With the innovations in technology, the interest in the use of mobile devices is increasing day by day. Any defect that may occur during the production of smart mobile phones, which is among mobile devices, causes significant damage to both the manufacturer and the user. The careful detection of defects that may occur on the screen glass, which is one of the most striking defects among these defects, with the human eye significantly affects the workforce cost. Therefore, it is important to detect defects with the help of software. In recent years, many methods based on machine vision have been developed for the detection of any object or difference in the image.
 In this study, a new model structure called Yolo-MSD, based on machine vision and the Yolo-v3 deep learning model, which detects and classifies oil, scratch, and stain defect types on the glass on the touch screen surface used in the design of smart mobile phones, is proposed. The proposed model structure (Yolo-MSD) is obtained by reducing the number of blocks in the Darknet-53 network structure developed in Yolo-v3. As a result of the training, a success rate of 98.50% with the Yolo-v3 model and 98.72% with the Yolo-MSD model was achieved in detecting and classifying defect types. Therefore, it has been observed that the Yolo-MSD model structure is better than the Yolo-v3 model structure by making better feature extraction from the types of defects on the screen glass since it is both faster and has less complexity.

This study synthesized the available evidence of simulation-based electronic health records (EHRs) training in educational and clinical environments for healthcare providers in the literature. The Arksey and O?Malley methodological framework was employed. A systematic search was carried out in relevant databases from inception to January 2020, identifying 24 studies for inclusion. Three themes emerged: (a) role of simulation-based EHR training in evaluating improvement interventions, (b) debriefing and feedback methods used, and (c) challenges of evaluating simulation-based EHR training. The majority of the studies aimed to emphasize the practical skills of individual medical trainees and employed post-simulation feedback as the feedback method. Future research should focus on (a) using simulation-based EHR training to achieve specific learning goals, (b) investigating aspects of clinical performance that are susceptible to skill decay, and (c) examining the influence of simulation-based EHR training on team dynamics.

<sec> <title>BACKGROUND</title> The approval of novel therapies for patients diagnosed with hematologic malignancies have improved survival outcomes but increased the challenge of aligning chemotherapy choices with patient preferences. We previously developed paper versions of a discrete choice experiment (DCE) and a best-worst scaling (BWS) instrument to quantify the treatment outcome preferences of patients with hematologic malignancies to inform shared decision making. </sec> <sec> <title>OBJECTIVE</title> We aim to develop an electronic health care tool (EHT) to guide clinical decision making that uses either a BWS or DCE instrument to capture patient preferences. The primary objective of this study is to use both qualitative and quantitative methods to evaluate the perceived usability, cognitive workload (CWL), and performance of electronic prototypes that include the DCE and BWS instrument. </sec> <sec> <title>METHODS</title> This mixed methods study includes iterative co-design methods that will involve healthy volunteers, patient-caregiver pairs, and health care workers to evaluate the perceived usability, CWL, and performance of tasks within distinct prototypes. Think-aloud sessions and semistructured interviews will be conducted to collect qualitative data to develop an affinity diagram for thematic analysis. Validated assessments (Post-Study System Usability Questionnaire [PSSUQ] and the National Aeronautical and Space Administration’s Task Load Index [NASA-TLX]) will be used to evaluate the usability and CWL required to complete tasks within the prototypes. Performance assessments of the DCE and BWS will include the evaluation of tasks using the Single Easy Questionnaire (SEQ), time to complete using the prototype, and the number of errors. Additional qualitative assessments will be conducted to gather participants’ feedback on visualizations used in the Personalized Treatment Preferences Dashboard that provides a representation of user results after completing the choice tasks within the prototype. </sec> <sec> <title>RESULTS</title> Ethical approval was obtained in June 2021 from the Institutional Review Board of the University of North Carolina at Chapel Hill. The DCE and BWS instruments were developed and incorporated into the PRIME (&lt;i&gt;P&lt;/i&gt;reference &lt;i&gt;R&lt;/i&gt;eporting to &lt;i&gt;I&lt;/i&gt;mprove &lt;i&gt;M&lt;/i&gt;anagement and &lt;i&gt;E&lt;/i&gt;xperience) prototype in early 2021 and prototypes were completed by June 2021. Heuristic evaluations were conducted in phase 1 and completed by July 2021. Recruitment of healthy volunteers began in August 2021 and concluded in September 2021. In December 2021, our findings from phase 2 were accepted for publication. Phase 3 recruitment began in January 2022 and is expected to conclude in September 2022. The data analysis from phase 3 is expected to be completed by November 2022. </sec> <sec> <title>CONCLUSIONS</title> Our findings will help differentiate the usability, CWL, and performance of the DCE and BWS within the prototypes. These findings will contribute to the optimization of the prototypes, leading to the development of an EHT that helps facilitate shared decision making. This evaluation will inform the development of EHTs to be used clinically with patients and health care workers. </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> DERR1-10.2196/39586 </sec>

Patients diagnosed with hematologic malignancies account for 10% of cancer related deaths. The growth of treatment options for hematologic malignancies has led to increased focus on treatment decision-making. However, little research has been done integrating patient-generated data and shared decision making to facilitate patient-clinician collaboration and understand patient preferences in cancer care. Our study aims to develop and evaluate data visualizations to support an electronic healthcare tool (EHT) to facilitate patient understanding of treatment outcomes using human-centered design methods. Data visualizations were developed and updated based on feedback from healthy volunteers, older adults with hematologic malignancies (patients), caregivers, and clinicians. We conducted a content analysis on the qualitative data gathered from participants. Our findings showed that users preferred easy to understand visualizations with simple, explanatory text compared to visualizations that were not immediately intuitive. Users also preferred visualizations that were more reflective of the individual's cancer treatment rather than a comparison to the patient population. Iterative improvements were made to the visualizations to reflect user feedback and will be used to inform the next iteration of visualizations for user testing in the clinic. This paper demonstrates the benefit of human- and user-centered design to iterate on data visualizations used to support a patient preference tool.

Sodium-ion batteries (SIBs) are emerging as a viable alternative to lithium-ion batteries (LIBs) due to their material sustainability and cost-effectiveness, helping address the high costs, supply limits, and environmental concerns associated with lithium. This paper reviews SIB materials, designs, and applications, and surveys their electrochemical performance, challenges, and future prospects. Recent advances in electrode materials (e.g., layered oxides, hard carbon composites, metallic alloys) are greatly improving SIB stability, conductivity, capacity, and cycle life. Improvements in both solid-state and liquid electrolytes have likewise enhanced ionic conductivity, capacity retention, thermal stability, and safety. Despite their lower energy density, SIBs tolerate wider temperature ranges and carry a significantly lower risk of thermal runaway compared to lithium-based systems, making them attractive for industrial, transportation, and large-scale power storage. Continuous progress in materials and cell engineering is narrowing the performance gap between SIBs and LIBs. Meanwhile, nascent battery recycling strategies for SIBs show promise for economic and environmental viability. Overall, SIBs represent a promising option for safer, more accessible, and more sustainable energy storage technology.

Radiation therapy therapists (RTTs) face challenging daily tasks that leave them prone to high attrition and burnout and subsequent deficits in performance. Here, we employed an accelerated alpha-theta neurofeedback (NF) protocol that is implementable in a busy medical workplace to test if 12 RTTs could learn the protocol and exhibit behavior and brain performance-related benefits. Following the 3-week protocol, participants showed a decrease in subjective cognitive workload and a decrease in response time during a performance task, as well as a decrease in desynchrony of the alpha electroencephalogram (EEG) band. Additionally, novel microstate analysis for neurofeedback showed a significant decrease in global field power (GFP) following neurofeedback. These results suggest that the RTTs successfully learned the protocol and improved in perceived cognitive workload following 3 weeks of neurofeedback. In sum, this study presents promising behavioral improvements as well as brain performance-related evidence of neurophysiological changes following neurofeedback, supporting the feasibility of implementing neurofeedback in a busy workplace and encouraging the further study of neurofeedback as a tool to mitigate burnout.

Fish remains popular among the body's most essential nutrients, as it contains protein and polyunsaturated fatty acids. It is extremely important to choose the fish consumption according to the season and the freshness of the fish to be purchased. It is very difficult to distinguish between non-fresh fish and fresh fish mixed in the fish stalls. In addition to traditional methods used to determine meat freshness, significant success has been achieved in studies on fresh fish detection with artificial intelligence techniques. In this study, two different types of fish (anchovy and horse mackerel) used to determine fish freshness with convolutional neural networks, one of the artificial intelligence techniques. The images of fresh fish were taken, images of non-fresh fish were taken and two new datasets (Dataset1: Anchovy, Dataset2: Horse mackerel) were created. A novel hybrid model structure has been proposed to determine fish freshness using fish eye and gill regions on these two datasets. In the proposed model, Yolo-v5 and Inception-ResNet-v2 and Xception model structures are used through transfer learning. Whether the fish is fresh in both of the Yolo-v5 + Inception-ResNet-v2 (Dataset1: 97.67%, Dataset2: 96.0%) and Yolo-v5 + Xception (Dataset1: 88.00%, Dataset2: 94.67%) hybrid models created using these model structures has been successfully detected. Thanks to the model we have proposed, it will make an important contribution to the studies that will be conducted in the freshness studies of fish using different storage days and the estimation of fish size.

3-Hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase inhibitors, or statins, reduce the incidence of strokes and reduce infarct volume after cerebral ischemia in mice. Excitoxicity caused by overstimulation of glutamate receptors is a major cause of neuronal death after an ischemic brain insult. Experiments presented here explored whether statins protect cultured neurons from excitotoxic death caused by the glutamate receptor agonist NMDA. Treatment with statins preserved NMDA receptor-expressing cortical neurons and potently and substantially reduced lactate dehydrogenase release caused by exposure of embryonic mouse neocortical cultures to NMDA. The rank order of neuroprotective potency was rosuvastatin = simvastatin \> atorvastatin = mevastatin \> pravastatin, which is similar to the known rank order of potency for inhibition of the HMG-CoA reductase enzyme. Resistance of cultures to NMDA excitotoxicity developed after several days of statin exposure. Neuroprotection by rosuvastatin was coincident with a decrease in cell sterols and occurred with a similar potency as inhibition of cholesterol biosynthesis. Neuroprotection was substantially attenuated by cotreatment with either mevalonate or cholesterol and was mimicked by acute treatment with the cholesterol-extracting agent β-cyclodextrin, suggesting that neuroprotection was mediated by depletion of a cellular pool of cholesterol because of the inhibition of cholesterol biosynthesis. These results suggest the possibility that, in addition to effects on cerebrovascular function, statins have the potential to render cortical neurons more resistant to NMDA-induced excitotoxic death as a result of changes in cell cholesterol homeostasis.

Printed Circuit Boards (PCBs) are electronic boards that hold electronic components together and provide the electrical connection between these components. Printed circuit boards offer many advantages over traditional wired circuits, such as durability, less heat, minimal wiring, and ease of assembly. Correct design and production of printed circuit boards significantly affect the quality and efficiency of printed circuit boards. In this study, a defect detection system based on machine learning and deep learning algorithms is proposed to help produce printed circuit boards accurately and minimize the error rate. In the proposed system, missing hole, mouse bite, open circuit, short, spur, and spurious copper defects on the printed circuit have been determined. According to the results obtained, According to the results obtained, success accuracies of 74.62% were obtained with YOLO-v4, 47.83% with HOG+SVM, and 39.86% with HOG+KNN. It has been seen that the algorithms discussed in the study are applicable in the detection of defects in printed circuit boards.

Background Health literacy enables the patients in understanding the basic healthcare information and taking informed health decisions; thus, it is a desirable goal of any healthcare system. It increases patients’ adherence to treatment, improves the quality of care and eases the overall burden on the healthcare system. In recent years, technological solutions are being increasingly used in educating patients and achieving better health literacy. Augmented reality (AR) provides powerful, contextual and situated learning experiences and supplements the real world with virtual objects. AR could potentially be an effective learning methodology for the patients, thus, warranting a comprehensive overview of the current state of AR in patient education and health literacy. Methods The proposed scoping review will be based on the framework developed by Arksey and O’Malley, including the refinements suggested by Levac et al . A systematic search for references in the published literature will be conducted in nine research databases—Institute of Electrical and Electronics Engineers (IEEE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, PsycInfo, Embase, Web of Science, Scopus, Association for Computing Machinery (ACM) and Association for Information Systems eLibrary (AISeL). The unpublished studies from ProQuest Dissertations and Theses, Conference Proceedings Citation Index and grey literature references obtained from a web search will also be included. Databases will be searched from inception to 14 January 2020. Two independent reviewers will screen the studies from the search results in two successive stages of title/abstract screening followed by full-text screening. Data variables will be extracted from the selected studies to characterise study design, type of AR technology employed and the relational factors affecting patient education. Lastly, key stakeholders will be consulted to gather their insights about the study findings. Ethics and dissemination The results will be disseminated through stakeholder meetings and conference presentations. The data used are from publicly available secondary sources, so this study does not require ethical review.