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Application of additive manufacturing (AM)/three-dimensional (3D) printing has been a very hot area of research in recent years. The fast and easy way to get the desired product and additive function, such as anti-bacterial materials, embedded nano detective sensors. This study reviewed 197 articles on application of AM/3D printing in rehabilitation and healthcare. The distribution of these articles was investigated in terms of journal type, year of publication, methodologies, and research contexts, etc. Based on the use of citation network analysis (CNA), the result shows the selected articles (197 articles) can be divided into two directions, healthcare and rehabilitation, and healthcare monitoring and materials for 3D printing purposes. All of them represent possible opportunities for future research. Further investigation was done by conducting a Main Path Analysis of the articles to draw a map of knowledge structure within each research domain. Finally, future research opportunities and directions are proposed for each research domain, rehabilitation and healthcare. In addition, use of 3D printing materials with antimicrobial properties for healthcare and rehabilitation is having more potential in future research. [ABSTRACT FROM AUTHOR]

While various screening instruments for autism spectrum disorders are widely used in diagnostic assessments, their psychometric properties have not been simultaneously evaluated in the outpatient setting where these instruments are used most. In this study, we tested the Ritvo Autism Asperger Diagnostic Scale-Revised and two short versions of the Autism-Spectrum Quotient, the AQ-28 and AQ-10, in 210 patients referred for autism spectrum disorder assessment and in 63 controls. Of the 210 patients, 139 received an autism spectrum disorder diagnosis and 71 received another psychiatric diagnosis. The positive predictive values indicate that these tests correctly identified autism spectrum disorder patients in almost 80% of the referred cases. However, the negative predictive values suggest that only half of the referred patients without autism spectrum disorder were correctly identified. The sensitivity and specificity of each of these instruments were much lower than the values reported in the literature. In this study, the sensitivity of the Ritvo Autism Asperger Diagnostic Scale-Revised was the highest (73%), and the Autism-Spectrum Quotient short forms had the highest specificity (70% and 72%). Based on the similar area under the curve values, there is no clear preference for any of the three instruments. None of these instruments have sufficient validity to reliably predict a diagnosis of autism spectrum disorder in outpatient settings.

Abstract This article examines the strong consistency of the conditional value-at-risk (CVaR) estimate for asymptotic negatively associated (ANA or ρ − $\rho ^{-}$ , for short) random samples under mild conditions. It is demonstrated that the optimal rate can achieve nearly O ( n − 1 / 2 ) $O (n^{-1/2})$ under certain appropriate conditions. Furthermore, we present numerical simulations and a real data example to corroborate our theoretical results based on finite samples.

Objective Shanghai′s rail transit boasts of huge commuter flow, and it is necessary to analyze the spatial distribution, connection and other characteristics of the passengers using urban rail transit for commuting. Method Based on the Baidu location big data and the passenger flow data from the automatic fare collection system of Shanghai rail transit, the commuters of Shanghai rail transit are statistically analyzed from the perspectives of total commuting volume, spatial distribution, commuting route, in-and-out station, and traffic connection characteristics between residence and workplace, and relevant suggestions are put forward. Result & Conclusion It is suggested that the tangential line shall be increased in urban rail transit, the land use layout around station be optimized, and the work place and residence along the urban rail transit network corridor keep a better balance.

The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of ‘water quality distance’ was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively. HIGHLIGHTS Validation of three unsupervised learning clustering algorithms in six real cases.; L2 distance improved based on water quality in the clustering algorithms.; Practical engineering applications consideration.; Machine clustering compared with human and ChatGPT's clustering.;

In order to enhance the support capability of photovoltaic inverters for new energy microgrid systems, grid-forming control technology has attracted widespread attention, with Virtual Synchronous Generator (VSG) emerging as a research frontier. This paper integrates hybrid energy storage systems with photovoltaic generation to provide stable voltage support and power compensation for the system. In addition, leveraging the variability of the virtual parameters J and D in traditional VSGs, an adaptive grid-forming photovoltaic inverter control strategy based on fuzzy algorithm is proposed. Furthermore, to reduce the frequency deviation caused by load transients during islanding operation, an adaptive droop coefficient based on frequency power limits is introduced. Finally, simulations are conducted under grid-connected and islanded conditions to compare three control methods: fixed parameter control, traditional adaptive control, and the proposed adaptive with fuzzy control. The results demonstrate that integrating adaptive droop coefficients with fuzzy control can effectively improve the frequency stability and dynamic response capability of microgrids.