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Abstract Objective This scoping review aims to explore published literature testing Virtual Reality (VR) interventions for improving upper limb motor performance in children and adolescents with Developmental Coordination Disorder (DCD). Our primary focus was on the types of VR systems used and the measurement tools employed within the International Classification of Functioning, Disability and Health Children and Youth Version (ICF-CY) domains in these studies. Methods A comprehensive search of six electronic databases up to 11th January 2024 was conducted using predefined terms. Inclusion and exclusion criteria were applied to determine study eligibility, with two authors independently assessing titles, abstracts, and full-text articles. Results Out of 788 potential studies, 14 met the eligibility criteria. Studies predominantly utilized non-immersive VR (nVR) systems, for example, commercial platforms such as Nintendo Wii. Most interventions targeted general motor coordination or balance, with only four studies specifically focusing on upper limb motor performance. The Movement Assessment Battery for Children-2 was the predominant assessment tool. However, the use of game scores and trial durations raised concerns about the accuracy of assessments. The majority of studies reported no significant improvement in upper limb motor performance following VR interventions, though some noted improvements in specific tasks or overall outcomes. Conclusion The findings suggest that, while nVR interventions are being explored for paediatric motor rehabilitation, their impact on enhancing upper limb motor performance in children with DCD is unclear. The variability in intervention designs, outcome measures, and the predominant focus on general motor skills rather than specific upper limb improvements highlight the need for more targeted research in this area. Impact This review underscores the importance of developing precise and clinically relevant measurement tools in a broader range of VR technologies to optimize the use of VR in therapy for children with DCD. Future research should aim for more rigorous study designs and emerging immersive technologies to maximize therapeutic benefits.

The new energy industry development is affected by many factors. Among them, the resources utilization ratio is a major reason for the low productivity of enterprises. As the core problem of cloud computing, the resource allocation problem has been widely concerned by the people, and the resource allocation problem of the new energy industry as the key to energy innovation and transformation should be more paid attention to. In multi-resource cloud computing scenarios, requests made by users often involve multiple types of resources. Traditional resource allocation algorithms have a single optimization object, typically time efficiency. In order to achieve cluster load balancing, utilization of system resources and improvement of system work efficiency, this paper proposes a new cloud computing allocation algorithm based on improved ant colony algorithm. According to the limit conditions of cloud computing environment and computing resources, this paper finds the shortest response time of all resource nodes and gets a set of best available nodes. This method can meet the quality requirements of cloud computing, and the task completion time of the improved algorithm is shorter, the number of algorithm iterations is less, and the load balancing effect is better. Through MATLAB simulation experiments, the effectiveness of the proposed method is verified.

Accurate classifications of land use/land cover (LULC) in arid regions are vital for analyzing changes in climate. We propose an ensemble learning approach for improving LULC classification accuracy in Xinjiang, northwest China. First, multisource geographical datasets were applied, and the study area was divided into Northern Xinjiang, Tianshan, and Southern Xinjiang. Second, five machine learning algorithms—k-nearest neighbor, support vector machine (SVM), random forest (RF), artificial neural network (ANN), and C4.5—were chosen to develop different ensemble learning strategies according to the climatic and topographic characteristics of each subregion. Third, stratified random sampling was used to obtain training samples and optimal parameters for each machine learning algorithm. Lastly, each derived approach was applied across Xinjiang, and subregion performance was evaluated. The results showed that the LULC classification accuracy achieved across Xinjiang via the proposed ensemble learning approach was improved by ≥6.85% compared with individual machine learning algorithms. By specific subregion, the accuracies for Northern Xinjiang, Tianshan, and Southern Xinjiang increased by ≥6.70%, 5.87%, and 6.86%, respectively. Moreover, the ensemble learning strategy combining four machine learning algorithms (i.e., SVM, RF, ANN, and C4.5) was superior across Xinjiang and Tianshan; whereas, the three-algorithm (i.e., SVM, RF, and ANN) strategy worked best for the Northern and Southern Xinjiang. The innovation of this study is to develop a novel ensemble learning approach to divide Xinjiang into different subregions, accurately classify land cover, and generate a new land cover product for simulating climate change in Xinjiang.

The effects of surface dissolution on dislocation activation in FeSi6.5 steel are quantitatively studied by analyzing the stress relaxation data using the thermal activation theory of dislocation. The stressed FeSi6.5 steel sample in acid solutions (H2SO4 or HCl) exhibits a much higher rate of stress reduction with time compared with that in air or deionized water. As the stress relaxation time is prolonged to 20 min, the relaxation rates are 0.055 MPa·min−1 in water and 0.074, 0.1, 0.11 MPa·min−1 in H2SO4 solutions with pH 4, 3, and 2, respectively. In a NaCl solution, a slight increase in the relaxation rate compared with air was found. Higher acidity (lower pH) of the solution inducing higher stress relaxation rate implies the softening is associated with the anodic dissolution of the surface layer and the accelerated (additional) flow of dislocations. The analyses using the thermal activation theory of dislocation during relaxation reveal the mechanism for the accelerated plastic flow induced by the corrosive medium. The variations of these parameters are related to the relaxation of the stress field of dislocations and the weakening of interaction between slip dislocations and short-range obstacles. The chemomechanical effect, including a reduction in apparent activation energy and a decrease in waiting time for dislocation to obtain sufficient thermal activation energy to cross obstacles, causes an increase in the stress relaxation rate (plastic strain rate). The study confirms that surface dissolution accelerates the plastic flow of metals and supports the view that surface dissolution facilitates dislocation slip. It is helpful to improve the formability of brittle metals.

Human height is a polygenic trait, influenced by a large number of genomic loci. In the pre-genomic era, height prediction was based largely on parental height. More recent predictions of human height have made great strides by integrating genotypic data from large biobanks with improved statistical techniques. Nevertheless, recent studies have not leveraged parental height, an added feature that we hypothesized would offer complementary predictive value. In this study, we assessed the predictive power of polygenic risk scores (PRS) combined with the traditional parental height predictors. Our study analyzed genotypic data and parental height from 1,071 trios from the United Kingdom Biobank and 444 trios from the Framingham Heart Study. We explored a series of statistical models to fully evaluate the performance of several PRS constructed together with parental information and proposed a model we call PRS++ that includes gender, parental height, and PRSs of parents and proband. Our estimate of height with an R2 of ∼0.82 is, to our knowledge, the most accurate estimate yet achieved for predicting human adult height. Without parental information, the R2 from the best PRS-driven model is ∼0.73. In summary, using adult height prediction as an example, we demonstrated that traditional predictors still play important roles and merit integration into the current trends of intensive PRS approaches.

China’s river basin ecosystems are facing serious environmental and structural fragmentation problems. However, there are no complete ecological redlines specifically for watershed ecological conservation. This study considers the Xiangjiang River Basin (XJRB) to propose a framework delineating the ecological redline area (ERA) on a watershed scale and highlights policy recommendations. It combines several evaluating indicators for coupling the response mechanism between human demand and ecological protection. Overall, the water supply in the XJRB meets the social demand. However, water supply in some places, such as the core area of Changsha Zhuzhou Xiangtan Urban Agglomeration and the Hengyang urban area, is still insufficient. This study considers ecosystem service functions, ecological sensitivities, and social needs and estimates the ERA of the XJRB to be 36,057 km2, accounting for 38% of the total watershed. The key ecological functional areas account for 60% of the total redline area and are concentrated in the upper reaches. Compared with the current method conducted at the national and administrative level, the redline area defined in this study by the sub-basin scale exhibits better connectivity and integrity. Moreover, the study can help ensure an accurate placement of the ecological redline while also providing an important reference for redline delineation of other river basins.

With high resolution and wide coverage, satellite precipitation products like Global Precipitation Measurement (GPM) could support hydrological/ecological research in the Tianshan Mountains, where the spatial heterogeneity of precipitation is high, but where rain gauges are sparse and unevenly distributed. Based on observations from 46 stations from 2014–2015, we evaluated the accuracies of three satellite precipitation products: GPM, Tropical Rainfall Measurement Mission (TRMM) 3B42, and the Climate Prediction Center morphing technique (CMORPH), in the Tianshan Mountains. The satellite estimates significantly correlated with the observations. They showed a northwest–southeast precipitation gradient that reflected the effects of large-scale circulations and a characteristic seasonal precipitation gradient that matched the observed regional precipitation pattern. With the highest correlation (R = 0.51), the lowest error (RMSE = 0.85 mm/day), and the smallest bias (1.27%), GPM outperformed TRMM and CMORPH in estimating daily precipitation. It performed the best at both regional and sub-regional scales and in low and mid-elevations. GPM had relatively balanced performances across all seasons, while CMORPH had significant biases in summer (46.43%) and winter (−22.93%), and TRMM performed extremely poorly in spring (R = 0.31; RMSE = 1.15 mm/day; bias = −20.29%). GPM also performed the best in detecting precipitation events, especially light and moderate precipitation, possibly due to the newly added Ka-band and high-frequency microwave channels. It successfully detected 62.09% of the precipitation events that exceeded 0.5 mm/day. However, its ability to estimate severe rainfall has not been improved as expected. Like other satellite products, GPM had the highest RMSE and bias in summer, suggesting limitations in its way of representing small-scale precipitation systems and isolated deep convection. It also underestimated the precipitation in high-elevation regions by 16%, suggesting the difficulties of capturing the orographic enhancement of rainfall associated with cap clouds and feeder–seeder cloud interactions over ridges. These findings suggest that GPM may outperform its predecessors in the mid-/high-latitude dryland, but not the tropical mountainous areas. With the advantage of high resolution and improved accuracy, the GPM creates new opportunities for understanding the precipitation pattern across the complex terrains of the Tianshan Mountains, and it could improve hydrological/ecological research in the area.

Abstract Background Over 80% of patients with stroke experience finger grasping dysfunction, affecting independence in activities of daily living and quality of life. In routine training, task-oriented training is usually used for functional hand training, which may improve finger grasping performance after stroke, while augmented therapy may lead to a better treatment outcome. As a new technology-supported training, the hand rehabilitation robot provides opportunities to improve the therapeutic effect by increasing the training intensity. However, most hand rehabilitation robots commonly applied in clinics are based on a passive training mode and lack the sensory feedback function of fingers, which is not conducive to patients completing more accurate grasping movements. A force feedback hand rehabilitation robot can compensate for these defects. However, its clinical efficacy in patients with stroke remains unknown. This study aimed to investigate the effectiveness and added value of a force feedback hand rehabilitation robot combined with task-oriented training in stroke patients with hemiplegia. Methods In this single-blinded randomised controlled trial, 44 stroke patients with hemiplegia were randomly divided into experimental (n = 22) and control (n = 22) groups. Both groups received 40 min/day of conventional upper limb rehabilitation training. The experimental group received 20 min/day of task-oriented training assisted by a force feedback rehabilitation robot, and the control group received 20 min/day of task-oriented training assisted by therapists. Training was provided for 4 weeks, 5 times/week. The Fugl-Meyer motor function assessment of the hand part (FMA-Hand), Action Research Arm Test (ARAT), grip strength, Modified Ashworth scale (MAS), range of motion (ROM), Brunnstrom recovery stages of the hand (BRS-H), and Barthel index (BI) were used to evaluate the effect of two groups before and after treatment. Results Intra-group comparison: In both groups, the FMA-Hand, ARAT, grip strength, AROM, BRS-H, and BI scores after 4 weeks of treatment were significantly higher than those before treatment (p 0.05). Inter-group comparison: After 4 weeks of treatment, the experimental group’s FMA-Hand total score, ARAT, grip strength, and AROM were significantly better than those of the control group (p 0.007). In addition, there were no statistically significant differences in MAS, BRS-H, and BI scores (p > 0.05). Conclusion Hand performance improved in patients with stroke after 4 weeks of task-oriented training. The use of a force feedback hand rehabilitation robot to support task-oriented training showed additional value over conventional task-oriented training in stroke patients with hand dysfunction. Clinical trial registration information NCT05841108

Over the last decades, various cost-based optimizers have been proposed to generate optimal plans for SQL queries. These optimizers are key to achieving good performance in database systems and can speed up query execution. Still, they may need enormous expert efforts and perform poorly on complicated queries. Learning-based optimizers have been shown to achieve high-quality plans by learning from past experiences. However, these solutions treat each query separately and neglect the semantic equivalence among different queries. Intuitively, a high-quality plan may be obtained for a complicated query by discovering a simple equivalent query. Motivated by this, in this paper, we present Celo, a novel constraint-enhanced learned optimizer to directly integrate the equivalent information of queries into the learning-based model. We apply denial constraints to identify equivalent queries by replacing equivalent predicates. Given a query, we augment the query plans generated by the learning-based model with the high-quality plans of its equivalent queries. Then, a more potentially well-performed plan will be predicted among the augmented query plans. Extensive experiments using real-world datasets demonstrated that Celo outperforms the previous state-of-the-art (SOTA) results even with few constraints.

The clinical efficacy of ESWT in treating bone non union has been widely recognized, but the biological mechanism of ESWT promoting bone non union healing is still unclear. ESWT can make old callus micro fracture through mechanical conduction, form subperiosteal hematoma, promote the release of bioactive factors, reactivate the fracture healing mechanism, rebalance the activities of osteoblasts and osteoclast, promote the angiogenesis of fracture site, and accelerate the healing of bone nonunion.Over recent years, great efforts have been made by both scientists and clinicians to explore the underlying mechanism behind the healing effect of ESWT on bone fractures. In this review, we introduced the growth factors during osteogenesis induced by ESWT hoping to provide new insights in the clinical use of ESWT.

Multiple sclerosis is a slowly progressive disease, immunosuppressants and other drugs can delay the progression and progression of the disease, but the most patients will be left with varying degrees of neurological deficit symptoms, such as muscle weakness, muscle spasm, ataxia, sensory impairment, dysphagia, cognitive dysfunction, psychological disorders, etc. From the early stage of the disease to the stage of disease progression, professional rehabilitation treatment can reduce the functional dysfunction of multiple sclerosis patients, improve neurological function, and reduce family and social burdens. With the development of various new rehabilitation technologies such as transcranial magnetic stimulation, virtual reality technology, robot-assisted gait, telerehabilitation and transcranial direct current stimulation, the advantages of rehabilitation therapy in multiple sclerosis treatment have been further established, and more treatment means have also been provided for patients.

Spasticity is one of the most common complications after stroke. With the gradual intensification of spasticity, stroke patients will have a series of problems such as joint ankylosis and movement restriction, which affect the daily activities and increase the burden on patients' families, medical staff and society. There are many ways to treat post-stroke spasticity before, including physical therapy and exercise therapy, drug therapy, surgery and so on, but not satisfied because of a few shortcomings. In recent years, many researchers have applied extracorporeal shock wave therapy (ESWT) for the treatment of post-stroke spasm and achieved good clinical effect, because it is non-invasive, safe, easy to operate, low cost and other advantages compared with other treatment methods. This article reviews the research progress and existing problems of ESWT in the treatment of post-stroke spasticity.

In the era of Big Data, integrating information from multiple sources has proven valuable in various fields. To ensure a high-quality supply of multi-source data, repairing different types of errors in the multi-source data becomes critical. This paper categorizes errors in multi-source data into entity information overlapping, attribute value conflicts, and attribute value inconsistencies. We first summarize existing repairing methods for these errors and then examine and review the study of the detection and repair of compound-type errors in multi-source data. Finally, we indicate further research directions in multi-source data repair.