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With the increase of the volume of world-wide trade, the continuous improvement of tonnage and dynamic performance of ships, and the density of ships on sea routes, the operation level of ship navigator has been challenged seriously. Therefore, it is imminent to improve the operation level of seafarers. Virtual reality technology can be used for navigator training. Environment and motion simulation, using virtual reality technology, has great application potential. In this paper, based on virtual reality technology and three-dimensional simulation software, a ship simulation platform is built, and the motion model are introduced in detail. The three-dimensional modeling of the vessels have been completed in the three-dimensional software 3DS MAX. In this paper, the work of motion simulation is accomplished by solving the motion equation of hull maneuvering, while visualization is realized by using VR toolbox. After ignoring the influence of ship oscillation on its hydrodynamic coefficients, and the vibration factor of propeller when establishing the power system model, the MMG equation has been established. In order to verify the correctness of the model, Simulink simulation is used to verify the model. For the reason of easy to use, low cost, powerful modeling function and good scalability and so on, XNA, a game development environment based on Direct X developed by Microsoft, has been chosen as the driver platform.

•Myofascial Release Technique has significant effects on reducing back disability in patients with low back pain.•MFR can improve the effect of physical therapy alone and exercise therapy alone, and that MFR can be an effective adjuvant therapy. The purpose of this meta-analytic review was to quantitatively examine the effects of myofascial release technique (MFR) on pain intensity, back disability, lumbar range of motion, and quality of life in patients with low back pain (LBP). Potential articles were retrieved using five electronic databases (Web of Science, PubMed, Scopus, China National Knowledge Infrastructure, and Wanfang). The search period was from inception to January 27, 2021. Two researchers independently completed record retrieval and selection, data extraction, and methodological quality assessment. Randomized controlled trials (RCTs) assessing the effect of MFR on pain intensity, back disability, lumbar range of motion, and quality of life in LBP patients were included. Pooled effect sizes were calculated using random effects models and 95 % confidence interval (95 % CI). Data from eight RCTs (386 patients with back pain) meeting the inclusion criteria were extracted for meta-analysis with methodological quality assessment scores ranging from 6 to 10. Compared to the control intervention, MFR induced significant decrease in back disability (SMD = −0.35, 95 % confidence interval [CI] = −0.68, –0.02, P = 0.04, I² = 46 %, n = 284). MFR induced non-significant decrease in the pain intensity (SMD = −0.12, 95 % confidence interval[CI] = −0.35, 0.11, P = 0.32, I² = 0%, n = 294), non-significant improvement in quality of life (SMD = −0.09, 95 % confidence interval [CI] = −0.46, 0.28, P = 0.62, I² = 0%, n = 114), and non-significant improvement in lumbar range of motion (Flexion SMD = 0.57,95 % confidence interval [CI] = −0.09, 1.24, P = 0.09, I² = 54 %, n = 80) (Extension SMD = 0.68, 95 % confidence interval[CI] = −0.72, 2.08, P = 0.34, I² = 89 %, n = 80) (Right flexion SMD = 0.05, 95 % confidence interval[CI] = −0.90, 0.99, P = 0.92, I² = 78 %, n = 80) (Left flexion SMD = 0.14, 95 % confidence interval[CI] = −0.59, 0.88, P = 0.70, I² = 64 %, n = 80). The findings suggest that MFR can improve the effect of physical therapy alone and exercise therapy alone, and that MFR can be an effective adjuvant therapy. Meta-analysis showed that MFR has a significant effect on reducing back disability in patients with low back pain, but no significant effect on reducing pain intensity, improving quality of life, and improving lumbar range of motion.

Virtual reality (VR) technology has shown significant potential in improving the social skills of children and adolescents with autism spectrum disorder (ASD). This study aimed to systematically review the evidence supporting the effectiveness of VR technology in improving the social skills of children and adolescents with ASD. The search for eligible studies encompassed 4 databases: PubMed, Web of Science, IEEE, and Scopus. Two (XY and JW) researchers independently assessed the extracted studies according to predefined criteria for inclusion and exclusion. These researchers also independently extracted information regarding gathered data on the sources, samples, measurement methods, primary results, and data related to the main results of the studies that met the inclusion criteria. The quality of the studies was further evaluated using the Physiotherapy Evidence Database scale. This review analyzed 14 studies on using VR technology interventions to improve social skills in children and adolescents with ASD. Our findings indicate that VR interventions have a positive effect on improving social skills in children and adolescents with ASD. Compared with individuals with low-functioning autism (LFA), those with high-functioning autism (HFA) benefited more from the intervention. The duration and frequency of the intervention may also influence its effectiveness. In addition, immersive VR is more suitable for training complex skills in individuals with HFA. At the same time, nonimmersive VR stands out in terms of lower cost and flexibility, making it more appropriate for basic skill interventions for people with LFA. Finally, while VR technology positively enhances social skills, some studies have reported potential adverse side effects. According to the quality assessment using the Physiotherapy Evidence Database scale, of the 14 studies, 6 (43%) were classified as high quality, 4 (29%) as moderate quality, and 4 (29%) as low quality. This systematic review found that VR technology interventions positively impact social skills in children and adolescents with ASD, with particularly significant effects on the enhancement of complex social skills in individuals with HFA. For children and adolescents with LFA, progress was mainly observed in basic skills. Immersive VR interventions are more suitable for the development of complex skills. At the same time, nonimmersive VR, due to its lower cost and greater flexibility, also holds potential for application in specific contexts. However, the use of VR technology may lead to side effects such as dizziness, eye fatigue, and sensory overload, particularly in immersive settings. These potential issues should be carefully addressed in intervention designs to ensure user comfort and safety. Future research should focus on optimizing individualized interventions and further exploring the long-term effects of VR interventions. International Platform of Registered Systematic Review and Meta-analysis Protocols INPLASY202420079U1; https://inplasy.com/inplasy-2024-2-0079/.

Background Genome structural variations (SVs) have been associated with key traits in a wide range of agronomically important species; however, SV profiles of peach and their functional impacts remain largely unexplored. Results Here, we present an integrated map of 202,273 SVs from 336 peach genomes. A substantial number of SVs have been selected during peach domestication and improvement, which together affect 2268 genes. Genome-wide association studies of 26 agronomic traits using these SVs identify a number of candidate causal variants. A 9-bp insertion in Prupe.4G186800 , which encodes a NAC transcription factor, is shown to be associated with early fruit maturity, and a 487-bp deletion in the promoter of PpMYB10.1 is associated with flesh color around the stone. In addition, a 1.67 Mb inversion is highly associated with fruit shape, and a gene adjacent to the inversion breakpoint, PpOFP1 , regulates flat shape formation. Conclusions The integrated peach SV map and the identified candidate genes and variants represent valuable resources for future genomic research and breeding in peach.

Background Perinatal depression can have profound impacts on both families and society. Exercise therapy is gradually becoming a widely used adjunct treatment for perinatal depression. Some studies have already focused on the relationship between physical activity and perinatal depression (PND). However, there is currently a lack of systematic and comprehensive evidence to address the crucial question of making optimal choices among different forms of physical activity. This study aims to compare and rank different physical activity intervention strategies and identify the most effective one for perinatal depression. Methods Four databases, namely PubMed, Cochrane Library, Embase, and Web of Science, were searched for randomized controlled trials assessing the impact of physical activity interventions on perinatal depression. The search covered the period from the inception of the databases until May 2024. Two researchers independently conducted literature screening, data extraction, and quality assessment. Network meta-analysis was performed using Stata 15.1. Results A total of 48 studies were included in the analysis. The results indicate that relaxation therapy has the most effective outcome in reducing perinatal depression (SUCRA = 99.4%). Following that is mind-body exercise (SUCRA = 80.6%). Traditional aerobics and aquatic sports were also effective interventions (SUCRA = 70.9% and 67.1%, respectively). Conclusion Our study suggests that integrated mental and physical (MAP) training such as relaxation therapy and mind-body exercise show better performance in reducing perinatal depression. Additionally, while exercise has proven to be effective, the challenge lies in finding ways to encourage people to maintain a consistent exercise routine. Trial registration This study has been registered on PROSPERO (CRD 42,023,469,537).

Background: Virtual reality (VR) training is a promising intervention strategy that has been utilized in health care fields like stroke rehabilitation and psychotherapy. Current studies suggest that VR training is effective in improving the locomotor ability of stroke patients. Objective: This is the first meta-meta-analysis of the effects of VR on motor function in stroke patients. This study aimed to systematically summarize and quantify the present meta-analyses results of VR training and produce high-quality meta-meta-analysis results to obtain a more accurate prediction. Methods: We searched 4 online databases (Web of Science, Scopus, PubMed, and Chinese National Knowledge Infrastructure) for meta-analysis studies. After accounting for overlap, 10 studies (accounting for almost 550 stroke patients) were obtained. Based on the meta-meta-analysis of these patients, this study quantified the impact of VR training on stroke patients’ motor performance, mainly including upper limb function, balance, and walking ability. We combined the effects under the random effect model and pooled the estimates as standardized mean differences (SMD). Results: The results of the meta-meta-analysis showed that VR training effectively improves upper limb function (SMD 4.606, 95% CI 2.733-6.479, P<.05) and balance (SMD 2.101, 95% CI 0.202-4.000, P<.05) of stroke patients. However, the results showed considerable heterogeneity and thus, may need to be treated with caution. Due to the limited research, a meta-meta-analysis of walking ability was not performed. Conclusions: These findings represent a comprehensive body of high-quality evidence that VR training is more effective at improving upper limb function and balance of stroke patients.

Background Plant metabolites reshaped by nature and human beings are crucial for both their lives and human health. However, which metabolites respond most strongly to selection pressure at different evolutionary stages and what roles they undertake on perennial fruit crops such as peach remain unclear. Results Here, we report 18,052 significant locus-trait associations, 12,691 expression-metabolite correlations, and 294,676 expression quantitative trait loci (eQTLs) for peach. Our results indicate that amino acids accumulated in landraces may be involved in the environmental adaptation of peaches by responding to low temperature and drought. Moreover, the contents of flavonoids, the major nutrients in fruits, have kept decreasing accompanied by the reduced bitter flavor during both domestication and improvement stages. However, citric acid, under the selection of breeders’ and consumers’ preference for flavor, shows significantly different levels between eastern and western varieties. This correlates with differences in activity against cancer cells in vitro in fruit from these two regions. Based on the identified key genes regulating flavonoid and acid contents, we propose that more precise and targeted breeding technologies should be designed to improve peach varieties with rich functional contents because of the linkage of genes related to bitterness and acid taste, antioxidant and potential anti-cancer activity that are all located at the top of chromosome 5. Conclusions This study provides powerful data for future improvement of peach flavor, nutrition, and resistance in future and expands our understanding of the effects of natural and artificial selection on metabolites.

Background: Prior research has provided evidence supporting the claim that actigaming may offer benefits for individuals’ emotions. However, the impact of actigaming on emotional attentional biases remains uncertain. Objective: To address this gap in the literature, this study aims to examine whether actigaming affects emotional attentional biases. Methods: We conducted a randomized crossover experiment in this study. A total of 18 college students completed a single session of actigaming and aerobic bicycle exercise for 40 min. Before and after the exercises, each participant completed the Profile of Mood States Questionnaire (POMS), Emotion Regulation Questionnaire (ERQ), and emotional attentional biases task. The heart rates of study participants were measured during the intervention to determine their maximum and average rates. Results: The following two findings were identified in this study: (1) There are no significant differences in maximum heart rate and average heart rate between participants of the aerobic bicycle intervention and the actigaming intervention (p = 0.352; p = 0.643, respectively). (2) Cores of POMS under the baseline condition present no significant difference between the two interventions (p = 0.872). However, the POMS post-test scores of participants in the actigaming intervention are significantly lower than those of participants in the aerobic bicycle intervention (p = 0.002). The main finding of the study is that, under baseline conditions, there are no significant differences in emotional attentional bias scores between the two interventions (p = 0.352). However, post-test scores show that participants in the actigaming intervention exhibit significantly lower attentional bias toward negative emotions compared to those in the aerobic bicycle intervention (p = 0.001). Conclusions: Actigaming more favorably post-exercise mood and significantly attenuates attentional bias toward negative stimuli compared with aerobic cycling. Therefore, the results of this study need to be confirmed by higher-quality studies in the future.

The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning models for diabetes prediction using lifestyle data from the National Health and Nutrition Examination Survey (NHANES) database. The 1999–2020 NHANES database yielded data on 17,833 individuals data based on demographic characteristics and lifestyle-related variables. To screen training data for machine models, the Akaike Information Criterion (AIC) forward propagation algorithm was utilized. For predicting diabetes, five machine-learning models (CATBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM)) were developed. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic (ROC) curve. Among the five machine-learning models, the dietary intake levels of energy, carbohydrate, and fat, contributed the most to the prediction of diabetes patients. In terms of model performance, CATBoost ranks higher than RF, LG, XGBoost, and SVM. The best-performing machine-learning model among the five is CATBoost, which achieves an accuracy of 82.1% and an AUC of 0.83. Machine-learning models based on NHANES data can assist medical institutions in identifying diabetes patients.

Bulk polytetrafluoroethylene (PTFE) foam is a great potential oil–water separation material, owing to the high hydrophobicity and lipophilicity property. However, the technique of fabricating PTFE foam with low cost, controllable porosity and high mechanical strength remains an urgent requirement due to its high chemical inertness induced limitation. A kind of bulk PTFE foam material is prepared by cold pressing co-sintering method, employing sodium chloride (NaCl) as pore forming agent. SEM measurements reveal that the size and distribution of pore structure are mainly determined by the particle size and content of NaCl, because the migration and diffusion behaviors of PTFE particles suffer from more inhibition induced by increased NaCl particles during sintering. The PTFE foam with uniformly distributed pore structures can be obtained from composite mixed with 70 wt% NaCl and 30 wt% PTFE, with a low thermal conductivity as low as 0.069 W/(m K). With the decrease of PTFE content in raw composite, the density and mechanical properties of PTFE foam gradually declines. However, the tensile strength remains as low as 0.2 MPa, with an elongation rate of 21%, which guarantees the required mechanical strength avoiding being broken when use as a bulk oil absorption material. Remarkably, the PTFE foam shows a strong hydrophobicity, with a mean water contact angle of 137°, and with a mean surface roughness of 4.41 μm. It exhibits separation efficiency larger than 99% in 30 cycles, with separation flux larger than 4000 L/m 2 /h within 15 cycles, indicating excellent recyclability and stability. The PTFE foam sample also shows desirable continuous separation performance for oil/water mixture. The proposed bulk PTFE foam with low manufacturing cost and stable chemical and mechanical properties will provide data and theoretical support for the material applied in oil/water separation and thermal-protective coating.