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In this paper, lower-order mathematical thinking skills within finance were studied from the viewpoint of financial employees in the Iranian Bank of Industry and Mine. To conduct this research, a questionnaire was developed after reviewing lower-order mathematical thinking skills in finance. In accordance with the revised Bloom's taxonomy, the skills considered in the questionnaire were "remembering mathematics in finance", "understanding mathematics in finance", and "applying mathematics in finance". In order to develop the questionnaire, we conducted interviews with employees and scholars, then a suitable sample familiar with mathematics and finance, consisting of 141 bank employees, was studied. Descriptive and inferential statistics were used for data analysis. Our findings show a hierarchical relationship between the first three mathematical thinking skills in finance, which confirms the revised Bloom's taxonomy. In addition, the attitudes of participants were positive concerning the importance of these skills. Participants believed that, in order to achieve proper functioning, it is essential to improve the skills of financial employees. The results were analysed with a T-test and ANOVA. This showed that the gender, position, experience, department, degree, and field of study of participants did not affect their attitudes. This research indicates that, for the successful utilisation of skills, it is essential to form an effective relationship between mathematical science and its practical application in the banking world. It is recommended to hold on-the-job training courses for financial employees in banks to empower them to use their computational skills.

The purpose of this article is to identify the factors which affect students' creative thinking in problem solving. The research which was performed was quasi-experimental. It used one experimental group and two control groups from three second-grade high school classes. They received either traditional, active or heuristic problem-solving training. In the traditional method, no teaching aids were used, and the material was taught traditionally. In the active method, 34 students were trained using teaching aids, such as coloured balls, and handicrafts, such as shirts and pants. They were trained to use the teaching aids in order to solve problems. In the heuristic method, the researcher introduced problems which the students thought about. After a few sessions, I commenced teaching the procedures--in each session, two heuristics were introduced. They learned to use these strategies to solve problems. Creativity and mathematics tests were given to the students, both before and after they received training in problem-solving. The findings show that parents' education (but not gender) and parents' job type affected students' ability to think creatively. Students' creative thinking was improved by active training, but not by the heuristic or traditional methods.

Background: Rare diseases, characterized by low prevalence and high complexity, pose significant challenges to health systems due to the uncertainty surrounding the best diagnostic methods and availability of effective treatments. Objectives: This study aimed to introduce new methods for prioritizing orphan drugs, with a pilot application to hemophilia, spinal muscular atrophy (SMA), cystic fibrosis (CF), and multiple sclerosis (MS). Methods: This quantitative research, conducted at Iran's National Institute for Health Research from 2021 to 2023, employs multi-criteria decision-making models (MCDA) to evaluate the efficacy of health care technologies for "rare and hard to cure" diseases. A preliminary model was developed based on a pilot selection of seven medications, for which comprehensive data was extracted. The clinical efficacy was assessed using quality adjusted life years (QALY) as a metric. The model design was grounded on three critical factors: The annual cost of intervention per individual, the annual number of eligible patients for intervention, and the Proportion of expenses covered by the governmental budget. Additionally, the model incorporated various constraints and a regulatory coefficient, denoted as "w," to enhance its robustness. Results: By running the model, the coverage of selected medications through model optimization, revealing the following percentages: Alemtuzumab (30%), ocrelizumab (30%), emicizumab (6%), dornase alfa (29%), tobramycin (2%), and spinraza (0.02%). Additionally, the corresponding monetary coverage in Iranian Rials is reported as follows: Alemtuzumab (27,144,000,000 IRR), ocrelizumab (109,645,200,000 IRR), emicizumab (17,360,490,000 IRR), dornase alfa (43,350,930,000 IRR), tobramycin (1,268,505,000 IRR), and spinraza (350,000,000 IRR). Conclusions: This model has tried to solve the shortcomings of the existing models regarding the prioritization of orphan drugs by combining different factors to improve access to essential treatments for rare diseases, although it can be generally asserted that no unique model can answer all policymakers' questions regarding budget allocation of rare diseases and orphan products. [ABSTRACT FROM AUTHOR]

We developed a DEA-based resource re-allocation model based on environmental DEA technology for organizations with a central decision-making environment. The proposed model considered a weak disposability axiom for undesirable outputs and combined Data Envelopment Analysis (DEA) with Multiple-Objective Programing (MOP). The objective was to find the appropriate re-allocation model in order to save energy and reduce environmental pollution, so that the next steps could be taken toward improvement. Given that reducing the inputs and outputs of inefficient units is sometimes not achievable and does not seem logical, for the reduction in the values to be logical and achievable, we divided the Decision-Making Units (DMUs) into different levels of efficient frontier using the context-dependent DEA technique. For this purpose, the model was designed to move the DMUs from the current frontier to the efficient frontier of the previous layer, which has better efficiency conditions, or keep them on their own frontier. In addition, the opinion of the central decision maker regarding the amount of reduction in the inputs and outputs was expressed using Goal Programing (GP) in a way that does not make the model infeasible. By implementing the model in 8 regions of the world, suggestions were made regarding the amounts of energy saving and CO2 pollution reduction based on the conditions determined by the central decision maker aiming improve the efficiency of inefficient units in the next step. [ABSTRACT FROM AUTHOR]

Since companies are inclined to implement lean production, researchers have proposed a number of fundamental success factors to facilitate the implementation of this production approach. This study analyzes the critical success factors (CSFs) in lean production extracted from 14 review studies. The interpretive structural modeling approach is utilized to analyze the impact of these critical success factors on one another. The aim is to enhance insights into lean production and facilitate informed decision-making. In this article, a seven-tiered model is presented. According to the conceptual model of success factors in lean production, leadership is positioned at the base of the model and serves as the origin for other factors. It should be regarded as the foremost critical success factor in lean production. When establishing lean production systems, organizations and senior managers should focus on higher levels and critical success factors that underlie the model. Subsequently, nonfuzzy and fuzzy driving and dependence power analyses were conducted that the fuzzy matrix cross-reference multiplication applied to a classification (MICMAC) analysis provides deeper insights into the analysis of driving and dependence power. The fuzzy matrix cross-reference multiplication applied to a classification analysis helped identify some key factors that are highly effective for successfully implementing lean manufacturing. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to present a new approach for measuring the performance of n-stage series systems in the presence of uncertain data, which are two challenging issues in evaluating the efficiency of Decision-Making Units (DMUs) using traditional Data Envelopment Analysis (DEA) models. By using a Network DEA model and its dual, as well as using Bertsimas et al.'s robustness technique, two Robust Network DEA have been presented. These models can display a range of DMUs performance with appropriate accuracy. Proposed models were used to determine the efficiency range of Iranian dairy companies' supply chain with three stages. The results show that the proposed models are applicable and effective. Total efficiency bounds are obtained with percentage deviations of 20%, 10% and 5%. The lower bounds have relative errors of 0.39, 0.23 and 0.12 and a correlation coefficient of more than 97%, and the upper bounds have relative error of 1.1, 0.84 and 0.62 and a correlation coefficient of about 90%. Therefore, the proposed model for calculating the lower bound is more accurate. The calculation of the efficiency bounds of the sub-stages also confirms this issue. Finally, the obtained results have been compared with the values obtained through a fuzzy three-stage DEA model, our results have a higher correlation coefficient and more accurate upper bounds. [ABSTRACT FROM AUTHOR]

The data envelopment analysis method is commonly used to measure efficiency. An estimate of the relative efficiency of this model is derived by calculating the ratio between inputs and outputs. Data envelopment analysis models can also be applied to network structures due to the extension of these models. Supply Chain Management (SCM) is a novel approach that governed production management in recent years. In complex and dynamic environments, the petrochemical industry requires an investigation system similar to those used by other organizations to inform about its activity's desirability, especially in complex and dynamic environments. This research focused on the petrochemical company supply chain. Laboratory studies, experts, and visits to petrochemical sites were used to identify production processes and determine indicators. After that, they were evaluated with an envelopment model and a coefficient corresponding to the identified petrochemical supply chain structure. The aggregate and componentwise efficiency of the studied units in petrochemical were also examined from 2016 to 2019. [ABSTRACT FROM AUTHOR]

In this paper, while taking into account the cooperative relationships between units, the problem of revenue allocation is considered as a coalitional game. In order for the allocation to be equitable, by relying on the concept of DEA efficiency, a new characteristic function is presented, and then using the concept of the Shapley value, which is a wellrecognized concept in coalitional game theory, a unique solution is obtained for the revenue allocation problem. And finally, to evaluate the equitability of the performed revenue allocation, the Gini coefficient is utilized. A comparison of the Gini coefficient obtained for our method with those of some existing methods showed that our method is more equitable than the previous ones. This demonstrates how impactful the wise and accurate selection of the characteristic function is in the equitability of the results. [ABSTRACT FROM AUTHOR]

Purpose: The current paper tries to determine the type of returns to scale in a decision-making unit under the condition that integer-valued inputs or outputs are present. Methodology: This paper introduces radial models for determining the value and type of Returns to Scale (RTS) in 4 scenarios, including single integer-valued input -- single real output (scenario one), mixed inputs -- exclusively real outputs (scenario two), exclusively integer-valued inputs -- exclusively real outputs (scenario three), and exclusively integer-valued inputs -- exclusively integer-valued outputs (scenario four); in each scenario, the values of the left RTS and right RTS are determined, and the RTS type is then determined on that basis. Finally, by presenting three examples based on two scenarios, namely single integervalued input -- single real output and single integer-valued input -- single integer-valued output, the new method is compared with previous methods using GAMS software, and the conclusions are provided. Findings: The type of returns to scale differs when integer-valued inputs or outputs are present as compared with the case where the inputs and outputs are assumed to have real values. Originality/Value: This study focuses on the value and type of returns to scale for integer-valued data. For this purpose, returns to scale was modeled in 4 scenarios using input-oriented radial models, and in the fourth scenario (exclusively integervalued inputs -- exclusively integer-valued outputs), the modeling was carried out for output orientation as well. The existence of a difference between the results produced by our proposed model and those of the classical model was demonstrated through two examples, one using hypothetical data and the other real-world data. [ABSTRACT FROM AUTHOR]