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The aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine learning (ML) algorithms. In this study, 130 male and 152 female volunteers aged between 6 and 11 years were included. After obtaining demographic information of the participants, skinfold thickness, diameter and circumference measurements, and 20-m sprint performance were determined. The study conducted three distinct experiments to determine the optimal ML technique for predicting outcomes. Initially, the entire feature space was utilized for training the ML models to establish a baseline performance. In the second experiment, only significant features identified through correlation analysis were used for training and testing the models, enhancing the focus on relevant predictors. Lastly, Principal Component Analysis (PCA) was employed to reduce the feature space, aiming to streamline model complexity while retaining data variance. These experiments collectively aimed to evaluate different feature selection and dimensionality reduction techniques, providing insights into the most effective strategies for optimizing predictive performance in the given context. The correlation-based selected features (Age, Height, waist circumference, hip circumference, leg length, thigh length, foot length) has produced a minimum Mean Squared Error (MSE) value of 0.012 for predicting the sprint performance in children. The effective utilization of correlation analysis in the selection of relevant features for our regression model suggests that the features selected exhibit robust linear associations with the target variable and can be relied upon as predictors. [ABSTRACT FROM AUTHOR]

Background: The maternal diet is essential to offspring development, but the specific effects on tooth morphology are still unknown. The aim of this study was to evaluate the effects of altering maternal calcium (Ca) and phosphorus (P) supplementation during gestation and lactation on offspring dentition. Methods: Pregnant mice were fed an experimental diet containing a threefold increase in Ca and a threefold decrease in P compared to the standard mouse chow diet at embryonic Day 0.5 (E0.5). Offspring mice were maintained on standard or experimental diets from post‐natal Day 0 to weaning, then fed control diets until 6 weeks of age. Six‐week‐old offspring heads were collected and scanned using micro‐computed tomography. Dental morphometrics of offspring maxillary and mandibular first and third molars (n = 5–6 per diet/per sex) were determined. A two‐way ANOVA test was employed to verify the existence of any significant differences between groups. The significance level was set at P <.05. Results: A two‐way ANOVA revealed a statistically significant interaction between the effects of diet and sex on the upper and lower dentition. Moreover, experimental diet‐fed female offspring exhibited smaller molars with shorter mesiodistal width and larger pulp chambers relative to controls, while experimental diet‐fed male offspring possessed larger molars with wider mesiodistal width and smaller pulp chambers. Conclusion: Our findings reveal that altering the maternal and offspring dietary Ca:P ratio during gestation, lactation and weaning led to significant, sex‐specific changes in the offspring dentition. The differences in dentition appeared to be correlated with the sex‐specific changes in the craniofacial skeleton. [ABSTRACT FROM AUTHOR]

The turn of the twenty-first century has witnessed a growing preoccupation with multilingual texts across the world. Literary code-switching is becoming significantly valuable as it allows readers access to the trans-lingual and transcultural experiences of bilinguals in monolingual majority societies. More importantly, as the recent surge in the body of contact literature in general, and Anglo-Arab fiction in particular, has witnessed a major shift in its purpose, in this paper I argue that it is high time researchers made a similar shift in scholarly investigations of literary code-switching. The new texts are becoming less concerned with contesting the colonizer. Instead, contact literature is becoming increasingly focused on cross-cultural negotiations. Therefore, I attempt to explore the purposes of code-switching in Ahdaf Soueif's "The Map of Love" (1999) and Leila Aboulela's "The Translator" (2005), while re-evaluating its assumed direct link to asserting the author's primary identity--a purpose that has been the focus of scholarly work tackling literary code-switching for decades. Two other main purposes are highlighted in this paper, namely: (i) changing attitudes towards multilingualism; and (ii) adding authenticity to the experiences delineated in a foreign language.

Objective: Unknown etiology Background: Immunoglobulin A (IgA) vasculitis is a small-vessel vasculitis characterized by the deposition of IgA immune complexes primarily in the skin, kidneys, and gastrointestinal tract. While it predominantly affects children, cases in adults are associated with more severe manifestations. Evidence suggests that infectious triggers play a pivotal role in its etiology. Often, it follows a self-limiting course and doesn't necessitate intervention. Case Report: We present the case of a 51-year-old man who presented with a maculopapular rash, arthralgia, and abdominal pain. An examination revealed a purpuric rash on lower extremities and abdomen. A lower extremity duplex ultrasound identified deep vein thrombosis (DVT) in the right leg. Skin biopsy of the rash confirmed the diagnosis of IgA vasculitis, demonstrating perivascular neutrophilic infiltrate and IgA complex deposition. Stool studies revealed co-infection with Cryptosporidium and Giardia. The patient was treated with a prednisone taper with significant improvement in symptoms. Conclusions: This case highlights the potential role of Cryptosporidium as a trigger for IgA vasculitis. The presence of concurrent infections underscores the complex interplay between infections and the development of IgA vasculitis. The co-infection with Giardia suggests that a secondary infection may be involved, further complicating the disease's etiology. The observation of DVT suggests a possible link between IgA vasculitis and a prothrombotic state. This report serves to expand the knowledge of IgA vasculitis triggers and associated complications, guiding clinicians in diagnosing and managing similar cases while emphasizing the importance of vigilance in adults with these symptoms. [ABSTRACT FROM AUTHOR]

The tremendous growth of about 8 billion devices connected to each other in various domains of Internet of Things (IoT)-based applications have attracted researchers from both industry and academia. IoT is a network of several devices connected with each other to provide sensing capabilities, particularly in smart grid (SG) environment. Various challenges such as the efficient handling of massive IoT data can be addressed with advances in fog computing. The secure data aggregation challenge is one such issue in IoT-based smart grid systems, which include millions of smart meters. Typical SG-based data aggregation approaches have high computation and communication costs, however, many efforts have been made to overcome these limitations while leveraging fog computing but no satisfactory results have been obtained. Moreover, existing solutions also suffer from high storage requirements. The traditional data aggregation schemes such as GCEDA (Grouping of Clusters for Efficient Data Aggregation) and SPPDA (Secure Privacy-Preserving Data Aggregation) also suffer from a few shortcomings. SPPDA follows a mixed aggregation architecture that includes trees and clusters which can lead to some performance complexities and is not energy-efficient, whereas GCEDA does not support heterogeneity. To overcome these problems, this research provides a fog-assisted strategy for secure and efficient data aggregation in smart grid. The concept of smart grid is implemented in fog environment, which was not the case in previous schemes. We used communication between smart meters (SMs) and fog nodes (FNs) to transmit confidential data in compressed form towards FN. The FN further aggregates the received data which can then be updated in cloud repositories later. We presented two algorithms—data aggregation and data extraction at FN and cloud, respectively, to achieve secure communication. The performance of the proposed strategy has been evaluated against existing data aggregation techniques GCEDA and SPPDA for various performance parameters such as storage, communication cost and transmission cost. The proposed scheme overcomes the limitation of heterogeneity and mixed aggregation which was faced in GCEDA and SPPDA and the results revealed outstanding performance in comparison with both, so the proposed solution can be used in a smart grid environment for efficient and secure data transmission. [ABSTRACT FROM AUTHOR]