Indole is a hazardous substance existing in the curl oil, which released to the environment by various industrial activities; thus, decreasing its ratio from nature seems to be essential. In this study, a facial pathway for loading Cu(II) particles on MCM-41 mesoporous material was introduced as an efficient catalyst for indole oxidation. The obtained catalyst was characterized by XRD, FESEM, EDS, TEM and BET/BJH techniques. Based on Taguchi method, indole oxidation via prepared catalyst at mild condition was evaluated. By applying this method, the operating factors including Indole initial concentration (mg/L), pH value and the mass of catalyst (g) were optimized. It was found that pH plays the most crucial role in indole oxidation, on the other word, in alkaline environment; indole oxidation was happened with higher yield.

Machine vision systems are among the novel tools proven to be useful in different applications, among which monitoring and controlling manufacturing processes is one of the most important ones. However, due to the complexity resulted from high-dimensional image data and their inherent correlations, the acquisition of traditional statistical process control tools seems inapplicable. To overcome the shortcomings of the traditional methods in this regard, a statistical model is proposed in this paper which utilizes the concepts of both the PCA-based T2 control chart and the classification methods to develop a tool capable of controlling an image-based process. By defining the warning zones, collected data taken from an image-based process are classified into more than the two classes related to in-control and out-of-control processes. This helps practitioners to define rules to make it easier to realize when the process is getting out of control. Through simulation, the accuracy performance and the speed of four different types of classifiers including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), kth nearest neighbors (KNN), and support vector machine (SVM) are assessed in different scenarios, based on which the functionality of the proposed approach is evaluated in in-control and out-of-control conditions.

In order to investigate the isomerization and conversion mechanism of the advantageous and widely used nonsteroidal anti-inflammatory medicine flurbiprofen, the hybrid density functional theory was applied. According to the results, the rearrangement reaction of (R)-flurbiprofen to its (S)-enantiomer happens in a [1,3]-hydrogen shifts with inversion of configuration at chiral center C14. From the calculated energies, it can be understood that the rate-limiting step in the flurbiprofen isomerization is the excitation of (R)-flurbiprofen methyl ester in its initial form to the first excited singlet state S1 at λ=243.91 nm. we studied this process by scanning the C14-H17 distance for the excited singlet to get more information about the process of isomerization occurring upon excitation. The results of calculations demonstrated that the isomerization process should pass through a ~71 kcal/mol barrier. The (S)-flurbiprofen methyl ester is more photostable than its related (R)-enantiomer. This issue can be attributed to the -1.50 kcal/mol of thermodynamic stability of the (S)-flurbiprofen methyl ester.

Agri-food supply chains have received the attention of many researchers in recent years for various reasons, including food security and health-related issues. Wheat, as a staple food in many countries, is the most cultivated crop in the world. Due to the importance of wheat, this paper proposes a mixed-integer linear mathematical model for redesigning and planning of the wheat supply chain. The proposed model determines the location and capacity of new storage facilities while addressing supplier selection, ordering, storing, transportation, and distribution problems. This model considers the differences between long-term and short-term storage facilities and the quality of wheat. Moreover, the proposed model addresses the uncertainties associated with the quantity of domestic supply and demand through a stochastic scenario-based programming approach. Applicability of this model is investigated using real data from the wheat supply chain of Iran. Results show that seven new long-term storage facilities should be opened, which decreases total costs by 3.45 percent.

Community detection has gained much attention during the past few decades. So many algorithms have been developed to tackle this problem. In previous related works the weight of the edges and directionality were not considered at the same time in the models. Considering weights and directionality makes the models more realistic and prevents the loss of information in the network. In this article, we propose an overlapping community detection algorithm for networks with weighted and directed edges. We used the concept of information flows among the vertices i.e. the more flows exist in a community, the stronger the community. We implemented the concept of flow using weighted closed flows starting from a given node and ending to the same node. By using the mentioned assumption we developed a new modularity measure called weighted flow modularity (WFM) based on M function modularity. In addition, we developed an overlapping score criteria which considers overlap in vertices and edges at the same time and is much faster in the terms of run time. We compared the community detection results in terms of accuracy and running time with Order statistics local optimization method (OSLOM) on 74 LFR benchmark networks using normalized mutual information score. We also implemented the community detection process using LCFE on real world datasets and evaluated the community detection results using EQ measure. The experimental analysis results show that the LCFE is more accurate in most cases and is competitive in other cases with OSLOM.

In this study, the selective oximation of structurally diverse aromatic aldehydes (versus ketones) to the corresponding aldoxime derivatives was investigated using the combination system of NH₂OH.HCl and bisthiourea complexes of cobalt, nickel, copper and zinc chlorides, M^II(tu)₂Cl₂, in a mixture of CH₃CN‒H₂O (1:1 mL). All reactions were carried out successfully at room temperature within the immediate time up to 130 min giving the products in high yields. Investigation of the results exhibited that the applied bis-thiourea metal complexes show the catalytic activity in order of Co(tu)₂Cl₂₂Cl₂₂Cl₂₂Cl₂ in their oximation reactions.

Economists have exploited the social accounting matrix in linear economic models to analyze the effect of some variables such as government spending, investment, and export on other economic indicators including total output, employment, household income, and economic growth. In this paper, the influence of investment injection on some economic indicators is analyzed. In addition, to gather different indicators in a general view, these economic indicators are applied as inputs and outputs in a data envelopment analysis model. Overall, to get to the best possible economic conditions, a fair revenue allocation method is used based on a data envelopment analysis model to determine each economic sector’s quota of investment. Next, a kind of fair-biased allocation method improving the economic conditions in comparison to the prior model is proposed. Finally, the whole process for Iran’s social accounting matrix and subsequent results are presented.

Patient punctuality significantly impacts resource utilization and patient waiting times, among other quality indicators, within psychiatry clinics. In pursuit of service improvement, this study endeavors to develop effective appointment scheduling systems that optimally distribute patients' needs during clinical sessions, thereby enhancing resource utilization and patient satisfaction. In developing these scheduling rules, three patient-related uncertainties are considered: preference, availability, and punctuality. Various scheduling rules are evaluated based on their average total cost under different scenarios. The HSBGDM rules have emerged as a balanced approach for clinic operations, effectively managing physician time but occasionally leading to overtime variations. Increased patient delays often exacerbate physician idle times, particularly under IBVST and VBVST rules. Hybrid rules, such as the HSBGDM series, adapt well, improving patient wait times and managing unscheduled patients. However, scheduling systems like REPDM may prolong waits, potentially impacting patient satisfaction. Systems prioritizing new appointments can increase physician idle times due to unpredictability. While accommodating unscheduled patients enhances service quality, it may also cause disruptions. This study provides valuable insights into scheduling dynamics, assisting administrators in balancing efficiency, cost, and patient satisfaction.

One of the most important solutions for dimensionality reduction in data preprocessing, and improving classification performance is gene selection in microarray data since they usually have several thousand genes with very few samples. Because of these characteristics, the complexity of classification models increases and their efficiency decreases. The gene selection problem inherently pursues two goals: reducing the number of genes and increasing the classification efficiency. Therefore, this paper presents a novel hybrid filter-wrapper solution based on the Fisher-score method and Multi-Objective Forest Optimization Algorithm (MOFOA). In the proposed method, as a preprocessing step, the Fisher-score method selects 500 discriminative genes by removing redundant/irrelevant genes. Then, MOFOA searches to find the subset of optimal genes using concepts such as repository, crowding-distance, and binary tournament selection. Moreover, the proposed method solves the gene selection problem and, at the same time, optimizes the kernel parameters in the SVM classification model. Six microarray datasets were used to evaluate the performance of the proposed method. Afterward, a comparison was made between its results and those of the four multi-objective hybrid methods presented in the literature in terms of classification performance, the number of selected genes, running time, and hypervolume criteria. According to the results, in addition to selecting fewer genes, the proposed solution has achieved greater classification accuracy in most cases and has been able to obtain a performance similar to or better than that of other multi-objective gene selection approaches.

Hub networks play a crucial role in optimizing transportation costs in air and road systems. Their main objective is to strategically locate hubs and allocate non-hub nodes within the network. The modular hub location problem is a specific area of hub network design that focuses on accurately calculating transportation costs, considering factors like trip numbers and capacity constraints in network routes. This study proposes a mixed-integer programming model to address the modular hub location problem with multiple allocations. It considers dependent and independent costs associated with vehicles per trip between hub network routes, considering specific vehicle capacities. Two datasets are utilized for validation: the CAB dataset representing 25 nodes of US airports and the TR dataset representing the Turkish transportation system with 81 nodes. To tackle the NP-hard nature of hub location models and the computational complexity of the proposed model, two solutions are developed. Firstly, a novel LP relaxation-based method using GAMS software provides near-optimal solutions for medium-sized instances. Additionally, a Genetic Algorithm (GA) implemented in MATLAB handles larger instances. The GA's efficiency is enhanced by tuning its parameters using the Taguchi method. Results analysis shows that both proposed algorithms yield high-quality solutions within significantly reduced timeframes compared to the CPLEX solver in GAMS software. The LP relaxation-based method performs well for medium-sized instances, while the GA approach is efficient for larger instances after parameter tuning with the Taguchi method.