Boundary layer and heat transfer Williamson fluid flow over a stretching sheet with Newtonian heating

The Aligned magnetic field with Williamson fluid has been analyzed using a stretching sheet with Newtonian heating. The governing partial differential equations are transformed to the nonlinear ordinary differential equation by employing the similarity transformations and then solved by using the MATLAB inbuilt solver bvp4c. The influence of various parameters on dimensionless velocity and temperature was graphically explored. Comparisons of all conditions for a particular situation have been made and a very effective agreement has been reached

Advances in the use of 3D Convolutional Neural Network for the detection of lung cancer

Lung cancer is one of the most prevalent cancer-related diseases with high mortality rate, and this is largely due to the lateness in detecting the presence of malignancy. Again, the conventional methods used in the diagnosis of lung cancer have had their shortfalls. While the effectiveness of computerized tomography in detecting this malignancy, the large volumes of data that radiologists have to process not only present an arduous task, but may also slow down the process of detecting lung cancer early enough for treatment to take its course.  It is against this backdrop that computer-aided diagnostic (CAD) systems have been designed. One of such is the convolutional neural network, a method that best describes a group of deep learning models featuring filters that can be trained with local pooling operations being incorporated on input CT images in an alternating manner for the purpose of creating an array of hierarchical complex features. The need to have this type of data-driven technique is further informed by the attempt to ensure successful segmentation of lung nodules, a step that cannot be overruled when striving for a good model of detection or diagnosis. There are variations and models of the convolutional neural networks that have been effectively put to use in the lung nodule detection. The 2D CNN model has been utilized in the medical field for quite a while now, and as it has displayed its many strengths, so could the limitations not be hidden. It is in addressing these limitations and improving on the detection prowess of the convolutional neural network that 3D model is now fast gaining traction. The 3D models have been reported to return pronounced sensitivity and specificity in detection of lung nodules, but the issues of time-consumption, training complexities and hardware memory usage could make it difficult to implement the 3D model in the medical field. In this paper, review the advances that have been made in the area of adopting 3D CNN model in the diagnosis of lung cance

Stability analysis of Automatic Voltage Regulator using Fractional Order Controller

We aim to design a fractional order robust control system. It is an advanced model of classic PID controller whose order will be non-integer.PID controller that we generally use has many advantages and disadvantages with respect to the disadvantages like, it doesn’t give accurate values of constants, exact values of the time domain parameters as well as frequency domain parameters of the control system and we have more robust problem. Wearable electronic based an automatic voltage regulator can automatically preservesthe terminal voltage of generator at a fixed value under varyingly load and operating temperature. AVR controls output by sensing the output voltage at a power-generating coil and compares it to a stable reference. The combination of fractional order controller with an automatic voltage regulator is proved to be better than conventional controllers

Enhancing DDoS Attack Detection in SDNs with GAN-Based Imbalanced Data Augmentation

Securing computer networks has become crucial due to the ongoing emergence of diverse network attacks. The popularity of Software Defined Networks (SDN) has risen because of its ability to enhance network agility, efficiency, and adaptability to recent networking challenges. However, it is essential to note that SDNs, which depend on centralized controllers, can be severely affected by Distributed Denial of Service (DDoS) attacks. The threat of DDoS attacks has grown exponentially, resulting in the evolution of robust Machine Learning-based DDoS attack detection systems within SDN. DDoS attack detection systems may deliver poor performance when trained on imbalanced datasets. Traditional techniques for handling imbalanced datasets need to be revised. Recent advances in generative adversarial networks (GANs) have revealed significant potential in generating synthetic data while preserving the probability distribution of the original data. This innovative procedure offers a promising solution to mitigate the challenges of imbalanced data in DDoS attack detection. To address challenges originating from imbalanced training datasets, we employed Generative Adversarial models to generate adversarial attacks from one viewpoint and evaluate their quality from another perspective. We chose Generative Adversarial Networks (GANs), Bidirectional GANs (Bi-GANs), and Wasserstein GANs (WGANs) based on extensive usage and reliability criteria in various domains. We conducted a comprehensive assessment to evaluate their effectiveness and resilience in generating high-quality attacks. It helps to develop, train, and fine-tune machine and deep learning models to estimate their impacts. We utilized NSL-KDD and CICIDS-2017 datasets to ensure generalization, implementing both ML and DL approaches. The outcomes demonstrate that the WGAN model outperformed GAN, Bi-GAN, and the models trained on the original imbalanced dataset and traditional sampling techniques in binary and multiclass classifications for both datasets

Factors Affecting Caregiver Burden in Informal Caregivers of Patients with Autism Spectrum Disorder

Individuals with Autism Spectrum Disorder (ASD) often require lifelong care to meet their daily needs, which is typically provided by informal sources like family members as well as formal caregivers from home health agencies. The persistent stress of raising a child with ASD can potentially lead to parental burnout, highlighting the importance of understanding the struggles faced by these caregivers. Clinicians must prioritize the well-being of both the individuals with ASD and their dedicated caregivers by gaining a comprehensive understanding of the challenges they encounter. Our research aims to investigate and comprehend the specific challenges faced by caregivers of individuals diagnosed with ASD. By utilizing the Caregiver Burden Inventory (CBI; Novak & Guest, 1989), we sought to pinpoint the primary elements that contribute to caregiver burden and evaluate how it affects the wellbeing of caregivers. These findings can ultimately lay the foundation for targeted interventions and strategies aimed at lessening the burden on caregivers, ensuring they receive the necessary support to provide optimal care for their loved ones with ASD while maintaining their own well-being

Variables Contributing to The Psychosocial Strain on Caregivers of Patients with Autism Spectrum Disorder

This research, conducted at the Rowan-Virtua Integrated Special Needs (RISN) Center, Sewell, New Jersey, explores the psychosocial strain on caregivers of patients with Autism Spectrum Disorder (ASD). Utilizing the Caregiver Burden Inventory (CBI), we assessed multiple psychosocial dimensions, including time dependency, emotional health, development, social relationships, and physical health. Data was extracted from a total of 295 patient charts; this poster analyzes the initial sample of 99 patients by incorporating demographic variables and caregiver burden scores into a tailored database for comprehensive analysis. Our findings reveal a statistically significant correlation between the age of patients and increased developmental scores in the CBI, suggesting that older patients tend to impose higher developmental demands on caregivers. Gender analysis showed no significant correlation with CBI scores. The study underscores the persistent psychosocial challenges faced by caregivers, pointing to the necessity for enhanced supportive measures and further research into the factors influencing caregiver burden. This ongoing project continues to expand with the aim of contributing to better-informed care strategies for both ASD patients and their caregivers

Pallister-Killian Mosaic Syndrome in an Omani Newborn: A Case Report and Literature Review

Pallister-Killian mosaic syndrome (PKS) is a rare sporadic condition with multiple congenital anomalies and intellectual deficits caused by mosaic tissue-limited tetrasomy of the short arm of chromosome 12 (12p). The clinical features are highly variable, ranging from mild to severe. Diagnosis is usually missed because of the low level of mosaicism in peripheral lymphocytes. We present a case of an Omani newborn with PKS with severe clinical presentation and multisystem involvement that lead to postnatal death. Karyotype and fluorescent in situ hybridization studies confirmed the presence of chromosome 12p duplication. This is the first case of PKS reported in the literature from Oman and the Arab world

Massively Parallel Sequencing and Analysis of the Necator americanus Transcriptome

The blood-feeding hookworm Necator americanus infects hundreds of millions of people. To elucidate fundamental molecular biological aspects of this hookworm, the transcriptome of adult Necator americanus was studied using next-generation sequencing and in silico analyses. Contigs (n = 19,997) were assembled from the sequence data; 6,771 of them had known orthologues in the free-living nematode Caenorhabditis elegans, and most encoded proteins with WD40 repeats (10.6%), proteinase inhibitors (7.8%) or calcium-binding EF-hand proteins (6.7%). Bioinformatic analyses inferred that C. elegans homologues are involved mainly in biological pathways linked to ribosome biogenesis (70%), oxidative phosphorylation (63%) and/or proteases (60%). Comparative analyses of the transcriptomes of N. americanus and the canine hookworm, Ancylostoma caninum, revealed qualitative and quantitative differences. Essential molecules were predicted using a combination of orthology mapping and functional data available for C. elegans. Further analyses allowed the prioritization of 18 predicted drug targets which did not have human homologues. These candidate targets were inferred to be linked to mitochondrial metabolism or amino acid synthesis. This investigation provides detailed insights into the transcriptome of the adult stage of N. americanus