Islamic Azad University South Tehran Branch

Wellbore stability is a primary objective in the the oil and gas industry, as it significantly reduces drilling risks and operational costs. In recent years, numerous studies have utilized geomechanical approaches to address this issue. However, due to insufficient and limited availability of necessary data, geomechanical studies remain a major challenge. This paper has tried to investigate wellbore stability conditions and the optimal mud weight in the Lali field using a geomechanical model to ensure efficient drilling operations in the field. In this regard, shear wave velocity estimation using the adaptive neuro-fuzzy inference system (ANFIS), was evaluated in comparison with two other approaches, multilayer perceptron (MLP) and radial basis function (RBF). To predict failure occurrence in well walls and assess stress conditions, failure criteria such as Mohr-Coulomb, Mogi-Coulomb, and Hoek-Brown were compared. A three-dimensional (3D) geomechanical model was implemented and analyzed in Petrel software based on the one-dimensional (1D) geomechanical model, considering the importance of the region’s geomechanical characteristics. The results indicate that the Mohr-Coulomb failure criterion provides reliable predictions of instabilities occurring in the Lali field. It was found that the stress regime in the field is predominantly of a reverse fault nature, occasionally transitioning to strike-slip faulting. Furthermore, the findings reveal that zones 6 and 7 of the Asmari reservoir have the narrowest safe mud weight windows, with averages of 23.64 MPa and 31.87 MPa, respectively.

Scalability is a challenging issue in blockchain technology, becoming more critical as blockchain systems grow in the amount of transaction data or number of users. Some key metrics affecting scalability in blockchain technology include transaction throughput and latency as well as the storage and communication capabilities the nodes need to participate in the network. Most proposed solutions for blockchain scalability handle only one or a few of these scalability issues assuming that all the blockchain nodes are homogeneous in computational, communication, and storage capabilities. This assumption results in degrading system efficiency and worsens scalability issues due to the unfair distribution of workload among the blockchain nodes. This paper presents a new solution for blockchain scalability that enhances scalability using multi-level sharding based on the heterogeneity of network nodes. In fact, the heterogeneous nodes are dynamically scored based on their resource capacities and contributions to the protocol and then are organized into multiple levels based on their calculated scores and form a hierarchical tree structure. Additionally, a new mechanism for consensus is introduced which makes use of the hierarchical structure to enable a more secure and decentralized consensus-making process. The simulation experiments showed that the proposed solution significantly improves various scalability measurements, including throughput, latency, storage efficiency, and communication overhead while increasing decentralization and maintaining security. The improvement rate over the scalability metrics achieved by the proposed work is proportional to the characteristics of the hierarchical tree structure such as depth and branching ratio.

In underground coal mining, erratic roof failures pose significant safety risks, often attributed to the inherent variability in rock mass properties. Traditional deterministic models typically rely on averaged values, which can lead to inaccurate strength estimations and unsafe mining conditions. This research addresses these limitations by proposing a probabilistic framework that treats rock properties as spatially correlated random variables. The primary objective is to enhance the accuracy of rock strength assessments and to improve the safety of mining operations through a robust stochastic modeling approach. To achieve this, a comprehensive database of mechanical properties was generated using the Extreme Value stochastic model, effectively capturing the variability of rock mass characteristics. The study compares results from deterministic models, which assume uniform properties, with those derived from the proposed stochastic approach. A series of laboratory tests were conducted to validate the model, and 152 random sample data sets were generated to analyze stress distribution under various loading conditions. The findings reveal that the stochastic model significantly improves predictions of rock strength variability, demonstrating an average accuracy increase of approximately 30% compared to traditional methods. This probabilistic approach provides a more realistic estimation of rock behavior and underscores the critical importance of incorporating randomness into mine design. Ultimately, this research advocates for integrating stochastic models into mining practices, enhancing safety and operational efficiency in underground coal mining.

Recent research studies have shown the advantages of using double-skin façade (DSF) and phase change material (PCM) in reducing the amount of building heat, especially for tropical and subtropical climates. Also, by adding photovoltaic (PV) cells in the facade of buildings, the solar heat of the building can be reduced and the electricity generated can lower the energy cost of the building. Since there is a strong interdependence among the energy performance of DSF, PV, and PCM as well as weather conditions, geography, and building orientation, in this paper, their simultaneous energy performance is studied under different climatic conditions of Iran. Also, all the cases are compared from an economic point of view. Based on the results obtained in this research, it can be concluded that the implementation of DSF and integration of PCM with it in the Mediterranean climate, the cold semi-arid climate, and the warm semi-arid climate of Iran is technically possible and offers the benefits of energy saving by changing the quality of spaces by using daylight as well as controlling the solar system, natural ventilation, sound insulation, and thermal insulation in winter and summer.

According to the research, zeolitic imidazolate framework (ZIF‐67) and graphene oxide (GO) nanosheets were synthesized with a guar gum (GG) biopolymer substrate to form two‐component hybrid biocomposites: GO/ZIF‐67 (GOZ), GG/ZIF‐67 (GZ), and three‐component hybrid with GO/GG/ZIF‐67 (GGZ) substrate polymer. These composites were used to adsorb malachite green (MG) cationic dye from an aqueous solution at room temperature. The chemical fractions, morphological and structural properties of the hybrid biocomposites were determined using a Fourier transform infrared spectrometer (FTIR), scanning electron microscope (SEM), X‐ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) analyses. The adsorption of MG dye was carried out on the two‐component and three‐component hybrid biocomposites with a polymer substrate under different experimental conditions. The high surface area of GZ, GOZ, and GGZ was 1362.89, 754.89, and 833.67 m² g⁻¹, respectively, and the total pore volume was 0.90 cm³ g⁻¹ for GZ, and 0.51 cm³ g⁻¹ for GOZ and GGZ, respectively. The removal of MG pollutant follows the pseudo‐second order model and the Langmuir model. The adsorption mechanism involves hydrogen bonding, π‐π stacking, and electrostatic interactions. The GOZ, GZ, and GGZ hybrid biocomposites showed the maximum removal efficiency at 66.6%, 74.4%, and 90.1%, respectively. The data show that the removal of MG after 3 cycles was 90%, 86%, and 82%.

This paper presents high-performance quaternary circuit cells, including adders and a multiplier, based on carbon nanotube field-effect transistors (CNTFETs). The proposed circuits consist of two separate parts, each of which is designed independently. The first part is a new quaternary decoder, and the second part is the main circuit body constructed by pass-transistor logic (PTL) and transmission-gate logic (TGL). These circuit methodologies result in novel quaternary designs with fewer transistors compared to the existing circuits in the literature. Several simulations by HSPICE and the 32nm CNTFET library are performed to evaluate the performance of the new circuits. Compared to previous works, the proposed designs reduce power-delay product (PDP) and energy-delay product (EDP) considerably. For example, the new quaternary full adder (QFA), with 46 fewer transistors, decreases the PDP and EDP of the best existing competitor by 32.6% and 65.3%, respectively.

The development of an effective and rapid method for healing the skin is of crucial importance. In this study, we prepared a porous scaffold made of polycaprolactone (PCL) and carbon quantum dots (CQDs), Fe, and Chitosan (Cs) as the scaffold core to cover the skin. Then evaluated antibacterial, biocompatibility, and wound healing properties as well as the expression of genes effective in wound healing. The PCL/Cs/CQD‐Fe scaffold was synthesized via electrospinning and was evaluated of morphology, functional groups, and structure through Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), and x‐ray diffraction (XRD). The viability of the L929 fibroblast stem cells was obtained. The antibacterial effect, biocompatibility, and wound healing efficiency of the scaffold were investigated through minimum inhibitory concentration (MIC), (3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT), and tissue analysis. The relative expression of genes platelet‐derived growth factor (PDGF), transforming growth factor beta (TGF‐β), and matrix metalloproteinase‐1 (MMP1) was assessed through RT‐PCR. The results of SEM showed the successful integration of the PCL scaffold with CQD‐Fe and Cs. The mean size of PCL/Cs/CQD‐Fe nanocomposite was in the range of 0.135–32.6 nm. The results of FTIR showed the formation of a link between CQD nanoparticles and Fe. The vibrating‐sample magnetometer (VSM) proved the super para magnetism of the CQD‐Fe magnetic nanoparticles (0.38 emu/g). The MIC of Cs/CQD‐Fe against Staphylococcus aureus and Escherichia coli bacteria was 0.08 and 0.04 µg/mL, respectively. The mean expression of genes TGF‐β and PDGF in the nanocomposite group were 0.05 and 0.015 on day 5 and 0.18 and 0.34 on day 15 and significantly increased after 15 days, whereas the mean expression of MMP1 in the nanocomposite group was 0.63 on day 5 and 0.12 on day 15 and significantly decreased after 15 days. According to the histological analysis, the thickest layer on Day 15 pertained to the nanocomposite group. Our findings indicated that PCL/Cs/CQD‐Fe can improve skin regeneration due to its antibacterial effect, biocompatibility, and non‐toxicity. This biocompatible nanocomposite is a scaffold that can be used for covering the skin.

Automotive companies have a stable supply chain due to extensive vehicle production and global supply networks. The purpose of sustainable supply chain intelligence in this study is to minimize system costs and environmental pollution. This study is descriptive-analytical, and transportation costs, which have a significant role in environmental pollution, were considered the main parameter, using time series forecasting by Narnet. The results showed significant differences between the predicted shipping costs from the supplier to the factory, from the factory to the distributor, from the distributor to the customer, from the customer to recycling, and from recycling back to the factory. The findings show artificial intelligence in the sustainable automotive supply chain can improve efficiency, reduce resource waste, enhance risk management, and maintain the sustainability of the supply chain.

The purpose of this research is to use the Concentration-Distance (C-D) fractal model to determine the relationship between the concentrations of ƩREEs and faults in coal seams of the North Kochakali coal deposit. For this purpose, three Concentration-Distance fractal models including: ƩREEC–DDF, ƩREEC-DSF, and ƩREEC– DTF were created based on ƩREEs concentrations and the distance from dextral, sinistral, and thrust faults, respectively. Four different geochemical populations were obtained according to fractal diagrams. The ƩREEC –DDF fractal model indicate that the very high geochemical population that includes the highest concentrations of ƩREEs in coal seams are located at a distance of 36–76 m from dextral faults with normal component, which indicates a positive relationship between ƩREEs mineralization and the distance from the dextral faults. Therefore, dextral faults with normal component were determined as the main factor of ƩREEs enrichment in coal seams of North Kochakali. Dextral faults with normal component acted after the formation of coal seams in North Kochakali coal deposit. So, the mineralization of REEs in North Kochakali coal deposit is epigenetic. Finally, the Concentration-Distance fractal model can be used as a suitable method to isolate the main mineralization and detect the relationship between faults and mineralized zones.

Metallic dampers are a common energy-dissipating tool in structural engineering. Meanwhile, these dampers have a disadvantage compared to viscous rivals, as the yielding force must be exceeded to activate the device, limiting their effectiveness across a range of potential events. Multi-level control can be employed as a way of addressing this limitation. In this paper, a novel multi-level yielding energy dissipating device, termed the Trapezoidal Multi-Level Yielding Damper (TMYD), is introduced. The TMYD employs pairs of trapezoidal plates as mechanical fuses, with each pair designed to absorb energy during specific earthquake intensities. While the number of plates is not analytically constrained, this study focuses on three pairs. The hysteretic behavior of TMYD is investigated through both experimental and numerical methods. Uniaxial tests are conducted to determine the axial force–displacement curve of a full scale prototype. A continuum-scale numerical model of the device is developed and validated against experimental results for further analysis. Additionally, analytical methods are employed to derive formulas for the optimal design of the damper. The findings demonstrate that the suggested damper has high energy absorption capabilities through reliable hysteretic loops, enhancing the performance of structures subjected to earthquake loads of varying intensities. The force–displacement characteristics of TMYD, including yield load, dissipated energy, and effective stiffness over consecutive loading cycles, are calculated. Furthermore, the experimental, analytical and numerical results are found to be in close agreement.

Self-regulated learning strategies have recently received a remarkable attention by researchers. The aim of this study was to explore the relationship between self-regulated learning strategies and students’ language proficiency as well as their reading comprehension. To do so, 115 Iranian EFL university students were selected. First, a TOEFL test was given to the participants so as to determine their language proficiency and reading comprehension. Then, they were asked to fill out Self-Regulated Learning Strategies Questionnaire (Al Asmari & Mahmoud Ismail, 2012). To analyze the data, descriptive statistics and Pearson correlation were conducted. The results revealed that there is a significant relationship between the students’ use of self-regulated learning strategies and their language proficiency. Also, a significant relationship between the students’ use of self-regulated learning strategies and their reading comprehension was found. Finally, the pedagogical message of this study is that teachers and students should incorporate self-regulated learning strategies into their teaching and learning process.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the autoregressive integrated moving average (ARIMA) model and seasonal autoregressive integrated moving average (SARIMA) model. Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

In this study, nanocomposite membranes were fabricated and characterized for use in membrane bioreactors (MBRs) to treat oily wastewater.

Due to rapid changes of technology and scientific advances in health systems and need for fast planning in health care, entrepreneurial spirit among employers and employees is a crucial element. According to the field of entrepreneurship research has not been solved and where learning and innovation for healthcare organizations due to the nature of the work required. This study aims to examine the entrepreneurial activities within the hospitals affiliated to Tehran University of Medical Sciences, Iran. To achieve the aim of the study, a questionnaire containing 29 items regarding the areas of innovation, creative behavior, flexibility, empowerment, rewarding systems and the management support was distributed among the hospitals’ managers. Establishment of a culture of entrepreneurship in healthcare organizations led to the development unit controlled, changing the culture of the hospital. The analysis of the data showed that the majority of the managers agreed with all five areas of entrepreneurship namely the existence of innovation and innovative behavior, flexibility, decision making, rewarding and encouraging system, as well as management supportive system of personnel's new ideas. In fact, the managers generally had positive attitude towards entrepreneurship in their organizations The Pearson correlation test also showed that there is a significant relationship between the areas of entrepreneurship and the managers’ age as well as their working experience (P<0.05). Entrepreneurial activities in healthcare can be improved through providing a suitable environment, adjusting reward and encouragement systems, giving more authority to subordinates, promoting awareness and education, and mobilizing managers to attract appropriate opportunities for organization. Further active involvement of employees, more stable in front of changes and increased ability managers to capture opportunities in domestic and foreign situation.

Complex sociotechnical systems with multiple competing objectives and nonlinear dynamics pose significant challenges for policy optimization. Traditional simulation-based methods often struggle with high-dimensional policy spaces. This study addresses these challenges by combining genetic algorithms (GAs) with system dynamics (SD) modeling to optimize policy configurations in complex environments. Our approach merges SD’s capacity to simulate intricate system behaviors with GA’s prowess in multi-objective optimization. The SD model uses predefined decision variables to simulate system behavior, while GA iteratively adjusts these variables to find optimal policy configurations. We apply this hybrid approach to a media industry case study, focusing on balancing profit, competitiveness, and audience satisfaction. The research methodology integrates SD’s ability to capture complex system behaviors with GA’s strength in optimizing multiple objectives. An SD model simulates system behavior based on predefined decision variables representing key policy levers. GA then iteratively adjusts these variables based on fitness objectives derived from the SD model. This process evaluates performance and identifies the globally optimal policy configurations. The results show that the hybrid SD–GA framework significantly improves policy solutions compared to conventional methods. Sensitivity analysis confirms the optimized policies’ robustness and comparative assessments highlight our approach’s advantages in navigating complex policy spaces. This study introduces a new SD–GA framework that improves data-driven policy formulation in complex systems. It combines policy informatics and evolutionary algorithms to offer a comprehensive approach to multi-criteria decision-making. Future research could include long-term validation of this approach in the broadcasting industry to further refine and apply this methodology across various domains.

This study examines a closed-loop supply chain (CLSC) comprising a manufacturer, a retailer, and a third party entity. The manufacturer presents a commodity and distributes it to the market via the retailer. The responsibility of the third party is to retrieve the used items from the consumers and deliver them to the producer for remanufacturing purposes. The producers remanufacture the secondhand items so that they turn out to have the identical quality to the renewed items. The retailer implements a return policy for their customers. The demand function is influenced by retailer pricing, goods quality, marketing effort level, and return policy. The proposed CLSC model is studied in four distinct scenarios: centralized, decentralized or Nash game, and manufacturer (leader) and retailer (leader) Stackelberg games. We determine the optimal policy in each scenario. Then give an example for evaluating the optimal outcomes, identify a preferable scenario among the four considered, and explore how important factors affect the best decisions.

With increasing worldwide attention on environmental sustainability, microgrids that harness renewable sources have become more prominent. The changing characteristics of renewable energy sources and energy demand’s unpredictable patterns might cause disruptions in the sustainable working of microgrids. Moreover, EVs (electric vehicles), being dynamic loads, might significantly affect the security administration of the microgrid. However, the persistent problem of PSOAs (particle swarm optimization algorithms) being affected by local optima emphasizes the need for more improvements to these algorithms. In order to tackle these difficulties, a framework for dual-objective optimization was developed with the aim of improving both economic efficiency and environmental sustainability in microgrids that incorporate electric vehicles. This model employs a linear weighting strategy under a TPZSG (two-person zero-sum game) to maximize the utilization of renewables options and provide support for the load. The ultimate objective is to achieve a more efficient balance between these two goals. In addition, a more advanced approach called enhanced ASA-PSOA (adaptive simulated annealing-PSOA) is employed to find the best solutions in this context. The simulation outcomes indicate that the multi-function weighting strategy can reduce the impact of uncertainties, hence optimizing the use of renewable resources and load management. Furthermore, implementing systematic charging and discharging procedures for electric vehicles has the potential to decline both operational and environmental expenses in microgrids. The total expense of the system under the proposed algorithm (ASA-PSOA) can be reduced by 11.1%, 10.1%, 6.5%, and 4.5% compared to the PSOA, standard-PSOA, adaptive-PSOA, and simulated annealing-PSOA, respectively. Therefore, the improved optimization technique greatly enhances the economic and ecological efficiency of the microgrid.

In the era of the Internet of Things (IoT), where technology has revolutionized our interaction with the world around us and bridged the gap between the physical and digital realms, providing an effective fine-grained access control system is paramount to safeguarding security of the IoT ecosystem. This paper introduces SecShield, a novel Software Defined Network (SDN)-based framework, particularly designed for IoT environments. SecShield operates by evaluating access requests and granting access to IoT services only when the set of defined access policies are satisfied. Utilizing the Attribute-Based Access Control (ABAC) model, SecShield specifies fine-grained access policies for IoT services and employs an algorithm for evaluating access requests. Additionally, the framework incorporates a local cache at the edge of the IoT network, enhanced with a Least Recently Used (LRU) algorithm, to optimize the process of access request evaluation. Experimental results validate the efficiency and feasibility of SecShield, positioning it as a viable solution for improving security of real-world IoT networks.

This research uses Python programming to explore the influence of magnetic field strength on a two-dimensional squeezing nanofluid flow confined between two parallel plates. The primary objective is to examine an aluminum oxide nanofluid's velocity and heat transfer characteristics which are governed by dimensionless parameters such as the Prandtl number and friction coefficient. The non-dimensionalized differential equations are solved employing two analytical techniques: the Akbari-Ganji Method and the Homotopy Perturbation Method, both implemented through Python programming. Using Python programming to solve these equations represents a novel approach that offers accurate and efficient results. The study's findings reveal that as the Prandtl number increases, the temperature and thermal properties of the nanofluid flow also increase while the concentration decreases. Additionally, the Nusselt number experiences a decline. The implementation of Python programming in this research showcases the versatility of the language in solving complex mathematical problems, particularly in fluid dynamics. Python's ability to provide accurate solutions efficiently enhances its potential for further applications and advancements in this area.