Hardware realization of a novel Automatic Censored Cell Averaging CFAR detection algorithm using FPGA

In this paper we present hardware realization of a novel Automatic Censored Cell Averaging (ACCA) Constant False Alarm Rate (CFAR) detection algorithm based on Ordered Data Variability (ODV) using Field Programmable Gate Array (FPGA). This algorithm has been recently proposed in the literature for radar target detection in non-homogeneous environments. The unknown background level can be estimated by dynamically selecting a suitable set of ranked reference window cells and by doing successive hypothesis tests. The ACCA-ODV based CFAR detector does not require any prior information about the background environment and uses the variability index statistic as a shape parameter to reject or accept the ordered cells under investigation. Recent advancements in modern FPGAs and availability of sophisticated electronic design tools have made it possible to realize the ACCA-ODV CFAR detector in a cost-effective way. The designed hardware is modular and has been physically realized in Altera Stratix II FPGA device. © 2008 IEEE.International Conference on Signal Processin

Influence of Hydroxyapatite Nanospheres in Dentin Adhesive on the Dentin Bond Integrity and Degree of Conversion: A Scanning Electron Microscopy (SEM), Raman, Fourier Transform-Infrared (FTIR), and Microtensile Study

An experimental adhesive incorporated with different nano-hydroxyapatite (n-HA) particle concentrations was synthesized and analyzed for dentin interaction, micro-tensile bond strength (μTBS), and degree of conversion (DC). n-HA powder (5 wt % and 10 wt %) were added in adhesive to yield three groups; gp-1: control experimental adhesive (CEA, 0 wt % HA), gp-2: 5 wt % n-HA (HAA-5%), and gp-3: 10 wt % n-HA (HAA-10%). The morphology of n-HA spheres was evaluated using Scanning Electron Microscopy (SEM). Their interaction in the adhesives was identified with SEM, Energy-Dispersive X-ray (EDX), and Micro-Raman spectroscopy. Teeth were sectioned, divided in study groups, and assessed for μTBS and failure mode. Employing Fourier Transform-Infrared (FTIR) spectroscopy, the DC of the adhesives was assessed. EDX mapping revealed the occurrence of oxygen, calcium, and phosphorus in the HAA-5% and HAA-10% groups. HAA-5% had the greatest μTBS values followed by HAA-10%. The presence of apatite was shown by FTIR spectra and Micro-Raman demonstrated phosphate and carbonate groups for n-HA spheres. The highest DC was observed for the CEA group followed by HAA-5%. n-HA spheres exhibited dentin interaction and formed a hybrid layer with resin tags. HAA-5% demonstrated superior μTBS compared with HAA-10% and control adhesive. The DC for HAA-5% was comparable to control adhesive

eHomeCaregiving: A Diabetes Patient-Centered Blockchain Ecosystem for COVID-19 Caregiving

The pandemic has triggered an unprecedented global demand for home caregiving to manage asymptomatic and mild COVID-19 cases. Older people and others with pre-existing medical conditions (including diabetes) appear to be more vulnerable to severe illness caused by the severe acute respiratory syndrome coronavirus 2. Approximately 25% of Saudis suffer from diabetes; these 4 million patients require 5.5 million consultations and follow-up visits each year to manage their disease. Furthermore, with the increasing number of patients with diabetes and their need for professional care, it is difficult and time consuming to share patient-care information among caregivers in a traditional way; this increases the financial and psychological burden of home caregivers. Although the pandemic has also triggered a global demand for digital health technology adoption worldwide to achieve higher standards of health, recent developments in advanced technologies and mobile health (mHealth) applications have failed to equip the caregivers with the right ecosystem for patient-centered information sharing to allow for informed care decisions. Therefore, there is a gap in the literature as the current solutions fall short of facilitating an effective communication channel among caregivers and between them and their patients, supporting diverse caregiving groups with multiple languages, distributing tasks between caregivers to alleviate the burden on one caregiver, providing a treatment plan by a specialized care team to be viewed and followed by caregivers and patients, and alerting everyone in case of an emergency. Based on the need for empowering home caregivers to cope with the pressure, we propose eHomeCaregiving, an mHealth solution that can build a transparent blockchain-based patient-centered family caregiving ecosystem. eHomeCaregiving facilitates care continuity in patients with type 2 diabetes in Saudi Arabia by integrating care, saving time and efforts of all caregivers, and improving the patient’s quality of life and outcomes, particularly in terms of facing emerging challenges amid the pandemic

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

COVID-19 pandemic has caused a global health crisis, resulting in endless efforts to reduce infections, fatalities, and therapies to mitigate its after-effects. Currently, large and fast-paced vaccination campaigns are in the process to reduce COVID-19 infection and fatality risks. Despite recommendations from governments and medical experts, people show conceptions and perceptions regarding vaccination risks and share their views on social media platforms. Such opinions can be analyzed to determine social trends and devise policies to increase vaccination acceptance. In this regard, this study proposes a methodology for analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset. The study relies on two techniques to analyze the sentiments: natural language processing and machine learning. To evaluate the performance of the different lexicon-based methods, different machine and deep learning models are studied. In addition, for sentiment classification, the proposed ensemble model named long short-term memory-gated recurrent neural network (LSTM-GRNN) is a combination of LSTM, gated recurrent unit, and recurrent neural networks. Results suggest that the TextBlob shows better results as compared to VADER and AFINN. The proposed LSTM-GRNN shows superior performance with a 95% accuracy and outperforms both machine and deep learning models. Performance analysis with state-of-the-art models proves the significance of the LSTM-GRNN for sentiment analysis

Prevalence, Predictors, and Awareness of Coffee Consumption and Its Trend among Saudi Female Students

This study aimed to investigate the prevalence, trends, and predictors of coffee consumption among Saudi female students and its association with anthropometric and demographic variables. A survey-based study using a face-to-face interview was designed, and 930 (aged 21.5 ± 2.1 years) apparently healthy female students from different departments of King Saud University participated. The prevalence of coffee consumption was significantly higher (88.2%, p < 0.03) in the central Riyadh region. Coffee consumers had significantly higher prevalence of being overweight than non-consumers (p = 0.02). The frequency of coffee consumption was significantly higher (p < 0.02) in students who were single and belonged to families with a moderate income level. Coffee consumption was significantly higher among first-year students with a high-scale grade point average (GPA) (p < 0.001 and p = 0.03, respectively). Increased coffee consumption during exam and stress conditions was associated with unhealthy dietary habits such as using more sugar and spices. The prevalence of coffee consumption was high among Saudi females. High body mass index (BMI) and increased family income level were strong determinants for coffee consumption. Continued nutritional education and awareness about the potential positive and negative health effects of coffee consumption and the importance of food label use should be provided to younger generations in order to correct the wrong perceptions

Updates on Epstein–Barr Virus (EBV)-Associated Nasopharyngeal Carcinoma: Emphasis on the Latent Gene Products of EBV

Nasopharyngeal carcinoma (NPC) is an uncommon type of malignancy/cancer worldwide. However, NPC is an endemic disease in southeast Asia and southern China and the reasons behind the underlying for such changes are unclear. Even though the Epstein–Barr infection (EBV) has been suggested as an important reason for undistinguishable NPC, the EBV itself is not adequate to source this type of cancer. The risk factors, for example, genetic susceptibility, and environmental factors might be associated with EBV to undertake a part in the NPC carcinogenesis. Normal healthy people have a memory B cell pool where the EBV persists, and any disturbance of this connection leads to virus-associated B cell malignancies. Less is known about the relationship between EBV and epithelial cell tumors, especially the EBV-associated nasopharyngeal carcinoma (EBVaNPC) and EBV-associated gastric carcinoma (EBVaGC). Currently, it is believed that premalignant genetic changes in epithelial cells contribute to the aberrant establishment of viral latency in these tumors. The early and late phases of NPC patients’ survival rates vary significantly. The presence of EBV in all tumor cells presents prospects for the development of innovative therapeutic and diagnostic techniques, despite the fact that the virus’s exact involvement in the carcinogenic process is presently not very well known. EBV research continues to shed light on the carcinogenic process, which is important for a more comprehensive knowledge of tumor etiology and the development of targeted cancer therapeutics. In order to screen for NPC, EBV-related biomarkers have been widely used in a few high-incidence locations because of their close associations with the risks of NPC. The current review highlights the scientific importance of EBV and its possible association with NPC

AGREEing on Clinical Practice Guidelines for Autism Spectrum Disorders in Children: A Systematic Review and Quality Assessment

Background: Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder requiring multimodal intervention and an army of multidisciplinary teams for a proper rehabilitation plan. Accordingly, multiple practice guidelines have been published for different disciplines. However, systematic evidence to detect and intervene must be updated regularly. Our main objective is to compare and summarize the recommendations made in the clinical practice guidelines (CPGs) for ASD in children released from November 2015 to March 2022. Methods: CPGs were subjected to a systematic review. We developed the inclusion and exclusion criteria and health-related questions, then searched and screened for CPGs utilizing bibliographic and CPG databases. Each of the CPGs used in the study were critically evaluated using the Appraisal of Guidelines for REsearch and Evaluation II (AGREE II) instrument. In a realistic comparison table, we summarized the recommendations. Results: Four eligible CPGs were appraised: Australian Autism CRC (ACRC); Ministry of Health New Zealand (NZ); National Institute for Health and Care Excellence (NICE); and Scottish Intercollegiate Guidelines Network, Healthcare Improvement Scotland (SIGN-HIS). The overall assessments of all four CPGs scored greater than 80%; these findings were consistent with the high scores in the six domains of AGREE II, including: (1) scope and purpose, (2) stakeholder involvement, (3) rigor of development, (4) clarity of presentation, (5) applicability, and (6) editorial independence domains. Domain (3) scored 84%, 93%, 86%, and 85%; domain (5) 92%, 89%, 54%, and 85%; and domain (6) 92%, 96%, 88%, and 92% for ACRC, NICE, NZ, and SIGN-HIS, respectively. Overall, there were no serious conflicts between the clinical recommendations of the four CPGs, but some were more comprehensive and elaborative than others. Conclusions: All four assessed evidence-based CPGs demonstrated high methodological quality and relevance for use in practice

Saudi Expert Consensus-Based Autism Spectrum Disorder Statement: From Screening to Management

Background: There is a large gap between the needs of individuals diagnosed with autism spectrum disorder (ASD) and the currently available services in Saudi Arabia. Services are often difficult to access, inconsistent in quality, incomplete, unsatisfactory, and costly. As such, there is a national need for expert consensus on the appropriate standards for the assessment and management of children on the autism spectrum. Methodology: A guideline development group (GDC) was formed by professionals representing all related specialties and institutions involved in the management of individuals on the autism spectrum in Saudi Arabia. They met on a regular basis over 21 months. The guideline development process consisted of five steps starting from reviewing existing guidelines and ending with discussing and writing this manuscript. A formal voting process was utilized and recommendations were discussed until a consensus was reached. Results: There was consensus on the following: A specialized diagnostic assessment needs to be carried out by an experienced multidisciplinary team for children referred to assess for ASD. They should be assessed for medical etiology, their behavioral history carefully reviewed, and symptoms directly observed. Longitudinal assessments are encouraged to reflect the effects of symptoms on the individual’s ability to function while with their family, among peers, and in school settings. An additional formal assessment of language, cognitive, and adaptive abilities as well as sensory status is essential to complete the diagnostic process. Interventions should be individualized, developmentally appropriate, and intensive, with performance data relevant to intervention goals to evaluate and adjust interventions. Target symptoms must be identified to address and develop monitoring systems to track change. Conclusion: ASD is a complex condition with widely varying clinical manifestations, thus requiring evaluation and intervention by a range of professionals working in coordination. Behavioral and environmental interventions are the key to optimal outcomes, in conjunction with medications when indicated for specific symptoms. Parental involvement in interventions is vital to sustaining therapeutic gains