Application Strategies of Virtual Reality Technology in the Teaching Design of Vocational Courses from the Perspective of Learning Transfer Theory

With the rapid development of virtual reality technology,it has been widely used in the field of education.It can promote the development of learning transfer,which is an effective method for learners to learn effectively.Therefore,this paper describes how to use virtual reality technology to achieve learning transfer in order to achieve teaching goals and improve learning efficiency.

Leadership and Top Management Influence on Total Quality Management Implementation in the Ghanaian Construction Industry

This study aims to determine the key and underlying Leadership and Top Management (LTM) factors that have a significant impact on sustaining the implementation of Total Quality Management (TQM) within the construction industry in Ghana. The research methodology employed in this study was a quantitative technique. Questionnaires were distributed to 641 participants within construction industry in Ghana. Questionnaires retrieved for the analysis were 536. Three steps approached were used for the data analysis. These include Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) analysis. After conducting the EFA and CFA, SEM was also used to analyze the construct validity. The SEM analysis helps to determine four key indicator variables for the leadership and top management construct. These include Leadership/Top Management approach to employees’ management, Leadership/Top Management understanding of TQM, Leadership/Top Management empowerment of employees to resolve quality issues, and Leadership/Top Management endorsement of TQM. All the four indicator variables were found to be good of fit and closely associated with the dependent variable. The study adds to the body of knowledge by using EFA, CFA and SEM techniques to establish key leadership and top management factors affecting TQM implementation in Ghana’s construction industry. The findings in general suggested that leadership and top Management factors identified have a direct positive impact on sustaining TQM implementation in the Ghanaian construction industry. Consequently, the leadership and top management factors identified in this study can help improve TQM in the Ghanaian construction industry.

Optimized Resource Allocation and Queue Management for Traffic Control in MANET

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks(MANETs).Offering better communication services among the users in a centralized organization is the primary objective of the MANET.Due to the features of MANET,this can directly End-to-End Delay(EED)the Quality of Service(QoS).Hence,the implementation of resource management becomes an essential issue in MANETs.This paper focuses on the efficient Resource Allocation(RA)for many types of Traffic Flows(TF)in MANET.In Mobile Ad hoc Networks environments,the main objective of Resource Allocation(RA)is to process consistently available resources among terminals required to address the service requirements of the users.These three categories improve performance metrics by varying transmission rates and simulation time.For solving that problem,the proposed work is divided into Queue Management(QM),Admission Control(AC)and RA.For effective QM,this paper develops a QM model for elastic(EL)and inelastic(IEL)Traffic Flows.This research paper presents an AC mechanism for multiple TF for effective AC.This work presents a Resource Allocation Using Tokens(RAUT)for various priority TF for effective RA.Here,nodes have three cycles which are:Non-Critical Section(NCS),Entry Section(ES)and Critical Section(CS).When a node requires any resources,it sends Resource Request Message(RRM)to the ES.Elastic and inelastic TF priority is determined using Fuzzy Logic(FL).The token holder selects the node from the inelastic queue with high priority for allocating the resources.Using Network Simulator-2(NS-2),simulations demonstrate that the proposed design increases Packet Delivery Ratio(PDR),decrease Packet Loss Ratio(PLR),minimise the Fairness and reduce the EED.

A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images

Deep Convolutional Neural Networks(CNNs)have achieved high accuracy in image classification tasks,however,most existing models are trained on high-quality images that are not subject to image degradation.In practice,images are often affected by various types of degradation which can significantly impact the performance of CNNs.In this work,we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model(DTA-ICM)to improve the existing CNNs’classification accuracy on degraded images.The proposed DTA-ICM comprises two key components:a Degradation Type Predictor(DTP)and a Degradation Type Specified Image Classifier(DTS-IC)set,which is trained on existing CNNs for specified types of degradation.The DTP predicts the degradation type of a test image,and the corresponding DTS-IC is then selected to classify the image.We evaluate the performance of both the proposed DTP and the DTA-ICMon the Caltech 101 database.The experimental results demonstrate that the proposed DTP achieves an average accuracy of 99.70%.Moreover,the proposed DTA-ICM,based on AlexNet,VGG19,and ResNet152,exhibits an average accuracy improvement of 20.63%,18.22%,and 12.9%,respectively,compared with the original CNNs in classifying degraded images.It suggests that the proposed DTA-ICM can effectively improve the classification performance of existing CNNs on degraded images,which has important practical implications.

Automatic Rule Discovery for Data Transformation Using Fusion of Diversified Feature Formats

This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.

Noscapine shows antimalarial activity against Plasmodium falciparum 3D7,its clinical isolate Pf140/SS,and Plasmodium berghei ANKA

Objective:To evaluate the antimalarial activity of noscapine against Plasmodium falciparum 3D7 strain(Pf3D7),its clinical isolate(Pf140/SS),and Plasmodium berghei ANKA(PbA).Methods:Using ring-stage survival assay,phenotypic assessments,and SYBR-green-based fluorescence assay,the antimalarial activities of noscapine were assessed compared with dihydroartemisinin(DHA)in in vivo and in vitro studies.In addition,hemolysis and cytotoxicity tests were carried out to evaluate its safety.RT-PCR assay was also conducted to determine the effect of noscapine on papain-like cysteine protease Plasmodium falciparum falcipain-2(PfFP-2).Results:The antimalarial efficacy of noscapine against Pf3D7 and Pf140/SS was comparable to DHA,with IC50 values of(7.68±0.88)and(5.57±0.74)nM/mL,respectively,and>95%inhibition of PbA infected rats.Noscapine also showed a safe profile,as evidenced by low hemolysis and cytotoxicity even at high concentrations.Moreover,PfFP-2 expression was significantly inhibited in both noscapine-treated Pf3D7 and Pf140/SS(P<0.01).Conclusions:Noscapine has antimalarial properties comparable to standard antimalarial DHA with better safety profiles,which may be further explored as a therapeutic candidate for the treatment of malaria.

Characteristics of β-Lactamase Synthesis in E. coli and K. pneumanie Strains in Nosocomial Infections

Background: Recently micro-organisms that synthesize extended-spectrum β-lactamase (ESBLs) were increased. The peculiarities of ESBL synthesis of Escherichia coli and Klebsiella pneumoniae strains that cause nosocomial urinary tract infections, surgical site infections and pneumonia in surgical clinic were studied. ESBL synthesis were observed 38.9% of E. coli strains obtained from urine, 92.3% of strains obtained from surgical site infections, and 50% of strains obtained from sputum. ESBL synthesis were observed 37.5% of K. pneumoniae strains obtained from urine, 85.7% of strains obtained from surgical site infections, and 60% of strains obtained from sputum. Different levels of ESBL synthesize of E. coli and K. pneumoniae strains isolated from different pattern is discussed. Conclusion. ESBL synthesis is common in E. coli and K. pneumoniae strains, which cause nosocomial infections. The frequency of occurrence of ESBL s synthesis among of these strains depends on clinical forms of nosocomial infections.

Analysis of Gestational Diabetes Mellitus (GDM) and Its Impact on Maternal and Fetal Health: A Comprehensive Dataset Study Using Data Analytic Tool Power BI

Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corroborate and expand upon the findings of this research. This underscores the ongoing need for continued investigation into GDM to refine our understanding and improve clinical management strategies.

Secure and Reliable Routing in the Internet of Vehicles Network:AODV-RL with BHA Attack Defense

Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether.Recognizing the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against BHA.An additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network behaviors.Rigorous testing of our solution has revealed its remarkable ability to enhance communication in VANETs.Specifically,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and security.These results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

Effect of Lateritic Stone Aggregate and Coconut Husk Fiber on the Properties of Concrete

Natural stone aggregate forms the bulk volume of concrete and has contributed to the increased cost of concrete production. This has led to the search for alternate aggregates such as lateritic stone for concrete production. This paper investigates the engineering properties of concrete produced with lateritic aggregate (LA) as the coarse aggregate replacement and coconut husk fibre (CHF) as reinforcement. Natural stone aggregate was replaced by LA at 0%, 10%, 20%, 30%, 40%, and 50%, with 0.25% constant CHF by weight. A mix proportion of 1:1.5:3 with a water-cement ratio of 0.6 was used for producing concrete. A total of 162 specimens (90 cubes and 72 beams) were prepared and tested at the 7, 14, 21, and 28 days of curing. The highest compressive strength was 43.36 N/mm2 (10% LA replacement) as compared to the control of 41.51 N/mm2. The 10% LA replacement obtained the highest flexural strength of 5.35 N/mm2 as compared with the 5.29 N/mm2 for the control. The water absorption of the concrete increased from 2.8% (control) to 3.57% (50% replacement LA). Scanning electron microscopy (SEM) revealed micro gaps between CHF and LA concrete. The study, therefore, concludes that the use of LA and CHF positively influenced the strength properties of concrete. 10% LA replacement of coarse aggregate and 0.25% CHF is recommended to practitioners for use.

Software and Mobile Apps as a Strategy for Productivity Improvement in the Construction Industry

The construction industry, known for its low productivity, is increasingly utilising software and mobile apps to enhance efficiency. However, more comprehensive research is needed to understand the effectiveness of these technology applications. The PRISMA principles utilised a scoping review methodology to ascertain pertinent studies and extract significant findings. From 2013 onwards, articles containing data on mobile applications or software designed to enhance productivity in the construction sector were obtained from multiple databases, including Emerald Insight, Science Direct, IEEE Xplore, and Google Scholar. After evaluating 2604 articles, 30 were determined to be pertinent to the study and were subsequently analysed for the review. The review identified five key themes: effectiveness, benefits, successful implementation examples, obstacles and limitations, and a comprehensive list of software and mobile apps. In addition, 71 software and mobile apps have shown potentially how these technologies can improve communication, collaboration, project management, real-time collaboration, document management, and on-the-go project information and estimating processes in the construction industry, increasing efficiency and productivity. The findings highlight the potential of these technologies such as Automation, Radio-Frequency Identification (RFID), Building Information Modeling (BIM), Augmented Reality (AR), Virtual Reality (VR), and Internet of Things (IoT) to improve efficiency and communication in the construction industry. Despite challenges such as cost, lack of awareness, resistance to change, compatibility concerns, human resources, technological and security concerns and licensing issues, the study identifies specific mobile applications and software with the potential to enhance efficiency significantly, improve productivity and streamline workflows. The broader societal impacts of construction software and mobile app development include increased efficiency, job creation, and sustainability.

Contractor Monitoring and Performance of Road Infrastructure Projects in Uganda: A Management Model

An understanding of the influence of contractor monitoring on performance of road infrastructural projects in Uganda provided an impetus for this study. The objectives of the study were to: assess the relationship between contractors monitoring and performance of national road infrastructure projects and the relationship between contractor monitoring components and performance of national road infrastructure projects in Uganda. Purposive sampling was employed in selecting the procurement professionals, engineers and simple random sampling was adopted in selecting private consultants, members of parliament and respondents from the civil society organizations. Data for this study were collected using a closed ended questionnaire and interviews. Some of the major finding from this study include: weak procurement rules which lead to awarding road projects to incompetent contractors;contractor monitoring being handled by unqualified, incompetent and inexperienced professionals;lack of contractors and contract supervisors appraisal system;delay of contractors payments which affects timelines in services delivery;lack of a strong internal project monitoring and evaluation mechanism at the Uganda National Roads Agency (UNRA). The research therefore recommends the establishment of an Independent Public Infrastructure Development and Monitoring Unit by government and adoption of systems that appraise both contractors and contract supervisors with clear disciplinary actions for unsatisfactory performance by the UNRA.

A Study on Effects of Turn-over Technology on the Quality of Jinmudan Oolong Tea

This paper reported the effect of oolong tea processing procedure of turn-over on quality of the Jinmudan Oolong tea,including taste components and volatile compounds.The content of the water extractable solids was gradually increased,but the content of amino acid decreased and then increased,and the content of the soluble sugar and tea polyphenols increased after the first turn-over processing.The major volatiles of the three tested Jinmudan Oolong tea samples were nerolidolcistrans,α-farnesene,palmitic acid,indole and 9,12,15-octadecatrienoicacid and methyl ester.The sensory evaluation results showed that an appropriate increase in the number of turn-over was helpful to quality of the Jinmudan Oolong tea.

Integrating Renewable Energy and Smart Grid Technology into the Nigerian Electricity Grid System

The electricity situation in Nigeria can be described as epileptic with no sign in view of improvement. This epileptic power situation affects the manufacturing, service and residential sectors of the economy which in turn affects the country’s economic growth. Even with the recent reforms in the power sector, more than half of the country’s population still lack access to electricity. The epileptic condition of the power sector can be attributed to the inadequate and inefficient power plants, poor transmission and distribution facilities, and outdated metering system used by electricity consumers. This paper attempts to present the way forward for the Nigerian poor electricity situation by reviewing the power sector as a whole and the renewable energy potentials. We identified the problems in the national grid and then proposed a smart grid model for the Nigerian power sector which will include renewable energy source. We believe that the content of this review paper will solve the poor epileptic condition of the power sector in Nigeria and also enable the proper integration of smart grid technology into the national grid.

CREATIVE DESIGN BASED ON KNOWLEDGE MANAGEMENT IN ENGINEERING DESIGN

To support and serve engineering design, creative design based on knowledge management is proposed. The key knowledge factors of creative design are analyzed and discussed, and knowledge extraction tools are utilized to distill the important knowledge to serve for knowledge resource of creative design. The implementation of creative design mode is described and executed, which can promote the intelligent asset of the enterprise and shorten the period of creative design. With this study, design afflatus and conceptual design can be achieved expediently and effectively.

Influence Factors Concept Model for Public Participation in Nanotechnology Risk Communication

Based on risk social cognition theory and planned behavior theory, the influence factors of the public participation in the nanotechnology risk communication are analyzed,and the concept model is presented to analyze their relationships and functional mechanism. In the model,the risk communication behaviors are divided into two variables of the public opinion expression and information acquisition,and the subjective norm divided into the internal and external social networks. Then the questionnaire is designed so as to verify the proposed model and analyze their relationships. The survey data are analyzed using SMART PLS software. The validity analysis shows that the questionnaire has a high convergence and discriminant validity. In addition, the Cronbach'a coefficient of each item is more than 0. 70,which shows the questionnaire has a high credibility. Based on the statistical data analysis, relationships of the influence factors are obtained.Further,the influence path model of the public attitude,intention,self regulation and behavior control in the risk communication is established. According to the Bootstrap algorithm in SMART,the path coefficient and its explanation variance of each influence factor are obtained. The calculation results show that the explanation variance values are all bigger than 10% and the path is reliable.Based on the path model, the influence path and function relationships of the participation attitude, subjective norm and behavior control on the participation intention and behavior are obtained. This will provide theoretical and data supports for the risk management and strategy making of the public participation in nanotechnology risk communication.