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It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases—both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50–100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect.

Artificial intelligence (AI) is increasingly applied in medical imaging, yet its ability to predict biological sex from pediatric radiographs remains unclear. This study investigates the performance of convolutional neural network (CNN) models in sex classification using a large dataset of pediatric trauma imaging and compares results with human raters. Radiographs from computed and digital radiography systems were processed to normalize grayscale and enhance contrast. The EfficientNet family of CNN models (B0–B7) was trained on this dataset, with attention to balancing the test set by age, sex, and fracture visibility. A subset of 1,000 images was independently assessed by human raters for comparison. AI models achieved a mean precision of 0.731 ± 0.035, recall of 0.718 ± 0.110, accuracy of 0.722 ± 0.032, and F1-score of 0.724 ± 0.050 across all network variants. Performance improved with age, peaking in the 13–18 group. Pelvic X-rays achieved the highest classification metrics. Human raters showed significantly lower agreement. AI can classify biological sex from pediatric radiographs with high accuracy, surpassing human performance. Results vary by age and body region, supporting the potential for AI-assisted imaging in pediatric clinical practice.

Background. The prevalence of childhood obesity has been increasing worldwide. This may explain the emergence of nonalcoholic fatty liver as the leading cause of liver disease. Several previous studies have addressed the association between thyroid function and nonalcoholic fatty liver disease. Objectives. To study the impact of weight reduction through lifestyle modifications in adolescents with obesity. Methods. A prospective cohort study was done on 61 adolescents with obesity. Patients were evaluated at the first visit by the full history, clinical examination, and investigations (thyroid profile, lipid profile, liver function tests, HbA1c, and liver ultrasonography) as basal information. The intervention program included a dietary program, increasing physical activity, and decreasing sedentary activity. A postintervention evaluation was done at the end of six months which included anthropometric measures, laboratory results, and ultrasonographic estimation. Results. It was shown that the mean BMI of the participants had significantly decreased after lifestyle modification from (32.05 ± 3.36 kg/m2) to (28.1 ± 2.77 kg/m2) (P<0.001). It also showed that the percentage of studied adolescents with elevated TSH decreased from 47.5% to 19.7% after the weight reduction program. Improvement was also achieved in the lipid profile and liver functions. The percentage of studied adolescents with ultrasound appearance of NAFLD decreased from 31.1% to 26.2% after weight reduction. Conclusions. Lifestyle modification positively influences the metabolic derangement in obesity without medical treatment. ΔTSH is a significant predictor of the change in BMI z-score. It is also possible that hepatic steatosis affects thyroid function rather than the other way around.

Objective: The aim of this analysis was to assess glycemic control before and during the coronavirus disease (COVID-19) pandemic. Methods: Data from 64 (main analysis) and 80 (sensitivity analysis) people with type 1 diabetes (T1D) using intermittently scanned continuous glucose monitoring (isCGM) were investigated retrospectively. The baseline characteristics were collected from electronic medical records. The data were examined over three periods of three months each: from 16th of March 2019 until 16th of June 2019 (pre-pandemic), from 1st of December 2019 until 29th of February 2020 (pre-lockdown) and from 16th of March 2020 until 16th of June 2020 (lockdown 2020), representing the very beginning of the COVID-19 pandemic and the first Austrian-wide lockdown. Results: For the main analysis, 64 individuals with T1D (22 female, 42 male), who had a mean glycated hemoglobin (HbA1c) of 58.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration 13.5 years (5.5 to 22.0 years) were included in the analysis. The time in range (TIR[70–180mg/dL]) was the highest percentage of measures within all three studied phases, but the lockdown 2020 phase delivered the best data in all these cases. Concerning the time below range (TBR[<70mg/dL]) and the time above range (TAR[>180mg/dL]), the lockdown 2020 phase also had the best values. Regarding the sensitivity analysis, 80 individuals with T1D (26 female, 54 male), who had a mean HbA1c of 57.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration of 12.5 years (5.5 to 20.7 years), were included. The TIR[70–180mg/dL] was also the highest percentage of measures within all three studied phases, with the lockdown 2020 phase also delivering the best data in all these cases. The TBR[<70mg/dL] and the TAR[>180mg/dL] underscored the data in the main analysis. Conclusion: Superior glycemic control, based on all parameters analyzed, was achieved during the first Austrian-wide lockdown compared to prior periods, which might be a result of reduced daily exertion or more time spent focusing on glycemic management.

Background A safe and effective vaccine is the ultimate key to mitigating the COVID-19 pandemic. Vaccine acceptance is influenced by various factors, including perceptions about the vaccine’s safety and side effects. The side effects vary depending on the type of the vaccine, but they are mainly mild, local, temporary, and self-limiting. Methods A cross-sectional study was carried out at Tanta University Hospitals, including 1246 healthcare workers who received either the first or the second dose of the COVID-19 vaccine, selected via a systematic random sampling technique using a self-administered structured validated questionnaire for data collection from November 2021 to January 2022. Qualitative data were presented as frequencies and percentages and analyzed using Chi-square and Fisher’s exact tests. Results The prevalence of one or more side effects was 91.3%. Among participants, about two-thirds believed in vaccine safety and its necessity (65.4% and 63.6%, respectively). Significantly more participants (46.9%) were concerned about AstraZeneca thrombotic complications than other vaccine types. The top five side effects reported by participants were injection site pain (64.8%), sense of fatigue (57.1%), headache (49.9%), muscle pain (48.7%), and fever (46.5). Most of the side effects were significantly higher among participants vaccinated with AstraZeneca. Side effects impacted work capacity of 23.4%, which was significantly higher among participants who received AstraZeneca (33.6%). Conclusion Participants had a good level of belief in vaccination safety and necessity. Healthcare workers who got the AstraZeneca vaccination reported more adverse effects than other vaccines. Injection site pain, fatigue, headache, muscle pains, and fever were the most frequently reported side effects. More research on vaccination safety is needed to understand the long-term adverse effects of vaccinations better, improve the public trust, and accelerate vaccine adoption.

Background: Advancements in artificial intelligence (AI) and machine learning (ML) are set to revolutionize healthcare, particularly in fields like endoscopic surgery that heavily rely on digital imaging. However, to effectively integrate these technologies and drive future innovations, pediatric surgeons need specialized AI/ML skills. This survey evaluated the current level of readiness and educational needs regarding AI/ML among members of the European Society of Pediatric Endoscopic Surgeons (ESPES). Methods: A structured survey was distributed via LimeSurvey to ESPES members via email before and during the 2024 Annual Conference. Responses were collected over four weeks with voluntary, anonymous participation. Quantitative data were analyzed using descriptive statistics. Results: A total of 125 responses were received. Two-thirds (65%) of respondents rated their AI/ML understanding as basic, with only 6% reporting advanced knowledge. Most respondents (86%) had no formal AI/ML training. Some respondents (31%) used AI/ML tools in their practice, mainly for diagnostic imaging, surgical planning, and predictive analytics; 42% of the respondents used these tools weekly. The majority (95%) expressed interest in further AI/ML training, preferring online courses, workshops, and hands-on sessions. Concerns about AI/ML in pediatric surgery were high (85%), especially regarding data bias (98%). Half of respondents (51%) expect AI/ML to play a significant role in advancing robotic surgery, oncology, and minimally invasive techniques. A strong majority (84%) felt that the ESPES should lead AI education in pediatric surgery. Conclusions: This survey presents the ESPES with a unique opportunity to develop a competency map of its membership’s AI/ML skills and develop targeted educational programs, thus positioning the society to take the lead in AI education and the advancement of AI solutions in pediatric endosurgery.

There is no doubt that the problem of climate change has become imperative for all parties oа the international community to join forces to resolve this crisis. With the signing of the Paris Agreement and following many deliberations and negotiations, this agreement is a new phase in dealing with the reduction of greenhouse gas emissions. With the participation of everyone, whether developed or developing countries, that opens the door to a new system of investment in the implementation of this commitment. Article 6 of the Paris Agreement is the cornerstone of this new type of emissions exchange between the parties. The old emissions trading system under the Kyoto Protocol has shown many advantages and disadvantages, which open the door to the need to find a new system that achieves the actual emission reduction target and opens the way for a new type of FDI. Although the features of the new system have not yet been formed, but many of the ideas and theses dealt with by many intellectuals and economists on how to develop an emissions trading system that achieves the goals of sustainable development. In this paper, a new vision will be taken to put Article 6 of the Paris Agreement into effect, in order to achieve the objective of the main agreement which is to reduce emissions without having a significant impact on development plans, especially in developing and poor countries, on the one hand, and the opportunity to attract foreign investment and capital flows towards more attention to the green economy.

Background Emergency cholecystectomy is the mainstay in treating acute cholecystitis (AC). In actual practice, perioperative prophylactic antibiotics are used to prevent postoperative infectious complications (PIC), but their effectiveness lacks evidence. We aim to investigate the efficacy of prophylactic antibiotics in emergency cholecystectomy. Methods We searched PubMed, Embase, Cochrane CENTRAL, Web of Science (WOS), and Scopus up to June 14, 2023. We included randomized controlled trials (RCTs) that involved patients diagnosed with mild to moderate AC according to Tokyo guidelines who were undergoing emergency cholecystectomy and were administered preoperative and/or postoperative antibiotics as an intervention group and compared to a placebo group. For dichotomous data, we applied the risk ratio (RR) and the 95% confidence interval (CI), while for continuous data, we used the mean difference (MD) and 95% CI. Results We included seven RCTs encompassing a collective sample size of 1747 patients. Our analysis showed no significant differences regarding total PIC (RR = 0.84 with 95% CI (0.63, 1.12), P  = 0.23), surgical site infection (RR = 0.79 with 95% CI (0.56, 1.12), P  = 0.19), distant infections (RR = 1.01 with 95% CI (0.55, 1.88), P  = 0.97), non-infectious complications (RR = 0.84 with 95% CI (0.64, 1.11), P  = 0.22), mortality (RR = 0.34 with 95% CI (0.04, 3.23), P  = 0.35), and readmission (RR = 0.69 with 95% CI (0.43, 1.11), P  = 0.13). Conclusion Perioperative antibiotics in patients with mild to moderate acute cholecystitis did not show a significant reduction of postoperative infectious complications after emergency cholecystectomy. (PROSPERO registration number: CRD42023438755).

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Medical students are vulnerable to infection by the coronavirus. Their awareness of the disease is crucial for their safety and for the management of the epidemic by spreading supportive information in their communities. The aim of this study was to assess coronavirus disease 2019 (COVID-19)-related knowledge, attitude, and preventive practices among Egyptian medical students. We conducted a cross-sectional study from the beginning of April to June 2020; a total of 439 undergraduate medical students (1st to 6th academic years) were assessed using an online questionnaire. The questionnaire consisted of 33 questions, including 5 items regarding socio-demographic features, 23 items concerning COVID-19 related knowledge, 2 items regarding attitude, and 3 items related to preventive measures. We observed an acceptable level of knowledge (74.3%) among the sample studied. Preclinical and female students were significantly more optimistic as 69.1% expected successful control of COVID-19, and 48.9% predicted that Egypt will win the fight against COVID-19. The majority of participants reported wearing a facemask in public places as a preventive measure (56.7%). Egyptian medical students had an acceptable level of knowledge, positive attitude, and good practices of preventive measures regarding the COVID-19 virus. There is no significant difference in almost all items of knowledge, attitude, and practices in relation to gender or academic grade.