Design and application of virtual simulation teaching platform for intelligent manufacturing.

Aiming at the practical teaching of intelligent manufacturing majors faced with lack of equipment, tense teachers and other problems such as high equipment investment, high material loss, high teaching risk, difficult to implement internship, difficult to observe production, difficult to reproduce the results, and so on, we take the electrical automation technology, mechatronics technology and industrial robotics technology majors of intelligent manufacturing majors as an example, and design and establish a virtual simulation teaching platform for intelligent manufacturing majors by using the cloud computing platform, edge computing technology, and terminal equipment synergy. The platform includes six major virtual simulation modules, including virtual simulation of electrician electronics and PLC control, virtual and real combination of typical production lines of intelligent manufacturing, dual-axis collaborative robotics workstation, digital twin simulation, virtual disassembly of industrial robots, virtual simulation of magnetic yoke axis flexible production line. The platform covers the virtual simulation teaching content of basic principle experiments, advanced application experiments, and advanced integration experiments in intelligent manufacturing majors. In order to test the effectiveness of this virtual simulation platform for practical teaching in engineering, this paper organizes a teaching practice activity involving 246 students from two parallel classes of three different majors. Through a one-year teaching application, we analyzed the data on the grades of 7 core courses involved in three majors in one academic year, the proportion of participation in competitions and innovative activities, the number of awards and certificates of professional qualifications, and the subjective questionnaires of the testers. The analysis shows that the learners who adopt the virtual simulation teaching platform proposed in this paper for practical teaching are better than the learners under the traditional teaching method in terms of academic performance, proportion of participation in competitions and innovative activities, and proportion of awards and certificates by more than 13%, 37%, 36%, 27% and 22%, respectively. Therefore, the virtual simulation teaching platform of intelligent manufacturing established in this paper has obvious superiority in solving the problem of "three highs and three difficulties" existing in the practical teaching of engineering, and according to the questionnaire feedback from the testers, the platform can effectively alleviate the shortage of practical training equipment, stimulate the interest in learning, and help to broaden and improve the knowledge system of the learners.

Mental Health Priorities in the Arab World: Insights From Jordan.

War, geopolitical instability, and natural disasters have contributed to enormous unmet mental health needs in the Middle East and North Africa. Jordan is a middle-income country that needs internal and international stakeholder engagement and support to effectively provide mental health services to its citizens and to the millions of immigrants and refugees who reside there. This column presents a snapshot of the state of mental health care in Jordan and outlines areas for future investment. Potentially high-yield areas for development include digital health technology, integrated care, and youth-focused interventions.

The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation. PubMed, Embase, Web of science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database were searched from inception to December 2021. Studies involving the utilization of computer technology and AI to promote the implementation of CPGs were eligible for review. A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems. We conclude that the application of computer technology and AI to CPG implementation mainly concentrated on the guideline content classification and extraction, guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration. The AI methods used for guideline content classification and extraction were pattern-based algorithm and machine learning. In guideline knowledge representation, guideline knowledge verification, and guideline knowledge integration, computer techniques of knowledge representation were the most used.

Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development.

With the continuous development of the pharmaceutical industry, people have always paid attention to the safety and effectiveness of drugs, including innovative drugs and generic drugs. For pharmaceutical companies as manufacturers, drug development is a very lengthy process that requires high costs, millions of man-hours, thousands of trials, and the mobilization of hundreds of researchers. Therefore, efforts need to be made to develop drugs with high safety and effectiveness. Drug research and development plays an important role today. Based on this, this article applied computer molecular simulation embedded technology and artificial intelligence technology to drug research and development. First, the problems faced in the research and development of anti-inflammatory disease-dependent tumor drugs were introduced, and then the applications of computer molecular simulation embedded technology and artificial intelligence technology in drug research and development were analyzed. Subsequently, the application of artificial intelligence in drug research and development teaching was analyzed, and a teaching system based on computer molecular simulation embedded technology and artificial intelligence was designed. Finally, the application effects of computer molecular simulation embedded technology and artificial intelligence technology were analyzed, and a feasible conclusion was drawn. The use of computer molecular simulation embedded technology and artificial intelligence technology can greatly improve the efficiency of drug research and development, and the research and development safety of imatinib mesylate has been improved by 7%. On the other hand, it can improve students' learning interest and stimulate their learning interest, and students' drug research and development capabilities have been improved. Drug research and development for inflammatory-dependent tumors has good application prospects.

Prospective study of computer-aided detection of colorectal adenomas in hospitalized patients.

BACKGROUND: Adenoma detection with polypectomy during total colonoscopy reduces the incidence of colorectal cancer (CRC) and colorectal cancer-associated mortality. The adenoma detection rate (ADR) is an established quality indicator, which is associated with a decreased risk for interval cancer. An increase in ADR could be demonstrated for several artificially intelligent, real-time computer-aided detection (CADe) systems in selected patients. Most studies concentrated on outpatient colonoscopies. This sector often lacks funds for applying costly innovations like CADe. Hospitals are more likely to implement CADe and information about the impact of CADe in the distinct patient cohort of hospitalized patients is scarce. METHODS: In this prospective, randomized-controlled study, we compared colonoscopies performed with or without computer-aided detection (CADe) system (GI Genius, Medtronic) performed at University Medical Center Schleswig-Holstein, Campus Luebeck. The primary endpoint was ADR. RESULTS: Overall, 232 patients were randomized with n = 122 patients in the CADe arm and n = 110 patients in the control arm. Median age was 66 years (interquartile range 51-77). Indication for colonoscopy was most often workup for gastrointestinal symptoms (88.4%) followed by screening, post-polypectomy and post-CRC surveillance (each 3.9%). Withdrawal time was significantly prolonged (11 vs. 10 min, p = 0.039), without clinical relevance. Complication rate was not different between the arms (0.8% vs. 4.5%; p = 0.072). The ADR was significantly increased in the CADe arm compared to the control (33.6% vs. 18.1%, p = 0.008). ADR increase was particularly strong for the detection in elderly patients aged ≥50 years (OR 6.3, 95% CI 1.7 - 23.1, p = 0.006). CONCLUSION: The use of CADe is safe and increases ADR in hospitalized patients.

Vascular features around the optic disc in familial exudative vitreoretinopathy: findings and their relationship to disease severity.

BACKGROUND: Familial exudative vitreoretinopathy (FEVR) is a rare congenital disorder of retinal vascular development. We aimed to study the vascular characteristics around the optic disc in neonates with FEVR and the relationship with disease severity. METHODS: A retrospective, case-control study including 43 (58 eyes) newborn patients with FEVR at stages 1 to 3 and 30 (53 eyes) age-matched normal full-term newborns was conducted. The peripapillary vessel tortuosity (VT), vessel width (VW) and vessel density (VD) were quantified by computer technology. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was used to visualize the relationship between the severity of FEVR and the characteristics of perioptic disc vascular parameters. RESULTS: The peripapillary VT, VW and VD were significantly increased in the FEVR group compared with the control group (P < 0.05). Subgroup analysis showed that VW and VD increased significantly with progressing FEVR stage (P < 0.05). And only VT in stage 3 FEVR was significantly increased compared with stage 1 and stage 2 (P < 0.05). After controlling the confounders, ordinal logistic regression analysis indicated that the VW (aOR: 1.75, P = 0.0002) and VD (aOR: 2.41, P = 0.0170) were significantly independent correlated with the FEVR stage, but VT (aOR: 1.07, P = 0.5454) was not correlated with FEVR staging. Visual analysis based on the t-SNE algorithm showed that peri-optic disc vascular parameters had a continuity along the direction of FEVR severity. CONCLUSIONS: In the neonatal population, there were significant differences in peripapillary vascular parameters between patients with FEVR and normal subjects. Quantitative measurement of vascular parameters around the optic disc can be used as one of the indicators to assess the severity of FEVR.

Profile and predictors of high school teachers' attitude and self-efficacy in utilizing ICT: an investigation from Indonesia.

This study investigated the relationship among several predictors that could influence high school teachers' attitudes and self-efficacy in utilizing ICT, such as length of computer use, computer competencies, computer access, computer training, and frequency of using computer programs. Quantitative study through descriptive and correlational design has been used to answer the hypothesis of this study. A total of 345 active high school teachers participated in this study. The correlational person product moment shows that all of the computer-related variables have a positive relationship with attitude and self-efficacy, although the relationships vary from low (p < 0.05) to high (p < 0.01). Hierarchical analysis showed that the predictors of attitude included computer competencies, computer access, training in computer, and level of using computer's program/apps variables; meanwhile, computer competencies, training in computer, and level of using computer's program/apps become good predictors of self-efficacy. This finding suggests that teachers should frequently access the computer and its app, have competency in using the computer, and have training or professional development in computer for improving teachers' attitudes and self-efficacy in utilizing and implementing ICT for the learning process.

Application of modern computer technology in classical genetics lab course--Development of a mobile, lightweight and high-precision batch identification system for genetic traits of Drosophila.

Drosophila is a crucial biological experimental teaching material extensively utilized in experimental teaching. In this experimental teaching, each student typically needs to manually identify hundreds of fruit flies and record multiple of each fly. This task involves substantial workload, and the classification standards can be inconsistent. To address this issue, we introduce a deep convolutional neural network that classifies the traits of every fruit fly, using a two-stage consisting of an object detector and a trait classifier. We propose a keypoint-assisted classification model with tailored training session for the trait classification task and significantly enhanced the model interpretability. Additionally, we've enhanced the RandAugment method to better fit the features of our task. The model is trained with progressive learning and adaptive regularization under limited computational resources. The final classification model, which utilizes MobileNetV3 as backbone, achieves an accuracy of 97.5%, 97.5% and 98% for the eyes, wings, gender tasks, respectively. After optimization, the model is highly lightweight, classifying 600 fruit fly traits from raw images in 10 seconds and having a size less than 5 MB. It can be easily deployed on any android device. The development of this system is conducive to promoting the experimental teaching, such as verifying genetic laws with Drosophila as the research object. It can also be used for scientific research involving a large number of Drosophila classifications, statistics and analyses.

Understanding Barriers and Facilitators to Online and App Activities for People Living With Dementia and Their Supporters.

BACKGROUND: Stigma often surrounds people with dementia when it comes to use of computer technology, although evidence does not always support this. More understanding is needed to investigate attitudes and experience in relation to computer technology use among those living with dementia and their readiness to use it to support self-management. METHODS: An online self-report questionnaire was completed by adults living with a dementia diagnosis and those living with them. Questions explored how long the participants had been using computer technology; how regularly they used it; the popularity of common communication apps; and whether they were interested in using an app to support their independence. RESULTS: 47 participants with dementia and 62 supporters responded to the questionnaire. There were no obvious differences between those with dementia and supporters when it came to regular technology usage and both groups showed positive attitudes to the use of it for independence in dementia. CONCLUSIONS: There was active use of computer technology among this population. Benefits were shown to include communication, increasing individuals' understanding of dementia diagnoses, and enabling independent activities for both those with dementia and supporters.

Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.

The disability determination process of the Social Security Administration's (SSA's) disability program requires assessing work-related functioning for individual claimants alleging disability due to mental impairment. This task is particularly challenging because the determination process involves the review of a large file of information, including objective medical evidence and self-reports from claimants, families, and former employers. To improve this decision-making process, SSA entered an interagency agreement with the Rehabilitation Medicine Department, Epidemiology and Biostatistics Section, in the Clinical Center of the National Institutes of Health, intending to use data science and informatics to develop decision support tools. This collaborative effort over the past decade has led to the development of the Work Disability-Functional Assessment Battery and has initiated an approach to applying natural language processing to the review of claimants' files for information on mental health functioning. This informatics research collaboration holds promise for improving the process of disability determination for individuals with mental impairments who make claims at the SSA.

An integration of hybrid MCDA framework to the statistical analysis of computer-based health monitoring applications.

The surge in computer-based health surveillance applications, leveraging technologies like big data analytics, artificial intelligence, and the Internet of Things, aims to provide personalized and streamlined medical services. These applications encompass diverse functionalities, from portable health trackers to remote patient monitoring systems, covering aspects such as heart rate tracking, task monitoring, glucose level checking, medication reminders, and sleep pattern assessment. Despite the anticipated benefits, concerns about performance, security, and alignment with healthcare professionals' needs arise with their widespread deployment. This study introduces a Hybrid Multi-Criteria Decision Analysis (MCDA) paradigm, combining the strengths of Additive Ratio Assessment (ARAS) and Analytic Hierarchy Process (AHP), to address the intricate nature of decision-making processes. The method involves selecting and structuring criteria hierarchically, providing a detailed evaluation of application efficacy. Professional stakeholders quantify the relative importance of each criterion through pairwise comparisons, generating criteria weights using AHP. The ARAS methodology then ranks applications based on their performance concerning the weighted criteria. This approach delivers a comprehensive assessment, considering factors like real-time capabilities, surgical services, and other crucial aspects. The research results provide valuable insights for healthcare practitioners, legislators, and technologists, aiding in deciding the adoption and integration of computer-based health monitoring applications, ultimately enhancing medical services and healthcare outcomes.

Novel antimicrobial agents targeting the Streptococcus mutans biofilms discovery through computer technology.

Dental caries is one of the most prevalent and costly biofilm-associated infectious diseases worldwide. Streptococcus mutans (S. mutans) is well recognized as the major causative factor of dental caries due to its acidogenicity, aciduricity and extracellular polymeric substances (EPSs) synthesis ability. The EPSs have been considered as a virulent factor of cariogenic biofilm, which enhance biofilms resistance to antimicrobial agents and virulence compared with planktonic bacterial cells. The traditional anti-caries therapies, such as chlorhexidine and antibiotics are characterized by side-effects and drug resistance. With the development of computer technology, several novel approaches are being used to synthesize or discover antimicrobial agents. In this mini review, we summarized the novel antimicrobial agents targeting the S. mutans biofilms discovery through computer technology. Drug repurposing of small molecules expands the original medical indications and lowers drug development costs and risks. The computer-aided drug design (CADD) has been used for identifying compounds with optimal interactions with the target via silico screening and computational methods. The synthetic antimicrobial peptides (AMPs) based on the rational design, computational design or high-throughput screening have shown increased selectivity for both single- and multi-species biofilms. These methods provide potential therapeutic agents to promote targeted control of the oral microbial biofilms in the near future.

Information Seeking Practices and Availability of Smart Devices Among Healthcare Professionals in a Lower-Middle-Income Country: An Analysis From Pakistan.

Introduction Evidence-based medicine (EBM) is a principle that integrates clinical experience with relevant information available to provide adequate healthcare. It requires access to current medical literature. This paper analyzes the information requirements of a lower-middle-income country (LMIC) and the resources available and preferred by medical professionals. Methods A survey-based cross-sectional study was carried out among 160 participants, ranging in expertise from students to attending physicians in Karachi, Pakistan. The survey comprised questions to assess the clinical background, technology access, need for health-related information, and the preference for resources to obtain that information in different scenarios. They were also asked if they use PubMed and their recommended methods to improve information access. Statistical Package for the Social Sciences (SPSS; IBM, NY, USA) software was used for all analyses. Results A basic mobile phone (with limited internet connectivity) was the most common device used at home (n=159; 99.4%) and work (n=141; 88.1%). No smart devices were available to 28 (17.5%) participants at work. Internet connectivity was available for 155 (96.9%) participants at home but only for 118 (73.7%) participants at work. About one-third (n=49; 30.6%) experienced questions arising in practice two to four times a day, and half of the participants (n=80; 50%) were very likely to look up a reference. The most common resource for the majority of given clinical scenarios was a senior colleague. At the same time, medical websites (Medscape, Up-to-Date, WebMD) were the first choice for a non-specific general medical query. About 68.75% (n=110) of participants claimed to use PubMed in daily practice. The most common reason for not using PubMed was the ease of using other search engines (like Google). Conclusions Improved access to the internet and well-reputed journals can enhance the practice of EBM in Pakistan. Limitations of technological access must be considered while designing information resources in lower-middle-income countries.

Dataset of TPACK in teaching practice: Adversity quotient, attitude computer technology and self-efficacy among Indonesian teachers.

This dataset describes the measurement of adversity quotient (AQ), attitude computer technology (ACT), and self-efficacy with computer technology (SCT) of Indonesian teachers in implementing the technological pedagogical content knowledge (TPACK) concept in their teaching practice. The online survey was distributed to collect data on demographic information (4 items), AQ (11 items), ACT (19 items), SCT (22 items), and TPACK (5 items). It was carried out from August to September 2022. A total of 901 teachers from 28 provinces in Indonesia were recruited using probability sampling technique. Data from the survey were analyzed using the statistical analysis of One Way Anova and Partial Correlation. This dataset can help teacher institutions design effective programs to develop teacher digital competencies in integrating technology. Future researchers can compare this dataset with more rigorous data from developing countries.

Robust Algorithms for Drone-Assisted Monitoring of Big Animals in Harsh Conditions of Siberian Winter Forests: Recovery of European elk (Alces alces) in Salair Mountains.

There are two main reasons for monitoring the population of forest animals. First, regular surveys reveal the real state of biodiversity. Second, they guarantee a prompt response to any negative environmental factor that affects the animal population and make it possible to eliminate the threat before any permanent damage is done. The research objective was to study the potential of drone planes equipped with thermal infrared imaging cameras for large animal monitoring in the conditions of Siberian winter forests with snow background at temperatures -5 °C to -30 °C. The surveyed territory included the Salair State Nature Reserve in the Kemerovo Region, Russia. Drone planes were effective in covering large areas, while thermal infrared cameras provided accurate statistics in the harsh winter conditions of Siberia. The research featured the population of the European elk (Alces alces), which is gradually deteriorating due to poaching and deforestation. The authors developed an effective methodology for processing the data obtained from drone-mounted thermal infrared cameras. The research provided reliable results concerning the changes in the elk population on the territory in question. The use of drone planes proved an effective means of ungulate animal surveying in snow-covered winter forests. The designed technical methods and analytic algorithms are cost-efficient and they can be applied for monitoring large areas of Siberian and Canadian winter forests.

The Promise of Digital Mental Health for LGBTQ+ Youths.

Lesbian, gay, bisexual, transgender, queer, plus (LGBTQ+) youths face increased risk for psychiatric morbidity, and the stress of being in a minority group drives this disparity. Affirmative treatments can improve mental health among LGBTQ+ youths, but barriers are encountered in accessing care. Digital mental health offers the opportunity to increase access, therefore potentially reducing mental health disparities. However, insufficient attention has been devoted to addressing the needs of LGBTQ+ youths through digital tools. In this column, the authors outline strategies to improve the inclusiveness of existing digital mental health content and to move toward equitable care with the development of new content.

Mobile Health for Caregivers of Young Adults With Early Psychosis: A Survey Study Examining User Preferences.

OBJECTIVE: Caregivers play a key role in supporting the recovery of young adults with early psychosis. This role often involves considerable responsibilities and burden. Despite the considerable needs of caregivers, troubling service gaps addressing these needs remain. Digital technologies may increase caregivers' access to supportive resources; however, technologies developed specifically for caregivers lag far behind those developed for their relatives affected by early psychosis. In particular, little is known about the mobile health (mHealth) features that may be most acceptable to caregivers. METHODS: The authors surveyed a sample of 43 caregivers on their interests regarding various features of a proposed mHealth intervention. RESULTS: Caregivers of young adults with early psychosis were highly interested in a caregiver-facing mHealth intervention, specifically one providing information about psychosis, treatments, and communication with their affected family member. CONCLUSIONS: Future caregiver-focused mHealth intervention interventions may be highly acceptable to this population and may address pressing service gaps.