همه گیری

Heart diseases are one of the most common and important diseases in the world. These diseases kill millions of people every year and impose huge costs on societies. Therefore, accurate and precision diagnosis and measurement of cardiac drugs are of particular importance. Voltammetry is one of the most common techniques for measuring drugs. Also, porous nanostructures have shown a high potential to improve the sensitivity and precision of the measurement of cardiac drugs using the voltammetry techniques. Recently, various types of porous nanostructures have been used to modify the voltammetric electrodes for the measurement of cardiac drugs. In the present paper, firstly, the porous nanostructures that are used to measure various types of drugs are introduced, and then, the ability of the modified electrodes to measure these drugs by the voltammetric techniques has been investigated and discussed.

Understanding trends in the decision-making domain is essential for advancing this field and addressing the complex challenges of decision-making in an increasingly technological and interconnected world. This study examines the conceptual structure of decision-making research in ISC database and identifies key sub-domains and dimensions of the decision-making concept.

This research was conducted using bibliometric techniques and co-occurrence analysis of keywords. A total of 2619 documents from the ISC were reviewed to identify research trends related to decision-making in the period from 1378 to 1402. Cluster analysis and strategic diagrams were used to map the conceptual structure of decision-making research.

Twelve clusters were identified in the field of decision-making, including: 1) decision-making and financial management, 2) decision-making methods, 3) decision-making in crises, 4) clinical decision-making, 5) ecology and decision-making, 6) technology and decision-making, 7) organizational participation in decision-making, 8) consumer behavior and decision-making, 9) decision-making and risk, 10) decision-making and rural development, 11) decision-making and work environment, 12) ethics in decision-making. Additionally, by analyzing the content of articles published over the past 25 years, emerging topics in the field of decision-making were identified.

Originality/Value:

 Before this study, there had not been a comprehensive research on the concept of decision-making in ISC database. Due to this research gap, a review of studies in this field was conducted, and emerging thematic areas, notable publications, and potential areas for future research were identified. Emerging topics in this field include crisis decision-making, clinical decision-making, organizational participation in decision-making, consumer behavior and decision-making, and ethics in decision-making.

The current research was conducted with the aim of explaining the mutual and synergistic role of big data analysis and strategic management at the national level.type of research is applied - developmental and descriptive - analytical . The examination of big data concepts has started since 2011, and in many countries this concept has entered the field of strategic management.The findings of the research show that according to the mutual influence of data and strategy and the formation of big data in different strategic areas, there is the creation of momentary situational awareness through their analysis in each of the stages of the strategic management program at the national level. It was also found that big data technology can affect the implementation of strategic management policies atThe National Level In Two ways:first , through increasing precision , this advantage will lead to accuraterecognition of strategy and policy implementation second , implementation of new policies andpolicies creates a large amount of data that can lead to the creation of strategies at the national level, which is a clear reason for the synergy of these two categories . The intelligent integration of big data technology with strategic management at the national level has undergone fundamental changes in the structure of this management and due to synergy, it creates a new cycle in which the continuous evaluation environment applies to all stages of strategic management at the national level. This innovation is presented in the form of a new model in this research.

The spread of Covid-19 has become one of the most important challenges in the world. This disease spread rapidly in the world and involved all countries. Most countries tried to deal with it with non-pharmacological interventions, including the imposition of coronavirus restrictions. Predicting this disease is one of the basic ways to deal with it. By predicting the condition of this disease, control measures to deal with this disease can be better managed. Also, in various studies, factors affecting the spread of this disease have been investigated. Paying attention to these factors can play a significant role in improving the management of this disease. Smart application of restrictions for urban management can prevent social, economic, and environmental losses in addition to controlling the spread of disease. Therefore, a two-stage model was designed for the intelligent application of restrictions. In the first stage, the adaptive neural fuzzy system was used to predict the disease, and in the second stage, the fuzzy expert system was used to apply the influence of the influencing factors on the disease outbreak. Finally, the created vulnerability map was used to apply restrictions. For the first step, the results of the comparison of the root mean square error for three clustering methods showed that the combined clustering with a value of 0.53294 works better than the two fuzzy and reduced clustering methods. In the second stage, factors of population density, elderly population density, spatial displacement, distance from busy places, access to medical resources, and cases of this disease were used and a vulnerability map was calculated and produced for the considered period. This map was used to decide the smart application of restrictions for decision-making bodies, and the map of smart application of restrictions was produced for four education and training bodies, the municipality, the health organization, and the governor's office. The results show that despite the application of complete measures for all works on December 30, 2019, in Qom city, some areas of dangerous situation were not areas without the need to apply restrictions in these areas. In education, there was no need to close 58% of the province's schools in this history, and in the municipality, there was no need to close 40% of the parks and gardens, and they could continue their activities by following health protocols.

The purpose of this research is to examine auditors' decision making in the face of ethical dilemmas in small and medium sized audit firms that are trusted by the stock exchange. This research seeks to investigate the relationship between the effective factors (such as auditor experience, audit firm size) on auditors' ethical decisions and to evaluate the role of these factors on audit quality and accuracy.

The statistical population in this study consists of the trusted auditing firms of the stock exchange, which includes 77 firms that have been studied by the full number method. The data of this research have been collected through the design of questionnaires among the auditors of small and medium audit firms trusted by the stock exchange. The collected data were analysed using SPSS20 statistical software and qualitative analysis techniques.

Four hypotheses were stated for this research. The results of the research show that audit experience and ethical decision making on audit problems (dilemmas) have a meaningful relationship. The results show that the size of the audit firm and ethical decision-making in audit problems (dilemmas) do not have a significant relationship. Also, there is no significant relationship between the ethical decision-making of audit problems (dilemmas) and the pressure received by the auditor from the client (employer) or the auditing firm.

 This research can help improve the quality of audits in small and medium enterprises and increase public confidence in financial reports. Also, the results of this research can help policy makers and regulators to establish more effective rules and regulations to support the independence and professional ethics of auditors through a better understanding of the ethical challenges faced by auditors.

Nowadays, with advances in technology and the use of industrial methods instead of traditional methods, the production of rose water and herbal distillates has significantly increased. In producing rose water and herbal distillates, a large amount of wastewater is generated. This is happening while drought, water crisis, and industrial growth have pushed Iran to purify and reuse this wastewater. In this research, the purifiability of wastewater from herbal distilleries and rose water factories in the large industrial townships of Kashan city, located in Isfahan province, was investigated. To this end, the available records and documents were reviewed, followed by field visits and the collection of wastewater samples. Necessary analyses were then conducted on the collected data. In the stage of suspended solids removal, the use of cloth filters, alum coagulant (Al2 (SO4)3), ferric chloride (FeCl3), and polyaluminum chloride (PAC) were studied. Furthermore, in the stage of organic matter removal, the use of potassium permanganate (KMnO4) and the experiences of other researchers, as well as the use of ozone, was examined. Subsequently, ozone treatment and simultaneous use of ozone and UV were investigated and analyzed, and a corresponding pilot was constructed. In the process of producing rose water and herbal distillates, a large amount of wastewater is generated. This is happening while drought, water crisis, industrial growth have pushed Iran to purify and reuse this wastewater. In this research, the profitability of wastewater from herbal distilleries and rose water factories in the large industrial townships of Kashan city, located in Isfahan province, was investigated. To this end, the available records and documents were reviewed, followed by field visits and collection of wastewater samples. Necessary analyses were then conducted on the collected data. In the stage of suspended solids removal, the use of cloth filters, alum coagulant (Al2 (SO4)3), ferric chloride (FeCl3), and polyaluminum chloride (PAC) were studied. Furthermore, in the stage of organic matter removal, the use of potassium permanganate (KMnO4) and the experiences of other researchers, as well as the use of ozone, was examined. Subsequently, ozone treatment and simultaneous use of ozone and UV were investigated and analyzed, and a corresponding pilot was constructed. The results showed that approximately 50% of the organic matter can be removed through coagulation and flocculation, and the remaining can be eliminated through an advanced oxidation process (ozone + UV) with sufficient retention time. As a result, in high capacities of wastewater it is recommended to use an anaerobic treatment method, ozonation of the outlet of the treatment plant and filtrating leads to achieve complete wastewater treatment in accordance with the discharge standards into the environment. Therefore, this method has been introduced as a suitable method for wastewater treatment in Kashan Industrial Towns Company.

A self-driving car, known as an autonomous car (AC),, is a car that is capable of traveling without human input. Self-driving cars are responsible for perceiving the environment, monitoring important systems, and control, including navigation.Also, automated driving is a new technology to reduce traffic accidents and improve driving efficiency. In this study, a deep reinforcement learning (DRL)-based decision-making policy for self-driving cars for Highway overtaking scenario is presented. In fact, first, a highway traffic environment is created where the target is to pass the agent through the surrounding vehicles with an efficient and safe maneuver. Then,, a hierarchical control framework is provided for the control of these vehicles, with high-level orders managing driving decisions and low-level orders monitoring the speed and acceleration of the vehicle. Then, a specific method based on deep reinforcement learning (DRL) called the deep deterministic policy gradient algorithm(DDPG) is used and evaluated to extract the decision-making strategy on the highway. The performance of the deep deterministic policy gradient (DDPG) algorithm has then been compared to the deep Q network algorithm (DQN) and results will be evaluated and examined. The simulation results show that the deep deterministic policy gradient (DDPG) method performs highway driving tasks efficiently and safely.

Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it.

In a competitive environment where business competition has become a fundamental challenge for managers, various ways to maintain, survive, or grow the organization are conceivable. Among these, marketing experts believe that customer loyalty is one of the most effective tools in facing this challenge. To achieve customer loyalty, various and diverse strategies have been mentioned in the literature by researchers and experts, which organizations can use, depending on the conditions, one or a combination of them. In such circumstances, managers usually have several strategies and must choose the most appropriate one(s). The present study aims to provide a combined approach for prioritizing customer loyalty strategies. This research uses a matrix-based approach to robustness analysis, which can deal with complexity and uncertainty. The proposed algorithm combines it with strategic planning tools (strategies derived from strategic objectives and SWOT analysis) for prioritizing and selecting strategies. The proposed approach was implemented in a case study on prioritizing customer loyalty strategies for a women's clothing boutique in Ramsar City. Available strategies, influential environmental variables, definitions of future scenarios, and the performance of strategies in different environmental conditions were determined based on the judgments of the problem owner. The results showed that considering influential environmental variables (national currency value, market access and raw materials, lifestyle changes, investment security, government-private sector relations, and the speed of technological change), supplier selection, contractor selection, and attracting a sponsor have the highest priority strategies. Afterwards, environmental advertising, collaborative production, and customer relationship management were placed in subsequent rankings. The outputs of the proposed approach indicate that considering the country's foreseeable future conditions, higher-priority strategies minimize environmental risks and their impact on the business.Originality/Value: The problem of choosing a customer loyalty strategy is a decision-making problem that can be addressed with the help of decision-making knowledge. However, the literature suggests that classical strategic planning approaches Quantitative Strategic Planning Matrix (QSPM) or multi-criteria Decision-Making Approaches (MCDM) are used in most cases of such decision-making. Despite their capabilities and features, these approaches face challenges in dealing with variable and evolving conditions (future uncertainty). An alternative approach, robustness analysis, can consider alternative futures but cannot define available strategies. Based on this, combining the matrix approach to robustness analysis with classical strategic planning approaches will be a response to the above problem.

Today, pilot wells are used for sampling the layers of the earth during deviation drilling. In order to reduce costs and facilitate operations in situations where it is not possible to use a pilot well for coring, coring studies are carried out in a deviant manner. Investigating the effect of drilling parameters on penetration rate in core drilling of deviated wells is important to optimize operations. In this regard, the rate of penetration is studied and simulated by preparing concrete samples for coring in a deviant way with laboratory devices, so that it reaches the most optimal state with the least amount of time, along with the necessary safety. In this research, cubic concrete samples with uniaxial compressive strength of 30 MPa and porosity of 31% have been used, and the effect of these parameters on the rate of penetration has been investigated by applying various weight on bit and angles of deviation. According to the experiments, the direct effect of the weight on bit on the penetration rate until reaching the turning point and the optimal value was observed. Also, by increasing the extra weight on bit, it causes a change in the mechanism of the bit's performance in drilling and loses its tangential state and causes the small particles of the sample to be torn off, which reduces the life of the drill bit.

Considering that the road factor along with the two factors of human and vehicle plays an important role in road accidents, so optimal and suitable road safety is improved through road geometric design, road surface structure (road pavement), and the installation of a variety of signs And road safety equipment will be important in reducing accidents and road accidents. Studies on the role of road accidents as the most important driving force in our country indicate that 75% of accidents occur in 20% of the total number of different paths, which, for various reasons, are included in the paths of the accident. . In other words, about 36,000 km out of a total of 180,000 km of roads in the country are affected by road accidents. In this research, the evaluation and ranking of the factors affecting road accidents in Markazi province were carried out using the equation structure (SEM) by preparing a questionnaire and a decision procedure Multidimensional Inventory (MCDM) of 80 factors involved in the ways of the central province. The results show that the index of road barrier obstructions in Friedman rank of 959/5 is of prime importance. It shows that most of the trails in this province are completely separate. The majority of crashes in the central provinces were due to lack of Enough to be seen. Direction Lighting Index 553/5 is ranked second. The construction quality index and geometric specifications and asphalt pavement are rated at 125/5 points in third place.

Calculation of ship sound source level in shallow waters using acoustic propagation models has attracted the attention of many researchers in recent decades. Source level measurement is important because of its wide application in subsurface communication and telecommunications, marine life and biology of marine animals such as whales and dolphins, and many other diverse applications.In this article, we first measure the bandwidth source level in the frequency range of less than 2500 Hz under specific operational and environmental conditions and under a specific standard. Then, the source level was measured for different floats in one-third octave of normal frequencies (25, 63, 125, 250 and 1000 Hz) and the sound transmission loss was calculated using acoustic models. This paper introduces a new modeling technique formulated based on finite difference time domain (FDTD).In this modeling method, factors such as layering of water and bed and depth changes of transmitter and receiver are considered and calculated as inevitable variables in field measurements. This technique can provide a suitable computational approach for standard source level measurement in shallow water.

To evaluate the software quality it is required to measure factors affecting the qualityof the software . Reliability and number of faults are examples of quality factors. If these factors are measured during the software development life cycle, more efficient and optimal activities can be performed to improve the software quality. The difficulty is that these factors could be measured only at the ending steps of the life cycle. To resolve the difficulty, these factors are indirectly measured through some software metrics which are available at the early stages of life cycle. These metrics are used as the input to fault prediction models and software components which may be faulty are the output of these models. Prediction of fault prone modules is a well known approach in software testing phase. When a module is predicted to be faulty, apparently more efforts have to be paid for correcting it. In addition to the module, all its dependent modules require specific consideration. When modifying a module all its dependent modules may be affected. The difficulty is that current known metrics for fault prediction do not reflect this situation. To resolve the difficulty, in this thesis, new metrics are introduced. Our experimental results show that the more the dependees of a module are changed, the more fault prone the module will be

During the pandemic, maintaining the survival of the supply chain partners and creating financial sustainability, especially in the food industry, becomes very important, so the purpose of this research is to identifying and ranking the factors affecting the financial sustainability of the food supply chain in a pandemic. This research is mixed method in terms of methodology and practical in terms of purpose. The statistical sample of this research includes 15 financial experts in food industry supply chain management. Factors have been identified based on literature review. Then, based on the Delphi method and with the help of a questionnaire, it was screened in three stages, and finally, among the 22 identified criteria, the experts reached a consensus in confirming 14 criteria, and in order to rank the final factors, the Analytic Hierarchy Process (AHP) technique was used. According to the results of AHP, the participation of stakeholders during the pandemic is the first priority, the management of service delivery policies is the second priority, and the waste management is the third priority during the pandemic. The findings of this research can open the way for the managers of the food industry during the crisis.

Considering the ever-increasing growth of the world's population and the increasing needs for cellulosic products, it is very important to maintain sustainability in this industry. Therefore, it is necessary to determine the importance of sustainability indicators in cellulose industries, to improve the performance and environmental, social and economic effects of this industry. Because, by determining the importance of sustainability indicators in cellulose industries, it is possible to determine appropriate strategies and policies to improve sustainable performance in this industry and help achieve sustainable development and environmental protection.Therefore, the main goal of this research is to rely on the presented importance of developing a decision-making model that is able to identify the importance of categories (indices) affecting the sustainability of cellulose industries and also calculate the prioritization of sub-factors by analyzing through multi-criteria decision-making techniques. In this research, a combined method using hierarchical analysis and best-worst methods to calculate the weight of categories and Aras prioritization method is used to select the best option and suggest the order of preferred ranking of the options. From the analysis, it has been determined that 11 main categories and three sub-categories affect the sustainability of cellulose industries. According to the results, among the main categories of strategic supplier cooperation with a consolidated weight score of 0.1801, continuous improvement is the least important with a consolidated weight score of 0.0252. Also, among the sub-factors, environmental management with a desirability degree of 1 has the first priority and environmental pressure with a desirability degree of 0.555 has the last priority. In addition, with the introduction of this decision-making framework, the analyzes of previous researchers have been reconstructed, and the framework of alternative rankings has been proposed in a hybrid way.

One of the influencing factors in failure asphalt layer tensile strength. For that reason validate benchmark for assessing and evaluating the quality of asphalt in road projects. Coring is the usual method for measuring quality of asphalt layer in the implemented road projects. But  the mentioned  method is costly  and    time-consuming  and also show the quality of  asphalt  layer in  a  specific  point . In the   present  article determine  tensile  strength  and  also detection  potential  failure of  the  implemented  asphalt  layer with using the  device    GPR Ground   Penetrating   Radar )   reviewed. In the  research  firstly a  part of  kerman  network  roads selected as  a sample for study  then evaluated  mechanized with  the help device  GPR  done . In  the  following  taken core  samples from locations rand only  and  then experiments determine  percentage  empty space  and  density done  on   them. In comparison results destructive experiments with mechanized scrolling transpire by increase tensile strength in the asphalt layer increase dielectric.so that by reducing the tensile strength less and equal to 870 (kpa) decreases in dielectric constant value from 5 was observed due to nondestructive evaluation . So the using output results radar can be obtained continuous profile from state tensile strength of the project study can be obtained.

Unexpected events, whether natural, industrial or man-made, are considered as surprising events. If surprised at the time of unexpected events, irreparably reduces the public trust on the ability of the government to reduce the damage caused by these events. The purpose of this investigation is to pay attention to the crisis, to extract the factors of surprises in the crisis and to provide solutions to prevent it. Q method is used in this investigation and the statements related to surprise factors in crisis are extracted and scored by statistical society and research colleagues. By using the colonial competition algorithm and programming the algorithm in MATLAB software, viewpoint 4 was extracted as the most important one and appropriate solutions were presented, which include the study and proper location of the warehouses of the relief facilities and the investigating the appropriate number and location of these warehouse. and to prepare the people to familiarize themselves with the possible crises and analyze the response at the time of crisis, through public media and social media. Furthermore studying and predicting widespread crises and designing relief facilities and suitable routes to help the more vulnerable points are derived as the proper solutions in this investigation.

Choosing stocks has always been one of the investors' concerns. The primary purpose of this study is to identify the factors affecting the decision-making and ranking of stocks in the stock exchange's three metal, chemical and pharmaceutical industries according to the importance of these industries.

The statistical sample of this research includes the shares of 84 companies in these three industries, which have been examined based on the data of 2021. First, stock rating factors were extracted by reviewing the research background. Experts used these factors in this field, and the final factors were selected after screening. Weighting and prioritization of these factors were done using the fuzzy SWARA method. According to the weight of the factors obtained from the fuzzy ride method and companies' financial information, the COCOSO method was used to rank the target stocks.

The results showed that price-income ratio, operating profit margin, and percentage of return on capital are the essential criteria for experts. Also, Fasabezvar, Fasmin, and Vetoka from the metal group, Vepakhsh, Desobha, and Depars from the pharmaceutical and shoyande, Shepdis and Shefan from the chemical group won the first and third places.

Originality/Value: 

In the last four years, various works have been done in this field, but less work has paid attention to the uncertainty in experts' opinions. Regarding other innovations of this article, it can be pointed out that the three industries of metal, chemical, and pharmaceutical, due to the importance of these industries, have not been specifically studied. Regarding the method of prioritizing criteria and stocks, less attention has been paid to new decision-making methods and uncertainty in decisions. Therefore, using new techniques, the importance of criteria can be determined with higher accuracy, and a better return on investment can be obtained.