bluuebunny/crossref_metadata_2025 huggingface dataset:
Financial Implication of COVID-19: A Story of Malaysian Dental Practitioner
Authors:   Centre of Restorative Studies, Faculty of Dentistry, Universiti Teknologi MARA, Jalan Hospital 47000 Sungai Buloh, Selangor, Malaysia, faharina@uitm.edu.my, Faharina Abdul Hamid Nor , Jaafar Azlan ,   Department of Periodontology & Community Oral Health, Faculty of Dentistry, Universiti Sains Islam Malaysia, 55100 Kuala Lumpur, Malaysia, drazlan_jaafar@usim.edu.my, Haini Najwa Mohamd Mahmod Nurul ,   Faculty of Dentistry, Universiti Teknologi MARA, Jalan Hospital 47000 Sungai Buloh, Selangor, Malaysia, haininajwa99@gmail.com, Nur Nabiha Raja Amir Hamzah Raja ,   Faculty of Dentistry, Universiti Teknologi MARA, Jalan Hospital 47000 Sungai Buloh, Selangor, Malaysia, rajanurnabiha@gmail.com
Published: January 2022

Coronavirus disease 2019 (COVID-19) has caused series of lockdown in Malaysia which led to the significant financial impact to dental practitioner in Malaysia. Objectives: The aim of this study is to investigate factors affecting dental practice and its implication to financial situation during the pandemic in Malaysia. Methods: Registered Malaysian dental practitioners were invited to participate in online questionnaire via google form. Descriptive and Pearson’s chi-square test analysis were conducted (p<0.05). Results: 468 of dental practitioners had responded to our survey. More than three-quarter of dental practitioners worked in government sectors while almost a quarter worked in private sectors. 49.2% of respondents opened for emergency cases only during movement control order (MCO), 42.1 % of them limit for dental emergency, appointment-based cases and non-aerosol generated procedure during Conditional MCO and 62.5% operated as usual following a strict standard operating procedure during Recovery MCO. More than three quarter of private dental practitioners indicated that pandemic affected their daily monetary income (p<0.001) while most of them had to spend other source of income (p=0.004). All working sectors dictated that the working volume and number of patients had statistically decrease (p<0.05). Conclusion: COVID-19 pandemic had an impact on the practice of dentistry and financial position especially for private dental practitioners. Major government assistance is important to reduce the burden of dental practitioner and preserving their future practice.
Comparison of Smart Governance in Response to Handling COVID-19 (Case Study: South Tangerang City, Yogyakarta City, Surabaya City)
Authors: Kawuriyan Megandaru Widhi , Sadayi Delila Putri , Purnomo Eko Priyo , Fathani Aqil Teguh
Published: January 2022

This study aims to look and compare how the Implementation of Smart Governance conducted by the Government of South Tangerang City, Yogyakarta City, and Surabaya in handling the impact of COVID-19. In addition, this study also wants to see how smart governance ideas and the readiness of the governments of the three cities in the impact of COVID-19. This research method uses explorative qualitative research method in analyzing more in-depth related to ideas, government readiness both from human resources and technology used, and how it is implemented in handling the impact of COVID-19. The data source in this study uses secondary data derived from articles, journals, official government websites, and online media time series in one week analyzed using Netlytics. The findings obtained in this study show that first, it has similarities in the delivery of information through a special website for COVID-19 services and information. The third city is actively providing information about COVID-19, social and economic impacts, health protocols, healthy lifestlyes, efforts to prevent, spread, and recover from the effects of COVID-19, which is integrated with ICT. However, the findings by analyzing the Twitter social media found that the Yogyakarta city government was more active and dominant in responding to COVID-19 than the City of Surabaya and South Tangerang.
Analysis of Adaptive Model Success of Integrated Financial Information System through User Satisfaction in Local Governments
Authors: Nasution Budi Anshari , . Erlina , Lumbanraja Prihatin , Muda Iskandar
Published: January 2022

This research is to anayze adaptive model success of integrated financial information system through user satisfaction in local governments. The population in this study were the parties managing the regional financial information system totaling 612 people. The sampling technique uses saturated samples. The processing of data used Smart Partial Least Squares (PLS) application for the SEM Methods. The results showed that: partially, the reliability of information systems, organizational support, benefits, user behavior, has an effect on user satisfaction with the government regeny/cities in North Sumatra; partially, the reliability of information systems, organizational support, benefits, user behavior, use of the intention to the local government in North Sumatra; the user satisfaction factor affects the intention to use; Partially, the reliability of information systems, organizational support, benefits, and user behavior affects user satisfaction and its impact on intention to use in local governments in North Sumatra.
Utilizing Dinesh Verma Transformation (DVT) and Differential Equations in a Cryptography Model
Authors: Meftin Dr. Noor Kadhim
Published: January 2022

The ever-expanding area of cryptography fostered the development of numerous cryptographic models utilizing a variety of mathematical and logical methodologies, and integral transformations were no exception. This paper proposes a cryptographic model based on the application of the Dinesh Verma integral Transformation (DVT) to the series produced by the production of a general polynomial P(t) of degree n with the Taylor series to increase the complexity of the resulting ciphertext, and in which key elements required for encryption and decryption are transmitted over channels of various security measurements to complicate the attacker's work in gathering the required information.
Determination Work Life Balance, Commitment Organization, Work Motivation and Compensation Financial on the Job Satisfaction Generation Millennial Performance as an Intervening Variable (Case Study on Driver Gojek Kota Medan)
Authors: Sofiyah Fivi Rahmatus , . Yulinda
Published: January 2022

This research to examine work life balance, organizational commitment, work motivation and financial compensation on job satisfaction through the millennial generation on Gojek drivers in Medan City. The population in this study are gojek drivers in Medan City who have worked for at least 6 months and are included in the millennial generation category. Data collection techniques were carried out using questionnaires and interviews. The analysis method uses path analysis using SmartPLS. The results showed that work life balance, organizational commitment, work motivation and financial compensation on job satisfaction through the performance of the millennial generation on gojek drivers in Medan City. Performance can also indirectly mediate work life balance, organizational commitment, work motivation and financial job satisfaction.
Development of an Integrate E-Medical System Using Software Defined Networking and Machine Learning
Authors: Abed Abdullah Suhail , Ahmed Brwa Khalil Abdullah , Ibrahim Sura Khalil , Zahra Musaddak Maher Abdul , Salih Mohanad Ahmed , Jaleel Refed Adnan
Published: January 2022

Scholars and medical professionals have recognizes the importance of electronic medical monitoring services for tracking elderly people's health. These platforms generate a large amount of data, requiring privacy and data security. on the contrary, Using Software Defined Networking (SDN) to maintain network efficiency and flexibility, which is especially important in the case of healthcare observation, could be a viable solution. Moreover, machine learning can additionally utilized as a game changing tool which incorporated with SDN for optimal level of privacy and security. Even so, integrating SDN into machine learning, which heavily relies on health sensors of patients, is incredibly difficult. In this paper, an Integrate Medical Platform (IMP) with a focus on SDN and Machine learning integration is proposed. We produce a platform that reduces complexity by identifying high level SDN regulations based on the extracted flow classes and utilizing machine learning traffic flow classification techniques. F or various types of traffic, We employ supervised learning approaches based on models that have already been trained. We use four algorithms for supervised learning: Random forest, Logistic Regression classifiers, K-NN, and SVM, with different characteristics. Finally, we evaluated IMP by using accuracy, precision, TPR, TNR, FPR, MAE, and energy consumption.
E-learning and COVID-19: Predicting Student Academic Performance Using Data Mining Algorithms
Authors: Alsammak Ihab L. Hussein , Mohammed Ali Hussein , Nasir ntedhar Shakir
Published: January 2022

The satisfaction of E-learners has the main effect on the success of the E-learning process and leads to improvements in the E-learning system's quality and several factors affect this satisfaction. Based on the dimensions of e-learning, the main objective of this study was to evaluate the factors that contributed to students' satisfaction with e-learning during pandemic the Covid-19 and to give a thorough understanding and knowledge of different data mining techniques that have been used to predict student performance and development, as well as how these techniques help in the identification of the most relevant student attribute for prediction. Currently, to search for information in large databases, data mining techniques have become very popular and proven itis effective. Because of the performance and effectiveness of data mining techniques, it has been adopted by many areas such as telecommunication, education, sales management, banking, etc. In this paper, data mining algorithms were relied on to build e-learning classification models for a "student performance" data set, the proposed model includes 1000 instances with 35 attributes. Data mining algorithms have been implemented on the student performance data set in E-learning. Among these algorithms are the Decision Tree algorithm, Random Tree algorithm, Naive Bayes algorithm, Random Forest algorithm, REP Tree algorithm, Bagging algorithm and KNN algorithm. After comparing the results and conducting the assessment, the impact of the proposed features in e-learning on the student's performance was clarified. The final result of this study is important for providing greater insight into evaluating student performance in the COVID-19 pandemic and underscores the importance of data mining in education.
The Economic Prosperity of Modern Cooperatives Members in Economic Education Perspective
Authors: Farid Muhammad Miftah , . Wahjoedi , Murwani F. Danardana , . Sunaryanto
Published: January 2022

This study aims to examine the prosperity of modern cooperative members in the perspective of economic education. Researcher used qualitative approach with multicentus model. This research was conducted in several cooperatives branch, such as Sinau Andadani Ekonomi Pujon (SAE Pujon Cooperative), Bebarangan Anggayuh Tentreme Urip (BATU Village Unit Cooperative), and Saiyeg Usaha Butuhing Urip (Subur Village Unit Cooperative) in Malang, East Java. In this study, the stages of research used interviews, observations, and documentation. The research result, theoretical and empirical studies of this study can be described as following below: the presence cooperatives of multi-site studies in general help to improves the prosperity of members and non-member communities in cooperative environment, the existence of cooperatives in multisite study Malang Raya increase the intelligence of members and non-member communities to produce and create new economic opportunities and the existence of cooperatives of multisite study in Malang to build a wisdom for cooperative members in consuming their needs.
Predicting Consumers' Online Grocery Purchase Intention Within Middle-Class Families
Authors: N.L. Dr. Balasudarsun , M. Dr. Sathish , P.S. Dr. Venkateswaran , Byloppilly Dr. Rameshwaran , Devesh Sonal , Naved Dr. Mohd
Published: January 2022

The goal of this study was to explore the factors that affect Generation Y's purchasing intentions to the fast-food industry in India. In the fast-food sector, purchasing intentions today are a significant component of long-term corporate sustainability. In addition, in comparison with other competitive markets, buying intentions are critical; thus, the fast-food industry must resolve this problem. In this report, the main determinants for the purchasing of Generation Y students in the Fast Food Industry in India were established. A total of 313 surveys were distributed with a response rate of 91 percent using the quantitative approach in this sample. Another goal of this study was to provide fast-food restaurants with valuable insights into how to perceive customers' purchasing intentions. By knowing the main buying determinants in the fast-food industry, leaders and managers from local fast-food restaurants in India will boost customers' experiences, overcome their problems and eventually achieve a high-quality market.
Hybrid Facial Chaotic-based Graphical Encryption Technique for Cloud Environment
Authors: A. Manikandan , R. Anandan
Published: January 2022

Pictographic representations are everywhere in this digital world. IoT, Cloud, Fog, and 5G systems are becoming data transfer boosters for each user. In a real-world situation, secure data transmission is critical through open networks. Many conventional cryptosystems are inadequate for graphical data privacy in terms of computational overhead, latency, and more sensitive to the unknown attacks. In this paper, the secured and low-complex chaotic-based facial image cryptosystem has been developed for computer vision image data. The proposed crypto system utilizes the facial features, Lorentz chaotic maps for private keys production during the encoding process and the same is decrypted using the diffusion process. Facial depictions are merged with chaotic maps that are segmented and decrypted with mutual keys. The performance of the proposed hybrid cryptosystem is validated using the standard facial datasets and NCPR, UACI metrics are measured. Entropy and adjacent pixels correlation metrics also evaluated through proposed cryptosystems.
The Federal General Budget between Reality and Challenges
Authors: Al-Muttar Mohammed Yousif Oudah
Published: January 2022

The general budget of the State of Iraq and its four stages, starting with enemies, implementation and control over it, are based on a set of legislations, including the amended Financial Management and Public Debt Law No. 95 of 2004. In light of this, the general budget cycle faces several failures, some of which are caused by shortcomings. In the adopted legislation, which affects in one way or another on the general budget, which calls for studying it, drawing attention to it, and submitting proposals that contribute to addressing this to improve the reality of the general budget. This paper aims to know the problems in the general budget and to know the shortcomings in the Iraqi legislation organizing the budget. As well as knowing the defect resulting from the lack of commitment to the application of the Iraqi legislation related to the general budget and facing the challenges facing the federal public budget.
Automated Test Suite Generation Tool based on GWO Algorithm
Authors: Altaie Atica M. , Tawfeeq Tawfeeq Mokdad , Saeed Mustafa Ghanem
Published: January 2022

In succession, the size and complexity of the program increase and the scope of testing expand. So, to ensure deadline delivery and reduce development testing costs, program testing efficiency must be improved. Therefore, to ensure that the product is delivered on the deadline and the cost of testing development is reduced, the efficiency of the program's testing must be enhanced. In this study, highlighting is placed to generate test suite automatically to reach increase the coverage of paths based on two algorithms Grey Wolf Optimizer algorithm (GWO) and Particle swarm optimization (PSO). The results of the implementation will be compared with each other to demonstrate the performance and efficiency of the proposed methodology and the results show that the PSO algorithm has better than the GWO algorithm.
Political Violence and Children in the City of Bulawayo
Authors: Le Kien
Published: January 2022

This research aims to explore how political violence threatens the health of young children in the city of Bulawayo, Zimbabwe. Utilizing a distinction in-contrasts relapse procedure, we uncover the harmful results of political viciousness on Zimbabwean youngster wellbeing. Specifically, political viciousness makes kids weigh 0.720 and 0.565 standard deviations less for their age and stature, separately, when presented to it. Our exploration prescribes embracing opportune and successful drives to limit the pernicious outcomes of political brutality.
Neural Network Principles and its Application
Authors: Hamdan Baida Abdulredha
Published: January 2022

Neural networks which also known as artificial neural networks is generally a computing dependent technique that formed and designed to create a simulation to the real brain of a human to be used as a problem solving method. Artificial neural networks gain their abilities by the method of training or learning, each method have a certain input and output which called results too, this method of learning works to create forming probability-weighted associations among both of input and the result which stored and saved across the net specifically among its data structure, any training process is depending on identifying the net difference between processed output which is usually a prediction and the real targeted output which occurs as an error, then a series of adjustments achieved to gain a proper learning result, this process called supervised learning. Artificial neural networks have found and proved itself in many applications in a variety of fields due to their capacity to recreate and simulate nonlinear phenomena. System identification and control (process control, vehicle control, quantum chemistry, trajectory prediction, and natural resource management. Etc.) In addition to face recognition which proved to be very effective. Neural network was proved to be a very promising technique in many fields due to its accuracy and problem solving properties.
The Effect of Tax Officials’ Organizational Communication on Job Performance: Focused on the Moderating Effect of Trust
Authors: Hong Soon-Bok , .
Published: January 2022

The purpose of this study is to investigate whether organizational communication factors - top-down, bottom-up, and horizontal communication - affect job performance when tax officials perform their duties, and to empirically analyze their relationship using the moderating variables of trust to see if organizational communication on job performance differs according to trust. The results of the analysis show that factors of organizational communication that have a positive effect on job performance are top-down communication and horizontal communication. Among them, horizontal communication has a greater effect. In addition, the analysis result of interaction effect by moderating regression analysis shows that only horizontal communication x trust has a significant positive effect on job performance except top-down communication and bottom-up communication among organizational communication factors. This means that the more smooth the horizontal communication in the tax organization is, the higher the trust gets. It suggests that, given that related duties in tax affairs are handled jointly and the organizational transactions are done together though the progress of tax affairs is mostly handled individually, horizontal communication has a positive effect on forming a network between colleagues and departments in the organization and on solving problems and conflicts in the work, thereby enhancing the job performance. The suggestions for future research are as follows. First, this study is the result of analyzing tax officials working in Busan, Ulsan and Gyeongnam-do (province), so the number of samples is limited. It is necessary to expand the region and organization because there is a limit to generalizing the results of the study. Second, it is considered that the same method has a problem in that the data were collected using the questionnaire limited to a few items by the self-report questionnaire, so further research that can increase external validity using various data should be conducted.
Design and Implementation of Home Training System based on Personal Fitness Using Kinect Sensor
Authors: Park Jung Kyu , Park Eun Young
Published: January 2022

In this paper, we implemented and developed a home training program using sensors. The implemented program grasps the user's athletic ability and suggests personalized exercise program. In modern society, the aging population is growing rapidly, and most of them are working and living in the interior. For this reason, health management of the aging population is becoming an important social issue, and it is recognized that muscle strength is an important factor. In this paper, we designed and developed a home training program for aged people to exercise in a comfortable environment like natural environment. In the developed program, we extract the skeletal feature points of the user using Microsoft's Kinect sensor and generate feature vectors to check the user's motion state. The proposed algorithm measures the user 's athletic ability and suggests a suitable exercise based on the information. As a result of applying kinematic sensor measurements to the proposed algorithm, it showed up to 97% accuracy. We measured the user's athletic ability through the program using the proposed algorithm and suggested the exercise suitable for the individual ability based on this information.
Data Quality Test Method for Factory Energy Management System
Authors: Ju Seung-Hwan , Seo Hee-Suk
Published: January 2022

Because data is an important factor in the software industry, how to reliably test data is important. This is even more essential for building Industry 4.0 and smart industrial complexes. This study prepares ISO/IEC 25024-based test methods and guidelines and uses them for energy management at the industrial complex level. In order to provide services by collecting energy data from industrial complexes, it is necessary to verify data quality based on data reliability and compatibility of each plant. Data quality technology needs to conform to ISO TC184/SC4/WG13 (industrial data quality standard) based technology. The study defines the data quality evaluation matrix for the energy management system of industrial parks and factories. It defines five categories and maps detailed indicators to each. The category has three detailed items, which are evaluation items for core requirements, interoperability, and conformity to standards. Each data requirement category covers functionality and reliability, usability and efficiency, and portability as data requirements in the system. Core requirements for system operation such as data consistency are basic evaluation items, and interoperability, which is the semantic compatibility of data for integrated operation of multiple sites, is verified. In addition, data quality is evaluated by verifying standard conformance. Through this evaluation system, the requirements for linking the factory energy management system data with the industrial complex energy management system can be evaluated. This can be used to monitor data quality and develop improvement technologies by developing a master data quality management technology standard suitable for industrial sites.
Implementation of Autonomous Driving on RC-CAR with Raspberry PI and AI Server
Authors: Hwang Kitae , Jung In Hwan , Lee Jae Moon
Published: January 2022

A lot of research is being done on autonomous driving vehicles or robots that recognize objects and drive themselves without human intervention. In order to develop autonomous driving technology, there is a fundamental difficulty in securing expensive real cars equipped with various sensors. In this paper, an autonomous driving system development platform was developed using an inexpensive RC-Car, and a test system that can test various algorithms related to autonomous driving was introduced. In the system developed in this study, the single board computer Raspberry PI was mounted on the RC-Car to control the car, and the autonomous driving-related algorithms were implemented in a separate AI server, and they communicated with the message-based ROS protocol. In addition, those who want to develop an autonomous driving system can easily attach desired sensors to the RC-Car, increasing scalability. In this paper, almost all algorithms related to autonomous driving have been implemented. A simple autonomous driving RC-Car system was actually implemented and operation was verified by designing and implementing algorithms such as lane recognition, driving along the lane, obstacle detection and stopping, traffic light recognition, driving between smooth and sharp curves, and autonomous parking. In sharp curves, the angle of the lane was tracked in a short period to prevent the vehicle from crossing the lane. In addition, we developed an Android app that can manually control the car and monitor the video from the camera in time. This study presented and solved various difficulties that could not be known by developing an autonomous driving algorithm using simulators.
Vedic Multiplier-based International Data Encryption Algorithm Crypto-Core for Efficient Hardware Multiphase Encryption Design
Authors: Kanda Guard , Ryoo Kwangki
Published: January 2022

At present, there are several pieces of research on designing and implementing new cryptographic algorithms that are lightweight and resistant to several, if not major forms of security attacks. However, some algorithms such as the International Data Encryption Algorithm (IDEA), which has been around for some time is yet to record any real threat against its functionality. To ensure its continued usage, current implementations rely on multiphase encryption where it is combined with other algorithms such as ROTation (ROT) and Data Encryption Standard (DES) for maximum security strength. Multiphase encryption implies that there is a tendency for an increase in hardware area and a reduction in overall speed. In such cases, having fast and reduced area algorithms are much desired. This paper, therefore, proposes an efficient hardware implementation of the IDEA cipher that is based on arithmetic modulo multiplication—one of the main computations of the IDEA—on a novel Vedic multiplier architecture. The increase in efficiency of the IDEA crypto architecture and the reduction in resources utilization is achieved through an enhancement of its structural architecture to utilize a fixed set of resources for all eight identical rounds of computation and the use of a proposed fast and lightweight Vedic hardware multiplier. The proposed hardware modification and resulting architecture are designed using the Xilinx ISE and Vivado tools. The architecture is synthesized using Precision Synthesis Tool (PS) and simulated using Modelsim SE 10.6d and ISIM simulation tools. The proposed IDEA cipher is 100% more efficient when designed based on the Vedic multiplier compared to existing designs. The hardware architecture is implemented on Spartan-6-FGG484 Field Programmable Gate Array (FPGA) using Verilog HDL. Verified results show that the proposed Vedic-based IDEA occupied 212 Slices with the Vedic multiplier only occupying 28 Slices out of the total 212. The proposed architecture operates at a maximum frequency of 253.3 MHz.
An Empirical Study of Intelligent Security Analysis Methods Utilizing Big Data
Authors: Chun Yang-ha , Cho Moon-ki
Published: January 2022

The frequency of cyber-terrorist attacks targeting broadcasting companies or key financial institutions, in which PCs affected by malicious code obstruct normal operations, has been increasing. With the widespread availability of internet technology, the frequency of intelligent cyber-attacks in virtual cyber environments, such as APT attacks, continues to rise. Through an intelligent analysis of cyber threats which are still unknown, it is possible to prevent security accidents before they occur through the use of intelligent security analysis methods based on big data. The researcher conducted this empirical study concerning intelligent security analysis methods utilizing big data which are capable of preventing cyber threats, while protecting information assets from risks through evaluation and prediction.
Study on the Case of Songdo Smart City based on the Citizen Participation Approach
Authors: Kim Myung-Hee
Published: January 2022

The purpose of this paper is to explore the level of citizen participation in Songdo smart city project, once called U-city project, in order to provide citizens with U-service anywhere by using new IT technology in the Incheon Free Economic Zone, a landfill site. To this end, it reviews the literature on smart city, and derives project’s problems, evaluates according to the framework, and proposes implications. This paper uses the “Citizen Participation Evaluation Framework” formalized by Simonofsky and his colleagues and analyzes civic participation method by dividing it into democratic participants, co-creators, and ICT users. The level of citizen participation in the Songdo project is evaluated according to the criteria presented by this framework and the results are presented. The analysis period is from 2003 to 2016, when the government office was opened and the initial foundation was completed. As a result of analysis, Songdo had built open data and infrastructure for citizens, but citizens were participated as ICT users only. Although citizens living in smart cities should be the co-creators and participants in democratic decision-making for solving urban problems, Songdo was missing these factors. In conclusion, this paper argues that Songdo remains in the U-city paradigm as the 'smart people', a key element among the smart city components, has been neglected and ultimately reveals a fundamental limit to its growth into a sustainable smart city. In addition, suggestions for improvement are presented. This paper is meaningful in that it presented lessons and implications that the human factors should be emphasized as the subject of technology and services to develop into an authentic smart city through the case of Songdo.
Personal Donation System Using a Blockchain for Enhanced Transparency
Authors: Seo Aria , Son Yunsik , Lee Yangsu , Jeong Junho
Published: January 2022

Driven by the development of information and communication technology (ICT) along with the social consciousness of civil societies, a cultural practice of donation using ICT resources is emerging. However, owing to problems with the trust and transparency of donation systems and public revelations of corruption on the part of some groups accepting donations, the donation participation rate is gradually decreasing. Because social welfare is considered a problem that cannot be solved only by the state, the operation of sound social welfare organizations that individuals can consistently trust and donate to is necessary. In this study, we propose a personal donation system designed to help ensure transparency in the operation of groups accepting donations and to encourage personal donations. To this end, we review the main factors in participant decisions to donate and make continuous donations. The proposed system and detailed module were designed based on a consortium blockchain and implemented by configuring the blockchain channel. We aim to provide a meaningful solution to important social problems using ICT and blockchain technology.
Application of Data Mining and Knowledge Discovery in Medical Databases
Authors: Abdulkadium Ahmed Mahdi , Shekan Raid Abd Alreda , Hussain Haitham Ali
Published: January 2022

While technical improvements in the form of computer-based healthcare information applications as well as hardware are enabling collecting of and access to healthcare data wieldier. In this context, there are tools to analyse and examine this medical data once it has been acquired and saved. Analysis of documented medical data records may help in the identification of hidden features and patterns that could significantly increase our understanding of disease onset and treatment therapies. Significantly, the progress in information and communications technologies (ICT) has outpaced our capacity to assess summarise, and extract insight from the data. Today, database management system has equipped us with the fundamental tools for the effective storage as well as lookup of massive data sets, but the topic of how to allow human beings to interpret and analyse huge data remains a challenging and unsolved challenge. So, sophisticated methods for automated data mining and knowledge discovery are required to deal with large data. In this study, an effort was made employing machine learning approach to acquire knowledge that will aid various personnel in taking decisions that will guarantee that the sustainability objectives on Health is achieved. Finally, the present data mining methodologies with data mining methods and also its deployment tools that are more helpful for healthcare services are addressed in depth.
Perception of Eliminating Service Waste for Better Efficiency and towards Lean Management in Vietnamese SMEs
Authors: Nguyen Huy Khanh , Nguyen Xuan Duc
Published: January 2022

The research has been conducted to clarify current waste elimination perceptions among SMEs in Vietnam. Besides, it delivers further suggestions for Vietnamese SMEs’ lean management in the near future. The samples of 237 SMEs have been surveyed to collect significant data about the perception of service waste and service efficiency in Vietnam. To examine the relationship between service waste and service efficiency, SPSS 26 software is conducted with two types of analyses, including descriptive analysis and statistical analysis. The main research findings are (1) Service waste and waste elimination has been highly appreciated by medium-sized Vietnamese SMEs rather than micro-sized SMEs; (2) Human-related waste, motion and defects are the most popular waste identifications and (3) Inventory waste has the least effect on service efficiency. The study becomes significant contribution to further papers about elimination awareness among different waste types and lean management considerations for SMEs in Vietnam.
A Hybrid Approach for Detection of Brain Tumor with Levy Flight Cuckoo Search
Authors: Singh Ravendra , Agarwal Dr. Bharat Bhushan
Published: January 2022

The identification and segmentation of tumors in the human brain MRI are very difficult and time-consuming tasks. In the segmentation process, the optimum threshold value is obtained by the methods called Otsu’s and Kapur entropy. The objective functions of these methods are computed using the Cuckoo-McCulloch procedure with levy flight for its accuracy and computational time. In this study, the proposed method incorporating McCulloch’s method for the levy-flight generation which is a computationally efficient image segmentation algorithm, the proposed method has compared the performance of efficient CS with Mantegna’s method. The overall-performance of an efficient CS algorithm is also compared and discussed with other popular methods and also the proposed efficient CS method is prominent and computationally efficient for magnetic resonance imaging. Experimental results encourage related researches in image processing, computer vision.
Gender and Women of Eligible Couple’s Decision Entering the Job Market in Central Java
Authors: Sundari Dyah Siti , Santosa Purbayu Budi , . Firmansyah
Published: January 2022

This research aims to analyze the factors that influence the decision of women of eligible couple to enter the labor market. The research method used is quantitative methods using statistical analysis techniques Binary Logistic Regression. The research data used is primary data with cross section data type. The number of respondents in this study was 1200 observations determined by the multistage random sampling method. Sampling was carried out in stages from the scope of Central Java Province, one district/city per residency was selected. The results of the study stated that the variables of education level and family planning participation did not significantly influence the decision of women of eligible couple to enter the labor market. Meanwhile, the family welfare variable was influential but not significant with a statistical Z value of 1.62 but still smaller than the Z table value of 1.645. Then the variables of age, number of children and understanding of gender have a significant effect on the decision of women of eligible couple to enter the labor market. Based on the results of the probability estimation, it is stated that the increasing age of an eligible couple woman, the higher the probability of an eligible couple woman entering the labor market, the more children an eligible couple woman has, the lower the probability of an eligible couple woman entering the labor market. The probability of an woman of eligible couple who has one child to enter the labor market is 43.68%, while the probability of having 5 children or more is only 21.93%. And the higher the value of understanding about the gender of women of eligible couple, the higher the chances of entering the labor market. Women of eligible couple with an average gender score of 1, have a probability of entering the labor market of 0.5 or 50%. Meanwhile, women of eligible couple with an average gender score of 5 have a probability of entering the labor market of 0.5915 or 59,15%. The policy implications that need to be implemented are increasing IEC and promoting birth control through family planning programs, increasing the socialization of gender concepts and gender mainstreaming and increasing women's empowerment programs, especially in the economic sector.
Knowledge as an Intangible Factor of Growth in Papua, Indonesia: Examining the Role of Education, Vocational Program, Information and Communication Technology
Authors: Sihombing Meiske M.N. , . Firmansyah , F.X. Sugiyanto , Widodo Wahyu
Published: January 2022

Knowledge is capable to produce economic growth, but also the most challenging to capture. By endogenous growth theory, it can sustain the long-term wealth through the increase in capital and goods. In this study, knowledge as an intangible factor, described by duration of formal education, the application of information technology and culture. The aim of this study is to examine whether the duration of formal education, the application of information technology and culture can produce growth in less developed region. The analysis applied through Fixed Effect Model on panel data from 25 regencies and city in Papua Province, Indonesia, from 2012-2017. The results show that there are only few indicators such as years of schooling, numbers of people who have the computers and internets which had significant effect on growth.
Sustainability Reporting by State Universities in Indonesia: An Examination of Influential Characteristics
Authors: Sudaryati Dwi , Raharja Surya
Published: January 2022

The aim of this study was to analyze the characteristics of sustainability information of higher education institutions in Indonesia. The population and research sample were all state universities in Indonesia, as many as 125. The method used was logistic regression analysis. The results showed that 29.6% of state universities in Indonesia had disclosed sustainability information and the remaining 70.4% had not disclosed sustainability information. The logistic regression results show that college size has a significant effect on sustainability information disclosure. The bigger the university, the more likely it is that the university will disclose its sustainability information. On the other hand, age and reputation did not significantly influence the disclosure of sustainability information.
Speculating the Threat of Cardiovascular Disease Using Classifiers with User-Focused Security Evaluations
Authors: S.R. Chandrasekaran , N. Dr.Sabiyath Fatima
Published: January 2022

In recent decades, cardiovascular disease (CVD) is the most common type of disease that is prevailing all over the world. It is a class of diseases that involve the heart and its vessels. Strokes and heart attacks are normally critical events that are largely provoked by congestion that restricts blood from streaming to the parts of the body. The principle aim of this research is to find the feature that accounts for cardiovascular disease risks. The collection of data from the hospitals and laboratories can determine the risk of patients having cardiovascular disease by analysing the trends and correlations between the dataset. First, the data undergoes a security process that involves user-level security. The data is further processed to show the comparison between the 12 features and find out the top few features that account for the risk of positive cardiovascular disease. This Machine learning techniques can be widely used in the medical field, to determine the risk of cardiovascular disease early. These collected data from the patients may be used to warn them. To identify the risk of having cardiovascular disease early there are some proposed machine learning algorithms like K-nearest neighbours, XG Boost, Gradient boost and Random Forest Classifier by measuring the metrics like precision, accuracy, f-1 score and recall. Out of all these algorithms, XG Boost yields the highest accuracy of 90%. To increase the overall accuracy stacking algorithm is used to combine all the base learner algorithms to produce a result at once. After stacking the overall accuracy boosted to 92% for the respective dataset.
A national surveillance program for evaluating new reagent lots in medical laboratories
Authors: Solsvik Anne Elisabeth , Kristoffersen Ann Helen , Sandberg Sverre , Gidske Gro , Stavelin Anne Vegard , Eikeland Joakim , Amundsen Erik
Published: January 2022

Objectives

Differences between laboratory results attributable to the use of different reagent lots can potentially affect the diagnosis and monitoring of patients. To minimize patient risks, all laboratories should verify that new reagent lots meet agreed analytical performance specifications (APS). We propose a simplified, pragmatic approach for laboratories that involves compilating results into a national surveillance program, and present the first results obtained when applying this approach to troponins, glycated hemoglobin (HbA1c), prostate-specific antigen (PSA) and D-dimer.

Methods

In the surveillance program we have (i) determined APS for selected analytes, (ii) implemented a simplified procedure for lot evaluation with patient samples used in laboratories across Norway and (iii) performed central processing of the results from the participating laboratories.

Results

Over a one-year period, 27 Norwegian laboratories returned results from 28 lot changes for troponin I, 11 for troponin T, and 29 for HbA1c, PSA and D-dimer. The mean difference between two reagent lots was 4.5% for troponin I (for a concentration interval of 20–32 ng/L), 5.1% for troponin T (10.7–17.5 ng/L), 2.2% for HbA1c (40–50 mmol/mol), 3.7% for PSA (3–5 μg/L) and 5.5% for D-dimer (0.4–1.0 mg/L FEU).

Conclusions

A novel procedure for reagent lot evaluation is proposed in which information about multiple lot changes from different medical laboratories can be accumulated nationally. Sharing this information allows simplification of lot evaluations in individual laboratories and provides real-world data about lot-to-lot variations.
Comprehensive review on synthesis, physicochemical properties, and application of activated carbon from the Arecaceae plants for enhanced wastewater treatment
Authors: Hoang Ngoc Bich , Ngo Thi Cam Quyen , Tran Thi Kim Ngan , Lam Van Tan
Published: January 2022

Arecaceae presents one of the plant families distributed mainly in the equatorial and subequatorial regions. Arecaceae are widely applied in many fields such as food, cosmetics, fuel, and chemical industries. However, a large amount of agricultural waste from the Arecaceae trees has been released into the environment. The objective of this report is to gain more insights into the potentials and applications of activated carbon (AC) from the Arecaceae trees in wastewater treatment, in which, the ability to handle organic pigments, metals, and antibiotics is focused. The physical properties and processability of AC are statistically evaluated. With a uniform structure, large specific surface area, processing ability according to Langmuir and pseudo-second-order models, we showed that ACs from Arecaceae trees are promising materials for water treatment applications. This is the basis for the development and reduction of by-products that affect the environment.
Functional A Posteriori Error Estimates for the Parabolic Obstacle Problem
Authors: Apushkinskaya Darya , Repin Sergey
Published: January 2022

The paper is concerned with functional-type a posteriori
estimates for the initial boundary value problem for a parabolic partial
differential equation with an obstacle. We deduce a guaranteed and
computable bound of the distance between the exact minimizer and any
function from the admissible (energy) class of functions. Applications
to the analysis of modeling errors caused by data implification are discussed. An important case of time incremental approximations is specially studied. Numerical examples presented
in the last section show how the estimates work in practice.
Prodrug-Activating Chain Exchange (PACE) converts targeted prodrug derivatives to functional bi- or multispecific antibodies
Authors: Dickopf Steffen , Buldun Can , Vasic Vedran , Georges Guy , Hage Carina , Mayer Klaus , Forster Matthias , Wessels Uwe , Stubenrauch Kay-Gunnar , Benz Jörg , Ehler Andreas , Lauer Matthias E. , Ringler Philippe , Kobold Sebastian , Endres Stefan , Klein Christian , Brinkmann Ulrich
Published: January 2022

Driven by the potential to broaden the target space of conventional monospecific antibodies, the field of multi-specific antibody derivatives is growing rapidly. The production and screening of these artificial proteins entails a high combinatorial complexity. Antibody-domain exchange was previously shown to be a versatile strategy to produce bispecific antibodies in a robust and efficient manner. Here, we show that the domain exchange reaction to generate hybrid antibodies also functions under physiological conditions. Accordingly, we modified the exchange partners for use in therapeutic applications, in which two inactive prodrugs convert into a product with additional functionalities. We exemplarily show the feasibility for generating active T cell bispecific antibodies from two inactive prodrugs, which per se do not activate T cells alone. The two complementary prodrugs harbor antigen-targeting Fabs and non-functional anti-CD3 Fvs fused to IgG-CH3 domains engineered to drive chain-exchange reactions between them. Importantly, Prodrug-Activating Chain Exchange (PACE) could be an attractive option to conditionally activate therapeutics at the target site. Several examples are provided that demonstrate the efficacy of PACE as a new principle of cancer immunotherapy in vitro and in a human xenograft model.