Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

Reading Loss in Arabic Language During COVID-19 in the UAE and Proposed Solutions:The Perspectives of Primary-Grade Arabic Language Teachers

The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.

The Use of Art Therapy in Alleviating Mental Health Symptoms in Refugees: A Literature Review

There are over thirty million refugees globally with severe experiences of trauma.Art therapy intervention allows for nonverbal expression and could alleviate mental health symptomatology among refugees.The present review’s aim was to integrate and summarize the previous research which examined the effects of visual arts on alleviating psychological conditions of refugees.However,due to the paucity of studies which solely used visual arts,we included studies that used visual arts alongside other modalities as part of an expressive arts therapy intervention.The present review synthesizes studies that examined the effect of art therapy on mental health issues of refugees from January 2000 to March 2021.Seven studies(child and adolescent sample,N=5 and adult sample,N=2)with a total of 298 refugee participants(n=298)met our inclusion criteria.The participants were from the Middle East and North Africa(MENA),Southeast Asia,and Europe.We found three commonly reported mental health disorders,namely Post Traumatic Stress Disorder(PTSD),anxiety,and Major Depression Disorder.The research highlights how art therapy interventions could be a great starting point to alleviate symptomatology among refu-gees.Four additional benefits of art therapy which were commonly reported across the seven studies emerged from this review:working with traumatic experience/loss,rebuilding social connection and trust,nonverbal com-munication and self-expression of loss and trauma,and retelling stories.Art therapy interventions could be used as a starting point in the healing process of traumatized refugees to encourage verbal articulation of their feelings and reduce mental health symptoms.Despite these promisingfindings,due to a dearth of robust methodologies,further research is required to assess the long-term effectiveness of art therapy.

Differential Scheme for Reconfigurable Intelligent Surface-Based Spatial Modulation System

In this paper,a differential scheme is proposed for reconfigurable intelligent surface(RIS)assisted spatial modulation,which is referred to as RISDSM,to eliminate the need for channel state information(CSI)at the receiver.The proposed scheme is an improvement over the current differential modulation scheme used in RIS-based systems,as it avoids the high-order matrix calculation and improves the spectral efficiency.A mathematical framework is developed to determine the theoretical average bit error probability(ABEP)of the system using RIS-DSM.The detection complexity of the proposed RIS-DSM scheme is extremely low through the simplification.Finally,simulations results demonstrate that the proposed RIS-DSM scheme can deliver satisfactory error performance even in low signal-to-noise ratio environments.

COVID-19 pandemic and the cyberthreat landscape:Research challenges and opportunities

Although cyber technologies benefit our society,there are also some related cybersecurity risks.For example,cybercriminals may exploit vulnerabilities in people,processes,and technologies during trying times,such as the ongoing COVID-19 pandemic,to identify opportunities that target vulnerable individuals,organizations(e.g.,medical facilities),and systems.In this paper,we examine the various cyberthreats associated with the COVID-19 pandemic.We also determine the attack vectors and surfaces of cyberthreats.Finally,we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals,organizations,and systems.

TC-Net:A Modest&Lightweight Emotion Recognition System Using Temporal Convolution Network

Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines.Speech Emotion Recognition(SER)is one of the critical sources for human evaluation,which is applicable in many real-world applications such as healthcare,call centers,robotics,safety,and virtual reality.This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state.The authors designed a Temporal Convolutional Network(TCN)core block to recognize long-term dependencies in speech signals and then feed these temporal cues to a dense network to fuse the spatial features and recognize global information for final classification.The proposed network extracts valid sequential cues automatically from speech signals,which performed better than state-of-the-art(SOTA)and traditional machine learning algorithms.Results of the proposed method show a high recognition rate compared with SOTAmethods.The final unweighted accuracy of 80.84%,and 92.31%,for interactive emotional dyadic motion captures(IEMOCAP)and berlin emotional dataset(EMO-DB),indicate the robustness and efficiency of the designed model.

Flexible Global Aggregation and Dynamic Client Selection for Federated Learning in Internet of Vehicles

Federated Learning(FL)enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles(IoV)realm.While FL effectively tackles privacy concerns,it also imposes significant resource requirements.In traditional FL,trained models are transmitted to a central server for global aggregation,typically in the cloud.This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server.The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments.These include diverse and distributed data sources,varying data quality,and limited communication resources.By employing dynamic client selection,we can prioritize relevant and high-quality data sources,enhancing model accuracy.To address this issue,we propose an FL framework that selects global aggregation nodes dynamically rather than a single fixed aggregator.Flexible global aggregation ensures efficient utilization of limited network resources while accommodating the dynamic nature of IoV data sources.This approach optimizes both model performance and resource allocation,making FL in IoV more effective and adaptable.The selection of the global aggregation node is based on workload and communication speed considerations.Additionally,our framework overcomes the constraints associated with network,computational,and energy resources in the IoV environment by implementing a client selection algorithm that dynamically adjusts participants according to predefined parameters.Our approach surpasses Federated Averaging(FedAvg)and Hierarchical FL(HFL)regarding energy consumption,delay,and accuracy,yielding superior results.

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

CEER:Cooperative Energy-Efficient Routing Mechanism for Underwater Wireless Sensor Networks Using Clusters

Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.

LSTDA: Link Stability and Transmission Delay Aware Routing Mechanism for Flying Ad-Hoc Network (FANET)

The paper presents a new protocol called Link Stability and Transmission Delay Aware(LSTDA)for Flying Adhoc Network(FANET)with a focus on network corridors(NC).FANET consists of Unmanned Aerial Vehicles(UAVs)that face challenges in avoiding transmission loss and delay while ensuring stable communication.The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network.The protocol uses a Red-Black(R-B)tree to achieve maximum channel utilization and an advanced relay approach.The paper evaluates LSTDA in terms of End-to-End Delay(E2ED),Packet Delivery Ratio(PDR),Network Lifetime(NLT),and Transmission Loss(TL),and compares it with existing methods such as Link Stability Estimation-based Routing(LEPR),Distributed Priority Tree-based Routing(DPTR),and Delay and Link Stability Aware(DLSA)using MATLAB simulations.The results show that LSTDA outperforms the other protocols,with lower average delay,higher average PDR,longer average NLT,and comparable average TL.

Role of diet on non-alcoholic fatty liver disease: An updated narrative review

The purpose of this article review is to update what is known about the role of diet on non-alcoholic fatty liver disease(NAFLD). NAFLD is the most common cause of chronic liver disease in the developed world and is considered to be a spectrum, ranging from fatty infiltration of the liver alone(steatosis), which may lead to fatty infiltration with inflammation known as non alcoholic steatohepatitis While the majority of individualswith risk factors like obesity and insulin resistance have steatosis, only few people may develop steatohepatitis. Current treatment relies on weight loss and exercise, although various insulin-sensitizing medications appear promising. Weight loss alone by dietary changes has been shown to lead to histological improvement in fatty liver making nutrition therapy to become a cornerstone of treatment for NAFLD. Supplementation of vitamin E, C and omega 3 fatty acids are under consideration with some conflicting data. Moreover, research has been showed that saturated fat, trans-fatty acid, carbohydrate, and simple sugars(fructose and sucrose) may play significant role in the intrahepatic fat accumulation. However, true associations with specific nutrients yet to be clarified.

Dynamic spillovers between the term structure of interest rates,bitcoin,and safe‑haven currencies

This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.

Mapping of Compositional Diversity and Chronological Ages of Lunar Farside Multiring Mare Moscoviense Basin:Implications to the Middle Imbrian Mare Basalts

The Mare Moscoviense is an astonishing rare flatland multi-ring basin and one of the recognizable mare regions on the Moon's farside.The mineralogical,chronological,topographical and morphological studies of the maria surface of the Moon provide a primary understanding of the origin and evolution of the mare provinces.In this study,the Chandrayaan-1 M^(3)data have been employed to prepare optical maturity index,FeO and TiO^(2)concentration,and standard band ratio map to detect the mafic indexes like olivine and pyroxene minerals.The crater size frequency distribution method has been applied to LROC WAC data to obtain the absolute model ages of the Moscoviense basin.The four geological unit ages were observed as 3.57 Ga(U-2),3.65 Ga(U-1),3.8 Ga(U-3)and 3.92 Ga(U-4),which could have been formed between the Imbrian and Nectarian epochs.The M^(3)imaging and reflectance spectral parameters were used to reveal the minerals like pyroxene,olivine,ilmenite,plagioclase,orthopyroxene-olivine-spinel lithology,and olivine-pyroxene mixtures present in the gabbroic basalt,anorthositic and massive ilmenite rocks,and validated with the existing database.The results show that the Moscoviense basin is dominated by intermediate TiO^(2)basalts that derived from olivine-ilmenite-pyroxene cumulate depths ranging from 200 to 500 km between 3.5 Ga and 3.6 Ga.

Scribble-Supervised Video Object Segmentation

Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.To tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.

LogUAD: Log Unsupervised Anomaly Detection Based on Word2Vec

System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.

Data-Fusion for Epidemiological Analysis of Covid-19 Variants in UAE

Since December 2019,a new pandemic has appeared causing a considerable negative global impact.The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time.The virus was further observed to be spreading rapidly and mutating at a fast pace,with over 5,775 distinct variations of the virus observed globally(at the time of submitting this paper).Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour,influence and more importantly,ways for reducing its impact.Data analytics has been playing a pivotal role in this research to obtain valuable insights into understanding and fighting against the spread of infection.However,this is time and resource intensive,making it difficult to observe and quickly identify the impact of mutations.Factors such as the spread or virulence could explain the three months delay in revealing the new virus variant in the UK.This paper presents an extensive correlation analysis of the effect caused by the different SARS-CoV-2 strains,and their influence on the population across diverse factors,such as propagation and fatality rates,during the peak of the pandemic,with a focus on two major countries in the Middle East,the United Arab Emirate(UAE)and the Kingdom of Saudi Arabia(KSA).This research aims to investigate the epidemiological behaviour of the Coronavirus’genomic variants over time in the UAE,compared with the KSA,where correlation analysis is carried out for a number of cases,deaths and their statistical deviations.The results of the analysis highlight very interesting insights into the epidemiological impact of the Covid-19 genomic behaviour in both countries,which could lead to important actions taken to minimize the impact on wider public health,possibly saving lives,and the economy.For instance,our method identifies a potential correlation between a spike in the number of deaths per case of 5.5 observed in the UAE by March 24th,with the emergence of new genomic variants of the Coronavirus(G0_c,G0_e1 and G0_e2).Our proposed methodology can be instrumental in identifying and classifying new variations of the virus earlier,and possibly predicting foreseeable mutations through pattern analysis,hence creating proactive measures to control its spread,such as the recent case of the new virus variant,recently discovered in the UK.

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Lear

Airline industry has witnessed a tremendous growth in the recent past.Percentage of people choosing air travel as first choice to commute is continuously increasing.Highly demanding and congested air routes are resulting in inadvertent delays,additional fuel consumption and high emission of greenhouse gases.Trajectory planning involves creation identification of cost-effective flight plans for optimal utilizationof fuel and time.This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required.In this paper,an algorithm for dynamic planning of optimized flight trajectories has been proposed.The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather,aerodynamic drag and air traffic within that virtual cube.There are several constraints like simultaneous flight separation rules,weather conditions like air temperature,pressure,humidity,wind speed and direction that pose a real challenge for calculating optimal flight trajectories.To validate the proposed methodology,a case analysis was undertaken within Indian airspace.The flight routes were simulated for four different air routes within Indian airspace.The experiment results observed a seven percent reduction in drag values on the predicted path,hence indicates reduction in carbon footprint and better fuel economy.

Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.

Validation of a Food Frequency Questionnaire for Vitamin D and Calcium Intake in Healthy Female College Students

Objective: The objective of this study was to examine the reproducibility and validity of a Food Frequency Questionnaire (FFQ) and assess calcium and vitamin D intake in health female college students. Methods: Thirty-five healthy female students were conveniently selected to participate in the study. None of the subjects were taking any supplements. The FFQ was validated against intakes from a three-day diet food record report (FR). Results: Positive correlations were observed of daily vitamin D (r = 0.82, p –8, 9, p < 0.676 and 43 mg/d (95% CI: 20, 65, p < 0.01). Conclusions: The FFQ used in this study shows promising validation evidence to be used in the future for assessing vitamin D and calcium intakes in female students.