Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.

The housing sector has always played a crucial role in the economy, with its fluctuations exerting significant effects on various economies. Prior to the 2007 financial crisis, standard models were commonly employed to explain price changes. These models assumed that Agents were rational and well-informed, disregarding factors like irrationality and the heterogeneity of individuals that could contribute to such crises. However, Agent-based models offer a different perspective, viewing the economy as a complex system with heterogeneous Agents possessing limited information, engaging in interactions with each other. As a result, this study aims to evaluate a spatial Agent-based model, specifically developed to analyze the housing market in Tehran. The simulation's results over an eleven-year period revealed that the growing demand from young households with limited savings for residential units under 100 square meters significantly drove up the prices of these particular units, outpacing other residential properties. Moreover, the findings indicated the higher growth of housing prices in the central areas of the city, primarily triggered by the influx of young households into these regions, seeking investment opportunities.  JEL Classification: R31، C61، C25

A B S T R A C TFor choosing suitable place multiple methods are introduced, which examine the qualities, methods and patterns of location selection. The current methods use global approach and do not pay attention to spatial differences, environmental variation in variables, local changes modeled by local weighting indicators. It has been selected as a case study to evaluate the EMS center of Fars province and to evaluate the indicators of distance from the power plant, distance from logistics centers, industries, transportation terminals, hospitals, hotels and tourism, cities, rural areas and custom centers. has been used, using the AHP method to weight the qualities, and then for local weights, non-overlapping neighborhood filtering within a radius of 25 km are used and calculate the corresponding changes, quantity, ratio Presented in this article, the obtained weights, then the compute local weighted layers, and then local weight was multiplied in the normalized layers and the final layer were obtained. In the next step, it was combined and calculated to compare the data using the global method. The results of the two methods were transferred to the points of the EMS and the results were analyzedExtended AbstractIntroductionIn geographical studies and spatial planning, there are different approaches to choosing the right place for all kinds of applications and uses, and a process is followed to choose the best places. This process includes stating the research problem (cognition), choosing the criteria and preferences of decision makers and how to determine the weight of indicators, combining criteria (design) and finally, choosing the best option. In the current methods of spatial decision, the criteria are given a fixed weight, while in geographical locations, the distribution of geographical phenomena is not uniform. In this case, attention should be paid to the spatial distribution pattern of geographical phenomena based on the principle of sensitivity of the range of changes, the size of the range of changes affects the outputs, and the data with a smaller range of changes also have a lesser effect on the output. The data with a larger range of changes will have a more significant effect on the output. Therefore, in spatial analysis, indicators that have small changes are less important. Local models have been presented to model local changes in spatial multi-criteria decision-making; in this research, the main issue is how to model the local changes that affect the expected outputs. MethodologyThe research process is based on multi-criteria spatial decision-making methods (national and local). First, spatial patterns of indicators were analyzed through exploratory analyzes of ESDA spatial data. Then based on the review of the research criteria, the direct weighting method was used to calculate the weight of the indicators, and the experts were asked to specify the weight of the criteria in the range of 1 to 10. The opinions were combined, and the final weight was obtained. First, the data were spatially modelled to model and calculate the local weights. Then Block Statistics spatial filter without overlap was used to determine the neighborhood. The range of local changes, maximum and minimum values, were calculated, and local weights were obtained based on that. Finally, the information layers were descaled. After that, the unscaled criterion layers were multiplied by the national weights and the final national scores were obtained. In the next step, the data of the evaluated information layers (local and national) were transferred to the points of emergency databases. based on the analysis, the fit was done. Results and discussionGeneral or global weights pay attention to the relative importance of indicators, which is the starting point for calculating local weights. based on the research results, the most critical indicators are industries, cities and hospitals, which are more important in the location of road emergency stations. However, in order to achieve better results, it is necessary to build a local weight layer. The results of the research show that the indicators' weight and the difference in the criteria (general weight) are not equal in the entire geographical area, and the areas with a larger range of changes have a higher coefficient.Because, unlike general weights that are obtained based on different methods, local weights are estimated based on the principle of sensitivity of the range of changes and are changed spatially, and a fixed value is not considered for all areas. According to the research findings, the highest level in the general and local evaluation method corresponds to 0.5 to 0.7. based on the local (geographic) weight method, it is less than the national or general method in the range of 0.9 to 1. Furthermore, this is due to the more precise nature of this method, which models the effects of local changes. To evaluate the compliance of the emergency centers with the final maps of suitability assessment, the information on the suitability maps was transferred to the map of the emergency center points of Fars province. based on the evaluation of 207 emergency stations, according to the general method, 14 percent are in the high group (0.9-1), and according to the local or geographical method (16.9), percent are in this group. ConclusionAt the level of space and geographical regions, phenomena and geographical complications are not uniformly distributed, some phenomena have a homogeneous distribution, and some have a heterogeneous distribution. On the one hand, the phenomena and decision issues are affected by the context in which they are formed and evolve; on the other hand, these phenomena are placed in the network of mutual relations. Furthermore, the results of the set of phenomena may differ from the results of the phenomena individually and separately; in this article, an attempt has been made to address the issue that in spatial analysis and planning, it is necessary to pay attention to geographical differences in decision-making. Furthermore, it should not be evaluated in general terms of spatial decision problems; one of the methods of modelling these differences and spatial changes is to consider the local weight of the criteria. based on the research findings, local (geographical) and national analysis results are different and can better show these changes. On the other hand, the pattern of spatial differences also has its spatial pattern, and paying attention to these differences in spatial differences can be the subject of further studies. FundingThere is no funding support. Authors’ ContributionAuthors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of InterestAuthors declared no conflict of interest. Acknowledgments We are grateful to all the scientific consultants of this paper.

Proper task allocation among Agents enhances a system’ s performance and reduces the probability of disorder in resolving a wide range of issues. Appropriate allocations are critical for the efficient implementation of tasks undertaken in natural hazard environments. Task allocation plays an important role in coordinating a multi-Agent system (MAS) among a set of Agents. Multi-Agent systems consist of several automatic and autonomous Agents that coordinate their activities to achieve a goal. Agents fail to reach their ultimate goal without the proper assignment of tasks. A proper approach to task allocations plays an important role in decision-making، particularly in urban search and rescue (USAR) operations in crisis-ridden areas. In the last decade، several studies were conducted regarding task allocation and different approaches have been presented to consider assigning tasks in MASs. This paper intends to provide an approach to task allocation in disaster environments through the consideration of appropriate spatial strategies to deal with disturbances. The challenge of this study is to provide the possibility of task reallocation in order to deal with uncertainties and events during the implementation. The main innovation of the study is that it presents an approach to improve conditions during reallocations، or future allocations، when initial allocations face problems due either to available uncertainties، or the addition of a new task. In other words، based on the nature of the implementation environment (natural disaster environments)، current allocation is not only considered but it is performed with regard to future allocations. The selected spatial strategies for a change the order of tasks (preparation for reallocation) are different in accordance with the conditions and the studied phenomenon. In general، strategies are selected in such a way that the final cost of the system will not increase abnormally if initial allocations face a problem. For example، building destruction level are uniformly distributed after an earthquake. Therefore، the convergence of rescue groups should be prevented as much as possible and the initial allocation should be done in such way to decrease Agent movement in future allocations. The proposed method is presented in five phases: ordering existing tasks، finding coordinating Agent، holding an auction، applying allocation strategies and implementation and observation of environmental uncertainties. The scalability of the proposed method was evaluated with the contract net protocol (CNP) method. In comparison with CNP، the standard time of rescue operations in the proposed approach includes at least 12% of improvement and the maximum improvement of 30% and the average percentage of recovery was 19%. Then obtained from the simulation of the proposed approach indicated that the time of rescue operations in the proposed scenarios was always less than the time required in the CNP method. Further، the evaluations based on deceased people and incorrect allocations indicated the feasibility of the proposed approach. The comparison of the proposed strategies at different levels of uncertainty showed that an increase in uncertainty leads to an increased rescue time for CNP. An effective assigning approach should consider strategies for replanning in order to waste the least time during system disruptions. This optimizes planning to achieve better implementation time and provides conditions for fault tolerance. Also، considering strategies in the task allocation process، especially spatial strategies، resulted in the optimization and increased flexibility of the allocation as well as conditions for fault tolerance and Agent-based cooperation stability in emergency management.

Since there are various social factors and differences between different sectors of the system, ignoring water users’ attributes and their social behavior as well as considering only the homogeneous and up-down management scheme, would not be a successful approach in sustainable water management. Agent-based Modeling is a relatively new approach that provides helpful tools to simulate social behaviors in sustainable water management. In this study, the agriculture sector’ s water use is simulated using a conceptual framework and an Agent-based Model to study the behavior of the decision-making Agents. Therefore, to prepare the conceptual model and to simulate and analyze the social behavior of water users (in three decision levels of the Government, local organizations, and farmers) to decide on the cropping pattern, the irrigation method, and consequently the water withdrawal volume, the MAIA framework has been applied. In this regard, the Agent-based Model for a pilot study area (the Hablehroud River basin, Iran) was coded, verified, calibrated, and validated. This model had a good performance in simulating the basin’ s conditions, including cropping patterns and areas, and therefore the stream flow and groundwater use. Furthermore, to assess the impacts of the government’ s water conservation policies on the hydrologic conditions, different scenarios of taking and increasing water costs were defined and modeled (one to ten thousand IRR per cm of water use led to a decrease in the total water withdrawals in the range of 8-32 million cubic meters per year).

This paper survey beliefs of investor on Tehran stock exchange at three break point date (BPD). at first three BPD with several criterion: average of the share price indices, average value of the stock market turnover, average value of the stock market capitalization. according this three BPD are: the election of Mahmood Ahmadinejad at 2005, financial crisis at 2008 and the election of Hassan rouhani at 2013. In addition this paper is base of Brock and Hommes heterogeneous Agent model (HAM) framework. Samples are the shares of companies that 40 days before and 40 days after was traded. then with MATLAB software code was writhed and simulation done. Finding shows that strategy of contrarian trend chaser is the best and we can with genetic algorithm optimize average and standard deviation of coefficient of investment strategy and adaption with real market at break point dates.

spatial epidemiology issues are outstandingly important, particularly the viral spread through populated areas is believed to be one of the major concerns. The outbreak of epidemic diseases in a community is inherently a spatio-temporal process of great importance for modern society. Modeling the spread and abrupt transmission of infectious diseases demands a better understanding of its dynamic behaviors to avoid sever consequences by appropriate preventive strategies. Agent based Modeling (ABM) is one of the innovative technologies for observing the spread of epidemic diseases. Agent-based models allow interaction among individuals and are capable to overcome the limitations of different approaches such as cellular automata and classical epidemic models, permitting to study specific spatial aspects of the spread of epidemics and addressing naturally stochastic nature of the epidemic process. Consisting of a population of individual actors or "Agents", an environment, and a set of rules, actions in ABM take place through the Agents, which are simple, self-contained programs that collect information from their surroundings and use it to determine how to act. Agent based simulation together with the improved Susceptible-Exposed-Infected-Recovered (SEIR) model, provides an opportunity for the study of interactions at the individual levels that includes social and casual relationships between individuals. The signature success of Agent-based modeling in public health is in the study of epidemics and infectious disease dynamics. ABMs have been used to study disease transmission at multiple scales, from individual communities to global pandemics. According to the previous researches, the relationships between factors affecting the outbreak diseases and its spread had not been ccomprehensively presented yet. Therefore, the purpose of this study is to provide a spatial Agent based modeling framework for simulating the spread of Seasonal Influenza. Due to the sudden and rapid spread of seasonal Influenza, the parameters of this disease were used for simulation. In this study, to investigate the effects of spatial units and other factors affecting outbreaks of seasonal influenza, simulation was performed, and then analyzed through five different scenarios. These scenarios were presented as the effects of population size, latent period, period of disease, transmission rate and polluted places on the spread of disease. Results showed that the output of epidemic follows a traditional epidemiological curve and also the output of scenarios lead to a better understanding of the factors in the spread of disease. Our results confirms the previous studies on this subject. For example, the results of the impact of spatial units on outbreak showed that considering the impact of polluted places leading to a significantly increase of pollution in the environment. Therefore, dynamic interactions between Agents and environment lead to explore the spread of disease in the model. Finally, the model can be used to inform and educate the public about the spread of infectious disease such as Seasonal Influenza, and can allow epidemiological researchers to assess systems’ behavior under various conditions. Therefore, This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak.