Qazvin Islamic Azad University

Background Cognitive networks impairments are common in neuropsychiatric disorders like Attention Deficit Hyperactivity Disorder (ADHD), bipolar disorder (BD), and schizophrenia (SZ). While previous research has focused on specific brain regions, the role of the procedural memory as a type of long-term memory to examine cognitive networks impairments in these disorders remains unclear. This study investigates alterations in resting-state functional connectivity (rs-FC) within the procedural memory network to explore brain function associated with cognitive networks in patients with these disorders. Methods This study analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ADHD, 49 with BD, 50 with SZ, and 50 healthy controls (HCs). A procedural memory network was defined based on the selection of 34 regions of interest (ROIs) associated with the network in the Harvard-Oxford Cortical Structural Atlas (default atlas). Multivariate region of interest to region of interest connectivity (mRRC) was used to analyze the rs-FC between the defined network regions. Significant differences in rs-FC between patients and HCs were identified (P < 0.001). Results ADHD patients showed increased Cereb45 l - Cereb3 r rs-FC (p = 0.000067) and decreased Cereb1 l - Cereb6 l rs-FC (p = 0.00092). BD patients exhibited increased rs-FC between multiple regions, including Claustrum r - Caudate r (p = 0.00058), subthalamic nucleus r - Pallidum l (p = 0.00060), substantia nigra l - Cereb2 l (p = 0.00082), Cereb10 r - SMA r (p = 0.00086), and Cereb9 r - SMA l (p = 0.00093) as well as decreased rs-FC in subthalamic nucleus r - Cereb6 l (p = 0.00013) and Cereb9 r - Cereb9 l (p = 0.00033). SZ patients indicated increased Caudate r– putamen l rs-FC (p = 0.00057) and decreased rs-FC in subthalamic nucleus r – Cereb6 l (p = 0.000063), and Cereb1 r – subthalamic nucleus r (p = 0.00063). Conclusions This study found significant alterations in rs-FC within the procedural memory network in patients with ADHD, BD, and SZ compared to HCs. These findings suggest that disrupted rs-FC within this network may related to cognitive networks impairments observed in these disorders. Clinical trial number Not applicable.

Background Diabetes mellitus is a global health epidemic affecting millions of individuals worldwide. Self-care practices, including regular physical activity, play a crucial role in managing diabetes and preventing its complications. However, most patients with diabetes fail to engage in sufficient physical activity. This study aimed to identify the barriers to participating in physical activities for diabetic patients upon referral to Qazvin City, Iran. Methods The current descriptive analytical study was conducted at Velayat Hospital in Qazvin, Iran. A total of 333 patients were enrolled in the study through a convenience sampling method. Data collection involved the use of a demographic questionnaire comprising 11 items, along with a questionnaire designed to assess barriers to physical activity, which included 44 items. Data analysis utilized a variety of statistical techniques, encompassing both descriptive and inferential statistics, such as independent t tests, one-way ANOVAs, and Pearson correlation coefficients. A significance level of p < 0.05 was maintained throughout the study. Results The average age of the participants was 64.4 ± 7.2 years. Among the participants, 168 individuals (50.5%) were female, while the remainder were male. Notably, the majority of participants (57.2%) had attained a high school education or higher. The most frequently reported barriers to physical activity were associated with structural and individual factors. Conclusion The results suggest that the availability of appropriate facilities and infrastructure is a critical factor in determining the amount of sports participation. Therefore, it is recommended that policymakers and healthcare providers prioritize the development and maintenance of accessible and inclusive sports facilities, as well as the implementation of policies that promote physical activity among diabetic patients. Furthermore, the establishment of a public non-governmental organization (NGO) can be a valuable strategy for increasing the participation of diabetic patients in sports activities.

The rapid growth and increasing popularity of cloud services have made effective resource management and energy consumption in data centers crucial. Virtual Machine (VM) consolidation is a widely adopted strategy to reduce energy consumption and minimize Service Level Agreement (SLA) violations. A key challenge in this process is the placement of VMs, which significantly impacts data center efficiency. Despite substantial progress in VM placement techniques, challenges remain, particularly in accurately identifying and managing underloaded and overloaded physical machines. To address these challenges, this paper proposes a novel stochastic process-based method for VM placement. The proposed approach uses a stochastic process-based prediction technique to estimate the probabilities of overload and underload in physical machines. By strategically placing VMs in machines that are predicted not to be underloaded or overloaded in the near future, our method optimizes resource allocation and reduces the frequency of migrations, energy consumption, and SLA violations. The effectiveness of the proposed method is validated using both the CloudSim simulator and the real-world PlanetLab dataset. Simulation results demonstrate that our approach outperforms existing methods in achieving a balance between energy efficiency and SLA compliance, while also minimizing VM migration overhead.

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.

This paper proposes a novel method for determining the initial strip width for thick-walled steel pipes in roll forming. The method introduces three criteria to define the minimum initial width, maximum initial width, and a refined selection within the specified range. An industrial roll forming line is simulated using the commercial finite element software Abaqus. The accuracy of the finite element model is validated by comparing its results with measurements from the production line. Subsequently, the appropriate initial strip width is determined for producing pipes from St37 steel, with an external diameter of 219.1 mm and thicknesses of 6, 10 and 14 mm. This determination is based on criteria defined for circumferential reduction, edge buckling and relative curvature of the strip deforming at the fin-pass forming stage. The results demonstrate that a thicker pipe requires a lower initial width but a higher reduction ratio in the fin-pass station. In conclusion, the proposed method shows great potential for use in the pipe industry.

Cognitive Innovative consumers are an important market segment for marketers. Revenue from new products adopted by innovative consumers plays a pivotal role for many firms. Hence having a correct understanding from the behavior and style of their purchase helps the firms to create and implement effective marketing plans for the new products. The current study investigates the behavior and shopping style of cognitive innovative consumers in electronic banking services through a hierarchical perspective. This research is quantitative and is practical in terms of the purpose. Yet it is a field study in terms of data gathering. The statistical population of this research includes the students of Azad University of Qazvin in Iran and the sample size is 384 persons. The resulted findings from this research, verify the hierarchy perspective of consumer innovativeness, specially the fact that cognitive innovativeness and domain-specific innovativeness are the best combination of predicting the adoption of new product behavior. Moreover the adoption behavior of these consumers follows the quality consciousness style. Results show that banks should target the cognitive innovative customers in order to have a successful marketing in regards with attracting customers and increasing the revenues from selling the electronic banking services.

In today’s global marketplace, individual firms do not compete as independent entities rather as an integral part ofa supply chain. Therefore, coordination and integration of key business activities undertaken by an enterprise, is ofgreatest value. For succeed in this competitive world, organizations must focus on their excellence position fromEFQM perspective. This paper proposes a fuzzy mathematical programming model for supply chain planningwhich considers excellence score, geographical score and capacity of each supplier and manufacturer anddistributor. The model has been formulated as a fuzzy mixed-integer linear programming model where some dataare fuzzy and modeled by triangular fuzzy numbers. This paper has been done in Sazeh Gostar Saipa Company inIran. This company supplies required automobile parts for SAIPA manufacturing group. Results show that thismodel can be applied for designing excellent supply chain network and has significant managerial implications.

As relationship marketing promotes products through word-of-mouth advertising, online consumer reviews can work as an alternative in developing modern digital businesses. In this research, four online review helpfulness measurements were employed for online review helpfulness prediction, using machine learning and meta-heuristic algorithms. The results indicated the most powerful algorithm for online review helpfulness prediction was the light gradient boosting machine regression. Also, the decimal logarithm of the number of helpful votes is the most suitable scale among online review helpfulness measurements. At last, the top 20 predictors were extracted from the two most accurate algorithms and the most efficient measurements.

Visual object tracking is required in many tasks such as video compression, surveillance, automated video analysis, etc. mean shift algorithm is one of popular methods to this task and has some advantages comparing to other tracking methods. This method would not be appropriate in the case of large target appearance changes and occlusion; therefore target model update could actually improve this method. KALMAN filter is a suitable approach to handle model update. We performed mean shift algorithm with model update ability for tracking in this paper and achieve good results.

AbstractAmong the ethnic groups living in Kermanshah Province[i], it is customary to perform a special therapy called (Yaar Arat Gerem[ii]) to treat individuals diagnosed with distress and neurosis, which is rooted in ancient indigenous traditions of the region. Effective techniques of this ritual are significantly similar to psychodrama. The supervisor of the group performing this procedure, called “Pary gir”, along with his assistants begins a therapeutic relationship with the patient, based on empathy and emphasizing on the present time. Through looking at the patient’s past and discussing his/her relationships with the important people in his/her life, “Pary gir” tries to understand his/her phenomenal world in order to start a proper treatment through finding the root causes of his/her present problems, and finally utilizes techniques that lead to his/her mental catharsis. In this paper, the researcher explains that psychodrama in its indigenous format, has been used therapeutically for centuries in Iran, through conducting a comparative study of this indigenous therapeutic ritual and modern psychodrama. The comparison highlights the necessity of recognition and revival of these types of indigenous therapeutic rituals, and emphasizes the necessity of their application in the treatment of psychological disorders.NotesNote 1. a province located in the west of IranNote 2. I’ll find you an auxiliary ego

In the Internet of Things (IoT) era, the demand for efficient and responsive computing systems has surged. Edge computing, which processes data closer to the source, has emerged as a promising solution to address the challenges of latency and bandwidth limitations. However, the dynamic nature of edge environments necessitates intelligent load-balancing strategies to optimize resource utilization and minimize service latency. This paper proposes a novel load-balancing approach that leverages learning automata (LA) to distribute real-time tasks between edge and cloud servers dynamically. By continuously learning from past experiences, the algorithm adapts to changing workloads and network conditions, ensuring optimal task allocation. The proposed algorithm employs a Service Time Measurement (STM) metric to evaluate servers' performance and make informed decisions about task distribution. The algorithm effectively balances the workload between edge and cloud servers by considering factors such as task complexity, server capacity, and network latency. Through extensive simulations, we demonstrate the superior performance of our proposed algorithm compared to existing techniques. Our approach significantly reduces average service time, minimizes task waiting time, optimizes network traffic, and increases the number of successful task executions on edge servers. Compared to previous approaches that partially addressed workload balancing, ALBLA offers a more comprehensive solution that optimizes resource utilization and minimizes energy consumption. Additionally, ALBLA's adaptive nature makes it well-suited for dynamic edge-cloud environments with fluctuating workloads. Our proposed approach contributes to developing more efficient, responsive, and scalable IoT systems by addressing the challenges inherent in edge computing environments.

Anaerobic bacteria, such as Lactobacillus plantarum (LP), are known to play a significant role in maintaining gut health and protecting against enteric pathogens in animals. The present study aimed to develop a safe, affordable, and eco-friendly method for producing LP-based probiotics and evaluate their efficacy in mitigating Salmonella-induced diarrhea in broiler chickens. The study employed three different culture media (MRS, TSB, and Baird Parker) to grow LP, which was then dried using a spray-drying technique to produce a stable probiotic formulation. When administered to broiler chickens, the LP probiotic derived from the MRS medium significantly improved body weight gain (4.147-fold increase over 4 weeks) compared to the other two culture conditions. Importantly, the LP probiotic treatment could substantially reduce the diarrhea index in broilers, with up to an 86.45% improvement in Salmonella-induced enteric infections. The beneficial effects were attributed to the ability of LP to modulate the gut microbiome, enhance the integrity of the intestinal mucosa, and mitigate the pathogenic effects of Salmonella. These findings demonstrate the potential of anaerobic Lactobacillus plantarum as a safe and effective probiotic intervention for controlling enteric diseases and improving production outcomes in poultry farming. The developed method provides a sustainable approach to harness the beneficial properties of this anaerobic bacterium for animal health and welfare. Graphical abstract

Although evaluating warehouses' efficiency using different approaches has been gaining prestige in recent years, this study is the first attempt to assess the efficiency of cross-docking systems. For this purpose, a comprehensive structure of a cross-docking system is examined, where a broad range of components affecting its performance are considered, including inbound and outbound doors, various means of transportation, inspection, kitting, storage, retrieval, and staging operations. In addition, an extensive set of key performance indicators (KPIs) are introduced for each component regarding automation, digitization, resiliency, sustainability, and lean concerns. More importantly, a novel network data envelopment analysis model is proposed to evaluate the efficiency of the cross-docking system with respect to undesirable factors. Furthermore, a new hybrid uncertainty approach is offered to handle the uncertainties in introduced KPIs and quantify some qualitative judgments. Finally, a case study is examined to illustrate the effectiveness of the proposed model and uncertainty approach. The obtained results reveal that the simultaneous exploitation of pre-distribution and post-distribution policies can increase the efficiency of the examined system by 36%. Also, modifying the layout to eliminate redundant transportation operations can lead to an 18% increase in efficiency.

This paper investigates the thermal and mechanical properties of carbon/high silica/phenolic composites with varying reinforcement ratios. Five hybrid samples were fabricated: 100% carbon, 75% carbon/25% silica, 50% carbon/50% silica, 25% carbon/75% silica, and 100% silica. A three-point bending test evaluated their strength, while an ablation test at 3000°C for 1 minute measured backside temperature, linear ablation rate, and mass ablation rate. Results indicated that the 100% carbon sample had the highest bending strength, while the 50% carbon/50% silica sample achieved the lowest linear and mass ablation rates, demonstrating an effective balance between fire retardancy and insulation, resulting in minimal backside temperature during ablation. Additionally, five machine learning models (Linear Regression, Decision Trees, Random Forests, Gradient Boosting Machines, and Neural Networks) were utilized to predict mass ablation rate, linear ablation rate, and strength. Decision Trees and Gradient Boosting Machines exhibited the highest prediction accuracy, while Linear Regression struggled with non-linear data, resulting in lower accuracy for ablation rate predictions. Notably, these models were also able to generalize to other percentages, showcasing their robustness and versatility in optimizing material compositions beyond the tested scenarios. This study highlights the potential of machine learning in predicting the properties of advanced composites, contributing to the development of high-temperature resistant materials.

Objectives Demoralization, a prevalent form of psychological distress, significantly impacts patient care, particularly in terminally ill individuals, notably those diagnosed with cancer. This study aimed to assess psychometric properties of Farsi version of Demoralization Scale-II (DS-II) in Iranian cancer patients. Methods This study was descriptive-analytical cross-sectional research. The statistical population was cancer patients who sought treatment at Imam Khomeini Hospital in Tehran throughout the 2021–2022. In the initial phase of the study, a preliminary sample comprising 200 patients was carefully selected through convenience sampling. After applying these criteria, 160 patients satisfactorily completed the questionnaires, forming the final study sample. They completed series of questionnaires that included sociodemographic information, DS-II, Scale of Happiness of the Memorial University of Newfoundland, and Beck Depression Inventory (BDI-II). The evaluation included exploratory factor analysis, confirmatory factor analysis (CFA), assessments of convergent validity, and internal consistency reliability. Results The CFA revealed a 2-factor model consistent with the original structure. The specific fit indices, including the Comparative Fit Index, Root Mean Square Error of Approximation, and Goodness-of-Fit Index, were 0.99, 0.051, and 0.86, respectively. Significant correlation coefficients ( p < 0.05) were found between the DS-II and the Beck Depression and MUNSH Happiness scales. The internal consistency of the DS-II, as measured by Cronbach’s alpha, yielded values of 0.91 for the meaning and purpose factor, 0.89 for the coping ability factor, and 0.92 for the total score. Significance of results The Farsi version of DS-II has demonstrated reliability and validity in evaluating demoralization among cancer patients in Iran. This tool can offer valuable insights into the psychological problems of terminally ill patients. Further research opportunities may include conducting longitudinal studies to track demoralization over time and exploring the impact of demoralization on the overall well-being and care of terminally ill patients in Iranian society.

Delay Tolerant Networks (DTNs) are a type of Mobile Ad-Hoc Networks (MANETs) where nodes are mobile, resulting in the absence of an end-to-end path between the source and destination nodes. Due to frequent disruptions in communication links, nodes in DTNs rely on a store, carry, and forward pattern to transmit messages. This forwarding and carrying of messages is achieved through cooperation among relay nodes. However, certain nodes may exhibit selfish behavior by avoiding cooperation to conserve their own resources, such as buffer and energy. In this paper, we propose a method for detecting selfish nodes in DTNs based on fuzzy logic. The method considers parameters such as the number of packets sent and received by a node, centrality degree, and buffer capacity as fuzzy inputs. The fuzzy outputs categorize nodes as active, semi-selfish, or selfish, and appropriate treatment is applied based on these categories. Simulation results demonstrate that the proposed method enhances the delivery rate by 10 ^% and reduces the average delay by 15% and hop count by 8% when compared to existing approaches.

Considering the significant growth of the Internet of Things network in recent years, the volume of requests sent to cloud servers was predicted to increase. Therefore, to solve the concern that has arisen in recent years, Cisco proposed the fog computing model to reduce the delay and load sent to the cloud servers. However, despite the mentioned advantages, the fog computing model has challenges, the most important of which is the appropriate method for allocating resources. In this challenge, it is necessary to pay attention to issues such as resource efficiency, increasing the profit of fog nodes, correct pricing, building trust between fog nodes and the Internet of Things, and creating a secure financial exchange system between nodes. For this reason, in this paper, a resource allocation method is proposed in the fog computing model based on the blockchain network. Combining blockchain technology with the fog computing model creates a safe and reliable environment where fog nodes can compete correctly and effectively for pricing. Also, in this article, a new auction method is proposed, in which fog nodes take into account the net profit of the requests and the chance of winning the auction to participate in the auction. The results of the simulation show that the proposed method has been able to perform significantly better than other existing methods in terms of the number of serviced requests, resource efficiency, and profit.

In this study, three extraction methods of bioactive compounds from the brown algae Nizimuddinia zanardini were ranked using the fuzzy weighting system in two stages, ranking between different conditions and choosing the optimal conditions for each extraction method separately. The inputs included extraction yield (EY), antioxidant activity (DPPH), total flavonoid content (TFC), total phenolic content (TPC), total phlorotannin content (TPhC), time, temperature, power, and cost. The top ranks of the first phase output included: Maceration Extraction (ME) with a score of 52.67, Ultrasound-Assisted Extraction (UAE) with a score of 54.31, and Microwave-Assisted Extraction (MAE) with a score of 73.09. The results of the second stage indicated that the lowest and highest extraction yields were obtained using UAE and MAE, respectively. The TFC in the UAE was determined as 103.29 mg QE (Quercetin Equivalent)/g as the lowest value and, in the ME, 140.95 mg QE/g was the highest value. The highest and lowest TPC and TPhC were observed with MAE and UAE, respectively. DPPH decreased in UAE, MAE, and ME, respectively. According to the fuzzy weighted results and considering the purpose of extraction, MAE can be introduced as the optimal method for extracting bioactive compounds from N. zanardini. The findings on extraction methods underscore the potential to reduce costs and improve the yields of bioactive compounds, such as antioxidants and polyphenols, thereby enhancing the economic viability of functional foods and nutraceuticals.

This study investigates the effects of adding fullerene and single-walled carbon nanotubes (SWCNT) on the strength and durability of bonded and bonded/bolted joints, specifically for composite-to-composite (CTC) and composite-to-aluminum (CTA) substrates under three-point bending, both before and after hygrothermal aging. Samples were categorized into neat specimens, specimens with added fullerene, specimens with added SWCNT, and specimens with a combination of 50% SWCNT and 50% fullerene. Results show that the optimal nanoparticle ratio differs for bonded versus bonded/bolted joints. Nanoparticles significantly reduced degradation from hygrothermal exposure, preventing interfacial debonding and slowing strength loss. Mixed formulations improved cohesive strength and shifted failure from the adhesive interface to within the adhesive layer, enhancing joint performance and durability under both unaged and aged conditions. Furthermore, six machine learning models—ridge regression, decision tree, random forest regressor, gradient boosting regressor, support vector regression, and neural networks—were applied to predict the static strength of joints. The support vector regression and decision tree models demonstrated superior performance for bonded joints, while ridge regression and gradient boosting regressor were most effective for bonded/bolted joints. The analysis highlights that joint type, substrate, nanoparticle type and percentage, and environmental aging significantly influence adhesive performance. This study offers valuable insights into the aging and durability of bonded and dissimilar joints, providing a framework to enhance joint performance and reduce the risk of failure during operational use.