مطالب

Objective : This research aims to identify recurring carbon reduction policies and their key variables, and analyze their relationships within pharmaceutical distribution in Iran. Methods : A mixed-method of qualitative and quantitative approaches was adopted. Firstly, a systematic literature review was employed to identify the policies and variables. Afterward, the Intuitive Fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method was used to analyze the causal relationships among identified variables in uncertain conditions. Following snowball sampling, data were gathered through expert questionnaires from 15 specialists in five groups, selected based on relevant expertise in carbon reduction policies, particularly for pharmaceutical distribution companies in Iran.  Results : The study identified three key carbon reduction policies—cap-and-trade, subsidy allocation, and financial penalties—all shaped by distinct variables. Cap-and-trade includes the emission cap, carbon selling price, and demand for carbon emission permits. Subsidy allocation and financial penalties cover the subsidy amount and the penalty rate, respectively. Notably, carbon emission level emerged as the most influential factor in shaping policy effectiveness, while carbon reduction cost was identified as the most impactful variable. These two variables are integral to all three policies, highlighting their pivotal role in policy formulation. While the demand for carbon emission permits remains neutral regarding influence and susceptibility, other variables demonstrate complex interdependencies, creating a dynamic system where policies interact directly through primary variables or indirectly through shared criteria. Conclusion : This study contributes to environmental policy research by offering an analytical framework that integrates uncertainty logic to assess relationships among key variables. The findings suggest that implementing a single policy may not be sufficient—a combination of strategies is recommended for more effective carbon reduction. Understanding how variables interact can help policymakers and businesses design targeted and efficient pharmaceutical distribution strategies in Iran.

The banking industry is undergoing a significant transformation driven by digital technologies, evolving customer behaviors, and increasing regulatory pressures. To remain competitive, banks must adopt governance frameworks that integrate digital innovations to enhance operational efficiency and improve environmental, social, and governance (ESG) performance. This study identifies governance framework archetypes essential to digital transformation in banks through a comprehensive research methodology, including a literature review of digital governance frameworks, a comparative analysis of 11 leading digital banks worldwide, and latent class analysis to uncover key archetypes. Our findings reveal nine distinct governance archetypes, categorized into three dimensions: structural, including Centralized, Semi-centralized, and Open Innovation-oriented banks; dedicated processes, comprising Continuous Improvement, Vanguard, and Fast Follower banks; and relational mechanisms, featuring Self-empowering, Explorer, and Relationship-oriented banks. This classification advances the understanding of governance approaches that effectively support banks in their digital transformation journeys. The implications of these archetypes are substantial, offering a framework for banks to align their strategies with digital transformation initiatives. By adopting these governance structures, banks can better navigate the complexities of the digital landscape, foster innovation, and ultimately enhance their service offerings while addressing the evolving demands of customers. This research contributes to the growing body of knowledge on digital governance in banking and provides guidance for financial institutions striving to succeed in an increasingly digital world.

Objective : This research aims to propose a large-scale vehicle routing model for the distribution network of a food industry product and apply the model in a real-world case study. Methods : A mathematical model is formulated to minimize the total variable transportation costs.  Considering the complexity of the model, a constrained clustering algorithm is used to decompose the problem. Then, vehicles are assigned to demand clusters according to their capacity. Finally, each cluster's symmetric traveling salesman problem (TSP) is solved using a genetic algorithm. The parameters of the proposed genetic algorithm were calibrated based on its widespread application in solving symmetric TSPs. A conservative approach was adopted to ensure the solution's validity by evaluating a worst-case scenario considering the highest node demands.  Results : By applying the proposed algorithm to the case study, over 2,000 demand nodes across Tehran were grouped into 91 clusters. Then, based on the demand level of each cluster, the vehicles are assigned, consisting of 26 small and 65 large cars. Within each cluster, the assigned vehicle followed an optimized route among the nodes, designed based on the optimal tour generated by solving the cluster-specific TSP using the genetic algorithm, and then returned to the central warehouse. Conclusion : Comparing the results with the current situation, the size of the proposed transportation fleet showed a 40% reduction. Additionally, reducing fleet size and optimizing the routes improved the total distribution network costs by 25%. Given the model's computational efficiency, this improvement is considered satisfactory.

Blended learning is a pedagogical approach that combines face-to-face instruction with online activities and has garnered significant interest in recent years. Despite the potential benefits of blended learning, it faces several critical issues that may dampen stakeholders' enthusiasm within universities. Consequently, the current study explores the key challenges of blended learning in higher education institutions. The data for this research were collected through a qualitative approach, using a descriptive phenomenological method and semi-structured interviews with experts in the field. The findings were then analyzed using Colaizzi’s (1978) seven-step method. During our research, interviews were conducted with 14 experts in the field through purposeful sampling. From these comprehensive discussions, a total of 112 significant statements were identified. These statements were then meticulously categorized into ten initial themes, providing a structured overview of the data. Further analysis allowed us to distill the challenges associated with the blended learning approach in higher education into three critical factors. The validity of the qualitative data, based on the four criteria of Lincoln and Guba (1985)—credibility, transferability, dependability, and confirmability—was examined through re-coding by two independent coders and confirmed. The findings reveal that the challenges associated with implementing blended learning in higher education can be categorized into personal, organizational, and support-related factors. Each of these dimensions encompasses various critical elements. Notably, these research outcomes hold significant potential for facilitating the successful adoption of a pragmatic blended teaching and learning approach within higher education.

This research work aims to present a robust deep learning framework by devising a deep learning-based ensemble method of YOLOv8 and EfficientNet. The suggested model is evaluated on the dataset collected from Kaggle, comprising 10,000 high-definition images of stems, leaves, and cut fruits of banana and papaya. These images are captured under different lighting conditions and thus expanded to 80,000 images. Authors have proposed an ensemble model comprising YoloV8 and EfficientNet as base deep learning models to enhance prediction and classification performance. Here, authors combine the merits of both models, i.e., speed of YoloV8 and the accuracy of EfficientNet, by putting a majority voting method in place. The final forecast is determined by majority voting, and EfficientNet is given higher significance in the situation of a tie owing to its enhanced accuracy. The proposed model presents a robust solution for agricultural disease management and demonstrates significant improvements in the detection of diseases in papaya and banana, opening avenues for its widespread employment in real life.