Computational Analysis of Novel Extended Lindley Progressively Censored Data

A novel extended Lindley lifetime model that exhibits unimodal or decreasing density shapes as well as increasing,bathtub or unimodal-then-bathtub failure rates, named the Marshall-Olkin-Lindley (MOL) model is studied.In this research, using a progressive Type-II censored, various inferences of the MOL model parameters oflife are introduced. Utilizing the maximum likelihood method as a classical approach, the estimators of themodel parameters and various reliability measures are investigated. Against both symmetric and asymmetric lossfunctions, the Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) technique with theassumption of independent gamma priors. From the Fisher information data and the simulatedMarkovian chains,the approximate asymptotic interval and the highest posterior density interval, respectively, of each unknownparameter are calculated. Via an extensive simulated study, the usefulness of the various suggested strategies isassessedwith respect to some evaluationmetrics such as mean squared errors, mean relative absolute biases, averageconfidence lengths, and coverage percentages. Comparing the Bayesian estimations based on the asymmetric lossfunction to the traditional technique or the symmetric loss function-based Bayesian estimations, the analysisdemonstrates that asymmetric loss function-based Bayesian estimations are preferred. Finally, two data sets,representing vinyl chloride and repairable mechanical equipment items, have been investigated to support theapproaches proposed and show the superiority of the proposed model compared to the other fourteen lifetimemodels.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Factors influencing health-related quality of life in chronic liver disease

AIM： To investigate the factors contributing to healthrelated quality of life （HRQL） in chronic liver disease （CLD）.METHODS： Patients with CLD and age- and sexmatched normal subjects performed the validated Thai versions of the short-form 36 （SF-36） by health survey and chronic liver disease questionnaire （CLDQ）. Stepwise multiple regression analysis was used to assess the impact of disease severity, demography, causes of CLD, socioeconomic factors, and self-rating health perception on HRQL.RESULTS： Two-hundred and fifty patients with CLD and fifty normal subjects were enrolled into the study. Mean age and the numbers of low educated, unemployed, blue-collar career and poor health perception increased significantly from chronic hepatitis to Child＇s Classes A to B to C. Advanced stage of CLD was related to deterioration of HRQL. Increasing age and female reduced physical health area. Low socioeconomic factors and financial burden affected multiple areas of HRQL. In overall, the positive impact of self-rating health perception on HRQL was consistently showed.CONCLUSION： Advanced disease, old age, female sex, stages of chronic liver low socioeconomic status and financial burden are important factors reducing HRQL. Good health perception improves HRQL regardless of stages of liver disease.

On a New Version of Weibull Model:Statistical Properties,Parameter Estimation and Applications

In this paper,we introduce a new four-parameter version of the traditional Weibull distribution.It is able to provide seven shapes of hazard rate,including constant,decreasing,increasing,unimodal,bathtub,unimodal then bathtub,and bathtub then unimodal shapes.Some basic characteristics of the proposedmodel are studied,including moments,entropies,mean deviations and order statistics,and its parameters are estimated using the maximum likelihood approach.Based on the asymptotic properties of the estimators,the approximate confidence intervals are also taken into consideration in addition to the point estimators.We examine the effectiveness of the maximum likelihood estimators of the model’s parameters through simulation research.Based on the simulation findings,it can be concluded that the provided estimators are consistent and that asymptotic normality is a good method to get the interval estimates.Three actual data sets for COVID-19,engineering and blood cancer are used to empirically demonstrate the new distribution’s usefulness inmodeling real-world data.The analysis demonstrates the proposed distribution’s ability in modeling many forms of data as opposed to some of its well-known sub-models,such as alpha powerWeibull distribution.

Impact of the Changes in Women’s Characteristics over Time on Antenatal Health Care Utilization in Egypt (2000-2008)

Objectives: This study empirically assesses the impact of the changes in women’s characteristics, empowerment, availability and quality of health services on woman’s decision to use antenatal care (ANC) and the frequency of that use during the period 2000-2008. Study Design: The study is a cross-sectional analytical study using 2000 and 2008 Egypt Demographic and Health Surveys. Methods: The assessment of the studied impact is conducted using the Zero-inflated Negative Binomial Regression. In addition, Factor Analysis technique is used to construct some of the explanatory variables such as women’s empowerment, the availability and quality of health services indicators. Results: Utilization of antenatal health care services is greatly improved from 2000 to 2008. Availability of health services is one of the main determinants that affect the number of antenatal care visits in 2008. Wealth index and quality of health services play an important role in raising the level of antenatal care utilization in 2000 and 2008. However, the impact of the terminated pregnancy on receiving ANC increased over time. Conclusions: Further research of the determinants of antenatal health care utilization is needed, using more updated measures of women’s empowerment, availability and quality of health services. In order to improve the provision of antenatal health care services, it is important to understand barriers to antenatal health care utilization. Therefore, it is advisable to collect information from women about the reasons for not receiving antenatal care.

Using Test Employee Capability Maturity Model for Supporting Gaps Bridging in Software Testing

This study addresses the challenge of assessing gaps among the differences of test people in eight groups by matching them based on four scenarios. The proposed model called Test Employee Capability Maturity Model (TEC-MM) helps find the gaps and measure the capability and maturity levels of each sub-maturity models in four dimensions as technicality, management, business, and personality. We applied TEC-MM to a software company which has instituted test people improvement strategy and plans to be a front runner in software testing industry. The findings reveal that gaps existed and consequently affect software testing process quality.

Fuzzy MCDM Model for Selection of Infectious Waste Management Contractors

Healthcare supply chains are under pressure to drive down costs because of digital business,shifting customer needs and new competition.Medical waste generated from medical facilities includes medical activities and daily-life activities of patients and their family members.According to statistics of the Department of Health EnvironmentalManagement,Vietnam currently has more than 13,500 medical facilities,including hospitals from central to provincial and district levels and private hospitals and medical facilities.Preventive medicine generates about 590 tons of medical waste/day and is estimated to be about 800 tons/day.Medical waste includes ordinary medical waste and hazardous medical waste;in which ordinary medical waste accounts for about 80%–90%,only about 10%–20%is hazardous medical waste including infectious waste and non-infectious hazardous waste.This is an environmental and occupational health issue that needs attention in developing countries like Vietnam.Handling large amounts of medical waste to ensure environmental and personal hygiene,doing so inefficiently creates potential hazards to the environment and increases operating costs.However,hospitals lack objective criteria and methods to evaluate and select the most optimal infectious medical waste,relying instead on their own subjective assessment and prior experience.Therefore,the author proposed a fuzzy multicriteria decision making(MCDM)model including Fuzzy Analytic hierarchy process(FAHP)and the Weighted Aggregated Sum-Product Assessment(WASPAS)for infectious medical waste contractors’selection in this research.The proposed Fuzzy MCDM method is in-tended to provide a more efficient,accurate method in the selection of infectious medical waste contractors than subjective assessment methods,thus reduce potential risks to hospitals.The results of this study can be applied to evaluate and select contractors in other industries.

Fuzzy MCDM for Improving the Performance of Agricultural Supply Chain

Fertilizer industry in Vietnam and globally have entered the saturation phase.With the growth rate slowing down,this poses challenges for the development impetus of the fertilizer industry in the next period.In fact,over the past few decades,Vietnam’s crop industry has abused excessive investment in chemical fertilizers,with organic fertilizers are rarely used or not at all,limiting crop productivity,increasing pests and diseases.To develop sustainable agriculture,Vietnam’s crop industry must limit the use of chemical fertilizers,increase the use of environmentally friendly organic and natural mineral fertilizers to produce clean agricultural products which is safe.Therefore,it is necessary to consider and choose the right supplier to ensure the goal of sustainable development.Spherical Fuzzy Analytic Hierarchy Process(SF-AHP),and the combinative distance-based assessment(CODAS)are new Multicriteria Decision Making(MCDM)method which can be used to solve supplier selection problem.This paper uses an effective solution based on a combined the concept of triple bottom line(TBL),SF-AHP and CODAS approach to help agriculture companies that need to select the best fertilizer supplier.This research can support supply chain managers to achieve supply chain systems that reduce not only sourcing costs,but also develop sustainable agriculture.

Optimization Agricultural Supply Chain:A Case Study of Fertilizer Supplier Selection

The 21st century is associated with the IndustrialRevolution 4.0 and the organic agriculture trend,making the utilization of high-quality fertilizers,abundant nutritional content,economical,and no affect to environment pollution.According to the new concept,clean agricultural production and organic agricultural products are not allowed to excessively use synthetic chemicals such as chemical fertilizers,and plant protection drugs,but priority is to use manure,organic fertilizers,and natural mineral fertilizers.Fertilizer must meet the balanced nutritional requirements of crops,maintain,and improve the fertility of the ground,protect the surrounding ecosystem,and leave harmful effects in agricultural products,products with high quality,safe for users and high economic efficiency for producers.To achieve the above goal,the selection of a fertilizer supplier is an important decision,supporting the supply chain’s sustainable development,fertilizer supplier selection is a multicriteria decision making model,the decision maker must assess all qualitative and quantitative factors.In this paper,the author proposed an integer decision making model including Fuzzy Analytic Hierarchy Process(FAHP)and Complex Proportional Assessment of Alternatives(COPRAS)for fertilizer supplier selection.The weightings of the criteria are calculated by using FAHP,COPRAS is then applied for ranking some potential fertilizer suppliers.The efficiency of the proposed models is proved by a case study conducted in a farm located in the south of Vietnam.This research is the first fertilizer supplier evaluation and se-lection model in Vietnam by interviewing experts and reviewing the literature.Re-search result is to provide a case study on evaluating supplier in agricultural supply chain utilizing the model proposed by the combination of FAHP and COPRAS models.

Prediction of BRIC Stock Price Using ARIMA,SutteARIMA,and Holt-Winters

The novel coronavirus has played a disastrous role in many countries worldwide.The outbreak became a major epidemic,engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline.This paper identifies two different models to capture the trend of closing stock prices in Brazil(BVSP),Russia(IMOEX.ME),India(BSESN),and China(SSE),i.e.,(BRIC)countries.We predict the stock prices for three daily time periods,so appropriate preparations can be undertaken to solve these issues.First,we compared the ARIMA,SutteARIMA and Holt-Winters(H-W)methods to determine the most effective model for predicting data.The stock closing price of BRIC country data was obtained from Yahoo Finance.That data dates from 01 November 2019 to 11 December 2020,then divided into two categories-training data and test data.Training data covers 01 November 2019 to 02 December 2020.Seven days(03December 2020 to 11December 2020)of datawas tested to determine the accuracy of the models using training data as a reference.To measure the accuracy of the models,we obtained the means absolute percentage error(MAPE)and mean square error(MSE).Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price(BVSP)while MAPE(0.50)and MSE(579272.65)with Holt-Winters(smaller than ARIMA and SutteARIMA),model SutteARIMA was found most appropriate to predict the stock prices of Russia(IMOEX.ME),India(BSESN),and China(SSE)when compared to ARIMA and Holt-Winters.MAPE andMSE with SutteARIMA:Russia(MAPE:0.7;MSE:940.20),India(MAPE:0.90;MSE:207271.16),and China(MAPE:0.72;MSE:786.28).Finally,Holt-Winters predicted the daily forecast values for the Brazil stock price(BVSP)(12 December to 14 December 2020 i.e.,115757.6,116150.9 and 116544.1),while SutteARIMA predicted the daily forecast values of Russia stock prices(IMOEX.ME)(12 December to 14 December 2020 i.e.,3238.06,3241.54 and 3245.01),India stock price(BSESN)(12 December to 14 December 2020 i.e.,.45709.38,45828.71 and 45948.05),and China stock price(SSE)(11 December to 13 December 2020 i.e.,3397.56,3390.59 and 3383.61)for the three time periods.

Reliability Analysis of HEE Parameters via Progressive Type-II Censoring with Applications

A new extended exponential lifetime model called Harris extended-exponential(HEE)distribution for data modelling with increasing and decreasing hazard rate shapes has been considered.In the reliability context,researchers prefer to use censoring plans to collect data in order to achieve a compromise between total test time and/or test sample size.So,this study considers both maximum likelihood and Bayesian estimates of the Harris extended-exponential distribution parameters and some of its reliability indices using a progressive Type-II censoring strategy.Under the premise of independent gamma priors,the Bayesian estimation is created using the squared-error and general entropy loss functions.Due to the challenging form of the joint posterior distribution,to evaluate the Bayes estimates,samples from the full conditional distributions are generated using Markov Chain Monte Carlo techniques.For each unknown parameter,the highest posterior density credible intervals and asymptotic confidence intervals are also determined.Through a simulated study,the usefulness of the various suggested strategies is assessed.The optimal progressive censoring plans are also shown,and various optimality criteria are investigated.Two actual data sets,taken from engineering and veterinary medicine areas,are analyzed to show how the offered point and interval estimators can be used in practice and to verify that the proposed model furnishes a good fit than other lifetimemodels:alpha power exponential,generalized-exponential,Nadarajah-Haghighi,Weibull,Lomax,gamma and exponential distributions.Numerical evaluations revealed that in the presence of progressively Type-II censored data,the Bayes estimation method against the squared-error(symmetric)loss is advised for getting the point and interval estimates of the HEE distribution.

Two-Stage Production Planning Under Stochastic Demand:Case Study of Fertilizer Manufacturing

Agriculture is a key facilitator of economic prosperity and nourishes the huge global population.To achieve sustainable agriculture,several factors should be considered,such as increasing nutrient and water efficiency and/or improving soil health and quality.Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields.Fertilizer supplies most of the necessary nutrients for plants,and it is estimated that at least 30%-50%of crop yields is attributable to commercial fertilizer nutrient inputs.Fertilizer is always a major concern in achieving sustainable and efficient agriculture.Applying reasonable and customized fertilizerswill require a significant increase in the number of formulae,involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae.An alternative solution is given by two-stage production planning under stochastic demand,which divides a planning schedule into two stages.The primary stage has non-existing demand information,the inputs of which are the proportion of raw materials needed for producing fertilizer products,the cost for purchasing materials,and the production cost.The total quantity of purchased material and produced products to be used in the blending process must be defined to meet as small as possible a paid cost.At the second stage,demand appears under multiple scenarios and their respective possibilities.This stage will provide a solution for each occurring scenario to achieve the best profit.The two-stage approach is presented in this paper,the mathematical model of which is based on linear integer programming.Considering the diversity of fertilizer types,themathematicalmodel can advise manufacturers about which products will generate as much as profit as possible.Specifically,two objectives are taken into account.First,the paper’s thesis focuses on minimizing overall system costs,e.g.,including inventory cost,purchasing cost,unit cost,and ordering cost at Stage 1.Second,the thesis pays attention tomaximizing total profit based on information from customer demand,as well as being informed regarding concerns about system cost at Stage 2.

Determinants of Antenatal Health Care Utilization in Egypt (2000-2014) Using Binary and Count Outcomes

Aim: This study seeks to investigate the factors determining the utilization of antenatal care services, the frequency of that use, and the timing of receiving antenatal care among Egyptian women utilizing a national representative data from Egypt Demographic and Health Surveys (EDHS) in 2000 and 2014. Methods: The paper estimates the logistic regression model, zero-inflated negative binomial model (ZINB), and negative binomial regression model (NB) to identify the most important determinants of antenatal health care utilization. Results: The findings indicate that the period 2000-2014 has experienced a significant increase in the use of antenatal health care services. The use of the public sector antenatal care services relative to that of the private sector has been decreasing over time. Moreover, wealth index, women’s education and quality of health services play significant roles in increasing accessibility of antenatal health care services. On the other hand, women’s empowerment has shown a positive effect in 2000 only. Conclusion: The study highlights the most vulnerable groups that are less likely to have access to antenatal health care services, mainly women who are less educated, poor and living in rural areas especially Upper Egypt. This certainly requires a more targeted health strategy with an equity lens.

Success Factors for Social Systems to Increase the Number of Organ Donations—From the Perspectives of Mechanisms and Organizational Behaviors

Deceased organ donation is much less prominent in Japan than it is in Western and other Asian countries. Because a shortage of organ donation is a serious social issue in Japan, various solutions to the issue have been considered. Although it was believed that the most critical factor in the organ shortage was the absence of a well-established social system, no prior studies attempted to analyze the issue from the perspective of the mechanisms and organizational behaviors. To identify common success factors of increasing organ donation, we conducted a qualitative survey in 5 countries promoting organ donations on a national level and increasing the number of organ donations. We found several important common factors: 1) to change from an explicit consent system to a presumed consent system with establishment of an appropriate in-hospital system, 2) to increase the level of job satisfaction of healthcare professionals and help them generating better results, 3) to demonstrate managements’ leadership for all staff to realize the importance of organ donations, 4) to establish an environment where medical professionals engaged in organ donations can appreciate autonomous working styles, the recognition of the importance of the work. It is suggested that these successful factors are introduced into Japan with long-, mid-term strategy to enhance organ donation.

Heuristic Scheduling of Job Orders in a Build-to-Order Manufacturing System

With the continuous development of technology,traditional manual work has been becoming more and more automated.Most large or medium-sized companies have applied Enterprise Resource Planning(ERP)software into their business and production activities.However,since many small firms cannot afford ERP because of its expensive cost,they often still employ manual work for the same tasks this software resolves,especially for scheduling.This paper aims to provide a possible solution for small businesses to try automated scheduling and discover whether it can help much.There are two main ways to make this determination:a mathematical model and a heuristic model,which are suitable for assessing low-and medium-sized workloads,respectively.This case study was carried out in a small domestic interior furniture company,particularly in scheduling for their customized products in two-stage flow shop.Normally,they produce according to the sequence of customers’orders.However,when we applied these supportive tools with batch-processing machines,they experienced enhanced production performance due to diminishing setup time for distinctive items and a more streamlined arrangement of job sequences.These changes were implemented for some small companies that do not use many production stages and have a suitable number of jobs and customers.If this method were applied to larger demands,it would need further improvement and development to become a complete tool that can perform like a part of an ERP system.

Scheduling Optimization Modelling:A Case Study of a Woven Label Manufacturing Company

Production scheduling involves all activities of building production schedules,including coordinating and assigning activities to each person,group of people,or machine and arranging work orders in each workplace.Production scheduling must solve all problems such as minimizing customer wait time,storage costs,and production time;and effectively using the enterprise’s human resources.This paper studies the application of flexible job shop modelling on scheduling a woven labelling process.The labelling process includes several steps which are handled in different work-stations.Each workstation is also comprised of several identical parallel machines.In this study,job splitting is allowed so that the power of work stations can be utilized better.The final objective is to minimize the total completion time of all jobs.The results show a significant improvement since the new planning may save more than 60%of lead time compared to the current schedule.The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process.The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries.A practical case study demonstrates the effectiveness of the proposed model.

Analyses of local ingredients and dishes on menus of the Budapest-Middle-Danube touristic region

The background of the study is based on previous studies dealing with the areas of Transdanubia of Hungary. Those works have proved the hypothesis of author that local ingredients and dishes represent minimum role on menus. The question is how similar or different the choice of menus of Budapest-Middle-Danube compared with the previously examined areas of Hungary. There can be another question about the usage of local ingredients available near to the catering units. This piece of work has studied 79 menus of Budapest-Middle-Danube, Hungary. The material was collected from different types of restaurants, small restaurants （vendeglo in Hungarian） and roadside restaurants （csarda in Hungarian）. The method of the work was on the one hand, data collecting, and on the other hand, examination of the differences between the catering units in details： the number of dishes, the right order of dishes, the appearance of local ingredients and dishes, the consideration of seasonality, the usage of different cooking methods and group of dishes. The study reflects the main problems of countryside restaurants in Hungary. Only minimum number of dishes could be connected to local dishes out of more than thousands. All in all, dishes are not as precise and perfect as the ＂old ones＂, so that is why it is important for every expert keep the traditions alive in daily routines and strategic mean too.

Threats and Opportunities Developing Nations of Africa Can Draw From the Foreign Direct Investment From Russia and China

The 21st century has seen the world superpowers,such as Russia and China and multi-lateral agencies encourage backward states to embrace and promote foreign direct investment(FDI)as an instrument to improve domestic production which will lead to economic growth and development.This is premised on the fact that FDI comes with transfer of technology,new and advanced management approaches,technical skills,and access to international markets.However,researchers have shown that occasionally FDI inflows achieve the opposite as they compete for the same markets with local industries and,as a result,they crowd out local industries leading to higher unemployment.Further,research has shown that most international financial crises were caused by rapid withdrawal of FDI.In essence,FDI can have positive and can also have negative effects on economic growth.Against this background,this discussion seeks to outline and look at the threats and opportunities that backward states,such as those in Africa will draw from the 21st century FDI from world superpowers.

Simulation-Based Evaluation for the Impact of Personnel Capability on Software Testing Performance

This study presents a decision making process in three steps of knowledge management for test organization using process simulation and financial analysis. First, project cost assessment of test knowledge management process subjects to different project duration and number of staffs. Two knowledge management simulation models representing experienced personnel with knowledge sharing and inexperienced personnel with internal training respectively are employed to contrast test personnel capability. Second, performance evaluation of software testing process by different personnel capability is conducted to simulate system test using three project metrics, namely, duration, effort cost, and quality. Third, a comparative financial analysis is prepared to determine the best solution by return on investment, payback period, and benefit cost ratio. The results from three stages of finding are discussed to arrive at the final scenario. We provide a case study evaluating how software testing industry needs to build effective test organization with high quality personnel for sustainable development and improvement.

A Water Line Network Failure Application of Network Design Problems

This study investigated a water supply recovery problem involving municipal water service piping. The problem consisted in recovering full service after network failure, in order to rapidly satisfy all urgent citywide demands. The optimal recovery solution was achieved through the application of so-called network design problems （NDPs）, which are a form of combinatorial optimization problem. However, a conventional NDP is not suitable for addressing urgent situations because （1） it does not utilize the non-failure arcs in the network, and （2） it is solely concerned with stable costs such as flow costs. Therefore, to adapt the technique to such urgent situations, the conventional NDP is here modified to deal with the specified water supply problem. In addition, a numerical illustration using the Sendai water network is presented.