Awareness of Hail Region Population about Facial Nerve Palsy (Seventh Cranial Nerve Palsy)

Background: Facial nerve palsy (seventh cranial nerve palsy) is a common neurological problem. The etiology is not fully understood but it is thought to be due to injury or compression throughout the seventh cranial nerve course. The study aimed to evaluate the awareness of the Hail region population about facial nerve palsy, its risk factors, methods of treatment, and prognosis. Methodology: This cross-sectional descriptive study was conducted in the Hail region from March to June 2022. The study involved 224 participants in the age group 18 - 65 years and data was collected through Google self-administered questionnaire. Results: 65.2% of the participants were 18 - 25 years, 20.1% were 26 - 35 years and 14.7 were above 35 years. 91.9% think that exposure to cold air is the leading cause followed by viral infections (30.4%), stroke (27.6%), and genetic factors (13.8%). diabetes (8.5%), evil eye (7.1%), magic (4%), pregnancy (0.4%) and vitamins deficiency (0.4%). 92% of participants think that facial nerve palsy is not contagious and 8% have no clear idea. 89.7% think that facial nerve palsy is curable, 83.9% think that physiotherapy is the treatment of choice, 94.6% agree that early medical assessment is essential for good outcomes and 92.9 believe that avoidance of cold air is the best method of prevention. Conclusion and Recommendations: The majority of participants show poor awareness regarding the etiology and the preferable treatment of facial nerve palsy. Public medical education and further wider studies are highly recommended.

Epidemiological Analysis of Type II Diabetes Mellitus among Hail Residents, Saudi Arabia

Type II diabetes is a global health concern. This epidemic is elevating in increasing rates in Saudi Arabia. Thus, the study investigates a number of risk factors of Type II diabetes in Hail region, one of Saudi Arabia’s highest regions in diabetes records among adults. Data are collected using diabetic subjects from the Diabetes Registry Records in King Khalid Hospital at the city of Hail, Saudi Arabia, where 200 subjects’ records from 2014 to 2018 were included. A binary logistic regression was utilized to assess the association between age, gender, obesity, hypertension, family history, hypercholesterolemia, and hyperglyceridemia as risk factors and Type II diabetes. Some risk factors yielded statistical significant associations such as age (OR = 486.00 for 61 and older;OR = 468.00 for 51 - 60;and OR = 130.50 for 41 - 50;p-values ≤ 0.01), obesity (OR = 3.088;p-value ≤ 0.01), and hypertension (OR = 8.476;p-value ≤ 0.01), while gender, family history, hypercholesterolemia, and hyperglyceridemia were insignificant risk factors in our study. Proper intervention measures targeting diabetes risk factors may tackle or delay this public health issue.

Molecular Phylogeny of Walterinnesia aegyptia (Reptilia, Elapidae) Isolated from Ha’il Province, Saudi Arabia

Walterinnesia aegyptia is one of the most venomous snakes belonging to the family Elapidae found in the Middle East and Africa. In addition to its ecological importance, it is accused of millions of deaths due to snakebites. Because molecular identification of snakes is crucial for the antivenom drug industry, mitochondrial genes are used to identify, characterize, and infer genetic diversity among different venomous snake species. Data of Walterinnesia collected from samples across Saudi Arabia were compared based on the mitochondrial 16S and 12S rRNA sequences with other Elapidae related taxa to assess the phylogenetic relationship. The phylogenetic analysis strongly supports the monophyly of the genus Walterinnesia based on two genes that represent different species of Elapidae. In addition, a close relationship between Walterinnesia aegyptia and W. morgani was found. Our molecular data showed that W. morgani from Riyadh, Saudi Arabia, is nearly genetically identical (D = 0) with W. aegyptia from Ha’il and Riyadh, Saudi Arabia, and Sinai, Egypt. Further study is required based on more material and detailed morphological and genetic analysis.

Electrochemically and Ultrasonically-Enhanced Coagulation for Algae Removal

At the global level, the augmenting presence of harmful algae blooms constitutes important dares to water treatment plants (WTPs). In WTPs, coagulation remains the primary process of the applied procedure to treat algae-contaminated water. Such a chemical process influences the following techniques;thus, regulating coagulation parameters to eliminate algae at the maximum degree without provoking cell deterioration is more than crucial. This work aims to review coagulation-founded methods for algae elimination. First, investigations concentrating on algae elimination using the chemical process are discussed. The introduction presents the widespread algae encountered in the water treatment field. Then, habitually utilized experimental techniques and emerging methods in coagulation investigations are summarized with typical findings. Next, the newest expansions in improved algae elimination, launched by electrochemically and ultrasonically-enhanced coagulation, are discussed. Workable thoughts for applying coagulation to eliminate algae in WTPs are also debated. The paper finishes by defining restrictions and dares related to the present literature and suggesting trends for subsequent studies. The charge neutralization mechanism efficiently removes solubilized microcystins (MCs), and enhanced coagulation configuration is also found to be more efficient for their removal. However, considerations should be taken to avert that the acid introduction has no unwanted effect in killing algae treatment to avoid the solubilized MCs level elevation. If such techniques are well-optimized and controlled, both algae and solubilized MCs could be efficaciously removed by ultrasound-enhanced coagulation and electrocoagulation/electrooxidation.

Solar-Driven Water Treatment: New Technologies, Challenges, and Futures

In this review, the new solar water treatment technologies, including solar water desalination in two direct and indirect methods, are comprehensively presented. Recent advances and applications of five major solar desalination technologies include solar-powered humidification–dehumidification, multi-stage flash desalination, multi-effect desalination, RO, and solar stills. Each technology’s productivity, energy consumption, and water production costs are presented. Also, common methods of solar water disinfection have been reviewed as one of the common and low-cost methods of water treatment, especially in areas with no access to drinking water. However, although desalination technologies have many social, economic, and public health benefits, they are energy-intensive and negatively affect the environment. In addition, the disposal of brine from the desalination processes is one of the most challenging and costly issues. In this regard, the environmental effects of desalination technologies are presented and discussed. Among direct solar water desalination technologies, solar still technology is a low-cost, low-tech, and low-investment method suitable for remote areas, especially in developing countries with low financial support and access to skilled workers. Indirect solar-driven water desalination technologies, including thermal and membrane technologies, are more reliable and technically more mature. Recently, RO technology has received particular attention thanks to its lower energy demand, lower cost, and available solutions to increase membrane durability. Disposal of brines can account for much of the water cost and potentially negatively affect the environment. Therefore, in addition to efforts to improve the efficiency and reduce the cost of solar technologies and water treatment processes, future research studies should consider developing new solutions to this issue.

Electrolytic Co-Deposition Mechanisms, Texture Layers, and Residual Stresses in Nanocomposite Coatings Processes: A Review

The composite coating has gained wider attention due to its property to protect materials used in energy, bridges, offshore platforms, underground pipelines, and the aviation industry from corrosion and deterioration. In this work, a literature review was conducted about the processes of nanocomposite coating, the mechanisms of electrolytic co-deposition, the texture of layers, and the residual stresses. An important aspect, residual stress, was emphasized, which represents the persistent stress after removing the external force affecting a metal in the plastic region. Because it cannot be measured directly and may be determined by measuring strain and indirect methods, the sources and methods for measuring residual stresses (XRD, SEM, TEM, EDS) were described in the last section to provide a comprehensive overview. Based on the thorough analysis of the published literature, it was concluded that nanoparticles could be electrodeposited with Ni on an Al substrate using a direct current and Ni sulfamate as an electrolytic solution, and Nickel will not reside on the oxide layer covering Al, so chemical changes are needed to prepare the Al surface. In addition, texture changes with the thickness of the coated layer must be investigated.

Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and education.In this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm.To achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and weights.Second,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point.During its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node.For this aim,the MR was equipped with an ultrasonic sensor used as obstacle detector.If an obstacle is found,the MR updates its diagraph by excluding the corresponding node.Then,Dijkstra algorithm runs on the modified diagraph.This procedure is repeated until reaching the target point.To verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 obstacles.The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors.This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

A Blockchain Based Framework for Stomach Abnormalities Recognition

Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured.The privacy of patients is very important and manual inspection is time consuming and costly.Therefore,an automated system for recognition of stomach infections from WCE frames is always needed.An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding.Initially,images are normalized in fixed dimension and passed in pre-trained deep models.These architectures are modified at each layer,to make them safer and more secure.Each layer contains an extra block,which stores certain information to avoid possible tempering,modification attacks and layer deletions.Information is stored in multiple blocks,i.e.,block attached to each layer,a ledger block attached with the network,and a cloud ledger block stored in the cloud storage.After that,features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function.The Softmax classifier is applied at the end for final classification.Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%.The statistical analysis and individual model comparison show the proposed method’s authenticity.

Genetic association study of P2x7 A1513C(rs 3751143) polymorphism and susceptibility to pulmonary tuberculosis: A meta-analysis based on the findings of 11 case-control studies

Objective:To summarize the precise association between pulmonary tuberculosis(PTB) and P2x7 A1513 C gene polymorphism.Methods:PubMed and Google Scholar web-databases were searched for the studies reporting the association of P2x7 A1513 C polymorphism and PTB risk.A meta-analysis was performed for the selected case-control studies and pooled odds ratios(ORs) and 95%confidence intervals(95%CIs) were calculated for all the genetic models.Results:Eleven studies comprising 2 678 controls and 2 113 PTB cases were included in this meta-analysis.We observed overall no significant risk in all the five genetic models.When stratified population by the ethnicity,Caucasian population failed to show any risk of PTB in all the genetics models.In Asian ethnicity,variant allele(C vs.A:P=0.001;QR=1.375,95%CI=1.159-1.632) and heterozygous genotype(AC vs.AA:P=0.001;OR=1.570,95%CI=1.269-1.944) demonstrated significant increased risk of PTB.Likewise,recessive genetic model(CC+AC vs.AA:P=0.001;OR=1.540,95%CI= 1.255-1.890) also demonstrated increased risk of PTB in Asians.Conclusions:Our meta-analysis did not suggest the association of P2x7 A1513 C polymorphism with PTB risk in overall or separately in Caucasian population.However,it plays a significant risk factor for predisposing PTB in Asians.Future larger sample and expression studies are needed to validate this association.

IoT & AI Enabled Three-Phase Secure and Non-Invasive COVID 19 Diagnosis System

Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019.Due to the similarity in initial symptoms with viral fever,it is challenging to identify this virus initially.Non-detection of this virus at the early stage results in the death of the patient.Developing and densely populated countries face a scarcity of resources like hospitals,ventilators,oxygen,and healthcare workers.Technologies like the Internet of Things(IoT)and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage.To minimize the spread of the pandemic,IoT-enabled devices can be used to collect patient’s data remotely in a secure manner.Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus.In this work,the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot,IoT,and deep learning technology.In phase one,an artificially assisted chatbot can guide an individual by asking about some common symptoms.In case of detection of even a single sign,the second phase of diagnosis can be considered,consisting of using a thermal scanner and pulse oximeter.In case of high temperature and low oxygen saturation levels,the third phase of diagnosis will be recommended,where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body.The proposed model reduces human intervention through chatbot-based initial screening,sensor-based IoT devices,and deep learning-based X-ray analysis.It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage.

Improving CNN-BGRU Hybrid Network for Arabic Handwritten Text Recognition

Handwriting recognition is a challenge that interests many researchers around the world.As an exception,handwritten Arabic script has many objectives that remain to be overcome,given its complex form,their number of forms which exceeds 100 and its cursive nature.Over the past few years,good results have been obtained,but with a high cost of memory and execution time.In this paper we propose to improve the capacity of bidirectional gated recurrent unit(BGRU)to recognize Arabic text.The advantages of using BGRUs is the execution time compared to other methods that can have a high success rate but expensive in terms of time andmemory.To test the recognition capacity of BGRU,the proposed architecture is composed by 6 convolutional neural network(CNN)blocks for feature extraction and 1 BGRU+2 dense layers for learning and test.The experiment is carried out on the entire database of institut für nachrichtentechnik/ecole nationale d’ingénieurs de Tunis(IFN/ENIT)without any preprocessing or data selection.The obtained results show the ability of BGRUs to recognize handwritten Arabic script.

Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

Wireless Sensor Networks(WSNs)can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions,such as temperature,sound,vibration,pressure and motion etc.WSNs may comprise thousands of Internet of Things(IoT)devices to sense and collect data from its surrounding,process the data and take an automated and mechanized decision.On the other side the proliferation of these devices will soon cause radio spectrum shortage.So,to facilitate these networks,we integrate Cognitive Radio(CR)functionality in these networks.CR can sense the unutilized spectrum of licensed users and then use these empty bands when required.In order to keep the IoT nodes functional all time,continuous energy is required.For this reason the energy harvested techniques are preferred in IoT networks.Mainly it is preferred to harvest Radio Frequency(RF)energy in the network.In this paper a region based multi-channel architecture is proposed.In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region.The Secondary User(SU)that are the licensed user is IoT enabled with Cognitive Radio(CR)techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy.To harvest energy efficiently and to reduce the energy consumption during sensing,the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel.From the experimental analysis,it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel.We also show that channel load can be highly reduced and channel utilization is proved to be more proficient.Thus,this proves the proposed architecture cost-effective and energy-efficient.

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease.In this work,a dataset containing medical,physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning,deep learning and a hybrid technique between deep learning and machine learning on theMagnetic Resonance Imaging(MRI)dataset for cerebral haemorrhage.In the first dataset(medical records),two features,namely,diabetes and obesity,were created on the basis of the values of the corresponding features.The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space.Meanwhile,the Recursive Feature Elimination algorithm(RFE)was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features.The features are fed into the various classification algorithms,namely,Support Vector Machine(SVM),K Nearest Neighbours(KNN),Decision Tree,Random Forest,and Multilayer Perceptron.All algorithms achieved superior results.The Random Forest algorithm achieved the best performance amongst the algorithms;it reached an overall accuracy of 99%.This algorithm classified stroke cases with Precision,Recall and F1 score of 98%,100%and 99%,respectively.In the second dataset,the MRI image dataset was evaluated by using the AlexNet model and AlexNet+SVM hybrid technique.The hybrid model AlexNet+SVM performed is better than the AlexNet model;it reached accuracy,sensitivity,specificity and Area Under the Curve(AUC)of 99.9%,100%,99.80%and 99.86%,respectively.

Hybrid Adaptive Synchronization of Hyperchaotic Systems with Fully Unknown Parameters

In this paper, an adaptive control scheme is developed to study the hybrid synchronization behavior between two identical and different hyperchaotic systems with unknown parameters. This adaptive hybrid synchronization controller is designed based on Lyapunov stability theory and an analytic expression of the controller with its adaptive laws of parameters is shown. The adaptive hybrid synchronization between two identical systems (hyperchaotic Chen system) and different systems (hyperchaotic Lorenz and hyperchaotic systems) are taken as two illustrative examples to show the effectiveness of the proposed method. Theoretical analysis and numerical simulations are shown to verify the results.

Wavelet Based Detection of Outliers in Volatility Time Series Models

We introduce a new wavelet based procedure for detecting outliers in financial discrete time series.The procedure focuses on the analysis of residuals obtained from a model fit,and applied to the Generalized Autoregressive Conditional Heteroskedasticity(GARCH)like model,but not limited to these models.We apply the Maximal-Overlap Discrete Wavelet Transform(MODWT)to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers.Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform(DWT).The series sample size does not need to be a power of 2 and the transform can explore any wavelet filter and be run up to the desired level.Simulated wavelet quantiles from a Normal and Student t-distribution are used as threshold for the maximum of the absolute value of wavelet coefficients.The performance of the procedure is illustrated and applied to two real series:the closed price of the Saudi Stock market and the S&P 500 index respectively.The efficiency of the proposed method is demonstrated and can be considered as a distinct important addition to the existing methods.

A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

The application of optimization methods to prediction issues is a continually exploring field.In line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective.The complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown parameters.We use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein model.Findings indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,respectively.Statistical performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

The Wheat Pathogenesis Related Protein (TdPR1.2) Ensures Contrasting Behaviors to <i>E. coli</i>Transformant Cells under Stress Conditions

The pathogenesis-related proteins 1 (PR-1) gene family play important roles in the plant metabolism in response to biotic and abiotic stresses. The wheat TdPR1.2 has been previously isolated and characterized. Here we showed by bio-informatic analysis that TdPR1.2 contains six cysteine residues that are conserved between all PR-1 proteins tested. Using ScanProsite tool, we found that TdPR1.2 structure has a CRISP family signature 1 and 2 located at the C-terminal part of the protein. Those two domains are conserved in many identified PR1.2 proteins in plants. Moreover, SignalIP-5.0 analysis revealed that TdPR1.2 contains a putative signal peptide formed by 25 amino acids at the N-terminal extremity. The presence of this signal peptide suggested that the mature proteins will be secreted after the cleavage of the signal sequence. Further, we investigate the role of the TdPR1.2 proteins in the growth of Escherichia coli transformants cells under different abiotic stresses. Our results showed that the full-length form of TdPR1.2 enhanced tolerance of E. coli against salt and osmotic stress but not to KCl. Moreover, TdPR1.2 protein confers bacterial tolerance to heavy metals in solid and liquid mediums. Based on these results, we suggest that the TdPR1.2 protein could play an important role in response to abiotic stress conditions.

An Intelligent Approach for Intrusion Detection in Industrial Control System

Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographically distributed control elements,and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions.In recent years,there has been a lot of focus on the security of industrial control systems.Due to the advancement in information technologies,the risk of cyberattacks on industrial control system has been drastically increased.Because they are so inextricably tied to human life,any damage to them might have devastating consequences.To provide an efficient solution to such problems,this paper proposes a new approach to intrusion detection.First,the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning process.Then,a prior estimation of the class is proposed based on a support vector machine.Simulation results show that the proposed approach has better anomaly detection performance than existing algorithms.

A Note on Bell-Based Bernoulli and Euler Polynomials of Complex Variable

In this article,we construct the generating functions for new families of special polynomials including two parametric kinds of Bell-based Bernoulli and Euler polynomials.Some fundamental properties of these functions are given.By using these generating functions and some identities,relations among trigonometric functions and two parametric kinds of Bell-based Bernoulli and Euler polynomials,Stirling numbers are presented.Computational formulae for these polynomials are obtained.Applying a partial derivative operator to these generating functions,some derivative formulae and finite combinatorial sums involving the aforementioned polynomials and numbers are also obtained.In addition,some remarks and observations on these polynomials are given.

A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

Diagnosing gastrointestinal cancer by classical means is a hazardous procedure.Years have witnessed several computerized solutions for stomach disease detection and classification.However,the existing techniques faced challenges,such as irrelevant feature extraction,high similarity among different disease symptoms,and the least-important features from a single source.This paper designed a new deep learning-based architecture based on the fusion of two models,Residual blocks and Auto Encoder.First,the Hyper-Kvasir dataset was employed to evaluate the proposed work.The research selected a pre-trained convolutional neural network(CNN)model and improved it with several residual blocks.This process aims to improve the learning capability of deep models and lessen the number of parameters.Besides,this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder phase.The research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization algorithm.These features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network classifier.The experiment worked on the HyperKvasir dataset,which consists of 23 stomach-infected classes.At last,the proposed method obtained an improved accuracy of 93.90%on this dataset.Comparison is also conducted with some recent techniques and shows that the proposed method’s accuracy is improved.