Numerical Treatments for Crossover Cancer Model of Hybrid Variable-Order Fractional Derivatives

In this paper,two crossover hybrid variable-order derivatives of the cancer model are developed.Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators.The existence,uniqueness,and stability of the proposed model are discussed.Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models.Comparative studies with generalized fifth-order Runge-Kutta method are given.Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented.We have showcased the efficiency of the proposed method and garnered robust empirical support for our theoretical findings.

Congenital absence of the lateral meniscus:A case report

BACKGROUND Congenital absence of the menisci is a rare anatomical variation characterized by the absence or underdevelopment of one or both menisci in the knee joint.The menisci are crucial in load distribution,joint stability,and shock absorption.Understanding the clinical presentation,diagnosis,and management of this condition is important for optimal patient care.CASE SUMMARY A 27-year-old male with a long-standing history of knee pain underwent diagnostic arthroscopy,revealing a congenital absence of the meniscus.The patient's clinical findings,imaging results,surgical procedures,and pertinent images are detailed.This case presents a unique aspect with the congenital absence of the meniscus,contributing valuable insights to the literature on rare anatomical anomalies.CONCLUSION This case of congenital absence of the menisci highlights the diagnostic challenges posed by rare anomalies.The diagnostic arthroscopy played a crucial role in identifying the absence of the meniscus and providing an explanation for the patient's persistent knee pain.The case underscores the importance of individualized treatment approaches,including physical therapy,for optimal management of rare meniscal anomalies.Further research is warranted to explore effective management strategies for the aforementioned cases and to expand our knowledge of these rare conditions.

Towards superior permeability and antifouling performance of sulfonated polyethersulfone ultrafiltration membranes modified with sulfopropyl methacrylate functionalized SBA-15

A non-solvent induced phase separation(NIPS)process was used to fabricate a series of sulfonated polyethersulfone(SPES)membranes blending with different concentrations of SBA-15-g-PSPA with the applications in the ultrafiltration(UF)process.SBA-15 was modified with 3-methacrylate-propyltrime thoxysilane(MPS)to form SBA-15-g-MPS.It was further modified with the charge tailorable polymer chains by reacting with 3-sulfopropyl methacrylate potassium salt.The nanoparticles were uniformly dispersed and finger-like channels were developed within the membrane.The adding of surface modified SBA-15-g-PSPA nanoparticles has significantly improved membrane water permeability,hydrophilicity,and antifouling properties.The pure water fluxes of the composite SPES membranes were significantly higher than the pristine SPES membrane.For the membrane containing 5%(mass)of SBA-15-g-PSPA(MSSPA5),the pure water flux was increased dramatically to 402.15 L
m^(-2)·h^(-1),which is ~1.5 times that of MSSPA0(268.0 L
m^(-2)·h^(-1)).The high flux rate was achieved with 3%(mass)of SBA-15 nanoparticles with retained high rejection ratio 98%for natural organic matter.The results indicate that the fashioned composite membrane comprising SBA-15-g-PSPA nanoparticles have a promising future in ultrafiltration applications.

Nasal Dermoid Cyst with Sinus Tract Intranasal Bone: A Case Report

Dermoid cysts are benign tumors originating from germ cells, which can form in various locations, including the nasal area in rare cases. They are of unknown exact etiology, but it is suggested that it is due to abnormal tissue migration during early embryonic development. Nasal dermoid cysts albeit rare, can present in various forms such as sinuses, fistulas, or intracranially extending tracts. They can be asymptomatic and incidentally discovered or present with a visible external mass or sinus that is either painful, infected or cosmetically concerning. If nasal dermoid cysts with an intra-nasal bone sinus tract are left untreated, they can lead to life-threatening complications. This report describes the case of a 6-year-old girl with a nasal dermoid cyst connected to a superficial punctum by an intra-nasal tract. She had undergone surgical excision of a nasal swelling previously diagnosed as a dermoid cyst. One year later, she returned to our clinic with a recurrence of the nasal swelling. Imaging tests revealed a nasal dermoid cyst with a tract extending to the nasal tip, without intracranial expansion. The cyst, along with the entire tract, was successfully removed surgically, and the postoperative follow-up indicated no complications. Histopathology confirmed the diagnosis of a dermoid cyst. This case underscores the significance of considering the dermoid tract in nasal cyst cases and the necessity of its complete removal to prevent recurrence.

Unilateral Choanal Atresia in Adults: A Case Series

Choanal atresia (CA) is a rare occlusion of the posterior choanae. Unilateral cases have been reported more than bilaterally, and it’s more often right-sided in those patients. According to the literature, mixed bony-membranous atresia is the most common type. There is a high incidence of craniofacial and visceral anomalies associated with congenital choanal atresia. Therefore, investigation for associated congenital anomalies is an important step before the surgery. We report 2 cases of incidental finding of unilateral choanal atresia in a 21- and 17-year-old with nasal discharge being the only complaint in the former and nasal obstruction with headache in the latter. The patients were then scheduled for day-surgery as a case of choanal atresia for transnasal, endoscopic repair and posterior septectomy. The patients were discharged home on the same day with the absence of restenosis or other complications.

The frequency and antimicrobial resistance patterns of nosocomial pathogens recovered from cancer patients and hospital environments

Objective:To determine the prevalence and antimicrobial resistance rates of nosocomial pathogens isolated from cancer patients and hospital environments.Methods:A descriptive cross-sectional study was conducted between December 2010 to May 2013 at Radiation and Isotopes Centre of Khartoum,Sudan.A total of 1 503 samples(505 clinical and 998 environmental)were examined.Isolates were identified,and their antimicrobial susceptibility was determined using standard laboratory procedures.Results:Out of 505 clinical samples,nosocomial pathogens were found as 48.1%.Among hospital environment samples,bacterial contaminants were detected in 29.7%of samples.The main microorganisms recovered from cancer patients were Proteus spp.(23.5%),Escherichia coli(22.2%),Pseudomonas aeruginosa(P.aeruginosa)(21.0%)and Staphylococcus aureus(20.2%).The most frequent isolates from hospital environments were Bacillus spp.(50.0%),Staphylococcus aureus(14.2%)and P.aeruginosa(11.5%).The proportions of resistance among Gram-negative pathogens from cancer patients were high for ampicillin,cefotaxime,ceftazidime and ceftriaxone.Moderate resistance rates were recorded to ciprofloxacin,such as 51.0%for P.aeruginosa,21.7%for Klebsiella pneumoniae and 55.5%for Escherichia coli.Except Klebsiella,there were no significant differences(P0.05)of resistance rates between Gram-negative isolates from cancer patients to those from the hospital environments.The proportions of extended-spectrum b-lactamase producing isolates from cancer patients were not differ significantly(P=0.763)from those collected from the hospital environments(49.2%;91/185 vs.47%;32/68).Conclusions:The prevalence of nosocomial infection among cancer patients was high(48.1%)with the increasing of antimicrobial resistance rates.Hospital environments are potential reservoirs for nosocomial infections,which calls for intervention program to reduce environmental transmission of pathogens.

Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language

The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa.To fill this gap,this study implements a CNN-based neural network,Convolutional Based Attention Module(CBAM),to recognise Malaysian Sign Language(MSL)in videos recognition.This study has created 2071 videos for 19 dynamic signs.Two different experiments were conducted for dynamic signs,using CBAM-3DResNet implementing‘Within Blocks’and‘Before Classifier’methods.Various metrics such as the accuracy,loss,precision,recall,F1-score,confusion matrix,and training time were recorded to evaluate the models’efficiency.Results showed that CBAM-ResNet models had good performances in videos recognition tasks,with recognition rates of over 90%with little variations.CBAMResNet‘Before Classifier’is more efficient than‘Within Blocks’models of CBAM-ResNet.All experiment results indicated the CBAM-ResNet‘Before Classifier’efficiency in recognising Malaysian Sign Language and its worth of future research.

Combining Monte Carlo simulations and dosimetry measurements for process control in the Tunisian Cobalt-60 irradiator after three half lives of the source

To check the dose uniformity and to determine the efficiency of medical devices sterilization by gamma irradiation after three half lives of the source, calculations of the absorbed dose were carried out. Monte Carlo simulations and dosimetry measurements, were established to study the radiation processing quality control. An isodose chart was created by GEANT4 Monte Carlo code to evaluate the absorbed dose rate uniformity inside the irradiation room from the year of the installation until the year of the source reload. The dose uniformity ratio(DUR) is deduced from maximum and minimum experimental doses in medical devices after three half lives of the source.

Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

A Mutual Authentication and Cross Verification Protocol for Securing Internet-of-Drones (IoD)

With the rapid miniaturization in sensor technology,Internet-ofDrones(IoD)has delighted researchers towards information transmission security among drones with the control station server(CSS).In IoD,the drone is different in shapes,sizes,characteristics,and configurations.It can be classified on the purpose of its deployment,either in the civilian or military domain.Drone’s manufacturing,equipment installation,power supply,multi-rotor system,and embedded sensors are not issues for researchers.The main thing is to utilize a drone for a complex and sensitive task using an infrastructureless/self-organization/resource-less network type called Flying Ad Hoc Network(FANET).Monitoring data transmission traffic,emergency and rescue operations,border surveillance,search and physical phenomenon sensing,and so on can be achieved by developing a robust mutual authentication and cross-verification scheme for IoD deployment civilian drones.Although several protocols are available in the literature,they are either design issues or suffering from other vulnerabilities;still,no one claims with conviction about foolproof security mechanisms.Therefore,in this paper,the researchers highlighted the major deficits in prior protocols of the domain,i.e.,these protocols are either vulnerable to forgery,side channel,stolen-verifier attacks,or raised the outdated data transmission flaw.In order to overcome these loopholes and provide a solution to the existing vulnerabilities,this paper proposed an improved and robust public key infrastructure(PKI)based authentication scheme for the IoD environment.The proposed protocol’s security analysis section has been conducted formally using BAN(Burrows-Abadi-Needham)logic,ProVerif2.03 simulation,and informally using discussion/pragmatic illustration.While the performance analysis section of the paper has been assessed by considering storage,computation,and communication cost.Upon comparing the proposed protocol with prior works,it has been demonstrated that it is efficient and effective and recommended for practical implementation in the IoD environment.

Energy-Efficient and Blockchain-Enabled Model for Internet of Things (IoT) in Smart Cities

Wireless sensor networks(WSNs)and Internet of Things(IoT)have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities.The data generated from these sensors are used by smart cities to strengthen their infrastructure,utilities,and public services.WSNs are suitable for long periods of data acquisition in smart cities.To make the networks of smart cities more reliable for sensitive information,the blockchain mechanism has been proposed.The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources;leading to extending the network lifetime of sensors.In this paper,a linear network coding(LNC)for WSNs with blockchain-enabled IoT devices has been proposed.The consumption of energy is reduced for each node by applying LNC.The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models.Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes,packet delivery ratio,throughput,and the optimized residual energy compared to other current techniques.

An Intelligent Cluster Verification Model Using WSN to Avoid Close Proximity and Control Outbreak of Pandemic in a Massive Crowd

Assemblage at public places for religious or sports events has become an integral part of our lives.These gatherings pose a challenge at places where fast crowd verification with social distancing(SD)is required,especially during a pandemic.Presently,verification of crowds is carried out in the form of a queue that increases waiting time resulting in congestion,stampede,and the spread of diseases.This article proposes a cluster verification model(CVM)using a wireless sensor network(WSN),single cluster approach(SCA),and split cluster approach(SpCA)to solve the aforementioned problem for pandemic cases.We show that SD,cluster approaches,and verification by WSN can overcome the management issues by optimizing the cluster size and verification time.Hence,our proposed method minimizes the chances of spreading diseases and stampedes in large events such as a pilgrimage.We consider the assembly points in the annual pilgrimage to Makkah Al-Mukarmah and Umrah for verification using Contiki/Cooja tool.We compute results such as verified cluster members(CMs)to define cluster size,success rate to determine the best success rate,and verification time to determine the optimal verification time for various scenarios.We validate ourmodel by comparing the results of each approach with the existing model.Our results showthat the SpCAwith SD is the best approach with a 96% success rate and optimization of verification time as compared to SCA with SD and the existing model.

Ambiguity Resolution in Direction of Arrival Estimation with Linear Antenna Arrays Using Differential Geometry

Linear antenna arrays(LAs)can be used to accurately predict the direction of arrival(DOAs)of various targets of interest in a given area.However,under certain conditions,LA suffers from the problem of ambiguities among the angles of targets,which may result inmisinterpretation of such targets.In order to cope up with such ambiguities,various techniques have been proposed.Unfortunately,none of them fully resolved such a problem because of rank deficiency and high computational cost.We aimed to resolve such a problem by proposing an algorithm using differential geometry.The proposed algorithm uses a specially designed doublet antenna array,which is made up of two individual linear arrays.Two angle observation models,ambiguous observation model(AOM)and estimated observation model(EOM),are derived for each individual array.The ambiguous set of angles is contained in the AOM,which is obtained from the corresponding array elements using differential geometry.The EOM for each array,on the other hand,contains estimated angles of all sources impinging signals on each array,as calculated by a direction-finding algorithm such as the genetic algorithm.The algorithm then contrasts the EOM of each array with its AOM,selecting the output of that array whose EOM has the minimum correlation with its corresponding AOM.In comparison to existing techniques,the proposed algorithm improves estimation accuracy and has greater precision in antenna aperture selection,resulting in improved resolution capabilities and the potential to be used more widely in practical scenarios.The simulation results using MATLAB authenticates the effectiveness of the proposed algorithm.

Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes.Therefore,Internet of Things(IoT)technology can be applied to monitor and detect harmful insect pests such as red palm weevils(RPWs)in the farms of date palm trees.In this paper,we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier,namely InceptionResNet-V2.The sound sensors,namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the farm.Palm trees are labeled based on the sensor node number to identify the infested cases.Then,the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model,i.e.,InceptionResNet-V2.The proposed infestation classifier has been successfully validated on the public TreeVibes database.It includes total short recordings of 1754 samples,such that the clean and infested signals are 1754 and 731 samples,respectively.Compared to other deep learning models in the literature,our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio recordings.The resulted classification accuracy score was 97.18%.Using 10-fold cross validation,the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53%and±1.69,respectively.Applying the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work.

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

Social media platforms provide new value for markets and research companies.This article explores the use of social media data to enhance customer value propositions.The case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress management.Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management practices.The aim is to analyze the tweets about stress management practices and to identify the context from the tweets.Thereafter,we map the tweets on pleasure and arousal to elicit customer insights.We analyzed a case study of a marketing strategy on the Twitter platform.Participants in the marketing campaign shared photos and texts about their stress management practices.Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants.The computational semantic analysis of the tweets was compared to the text analysis of the tweets.The content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

The Effect of Simulation-Based Training on Nursing Students’ Communication Skill, Self-Efficacy and Clinical Competence for Nursing Practice

Background: Simulation-based training is a new strategy in teaching that gives the students good opportunities to learn and apply what they learn in nursing care safely. Aim: This study conducted to evaluate the effects of simulation-based training on nursing students’ communication skill, self-efficacy and clinical competence in practice. Subjects and Methods: Quiz-experimental design was used in this study (pre-posttest intervention), it was carried out on 100 nursing students first semester in 2019 using low and high-fidelity simulators. This study was carried out at College of Applied Medical Sciences-Bshia University. Data Collection: demographic data, communication skill, self-efficacy and clinical competence questionnaires. Analysis is done by SPSS version 20 software. Results: Participants who received the simulation-based training, showed statistical significant improvement in communication skill, self-efficacy, and clinical competence scores after participation in the simulation program (t = −32.64, p = 0.001;t = −19.9, p = 0.001;16.4, p = 0.001). Also, there are significant relation between gender and clinical competency (t = 2.768, p Conclusion: Simulation-based training in medical courses is effective in enhancing communication skill, self-efficacy and clinical competence. Multiple-patient simulations as a teaching-learning strategy in the nursing curriculum are highly recommended.

Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

Education acts as an important part of economic growth and improvement in human welfare.The educational sectors have transformed a lot in recent days,and Information and Communication Technology(ICT)is an effective part of the education field.Almost every action in university and college,right from the process fromcounselling to admissions and fee deposits has been automated.Attendance records,quiz,evaluation,mark,and grade submissions involved the utilization of the ICT.Therefore,security is essential to accomplish cybersecurity in higher security institutions(HEIs).In this view,this study develops an Automated Outlier Detection for CyberSecurity in Higher Education Institutions(AOD-CSHEI)technique.The AOD-CSHEI technique intends to determine the presence of intrusions or attacks in the HEIs.The AOD-CSHEI technique initially performs data pre-processing in two stages namely data conversion and class labelling.In addition,the Adaptive Synthetic(ADASYN)technique is exploited for the removal of outliers in the data.Besides,the sparrow search algorithm(SSA)with deep neural network(DNN)model is used for the classification of data into the existence or absence of intrusions in the HEIs network.Finally,the SSA is utilized to effectually adjust the hyper parameters of the DNN approach.In order to showcase the enhanced performance of the AOD-CSHEI technique,a set of simulations take place on three benchmark datasets and the results reported the enhanced efficiency of the AOD-CSHEI technique over its compared methods with higher accuracy of 0.9997.

Saddle Point Optimality Criteria of Interval Valued Non-Linear Programming Problem

The present paper aims to develop the Kuhn-Tucker and Fritz John criteria for saddle point optimality of interval-valued nonlinear programming problem.To achieve the study objective,we have proposed the definition of minimizer and maximizer of an interval-valued non-linear programming problem.Also,we have introduced the interval-valued Fritz-John and Kuhn Tucker saddle point problems.After that,we have established both the necessary and sufficient optimality conditions of an interval-valued non-linear minimization problem.Next,we have shown that both the saddle point conditions(Fritz-John and Kuhn-Tucker)are sufficient without any convexity requirements.Then with the convexity requirements,we have established that these saddle point optimality criteria are the necessary conditions for optimality of an interval-valued non-linear programming with real-valued constraints.Here,all the results are derived with the help of interval order relations.Finally,we illustrate all the results with the help of a numerical example.

Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology.In this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach.The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text.Moreover,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any tampering.SFASCDW has been implemented using PHP with VS code IDE.The robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and deletion.The SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

Anti-inflammatory and antioxidant potential capacities of AD-MSCs and BM-MSCs in suppressing pancreaticβ-cells auto-immunity and apoptosis in rats with T1DM induced model

Since Type 1 diabetes(T1DM)occurs whenβ-cells mass is reduced to less than 20%of the normal level due to autoimmune destruction of cells resulting in the inability to secrete insulin,preservation or replenishment of the functionalβ-cells mass has become a major therapeutic focus for this diabetic type treatment.Thus,this 4-week work plan was designed to determine which mesenchymal stem cells(MSCs)type is more appropriate to alleviate pancreatic hazards resulting from diabetes induction;via tracking a comparative study between MSCs derived from adipose tissue(AD-MSCs)and from bone marrow(BM-MSCs)in management of T1DM considering their immunomodulatory,anti-apoptotic and antioxidative roles.Rats were divided randomly into 4 groups;control,STZdiabetic(D),D+AD-MSCs,and D+BM-MSCs groups.Both stem cells types in this study were allogenic.Herein,both oxidative stress and antioxidant markers were evaluated using colorimetric analysis,while inflammatory,immune and apoptotic markers were assessed through flow cytometric analysis.Results showed that diabetic rats treated with either AD-MSCs or BM-MSCs exhibited marked pancreatic antioxidant and anti-inflammatory activities that were able to initiate pancreatic immunomodulation and reducingβ-cells apoptotic death,thus,help to restore their normal insulin secretion and hypoglycemic abilities.However,AD-MSCs injection was shown to be superior as a pancreatic regenerative tool in overcoming diabetes;owing to their marked antioxidant,anti-inflammatory,immunomodulatory,and anti-apoptotic characteristics over BM-MSCs treatment.