Generally speaking, the severe financial constraints of the municipalities on the one hand, and the inefficiency of state investment companies on the other hand, affirm the necessity of private sector participation in the efficiency of providing services to cities. The performance of the private sector in comparison with the public sector has its own properties and characteristics, the most important of which are high performance and access to diverse resources. Valuable international experience in the field of municipalities’ utilization of private sector capacities to implement urban services projects confirms this issue. The present study was conducted aiming to apply fuzzy multi-criteria decision-making technique (FMCDM) in formulating and prioritizing strategies for developing economic participation in urban management of Ahvaz Metropolis. The main research approach was developmental-applied, descriptive, analytical, and survey research. Based on the SWOT analysis, identifying and evaluating the current situation were done and then, according to the identified criteria, four strategies were developed. Finally, using FDEMATEL technique, strategies were prioritized. The research results show that the strategies such as doing pathological study in Ahvaz municipality investment; designing and providing investment packages; increasing the positive interaction with the executive and necessary devices with the participation of other interested organizations for the promotion of services and performing tasks based on laws, regulations and guidelines delegated to the municipality; forecasting the incentive system to reduce the risk of projects; providing Cadastre and GIS; designing a database of investors and urban projects; setting up an in-vestment facilitation team with the participation of departments related to the municipality; and identifying regular investment capacities related to inherent duties of the municipality are among the most important strategies in developing economic participation in Ahwaz Metropolis.

This research aims to enhance portfolio selection by integrating machine learning regression algorithms for predicting stock returns with various risk measures. These measures include mean-value-at-risk (VaR) variance (Var), semi-variance mean-absolute-deviation (MAD) and conditional value-at-risk (C-VaR). Addressing gaps in existing literature. Traditional methods lack adaptability to dynamic market conditions. We propose a hybrid approach optimized by genetic algorithms. The study employs multiple machine learning models. These include Random Forest, AdaBoost XGBoost, Support Vector Machine Regression (SVR) K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN). These models are used to forecast stock returns. Utilizing monthly data from the Tehran Stock Exchange, the results indicate that the genetic algorithm prediction model combined with mean-VaR, Var semi-variance and MAD, produces the most efficient portfolios. These portfolios offer superior returns with minimized risk compared to other models. This hybrid strategy provides a robust and efficient method for investors aiming to optimize returns while managing risk effectively. To implement this approach successfully it is crucial to balance investments. This involves both traditional and alternative asset classes, ensuring diversification. It also capitalizes on market opportunities. Regular review and adjustment of fund allocation are essential. Maintain an optimized strategy for maximum returns and minimal risk.

This article introduces a comprehensive analysis of 20 leading companies, scrutinized through their financial metrics across various sectors. By deploying multi-criteria decision-making (MCDM) techniques, we aim to offer investors a clear and objective perspective on which companies stand out as the best investment options. Among the MCDM techniques, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized, renowned for its efficiency in handling complex decision-making scenarios which is conducted by two clauses. 1) Implementing TOPSIS with assigning equal weights and same share to every chosen metrics as criteria and 2) employ BWM (Best Worst Method) to calculate these weights base on their significance and relevancy to the prosses of ranking. According to the Result gained from the computation, ranks 1 to 5 belong to the similar companies with both assumptions which are Ford Motor Co, BP plc, Tesla Inc, General Motors Co and Exxon Mobil Corp. The consistency in rankings across two different weighting assumptions highlights the robustness of the criteria used, ensuring stable and reliable outcomes. This enhances the credibility of the findings, making them more trustworthy and citable for those who seek reliable and robust methodologies for informed investment decisions.

In this report, novel chromium incorporated fluorapatite encapsulated iron oxide (γ-Fe2O3@FAp@Cr) nanocatalyst was synthesized and characterized by FT-IR, TEM, SEM, XRD and EDX techniques. The catalyst was used in the synthesis of various derivatives of pyrazole-triazole hybrids via the reaction of thiosemicarbazide or semicarbaside and pyrazolecarbaldehydes at room temperature with excellent yields and short reaction times. The protocol was also used in the synthesis of bis-triazoles in high yield and reasonable reaction time. The nanocatalyst was comfortably separated from the reaction mixture by an external magnet and was reused in six consecutive cycles without any remarkable changes in its catalytic performance.

Portfolio optimization is a widely recognized strategy for investing that involves selecting a combination of assets that offers the optimal balance between potential gains and volatility. Traditional portfolio optimization typically focuses on a single period, considering only the current market conditions. However, multi-period portfolio optimization takes a more comprehensive approach by incorporating the dynamic nature of financial markets over multiple periods. Hence in this study, we focus on multi-period portfolio optimization. We conduct a bibliometric analysis of articles on multi-period portfolio optimization in the Web of Science (WoS) database. Through quantitative methods and the utilization of the Bibliometrix R package, we analyze publication trends, key research sites, and historical output in this field. Our findings provide valuable insights into the current state of research on multi-period portfolio optimization. This bibliometric analysis contributes to the existing literature on multi-period portfolio optimization and serves as a valuable resource for researchers, policymakers, and practitioners in the field of finance.

In a short runtime process (about 6 minutes), with isocratic method without need of reconditioning before the next run, a mixture of some molecules containing 4-aminophenol (4APh), phenol, 3-nitrosalicylic acid (3NSA), and mesalamine (5-aminosalicylic acid) (MLZ), were separated and detected by means of HPLC/UV-Vis system. The resolutions of the separated and sharp peaks referring to the mentioned compounds were at least more than 2, and the tailing factor (TF) was about 1, even in 1 µg/l (ppm) concentration. In order to optimize the method, the effects of the pH, eluent percentage, flow rate, and buffer concentration, and wavelength on the chromatogram were investigated. Also, the results of the validation processes showed that the method trustable to be used in laboratories.

Designing an efficient supply chain for organ transplant networks which is intimately related to humans’ life plays a primary role in improving the network’s performance. This research is focused on proposing a new multi-period location-allocation modeling approach to make appropriate strategic decisions for designing organ transplant networks under supply and budget uncertainties. To serve this purpose, a bi-objective possibilistic programming model is formulated the aim of which is to maximize network responsiveness and minimize the total cost. A fuzzy goal programming approach is adopted to solve multiple objective function models and control their deviations from the corresponding aspiration levels. As an important contribution of this study, the chance of success of transplantation processes is taken into consideration by proposing appropriate utility functions according to transportation criteria. Moreover, for the purpose of coping with the inherent uncertainty of the input parameters, a possibilistic programming model based on Me measure converted to three optimistic, realistic and pessimistic models is developed. Three new formulations have also been developed to tackle equality chance constraints. Finally, the optimal solutions of the developed models are analyzed through conducting a real case study in Iran. According to the results, for the considered organ transplant network, the possibilistic programming model based on the realistic measure is better than the optimistic and pessimistic measure in most confidence levels.

The majority of structural failures are attributable to errors in construction. This problem exists in all countries, but it is more frequent in developing communities. This study focuses on construction errors of structures in Tehran, the capital city of Iran. In this study, eighty-eight buildings have been investigated during the construction phase. These buildings have been categorized into ten types and have been distributed in twenty-two suburbs. Results showed that the buildings of Tehran can suffer from at least forty-nine major construction problems. In addition, for the first time, this research has introduced the following three terms in relation to prioritizing of construction errors: Relative Importance Factor (RIF), Priority Index (PI) and Structural Importance Index (SII). As a part of the conclusions, the results showed one hundred percent of investigated buildings are affected dramatically by the “use of untrained workers” and “lack of sampling or wrong sampling” too. In this regard, the RIF and PI of each “Lack of sampling or wrong sampling” and “use of untrained workers” are 100 and 1, respectively. Also, suburb 3 has the best construction conditions while suburb 10 has the worst.

A solid fuel launch vehicle is a rocket with an engine that has been widely used in aerospace missions. Utilizing such launch vehicles depends on the simplicity of the manufacturing, maintenance, operation and development of the control systems. The purpose of optimization in solid fuel launch vehicles design is to find the best possible design for the mission with regard to the available equipment, constraints and infrastructures. Therefore, the main purpose of this research is to optimally design a launch vehicle for customized missions based on successful experiences, as well as technology, manufacturing capabilities and facilities. In this context, NSGA-II Intelligent Optimization Algorithm is considered based on multi-objective optimization principles and Mass-Energetic concepts. The optimal design of the launch vehicle is performed by applying intelligent algorithms and technological opportunities and limitations. The result showed that the present optimization method can design the launch vehicle based on technological limitations.

Gamification as a new concept uses game elements in a novel way to engage users of a non-gaming system and can be used in many domains within an enterprise, to implement the organizational processes with lower costs, higher quality or in a more efficient way. Although there are many researches on gamification but a few studies can be found in the organizational gamification and there are few research works about framework and methodology for designing and implementing organizational gamification in the literature. The purpose of this article is to provide a comprehensive methodology for the enterprise gamification. This research is an attempt to overcome the mentioned gap via presenting a methodology by applying some important issues including organizational, humanity and gamification aspects together to design and implement customized enterprise gamification solutions through reviewing the related literature and experts’ commentaries. The evaluation of the methodology showed that it is an appropriate and perfect way to design gamification solutions in an organization, besides the enterprise needs to provide the necessary conditions for its implementation. This paper forwards an important debate on a comprehensive methodology for applying enterprise gamification, which explains how to properly use gamification in enterprises to increase productivity and better communication with employees, and thus contributes to literature on internal and enterprise gamification.