https://jii.araku.ac.ir/ Industrial Innovations en daily 1 Sat, 21 Mar 2026 00:00:00 +0330 Sat, 21 Mar 2026 00:00:00 +0330 https://jii.araku.ac.ir/article_732722.html In this research, the flow behavior in the secondary bottom outlet of the Roudbar- Lorestan Dam was numerically analyzed using ANSYS CFX software, with an emphasis on predicting the length of the issuing jet and the possibility of cavitation occurrence. For validation, the outputs of the numerical model were compared with experimental results obtained from the dam’s reduced-scale physical model (1:20). The meshing process and grid independence study showed that using cubic elements with approximately 900,000 elements could reduce the error in the issuing jet length to less than 8%. After evaluating various turbulence models, the K−ω model was selected as the superior turbulence model with a mean relative error of 7.88%. Furthermore, the results indicated that at the maximum flow rate, the simulated jet length was predicted to be about 22% shorter than the initial design state. Analysis of local pressures and the cavitation index also showed that in the high-velocity flow regions and near the gate, the cavitation index reached a minimum value of 0.78, which is close to the threshold for the occurrence of cavitation. In contrast, the values of this coefficient in the middle and end sections of the jet were higher than 1.2, and the probability of cavitation in these areas was assessed as very negligible. Overall, the findings of this research demonstrate that numerical simulation can be used with high accuracy, exceeding 95%, in predicting the jet length and evaluating cavitation conditions, and is considered an efficient tool for the analysis and optimization of the design of dam bottom outlet. https://jii.araku.ac.ir/article_733399.html This paper presents ultra-wideband continuous wave radars in the frequency sweep of 0.5 to 8 GHz, which search for moving and stationary objects behind walls. As electromagnetic waves propagate from one homogeneous medium to another create a change in the wave impedance at the boundary of the medium. Impedance mismatch generally leads to reflection, absorption, and transmission of electromagnetic waves. Therefore, it is necessary to investigate the reflection coefficient and transmission coefficient of different materials to understand their behavior towards electromagnetic waves. The main advantages of ultra-wideband for short-range radar tracking and imaging include very good resolution, high power efficiency, low interference, and the ability to detect moving or stationary targets in various industrial environments. Through-The-Wall (TTW) radars have emerged in recent years as a new method of industrial and non-destructive imaging. The operation of these radars is based on the emission and reception of electromagnetic waves through physical obstacles such as brick and concrete walls. https://jii.araku.ac.ir/article_727684.html Accurate metal and alloy identification plays a key role in reducing costs and increasing productivity in various industrial applications. However, some industries are unable to utilize advanced metal identification equipment, such as XRF, due to financial constraints. In such circumstances, the use of simple, fast, and low-cost methods such as spark testing to identify metals can be a suitable and practical alternative. Spark testing can identify different alloys and metals based on examining the shape, color, and different geometrical properties of sparks resulting from the contact of metal with a grinding machine. Given the importance of this issue, this study was conducted based on the identification of metals according to the spark pattern produced by grinding the sample. This research demonstrated that different grades of steels, such as 420 (stainless steel), 316(stainless steel), and A105 steel(carbon steel), can be reliably identified from one another by analyzing the spark patterns and geometrical features. The results indicate that the difference in the composition of alloying elements leads to the production of spark patterns with distinctive characteristics that can be identified with sufficient experience and accuracy in interpreting the spark patterns. As an example, based on the presented results in this paper, the spark length in A105 steel was approximately 33 mm longer than that of 316, and its spread angle exceeded that of 316 steel by 3.5 degrees. This research aims to explain the practical application of the spark test as an effective experimental method for rapid identification of metals in industrial environments. https://jii.araku.ac.ir/article_733389.html Investment in the stock market, despite its high return potential, is always associated with significant risks. The quality of a stock portfolio significantly depends on the future performance of the market and the selected industries for investment. This research aims to reduce this risk and improve returns by presenting an integrated four-stage approach for constructing an industry-based stock portfolio in the Iranian capital market.In the first stage, using Dempster-Shafer theory and three technical depth indicators, MACD, RSI, and SMA, the future state of the market (bullish or bearish) is predicted. If a bearish trend is predicted, investment is redirected to low-risk assets such as Islamic Treasury Bills. If a bullish trend is predicted, the second stage begins, and industries are grouped using the K-Means clustering algorithm to create a basis for diversification. In the third stage, industries within each cluster are ranked using the TOPSIS method based on the same technical indicators. Finally, in the fourth stage, the top industry from each cluster is selected, and an equal-weighted stock portfolio is formed.Model evaluation over a six-half-year period (from early 2021 to the end of 2022) on the top ten industries of the Iranian capital market showed that the model's accuracy in predicting market trends was 83%. Furthermore, the portfolio formed based on the model's selected industries had a significantly higher return than a portfolio consisting of the entire market (equal-weight index). The results of this study indicate the desirable performance of the proposed model, and it is suggested that active investors use this framework to form an optimal portfolio. https://jii.araku.ac.ir/article_734788.html In recent years, the application of machine learning techniques in the field of predictive vehicle maintenance has emerged as a highly effective approach for reducing unexpected breakdowns, enhancing operational safety, and optimizing maintenance and repair costs. Predictive maintenance aims to anticipate potential failures before they occur, thereby minimizing vehicle downtime, reducing maintenance expenses, and improving overall system reliability. The primary objective of this study is to develop, implement, and evaluate a range of machine learning models for predicting vehicle maintenance needs using operational and performance data collected from various vehicle subsystems, including engine, transmission, braking, and electrical systems. The dataset used in this research underwent thorough preprocessing, including data cleaning, normalization, and handling of missing values, followed by a division into 80% training and 20% testing sets. Twelve machine learning algorithms were implemented and compared, comprising traditional methods such as Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF), as well as ensemble methods including AdaBoost, Bagging, Stacking, XGBoost, CatBoost, and LightGBM. The models’ performance was systematically evaluated using multiple metrics: accuracy, precision, recall, F1-score, and ROC-AUC, providing a comprehensive assessment of both classification ability and predictive reliability. The experimental results demonstrated that tree-based algorithms and ensemble approaches consistently outperformed other methods in terms of predictive accuracy and robustness. Among them, LightGBM achieved the highest performance, with an AUC of 0.9475 and an F1-score of 0.9613, indicating superior capability in capturing complex patterns and correlations in vehicle operational data. The findings of this research highlight the significant potential of machine learning techniques—particularly ensemble models—in predictive maintenance applications. These methods can serve as a foundation for designing intelligent maintenance systems that enhance vehicle reliability, reduce operational costs, and support proactive decision-making in automotive fleet management. https://jii.araku.ac.ir/article_735284.html The main objective of this study is to identify and prioritize the various factors affecting innovation systems in the food industry and to assess the impact of innovation system factors on regional sustainability. To achieve this goal, a descriptive-correlational research method was used, employing a structured questionnaire as the primary data collection tool, and the statistical population of this study included professors and experts in innovation who were selected through purposive sampling methods. Data analysis was performed using structural equation modeling (SEM) and fuzzy analytic hierarchy process (Fuzzy AHP) methods. The SEM approach facilitates the examination of complex relationships between variables, while the fuzzy AHP method increases the accuracy of assessments and decision-making in the face of uncertainty. The combination of these two methods provides more comprehensive and reliable insights into the factors affecting innovation systems. The findings show that individual, organizational, environmental, and material factors significantly influence regional sustainability, and individual factors appear as the most important element in enhancing innovation processes. Furthermore, the validity and reliability of the proposed model demonstrate its applicability in both theoretical and practical research fields. Despite the recognized importance of innovation systems in enhancing regional sustainability and supporting food industry companies, there is still a lack of comprehensive models in the marketing field that address this issue. Consequently, it is essential to develop a model that clarifies the relationships between innovation systems and sustainability. Such a model can enable organizations to understand better and effectively manage regional sustainability dynamics. Ultimately, this research contributes to the existing body of knowledge by providing actionable insights that can guide policymakers and industry leaders in fostering innovation-driven sustainable practices in the food sector. https://jii.araku.ac.ir/article_735499.html In the contemporary world, e-commerce has emerged as a key pillar of the digital economy, bringing about profound transformations in the structures and traditional patterns of business. The rapid expansion of modern communication technologies and the evolving behavior of consumers have highlighted the growing need to reconsider the planning and design of commercial activity centers. This study aims to examine the impact of various dimensions of e-commerce on the planning and design of such centersFor data analysis, structural equation modeling was conducted using the Partial Least Squares (PLS) approach via SmartPLS software, which proved to be an appropriate and reliable method given the characteristics of the collected data. The results of the analysis revealed that components such as technical quality, functional quality, service perception, customer satisfaction, and brand image each have a positive and significant impact on the planning and design of commercial activity centers. Moreover, the validity of the conceptual model was confirmed through the evaluation of factor loadings and statistical indicators, indicating the reliability and validity of the measurement tools used.The findings of this research suggest that, within the context of e-commerce, attention to users’ qualitative and perceptual factors plays a crucial role in optimizing the design of commercial spaces. This can lead to improved user experience and enhanced operational efficiency in meeting new customer demands. These insights can inform the strategies of policymakers, urban designers, and commercial project managers in developing innovative approaches to planning and design in the digital age. Keywords: Electronic Commerce (EC) Customer Satisfaction Technology Business Center Planning and Design https://jii.araku.ac.ir/article_735500.html With the expansion of advanced technologies, particularly artificial intelligence, and the increasing complexity of competition in today’s markets, organizations are compelled to leverage technological and strategic tools to enhance their performance. Artificial intelligence strengthens the value creation process by extracting meaningful insights from data, enabling the development of more precise, predictive, and customer-centric marketing strategies. These insights, through personalization, analysis of user needs, and the design of data-driven marketing actions, contribute to creating sustainable competitive advantages and improving decision-making effectiveness. Knowledge-based companies, due to their innovative nature, are more influenced than other businesses by technological capacities, marketing strategies, and organizational behavior. However, the simultaneous examination of the effects of artificial intelligence and marketing on organizational performance, and the role of organizational capacities and behavior in this relationship, has received less attention. The present study is applied and descriptive–survey in nature. The statistical population included 170 managers of knowledge-based companies located in the Khorasan Razavi Science and Technology Park, of whom 118 were randomly selected. Data were collected using a standard 30-item questionnaire and validated through factor analysis, and its reliability was confirmed. Data analysis was conducted using descriptive statistics and structural equation modeling in SmartPLS. The results indicated that both artificial intelligence and marketing strategies have positive and significant effects on organizational performance. Organizational capacities play a mediating role, while organizational behavior acts as a moderating factor in these relationships. Leveraging insights from AI and data-driven marketing, along with strengthening infrastructure, employee training, and fostering a learning-oriented organizational culture, enhances organizational effectiveness and provides important practical implications for developing transformative strategies in knowledge-based companies. https://jii.araku.ac.ir/article_735501.html Given the increasing complexity of supply chains and the importance of the role of suppliers in improving the economic and environmental performance of organizations, selecting appropriate suppliers in large and reverse supply chains has become one of the fundamental challenges of manufacturing industries. The steel industry, as one of the parent industries, needs to use modern and systematic models in this area more than other industries. Therefore, the main goal of this research is to identify and classify the components affecting the selection of suppliers in the large and reverse supply chains in the steel industries of Chaharmahal and Bakhtiari province using the method of content analysis and interpretive structural modeling. This research is applied and mixed. Initially, through semi-structured interviews with 12 managers of the steel industry of Chaharmahal and Bakhtiari province and university professors who were selected by theoretical sampling method, the components were identified, data were collected and codes were collected, the components affecting the selection of large and reverse supply chain suppliers in the steel industry of Chaharmahal and Bakhtiari province were counted in 5 main categories and 12 subcategories and compiled in the form of a thematic analysis network. The method of analyzing the information in the quantitative part included interpretive structural modeling. The results of the quantitative part showed that there are 12 components affecting the selection of large and reverse supply chain suppliers, of which two sub-criteria of improving brand image and environmental pressures, demand management techniques and risk management resulting from sudden changes have the highest level and the most impact. And the sub-criteria of customer orientation has the lowest level and the most impact. The presented model can provide significant help and guidance to managers in selecting the most appropriate large and reverse supply chain suppliers. https://jii.araku.ac.ir/article_735502.html This study aims to develop an integrated analytical framework for evaluating and ranking distribution centers and sales channels within a viable supply chain in the lubricant industry (case study: Behran Oil Company), with simultaneous emphasis on circular economy principles and the requirements of Industry 5.0. The research adopts a multi-criteria decision-making approach within a two-stage scenario-based model. In the first stage, evaluation criteria were identified through literature review, industrial documents, and expert opinions, and categorized into six dimensions. The criteria were then weighted using a stochastic fuzzy Best–Worst Method under three scenarios: optimistic, most likely, and pessimistic. In the second stage, distribution centers and sales channels were assessed and ranked using stochastic fuzzy TOPSIS. The results indicate that resilience is the most significant dimension, followed by sustainability and agility, while digitalization received the lowest weight. Among sub-criteria, distribution capacity flexibility and operational recovery speed were the most influential factors. The ranking results show that Center 6 achieved the highest performance, whereas Center 3 demonstrated the weakest performance. Comparative analysis with conventional methods confirmed the robustness and reliability of the proposed approach. The framework provides a practical tool for strategic decision-making at the mid-network level. The aim of this research is to provide a comprehensive analytical framework for evaluating distribution centers and sales channels in a sustainable supply chain with an emphasis on the requirements of the Fifth Industrial Revolution. This study attempts to explain, with an integrated and forward-looking perspective, the role of economic, environmental, social, resilience, agility, and digitalization criteria in the evaluation and decision-making process and provide practical insight for industrial managers and policymakers, especially in the lubricant industry. In this regard, a two-stage decision-making model has been designed and developed that systematically identifies, weights, and ultimately uses effective criteria and sub-criteria to evaluate and score distribution centers and sales channels. https://jii.araku.ac.ir/article_735610.html Calcium oxide catalyst derived from eggshell waste was used for biodiesel production via transesterification of sunflower oil. The eggshell waste was calcined in air at 900 °C for 4 hours to convert the calcium carbonate present in the eggshells into active calcium oxide catalyst. To identify the composition of the CaO catalyst obtaiCalcium oxide catalyst derived from eggshell waste was used for biodiesel production via transesterification of sunflower oil. The eggshell waste was calcined in air at 900 °C for 4 hours to convert the calcium carbonate present in the eggshells into active calcium oxide catalyst. To identify the composition of the CaO catalyst obtained from eggshell waste, X ray fluorescence (XRF) was used. The fatty acid composition of the sunflower oil was also determined by gas chromatography (GC). A factorial design of experiments was employed to evaluate the effect of various parameters (reaction time, methanol to oil molar ratio, and catalyst amount). The use of CaO catalyst derived from eggshell waste for biodiesel production gave a high yield of >95 wt.%. The catalyst performance was achieved at 60 °C, with a reaction time of 4 hours and a methanol to oil molar ratio of 12. Producing CaO catalyst simply by calcining eggshell waste provides an opportunity to use this waste material as an effective catalyst for biodiesel production from sunflower oil. The biodiesel yield remained above 75 wt.% after five cycles of recovery and reuse, which are promising results for the industrial application of this catalyst. https://jii.araku.ac.ir/article_735611.html Given the importance of the role of managers as critical decision-makers and participants in firms, the purpose of this study is to examine the moderating effect of managerial discretion on blockchain technology and firms’ innovation quality. The statistical population of this study includes all academic experts, managers, and practitioners in manufacturing companies in Tehran Province who were in some way involved with blockchain technology, from whom 384 individuals were selected as the sample. The data collection instrument was a questionnaire, and the research data were analyzed using the structural equation modeling method and the statistical software SPSS and SmartPLS. In this study, the moderating effect of three types of managerial discretion namely environmental discretion, organizational discretion, and extra discretion on the relationship between blockchain technology and firms’ innovation quality was examined. The results showed that environmental discretion, with a path coefficient of 0.253 and a significance value of 3.326, strengthens the positive relationship between blockchain technology and firms’ innovation quality. Organizational discretion, with a path coefficient of −0.642 and a significance value of −8.465, weakens the positive relationship between blockchain technology and firms’ innovation quality. Moreover, extra discretion, with a path coefficient of 0.291 and a significance value of 3.865, strengthens the positive relationship between blockchain technology and firms’ innovation quality. In explaining the research findings, it can be stated that we have entered the golden age of information technology, which has blurred the managerial boundaries of firms, and this is especially true in companies where blockchain technology is well developed and widely used. Accordingly, companies should pay sufficient attention to innovations related to technical skills. This result provides an inspiring and useful reference for company managers when undertaking technological innovation and making strategic innovation decisions.