Islamic Azad University of Kashan

Due to the variety of engines in volume, number of cylinders and power, turbochargers on the market are often generally made for a specific range of engine power. This research shows one of the ways to improve the performance of a turbocharger with a wide range of performance in a specific engine. In this article, by changing the inlet angle of the turbocharger turbine blade compared to the turbine blade inlet angle of a selected turbocharger and three-dimensional flow simulation inside it, the goal is to improve turbine performance. A real model of a turbocharger turbine, including a volute and blades, has been photographed by precise devices and an image has been prepared in the form of a cloud of points. This image is modified by the software and a three-dimensional model is prepared from it and edited in different software environments, and finally the three-dimensional flow inside the turbine is simulated. For validation, the engine and turbocharger assembly placed on the test bench and the performance parameters of the turbocharger turbine has been measured at different engine speeds and compared with the simulation results. The results showed that changing the inlet blade angle of the turbine to the value of 4.7° compared to the initial entry angle of the blade in all engine speeds leads to the optimization of the values of the performance parameters of the turbine. This angle change, improves the pressure ratio of the turbine by about 11% and the efficiency and power by about 18%. At high speeds, due to the surge phenomenon in compressor, this pressure ratio may not be practical, but at low speeds, when the energy of exhaust gases from the engine is not enough for good turbine operation, this increase in power can be very beneficial.

Hydrogen has received a lot of attention and is regarded as the most promising energy source of the twenty-first century because of its high energy density and eco-friendliness. However, there are increasing concerns about its safe use, storage, and transportation. The present research study scrutinizes the chemi-resistive behavior of pure and Cu-doped TiO2 thin films that may be applied to the creation of hydrogen sensing devices. The spray technique was used to synthesize the samples with different levels of Cu content (0, 0.2 and 0.4 wt %) on thermally oxidized Si substrates. The samples were characterized by grazing incidence X-ray diffraction (GIXRD), atomic force microscopy (AFM), field emission scanning electron microscopy (FESEM), and energy dispersive X-ray (EDX) analysis. The hydrogen chemi-resistive behavior of the samples was assessed and the sensing mechanism of the pure and doped samples was analyzed. The results showed that Cu-doping significantly affected the hydrogen sensing performance of the TiO2 thin films due to the increase of oxygen adsorption sites. The sample of 0.4 wt % Cu-doped TiO2 (the most sensitive sample) showed a response value of 2600% with a reaction time of 55 s to 1000 ppm of hydrogen at the operating temperature of 200 °C. It was also selective to H2 compared to CO2, NO2, NH3, and LPG. The evaluation of the reliability of the mentioned sample revealed that it can be considered as a potential for hydrogen sensing devices.

Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of covering a variety of nodes and edges. The members of these networks usually have metadata whose analysis can lead to the discovery of knowledge. One way to analyze such data is clustered where high-quality clustering requires effective similarity calculation. Most of the existing clustering methods do not pay attention to the use of metadata or the characteristics of network members. On the other hand, they are only able to process small and medium-sized networks due to the amount of memory and execution speed. This paper presents a hybrid approach for heterogeneous network clustering to overcome these problems. The structural similarity in this approach is calculated by the graph embedding method, which we call learning-based. Attribute similarity is calculated by a scoring method that we call similarity-based. In the experimental study, we compared the proposed method with collaborative approaches based on similarity on the real-world networks. The experimental findings demonstrate the superiority of the proposed method in terms of entropy, memory consumption, execution time, and density in certain cases.

Today, with the expansion of online social networks and their impact on various aspects of human life, investigating the interactions between users and identifying influential users for various advertising applications and accelerating or preventing the dissemination of information has been the focus of researchers. One of the fundamental researches is investigating the fact that the similarity of users’ characteristics along with their interests leads to new relationships in the friendship network, a concept known as homophily. The study of homophily can provide significant insight into the flow of information and behaviors in a community to analyze the formation of online communities. In recent years, the emergence of location-based social networks (LBSNs) has created massive datasets by sharing spatial and temporal information better than ever before. This issue enables researchers to analyze the behavioral patterns of users and their impact on their social connections and friends. Throughout the present paper, a framework is being defined to examine the effect of combining structural similarity and homophily in determining users’ social influence under two scenarios. The experiments simulate performance of nodes on three LBSNs: Gowalla, Foursquare, and Brightkite. By calculating the correlation coefficient for the similarity methods applied, it can be displayed that with the increase in homophily, the correlation of the proposed method and the social influence increases. A new measure of centrality is also introduced by using the topological structure of the user's communication network, such as the eigenvector centrality along with the values of friendship influence and the number of spatial movements of the user. The results show that our proposed centrality matches up to 85% with baseline methods.

Introduction Rare studies have been done to investigate the association between dietary intakes of vitamin D and the risk of mental health disorders among athletes. The current study aimed to investigate the association between this vitamin intake and the risk of depression, anxiety, and sleep disorders among a group of Iranian physically active adults. Methods This cross-sectional study was conducted among 690 healthy athletes (18–50 years, mean BMI between 20 and 30) in Kashan, Iran. The usual dietary intake of participants was assessed by a 147-item FFQ. Depression was assessed by the Beck Depression Inventory-II (21-item), anxiety by the Beck Anxiety Inventory (21-item), and sleep disorders by the Pittsburgh Sleep Quality Index questionnaires. Statistical analyses were done by using SPSS version 18. p values < 0.05 were considered significant. Results No significant association was found between vitamin D dietary intake and risk of depression in the full-adjusted model (OR: 0.96, 95% CI: 0.62, 1.51). In contrast, participants at the highest tertile of vitamin D consumption had a 49% lower risk of anxiety than those at the lowest tertile (OR: 0.51, 95%: 0.29, 0.87). Moreover, a significant 46% lower risk of sleep disorders was found among those with the highest intake of vitamin D in comparison to participants with the lowest intake (OR: 0.54, 95% CI: 0.37, 0.78). Conclusion We found a significant association between dietary vitamin D intake and reduced risk of anxiety and sleep disorders, but not with depression, in this study. Further prospective studies are recommended for future investigations.

In this study, the solubilities of codeine phosphate, a widely used pain reliever, in supercritical carbon dioxide (SC-CO 2) were measured under various pressures and temperature conditions. The lowest determined mole fraction of codeine phosphate in SC-CO 2 was 1.297 × 10 −5 at 308 K and 12 MPa, while the highest was 6.502 × 10 −5 at 338 K and 27 MPa. These measured solubilities were then modeled using the equation of state model, specifically the Peng-Robinson model. A selection of density models, including the Chrastil model, Mendez-Santiago and Teja model, Bartle et al. model, Sodeifian et al. model, and Reddy-Garlapati model, were also employed. Additionally, three forms of solid-liquid equilibrium models, commonly called expanded liquid models (ELMs), were used. The average solvation enthalpy associated with the solubility of codeine phosphate in SC-CO 2 was calculated to be − 16.97 kJ/mol. The three forms of the ELMs provided a satisfactory correlation to the solubility data, with the corresponding average absolute relative deviation percent (AARD%) under 12.63%. The most accurate ELM model recorded AARD% and AICc values of 8.89% and − 589.79, respectively. List of symbols A 1 , B 1 Chrastil's model parameters (dimensionless, K) A 2 , B 2 , C 2 MT model parameters (K, K m 3 /kg, dimensionless) A 3 , B 3 , C 3 Bartle's model parameters (dimensionless, K m 3 /kg) A

Breast cancer is among the most common diseases and one of the most common causes of death in the female population worldwide. Early identification of breast cancer improves survival. Therefore, radiologists will be able to make more accurate diagnoses if a computerized system is developed to detect breast cancer. Computer-aided design techniques have the potential to help medical professionals to determine the specific location of breast tumors and better manage this disease more rapidly and accurately. MIAS datasets were used in this study. The aim of this study is to evaluate a noise reduction for mammographic pictures and to identify salt and pepper, Gaussian, and Poisson so that precise mass detection operations can be estimated. As a result, it provides a method for noise reduction known as quantum wavelet transform (QWT) filtering and an image morphology operator for precise mass segmentation in mammographic images by utilizing an Atrous pyramid convolutional neural network as the deep learning model for classification of mammographic images. The hybrid methodology dubbed QWT-APCNN is compared to earlier methods in terms of peak signal-to-noise ratio (PSNR) and mean square error (MSE) in noise reduction and detection accuracy for mass area recognition. Compared to state-of-the-art approaches, the proposed method performed better at noise reduction and segmentation according to different evaluation criteria such as an accuracy rate of 98.57%, 92% sensitivity, 88% specificity, 90% DSS, and ROC and AUC rate of 88.77.

In this research study, TiO2 thin films-based ethanol sensors were fabricated by RF magnetron sputtering technique. The devices' structure, surface morphology, and sensing performance were engineered by changing the sputtering power. The characteristics of sensing materials were studied by grazing incidence X-ray diffraction (GIXRD), atomic force microscopy (AFM), and field emission scanning electron Microscopy (FESEM). The produced samples at low powers (100 and 200 W) had a tetragonal structure with a mixed phase of anatase and rutile whereas the sample deposited at the power of 300 W represented a single phase of anatase. The results showed that the variation of sputtering power had a significant effect on the ethanol vapor sensing performance of the devices due to the change in crystallographic structure, phase, and surface morphology. The sample produced at the sputtering power of 200 W showed more sensitivity than the other samples. The mentioned device also showed enhanced sensitivity compared to the previous reports due to its dual structure.

Social Internet of Things (SIOT) is a paradigm in which thing communicate with one another based on social relation. The goals is for the things to search for services, retrieve and provide users with them independent from their owners. The existing approaches lack models for analyzing the efficiency of SIOT. The objective of the current research is to model SIOT with regard to various relationships and their different topological properties. In this approach, the graphs related to SIOT are modeled based on similarity of their topological properties and random graphs in such a way that these properties are preserved by increasing the size of the network, the intended topological properties are preserved. For this purpose, first, topological properties of real SIOT graphs have been extracted. Then, using numerical and intuitive comparisons, the degree of resemblance between SIOT topological properties and random graphs has been examined. In order to prove this resemblance and network scalability, the connection between average route length and descending gradient algorithm has been implemented. The obtained results have shown the resemblance of ownership object relationship (OOR) real graph to Erdos Renyi (ER) random graph per p = 0.9, parental object relationship (POR) graph to ER random graph per p = 0.009, co-location object relationship (CLOR) graph to ER random graph per p = 0.00009 and social object relationship (SOR) graph to Barbasi Albert (BA) random graph per m = 50. In order to evaluate the proposed framework, the real SIOT dataset has been used and the scalability and maintaining the topological properties have been proven.

The idea of community energy network is being advocated to enhance the elasticity of diverse energy systems required for efficiently integrating a substantial volume of distributed energy resources. On the other hand, the interest in renewables-based desalination systems has received significant interest recently to consider freshwater as an additional end-use product in the community energy network system. Within this context, this paper introduces a multifaceted method for community energy networks with a focus on desalination-capable systems. The central goals involve diminishing the cumulative long-term expenses of the configuration, all while concurrently augmenting the system's capacity to store electrothermal energy on a daily basis that varies – all aimed at enhancing the reliability and security of resource provisioning. Importantly, the model co-optimizes the community energy network expenditure and reserve capacities, whilst integrating electrical, thermal, and natural gas vectors, as well as providing a platform for supplying freshwater needs. The overall freshwater provisioning infrastructure incorporates a water storage system, a desalination unit, a water well component, and a water pumping system. Furthermore, for the purpose of enhancing the adaptability, the community energy network concept put forth here utilizes coordinated electrothermal responsive load initiatives. These are coupled with meticulously planned electrothermal reservoir setups to curtail the wastage of surplus renewable production amidst diverse origins of unpredictability. The normalized weighted sum method is employed to convert the proposed formulation to a single-objective problem that is amenable to commercially available solvers in GAMS software. Then, the modelling framework is adapted to a system populated for a hypothetical site. The results verify the validity of the model in yielding globally optimum results for complex community energy networks with intertwined vectors of energy and end-use products. They also indicate that relatively small raises in the size of the electric and thermal reservoirs – and insubstantial raises in the expenditure of the system – can have potentially significant impacts on the ability of the system in serving loads during contingency conditions. In particular, by implementing demand response programs a cost reduction of 2.07% is shown, which is significant in the day-ahead operational planning phase.

This paper presents the generation and transmission expansion planning (GTEP) in electricity and gas networks by considering their resilience against floods and earthquakes. These networks supply electricity, heat, and gas consumption energies as a multi‐carrier microgrid. The scheme is expressed in the form of bi‐level optimization, the upper level of which is the minimization of generation and transmission planning cost (total investment cost and expected operating cost) in the mentioned networks constrained to the investment budget and the planning model of the mentioned elements. Lower‐level formulation minimizes the total expected annual operating cost of these networks and the expected outage cost of electricity, heat, and gas consumers in the event of floods and earthquakes. This formulation is bound by the power flow equations of electricity and gas networks, the operation and resilience constraints of the networks, and the limitation on generation capability. In this problem, the expected energy not‐supplied and the outage cost during natural disasters are considered resilience indicators. Next, a single‐level model for the proposed design is extracted from the Karush–Kuhn–Tucker (KKT) method. The basic requirement of this method is the convexity of the lower‐level constraints. For this purpose, first, a linear approximation model is obtained for the lower‐level constraints of the problem. Furthermore, stochastic optimization is adopted to model the uncertainty of load, renewable power, and network equipment availability during floods and earthquakes. Finally, the extracted numerical results confirm the capability of the proposed scheme in improving the operation and resilience of the mentioned networks using optimal generation and transmission planning.

Human society is a combination of diverse strata and people with physical disabilities are also an integral part of society. In the past, this group and their needs and challenges they are faced with have been neglected due to lack of knowledge, lack of facilities as well as technology that has led to their low presence in society. One of the most important components of social sustainability is improving life quality, removing discrimination and ensuring individual independence and social identity. Therefore, the society should provide equal opportunities to everyone in the first step, and in the next step, there should be the right of individual choice for everyone independently. Health, efficiency and satisfaction in work and the feeling of human comfort are largely dependent on environmental physical criteria. The stable physical environment, which is the result of complex factors such as ventilation, temperature, lighting, acoustics and installation systems, directly affects people's work ability. Despite the many researches that have been done in the field of providing indoor thermal comfort for the general public, still few researches have focused on special people such as the elderly, the sick and people with physical disabilities. Accordingly, the aim of this study is to achieve a model of design using the definition of architecture and social sustainability and its application to meet the needs of these people, such a center that can be designed in one of the crowded areas of Tehran city to address the challenges these marginal groups are faced with.

Backgrounds and Aims Patients with multiple sclerosis (pwMS) need self‐management (SM) skills to manage their symptoms and problems. An essential step to SM improvement is accurate SM assessment using valid and reliable instruments. The aim of this study was to evaluate the psychometric properties of the Persian version of the Multiple Sclerosis Self‐Management Scale‐Revised (MSSMS‐R). Methods This cross‐sectional methodological study was conducted from December 2021 to June 2022. The face, content, and construct‎ validity of MSSMS‐R were evaluated. Construct validity was evaluated through confirmatory factor analysis (CFA) and evaluating convergent and discriminant‎ validity using the data obtained from 210 randomly selected MS patients. The reliability of the scale was also evaluated through the test–retest stability and the internal consistency evaluation methods. Results The face validity was confirmed and the content validity ratio and index values of all items were more than 0.62 and 0.79, respectively. CFA revealed the acceptable construct validity of the scale after omitting items 21 and 22. In convergent and discriminant validity evaluation, the total score of MSSMS‐R had significant positive correlation with the total mean scores of the Multiple Sclerosis Self‐Efficacy Scale (r = 0.36; p < 0.001) and the physical health composite‎ (r = 0.31; p < 0.001) and the mental health composite‎ (r = 0.39; p < 0.001) dimensions of the 54‐item Multiple Sclerosis Quality of Life scale ‎and significant inverse correlation with the total mean score of the Beck Depression Inventory (r = –0.28; p < 0.001). The Cronbach's alpha values of the scale and its subscales were 0.86 and 0.65–0.90 and their test–retest intraclass correlation coefficients were 0.97 and 0.95–0.99, respectively. Conclusion The Persian MSSMS‐R is a valid and reliable scale and can be used in future studies for SM assessment among pwMS.

Introduction Myocardial infarction (MI) is a cardiovascular emergency that needs immediate diagnosis and treatment. Ineffective self-management of MI may lead to adverse consequences and complications. This study was conducted to explore the process of ineffective prehospital self-management of MI. Methods This study was conducted using the grounded theory design. Sixteen patients with MI and six family members were purposefully and theoretically selected from a leading hospital in Kashan, Iran. Data were collected using unstructured and semi-structured interviews and were analyzed through the constant comparison method proposed by Corbin and Strauss (2015). The length of the interviews was 40–60 minutes and data collection was kept on to reach data saturation. Findings The onset of MI symptoms had encountered patients with unfamiliar conditions which required them to use different self-management strategies, namely fighting between awareness and preference, taking problematic arbitrary measures, and consulting lay people. These ineffective strategies together with contextual factors had aggravated their conditions. Contextual factors were loneliness at the time of MI, affliction by underlying diseases, occurrence of symptoms at inappropriate time, and referring to non-specialty centers. The outcomes of this process were symptom aggravation and close encounter with death. Conclusion Unfamiliarity with MI and its management makes MI management very difficult for patients and family members. Therefore, MI-specific educations are needed to improve patients’ self-management abilities.

This paper discusses the optimal deployment of a cluster consisting of connected AC-coupled, low voltage (48 V) multi-carrier microgrids within an integrated framework. The utilization of this integrated framework proves to be an effective approach for enhancing the reliability, resiliency, and operational quality of the clustered multi-carrier microgrids. Furthermore, it enables improved utilization of distributed energy resources in both grid-connected and stand-alone scenarios. In order to address local objectives, this paper presents a hybrid approach to determine the optimal integration and size of distributed energy resources in autonomous multi-carrier microgrids. Additionally, the proposed model identifies the ideal demand response intensity for each multi-carrier microgrid, which can result in energy savings and financial profits by modifying energy demands during peak hours. The primary objective is to minimize the development cost of clustered multi-carrier microgrids while ensuring a desired level of local reliability and online reserve. To address the planning problem of the proposed integrated parallel multi-carrier microgrid network, a mixed-integer programming model is formulated. Numerical results obtained from a three-microgrid system demonstrate the effectiveness of the proposed integrated planning model, validating the economic viability of the expansion project from various financial perspectives. Finally, a practical financing strategy is proposed to facilitate the successful implementation and deployment of parallel multi-carrier microgrids, thereby contributing to the achievement of sustainable development goals. The study examines the role of governments in facilitating capital investments for clustered multi-carrier microgrid projects, aligning with sustainable development goals. It proposes a feasible financing strategy through settled billing tax rates ranging from 4% to 26% for multi-carrier microgrid customers over ten years. This strategy can assist policymakers in formulating supportive policy programs to effectively implement and promote multi-carrier microgrids in diverse premises.

In recent years, researchers from academic and industrial fields have become increasingly interested in social network data to extract meaningful information. This information is used in applications such as link prediction between people groups, community detection, protein module identification, etc. Therefore, the clustering technique has emerged as a solution to finding similarities between social network members. Recently, in most graph clustering solutions, the structural similarity of nodes is combined with their attribute similarity. The results of these solutions indicate that the graph's topological structure is more important. Since most social networks are sparse, these solutions often suffer from insufficient use of node features. This paper proposes a hybrid clustering approach for link prediction in heterogeneous information networks (HINs). In our approach, an adjacency vector is determined for each node until, in this vector, the weight of the direct edge or the weight of the shortest communication path among every pair of nodes is considered. A similarity metric is presented that calculates similarity using the direct edge weight between two nodes and the correlation between their adjacency vectors. Finally, we evaluated the effectiveness of our proposed method using DBLP and Political blogs datasets under entropy, density, purity, and execution time metrics. The simulation results demonstrate that while maintaining the cluster density significantly reduces the entropy and the execution time compared with the other methods.

The Internet of Things (IoT) has grown rapidly over the past few years. The main challenges for people who design and make these systems are to make them use less energy and use as small hardware as possible. Recording and sending compressed images in IoT systems must be secure. Secure image transmission requires encryption, watermarking, and compression. In choosing these algorithms, one should always consider energy efficiency and low hardware complexity. So, we propose a secure way to send data in IoT systems by combining encryption, watermarking, and compression. Depending on what is needed, we use a lightweight encryption algorithm, discrete wavelet transform (DWT) watermarking, and Discrete cosine transform + Lempel‐Ziv‐Welch (DCT+LZW) compression. In this method, the main contribution is to make a security pack that is small, strong, and easy to implement for secure image transmission on IoT systems. In the experiments, several images are used to judge how well the proposed integrated method works and how well it compares to other methods. When the proposed method was compared to other methods, the peak signal‐to‐noise ratio (PSNR) parameter improved by more than 20% in the Lena image and by more than 11% in the Baboon image.

A supercritical fluid, such as supercritical carbon dioxide (scCO 2) is increasingly used for the micronization of pharmaceuticals in the recent past. The role of scCO 2 as a green solvent in supercritical fluid (SCF) process is decided by the solubility information of the pharmaceutical compound in scCO 2. The commonly used SCF processes are the rapid expansion of supercritical solution (RESS) and supercritical antisolvent precipitation (SAS). To implement micronization process, solubility of pharmaceuticals in scCO 2 is required. Present study is aimed at both measuring and modeling of solubilities of hydroxychloroquine sulfate (HCQS) in scCO 2. Experiments were conducted at various conditions (P = 12 to 27 MPa and T = 308 to 338 K), for the first time. The measured solubilities were found to be ranging between (0.0304 × 10-4 and 0.1459 × 10-4) at 308 K, (0.0627 × 10-4 and 0.3158 × 10-4) at 318 K, (0.0982 × 10-4 and 0.4351 × 10-4) at 328 K, (0.1398 × 10-4 and 0.5515 × 10-4) at 338 K. To expand the usage of the data, various models were tested. For the modelling task existing models (Chrastil, reformulated Chrastil, Méndez-Santiago and Teja (MST), Bartle et al., Reddy-Garlapati, Sodeifian et al., models) and new set of solvate complex models were considered. Among the all models investigated Reddy-Garlapati and new solvate complex models are able to fit the data with the least error. Finally, the total and solvation enthalpies of HCQS in scCO 2 were calculated with the help of model constants obtained from Chrastil, reformulated Chrastil and Bartle et al., models.