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Noting that the gap between individual communication abilities is amplified in the network era, we classify spreaders into super-spreaders and ordinary spreaders by the Pareto principle. In addition, when facing negative information, individuals with higher knowledge levels will recognize it. Based on these, in this study, an improved Ignorants-Hesitant-Super-spreaders-Ordinary spreaders-Wise-Removers (IEASWR) information propagation model was developed. Meanwhile, in view of the gradual saturation, density dependence and nonlinear relationship of the propagation process, the Holling-type II functional response function is introduced to information propagation modeling for the first time making it more realistic. First, the basic reproduction number ℛ 0 is deduced. Then, the global asymptotic stability of the information-free/prevailing equilibria is proved by constructing the Lyapunov function. Furthermore, using the Pontryagin maximum principle, a real-time optimization guidance measure is proposed to achieve the correct guidance of negative information within the expected time. Finally, the validity of the theory is verified through numerical simulation. [ABSTRACT FROM AUTHOR]

In this short paper, we build a simple model where a non-welfarist social welfare function can be transformed into and thus be identical to a welfarist social welfare function in form. Our analysis first suggests that non-welfarist methods of policy assessment can also obey the Pareto principle. More broadly, our analysis is a useful input in thinking rigorously about the formal underpinnings of standard welfare economics. [ABSTRACT FROM AUTHOR]

In the Analytic Hierarchy Process (AHP) the efficient vectors for a pairwise comparison matrix (PC matrix) are based on the principle of Pareto optimal decisions. To infer the efficiency of a vector for a PC matrix we construct a directed Hamiltonian cycle of a certain digraph associated with the PC matrix and the vector. We describe advantages of using this process over using the strong connectivity of the digraph. As an application of our process we find efficient vectors for a PC matrix, A, obtained from a consistent one by perturbing three entries above the main diagonal and the corresponding reciprocal entries, in a way that there is a square submatrix of A of order 2 containing three of the perturbed entries and not containing a diagonal entry of A. For completeness, we include examples showing conditions under which, when deleting a certain entry of an efficient vector for the square matrix A of order n, we have a non-efficient vector for the corresponding square principal submatrix of order n-1 of A. [ABSTRACT FROM AUTHOR]

Value pluralists believe in multiple dimensions of value. What does betterness along a dimension have to do with being better overall? Any systematic answer begins with the Strong Pareto principle: one thing is overall better than another if it is better along one dimension and at least as good along all others. We defend Strong Pareto from recent counterexamples and use our discussion to develop a novel view of dimensions of value, one which puts Strong Pareto on firmer footing. We conclude by defending Dimensionalism, the hypothesis that overall value relations are determined solely by how things compare along value dimensions. These are first steps towards a more systematic value pluralism. [ABSTRACT FROM AUTHOR]

The implications of the repugnant conclusion for consequentialist theories, such as total utilitarianism, have been extensively discussed since the work of Derek Parfit. These discussions make the paradoxes of population ethics depend on welfarist conditions, like the dominance condition (or the Pareto Principle). Thus, one might think that the repugnant conclusion is not a practical problem for deontologists, who deny that we always ought to do what produces the most aggregate welfare. In this study, we offer two impossibility results using what we call the limit aggregation property. This states that when welfaristic and non‐welfaristic considerations conflict, there is a threshold number of persons such that the former can override the latter as long as more people than the threshold number can enjoy increases in well‐being levels. We argue that this property should be accepted even by deontologists, insofar as they do not commit themselves to the implausible absolutist position that fails to assign any moral weight to aggregate welfare. Our results therefore state that any normative population theory that is not absolutist entails (a variant of) the repugnant conclusion or some other implausible conclusion. Therefore, the repugnant conclusion must be taken seriously even by non‐absolutist deontologists, not just consequentialists. [ABSTRACT FROM AUTHOR]

Optimization is getting more and more important due to its application in real engineering problems. Recently, the vibrating particles system algorithm has been developed as an efficient method for mono-objective optimization. However, in multi- and many-objective design problems, the vibrating particles system method is unable to handle simultaneously the conflicting objectives. The second drawback of the vibrating particles system algorithm is the variability of the obtained results at each independent test, due to its inability to balance exploitation and exploration capabilities. To address these issues, this paper proposes an enhanced vibrating particles system algorithm called the many-objective vibrating particles system algorithm. The proposed many-objective vibrating particles system algorithm uses the Pareto principle to store the non-dominated solutions of multiple conflicting functions. Moreover, the implementation of the particle position enhancement mechanism to boost this algorithm’s exploitation and exploration capabilities is another distinctive aspect of the suggested method. A variety of high-dimensional test functions and engineering design problems are used to evaluate the efficiency of the manyobjective vibrating particles system algorithm. The obtained results show that the proposed algorithm outperforms other popular methods in terms of convergence characteristics and global search ability. [ABSTRACT FROM AUTHOR]

It is taken into account that the solution to the problem of software quality provision depends significantly on one of the final stages related to the level of testing of software products, which necessitates the development of alternative approaches to the testing process that would effectively take into account various factors influencing its effectiveness, the complexity of production and organizational conditions, and the requirements regarding the quality of the final product. An informational interpretation of the concept of quality formation of this category is proposed using the tools of decision theory to model alternative and determine the optimal variants of the program testing process. The application of an alternative approach to the selection of the software testing process options based on the method of linear convolution of criteria, the Pareto concept, taking into account the weight preferences of factors influencing the quality of software testing and their share in the formation of alternatives is considered. The theoretical fundamentals of the method of linear convolution of criteria and its application to program testing are presented. Practical implementation of the method is investigated, the initial stage of which is the selection of a Pareto set from the general set of characteristics of software testing, which contains the dominant factors of priority impact on the testing process. The use of the Pareto principle ensured the determination of the optimal set of combinations of test cases that best meet the needs of testing with limited resources. Alternative variants of the software testing process are designed using utility functions and efficiency shares of factors in alternative variants. The matrices of pairwise comparisons are constructed and processed to calculate the refined weight priorities of the factors of the Pareto set and the values of the utility functions of the factors, taking into account their share in the formation of alternatives. The optimal variant of the software testing process among the alternatives is calculated and determined according to the criterion of the maximum value of the combined target functions, which ensures the proper quality of simulation modeling. [ABSTRACT FROM AUTHOR]

This article is devoted to the study of decision-making problems of hydrotreating process control in the production of high-quality gasoline under conditions of scarcity and fuzziness of the initial information, ultimately developing an approach to solve them. A systematic method is proposed that makes it possible to develop a package of mathematical models of a complex of interconnected units of chemical-technological systems based on available information of various types. Using the proposed system method, a package of models of the main interconnected units in which the hydrotreating process took place was developed. A decision-making problem was formulated to control the hydrotreating process in a fuzzy environment based on the developed system of models. By modifying the Pareto principle of optimality for fuzzy conditions, a heuristic method for solving the given decision-making problem was developed to control the hydrotreating process in a fuzzy environment. The novelty of the proposed heuristic method lies in the full use of the collected fuzzy information, which represents the knowledge, intuition and experience of the decision makers and experts. Accordingly, the proposed heuristic decision-making method makes it possible to achieve a high adequacy and efficiency of decisions made when solving production problems in a fuzzy environment. The results obtained were applied in practice to solve decision-making problems for hydrotreating process control at the Atyrau refinery. The results obtained show the advantages of the proposed heuristic method for solving decision-making problems of hydrotreating process control over known methods. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive overview of Branko Milanovic's book "Visions of Inequality," which examines income inequality from a theoretical perspective. Milanovic challenges the neoliberal trend and advocates for state intervention in income distribution. The book explores the theories of economists such as Quesnay, Smith, Marx, and others, highlighting the importance of correct income distribution for economic growth and societal well-being. The text also discusses the perspectives of economists Ricardo, Marx, Pareto, and Kuznets on income distribution and economic growth. The author concludes by emphasizing the need for global analysis and governance to address inequality. [Extracted from the article]

Although Big Deal contracts that provide access rights to all electronic journals published by the publisher initially gained favor with university libraries, some libraries have terminated these contracts owing to increased charges since the 2010s. Consequently, they are faced with the problem of selecting journals for purchase within their limited budgets. This study investigates the factors affecting the number of downloads, representing journal demand, to provide libraries with guidance on journal selection. The download equation for 1485 hybrid journals published by Springer Nature is formulated using ordinary least squares. The results found that 5% and 50% of the 1485 journals generated approximately 30% and 85% of the downloads in 2022, respectively. Downloads are concentrated in fewer journals, although the Pareto principle does not apply to hybrid journals. Demand concentration implies that libraries do not need to maintain access rights to all journals. Recently, a few leading publishers have provided access rights to almost all electronic journals based on transformative agreements aiming to promote open access. Therefore, this study’s findings raise the issue of the rationale for bundling electronic journals in transformative agreements, which is similar to Big Deal. Moreover, the results of the download estimation reveal that hybrid journals with more open access articles, larger citation scores, and longer histories acquire more downloads. These findings indicate that open access accelerates the dissemination of research.

The article focuses on the challenge of accurately forecasting demand in the Fast-Moving Consumer Goods (FMCG) sector. Topics include the 'Animal Farm' segmentation method for categorizing products by demand behavior, the importance of tailored forecasting and management strategies, and the role of Exception-Based Management in optimizing demand planning.

This study aims to dissect the direct effects of digital transformation on key economic and social segments in Ukraine. It underscores the critical role of adaptable digital solutions in sustaining economic viability. The investigation focuses on public service provision, entrepreneurial ventures, infrastructure development, mental health, and societal unity, all examined through the prism of digital resilience. Utilizing a comprehensive methodology involving formalization, abstract reasoning, and the Pareto principle, the research employs ABC analysis and linear scaling to assess and prioritize the impact of various digital aspects in the context of the current war scenario. The findings reveal nuanced impacts across different sectors: Public service dissemination scores a modest 0.32, indicating the need for urgent enhancement. Entrepreneurial activity demonstrates moderate adaptability with a rating of 0.43. Infrastructure reconstruction lags at 0.28, revealing critical vulnerabilities. Mental health recovery (0.56) and social cohesion (0.51) reflect more robust digital integration. The study suggests that prioritizing and investing in digital initiatives, especially in the higher-scoring areas, can provide substantial support for maintaining stability and fostering growth. These actionable insights are particularly relevant for policymakers and stakeholders, emphasizing the immediate and measurable benefits of targeted digital strategies in a wartime setting. [ABSTRACT FROM AUTHOR]

The purpose of this study is to systematically analyze the impact of Industry 4.0 technologies on workforce employability and skills in the South Asian region. The study investigates the driving success factors, challenges, and needed skills by analyzing 48 peer-reviewed articles. The authors searched keywords on the Web of Science database for articles published between 2013 and 2022. The review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA 2020) and pareto principles. The analysis identifies nine critical success factors, such as artificial intelligence, digital skills, and big data analytics, that contribute to Industry 4.0's productivity and efficiency. It also identifies six types of challenges, such as training and development, financial constraints, and regulatory issues that must be addressed to grab maximum potential. In addition, the research categorizes five different skills, including the technical, digital, and social skills that are essential for the evolving labor market. The proposed "Industry 4.0 SEI Framework" provides stakeholders with a comprehensive view of the dynamics of Industry 4.0, thereby facilitating policy and industry strategies. [ABSTRACT FROM AUTHOR]

One of the most used algorithms to solve the fuzzy clustering problem is Fuzzy C-Means; however, one of its main limitations is its high computational complexity. It is known that the efficiency of an algorithm depends, among other factors, on the strategies for its initialization and convergence. In this research, a new convergence strategy is proposed, which is based on the difference of the objective function values, in two consecutive iterations, expressed as a percentage of its value in the next to the last one. Additionally, a new method is proposed to optimize the selection of values of the convergence or stop threshold of the algorithm, which is based on the Pareto principle. To validate our approach, a collection of real datasets was solved, and a significant reduction in the number of iterations was observed, without affecting significantly the solution quality. Based on the proposed method and the experiments carried out, we found it is convenient to use threshold values equal to 0.73 and 0.35 if a decrease in the number of iterations of approximately 75.2% and 64.56%, respectively, is wanted, at the expense of a reduction in solution quality of 2% and 1%, respectively. It is worth mentioning that, as the size of the datasets is increased, the proposed approach tends to obtain better results, and therefore, its use is suggested for datasets found in Big Data and Data Science. [ABSTRACT FROM AUTHOR]

The ABC analysis is an established inventory control technique in the industry wherein the available inventory is divided into three categories. It is similar to the Pareto principle and provides a mechanism to segregate the material into different categories based on their values to the industries. This study presents a novel approach of extending the concept of ABC analysis to the companies visiting the institutes for conducting the campus interview. The primary objective of this study was to enhance the quality and number of placements in the institute by classifying the industries as per the ABC analysis. The data of 268 companies visiting the institute over a period of eight years was documented and analyzed. After careful deliberations, a formula was established to determine the value of each company offering placements. The percentage value obtained for every company was used to segregate the companies into A, B and C categories. For calculations, the study has considered only the number of placements and average package offered by the visiting companies. Thereafter certain initiatives were undertaken by the institute for engaging with the industries. The motive was to enhance the quality of placements and to increase the industry-institute interactions in the institute. It was observed that the implementation of various initiatives led to an increase in the number of placements, average and maximum package. Within a year, the placements increased by 121 while the maximum package increased to 4.6 Lakhs. Pro-active engagement with the 'A' category companies have also resulted in additional benefits such as scholarships for needy students, increase in internships for students and increase in footfall of the industry experts and establishment of Centre of Excellence. The present study is a unique attempt to enhance the quality and number of placements in educational institutions. It represents a logical extension of a simple and commonly used technique in a different domain. This approach can be applied to any educational institute with suitable modifications for increasing the industry-institute interactions. [ABSTRACT FROM AUTHOR]

Considering the trade‐off on the improvement of fuel economy performance and mode transition comfort for multimode hybrid electric vehicles (MMHEV), a bi‐objective trade‐off control strategy is designed based on the equivalent consumption minimization strategy (ECMS) with Pareto method. Aiming at the problem of optimal economy of multimode working area of vehicles, the equivalent fuel consumption cost and torque distribution MAP in different modes are determined based on ECMS. In order to solve the problem of torque instability caused by engine response lag and demand torque fluctuation, the motor torque optimization coefficient (MTOC) is introduced as the control variable by taking advantage of the motor's fast and accurate response to torque, and the genetic algorithm is used to optimize the MTOC, and a smoother torque distribution is obtained. Because there is a coupling relationship between the economy and ride comfort of the system, the trade‐off optimization is carried out using the NSGA‐II algorithm based on Pareto principle. The simulation verification conducted in nonslope and slope conditions, as well as hardware‐in‐the‐loop experiments, demonstrates the effectiveness of the proposed control strategy in managing the trade‐off between economy and ride comfort for the MMHEV. [ABSTRACT FROM AUTHOR]

The subject of this article is endocrinological diseases, namely, the analysis of complications in people with hypothyroidism and hyperthyroidism. It is known that these diseases occur asymptomatically or in a way that may indicate other possible diseases, so people do not suspect what exactly they are suffering from. Later, the diseases develop to the point where complications occur in the body, some of the most dangerous of which are psychological disorders: depression, mania, aggression, etc. Therefore, the aim of this work is to develop methods for predicting the occurrence of neurological deterioration in people who have already been diagnosed with endocrinological diseases. The article solves the problem of choosing the best models for predicting the occurrence of psychological disorders in people with endocrinological problems. Machine learning methods that are widespread in the medical field were analyzed and one of them was chosen that more optimally solves all the tasks of the task. The selection of criteria took into account potential problems with medical and psychological data. The method used was linear additive convolution, which is used to select the best alternatives according to the results, with the Pareto principle, which aims to exclude less suitable alternatives because all the features have lower values than in other options. For the experiment, all features were converted into quantitative ones to calculate convolution values. The evaluation criteria are given in the paper. The following results were obtained: the forecasting model in further study of this problem will be a random forest. Conclusions: the forecasting methods were studied and a more optimal model was chosen using linear additive convolution, namely, the random forest algorithm, its advantages and disadvantages were considered. A more detailed analysis of its development will be presented in the following articles. A mathematical description of the chosen forecasting method is provided, which includes potential ways of implementation and steps for building an algorithm for one of these methods.

Unsheltered persons experiencing homelessness (PEH) encounter many kinds of harm, and it is often unclear what data sources can be leveraged to connect PEH to services. The present study contributes by first examining crime near encampments, and then determining the utility of police tickets to aid outreach to PEH who camp in public spaces. Using Boulder Colorado and Boulder Colorado Police Department data from November 2021 to October 2022, location quotients suggest that crime is approximately three times more concentrated near encampments identified by residents, and seven times more concentrated near encampments identified with police data. Police tickets for outdoor camping were concentrated among a small number of all ticketed persons. People ticketed most camped in the same area multiple times, and had camps established within a small number of geographically proximate locations. Results suggest outreach efforts to specific PEH could be enhanced with police camping ticket geographies, and this supports a coordinated response to homelessness. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In "An Intrapersonal Addition Paradox," Jacob Nebel provides a novel intrapersonal argument for the Repugnant Conclusion. The most controversial premise of Nebel's argument is the "Probable Addition Principle," on which it is better for individuals to receive additional chances of existence with a life worth living. I provide an alternative intrapersonal argument for the Repugnant Conclusion which does not assume the Probable Addition Principle. I also show that Pareto principles alone, when conjoined with very minimal principles of prudence, imply a version of the Repugnant Conclusion. [ABSTRACT FROM AUTHOR]

Soon after the publication of A Theory of Justice, Rawls found himself swept up in the huge wave of enthusiasm his work had elicited from economists, while also having to respond to major critiques. Among the latter we find a largely unknown piece by Samuelson, a giant in the world of economics, devoted to a central question of Rawls's framework, namely the maximin, which he supplemented with several virulent letters strongly attacking Rawls's notions of justice or fairness. A few years later, in a paper written in response to Arrow's 1985 Tanner Lectures, Samuelson would dedicate a section to "Rawls's Gratuitism," caustically remarking that "if something true by definition is a 'truism,' then we may perhaps call something gratuitous a 'gratuitism'." Focusing on the dialogue between Rawls and Samuelson after A Theory of Justice, paying special attention to their correspondence, the paper aims to shed light on the reasons for Samuelson's furore against Rawls's maximin, and to draw some lessons concerning the complex dialogue between Rawls and the economists, both as regards the attitude of the economists towards Rawls and as regards Rawls's position towards economists and the economy. [ABSTRACT FROM AUTHOR]

The article is devoted to the analysis of the structure of Ukrainian exports based on the use of the ABC method. Its purpose is to assess the prospects of various product groups in the future model of Ukraine’s foreign trade based on the use of microeconomic analysis methods. The article substantiates methodical approaches to the division of individual goods of positions A, B and C depending on their specific weight in the total income from exports. Based on the data for 2021 (the last pre-war year of export growth), 5 commodity items that provide more than 75% of export income (group A) and 10 commodity items that make up more than 90% of exports (A+B) were selected. It is these positions that should be the focus of the state’s attention on the formation of export policy. The performed grouping was used to analyze the trends of changes in the export structure over the last 10 years. It was found that in group A, two product positions (chapters XV and XVI according to the Ukrainian classification of goods of foreign economic activity - UCG FEA) have a steady tendency to weaken positions, as well as two other positions (chapters II and III), on the contrary, constantly increase their specific weight in total exports. This phenomenon is qualified as deindustrialization of Ukrainian exports. In order to identify the growth potential of Ukrainian exports and justify the basic principles of the formation of the post-war foreign economic strategy, the ABC analysis was supplemented by the calculation of the elasticity coefficient for individual commodity items, which is defined as the ratio of the percentage change in income from the corresponding commodity item to the percentage change in the total value of commodity exports for the corresponding year. The analysis of the obtained results showed that goods from section XV (Precious metals and products thereof) are characterized by high elasticity both in periods of growth of total exports and its reduction. This gave reason to assume that the main factors of the dynamics of this part of exports lie outside the country’s borders, and therefore it should not form the basis of foreign trade strategy in post-war Ukraine. On the contrary, goods from sections I, II, III and IV (goods related to agro-industrial complexes) have low elasticity, their export is characterized by stability. However, due to these reasons, they also do not have significant growth potential, and therefore should be considered not as a driver, but as a stabilizer of foreign trade. The goods of group XVI (Machines, equipment and mechanisms; electrical equipment) have the greatest potential. Therefore, in the post-war strategy of foreign economic activity, a special place should be given to the stimulation of both the production and export of these goods, as well as the creation of special conditions for investors (domestic and foreign) who are ready to invest in export-oriented machine-building enterprises.

The article provides strategies for building and presenting a budget that garners Executive Board approval, emphasizing the importance of understanding executive priorities and preparing a compelling case. Topics discussed include establishing a cybersecurity steering committee, documenting and reporting progress continuously, and presenting budget options with clear risk assessments and benchmarking data.

Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm. [ABSTRACT FROM AUTHOR]

The Pareto principle, or 20:80 rule, describes resource distribution in stable communities whereby 20% of community members acquire 80% of a key resource. In this Burning Question, we ask to what extent the Pareto principle applies to the acquisition of limiting resources in stable microbial communities; how it may contribute to our understanding of microbial interactions, microbial community exploration of evolutionary space, and microbial community dysbiosis; and whether it can serve as a benchmark of microbial community stability and functional optimality? [ABSTRACT FROM AUTHOR]

In this paper, we study intertemporal social welfare evaluations when agents have heterogeneous preferences that are interpersonally noncomparable. We first show that even if all agents share the same preferences, there is a conflict between the axioms of Pareto principle, time consistency, and equity requiring society to reduce inequality regardless of the past. We argue that responsibility for past choices should be taken into account and, thus, the equity axiom is not compelling. Then we introduce another form of equity that takes the past into consideration and is compatible with time consistency. Using this form of equity and time consistency, we characterize maximin and leximin social welfare criteria that are history‐dependent. [ABSTRACT FROM AUTHOR]

When faced with a problem to be solved by running designed experiments we should first think of what strategy should be used for the experimentation. One aspect of successful experimental strategy is how to use screening experiments in practice. In this article the authors share their combined experiences of more than 90 years in using designed experiments in which screening experiments are an integral part of the strategy. Along the way the benefits of running screening experiments and why they work are discussed. Topics such as the Pareto principle applied to experimentation and the robustness of screening designs to assumptions are addressed. Case studies, tips, traps, and guidelines are provided. What emerges is a holistic approach to experimentation that results in practicing scientists and engineers getting the right data in the right amount at the right time. [ABSTRACT FROM AUTHOR]

The number of detected bugs by software test data determines the efficacy of the test data. One of the most important topics in software engineering is software mutation testing, which is used to evaluate the efficiency of software test methods. The syntactical modifications are made to the program source code to make buggy (mutated) programs, and then the resulting mutants (buggy programs) along with the original programs are executed with the test data. Mutation testing has several drawbacks, one of which is its high computational cost. Higher execution time of mutation tests is a challenging problem in the software engineering field. The major goal of this work is to reduce the time and cost of mutation testing. Mutants are inserted in each instruction of a program using typical mutation procedures and tools. Meanwhile, in a real-world program, the likelihood of a bug occurrence in the simple and non-bug-prone sections of a program is quite low. According to the 80–20 rule, 80 percent of a program's bugs are discovered in 20% of its fault-prone code. The first stage of the suggested solution uses a discretized and modified version of the Forrest optimization algorithm to identify the program's most bug-prone paths; the second stage injects mutants just in the identified bug-prone instructions and data. In the second step, the mutation operators are only injected into the identified instructions and data that are bug-prone. Studies on standard benchmark programs have shown that the proposed method reduces about 27.63% of the created mutants when compared to existing techniques. If the number of produced mutants is decreased, the cost of mutation testing will also decrease. The proposed method is independent of the platform and testing tool. The results of the experiments confirm that the use of the proposed method in each testing tool such as Mujava, Muclipse, Jester, and Jumble makes a considerable mutant reduction. [ABSTRACT FROM AUTHOR]

Lung cancer has is highly prevalent worldwide and is the leading cause of cancer-related deaths. In the clinic, a biopsy sample of the lesion is taken to determine whether a lung mass is benign or malignant. CT-guided percutaneous lung biopsy is a minimally invasive intervention and is commonly used to diagnose lung cancer. Path planning before surgery plays a crucial role in percutaneous lung biopsy. Traditionally, path planning for lung biopsy is performed manually by physicians based on CT images of the patient, which demands knowledge and extensive clinical experience of the operating physicians. In this work, a computer-assisted path planning system for percutaneous lung biopsy is proposed based on clinical objectives. Five constraints are presented to remove unqualified skin entry points and determine a feasible entry region based on clinical criteria. Inspired by the Pareto principle and the concept of geometric weighting, the loose-Pareto and adaptive heptagonal optimization (LPHO) method is introduced to plan the optimal puncture path. CT images of 29 patients were collected from Zigong First People's Hospital. Retrospective experiments and test experiments were conducted to evaluate the effectiveness of the algorithm. The planning paths obtained using the proposed method were clinically feasible for 89.7% of patients, demonstrating the applicability and robustness of the system in surgical path planning for lung biopsy. [ABSTRACT FROM AUTHOR]

This paper provides an axiomatic analysis of sufficientarian social evaluation. Sufficientarianism has emerged as an increasingly important notion of distributive justice. We propose a class of principles that we label generalized critical-level sufficientarian orderings. The distinguishing feature of our new class is that its members exhibit constant critical levels of well-being that are allowed to differ from the threshold of sufficiency. Our basic axiom assigns priority to those below the threshold, a property that is shared by numerous other sufficientarian approaches. When combined with the well-known strong Pareto principle and the assumption that there be a constant critical level, the axiom implies that the critical level cannot be below the threshold. The main results of the paper are characterizations of our new class and an important subclass. As a final observation, we identify the generalized critical-level sufficientarian orderings that permit us to avoid the repugnant conclusion and the sadistic conclusion, which are known as two fundamental challenges in population ethics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Business & Projects / Revista de Gestão e Projetos is the property of Revista de Gestao e Projetos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multi‐actor multi‐criteria analysis is a group decision‐making framework that allows multiple stakeholder groups to be involved in the decision‐making process, facilitating the understanding of the points of consensus and conflict among the stakeholder groups. Carefully selecting suitable criteria is important as they illustrate the possibly divergent priorities of the respective stakeholder group, and overlooking important criteria can lead to erroneous outcomes. Furthermore, the number of criteria needs specific consideration, as a too large number poses problems for human cognition, but a too small number inaccurately represents the stakeholder's interest. In stakeholder groups with many members, such as those representing citizens, defining a criteria set is likely to be even more complicated. Currently, there is no formal guideline to assist facilitators in defining these criteria sets. In this paper, we propose a novel framework for criteria preprocessing with stakeholder involvement that includes a guideline for firstly selecting criteria into a tentative list and secondly selecting the final criteria set. It provides a procedure on how to determine criteria considering the priorities of stakeholder groups with regard to the context. As a final step, we propose a mathematical model for selecting a number of criteria that are both cognitively manageable and representative for the participants' priorities. Based on the principles of the Pareto analysis, as well as the cognitive judgment theory "magic number seven plus or minus two", a recommendation list of the criteria is generated. It prevents key criteria from being omitted while at the same time limiting the overall number of criteria. This framework is applied to a social decision‐making case for construction logistics, and the results are compared with the conventional approach of criteria definition. [ABSTRACT FROM AUTHOR]

At present, the COVID-19 epidemic is still spreading at home and abroad, and the foreign exchange market is highly volatile. From financial institutions to individual investors, foreign exchange asset allocation has become important contents worthy of attention. However, most intelligent optimization algorithms (hereinafter IOAS) adopt the existing data and ignore the forecasted one in the foreign exchange portfolio allocation, which will result in a huge difference between portfolio allocation and actual demand; at the same time, many IOAS are less adaptable and have lower optimization ability in portfolio problems. To solve the aforementioned problems, this paper first proposed a DETS based on hybrid tabu search and differential evolution algorithms (DEAs), which has excellent optimization ability. Subsequently, the DETS algorithm was applied to support vector machine (SVM) model. Experiments show that, compared with other algorithms, the MAE and RMSE obtained by using DETS optimization parameters are reduced by at least 3.79 and 1.47%, while the CTR is improved by at least 2.19%. Then combined with the DETS algorithm and Pareto sorting theory, an algorithm suitable for multi-objective optimization was further proposed, named NSDE-TS. Finally, by applying NSDE-TS algorithm, the optimal foreign exchange portfolio is acquired. The empirical analysis shows that the Pareto front obtained by this algorithm is better than that of NSGA-II. Since the lower the uniformity index and convergence index, the stronger the optimization performance of the corresponding algorithm, compared with NSGA-II, its uniformity and convergence index decreased by 15.7 and 39.6%. [ABSTRACT FROM AUTHOR]