FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network. In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns a FL server and dedicated carrier frequencies to exchange the updated model's parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics. We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users' criteria and achieving high accuracy in a robust network

Quality of care of Egyptian asthmatic children: Clinicians adherence to asthma guidelines

<p>Abstract</p> <p>Background</p> <p>Despite the development and dissemination of guidelines for the diagnosis and management of asthma, a gap remains between current recommendations and actual practice.</p> <p>Objectives</p> <p>To assess the physicians attitude towards asthma guidelines and their adherence to its recommendations.</p> <p>Methods</p> <p>Three hundred and fifty two clinicians (101 General practitioners, 131 pediatric specialists, 35 pediatric consultants and 85 doctors did not report the qualification) engaged in direct childhood asthma care in Cairo, Egypt were subjected to a self-administered questionnaire with 35 questions of which most were multiple choices, aiming at assessment of three important aspects about the involved physicians; physician's knowledge, practice and attitude. 165 of the clinicians were working in governmental hospitals, 68 clinicians work in private clinics and 119 clinicians work in both.</p> <p>Results</p> <p>Agreement with asthma guidelines was present in 76.2% of the studied physicians, however those who not in agreement with the guidelines claimed that this was mainly due to patient factors, firstly the poor socioeconomic standard of the patient (18.1%) and secondly due to poor patient compliance (16%). Poor knowledge was found in 28.5%, poor practice was found in 43.6% and poor attitude was found in 14.4% of the studied physicians. There was positive highly significant correlation between qualification and knowledge, (p < 0.01), positive highly significant correlation between qualification and practice, (p < 0.01), and positive highly significant correlation between qualification and attitude, (p < 0.01).</p> <p>Conclusion</p> <p>The attitude of the studied physicians revealed agreement of their majority with the guidelines, while the disagreement was mainly explained by the poor socioeconomic standard of the patients. The degree of poor practice is more marked than that of poor knowledge or poor attitude reflecting resources limitations and applications obstacles in the physician's practice.</p

Evaluating the Effectiveness of Digital Inclusion at Private Educational Schools in Gaza Strip

The research aims to evaluate the process of digital inclusion and the usage of digital tools in the educational process at private schools in Gaza Strip - Palestine. Education is the beacon of knowledge and development that is accompanied by developmental processes aimed at rising and improving the quality of service through the use of digital tools in all aspects of the educational and administrative processes within a school. In this study, the research team sought to know the tools used in the process of digital integration, and the challenges and problems faced by teachers while using digital tools, in addition to providing recommendations to help solve or mitigate problems. The descriptive analytical method is adopted to carry out the research. The results of the study showed weakness in the effectiveness of the digital inclusion process and the use of digital tools in the educational process in private schools. Furthermore, recommendations to the schools’ administrations, teachers and the Ministry of Education are stated in order to alleviate the problems and challenges of using digital tools

Software Agent Simulation Design on the Efficiency of Food Delivery

Food delivery services have gained popularity since the emergence of online food delivery. Since the recent pandemic, the demand for service has increased tremendously. Due to several factors that affect how much time additional riders spend on the road; food delivery companies have no control over the location or timing of the delivery riders. There is a need to study and understand the food delivery riders' efficiency to estimate the service system's capacity. The study can ensure that the capacity is sufficient based on the number of orders, which usually depends on the number of potential customers within a territory and the time each rider takes to deliver the orders successfully. This study is an opportunity to focus on the efficiency of the riders since there is not much work at the operational level of the food delivery structure. This study takes up the opportunity to design a software agent simulation on the efficiency of riders' operations in food service due to the lack of simulation to predict this perspective, which could be extended to efficiency prediction. The results presented in this paper are based on the system design phase using the Tropos methodology. At movement in the simulation, the graph of the efficiency is calculated. Upon crossing the threshold, it is considered that the rider agents have achieved the efficiency rate required for decision-making. The simulation's primary operations depend on frontline remotely mobile workers like food delivery riders. It can benefit relevant organizations in decision-making during strategic capacity planning

Theileria lestoquardi in Sudan is highly diverse and genetically distinct from that in Oman

Malignant ovine theileriosis is a severe tick-borne protozoan disease of sheep and other small ruminants which is widespread in sub-Saharan Africa and the Middle East. The disease is of considerable economic importance in Sudan as the export of livestock provides a major contribution to the gross domestic product of this country. Molecular surveys have demonstrated a high prevalence of sub-clinical infections of Theileria lestoquardi, the causative agent, among small ruminants. No information is currently available on the extent of genetic diversity and genetic exchange among parasites in different areas of the country. The present study used a panel of T. lestoquardi specific micro- and mini-satellite genetic markers to assess diversity of parasites in Sudan (Africa) and compared it to that of the parasite population in Oman (Asia). A moderate level of genetic diversity was observed among parasites in Sudan, similar to that previously documented among parasites in Oman. However, a higher level of mixed-genotype infection was identified in Sudanese animals compared to Omani animals, consistent with a higher rate of tick transmission. In addition, the T. lestoquardi genotypes detected in these two countries form genetically distinct groups. The results of this work highlight the need for analysis of T. lestoquardi populations in other endemic areas in the region to inform on novel approaches for controlling malignant theileriosis

Modified arithmetic optimization algorithm with Deep Learning based data analytics for depression detection

Depression detection is the procedure of recognizing the individuals exhibiting depression symptoms, which is a mental illness that is characterized by hopelessness, feelings of sadness, persistence and loss of interest in day-to-day activities. Depression detection in Social Networking Sites (SNS) is a challenging task due to the huge volume of data and its complicated variations. However, it is feasible to detect the depression of the individuals by examining the user-generated content utilizing Deep Learning (DL), Machine Learning (ML) and Natural Language Processing (NLP) approaches. These techniques demonstrate optimum outcomes in early and accurate detection of depression, which in turn can support in enhancing the treatment outcomes and avoid more complications related to depression. In order to provide more insights, both ML and DL approaches possibly offer unique features. These features support the evaluation of unique patterns that are hidden in online interactions and address them to expose the mental state amongst the SNS users. In the current study, we develop the Modified Arithmetic Optimization Algorithm with Deep Learning for Depression Detection in Twitter Data (MAOADL-DDTD) technique. The presented MAOADL-DDTD technique focuses on identification and classification of the depression sentiments in Twitter data. In the presented MAOADL-DDTD technique, the noise in the tweets is pre-processed in different ways. In addition to this, the Glove word embedding technique is used to extract the features from the preprocessed data. For depression detection, the Sparse Autoencoder (SAE) model is applied. The MAOA is used for optimum hyperparameter tuning of the SAE approach so as to optimize the performance of the SAE model, which helps in accomplishing better detection performance. The MAOADL-DDTD algorithm is simulated using the benchmark database and experimentally validated. The experimental values of the MAOADL-DDTD methodology establish its promising performance over another recent state-of-the-art approaches

Memristor Multiport Readout: A Closed-Form Solution for Sneak Paths

In this paper, we introduce for the first time, a closed-form solution for the memristor-based memory sneak paths without using any gating elements. The introduced technique fully eliminates the effect of sneak paths by reading the stored data using multiple access points and evaluating a simple addition/subtraction on the different readings. The new method requires fewer reading steps compared to previously reported techniques, and has a very small impact on the memory density. To verify the underlying theory, the proposed system is simulated using Synopsys HSPICE showing the ability to achieve a 100% sneak-path error-free memory. In addition, the effect of quantization bits on the system performance is studied. © 2014 IEEE

MicroRNA-208a: a Good Diagnostic Marker and a Predictor of no-Reflow in STEMI Patients Undergoing Primary Percutaneuos Coronary Intervention

MicroRNA-208a is a cardiac specific oligo-nucleotide. We aimed at investigating the ability of microRNA-208a to diagnose myocardial infarction and predict the outcome of primary percutaneuos coronary angiography (PCI). Patients (n = 75) presented by chest pain were recruited into two groups. Group 1 (n = 40) had ST elevation myocardial infarction (STEMI) and underwent primary PCI: 21 patients had sufficient reperfusion and 19 had no-reflow. Group 2 (n = 35) had negative cardiac troponins (cTns). Plasma microRNA-208a expression was assessed using quantitative polymerase chain reaction and patients were followed for occurrence of in-hospital major adverse cardiac events (MACE). MicroRNA-208a could diagnose of MI (AUC of 0.926). After primary PCI, it was superior to cTnT in prediction of no-reflow (AUC difference of 0.231, P = 0.0233) and MACE (AUC difference of 0.367, P = 0.0053). Accordingly, circulating levels of miR-208a can be used as a diagnostic marker of MI and a predictor of no-reflow and in-hospital MACE

Freshwater microalgae-based wastewater treatment under abiotic stress

Wastewater treatment by microalgae is an eco-friendly and sustainable method for pollutant removal and biomass generation. Microalgae production under abiotic stress (such as salinity/salt stress) has an impact on nutrient removal and fatty acid accumulation. In this study, a freshwater microalgal strain (Desmodesmus communis GEEL-12) was cultured in municipal wastewater with various NaCl concentrations (ranging from 25–150 mM). The growth kinetics and morphological changes of the microalgae were observed. The nutrient removal, salinity change, fatty acid composition, and biodiesel quality under various groups were also investigated. The maximum growth of D. communis GEEL-12 was observed in the control group at 0.48 OD680nm. The growth inhibition was observed under high salt conditions (150 mM), which showed poor tolerance with 0.15 OD680nm. The nitrogen (N) and phosphorus (P) removal significantly decreased from 99–81% and 5.0–5.9% upon the addition of 100–150 mM salt, respectively. Palmitic acid (C16:0) and stearic acid (C18:0) were the most common fatty acid profiles. The abundance of C18:0 enhanced from 49.37%–56.87% in D. communis GEEL-12 upon high NaCl concentrations (100–150 mM). The biodiesel quality index of D. communis GEEL-12 under 50–75 mM salt concentrations reached the levels advised by international standards

A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlations Air Pollutions Global Risk Assessment

Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this pa-per proposes a new air pollution global risk assessment (APGRA) model for predicting spatial correlations air quality index risk assessment to address these issues. The APGRA model incorporates autoregressive integrated moving average (ARIMA), Monte-Carlo simulation, and collaborative multi-agent system, and prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models