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.

Due to the new hopes for treatment of multiple sclerosis (MS) diseases by Teriflunomide (TFN), in this project, a cheap, robust, and fully validated method has been developed both for determination of assay content in API (active pharmaceutical ingredient), and for related impurities analysis (RIA). To operate the method, a common C18, end-capped (250 × 4.6) mm, 5µm liquid chromatography column, was applied. The mobile phase A was prepared by dissolving 2.74 g (20mM) of PDP (potassium dihydrogen phosphate) and 3.72 g (50mM) of PC (potassium chloride) in water (1000 mL). Then, pH was adjusted to 3.0 by adding OPA (ortho-phosphoric acid) 85%; while, the mobile phase B was acetonitrile (ACN) (100%). In order to confirm the experimental data about the λmax of TFN, we have used the Born-Oppenheimer molecular dynamics (BOMD) simulations, quantum mechanics (QM), and TD-DFT calculations. According to the results, the method showed a high level of suitability, specificity, linearity, accuracy, precision, repeatability, robustness, and reliable detection limit.

In this study, we attempted to conduct a comprehensive review of the existing and pertinent literature on the topic of factor investment. We performed Scientometric analysis of studies published in reputable finance journals, i.e., The Journal of Portfolio Management, The Financial Analysts Journal, The Journal of Asset Management and others, during the years 2014 to 2023. To obtain the research data for our study, we gathered and examined a collection of 76 bibliographic records sourced from the Web of Science database. This database provided a comprehensive and reliable source of scholarly publications in the field of finance. To analyze the data, we employed Scientometric networks as part of our analytical approach. Scientometric networks allowed us to explore the relationships and connections between different publications, authors, and keywords within the domain of factor investment. To visualize and present the research findings, we utilized the Bibliometrix package for R, a powerful tool specifically designed for bibliometric analysis. This package enabled us to generate insightful visualizations that showcased the key patterns, trends, and interconnections within the literature on factor investment. By employing Scientometric analysis and leveraging the capabilities of the Bibliometrix package, we aimed to provide a comprehensive overview of the existing scholarly research in this field and contribute to the understanding of factor investment.

In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.

In recent years, the rapid transmission of information and interconnectedness of global financial markets have amplified the convergence and influence among them. Consequently, the occurrence of spillover effects in one market can significantly impact other markets. Accurately identifying and understanding these spillover effects is crucial for effectively managing and controlling market fluctuations. This research aims to measure and analyze the spillover effects between China's stock market and selected emerging economies in the Middle East, with a focus on exploring diversification opportunities. The analysis encompasses three distinct time periods, including the overall period from May 1, 2005, to May 31, 2023. The sub-periods consist of the first sub-period from May 1, 2005, to October 31, 2009, and the second sub-period from December 1, 2010, to May 31, 2023. Multivariate Generalized Heterogeneous Autoregression (MGARCH) is employed in this study to examine the spillover effects between China's economy and the emerging economies under consideration. The Granger causality analysis reveals a unidirectional causality running from the Chinese stock market to Jordan, as well as from the UAE to China throughout the entire observation period. However, no spillover effects are found between China and Saudi Arabia in either direction during any of the periods. Notably, a two-way causality is detected between the Chinese and UAE markets in the second sub-period. Furthermore, MGARCH results indicate no spillover effects from China to the emerging economies during the overall period, first sub-period, or second sub-period. The findings of this research offer valuable insights for investment portfolio managers in the Chinese economy, who may consider the examined emerging economies as potential destinations for risk diversification.