محمد فلاح

 Infection with Strongyloides stercoralis is endemic worldwide and its prevalence is higher in warm and humid regions and poor countries. The frequency of infection is higher in people with immunodeficiency and in collective centers, including centers for patients with psychiatric disorders and prisoners, where hygiene principles are not fully observed, and dangerous parasitic infections such as Strongyloides stercoralis have been frequently reported. As a result, the present study aimed to determine and investigate the prevalence of Strongyloides stercoralis and other intestinal parasites in patients with psychiatric disorders hospitalized in Sina Hospital in Hamadan.

The present descriptive-cross-sectional study was conducted in 2021. The study population were patients with psychiatric disorders hospitalized at Sina Hospital in Hamadan, Iran. The method of searching for Strongyloides stercoralis and other intestinal parasites in feces included direct vision, formalin ether sedimentation method, and modified agar plate culture. The collected data were analyzed using the chi-square statistical test.

The results obtained from the present study on the feces of 370 patients with psychiatric disorders hospitalized at Sina Hospital in Hamadan did not indicate any contamination with Strongyloides stercoralis. Infection with other parasitic agents included the protozoan cysts Blastocystis hominis (9 people) 2.43%, Giardia lamblia (6 people) 1.62%, Entamoeba coli (2 people) 0.54%, and Iodamoeba buetschlii (1 person) 1%. The chi-square test with p<0.05 indicated a higher frequency of intestinal parasites in men of the target population.

Apart from the lack of suitable environmental and climatic conditions for establishing a free-living cycle of Strongyloides stercoralis in Hamadan province, other reasons such as the increase in public health following the coronavirus pandemic and the possible antiparasitic effects of antidepressant drugs can be effective in controlling and eliminating Strongyloides stercoralis in the target population. The reduction in Giardia infection in the present study to 62.1% (compared to previous similar studies) was promising for improving water supply, sewage, and public health systems.

An effective approach to tackling climate change is to replace electric vehicles with their diesel counterparts. Although this replacement has many challenges, it is not impossible. In this paper, two multi-objective mathematical models - multiple deposit problem routing - charging station location and battery replacement of heterogeneous freight electric vehicles were presented. In addition, to increase customers' satisfaction, fines for violating time windows were applied in the models. Each of these two models pursues three goals. The first goal is to minimize (the total cost of routing, the cost of constructing charging stations or replacing the battery, and the cost of breaking time windows); the second goal is to minimize the number of used cars and the third goal is to minimize the number of charging or battery replacement stations. The models were solved by CPLEX solver with GAMS software in small size. Examination of the results shows that in the first proposed model, battery replacement stations were used. Fewer cars were used. Also, in the second proposed model that the stations offer partial charging; the total cost was reduced. Also, in both models, the cost decreases with increasing the number of depots.

Increasing shareholders' wealth is one of the most important financial management goals. This matter is always intertwined with the concepts of risk and return simultaneously so that shareholders always seek to increase portfolio returns by controlling and minimizing risk or reducing risk at a certain level of expected return. For this purpose, investors mainly use the concepts of fundamental analysis, which means paying attention to the internal structure and financial performance of companies, or technical concepts, which means paying attention to the changes and price fluctuations of stocks in the market, or a combination of both methods. The main objective of the present research is to provide a combined model that pays attention to both financial structure as a fundamental approach and stock price fluctuations in the historical chart as a technical approach. Ultimately, using the network concept in data envelopment analysis, it aims to investigate the impact of financial performance on the company's market value.  In addition, the concept of trapezoidal fuzzy numbers has been used to consider the uncertainty in returns.

In this research, we will combine the Markowitz model with fuzzy returns and the network data envelopment analysis model to create a multi-objective model. By considering company performance based on certain financial ratios, its impact on stock market value, and price fluctuations, we aim to introduce stock portfolios in the best possible scenarios in terms of risk, return, and portfolio efficiency. Ultimately, to utilize the model for selecting an optimal stock portfolio, 50 companies from various industries actively traded on the Tehran Stock Exchange were chosen, and the proposed model was applied to them. Additionally, a multi-objective genetic algorithm with non-dominated sorting was employed to solve the model.

The results obtained from the implementation of the model on 50 companies active in the stock exchange show that the use of the proposed model has better results in terms of efficiency than the use of any of the Markowitz models or network data coverage analysis alone, and also compared to the non-network model. It provides investors with risk and efficiency and has a more comprehensive approach than the previously presented models.

Originality/Value:

 The current research introduces a model that simplifies the decision-making process for choosing the optimal portfolio by combining the views of investors with a fundamental approach and investors with a (technical) price volatility approach. The presented method tries to express the uncertainty in the returns in terms of trapezoidal fuzzy numbers and also measures the effect of the companies' efficiency on the market value and the increase of the shareholders' wealth by using the data envelopment analysis network model.

Today, forecasting the actual costs of a project over a period is one of the fundamental aspects of project management. This is especially critical for construction projects, which play a significant role during natural crises. Effective cost and time management are crucial factors in the optimal execution of these projects. This study aims to identify the key factors influencing the accurate estimation of time and cost for construction projects using factor analysis. The case study focuses on commercial and residential buildings in the city of Arak. The study is applied in nature and employs an exploratory research method. The statistical population includes all senior managers, supervisors, and engineers from the Arak Engineering System Organization, with 170 individuals selected through a convenience sampling method. A factor analysis model was used to examine the relationships between various components. The results indicate that the most significant factors affecting accurate time and cost estimation include: failure to resolve objections promptly (factor load: 9.14), delays in preparing necessary maps during execution (factor load: 6.66), insufficient budget provision at the right time (factor load: 5.94), lack of practical and executive vision among designers (factor load: 5.85), serious errors in project design and contract documents (factor load: 4.04), inadequate planning for changes (factor load: 3.88), absence of a basic unit price list for inner-city works (factor load: 3.83), and project suspension by other institutions and organizations (factor load: 3.78). These factors were identified as the most critical for accurate time and cost estimation in the construction of commercial and residential buildings in Arak.

Estimating forest tree structural attributes such as height and diameter at breast height (DBH) is crucial for understanding the structure and management of forest resources. One important method for estimating these parameters is the individual tree detection (ITD) method using appropriate remote sensing data, such as airborne LiDAR data. However, it should be noted that different ITD methods have various limitations and capabilities and react differently to changes in forest tree species and the vertical structure of the canopy.

This study presents a hybrid individual tree detection method that combines raster-based and point-based methods in a multi-scale framework to identify single trees from LiDAR data. In this method, tree crown scale levels are obtained from morphological filters in the canopy height model (CHM). Segmentation is then performed using a multi-scale method, and the results are merged. To better separate adjacent and understory trees, the point cloud inside the segments is analyzed using the probability density function, and tree crown segments are modified. After detecting single trees, DBH and height parameters were estimated using ground control data and extracted features from LiDAR data with machine learning algorithms, including random forest (RF), support vector machine (SVM), and cubist (CB), in the form of 10-fold nested cross-validation (10-fold NCV). The Boruta feature selection algorithm was used to identify the most important metrics based on the LiDAR point cloud, which played an effective role in improving the performance of machine learning algorithms. Due to limited access to LiDAR and ground data from Iran’s forests, this study uses the NEWFOR single tree detection benchmark dataset, collected from forests of the Alpine region with a combination of different tree species and vertical canopy structures.

Although understory trees cannot be extracted with the same accuracy as overstory trees, the results of this study showed that, on average, the developed multi-scale individual tree detection (MSITD) method detected 89% of the tree crowns in the highest height layer and the highest number of small overstory trees with a detection rate of 48% in the lowest height layer (2-5 meters). The analysis of the machine learning algorithms’ results in estimating forest structural attributes showed that, despite slight differences in performance, the SVM algorithm performed better than the RF and CB algorithms in estimating both height and DBH attributes. For the height attribute, the mean values of RMSE, rRMSE, and R2 in the SVM algorithm were 1.75 m, 9%, and 0.85, respectively. For the DBH attribute, the values obtained for RMSE, rRMSE, and R2 were 4.74 cm, 19%, and 0.78, respectively.

The evaluation of the results showed that the methods presented in this study for identifying single trees and estimating forest tree structural attributes have high potential for practical applications.

Trichostrongylid helminths play a significant role in economic loss in livestock and human health. Due to the need to knowledge of the latest status of helminth infection of domestic ruminants, for proper planning to prevention and control, this study was conducted to determine of Trichostrongylidae helminths .

A total of 334 intestines and abomasums purchased from slaughterhouse of Sanadaj, Kurdestan. The samples were placed in 10% formalin separately and examined in the Parasitology Laboratory of Sanandaj and Hamadan Medical School. All nematodes identified using the appropriate keys. The results were recorded in a form along with information such as livestock type, sex, farming location .

Totally, 178 sheep samples (53.29%), 93 goat samples (27.85%) and 63 cattle samples (18.86%) were studied. The prevalence of intestine infection was 18.31% and abomasum was 81.69% and the overall infection in this study was 20.95%. Only two samples (0.6%) of Trichostrongylus sp. were isolated, most of them were female. The most isolated species was T. longispiculis. The highest percentage of isolated worms was Ostertagia ostertagi (22.18%). Most cases of intestinal nematode were Nematodirus abnormalis (14.28%) and in abomasum O. circumcicta (18.57%.)

Although Trichostrongylus infection has decreased in recent years; a 0.6% prevalence were found in present study. However, infection with other Trichostronegilids in the studied animals has a relatively high prevalence. Due to the economic loss in livestock and the possible role of these parasites in transmitting to humans, appropriate control and treatment measures should be taken

In the present age, evaluating and then improving the performance of production systems and supply chains as a whole is the only possible way to compete in the global business market. Data Envelopment Analysis (DEA) is a non-parametric method for measuring the relative efficiency of production systems and decision-making units with multiple homogeneous inputs and outputs as a mathematical programming method. Determining the type of return to scale (RTS) helps managers make more accurate predictions about the expansion or limitation of the decision-making unit (DMU). Due to the importance of RTS in relation to management decisions, various methods have been proposed by DEA researchers to introduce and determine its type. Research in RTS evaluation has led to a more general classification of RTS types, with the titles Left return to scale (L-RTS) and Right return to scale (R-RTS). This research studies the two-stage production process and introduces an approach to estimating the type of efficiency on the right and left scales of DMUs. In this paper, the application of the DEA network model to calculate the relative efficiency and return to scale of cement companies listed on the stock exchange has been studied. The data used in the model is related to the production process of 42 cement companies in 2020.

The achievement of this research project in the field of enabling the geometric monitoring of precast segments in the country and for the construction projects of precast segmental concrete bridges using the balanced girder method, which is of interest in the country, is widely used. The experimental results proposed in this project will lead to the achievement of more comprehensive knowledge in the field of geometric correction of precast segments and a significant reduction of errors and costs in the design and construction process. In this study, two approaches have been designed and implemented based on the transformation of the global and local coordinate systems and based on the matrix of direction cosines and the analysis of plane and height deflection angles in order to calculate the segment adjustment numbers and the global coordinates of the plates. Based on the results, the second method compared to the first method has about 20% improvement in offset and 10% improvement in line. Based on the results of the second method, according to the calculation of deviation angles in a decomposed way for the right, center and left side of the segment, the offset values have a significant improvement.

This paper focuses on modeling a sustainable electricity industry supply chain network under uncertainty. The aim of presenting this supply chain network is to meet customer demands for solar panels to generate clean energy.

A mixed-integer linear programming model, including facility location, supplier selection, optimal flow allocation, and determination of the optimal price of solar panels in the network, is considered. The sustainability objectives of the model include maximizing the profit of the supply chain network, minimizing greenhouse gas emissions, and maximizing reliability. A robust optimization method is also considered to control uncertain parameters, and precise and innovative techniques are used to solve the model.

The results of the model show that with an increase in network reliability, the current net value in the network decreases, and greenhouse gas emissions in the network increase. Additionally, the analysis of the results shows that with an increase in the network's uncertainty rate, the network's current net value and reliability decrease, and greenhouse gas emissions increase. Finally, the statistical test results also show that there was no significant difference between the averages of the number of practical solutions, the maximum spread, and the metric distance between the two algorithms, and only a significant difference exists between the solution times of the two algorithms. The results of the presented solution methods demonstrate their high efficiency in solving the sustainable electricity industry supply chain model.

Originality/Value:

 In the proposed model, essential decisions such as supplier selection, establishment of production centers, optimal product flow allocation, and pricing of solar panels are made. On the other hand, further analyses of 15 numerical examples show the high efficiency of the MOALO and MOWOA algorithms compared to the epsilon-constraint method.

After network and flight schedule design, routing and aircraft maintenance and repair issues as well as aircrew planning are among the major decisions that affect airline costs. When making decisions on each of these issues, there are specific operational restrictions and rules and regulations that must be followed. If these decisions are integrated and the optimal response is found, there will be a significant reduction in airline cost. In this research, assuming that an airline has designed the flight program at a strategic level, how to optimally implement this program is discussed; to this end, the optimal integration of aircraft routing decisions and the crew's movement plan (flight team) was considered. The desired problem for an airline is solved with net base and crew diversity, with the goal of minimizing total costs. One of the biggest challenges in solving this problem is the possible disruption of the original plan, which makes aircraft and crew planning ineffective in practice. Therefore, in this research, we define the possible disturbances in the initial plan as several different scenarios and by using the two-step robust scenario-oriented optimization approach, we provide a robust answer to the problem of integrating the decisions of aircraft network routing. And the crew's travel plan. In the proposed stable model, in addition to the decision integration, the adjustable variables of the second stage, including the decision to cancel the flight in the event of a disturbance, are also seen so that the model is adaptable to different disturbance scenarios. . The numerical results of the implementation of the proposed model confirm the applicability of the proposed model to provide an integrated and stable answer to the research problem.

Rice is one of the most important main food sources in Iran and the world. The correct identification of the type of pest in the early stages of preventive action has a significant role in reducing the damage to the crop. Traditional methods are not only time-consuming but also provide inaccurate results, As a result, precision agriculture and its associated technology systems have emerged. Precision agriculture utilizes information technology such as GPS, GIS, remote sensing, and machine learning to implement agricultural inter-farm technical measures to achieve better marginal benefits for the economy and environment. Machine learning is a division of artificial intelligence that can automatically progress based on experience gained. Deep learning is a subfield of machine learning that models the concepts of using deep neural networks with several high-level abstract layers. This capability has led to careful consideration in agricultural management. The diagnosis of disease and predicting the time of destruction, with a focus on artificial intelligence, has been the subject of much research in precision agriculture. This article presents, in the first step, a trained model of the Chilo suppressalis pest using data received from the smartphone, validated with the opinion of experts. In the second step, we introduce the developed system based on the smartphone. By using this system, farmers can share their pest images through the Internet and learn about the type of pest on their farm, and finally, take the necessary measures to combat it. This operation is done quickly and efficiently using the developed artificial intelligence. In the continuation of the article, the second part introduces the materials and methods, and the third part presents the results. The fourth section also discusses and concludes the research.

Chilo suppressalis is one of the most important pests of rice in temperate and subtropical regions of Asia. The conventional approach employed by villagers to gather the Chilo suppressalis pest entails setting up a light source above a pan filled with water infused with a pesticide. At sunset, these insects are attracted to the light and fall into the water in the pan. This method is known as optical trapping. After catching the pest using optical traps, they are collected from the water surface, and their photo is taken with a mobile phone based on the location of the optical trap.The proposed method in this research consists of three main steps. Firstly, the farmer utilizes the software provided by the extended version known as Smart Farm. The farmer captures an image of the Chilo suppressalis pest and sends it along with its location to the system. The Smart Farm software program carries out image processing and pest range detection operations. The user then verifies the accuracy of the pest detection. In the second step, the images sent by the farmer are processed by the pre-trained model within the system. The model analyzes the images and determines the presence of the pest. Finally, after identifying the type of pest, the results, along with recommended methods for pest control, are sent back to the farmer.In summary, In this method, farmers employ the Smart Farm software to capture and transmit images of the Chilo suppressalis pest. The captured images then undergo image processing and pest range detection as the next steps in the process. The results, including pest identification and control methods, are then returned to the farmer.

The model has been designed with 400 artificial neural network processing units (APCs), achieving accuracy percentages of 88% and 92%. To conduct a more detailed study of the proposed model, the statistical criteria of recall and F-score were used. Based on the calculations, the trained model demonstrated a recall score of 91%. This criterion shows that the model was able to identify a large percentage of what was expected to be identified by the model. Additionally, the F-score, with an acceptable percentage of 88%, confirmed the accuracy of the trained model.

Researchers have always been highly interested in the valuable data freely provided by farmers for their studies and analyses. In this study, an intelligent system was designed for identifying types of pests such as worms and stalk eaters, which can automatically determine the pest type from the image sent by the farmer using artificial intelligence and deep learning. By utilizing the developed system, farmers can be informed of the type of pest present on their farm in the shortest possible time, with minimal required software training.

The Muslim Brotherhood is one of the most important and influential political groups in Egypt's internal politics, since its formation in 1928. After the fall of Hosni Mubarak, the Muslim Brotherhood was a coherent and organized political actor capable of taking power in the post-Mubarak era. But only one year after coming to power, this group has been marginalized and has lost its influence and credibility in the eyes of many Egyptians. This research raises the question, "What were the reasons for the failure of the Muslim Brotherhood after 2011 AD?" Using the descriptive-analytical method and using the constructivist approach, it evaluates the hypothesis that the Egyptian military and opposition groups considered the Muslim Brotherhood as a cause of instability and the spread of division among Islamist groups, and because of this, they prevented this group from gaining power. This view of the opposition groups of the Brotherhood is the main reason for the failure of the Brotherhood, along with other structural reasons, after gaining power in 2011.

The purpose of the current research was to investigate the Islamic Revolutionary Guard Corps' jihadist management in containing the Corona virus. The present research method is descriptive and analytical, using questionnaires and surveys. The statistical population of this research is the officials who were in the field, operational and strategic in the process of curbing Corona during the Corona period. The statistical sample of this research includes 60 commanders and managers who played a role in curbing the Corona virus, who were selected by purposeful sampling. Data analysis was done using spss software. In this research, a questionnaire with 29 questions was used to analyze the obtained data and to examine all hypotheses and determine the impact of the field and short-term, operational and medium-term and strategic and long-term jihadist management of the IRGC in containing the Corona virus. The results obtained from the research indicate that the Islamic Revolutionary Guard Corps' jihadi management in the fight against the Corona virus was considered a new experience for managers and commanders and the military forces, that the control of this disease required the cooperation and interaction of all healthcare professionals and the General Staff of the Armed Forces. which to some extent were able to succeed in all three strategic, operational and field levels to a great extent, especially in helping to vaccinate and contain the corona virus and reduce deaths and epidemics of this disease and create life expectancy and return people's lives to normal do it yourself

Cystic echinocossosis (hydatidosis) caused by the larval stage of cestodes of the genus Echinococcus is an important and prevalent zoonotic disease. In human patients, surgical removal of the cysts is the treatment of choice in many cases however, leakage of the larva (protoscoleces) during the surgery and formation of new cysts is a concern. Presently, different solutions such as hypertonic saline and silver nitrate are used to inactivate the cyst-dwelling protoscoleces but each have some side-effects hence searching for novel protoscolicide formulations with high efficacy and low side-effects has been always in the focus. The aim of this study was to optimize the extraction conditions of Echinophora platyloba, to evaluate antioxidant properties of the extracts and to assess their protoscolicical effects in vitro. For this purpose, hydroalcoholic extracts of Echinophora platyloba were extracted in ethanol concentrations of 0, 50 and 100% and during 24, 48 and 72 hours. Response surface methodology (RSM) was perform to estimate optimal extract conditions. Finally, 200, 400 and 600 ppm concentrations of optimized extracts were tested on live protscoleces from sheep and goat livers collected from Hamedan abbatoir. Results showed that Echinophora platyloba extract prepared with absolute ethanol in 24 hours could inactivate 100% of protoscolicess after 5, 15 and 30 minutes of exposure. This in vitro study suggest that ethanolic extract of Echinophora platyloba can potentially be applied in vivo.

By the advent of video games as an inseparable part of technology and education, the impact of such games on enhancing the level of teamwork has reached an unprecedented attention. The uppermost objective of this study was to investigate the miscellany of aspects influential on the improvement of teamwork among Iranian Air Force freshmen cadets.

To this end, 50 freshmen cadets participated in this research by playing Counter Terrorist Game in groups of five (10 groups). They were randomly assigned to each of the groups, however, they all were chosen from among volunteers to play the game with no specific randomization (convenience sampling). Each group played four rounds of the game in two consecutive days (each day two rounds) competitively against the computer. After the second day, participants were in depth interviewed about their lived experiences of playing video games with their coursemates utilizing semi-structured questions concerning various aspects of teamwork.

The researchers also observed the participants playing games and employed field notes in order to record the phenomenon under study. The data collected were meticulously transcribed and analyzed and data-driven themes were obtained using NVivo qualitative data analysis software version 12.

By conducting this study, it was revealed that there are ten main themes or categories in the context of game playing that can potentially lead towards learning teamwork features. The themes are goal-setting, identity-formation, premeditation, interaction, implementation, leadership, esprit de corps, companionship, trait and tact.

The process of applied research and experimentation in the second half of the twentieth century and the beginning of the twenty-first century was not very integrated and its scope deserves research. The present study describes the status of the publication of documents in the field of couple therapy and the mapping of scientific cooperation networks of individuals, organizations, and countries, as well as the mapping of the peer network of reforms in the Scopus database from 1970 to 2020. The method of this research is descriptive and with a bibliometric approach. In this study, 2898 published documents on the subject of couple therapy from the Scopus database were analyzed by bibliography as a thematic search. Data analysis was performed using Scopus database analysis options and VOS viewer imaging software. The research findings show the upward trend in the publication of applied documents in the field of couple therapy in the last 20 years. The top five countries active in scientific production on the subject were the United States, Canada, the United Kingdom, Germany, and Australia, respectively. The most productive journals identified were " Journal of Marital and Family Therapy ", " Journal of Consulting and Clinical Psychology ", " Family Process ", "Journal of Family Psychotherapy" and " Behavior Therapy ", respectively. In addition, there were two prolific writers, Christensen, A, and Johnson, S.M. The evolution of keyword concepts has also been studied in this study. Based on the findings, it can be concluded that in addition to couple therapy, special attention should be paid to the field of psychological education to improve relationships; because at present, the most important issue in the field of couple therapy is in prestigious journals and our country, not enough studies have been done.

One of the traditional ways to improve the condition of the networks is the construction of new roads, which is not a desirable option today, especially in crowded urban areas due to the high cost of construction. One of the effective options in this regard is traffic management methods. The issue of traffic assignment, especially dynamic traffic assignment by providing a platform for examining the status of the network under study by providing a traffic flow model is one of the important tools and components in the discussion of traffic management. In this study, using a complete and general dynamic network loading method based on LWR network expansion model, the dynamic network loading problem is formulated as a system of differential algebraic equations that the resulting dynamic network loading model is able to form, propagate and lose physical queue. In this research, an algorithm based on the fixed point formula has been used to solve dynamic user equilibrium problems. Then the software package algorithm in C ++ language is defined to implement both systems of differential algebraic equations and the fixed point algorithm by considering the queue spillback. The program is developed in such a way that it can be used to solve dynamic user balance problems and load dynamic network in any large-scale network defined by the user. The C ++ program was implemented for the Chicago metropolitan area network with 86,179 origin-destination pairs and 250,000 routes, and after 69 repetitions, the convergence threshold was reached and the results were evaluated. Finally, the results of this study are evaluated and the accuracy of the results is shown.

In the real world, remanufacturing depreciated products for the purpose of cost reduction in supply chains, is recognized as a competitive advantage. In this paper, an appropriate approach for collecting and remanufacturing depreciated products in a closed-loop supply chain is considered with the aim of reducing costs using the Stackelberg game theory. To this end, the considered chain members include the manufacturer, the retailer, and the remanufacture contractor (as the third party to the contract). In this research, the four following scenarios are considered and the results are analyzed: 1) Payment by the manufacturer to collect and reproduce the depreciated product; 2) Joint payment of an amount by the retailer and the manufacturer to collect and reproduce the depreciated product; 3) Joint payment of an amount by the manufacturer and the third party to the contract to collect and reproduce the depreciated product; 4) Payment by the manufacturer for collection and non-reproduction of the product. The computational results show that the chain profit will reach its maximum amount with the optimal reproduction rate considering the third scenario, and the chain members will gain more benefits than other scenarios. The findings of this research indicate that when the reproduction process does not take place, the lowest possible profit is made in the chain. The sensitivity analysis performed by increasing the effect of the final product price on customer demand in different periods, shows a decreasing trend in the three decision variables of final product price, selling price to the retailer, and the amount paid to the customer for each consumed product.

Selecting the optimal stock portfolio is one of the most important issues in the field of financial research, which tries to choose the optimal combination of assets in order to create maximum utility for the investor, Given that the return on securities in the real world is often vague and inaccurate, one of the most important investment challenges is uncertainty about the future. In this paper the problem of selecting and optimizing securities portfolios with different modeling goals has been solved and compared. The designed models have considered both the nature of the portfolio selection issue and the considerations considered by the shareholder in the portfolio selection. The uncertainty quality of the future return of a given portfolio is estimated using fuzzy LR numbers, while its return torques are measured using possibility theory. The most important purpose of this paper is to solve the problem and compare portfolio selection models with simultaneous optimization of two, three, and four objectives. For this purpose, the NSGA-II genetic algorithm is used and the mutation and intersection operators are designed specifically to generate possible solutions to the cardinality constraint of the problem. Finally, the efficiency and performance of the models in case of using fuzzy logic and not using it have been compared and it has been determined that the use of fuzzy logic and possibility theory leads to the formation of portfolios with higher performance and higher efficiency.

Trichomoniasis is the most common non-viral sexually transmitted infection in the world, with resistant cases spreading. The aim of this study was to evaluate the effect of Ferulago angulata extracts and essential oil on Trichomonas vaginalis protozoan.

In this study, methanol, ethyl acetate and hexane extracts and essential oil of F. angulata aerial parts were prepared and tested to determine the minimum lethal concentration (MLC) and the percentage of growth inhibition (GI%). The tests were carried out on two clinical isolates of T. vaginalis under aerobic conditions and against metronidazole and control.  The tests were surveyed after 24 and 48 hours of incubation.

After 24 hours, the MLC of methanol, ethyl acetate and hexane extracts and essential oil were 250, 500, 250 and 500 μg/ml, respectively. The MLC of ethyl acetate and hexane extracts were reduced to 250 and 125 μg/ml, after 48 hours. GI% at Sub-MLC for extracts and essential oils were 24.3% to 93.2%, after 24 hours, and from 51.6% to 95.1% after 48 hours. Also, the mean MLCs of metronidazole after 24 and 48 hours were 9.3 and 4.6 μg/ml.

The results of this study showed that F. angulata has anti-trichomonial potential. Thus, more investigations it is necessary to purify the constituents of essential oil and extracts. Further in vitro and in vivo experiments will clarify the antimicrobial potential of the plant to make a more correct judgment.

IoT technology is one of long waves of technology that will act as a platform for countries to rise or fall in the economic path. If countries and companies that take advantage of the opportunities ahead of this technology, they can join the ranks of global leaders and consolidate their position for decades. In recent years, Iran has carried out extensive activities to develop various technologies, but due to the multiplicity of technology fields and the spread of the country's potential in various fields, sufficient accumulation of knowledge and capital in one sector has not taken place. In general, in order for economic coexistence, there must be leaps in several industrial and technological sectors so that a country can become the backbone of a developed country. But since IoT technology is a common technology and is used in all sectors, there is a risk of spreading the scientific and capital potential of the country in its various fields. Therefore, it is necessary to create accumulation of knowledge, capital and wealth by focusing on priority areas. Due to this, in this research, the applied parts of IoT technology that can cause technological and economic leaps have been identified and then the importance and priority of these parts can be determined using nonlinear modeling approach. The results showed that focusing on energy intelligence in Iran has the highest advantage and focusing on this sector can ensure economic growth and technology of society.

Enterobius vermicularis (pinworm) is one of the most common intestinal nematodes in the world and is found in all developed and developing communities. Children are the most vulnerable group in this regard. This study aimed to determine the prevalence of E. vermicularis infection amongst kindergartens in Hamadan, Iran, in 2019.

This descriptive cross-sectional study was conducted on 98 active kindergartens in Hamadan, Iran. A total of 18 kindergartens were selected, followed by the random selection of a number of children from each kindergarten. The adhesive cello-tape is a standard method for examining this disease, and mothers were trained to use this method. Subsequently, the sampling was conducted by this method.

Out of 451 children from 18 kindergartens, the overall prevalence of E. vermicularis infection was obtained at 0.4% (n=2). The mean age of the children in this study was 5.45±0.93 years. Since the prevalence of the disease was not significant, the results were not analyzed.

The results of this study show that the prevalence of this disease has decreased over a decade in Hamadan, Iran; however, this infection still exists. Therefore, specific measures should be taken to control the transmission cycle of E. vermicularis

Zoonotic intestinal parasites and intestinal nematodes of livestock are indirectly one of the human health challenges in developing countries. Considering the possible role of gastrointestinal parasites in domestic animals in the transmission of infection to humans, their economic significance in the production of protein products, and the lack of up-to-date information on the contamination status of ruminants in the region. As a result, the present study aimed at determining the frequency of Trichostrongylus species in ruminants slaughtered at Hamadan slaughterhouse in 2020. 

Abomasum‎ and intestinal samples were taken from 338 livestock by referring to Hamadan slaughterhouse. The samples were placed separately in sealed containers and transferred to the parasitology research laboratory of Hamadan University of Medical Sciences to diagnose parasitic infection. After opening and washing the contents of the samples in a large tray, these contents were filtered through a wire sieve several times and the obtained transparent material was gradually poured into other trays. At that point, the worms were separated with the help of a hand-held magnifying glass and the light of the study lamp and were placed within bottles containing alcohol-glycerin. The worms were then identified at the genus and species levels using diagnostic keys. Information about livestock such as gender, type of livestock, anatomical position, and habitat were recorded in the information form. Data were analyzed using descriptive statistics, and the results were presented as tables and graphs.

Out of 338 livestock, 70 (20.70%), 264 (78.10%), and 4 (1.18%) were Cattles, sheep, and goats, respectively. In addition, 240 (71%) and 98 (28.99%) livestock were male and female, respectively. 301 (89.05%) and 37 (10.94%) livestock were young and old, respectively. The highest percentage of infected specimens was related to Ostertagia Ostertagi (64 cases, 48.5%) and the lowest percentage of infected specimens was related to male Nematodirus filicollis (1 case. 1.6%). Most of the infestations were reported for the male livestock and from those raised in Qorveh area. In addition, no worm infestation was observed incattle and goats.

Ostertagia infection was highly prevalent in the studied livestock. The zoonosis Trichostrongylus parasite was not observed in the present study. The prevalence of intestinal nematodes in sheep in the region is still significant and should be controlled by the relevant authorities.

One of the things that causes responsibility under the doctrine of causality, is the rule of causality in the law and jurisprudence. This rule also applies to the crimes and violations found in the building and construction.Sometimes this rule conflicts with other rules in the subject matter.The present study examines the relationship between the rule of causation and its relationship with other rules in construction crimes and with the rules of warning, benevolence, no-harm, waste and deception in a descriptive-analytical method, using library tools with the aim of knowledge raising and awareness. The results of this research are:If effective warning conditions are observed in the construction industry, the warning rule governs the rule of causation, and also if benefactor has done the conditions of benevolence in the subject under discussion, that is, acting according to the laws and regulations, the rule of benevolence takes precedence. In the no-harm rule, the opposite point of which in the causality is the rule of domination, if the action of the owner is in his favor or the repulsion of the loss from himself, the rule of domination prevails, otherwise the no-harm rule is followed by that