-periodic orbits in the symmetric collinear four-body problem through a variational approach. In Section 3, we analyze the conditions under which homographic solutions minimize the action functional. We demonstrate that these solutions for four equal masses arranged in a linear configuration are indeed the minimizers of the action functional and compute the minimum value of the action functional for these solutions. Additionally, we employ numerical experiments using Poincaré sections to explore the existence and stability of periodic and quasi-periodic solutions within this dynamical system. Our results provide a deeper understanding of the variational principles in celestial mechanics and reveal complex dynamical behaviors that are crucial for further studies in multi-body problems. The findings have significant implications for theoretical research and practical applications in astrodynamics and space mission planning.

With the rising cost of animal feed protein, finding affordable and effective substitutes is crucial. Walnut kernel cake, a polyphenols, fiber, protein and fat-rich byproduct of walnut oil extraction, has been underexplored as a potential protein replacement in pig feed. In this study, we found that feeding large Diqing Tibetan pigs walnut kernel cake promoted adipose deposition and improved pork quality during pig growth. Transcriptome analysis revealed the upregulation of genes ANGPTL8, CCNP, ETV4, and TRIB3, associated with adipose deposition. Pathway analysis highlighted enrichment in adipose-deposition-related pathways, including PPAR, Insulin, PI3K-Akt, Wnt, and MAPK signaling. Further analysis identified DEGs (differentially expressed genes) positively correlated with adipose-related traits, such as PER2 and PTGES. Single-cell transcriptome data pointed to the specific expression of CD248 and PTGES in adipocyte progenitor/stem cells (APSCs), pivotal for adipocyte differentiation and adipose deposition regulation. This study demonstrates walnut kernel cake's potential to substitute soybean cake in pig feed, providing high-quality protein and promoting adipose deposition. It offers insights into feed protein replacement, human functional food, fat metabolism, and related diseases, with marker genes and pathways supporting pig breeding and pork quality improvement.

(1) The purpose of this study was to compare the percentages of quarterly change for each of the variables analysed (height, weight, body fat percentage and muscle mass) as a function of the quarter in a sample of adolescent football players. (2) The sample consisted of young male football players of different categories belonging to a high-performance football academy, (more than 5 years in football and have spent 1 full year at the academy). (3) Two-way repeated measures ANOVA revealed for the anthropometric variable, P_value

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

Background/Objectives: This study looks at how a kinematic chain exercise regimen that targets the lower, core, and upper body affects university shot put participants' shoulder muscle strength and throwing efficiency. This study fills an apparent research void on shot put training approaches by presenting a comprehensive kinematic chain workout program. It was anticipated that this method would improve performance the most, considering the complex biomechanical require-ments of the sport. Methodology: Athletes, seventy in age (19.87 ± 1.31 years), were assigned into two groups at random: experimental (n = 35) and control (n = 35). While the control group carried on with their usual training, the experimental group participated in an 8-week kinematic chain training program, pre- and post-training evaluations were carried out to evaluate shot put throwing ability, shoulder muscle strength, and participant satisfaction with the exercise regimen. Results: demonstrated that, when compared to the control group, the athletes in the kinematic chain program had a significantly improved throwing distance (p = 0.01) and shoulder muscle strength (p = 0.01). Furthermore, there was a significant difference (p = 0.005) in the athletes' satisfaction levels with the workout program among those in the experimental group. This implies that a kinematic chain-focused strategy improves performance and emphasizes the significance this type of training is for shot put athlete performance. Conclusion: As an improvement forward in sports science, the study's finding promotes the inclusion of kinematic chain workouts in shot put training programs and calls for more research on kinematic chain-based approaches in a variety of sports.

The material properties and structural characteristics of ballistic composites are crucial to their ballistic performance. Investigation for the influence of the material and structural parameters of UHMWPE body armor on anti-fragmentation penetration performance has been made by numerical simulation in this paper, with a numerical modeling simulating the penetration of a 1.1 g FSP into the target plate of UHMWPE body armor, by which the simulation results demonstrate that the failure process of the body armor target plate primarily involves shear damage, interlayer delamination, and tensile damage. Furthermore, the study evaluates the effects of elastic modulus, tensile strength, shear strength, number of layers, and interlayer strength on the ballistic limit velocity of UHMWPE body armor. The findings reveal that the ballistic limit velocity is most sensitive to changes in shear strength, with variation rates ranging from -18.02% to 10.81%, showing an approximate positive correlation, while the elastic modulus has the smallest impact on ballistic limit velocity, with variation rates ranging from -1.8% to 3.6%. Additionally, appropriate interlayer strength can improve the ballistic limit velocity of the body armor to a certain extent. This study provides theoretical methods and recommendations for optimizing anti-penetration performance of UHMWPE body armor.

This paper represents the first investigation into the consensus problem of linear time-varying multi-agent systems utilizing an event-triggered communication scheme. First, a general event-triggered consensus control scheme is proposed for a general category of linear time-varying multi-agent systems. Under some suitable assumptions, it is demonstrated that all agents’ states will converge exponentially, with Zeno behaviour being ruled out. Second, the consensus problem in a network of linear time-varying multi-agent systems with a spanning tree is investigated using the proposed control strategy. It demonstrates that the consensus issue for the specified system can be reformulated as a stabilization problem for an error system through a time-varying linear transformation. Then, the event-triggered consensus problem is just a special instance of the general event-triggered consensus problem mentioned above. Finally, to illustrate the efficacy of the event-triggered method proposed in this study, simulation results are shown.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels’ long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels’ correlations are remarkable and stable for images generated by a known physical principle, such as infrared images and medical CT images. And the corresponding non-extensive parameter q can be evaluated by using self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of Shannon entropy algorithm and the original Tsallis entropy algorithm, in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Throughout the eukaryotic tree of life, amoeboid organisms have evolved that aggregate upon starvation and form multicellular fruiting bodies, consisting of a ball of spores atop a stalk. This chapter discusses the remarkable convergent evolution of a stalked fruiting body in these different taxa. It then discusses a well-studied group of aggregative fruiters, the cellular slime molds, in more detail. These organisms exhibit substantial variation in their stalk formation and composition, which allows a better understanding of the evolution, maintenance and possible functions of stalked fruiting bodies, but also points to potential costs and benefits of different types of stalks.

Older adults with obesity have an increased risk for cognitive decline and subsequent Alzheimer’s disease. Previous research has focused on the impact of individual nutrients and overall dietary patterns to improve cognitive function. The Brain and Body Health (BB-Health) study aims to demonstrate the feasibility of a multicomponent nutritional supplement (MCNS) and weight loss (WL) intervention for individuals with overweight and obesity who are not consuming a healthy diet. BB-Health is a parallel-group randomized placebo-controlled 2x2 factorial pilot trial comparing the MCNS, WL, and MCNS+WL with a Control group. Eligible participants are 55 to 85 years of age with a body mass index 27.0 to 39.9 kg/m2. Major exclusion criteria include type 2 diabetes, moderate or severe cognitive impairment, and self-reported healthy dietary pattern. The BB-Health feasibility trial is registered as NCT05542199 at clinicaltrials.gov. The BB-Health objectives are to report: 1) recruitment and enrollment, including pace and descriptive statistics of the study sample; 2) adherence to the administered interventions; 3) successful data collection and performance of outcome assessments, including evaluation of change in key outcomes over time that are powered for this pilot study; and 4) summary statistics on outcomes for use in planning a subsequent, larger trial. The outcomes of interest are age-sensitive cognitive assessments, cerebral hemodynamics, and related health parameters. All measurements are collected at baseline, 3-, 6-, and 12-month visits. If the BB-Health protocol demonstrates feasibility, the novel MCNS with and without a parallel WL intervention has potential to be administered in a confirmatory randomized trial to evaluate ability to prevent age-related cognitive decline and improve brain health. The BB-Health Feasibility Study could significantly contribute to our understanding of the influence of diet and weight management on cognitive health

Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial-temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. We propose a hierarchical residual attention network using a scaled combination of multiple spectrograms, including STFT, Log-Mel, MFCC, and CST features (chroma, spectral contrast, and Tonnetz), to create a comprehensive sound representation. This model enhances focus on relevant spectrogram parts through attention mechanisms. Experimental results with the OpenMIC-2018 dataset show significant improvement in classification accuracy, especially with the "Magnified 1/4 Size" configuration. Future work will optimize CST feature scaling, explore advanced attention mechanisms, and apply the model to other audio tasks to assess its generalizability.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

This scoping review explores the intersection of Extended Mind Theory and Extended Reality (XR) technologies, focusing on how Virtual Reality, Augmented Reality, and Mixed Reality reshape human cognition and interaction. XR enables users to offload cognitive tasks and engage in embodied experiences, extending cognition beyond the brain into digital environments. The review highlights a wide range of XR applications, from immersive learning in STEM education and medical training, neuropsychological assessment to therapeutic interventions, arts and entertainment, professional skills development, retail and e-commerce, remote work, sports training, architecture and urban planning, and cultural heritage preservation. XR's integration with modalities like haptics, eye-tracking, face- and body-tracking, and brain-computer interfaces further enhances cognitive extension and user engagement. However, alongside these advancements come significant ethical, psychological, and societal challenges, such as data privacy concerns, the psychological effects of prolonged immersion, and social inequality arising from disparate access to XR technologies. This review emphasizes the need for robust ethical frameworks that address these challenges, ensuring that XR technologies enhance human development while maintaining autonomy, privacy, and mental well-being. As XR continues to evolve and integrate with artificial intelligence and other emerging technologies, its role in expanding human cognition will depend on responsible implementation and governance.

Existing methods of hyperspectral anomaly detection still face several challenges: 1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; 2) The ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; 3) Current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind spot network based on multi-scale blind spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial-spectral joint module and a background feature attention mechanism to enhance the interaction between spatial-spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that integrates pixel shuffle down-sampling (PD) operation with spatial spectral joint screening to address the issue of anomalous identity map-ping and facilitate finite-scale mask convolution to adapt to the detection of more scale targets. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the art methods.

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

This study investigated the effectiveness of virtual reality (VR) technology in table tennis education compared to traditional training methods. A 12-week randomized controlled trial was conducted with 120 participants divided equally between VR and traditional training groups. Performance metrics, learning motivation, and satisfaction were assessed at regular intervals. Results demonstrated significant advantages of VR training, with the VR group showing superior improvements in serve accuracy (23.5% vs. 15.8%, p < 0.001), rally endurance (increase of 8.2 vs. 5.7 shots, p < 0.01), and overall skill scores (18.7 vs. 13.2 points improvement, p < 0.001). The VR group also exhibited higher increases in learning motivation (23.5% vs. 12.8%, p < 0.001) and satisfaction (31.5% vs. 18.7%, p < 0.001). Subgroup analysis revealed particular benefits for novice players and younger participants. These findings suggest that VR technology offers a promising approach to enhance table tennis education, potentially revolutionizing sports training methodologies. Future research should focus on long-term skill retention and the optimization of VR training protocols.

The utilization of Artificial Intelligence (AI) in biotechnology is widespread, addressing challenges such as elucidating disease pathophysiology and novel therapeutic approaches. AI plays a critical role in tasks such as drug manufacturing, chemical analysis, RNA and DNA sequencing, and enzyme studies by automating processes and expediting data collection and analysis. AI has particularly transformed the analysis of omics data for researchers, providing tools to handle complex and multifaceted datasets effectively. Through AI, integrative analysis of multi-omics data can be conducted in efficient ways. This article discusses the application of multi-omics and AI technologies in studying various diseases related to infection (COVID-19), mutation (cancer), and senescence (alopecia) to understand their pathophysiology. The applications of AI were performed as the aspect of the diagnosis, prognosis, and treatment for each disease. We introduce AI-driven analysis of pathophysiology can overcome traditional limitations in pathology, with diverse features showing potential in defining the causes of diseases, and potentially enhancing primary omics data in the distinguish the subtype of diseases. In this context, integrated approaches using multi-omics data and AI technology could provide fresh biological insights by comprehending the molecular mechanism of diseases and is instrumental in advancing precision medicine. We anticipate that the rapidly advancing AI-based multi-omics data analysis will significantly contribute to promoting healthy longevity.

Background and Objective: Although whole body-electromyostimulation (WB-EMS) has been shown to favorably improve body composition in several research studies, it has not been confirmed how it affects adipose tissue-derived biomarkers and circumferences of the abdomen and thighs. Materials and Methods: Twenty participants of obese elderly women were randomly classified into an EMS group (EMSG; n = 10) or a non-EMS group (CON; n = 10). The WB-EMS suits used in this study enabled the simultaneous activation of 8 muscle groups with selectable intensities for each phase. Stimulation frequency was selected at 85 Hz, impulse-width at 350 μ seconds, and impulse-rise as a rectangular application. Impulse duration was 6 seconds with a 4-second break between impulses. The EMS sessions were combined with aerobic dancing 3 times a week for 12 weeks. Both groups received the same program; however, the CON did not receive electrical stimuli. Biomarkers and body composition including circumferences of the abdomen and thighs were measured on Week 0 and Week 12. Results: Compared with the CON, 1) a significant effect of the EMS-intervention concerning decreased body weight (p = 0.032) and increased basal metabolic rate (p = 0.029) were evident. The thigh subcutaneous fat and total fat of EMSG were not significantly changed, but those of CON were significantly increased after 12 weeks. 2) The biomarkers of CON either increased or showed no change, although the interleukin (IL)-6 was significantly (p = 0.028) decreased in EMSG. Specifically, C-reactive protein (CRP; p = 0.005) and alpha-fetoprotein (p = 0.050) of CON were significantly increased after 12 weeks. In addition, the IL-6 (p = 0.034), CRP (p = 0.001), carcinoembryonic antigen (p = 0.017), and low density lipoprotein (p = 0.005) of EMSG were lower than those of CON in the end of experiment. 3) Conclusions: The results indicate that EMS intervention improved body composition and reduced negative biomarkers, thus highlighting its effectiveness.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

Monte Carlo techniques have been extensively used for planning laser-based clinical procedures such as photobiomodulation. However, the effect of several biological tissue characteristics, including morphological structure and physiological parameters, has not been carefully addressed in many applications. Specifically, many questions remain concerning the effect of skin phototype and body mass index on the efficacy of photobiomodulation for extraoral therapies. To address these questions, a Monte Carlo simulation model was developed to analyze the effects of body mass index-dependent skin structure and Fitzpatrick skin type, specifically tailored for the morphological characteristics of cheek tissue. The model describes the settings of a typical oral photobiomodulation treatment protocol for pain relief, namely the use of 660 nm and 808 nm laser wavelengths and a therapeutic dose of 2.0 J/cm² on the masseter muscle. The simulations were used to train a machine learning predictive model aimed at accelerating the treatment planning stage and assessing the importance of patient-specific parameters. A multiple regression approach was adopted to predict muscle dose and treatment time for the effective delivered dose.

Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which require a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in that deep learning-based SAR ship detectors struggle due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed as Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher-student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch, but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and learning comprehensive diversity information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach.

In this review we aim to address the current status of phage therapy in the management of multidrug-resistant infections, from compassionate use cases to ongoing clinical trials, as well as the challenges this approach presents in the particular context of CF patients.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

Background: Smad4, a critical tumor suppressor gene, plays a significant role in pancreatic biology and tumorigenesis. Genetic background and sex are known to influence phenotypic outcomes, but their impact on pancreatic weight in Smad4-deficient mice remains unclear. This study investigates the impact of Smad4 deficiency on pancreatic weight in F1 mice from diverse Collaborative Cross (CC) lines, focusing on the influence of genetic background and sex. Methods: F1 mice were generated by crossbreeding female CC mice with C57BL/6J-Smad4tm1Mak males. Genotyping confirmed the presence of Smad4 knockout alleles. Mice were housed under standard conditions, euthanized at 80 weeks, and their pancreatic weights were measured, adjusted for body weight, and analyzed for effects of Smad4 deficiency, sex, and genetic background. Results: The overall population of F1 mice showed a slight but non-significant increase in adjusted pancreatic weights in heterozygous knockout mice compared to wild-type mice. Sex-specific analysis revealed no significant difference in males but a significant increase in adjusted pancreatic weights in heterozygous knockout females. Genetic background analysis showed that lines CC018 and CC025 substantially increased adjusted pancreatic weights in heterozygous knockout mice. In contrast, other lines showed no significant difference or varied non-significant changes. The interplay between genetic background and sex further influenced these outcomes. Conclusion: Smad4 deficiency affects pancreatic weight in a manner significantly modulated by genetic background and sex. This study highlights the necessity of considering these factors in genetic research and therapeutic development, demonstrating the value of the Collaborative cross-mouse population in dissecting complex genetic interactions.

Real-time object detection remains a pivotal topic in the realm of computer vision. Balancing the accuracy and speed of object detectors poses a formidable challenge for both academic researchers and industry practitioners. While recent transformer-based models have showncase the prowess of the attention mechanism, delivering remarkable performance improvements over CNNs, their computational demands can hinder their effectiveness in real-time detection tasks. In this paper, we elect to use YOLOX as our robust starting point and introduce a series of effective enhancements, culminating in a new high-performance detector named YOLOAX. To further exploit the power of the attention mechanism, we devise multi-dimensional attention-based modules that activate CNNs, emphasizing the most pertinent regions and bolstering the capacity to learn the most informative image representations from feature maps. Moreover, we introduce a novel leading label assignment strategy called STA, along with a groundbreaking loss function named GEIOU Loss, to further refine our detector's performance. We provide extensive ablation studies on the COCO and PASCAL VOC 2012 detection datasets to validate our proposed methods. Remarkably, our YOLOAX is trained solely on the MS-COCO dataset from scratch, without leveraging any prior knowledge, which achieves an impressive 54.2% AP on the COCO 2017 test set while maintaining a real-time speed of 72.3 fps, surpassing YOLOX by a significant margin of 3.0% AP. Our source code and pre-trained models are openly accessible at https://github.com/KejianXu/yoloax.

The monitoring of maternal and child health, using wearable devices made with wireless sensor technologies, is expected to reduce maternal and child death rates. Wireless sensor technologies have been used in wireless sensor networks to enable the acquisition of data for monitoring machines, smart cities, transportation, asset tracking, and tracking of human activity. Applications based on wireless body area network (WBAN) have been used in healthcare for measuring and monitoring of patient health and activity through integration with wearable devices. Wireless sensors used in WBAN can be cost-effective, enable remote availability, and can be integrated with electronic health record (EHR) management systems. Interoperability of WBAN sensor data with other linked data has the potential to improve health for all, including maternal and child health through the improvement of data access, data quality and healthcare access. This paper presents a survey of the state-of-the-art techniques for managing WBAN sensor data interoperability. The findings in this study will provide reliable support to enable policymakers and health care providers to take action to enhance the use of e-health to improve maternal-child health and reduce the mortality rates of women and children.

Imitation learning, also known as programming by demonstration, has been shown to be a promising paradigm for intuitive robot programming by non-expert users. However, the classical kinesthetic approach with physical hand guidance suffers from generalizability across different robot types and is impractical for demonstrating tasks with long horizons. Visual imitation learning enables the recording of multi-step tasks as a single continuous video, allowing non-experts to demonstrate tasks naturally. Existing approaches typically require a large amount of data to develop end-to-end deep learning models that map raw pixels to robot actions. This paper explores the application of visual imitation learning from one-shot demonstration, significantly reducing the data requirements and simplifying the programming process. To achieve this target, a framework is proposed to map hand trajectories to the robot end-effector, consisting of four essential components: hand detection; object detection; segmentation of the trajectories into elemental skills; and learning the skills. Methods are developed for each component and evaluated on recorded videos to demonstrate the effectiveness of the proposed framework.

Drag reduction by laminarization, an innovative technology in aerodynamics design, provides the possibility to improve significantly the aerodynamic performance of aircraft and remains to be one of the most promising and effective technologies. In this paper, the design method for transonic natural laminar flow (NLF) wing at high Reynolds number is investigated in details and a new optimization model is established to solve conflicts between the increase of a laminar flow region and the shock wave strength at the trailing edge. In order to numerically handle this competitive situation efficiently, a two-objective hierarchical variable fidelity Pareto evolutionary optimization model based on search space contraction is proposed and implemented. Numerical experiments show that it can capture simultaneously a Pareto front of the two-objectives: a wave drag minimization and a laminar flow region maximization. The results also show that both wave drag and skin friction drag performances of non-dominated Pareto members can be significantly improved via the optimal laminar wing shape and shock wave control device. Nearly 40% of the laminar flow area on the wing surface is achieved at the Reynolds number of 107. It is concluded that the present hierarchical variable-fidelity multi-objective evolutionary optimization method is computationally efficient, and illustrate its potential for solving complex engineering design problems.

With LiDAR (Light Detection and Ranging) time series being used for various applications, optimal realisation of a common geodetic datum over many epochs is a highly important prerequisite with direct impact on accuracy and reliability of derived measures. In our work, we develop and define several approaches to the adjustment of multi-temporal LiDAR data in a given software framework. These approaches ranging from pragmatic to more rigorous solutions are applied to an 8 year time series with 21 individual epochs. The analysis of the respective results suggests that a sequence of bi-temporal adjustments brings the best results while being more flexible and computationally viable than the most extensive approach of using all epochs in one single multi-temporal adjustment. With a combination of sparse control patches measured in the field and one selected reference block, we obtain relative datum discrepancies in the range of 1-2 cm for the complete time series. Based on our findings, we formulate design criteria for setting up and adjusting future time series with the proposed method.

Biological neural network (BNN) is the core brain for creature to accomplish its intelligent behaviors through unique network structure and mechanisms. It also inspires controlling human-designed autonomous agents including robots to generate more advanced intelligent behaviors by mimicking the neural control mechanism of creatures at a deeper and more interactive level. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of cellular synapse. The neural network simulation integrates computational programming and visualization of neurons and synapse connections of C. elegans, containing the specific controllable circuits summarization with their dynamic characteristics within the whole network. To illustrate its particular intelligent control capability in terms of robotics, we introduce the first innovative methodology for applying the BNN model to a 12-legged robot’s movement control based on the established numerical simulation platforms. We accomplished and designed two methods and corresponding encoding processes, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of BNN. Both simulation and experiment results indicate that the robot exhibits stronger autonomy and more intelligent moving performance under the control of BNN. We then summarized the contributions for digitalizing the C. elegans’ whole-brain neural network in real-time and utilizing it to control robot in a closed loop to validate its advanced intelligence control ability in terms of a scientific robot.

Abstract: With the growing integration of the Internet of Things (IoT) in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of Wireless Sensor Networks (WSNs) in a commercial multi-story building, specifically addressing the difficulty of dealing with variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights that have the potential to signifi-cantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. By leveraging the capabilities of Contiki operating system, this study employs a specialized simulator to generate simulated data, allowing for a comparison between real-world and simulated scenarios. We provide an in-depth analysis to scrutinize the disparities and commonalities between the datasets obtained from real-world deployments and simulated en-vironments. We highlight the necessity of precise simulation to validate real-life sensor data, shedding light on the challenges and opportunities associated with implementing WSNs in in-telligent buildings. Results obtained show the significance of accurate simulation models for re-liable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. The findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental pa-rameters. This includes insights into temperature, humidity, and light intensity, offering oppor-tunities for enhanced comfort and efficiency in intelligent environments.

The influence of short term water absorption on the mechanical properties of halloysite nanotubes-multi layer graphene reinforced polyester hybrid nanocomposites has been investigated. The addition of nano-fillers significantly increased the flexural strength; tensile strength and impact strength in dry and wet conditions. After short term water exposure; the maximum microhardness; tensile; flexural and impact toughness values were observed at 0.1 wt% MLG. The microhardness increased up to 50.3%; tensile strength increased up to 40% and flexural strength increased up to 44%. Compared to dry samples; the fracture toughness and surface roughness of all types of produced nanocomposites were increased that may be attributed to plasticization effect. Scanning electron microscopy revealed that the main failure mechanism is caused by the weakening of nano-filler-matrix interface induced by water absorption. It was further observed that synergistic effects were not effective at concentration of 0.1 wt% to produce considerable improvement in mechanical properties of produced hybrid nanocomposites.

This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

The awareness of the vulnerability of existing structures under both seismic and energy viewpoints highlight the need to develop integrated retrofit solutions combining the structural and thermal performances. In this perspective, the study explores the efficacy of the Resisto 5.9 Tube system, a seismic coat that improves the seismic behaviour of masonry and reinforced concrete (RC) structures, also enhancing their energy performances, thanks to the thermal coat integrated in the basic steel system. The research involves the application of a design procedure specifically developed for this retrofit system from the seismic perspective. Once all the system elements are designed, non-linear static analyses are performed with a Finite Element software comparing the building’s seismic performances before and after the application of Resisto 5.9 Tube. The results demonstrate a significant increase in seismic safety coefficient, proving that this intervention type can be useful to obtain the seismic upgrading of existing RC buildings.

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human keypoint detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of keypoints. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model's robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to re-weight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded data sets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.

Background Children often suffer the nonspecific musculosceletal pain as reported in literature. Aim To determine relationship between body weights with development of musculoskeletal pain and to determine whether growing in body height is associated with musculoskeletal pain in schoolchildren. Subjects/ Methods A prospective longitudinal study included 1315 school children aged 7-14 years (652 boys and 663 girls) and was performed in 13 elementary schools in B&H. Child body height and body weight were measured. The survey of perception of musculoskeletal pain in different body regions of subjects was conducted by adjusted Nordic Musculosceletal Questionnaire (NMQ). Results The highest prevalence of an overweight and obesity in the 10th year 35.7% and the lowest frequency 17.8% in the 14th year was. In the age 14th obesity was’nt found. Boys have more prevalence of overweight. Using logistic regression model, we found that school children with normal BMI were protected with increased body height of acute lower back pain (β= -0.089, 95%CI, -9.730- -0.023, P< 0.049), and increased body height was protector of obese school children of acute upper back pain (β= -0.356, 95%CI, -14.077- -3.878, P< 0.001) and chronic lower back pain (β= -0.356, 95%CI, -14.077- -3.878, P< 0.001). Conclusion Schoolchildren with normal weight more often have had musculosceletal pain than those with overweight or obesity. This can be associated with intense physical growth period in height, especially. The assumption is that the increase in height changes the relationship between excessive BMI and musculoskeletal pain in children of school age.

Background: Bioelectrical impedance (BIA) and ultrasound (US) have become popular for estimating body fat percentage (BF%) due to their low cost and clinical convenience. However, agreement of these devices with gold standard method still require investigation. The aim was to analyze the agreement between a gold standard %BF assessment method with BIA and US devices. Methods: Twenty-three men (age 30.1 ± 7.7 years, 82.5 ± 14.9 kg, 1.77 ± 0.05 m) underwent Dual Energy X-Ray Absorptiometry (DXA), BIA (tetrapolar) and US (three-site method) %BF assessment. Pearson and concordance correlations were analyzed. T-test compared means between methods, and Bland Altman plots analyzed agreement and proportional bias. Alpha was set at &lt; .05. Results: Pearson coefficients between BIA and US with DXA were high (BIA = 0.94; US = 0.89; both p &lt; .001). Concordance coefficient was high for BIA (0.80) and moderate for US (0.49). BF% measured by BIA (24.5 ± 7.5) and US (19.4 ± 7.0) were on average 4.4% and 9.6% lower than DXA (29.0 + 8.5%), respectively (p &lt; .001). Lower and upper agreement limits between DXA and BIA were -1.45 and 10.31, while between DXA and US were 2.01 and 17.14, respectively. There was a tendency of both BIA (p = .09) and US (p = .057) to present proportional bias and underestimate BF%. Conclusions: Despite the correlation, the mean difference between the methods were significant, and the agreement limits are very wide. This indicate that BIA and US, as measured in this study, have limited potential to accurately measure %BF compared to DXA, especially in individuals with higher body fat.

We previously reported our initial experience of subxiphoid single-port robotic-assisted thoracic surgery (SRATS) thymectomy using the single-port robotic surgical system (SPS). However, the potential benefits of this technique remain unknown. Thus, this study examined the multi-institutional experience of SRATS thymectomy and compared perioperative outcomes of this technique with those of subxiphoid single-port video-assisted thoracic surgery (SVATS) thymectomy. Data of patients who underwent subxiphoid SRATS and SVATS thymectomy performed by three thoracic surgeons at three institutions between September 2018 and May 2024 were retrospectively collected. In total, 110 patients were included, with 85 and 25 undergoing SRATS and SVATS thymectomy, respectively. No significant differences were noted in the total operative time (154.46±74.06 vs. 146.76±67.07 min, P=0.674) and conversion rate to median sternotomy (0% vs. 4 %, P=0.227) between the two groups. The SRATS group was associated with a lower conversion rate to multi-port surgery (2% vs. 20%, P=0.006), shorter chest tube drainage duration (1.40±0.94 vs. 2.00±1.29 days, P=0.001), and shorter postoperative hospital stay (2.87±1.26 vs. 5.08±5.20 days, P=0.007). Subxiphoid SRATS thymectomy using the SPS is feasible and can be a good alternative to conventional minimally invasive thymectomy. Further large-scale randomized controlled studies are needed to confirm its benefits.