An evaluation of technical and environmental complexity factors for improving use case points estimation

This paper presents a proposed method for improving the prediction ability of the Use Case Points method. Our main goal is to use the Least Absolute Shrinkage and Selection Operator Regression methods to find out which of the technical and environmental complexity factors significantly affect the accuracy of the Use Case Points method. Two regression models were used to calculate the selected significant variables. The results of several evaluation measures show that the proposed estimation method ability is better than the original Use Case Points method. The Sum of Squared Error of the proposed method is better than the results obtained by the original one. The study also enables project managers to understand how to assess the technical and environmental complexity factors better - since they do have an important impact on effort estimation. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

AdamOptimizer for the optimisation of Use Case Points estimation

Use Case Points is considered to be one of the most popular methods to estimate the size of a developed software project. Many approaches have been proposed to optimise Use Case Points. The Algorithmic Optimisation Method uses the Multiple Least Squares method to improve the accuracy of Use Case Points by finding optimal coefficient regressions, based on the historical data. This paper aims to propose a new approach to optimise the Use Case Points method based on Gradient Descent with the support of the TensorFlow package. The significance of its purpose is to conduct a new approach that might lead to more accurate prediction than that of the Use Case Points and the Algorithmic Optimisation Method. As a result, this new approach outweighs both the Use Case Points and the Algorithmic Optimisation Methods. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

Состояние провоспалительного цитокинового звена у больных с нестабильной стенокарадией и сахарным диабетом 2-го типа в зависимости от функционального класса хронической сердечной недостаточности

Проанализировано состояние провоспалительного звена цитокинов у больных с нестабильной стенокардией (НС) и сопутствующим сахарным диабетом (СД) 2−го типа в зависимости от функционального класса хронической сердечной недостаточности (ХСН). Нарастание проявлений сердечной декомпенсации у больных с НС и СД 2−го типа ассоциируется с высокой активностью провоспалительного цитокинового звена, представленного фактором некроза опухолей−α и интерлейкином−6. Повышение функционального класса ХСН характеризуется увеличением инсулинорезистентности у больных с НС и СД 2−го типа.Проаналізовано стан прозапальної ланки цитокінів у хворих із нестабільною стенокардією (НС) та супутнім цукровим діабетом (ЦД) 2−го типу залежно від функціонального класу хронічної серцевої недостатності (ХСН). Наростання проявів серцевої декомпенсації у хворих із НС та СД 2−го типу асоціюється з високою активністю прозапальної цитокінової ланки, представленої фактором некрозу пухлин−α та інтерлейкіном−6. Підвищення функціонального класу ХСН характеризується зростанням інсулінорезистентності у хворих із НС та ЦД 2−го типу.The state of pro−inflammatory cytokines in patients with unstable angina (UA) and associated type 2 diabetes mellitus (DM) was analyzed depending on the functional class of chronic heart failure (CHF). The increase in manifestations of cardiac decompensation in patients with UA and type 2 DM is associated with high activity of pro−inflammatory cytokine level represented by tumor necrosis factor−β and interleukin−6. Increase of functional class of CHF is characterized by increased insulin resistance in patients with UA and type 2 DM

INVESTIGATING THE EXPERIENCES OF STUDENTS WITH DISABILITIES WITH E-LEARNING DURING THE COVID-19 PANDEMIC IN VIETNAMESE HIGHER EDUCATION

This study uses a mixed-methods approach to investigate the experiences of Vietnamese university students with disabilities (visual/mobility impairments) with e-learning as a consequence of emergency remote teaching during the COVID-19 pandemic. An analysis of the ideas of 20 surveyed students with disabilities at eight universities in Ho Chi Minh City and six students interviewed afterward shows that students can change their study habits to adapt to e-learning and to enjoy this model of learning. However, the participants revealed that they also want to experience face-to-face learning so that they can interact with their lecturers and peers more effectively and in more diverse ways, as well as assimilate lectures more easily. Furthermore, the research shows that various adjustments should be made by system designers, universities, and lecturers to make e-learning friendlier to disabled students. The recommended adjustments include designing easy-to-use learning tools and platforms, providing lecturers with the necessary tools and facilities to design lessons appropriate for all students, providing psychological and technical support for disabled students, choosing user-friendly learning applications and platforms, providing students with suitable learning resources, and modifying testing and assessment methods

A new approach to calibrating functional complexity weight in software development effort estimation

Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.RVO/FAI/2021/002Faculty of Applied Informatics, Tomas Bata University in Zlin [RVO/FAI/2021/002

Parametric software effort estimation based on optimizing correction factors and multiple linear regression

Context: Effort estimation is one of the essential phases that must be accurately predicted in the early stage of software project development. Currently, solving problems that affect the estimation accuracy of Use Case Points-based methods is still a challenge to be addressed. Objective: This paper proposes a parametric software effort estimation model based on Optimizing Correction Factors and Multiple Regression Models to minimize the estimation error and the influence of unsystematic noise, which has not been considered in previous studies. The proposed method takes advantage of the Least Squared Regression models and Multiple Linear Regression models on the Use Case Points-based elements. Method: We have conducted experimental research to evaluate the estimation accuracy of the proposed method and compare it with three previous related methods, i.e., 1) the baseline estimation method – Use Case Points, 2) Optimizing Correction Factors, and 3) Algorithmic Optimization Method. Experiments were performed on datasets (Dataset D1, Dataset D2, and Dataset D3). The estimation accuracy of the methods was analysed by applying various unbiased evaluation criteria and statistical tests. Results: The results proved that the proposed method outperformed the other methods in improving estimation accuracy. Statistically, the results proved to be significantly superior to the three compared methods based on all tested datasets. Conclusion: Based on our obtained results, the proposed method has a high estimation capability and is considered a helpful method for project managers during the estimation phase. The correction factors are considered in the estimation process. AuthorFaculty of Applied Informatics, Tomas Bata University in Zlin [IGA/CebiaTech/2021/001, RO30216002025

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Accurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K-nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points-based single methods, optimization correction factors-based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p-value for the t-test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.Faculty of Applied Informatics, Tomas Bata University, (IGA/CebiaTech/2022/001, RVO/FAI/2021/002)Tomas Bata University in Zlin [RVO/FAI/2021/002, IGA/CebiaTech/2022/001

Economics Analysis of Different Aquaculture Systems in Coastal Buffer Zones of Protected Areas: a Case Study in Xuan Thuy National Park, Vietnam

peer reviewedThe paper aims to achieve two goals. First, it highlights the differences of aquaculture systems in the buffer zones of Xuan Thuy national park, Vietnam. Second, it serves as an assessment of the economic performance and factors that influence net returns of the systems. A formal survey was used to collect relevant data from 138 farmers in intensive shrimp (ISH) and integrated aquaculture-mangrove (IAM) farming systems. Results demonstrated that ISH produced a total 1,017 million Vietnam Dong (mil.VND) value of output per hectare annually which was more than 32.99 mil. VND from IAM. ISH carried a much higher cost of production (695.39 million VND/ha/year) than IAM (13.29 million VND), and ISH obtained relative higher net returns (321.17 million VND) than IAM (19.70 million VND). Ordinal least square (OLS) regression was used to assess relationships between social-economic-environmental factors on the net returns. Maintaining more forestry coverage and reduce the negative effects of polluted water from surrounding rivers are important to achieve greater income of IAM production. Meanwhile, stimulating formal and informal knowledge together with increasing farmers' power in bargaining prices simultaneously reducing negative effects of polluted water, disease occurrence and production costscan help to improve the net returns of ISH production. The expanded analysis is necessary to render value contributions to ensure farmers receive a higher level of economic returns while conserving the natural ecosystem for the protected areas

In vitro antioxidant activity and bioactive compounds from Calocybe indica

Nowadays, the use of mushrooms in medicine is ubiquitous and has achieved particular success. The antioxidants in mushrooms can deactivate free radicals. This study assesses the antioxidant potential of mushroom Calocybe indica with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical and 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) radical scavenging methods and the total antioxidant capacity. The mushroom’s ethanol extract exhibits acceptable activity with a low IC50 value (240.11 μg/mL), approximately 2.9 times lower than that of the mushroom Ophiocordyceps sobolifera extract. The ABTS scavenging rate of the extract is around 60% at 500 µg/mL, and the total antioxidant capacity is equivalent to 64.94 ± 1.03 mg of GA/g or 77.42 ± 0.42 μmol of AS/g.  The total phenolics, flavonoids, polysaccharides, and triterpenoids are equivalent to 29.33 ± 0.16 mg of GAE/g, 17.84 ± 0.11 mg of QUE/g (5.04 ± 0.04%), and 4.96 ± 0.04 mg of oleanolic acid/g, respectively. Specifically, the total triterpenoid content has been reported for the first time. The mushroom can have potential biomedical applications