Yazd University

Hosseini Bamakan S.M., Far S.B.

The fourth industrial revolution has significantly increased the adoption of Digital Twins (DTs) across various sectors, including intelligent manufacturing, automation, logistics, and medical analysis. Despite substantial progress in deploying DT projects, numerous challenges remain, such as managing distributed data flows, protecting commercial confidentiality, securing intellectual property, and ensuring privacy and security. This research introduces a novel approach to addressing these challenges by leveraging blockchain and Web3 technologies, including non-fungible tokens (NFTs) and Distributed Autonomous Organizations (DAOs). The study aims to develop a distributed, tamper-proof DT cooperation platform that facilitates traceable and trustworthy data sharing while preserving intellectual property rights and enabling decentralized governance. This platform enhances idea and invention ownership, promotes collective decision-making through consensus protocols, and explores innovative solutions like blockchain-based federated learning and efficient DT project fundraising tools. Relying on security models and analysis tools, this study addresses several important security analysis methods/tools/models (e.g., random oracle model, BAN logic, AVISPA tool, and TAMARIN prover) that are known as practical analysis methods. They can strongly prove every claimed security feature of DT projects. The proposed solutions set the stage for future academic and industrial advancements, supported by a comprehensive SWOT (Strongness - Weakness - Opportunity - Threat) analysis, and outline potential future research directions.

Sheikholeslami S.M., Ng P.C., Abouei J., Plataniotis K.N.

Ostovari A., Bozorgi Amiri A., Yousefi-Babadi A., Benyoucef L.

Aghashahi S., Zeinalpour-Yazdi Z., Tadaion A., Mashhadi M.B., Elzanaty A.

Lotfi R., Shafiei R.M., Komeleh M.G., Pasha F.G., Ferasat M.

This study aims to develop a solution for the Viable Vaccine Supply Chain Network Design (VVSCND) problem, which concurrently addresses multiple factors such as sustainability, resiliency, agility, risk, and robustness. To achieve this, the researchers propose a Mixed-Integer Linear Programming (MILP) model based on a Robust Stochastic Programming (RSP) approach, which minimizes the weighted expected and max cost function. Furthermore, the research considers factors such as limiting CO2 emissions, introducing flexible capacity, and ensuring the reliability and redundancy of facilities to establish a more agile and environmentally sustainable supply chain. The key decisions in the proposed methodology involve determining facility location and product flow within an efficient healthcare system. According to the findings, the cost function of the VVSCND problem was only marginally higher, by 0.04%, than its counterpart in a VSCND that did not consider any risks or worst-case scenarios. However, increasing the conservatism coefficient or agility coefficient by 50% and 10%, respectively, leads to similar increases in the cost function. Similarly, the resiliency coefficient has a direct relationship with the cost function. Overall, the study demonstrates that an optimal VSCND can be achieved by considering multiple factors through RSP-based MILP modeling.

Ahmadi-Nedushan B., Akbarzaghi M., Tajammolian H.

Ghoroghchian F., Du Y., Amiri E., He Z., Aliabad A.D., Rahideh A.

Shahbazi M., Najafi M., Fatehi Marji M., Abdollahipour A.

ABSTRACTThe mechanism of cavity growth in a UCG process is mainly dependent on the presence of fractures and microcracks in the coal seam. In this study, the rate of cavity growth and the crack propagation mechanism in brittle coal samples under high thermal conditions are investigated using a two‐dimensional particle flow code (PFC2D). Coal samples with different cleats' orientation under thermal environments are numerically simulated. The numerical modeling results show that the induced thermal stress is one‐third of the coal sample failure stress. This is due to the increase in particles' volume, the change in normal force between the particles' bonds, and the changes in thermal and mechanical parameters resulting from the applied source temperature, which breaks the bond around the particle. The effects of heat and heterogeneity on the strength of coal samples are also studied under different temperatures ranging from 50°C to 900°C. The results showed that the presence of high‐strength coal seams reduces the formation and propagation of heat‐induced cracks, consequently reducing the cavity growth rate. The soft coal sample has more plasticity, and the cavity growth rate in the soft coal is more than that of the hard coal. The elasticity modulus and uniaxial compressive strength decrease with the increase of the source temperature and the sample begins to deform in a plastic mode. Also, increasing temperature causes an exponential increase in thermal stress. From the fracture mechanics point of view, knowing the conditions and the mechanism of pre‐existing crack propagation in the coal seam can lead to a correct understanding of cavity growth during the UCG process.

Afaridegan E., Fatahi-Alkouhi R., Khalilian S., Moradi-Eshgafti A., Amanian N.

This study aims to accurately predict the energy dissipation rate (EDR) in modified semi-cylindrical weirs, which is essential for their efficient design. Three machine learning models—locally Weighted polynomial regression (LWPR), random forest (RF), and categorical boosting (CatBoost)—were applied individually to estimate the EDR. Additionally, four hybrid models combining these individual approaches were developed: LWPR-RF, RF-CatBoost, LWPR-CatBoost, and RF-CatBoost-LWPR. Sensitivity analysis using the Gamma Test and SHAP (Shapley Additive Explanations) was conducted to assess the influence of key dimensionless parameters on the EDR. The analysis revealed that the ratio of critical depth to the crest radius (dC/R) and the downstream ramp angle (θ) significantly affected the EDR. Laboratory data reflecting diverse hydraulic conditions were split into a 75% training set and a 25% testing set for model development and validation. For model evaluation, mean absolute error, mean percentage error, root mean square error, correlation coefficient (R2), mean absolute relative error, Scatter Index, Nash–Sutcliffe efficiency, and percent bias were used. To compare and rank the models, the Taylor diagram, regression error characteristic, and Performance Index (PI) were employed. The results showed that the hybrid models outperformed the individual models during training, with RF-CatBoost-LWPR achieving the highest PI = 4.64 and the lowest centered root mean square error (E' = 0.0091). The RF-CatBoost model followed closely with a PI of 4.6 and an E' of 0.0092. During the testing stage, all models performed similarly, with the single models slightly outperforming the hybrid models by a small margin. The LWPR model emerged as the top performer, achieving a PI of 0.44 and an E' of 0.0862. Closely following was the CatBoost model, ranking second with a PI of 0.43 and an E' of 0.0864. Despite these minor differences, all models demonstrated strong predictive capabilities during testing.

Soltaninejad S., Abdollahi M.S., BP N., Marandi S.M., Abdollahi M., Abdollahi S.

The Jiroft Dam, situated in Kerman province, Iran, serves as a crucial infrastructure for water management, flood control, and agricultural development in the region. However, the surrounding mountainous terrain presents considerable geotechnical challenges that threaten the stability of access roads and other essential infrastructure. This study is based on comprehensive field surveys and mapping, which have revealed significant ground displacements and evidence of slope instabilities in the area. The investigation identifies key factors, including soil composition, rock formations, groundwater flow, and seismic activity, that contribute to these shifts in the terrain. To ensure the accuracy of the elevation data, the study employed Monte Carlo simulation techniques to analyze the statistical distribution of the collected survey data. By simulating various possible outcomes, this study enhanced the precision of the elevation models, allowing for better identification of critical instability zones. Additionally, the Overall Equipment Effectiveness (OEE) was utilized to evaluate the effectiveness of the current monitoring equipment and infrastructure, providing a clearer understanding of operational efficiency and areas for improvement. The findings of this study highlight the immediate need for effective risk management strategies to mitigate the potential hazards of landslides and infrastructure failure. Addressing these challenges is essential to ensure the long-term sustainability of the region’s infrastructure. In response to these observations, this research proposes practical engineering solutions such as slope stabilization techniques and improved drainage systems to address the identified instabilities. Furthermore, this study underscores the necessity of the continuous monitoring and the implementation of early warning systems to detect further ground movements and mitigate associated risks.In addition to technical interventions, this research emphasizes the importance of integrating local knowledge and expertise into the risk management process.

Niazkar M., Piraei R., Goodarzi M.R., Abedi M.J.

This study aims to assess performances of eleven Machine Learning (ML) methods in predicting the Groundwater Quality Index (GWQI) for Yazd, an arid province in Iran. The ML models encompass Multiple Linear Regression (MLR), Support Vector Regression (SVR), K-Nearest Neighbors, Decision Tree Regression, Adaptive Boosting or AdaBoost, Random Forest Regression, Gradient Boosting Regression (GBR), XGBoost Regression (XGBR), Gaussian Process (GP), Artificial Neural Network (ANN), and Multi-Gene Genetic Programming (MGGP). After optimizing ML hyperparameters, ML-based estimation models were developed for three scenarios depending on which water quality parameters were used as input data: (1) K+ and pH; (2) K+, pH, Na+, Ca2+, SO42-, HCO3- and Mg2+; and (3) K+, pH, Na+, Ca2+, SO42-, HCO3-, Mg2+, Cl-, EC, TH, and TDS. For each scenario, ML-based models were assessed further by conducting (i) reliability analysis, (ii) ranking analysis, and (iii) confidence limits check. The results of the first scenario (with two input data) demonstrated the superiority of ANN, MGGP and GP, whereas ANN, MGGP and GBR were the most robust for the second scenario (with seven input data). Furthermore, the ranking analysis indicated that MLR, GP and ANN achieved the first highest ranks when eleven water quality parameters (third scenario) were used. The reliability analysis revealed that GP, MGGP, MLR, ANN, GBR, and XGBR achieved the highest reliability percentages across different scenarios, with ANN consistently ranking among the top models. Finally, the comprehensive comparative analysis of ML performances in this study reveals their potential for predicting GWQI. • Groundwater quality was assessed using a dataset collected from wells of an arid region • Eleven machine learning models were evaluated for estimating groundwater quality index • Three scenarios were compared based on WHO permissible limits • Reliability and ranking analyses were conducted for estimations of each ML model

Zarezadeh Mehrizi R., Bafghi A.F., Nasiri V., Sarafraz Ardakani M.R., Meybodi M.N., Zare-Zardini H.

Leishmaniasis remains a significant global health concern, ranking among the top ten infectious diseases and causing substantial mortality and socioeconomic burden. Effective and accessible treatments are needed. This study investigated the potential of a hydroalcoholic extract from readily available urban green algae as an anti-leishmanial agent, focusing on its impact on key weight-related indicators of Leishmania major infection in BALB/c mice. To evaluate the in vivo anti-leishmanial activity of the hydroalcoholic extract from the common green algae genus Spirogyra against Leishmania major in BALB/c mice, specifically by assessing its effects on weight loss, lesion size, liver weight, and spleen weight—key indicators of disease progression. Spirogyra algae were collected and identified in Yazd Province, Iran. A hydroalcoholic extract was prepared and administered via intraperitoneal injection into Leishmania major-infected BALB/c mice at doses of 3, 6, and 12 mg/kg/day, starting after lesion development. The control groups included untreated infected mice (negative control), healthy uninfected mice (control), and infected mice treated with Glucantime (positive control). We assessed treatment efficacy by monitoring weight loss, lesion diameter, liver weight, and spleen weight. Treatment with the highest concentration of Spirogyra extract (12 mg/kg/day) significantly mitigated weight loss in infected mice, demonstrating comparable efficacy to Glucantime. Both the 12 mg/kg/day algae extract and Glucantime significantly controlled lesion growth. Importantly, both treatments significantly reduced liver and spleen weight compared with the negative control group, indicating a reduction in organomegaly. Specifically, the negative control and 3 mg/kg extract groups exhibited the highest liver weights, whereas the negative control group showed significantly higher spleen weights than the other groups. The 12 mg/kg extract and Glucantime groups showed liver and spleen sizes comparable to the healthy control group, demonstrating effective control of organ size changes associated with leishmaniasis. The hydroalcoholic extract of urban Spirogyra green algae, particularly at a dose of 12 mg/kg/day, exhibited significant in vivo anti-leishmanial activity in BALB/c mice. Evaluated through weight indicators such as reduced weight loss, controlled lesion growth, and normalized liver and spleen weights, the extract showed promise in mitigating the detrimental effects of Leishmania major infection and warrants further investigation as a potential source for novel anti-leishmanial therapeutics.

Shahedi S., Daneshfar Z., Hasani S., Hashemi S.M., Mashreghi A.

Biomedical science, nowadays, benefits from ferromagnetic nanoparticles for diverse applications. Ferrites are a group of promising materials whose properties can be modified by changing synthesis parameters, doping, and coating to make them suitable for biomedical applications, like drug delivery. In this study, Dy-doped Mn-Zn ferrite (DMZF) nanoparticles were synthesized using a self-combustion sol-gel method and subsequently coated with chitosan (CS). Further polyethylene glycol (PEG) coating was conducted to increase their stability. In order to characterize and investigate the properties of coated DMZF nanoparticles, X-ray diffraction (XRD) with Rietveld refinement, transmission electron microscopy (TEM) and field emission scanning electron microscopy (FE-SEM), thermogravimetric and differential thermal analysis (TG-DTA), Fourier-transform infrared spectroscopy (FTIR), and vibrating sample magnetometer (VSM) analyses were performed. Dynamic light scattering (DLS), zeta potential, and UV-visible spectroscopy were also conducted to estimate the practicality of the nanocarriers. Based on the results obtained, the nanoparticles coated at a 1:1:1 mass ratio of particle: chitosan: glutaraldehyde (as a crosslinker) exhibited a notable performance with high saturation magnetization (~57 emu/g), high zeta potential value (~ −24 mV), and hydrodynamic size of ~460 nm. The oxaliplatin loading on the CS- and PEG-CS-coated nanoparticles was 48% after 48 h. 35% of the loaded drug on the PEG-CS-coated DMZF was released at an acidic pH of 5.5 during 24 h. The corresponding amount for the CS-coated DMZF was 68%.

Hassani Sadrabadi E., Davvaz B.

In this paper we verify a connection between fuzzy sets, biological inheritance and hyperstructures. We analyse the second type Supplementary of non-Mendelian inheritance also simple inheritance and determine the sequences of join spaces and fuzzy sets associated to each of its types, focusing on the calculation of their lengths that is called the fuzzy grade of H.

Dehghani H., Dehghan A.A., Mirjalily S.A.