In this study, the influence of the volume fraction of the martensite phase as well as the size of the martensite particles on the mechanism of particle fracture in dual-phase steel were examined. A combined continuum/dislocation based approach was used in order to model the average stress in the martensite particles. It was found that the model predictions are in accordance with the experimental results. For the same volume fraction of the martensite particles, the model predicts an increase of the internal stress and the average stress in the martensite particles with increasing the particles size. Since the fracture strength of the martensite depends on its volume fraction, the particle size has no effect on the mechanism of particle fracture. Increasing the volume fraction of the martensite particles results in the enhancement of the internal stress in the martensite particles. However, it has a slight influence on the average stress in the particles. Nevertheless, because of decreasing the fracture strength of martensite with increasing its volume fraction, this parameter has a main role in the occurrence of the particle fracture mechanism.

Asphaltic concrete mixtures are among the most common construction materials for the pavement of roads. As a multi-phase composite mixture with randomly distributed aggregates inside the mastic part, the mechanical properties of such materials can be influenced by different factors. Cracking and induced fracture is among the common degradation and failure modes in these construction mixtures that often takes place in cold regions. In this research, the effects of some influencing parameters including temperature, air void percentage and loading rate are investigated experimentally on the fracture toughness (K_Ic) and work of fracture (W_Ic) of hot mix asphalt material. Edge notched semi-circular bend (SCB) specimen was employed to conduct mode I fracture experiments. The thickness of SCB samples were considered as variable and the HMA mixtures were tested with two SCB thicknesses of 30 and 60 mm. The experimental results showed that both fracture toughness and fracture work are increased by increasing the thickness. However, the effect of thickness on the fracture work was much more significant than the K_Ic value. Also, the fracture and cracking resistance parameters were increased by decreasing the temperature and air void content. Both K_Ic and W_Ic values were also increased by increasing the loading rate in the investigated range of 1 to 8 mm/min. The most influencing parameters on the change of fracture resistance parameters were the temperature, loading rate, air void content and thickness, respectively.

Multicriteria Decision Making (MCDM) is one the most important branches of decision theory. Due to the fact that MCDM methods have the utmost significance in management, scholars try to develop more MCDM methods. Since calculating the weights of criteria is an important step in any MCDM method, increasing the accuracy of weight calculating methods can highly affect these methods. This accuracy can be improved by less pairwise comparison between criteria. To this end, the present study seeks to make a comparison between two new weight calculating techniques, namely BWM and FUCOM in a fuzzy environment using a real-world case study Results of this study shows that FUCOM-F provides more reliable results compared to FBWM since its consistency is less than FBWM by a great amount.

Cross-docking is a logistics strategy that consolidates the products of different inbound trucks according to their destinations in order to reduce the inventory, order picking, and transportation costs. It requires a high level of collaboration between inbound trucks, internal operations, and outbound trucks. This article addresses the truck-to-door sequencing problem. Truck-to-door sequencing has been studied by some researchers in different titles such as scheduling and sequencing of inbound and outbound trucks of the cross-dock center. However, previous studies have not considered repeat truck holding pattern. Therefore, it is important to determine the doors and the sequence of the inbound and outbound trucks that should be assigned in a cross-dock center. This paper focuses on optimizing truck-to-door sequencing with consideration of repeat truck holding pattern in inbound trucks in order to minimize makespan. Two methods are considered to solve this problem, including mathematical modeling and a heuristic algorithm. In the first method, a mixed integer-programming model is developed to minimize the makespan. Then, GAMS software is used to solve small-scale problems. In the second approach, a heuristic algorithm is developed to find near-optimal solutions within the shortest time possible and the algorithm is used to solve large-scale problems. The results of the mathematical model and the heuristic algorithm are slightly different and show the good quality of the presented heuristic algorithm.

This project was started when an unknown peak was being detected in the organic volatile impurity (OVI) analysis (using the head-space vapor sampling procedure) of bortezomib (BZB) active pharmaceutical ingredient (API), in pharmaceutical companies. During the OVI analysis of the API of BZB, a huge-area peak with an unknown source appeared in the chromatograms of the gas chromatography with flame ionization detector (GC-FID). The data prepared by GC-MAS revealed that the considered huge peak was 3-methylbutanal (3MBut). But, investigating the synthesis procedures showed that during all the synthesis steps, 3MBut or any other solvent containing this impurity was not being applied. Thus, we had concluded that there is a possibility for emergence of this aldehyde from bortezomib itself. To find out which part of bortezomib might turn into 3MBut, we began to investigate all its molecular structure, and hypothesized that the α-amino-boronic acid part of the molecule turned into 3MBut. The experimental analysis and theoretical quantum chemical calculations confirmed that the α-amino-boronic acid center of bortezomib molecule undergoes a rare and unexpected aerobic oxidation by O2 molecule, even in catalyst and solvent-free conditions. The result of this project not only might make clear the passive source of the 3MBut peak of the OVI of bortezomib, but also, it would suggest to store this API in an inert oxygen-free atmosphere to improve the long-term and accelerated (thermal) stability of this very expensive anti-cancer drug.

One of the most significant parameters which should be considered by all engineers is improving structures’ strength subjected to lateral load. Steel shear wall whose duty is to affect lateral load (wind and earthquake) is a wall which consists of shear part. Application of low yield point (LYP) steel in shear walls allows the employment of moderate and/or stocky infill plates with low yielding and high buckling capacities, which can result in enhanced buckling stability, serviceability, and energy dissipation capacity of such systems. Infill LYP plate is used to improve shear wall behavior which leads to enhancement of stiffness. In the present research, infill plate with spherical appendages is applied, and its impact on plate stiffness, cyclic behavior and energy absorption are investigated. The spherical diameter has been chosen respectively 10 and 20 cm distributed with two patterns (diagonal and plus form). The best performance is for a LYP plate with a 10 cm spherical diagonal pattern.

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document-centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.

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.