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Lung cancer is a leading cause of cancer deaths worldwide with an estimated 2 million new cases and 1·76 million deaths yearly. Early detection can improve survival, and CT scans are a precise imaging technique to diagnose lung cancer. However, analyzing hundreds of 2D CT slices is challenging and can cause false alarms. 3D visualization of lung nodules can aid clinicians in detection and diagnosis. The MobileNet model integrates multi-view and multi-scale nodule features using depthwise separable convolutional layers. These layers split standard convolutions into depthwise and pointwise convolutions to reduce computational cost. Finally, the 3D pulmonary nodular models were created using a ray-casting volume rendering approach. Compared to other state-of-the-art deep neural networks, this factorization enables MobileNet to achieve a much lower computational cost while maintaining a decent degree of accuracy. The proposed approach was tested on an LIDC dataset of 986 nodules. Experiment findings reveal that MobileNet provides exceptional segmentation performance on the LIDC dataset, with an accuracy of 93.3%. The study demonstrates that the MobileNet detects and segments lung nodules somewhat better than other older technologies. As a result, the proposed system proposes an automated 3D lung cancer tumor visualization.

Background: Agro-food supply chains possess specific characteristics due to the diverse nature of products involved and contribute to all three pillars of sustainability, making the optimal design of a sustainable agro-food supply chain a complex problem. Therefore, efficient models incorporating the unique characteristics of such chains are essential for making optimal supply chain decisions and achieving economically and environmentally sustainable agro-food supply chains that contribute to global food security. Methods: This article presents a multi-objective mixed-integer linear programing model that integrates agricultural-related strategic decisions into the tactical design of an agro-food supply chain. The model considers transportation, inventory, processing, demand fulfilment, and waste disposal decisions. It also accounts for seasonality and perishability, ensuring a comprehensive approach to sustainability. The model aims to maximize the total generated profits across the supply chain while simultaneously minimizing CO2 emissions as a measure of environmental impact. Results: By implementing the model on a sugar beet supply chain in the Netherlands, strategic crop rotation farm schedules for the crop rotation cycle and the optimum supply network decisions are obtained. Furthermore, different objectives are analyzed and the Pareto-efficient frontier is investigated to analyze the underlying trade-offs. Additionally, the model serves as a decision support tool for managers facilitating informed investment decisions in technologies that prolong product shelf life while maintaining profitability. Conclusions: The proposed multi-objective model offers a valuable framework for designing economically and environmentally sustainable agro-food supply chains. By aligning with sustainability goals and providing decision support, this research contributes to enhancing global food security and promoting sustainable resource utilization.

Variants of NR5A1 are often found in individuals with 46,XY disorders of sex development (DSD) and manifest with a very broad spectrum of clinical characteristics and variable sex hormone levels. Such complex phenotypic expression can be due to the inheritance of additional genetic hits in DSD-associated genes that modify sex determination, differentiation and organ function in patients with heterozygous NR5A1 variants. Here we describe the clinical, biochemical and genetic features of a series of seven patients harboring monoallelic variants in the NR5A1 gene. We tested the transactivation activity of novel NR5A1 variants. We additionally included six of these patients in a targeted diagnostic gene panel for DSD and identified a second genetic hit in known DSD-causing genes STAR, AMH and ZFPM2/FOG2 in three individuals. Our study increases the number of NR5A1 variants related to 46,XY DSD and supports the hypothesis that a digenic mode of inheritance may contribute towards the broad spectrum of phenotypes observed in individuals with a heterozygous NR5A1 variation.

Background This study aimed to assess the anthropometric measures and pubertal growth of children and adolescents with Type 1 diabetes mellitus (T1DM) and to detect risk determinants affecting these measures and their link to glycemic control. Patients and methods Two hundred children and adolescents were assessed using anthropometric measurements. Those with short stature were further evaluated using insulin-like growth factor 1 (IGF-1), bone age, and thyroid profile, while those with delayed puberty were evaluated using sex hormones and pituitary gonadotropins assay. Results We found that 12.5% of our patients were short (height SDS < -2) and IGF-1 was less than -2 SD in 72% of them. Patients with short stature had earlier age of onset of diabetes, longer duration of diabetes, higher HbA1C and urinary albumin/creatinine ratio compared to those with normal stature (p  <  0.05). Additionally, patients with delayed puberty had higher HbA1c and dyslipidemia compared to those with normal puberty (p  <  0.05). The regression analysis revealed that factors associated with short stature were; age at diagnosis, HbA1C > 8.2, and albumin/creatinine ratio > 8 (p  <  0.05). Conclusion Children with uncontrolled T1DM are at risk of short stature and delayed puberty. Diabetes duration and control seem to be independent risk factors for short stature.

Background Streptococcus pyogenes (Group A Streptococcus, GAS) is a significant pathogen that causes diverse infections, ranging from pharyngitis to severe invasive diseases. Asymptomatic carriage in children is pivotal for transmission. The COVID-19 pandemic’s health measures, including mask wearing and enhanced hand hygiene, likely influenced GAS transmission dynamics. This study evaluated the impact of these precautions on the prevalence of asymptomatic pharyngeal GAS carriage among schoolchildren in the southern West Bank, Palestine. Methods This cross-sectional study was conducted in two phases: pre-COVID-19 (November 2019–January 2020) and post-COVID-19 (November 2023–April 2024). Throat swabs were collected from 701 children (345 pre-COVID-19, 356 post-COVID-19) via cluster sampling. The samples were tested with the ABON Strep A rapid test and confirmed by culture. Sociodemographic, health, and household data were also collected. The statistical analyses included descriptive statistics, chi-square tests, and binary logistic regression. Results The prevalence of asymptomatic pharyngeal GAS carriage declined from 15.7% pre-COVID-19 to 10.4% post-COVID-19 ( p  = 0.038). Significant reductions were observed among urban residents (23.5–10.1%, p  = 0.003) and those from medium socioeconomic backgrounds (16.0–9.1%, p  = 0.008). Compared with urban residents, rural residents had lower GAS carriage rates (adjusted OR = 0.505, p  = 0.023). Carriage rates also decreased among children with frequent sore throats (17.6–7.3%, p  = 0.007) and those using private wells (52.5–14.9%, p  < 0.001). Higher BMI was a significant risk factor (adjusted OR = 17.68, p  < 0.001), whereas frequent tooth brushing (adjusted OR = 0.055, p  < 0.001) and hand washing (adjusted OR = 0.367, p  < 0.001) were protective factors. Conclusions COVID-19-related health precautions were correlated with a significant reduction in asymptomatic GAS carriage among Palestinian children. These findings suggest that public health measures, such as mask wearing and hand hygiene, can influence the transmission of respiratory pathogens. Ongoing surveillance and targeted interventions are essential for managing GAS infections, particularly in resource-limited settings.

Upper limb disabilities, often caused by conditions such as stroke or neurological disorders, severely limit an individual’s ability to perform essential daily tasks, leading to a significant reduction in quality of life. The development of effective rehabilitation technologies is crucial to restoring motor function and improving patient outcomes. This systematic review examines the application of machine learning and deep learning techniques in myoelectric-controlled systems for upper limb rehabilitation, focusing on the use of electroencephalography and electromyography signals. By integrating non-invasive signal acquisition methods with advanced computational models, the review highlights how these technologies can enhance the accuracy and efficiency of rehabilitation devices. A comprehensive search of literature published between January 2015 and July 2024 led to the selection of fourteen studies that met the inclusion criteria. These studies showcase various approaches in decoding motor intentions and controlling assistive devices, with models such as Long Short-Term Memory Networks, Support Vector Machines, and Convolutional Neural Networks showing notable improvements in control precision. However, challenges remain in terms of model robustness, computational complexity, and real-time applicability. This systematic review aims to provide researchers with a deeper understanding of the current advancements and challenges in this field, guiding future research efforts to overcome these barriers and facilitate the transition of these technologies from experimental settings to practical, real-world applications.

This research systematically reviews existing literature to evaluate the perceived value of forensic accounting. The review examines the evolution of forensic accounting, key services provided, necessary skills, professional standards, and educational requirements. It also addresses the expectation gap in forensic accounting education and identifies areas for future research. By synthesizing data from various studies, the paper presents a comprehensive overview of the current state of forensic accounting research. Key findings indicate a growing global demand for forensic accounting, evidenced by the establishment of specialized units within major firms and regional accounting entities. The review concludes with insights into the general characteristics and classification schemes of forensic accounting studies, offering a detailed examination of the field’s present and future directions.

Low power consumption has become one of the major requirements for most microelectronic devices and systems. Increasing power dissipation may lead to decreasing system efficiency and lifetime. The BULK metal oxide semiconductor field-effect transistor (MOSFET) has relatively high power dissipation and low frequency response due to its internal capacitances. Although the silicon-on-insulator (SOI) MOSFET was introduced to resolve these limitations, other challenges were introduced including the kink effect in the current-voltage characteristics. The selective buried oxide (SELBOX) MOSFET was then suggested to resolve the problem of the kink effect. The authors have previously investigated and reported the characteristics of the SELBOX structure in terms of kink effect, frequency, thermal and static power characteristics. In this paper, we continue our investigation by presenting the dynamic power characteristics of the SELBOX structure and compare that with the BULK and SOI structures. The simulated fabrication of the three devices was conducted using Silvaco TCAD tools in 90 nm complementary metal oxide semiconductor (CMOS) technology. Simulation results show that the average dynamic power dissipation of the CMOS BULK, SOI and SELBOX are compatible at high frequencies with approximately 54.5 µW. At low frequencies, the SOI and SELBOX showed comparable dynamic power dissipation but with lower values than the BULK structure. The difference in power dissipation between the SELBOX and BULK is in the order of nano watts. This power difference becomes significant at the chip level. For instance, at 1 MHz, SOI and SELBOX exhibit an average dynamic power consumption of 0.0026 µW less than that of the BULK structure. This value cannot be ignored when a chip operates using thousands or millions of SOI or SELBOX MOSFETs.

GEOTABS, a combination of TABS with a geothermal heat pump, is a promising heating and cooling system for decreasing greenhouse gas emissions in the building sector. However, TABS has a time delay when transferring energy from the pipes to the room. So, when the heat demand changes fast, TABS cannot properly compensate the heat demand. In order to solve this problem and maintain thermal comfort in the room, the concept of hybridGEOTABS proposes using a fast secondary system to assist the TABS. Yet, there is no integrated method for sizing both systems in a hybridGEOTABS building, considering the interaction between the secondary system and GEOTABS. This study will provide an integrated sizing methodology for hybridGEOTABS buildings. To that purpose, in this paper the interaction between the secondary system and TABS is investigated for two different scenarios by using a preference factor between the TABS and the secondary system. The methodology starts from heat demand curves, an analytic model for TABS, and optimal control principles for TABS to minimize the total energy use while providing thermal comfort. Finally, the method is used for 4 case studies in different scenarios with different secondary systems. Preliminary results of this research indicate that the secondary system type doesn’t have effect on the strategy of sizing. Therefore, designer can decide about secondary system type with investment and operating cost analysis.