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Background: Many studies across the globe have observed the significant effect of sleep disturbance on the academic achievement of medical students. Objective: This research was conducted to define the prevalence of sleep disturbance and to discover the consequences and management of this condition among undergraduate pharmacy students of Mohammed Al-Mana College for Medical Sciences (MACHS). Methods: We conducted a questionnaire-based online survey among different levels of MACHS pharmacy students. Results: A total of 165 students participated in the study, of whom 132 (80%) were females and 33 (20%) were males. Most of the participants, 142 (86.06%), were staying with their parents, whereas few of them were staying alone (23; 13.94%). While a handful of them, 29 (17.58%), did not have their bedrooms, most of them had their bedrooms (136; 82.42%). When asked about drinking caffeine-rich products in the afternoon or at night, most of them responded “sometimes” (78; 47.27%), followed by the category “always” (61; 36.97%), “rarely” (15; 9.09%), and then the category of students who responded “not at all” (11; 6.67%). Overall, 35 (21.21%) of the study participants had a smoking habit, 72 (43.63%) tried to treat their sleep disturbance, whereas 42 (25.45%) were getting treatment for insomnia. Conclusion: Most pharmacy students at MACH College, especially females, have sleep problems. Mostly, the prevalence is common during exam periods, and it mildly affects their grade point average.

Introduction: This study aims to investigate cervical kyphosis in children, which has gained increasing attention in recent years due to its higher incidence and its association with tumor surgeries, and to shed light on the unique anatomical and biomechanical differences between pediatric and adult populations regarding cervical sagittal alignment. Additionally, it explores the diverse causes and management approaches, which often pose significant challenges. Furthermore, this study presents the management outcomes from three spine centers in the Middle East. Methods: A retrospective analysis was conducted on patient records from 2009 to 2021 in three centers located in Saudi Arabia, Egypt, and Jordan. Demographic and clinical data were collected, imaging studies were reviewed, and various treatment modalities and their corresponding outcomes were documented and analyzed. Additionally, a literature review on pediatric cervical kyphosis and its management was performed. Results: Seventeen patients were included in this study. The average age at presentation was 11.9 years. Among the participants, 14 underwent surgical treatment, 1 was treated with Minerva orthosis, and 2 were observed. The mean follow-up period was 32.4 months. In surgically treated patients, a statistically significant higher degree of correction was achieved when combining anterior and posterior surgeries compared to performing standalone anterior or posterior surgery (P-value = 0.014). Conclusions: Although rare, pediatric cervical kyphosis is a significant condition within the spectrum of pediatric deformities and frequently occurs as a component of syndromes or as a result of iatrogenic factors. Neck pain and myelopathy are the most commonly observed symptoms. Thorough evaluation and complex surgical interventions are typically required for most cases.

Background/Objectives: We assessed the influence of local patients and clinical characteristics on the performance of commercial deep learning (DL) segmentation models for head-and-neck (HN), breast, and prostate cancers. Methods: Clinical computed tomography (CT) scans and clinically approved contours of 210 patients (53 HN, 49 left breast, 55 right breast, and 53 prostate cancer) were used to train and validate segmentation models integrated within a vendor-supplied DL training toolkit and to assess the performance of both vendor-pretrained and custom-trained models. Four custom models (HN, left breast, right breast, and prostate) were trained and validated with 30 (training)/5 (validation) HN, 34/5 left breast, 39/5 right breast, and 30/5 prostate patients to auto-segment a total of 24 organs at risk (OARs). Subsequently, both vendor-pretrained and custom-trained models were tested on the remaining patients from each group. Auto-segmented contours were evaluated by comparing them with clinically approved contours via the Dice similarity coefficient (DSC) and mean surface distance (MSD). The performance of the left and right breast models was assessed jointly according to ipsilateral/contralateral locations. Results: The average DSCs for all structures in vendor-pretrained and custom-trained models were as follows: 0.81 ± 0.12 and 0.86 ± 0.11 in HN; 0.67 ± 0.16 and 0.80 ± 0.11 in the breast; and 0.87 ± 0.09 and 0.92 ± 0.06 in the prostate. The corresponding average MSDs were 0.81 ± 0.76 mm and 0.76 ± 0.56 mm (HN), 4.85 ± 2.44 mm and 2.42 ± 1.49 mm (breast), and 2.17 ± 1.39 mm and 1.21 ± 1.00 mm (prostate). Notably, custom-trained models showed significant improvements over vendor-pretrained models for 14 of 24 OARs, reflecting the influence of data/contouring variations in segmentation performance. Conclusions: These findings underscore the substantial impact of institutional preferences and clinical practices on the implementation of vendor-pretrained models. We also found that a relatively small amount of institutional data was sufficient to train customized segmentation models with sufficient accuracy.

Background: Malnutrition is a major health concern among children especially in low and middle-income countries. However, there are limited studies on school health in Bangladesh. This study aimed to reduce malnutrition among primary school children in Bangladesh by increasing awareness and knowledge through school nurse-led health education. Methods and Materials: A prospective, open-label, parallel-group (1:1), cluster nonrandomized controlled trial on primary school children conducted in rural Bangladesh. The study lasted 13 months between September 2021 and September 2022. Four schools were selected and assigned to the intervention and control groups (CGs). Next, school nurses provided evidence-based health education to the children in the intervention group (IG) for 9 months to improve awareness and knowledge of malnutrition. Data were collected at baseline, midline, and endline. Results: Overall, 604 children were enrolled at the baseline; among them, 455 (CG, n = 220; IG, n = 235) completed the study. Changes in the malnutrition rate—the primary outcome—were not significant (P = 0.225). However, after adjusting the endline data with baseline and sociodemographic data, the children's body mass index improved significantly in the IG than in the CG (P < 0.05). Changes in eating behavior, and awareness and knowledge of malnutrition—the secondary outcomes—significantly differed between the groups (P < 0.001). Conclusion: The school nurse-led health education program significantly improved primary school children's awareness and knowledge of malnutrition. This study revealed the effectiveness of school nurses in reducing malnutrition among children, which may decrease future morbidity and mortality rates in children.

Developers often rely on online resources, such as Stack Overflow (SO), to seek assistance for programming tasks. To facilitate effective search and resource discovery, manual tagging of questions and posts with the appropriate programming language is essential. However, accurate tagging is not consistently achieved, leading to the need for the automated classification of code snippets into the correct programming language as a tag. In this study, we introduce a novel approach to automated classification of code snippets from Stack Overflow (SO) posts into programming languages using generative pre-trained transformers (GPT). Our method, which does not require additional training on labeled data or dependency on pre-existing labels, classifies 224,107 code snippets into 19 programming languages. We employ the text-davinci-003 model of ChatGPT-3.5 and postprocess its responses to accurately identify the programming language. Our empirical evaluation demonstrates that our GPT-based model (SCC-GPT) significantly outperforms existing methods, achieving a median F1-score improvement that ranges from +6% to +31%. These findings underscore the effectiveness of SCC-GPT in enhancing code snippet classification, offering a cost-effective and efficient solution for developers who rely on SO for programming assistance.

Partially exposed bedrock beneath Pleistocene glacial till in Erie County (north-central Ohio) displays unusual structural deformation in the Devonian Berea Sandstone, Bedford Shale, and Ohio Shale. These folded and faulted units are exposed in creeks as anticlines and synclines. Past studies of this area proposed Pleistocene ice movement and soft-sediment deformation during the Late Paleozoic as the deformation mechanisms, but these hypotheses cannot explain the extent of layer displacement or the contradiction between the southwest travel direction of the ice sheet and the structural sense of motion on the folded units. A new interpretation using field data and constructing geologic profiles explains the development of these structures. This study investigated 17 anticlines that trend in different directions. Four of these anticlines are tightly folded with steep or overturned flanks and thrust-faulted Ohio Shale in their cores. Structural analysis of these folds shows that the incompetent shaly units of the Plum Brook-Ohio-Bedford and competent Berea Sandstone were folded above the Delaware-Niagara carbonates as a result of the compressional stress during the Late Paleozoic. Development of these tight or overturned folds, and change in trend of the anticlines, is caused by unusual stratigraphic thickness variations in the Berea and Bedford units. Preserved and undeformed fine sedimentary structures, and sharply faulted beds, in the Berea and Bedford indicate that soft-sediment deformation was not the cause of the regional structural deformation. Finally, the absence of physical features of glacially deformed bedrock demonstrates that Pleistocene glacial ice shove was not the cause of deformed bedrock units in the study area., INTRODUCTION Unusual geologic structures near Chappel Creek in north-central Ohio have drawn the attention of geologists for many decades (Fig. 1). Unlike the horizontal to sub-horizontal sedimentary layers of most [...]

Objectives: This study was done to evaluate the knowledge of community pharmacists—working in Saudi Arabia—regarding over-the-counter opioid-related and opioid-containing medications. Materials and Methods: A quantitative cross-sectional study was conducted using an online questionnaire that was distributed through social media and taken to the community pharmacists in person. A total of 101 responders were reached in a period of around 3 weeks. Results: Most of the pharmacists working in community pharmacies in Saudi Arabia are non-Saudi (85.15%), males (88.12%), and the majority are holders of bachelor's degrees (85.15%). More than half of the participants spend a considerably sufficient time with the patient 6–10 minutes (60.4%). The availability of a private counseling room in community pharmacies is significantly low (21.78%). Only (69.41%) of participants always counsel the patient before dispensing opioid-related medications. Most of the participants (84.76%) believe that all over-the-counter opioid-related and opioid-containing medications should be restricted to avoid drug abuse. Conclusion: A little neglect from pharmacists' side regarding dispensing such medications was observed; thus, there is a significant need to raise their awareness regarding over-the-counter opioid abuse.

PurposeVariability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring.MethodsWe developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group.ResultsFor every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was –0.8 mm ([–37.9, 34.9], 4.2) and 0.3 mm ([–25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to –0.1 mm ([–16.2, 7.3], 0.8) and 0.1 mm ([–6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were –1.5 mm ([–22.9, 19.9], 3.4) and –0.2 mm ([–4.5, 1.5], 0.7) for the heart and 1.8 mm ([–16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV.ConclusionsA tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee’s contouring.

Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction.

The rapid evolution of video programming tutorials as a key educational resource has highlighted the need for effective code extraction methods. These tutorials, varying widely in video quality, present a challenge for accurately transcribing the embedded source code, crucial for learning and software development. This study investigates the impact of video quality on the performance of optical character recognition (OCR) engines and the potential of large language models (LLMs) to enhance code extraction accuracy. Our comprehensive empirical analysis utilizes a rich dataset of programming screencasts, involving manual transcription of source code and the application of both traditional OCR engines, like Tesseract and Google Vision, and advanced LLMs, including GPT-4V and Gemini. We investigate the efficacy of image super-resolution (SR) techniques, namely, enhanced deep super-resolution (EDSR) and multi-scale deep super-resolution (MDSR), in improving the quality of low-resolution video frames. The findings reveal significant improvements in OCR accuracy with the use of SR, particularly at lower resolutions such as 360p. LLMs demonstrate superior performance across all video qualities, indicating their robustness and advanced capabilities in diverse scenarios. This research contributes to the field of software engineering by offering a benchmark for code extraction from video tutorials and demonstrating the substantial impact of SR techniques and LLMs in enhancing the readability and reusability of code from these educational resources.

Background/Objectives: We assessed the influence of local patients and clinical characteristics on the performance of commercial deep learning (DL) segmentation models for head-and-neck (HN), breast, and prostate cancers. Methods: Clinical computed tomography (CT) scans and clinically approved contours of 210 patients (53 HN, 49 left breast, 55 right breast, and 53 prostate cancer) were used to train and validate segmentation models integrated within a vendor-supplied DL training toolkit and to assess the performance of both vendor-pretrained and custom-trained models. Four custom models (HN, left breast, right breast, and prostate) were trained and validated with 30 (training)/5 (validation) HN, 34/5 left breast, 39/5 right breast, and 30/5 prostate patients to auto-segment a total of 24 organs at risk (OARs). Subsequently, both vendor-pretrained and custom-trained models were tested on the remaining patients from each group. Auto-segmented contours were evaluated by comparing them with clinically approved contours via the Dice similarity coefficient (DSC) and mean surface distance (MSD). The performance of the left and right breast models was assessed jointly according to ipsilateral/contralateral locations. Results: The average DSCs for all structures in vendor-pretrained and custom-trained models were as follows: 0.81 ± 0.12 and 0.86 ± 0.11 in HN; 0.67 ± 0.16 and 0.80 ± 0.11 in the breast; and 0.87 ± 0.09 and 0.92 ± 0.06 in the prostate. The corresponding average MSDs were 0.81 ± 0.76 mm and 0.76 ± 0.56 mm (HN), 4.85 ± 2.44 mm and 2.42 ± 1.49 mm (breast), and 2.17 ± 1.39 mm and 1.21 ± 1.00 mm (prostate). Notably, custom-trained models showed significant improvements over vendor-pretrained models for 14 of 24 OARs, reflecting the influence of data/contouring variations in segmentation performance. Conclusions: These findings underscore the substantial impact of institutional preferences and clinical practices on the implementation of vendor-pretrained models. We also found that a relatively small amount of institutional data was sufficient to train customized segmentation models with sufficient accuracy. [ABSTRACT FROM AUTHOR]

The Saudi General Aptitude Test (GAT) aims to measure the analytical and inferential learning abilities of high school graduates seeking admission to higher education institutions. Given the need for effective preparation tools, this study investigates the potential of chat generative pre-trained transformers to assist students in preparing for the GAT, especially in Arabic. The primary objective is to assess the effectiveness of Large Language Models (LLMs) in answering questions related to mental and logical abilities, specifically in Arabic. The performance of GPT-4, GPT-4o, and Gemini was examined through 21 experiments to determine their accuracy in answering a range of GAT-related questions. The findings indicate that although GPT-4 and GPT-4o outperformed Gemini in providing accurate answers for the GAT, their current accuracy levels still require improvement. [ABSTRACT FROM AUTHOR]

This study sought to investigate the effectiveness of interactive whiteboards (IWB) on students' Arabic listening skills at Kuwaiti universities during the second semester of the 2018/2019 academic year. A review of the literature indicates that interactive whiteboards support the language learning, as well as to improve students' achievement listening skills and motivation in school. The study sample consisted of 60 male students who were equally divided into two groups: the experimental and control groups. Interactive whiteboards strategy was used to determine the effect of teaching method of Arabic listening skills on students' outcomes. The pretest and posttest scores of both groups were analyzed statistically using the Statistical Package for the Social Sciences (SPSS) program. The finding of the study indicated significant differences between the groups at the [alpha] [less than or equal to] 0.05 level in favor of the experimental group. The results showed the following: (1) the use of the interactive white board strategy showed better improvement among the students than the use of the traditional method; (2) the effect size of using the interactive white smart boards was high, and (3) there were statistically significant differences between the mean posttest scores of the two groups in the Arabic listening skills in favor of the experimental group. Based on the findings of this study, the research indicates that (IWB) strategies can be used in teaching Arabic listening skills because of their positive effect on student achievement scores. The study concludes with recommendations for future research in different contexts.

Diabetic Retinopathy (DR) is a disease caused by a high level of glucose in retina vessels. This malicious disease put millions of people around the world at risk for vision loss each year. Being a life-threatening disease, early diagnosis can be an effective step in the treatment and prevention of vision loss. To automate the early diagnosis process, computer-aided diagnosis methods are not only useful in detecting the diabetic signatures but also provide information regarding the diabetic grade for the optometrist to determine an appropriate treatment. Several deep classification models are proposed in the literature to solve the diabetic retinopathy classification task, however, these methods usually lack incorporate an attention mechanism to better encode the semantic dependency and highlight the most important region for boosting the model performance. To overcome these limitations, we propose to incorporate a style and content recalibration mechanism inside the deep neural network to adaptively scale the informative regions for diabetic retinopathy classification. In our proposed method, the input image passes through the encoder module to encode both high-level and semantic features. Next, by utilizing a content and style separation mechanism, we decompose the representational space into a style (e.g., texture features) and content (e.g., semantic and contextual features) representation. The texture attention module takes the style representation and applies a high-pass filter to highlight the texture information while the spatial normalization module uses a convolutional operation to determine the more informative region inside the retinopathy image to detect diabetic signs. Once the attention modules are applied to the representational features, the fusion module combines both features to form a normalized representation for the decoding path. The decoder module in our model performs both diabetic grading and healthy, non-healthy classification tasks. Our experiment on APTOS Kaggle dataset (accuracy 0.85) demonstrates a significant improvement compared to the literature work. This fact reveals the applicability of our method in a real-world scenario.