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Cai et al.(2013) and  Cai and Han (2014) presented the polynomial time algorithms for two-pair and three-pair networks with common bottleneck links, respectively. Also, Chen and HaiBin(2012) proposed a non-polynomial time algorithms for $n$-pair networks with common bottleneck links, where $n$ is an arbitrary integer. This paper presents a new sufficient and necessary condition to determine the solvability of single rate $n$-pair networks with common bottleneck links, which concludes a polynomial time algorithm for $n$-pair networks with common bottleneck links, where $n$ is an arbitrary integer. Our algorithm runs in $O(|V||E|^{2})$ time, where $|V|$ and $|E|$ are the number of nodes and links, respectively.

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During the COVID-19 pandemic and the resulting constraints, businesses have encountered changes in their customers’ behaviors and business environments. Scholars have emphasized on the role of digital transformation as a response to these challenges. This study investigates the level of digital maturity in seven dimensions of digital transformation presented by Kane (2017) in small service businesses (SSBs). A mixed method combining the logic of statistical accuracy of questionnaire analysis with the realistic aspects of discourse analysis was applied. The results revealed that digital transformation does not fully occur among the SSBs. Instead, some extent of digitalization happened in various areas such as communications with customers and the digitalization of some aspects of services. Besides, the study revealed that the most frequent pathways taken by SSBs toward digitalization are the less capital-intensive and technology-based ones  . Furthermore, customers are involved in three types of relational activities categorized as “transactional”, “intercommunication”, and “information sharing”.

 

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The expansion of gas transmission lines in Iran involves numerous risks, requiring regular assessments to ensure safe and efficient transport. This study examines six kilometers of Iran’s oldest gas pipeline, located in Tonekabon, a densely populated and touristic city. The pipeline was divided into six zones, considering pipeline class, population density, and intersections. In each zone, three events—leakage, rupture, and explosion—were assessed using four methods: simple matrix, weighted matrix, fuzzy weighted matrix, and a 3D uncertainty-based matrix. Four experts evaluated the probability and severity of consequences, categorized as technical, safety, environmental, and cost impacts. The consequences enabled risk calculation across all categories. Standard deviation was used to compute a three-dimensional uncertainty-based risk, incorporating uncertainty in both probability and consequence estimation. Risk management levels were then adjusted accordingly. Chang’s fuzzy AHP method and Mamdani’s fuzzy logic in MATLAB were applied to handle inherent uncertainties. Results showed discrepancies between simple and fuzzy matrices due to the exclusion of cost impacts, given the state-owned nature of the company. The 3D matrix further indicated that most risk cells require only preliminary review, attributed to the company’s regular inspections and access to reliable data.
 

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Supplier selection is one of the main discussions in the Supply Chain. The issue of assigning purchase orders to suppliers that act differently in terms of quality, cast, services, etc. criteria is one of the significant concerns of purchase managers in the supply chain. To adopt an optimal decision in this regard is related to a multi-objective problem that the objectives are contradicting each other and have different importance and priority depending on the location. In practice, the existence of kind of ambiguity in explaining the information related to the problem constraints and complicated. In this regard, the emergence of Fuzzy set theory as a tool to describe such conditions besides presenting question model realistically can help to solve such problems well. Despite the importance of the model with the mentioned structure, unfortunately, few original works have been done in this field. As a result, in this paper, in addition to presenting a new multi-objective Fuzzy model being modelled based on assigning purchase order to suppliers in a supply chain a solution method is introduced based on using Fuzzy linear programming. To clarify solution process modelling and description, a case study is included related to selecting flour supplier for providing industrial bread of Khoshkar factory. The proposed model includes four objective functions:

1. Aggregate costs of minimizing type,
2. Services of maximizing type (such as packing, being faithful to promise, factory heath, discount, correct transportation, good relationships, honestly, etc.),
3. Flour useful survival of maximizing type (regarding monthly flour buying by the factory),
4. The purchased flour quality of maximizing type (concerning product type).

 Especially in the solution process, a method is determined based on setting weight for each of the objectives concerning the major factory stockholders.
 

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The backup 2-median location problem on a tree T is to deploy two servers at the vertices such that the expected sum of distances from all vertices to the set of functioning servers is minimum. In this paper, we investigate the backup 2-median location problem on tree networks with trapezoidal interval type-2 fuzzy weights. We first, present  a new  method for comparing generalized trapezoidal fuzzy numbers and then develop it for trapezoidal interval type-2 fuzzy numbers. Then numerical examples are given to compare the proposed methods with other existing  methods. Finally, we apply our ranking method to  solve the the backup 2-median location problem on a tree network with trapezoidal interval type-2 fuzzy weights.

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Intelligence has long been an interesting and important topic in psychology and cognitive science. IQ is considered a basic measure of a person's cognitive abilities, which includes various aspects of reasoning, problem solving, memory, and overall intellectual ability. Considering the importance of IQ in cognitive and psychological evaluations, the main goal of this article was to provide a new and effective approach to improve the accuracy of estimating this measure through complex brain data processing. In this paper, we have analyzed and developed a hybrid model of GWO algorithm and CNN (GCNN) in order to estimate IQ using brain MRI images. The results of the experiments showed that the accuracy of the proposed model was significantly better than the traditional techniques, and this indicates the high capabilities of the model in interpreting complex medical data. By examining the results, we find that the accuracy of the proposed model with an estimation rate of 93.10% is better than other competing methods.

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In this paper, a modified hybrid three-term conjugate gradient (CG) method is proposed for solving unconstrained optimization problems. The search direction is a three-term hybrid form of the Hestenes-Stiefel (HS) and Liu–Storey (LS) CG parameters. It is established that the method ensures the sufficient descent property independent of line search techniques. The convergence analysis of the proposed method is carried out under standard assumptions for general functions. Numerical experiments on CUTEr problems and image denoising tasks demonstrate that our method outperforms existing approaches in terms of efficiency, accuracy, and robustness, particularly under high levels of salt-and-pepper noise.

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A sum-network is a directed acyclic network with multiple sources and multiple sinks where each sink  demands the sum of the independent information generated at the sources. The coding capacity of sum networks with independent sources has been investigated in Tripathy and Ramamoorthy(2015) and it was proven that the upper bound of the coding capacity of such networks is 1. In this paper,  it is shown that the upper bound of the coding capacity of a sum network with dependent sources is greater than 1 which is different from the obtained results in Tripathy and Ramamoorthy(2015).
 It is also shown that a non-solvable sum-network with independent sources can be converted to a solvable one when the sources have arbitrary dependencies

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This study explores the implementation of virtual platforms in supply chain management, emphasizing online production, procurement, and distribution without traditional factory infrastructures. Using a qualitative descriptive-survey approach with inductive reasoning, the research aims to enhance supply chain performance through advanced digital technologies. Rapid advancements in Information and Communication Technologies such as Internet of Things and Artificial Intelligence challenge conventional models by enabling real-time data exchange, improving forecasting accuracy, and reducing delays. Digital integration facilitates seamless communication among suppliers, manufacturers, distributors, and customers, enhancing coordination and cost efficiency. Semi-structured interviews with industry experts were analyzed through thematic analysis, yielding 139 initial codes refined into 25 categories and 5 key themes. These highlight critical dimensions: Digital Integration, Stakeholders Coordination, Edge Computing, Data Analytics and Agility Management. Advanced analytics, leveraging mathematical models and Intelligence algorithms, provide actionable insights for demand forecasting and inventory optimization, strengthening decision-making. The findings underscore the importance of flexibility and agility in addressing market disruptions, with edge computing and real-time data processing identified as vital for operational resilience. Practical recommendations include deploying simulation tools, developing logistics optimization algorithms, and implementing robust cybersecurity protocols. Overall, virtual platforms offer a transformative approach to supply chain management, improving efficiency, reducing costs, and enhancing competitiveness in dynamic markets.

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Prostate cancer is the most common cancer in men and the second leading cause of cancer-related death worldwide. Over the years, researchers from various fields, beyond medicine, have sought to expand their understanding of the disease to develop more effective treatments. Treatment planning for high-dose-rate (HDR) brachytherapy involves designing the trajectory of the radiation source to deliver sufficient doses to the target area while minimizing exposure to surrounding organs at risk (OAR) within clinically safe limits. Since the exact tumor volume is not known, the model uses gray numbers instead of tumor volume, which provides more accurate results.
In this study, four powerful multi-objective evolutionary algorithms (MOEAs) NSGA¹-II, PESA²-II, SPEA³-II, and MOPSO⁴ are employed. Instead of yielding a single best solution, these algorithms produce a set of Pareto-optimal solutions, each representing a trade-off where no one solution is definitively better than the rest. However, they demonstrate improved performance compared to other optimization methods. The results show that the MOPSO algorithm performs better than the other three powerful algorithms in terms of solution quality and maintaining diversity among solutions.
 

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Emotion recognition in Persian texts using data mining is a significant area within text analysis. Emotions are typically defined as individuals’ emotional reactions to situations, events, and information. Emotion recognition in text involves identifying and analyzing emotional content across various types of textual data. This paper presents a model for detecting different emotions in Persian texts using an enhanced transfer model. The proposed model comprises an encoder and a decoder, each equipped with a self-attention mechanism and RNN modules. Initially, a dataset of sentences annotated with emotional states—anger, happiness, sadness, and fear—is created by multiple users. These sentences are then converted into image representations and fed into the improved transfer model for emotion recognition. Experimental results demonstrate that the model effectively identifies the emotions of sadness, anger, happiness, and surprise with precision, accuracy, recall, and F1-score values of 90.25%, 91.4%, 91.6%, and 90.80%, respectively.
 

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Facility location and routing problems have attracted significant research attention since the 1960s due to their practical relevance and complexity. Efficiently establishing production facilities, optimizing vehicle routes, and implementing effective inventory systems are essential for improving organizational performance. In this study, we propose an integrated location-routing model for the pharmaceutical supply chain, designed to satisfy all retailer demands through an appropriate inventory policy, ensuring no demand is unmet. The proposed mixed-integer mathematical model considers a four-tier supply chain, including manufacturers, distributors, wholesalers, and retailers, with the objective of establishing cost-effective warehouses while fulfilling all demand requirements. Demand uncertainty is addressed using a scenario-based probabilistic approach. The model is solved using GAMS for a small-scale case study. For larger-scale instances, where exact solutions are computationally challenging, a meta-heuristic approach—specifically, a genetic algorithm—is employed to efficiently obtain near-optimal solutions.
 

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The conjugate gradient method (CGM) stands out as one of the most rapidly growing and effective approaches for addressing unconstrained optimization problems. In recent years, significant efforts have been dedicated to adapting the CGM for tackling nonlinear optimization challenges. This research article introduces a new modification of the Fletcher–Reeves (FR) conjugate gradient projection method. The proposed method is characterized by its sufficient descent property, and its global convergence has been established under specific assumptions. Numerical experiments conducted on a range of functions from the CUTEr collection demonstrate the potential and effectiveness of the proposed methods.
 

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Leukemia is one of the most common and dangerous types of cancer in the world. In many cases, the disease is curable if detected in its early stages. One of the effective tools for early detection is the analysis of microarray data, which measures the expression of thousands of genes simultaneously. However, the large volume of features and the presence of noise make the analysis process complex and time-consuming. Therefore, the selection of effective genes plays a key role in increasing the accuracy and reducing the computational cost of learning models. In this paper, a two-step hybrid approach is presented for feature selection and classification of leukemia types. In the first step, the features are filtered using the mutual information criterion and the genes with the highest correlation with the disease label are selected. In the second step, the XGBoost model is used to rank and stably select the features to identify the genes that are most important in different iterations. In the final stage, classification will be performed using the temporal fusion transformer method, which allows for fast and efficient learning of complex patterns among selected genes. Experimental results on real microarray datasets show that the proposed method outperforms the baseline methods with an accuracy of 99.2% and has been able to identify key genes effective in differentiating leukemia types by effectively reducing the data dimensions.

 

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Natural gas is a critical energy source that substantially contributes to meeting national energy demand. Iran possesses the world's second-largest natural gas reserves. In South Khorasan province, natural gas coverage is extensive, reaching 100% of urban households, 99.9% of rural households, and a large proportion of industrial facilities. This study examines the effects of natural gas supply to agricultural and production centers on production efficiency and economic profitability. The research is applied and employs a descriptive–survey design with a mixed-methods approach, combining structured questionnaires and expert interviews. The sample consisted of 165 respondents from the agricultural sector and 150 respondents from the production sector. Results indicate that supplying natural gas to agricultural and production centers enhances resource utilization, reduces energy costs, improves operational efficiency, and diminishes air pollution and greenhouse gas emissions.

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Evaluation of healthcare systems, as a key organization providing different health services, is essential. This issue becomes more crucial when occurring crises such as a pandemic. They need to keep track of their success in the face of the crisis to assess the effects of policy changes and their capability to respond to new challenges. The inverse data envelopment analysis (InvDEA) technique is an applicable method in order to estimate the input/output levels of decision-making units (DMUs) to preserve predetermined technical efficiency scores. In classic studies of InvDEA, decision-Making Units (DMUs) as black boxes, ignoring their internal structure. This paper estimates input levels and new intermediate products to achieve a predetermined efficiency score set by the decision maker. In traditional inverse data envelopment analysis models, precise data are required to determine the input and/or output levels of each decision-making unit. However, in many scenarios, such as system flexibility, social and cultural contexts information may be indeterminate. In these cases, experts’ opinions are used to model uncertainty. Uncertainty theory, a branch of mathematics, logically deals with degrees of belief. This paper aims to develop an inverse Network DEA model incorporating uncertainty theory. We assume that inputs and outputs of decision-making units are based on experts’ belief degrees. To demonstrate the model is performance, we explore efficiency of healthcare systems during COVID-19 pandemic.
 

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This paper presents a constrained multi-objective deep reinforcement learning framework for urban traffic signal control. The problem is modeled as a constrained Markov decision process in which an agent simultaneously optimizes efficiency objectives while respecting explicit safety and fairness constraints. A dueling double deep Q-network (D3QN) is combined with a Lagrangian cost estimator to approximate both the reward value function and cumulative constraint costs. The state representation includes queue lengths, phase indicators and elapsed green times, and the action space consists of a small set of interpretable decisions such as extending the current green or switching to the next phase. The proposed controller is trained and evaluated in a SUMO-based microscopic simulation of a four-leg urban intersection under various traffic demand patterns. Its performance is compared with fixed-time, vehicle-actuated and unconstrained DQN controllers. Simulation results show that the proposed method can substantially reduce average delay and maximum queue length while keeping queue spillback and delay imbalance within predefined limits. These findings indicate that constrained multi-objective deep reinforcement learning offers a promising and practically deployable framework for safe and fair traffic signal control in congested urban networks, and can be extended to more complex corridors and network-wide settings in future work.
 

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Fraud is a phenomenon that involves deviations and manipulations in financial statements. These actions can lead to tax non-compliance and erode the trust of investors and other stakeholders. Given the intricate nature and vast amount of financial data within organizations, leveraging artificial intelligence as a sophisticated tool can greatly enhance fraud detection in financial statements and bolster confidence in the face of evolving fraudulent tactics. Fraud or deception in the financial information of individuals or organizations reduces the level of trust and confidence that people have in the reliability and integrity of this information. This can lead to serious negative impacts, including loss of trust from customers, investors, and other entities, negative financial and legal consequences, and the exposure of illegal or improper operations that may involve financial crimes. This paper introduces an intelligent method for detecting fraud in financial statements. Initially, the Apriori algorithm is utilized to select pertinent features in the financial data. Subsequently, the performance of the proposed method is enhanced by augmenting the dataset using the GAN-CNN network. Finally, fraud detection is executed with the assistance of XGBoost. The results demonstrate that the proposed method has achieved a fraud detection accuracy of 95.3%.
 

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In this paper, we investigate a solution procedure for a fuzzy linear fractional optimization problem in which the input parameters are considered as convex fuzzy numbers. By applying a specific fuzzy ranking method which is based on the α-cut concept, and according to Charnes and Cooper’s approach of variable transformation, the solution of the original fuzzy linear fractional optimization model is transformed to the solution of at most two semi-infinite linear programs that are dis similar among themselves via a sign in a constraint and in the objective function. An appropriate cutting plane algorithm(CPA) of Fang is uti lized to obtain the optimal solution of the semi-infinite linear programs. Further, the application of our provided algorithm in facility location theory is discussed properly. Finally, an illustrative example is given to clarify the developed solution procedure.