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This study applies artificial neural networks (ANNs) to assess the impact of climate factors on the collaborative development of agriculture and logistics in Zhejiang, China. The ANN model investigates how average temperature and rainfall from 2017-2022 influence crop yield, water usage, energy demand, logistics efficiency, and economic growth at yearly and seasonal scales. By training the neural network using temperature and rainfall data obtained from ten weather stations, alongside output indicators sourced from statistical yearbooks, the ANN demonstrates exceptional precision, yielding an average R² value of 0.9725 when compared to real-world outputs through linear regression analysis. Notably, the study reveals climate-induced variations in outputs, with peaks observed in crop yield, water consumption, energy usage, and economic growth during warmer summers that surpass historical norms by 1-2°C. Furthermore, the presence of subpar rainfall ranging from 20-30 mm also exerts an influence on these patterns. Seasonal forecasts underscore discernible reactions to climatic factors, especially during the spring and summer seasons. The findings underscore the intricate relationship between environmental and economic factors, indicating progress in agricultural practices but vulnerability to short-term climate fluctuations. The study emphasizes the necessity of adapting supply management to address increased water demands and transitioning to clean energy sources due to rising energy consumption. Moreover, optimizing logistics requires strategic seasonal infrastructure planning.

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In addition to diagnostic measures in the early stages of the widespread disease of COVID-19, prevention of the presence of individuals in high-risk environments, along with the proper distribution of population and services, is also effective in controlling the spread of the disease. The epidemic model, is based on population and movement. The aim is to introduce hazardous maps at the outbreak of corona disease and to explain the framework for their preparation and application based on issues related to resident behaviors. This research has been done by the method of logical reasoning and by analytical study of the existing samples, the components that are effective in preparing these maps and updating them. To this end, after the typology of the maps, the results evaluation criteria were validated from the perspective of the outputs. According to the research results, the dynamics of human movement data are key to estimating spatial interactions in these maps; Because of the social distance, staying home, and closing down jobs, fundamental changes occur in individual and group movements. Using different sources of information can be provided, the platform for participation of different groups of users using mapping maps is provided with an active and inactive demographic approach and increased efficiency. The development of such maps is a collaboration between the fields of epidemiology, health, environmental psychology, and public planning and design, especially urban design, to ensure that integrated studies based on the dynamics of location-based behaviors greatly enhance the validity of the maps.

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There is undoubtedly general agreement that the efficiency of educational investments should he maximized through the managerial process as far as possible. Agricultural extension is one of the crucial tasks in developing agricultural societies calling for considerable consumption of intellectual investment. The management of agricultural extension projects (AEPs) however, needs careful planning in utilizing this investment specially in terms of meeting the right clientele. This paper reports on the use of a statistical device which can be applied for planning the social modeling of agricultural extension programs. This statistical device, the so-called Dichotomous Distribution of the Extension Clientele (DDEC) was designed and used by the author to determine the social modeling of agricultural extension projects in Iran and the degree to which the extension projects have been successful in reaching their target clientele. The procedure consisted of four major criteria: farmers, educational needs, participation in AEP: access to utilities needed for adoption and utilization of the innovation (advice given by the extension agents). As a result of using this method and interviewing 912 farmers throught 57 randomly selected AEPs, it was found that 66 percent of the projects in 1988 and 60 percent in 1989 were thoroughly efficient, and 16 percent in 1988 and 12 percent in 1989 were efficient. Four projects in each year were found to have a very low efficiency rate while one project in 1988 and four projects in 1989 were inefficient in terms of their social modeling. This procedur has been applied to study the social modeling along with the efficiency of the extension projects dealing with the biological control of rice stenborer in eastern part of Mazandaran province where rice is the dominant cash crop. Acording to this result obtained from the recent research projects, it was shown that the less differences among the number of trained farmers and the target groups the more efficient were the extension project In addition, there was statistically significant difference among those of target groups and none target groups in term of applying the extension boicontrol guidlines in rice production practices. The related extension projects were also efficient (r=0.73) in term of their social modelings.

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In this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and wavelet-artificial neural network (Wavelet-ANN) models were applied to model rainfall-runoff (RR) relationship. For this purpose, the daily stream flow time series of hydrometric station of Hajighoshan on Gorgan River and the daily rainfall time series belonging to five meteorological stations (Houtan, Maravehtapeh, Tamar, Cheshmehkhan and Tangrah climatologic stations) were used for period of 1983-2007. Root mean square error (RMSE) and correlation coefficient (r) statistics were employed to evaluate the performance of the ANN, ANFIS, ARX and ARMAX models for rainfall-runoff modeling. The results showed that ANFIS models outperformed the system identification, ANN and Wavelet-ANN models. ANFIS model in which preprocessed data using fuzzy interface system was used as input for ANN which could cope with non-linear nature of time series and performed better than others.

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The present research aims to model the structural equations of green marketing and the desire to buy customers through the mediation of social responsibility. The research method is a descriptive correlation, which has been done in field experiments. For this purpose, 384 customers of Tehran's sporting goods stores were randomly selected using the Monroe method as a statistical sample. Data were gathered by green marketing awareness and willingness to purchase Habibi Saravi (2016) (α=0.92), social responsibility of Park & et al. (2017) (α=0.73) with a Likert scale of 5 Became for data normalization, the Kolmogorov-Smirnov test was used to test the research hypotheses. Structural equation modelling, including confirmatory factor analysis and path analysis using AMOS software and statistical software SPSS22, were used at the significance level of P≤0.05. Inferential results showed a relationship between the marketing of green and the desire to buy sports products from customers with the mediating role of social responsibility of vendors. Also, the communication model between the three meters has adequate fitness.

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Context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. The most common way to implement this approach is via triphone modeling. Nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. One approach to solve this problem is via parameter tying. In this paper, clustering has been carried out on HMM state parameters and the states allocated to any cluster are tied to decrease the overall number of system parameters and achieve robust training. Two types of groupings, one based on the final trained model set parameters and their inter-model distances and the other based on the training data and a decision tree, have been carried out. In the implementation of the later, a decision tree based on the acoustic properties of the Persian (Farsi) language and the phonetic similarities and differences has been designed. The results obtained have shown the usefulness of both the approaches. However, the second approach has the advantage of making the estimation of unseen model parameters possible.

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Abstract

Research Subject: Sulfide removal from sour water is essential, before reuse or release of sour water into the environment. Regarding the high costs of traditional methods, biological removal can be used as a reliable alternative.
Research Approach: Biological sulfide removal from sour water was investigated in a batch reactor using Thiobacillus sp. as a dominant species of a mixed culture. A conceptual model was developed to describe the process of H₂S removal from sour water in the batch reactor. The model considers H₂S and O₂ transfer between liquid and gas phases, biological oxidation of H₂S to sulfate and elemental sulfur, and chemical oxidation of H₂S to thiosulfate in the liquid phase. The governing equations were derived using the principles of mass conservation and biochemical reactions. Several batch runs were performed to obtain experimental data on the variation of sulfide, sulfate, thiosulfate, and oxygen concentrations in the system as a function of time, and an algorithm was devised to use the method of Particle Swarm Optimization together with the numerical solution of the model equations to estimate biokinetic parameters. Additional batch runs under different conditions were performed to verify the accuracy of the model. These results indicated reasonable accuracy of the model to predict the performance of a batch reactor for the removal of H₂S from sour water. The novelty of this model is considering different pathways for sulfide oxidation which includes product selectivity.
Main Results: The maxim specific oxygen uptake rate (SOUR=OUR/X) is one of the most important parameters in the evaluation of the biological activity of the microorganisms. The calculated value for this parameter was almost constant (16 mg DO g^-1 VSS min^-1) during all sulfide oxidation tests indicating that the maximum specific oxidation capacity of the biomass is independent of substrate and biomass concentration. Results exhibited bacteria prefer to partially oxidized sulfide to elemental sulfur, however this preference is a function of dissolved oxygen and substrate availability.

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  Accepting differences between knowledge-based organizations and other organizations with respect to the philosophy of existence, nature of the activities and their differentiated employees, we intend to present a proper model with surveying existing models. The presented model should be applicable in the compensation system in knowledge based organizations. Components Influencing compensation systems identified and classified into two main groups of financial and non-financial and four sub-division categories through studying existing models. According to the experts, 8 main effective components in compensation of these organizations were identified. Finally, based on 8 selected components and with the exploiting of the experts opinion and using of Interpretive Structural Modeling (ISM) technique, a prioritizing and leveling model for compensation system of knowledge-based organizations was developed. In this designed model, nonfinancial factors such as job-related factors (job challenge and growth opportunities) and factors related to the job environment (having a floating work hours and workplace conditions) have higher priority. This means that these Factors in compensation system are more important and have more influence.  

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Among different models for runoff estimation in watershed management, the Soil Conservation Services-Curve Number (SCS-CN) method along with its modifications have been widely applied to ungauged watersheds because of quickly and more accurate estimation of surface runoff. This approach has been widely accepted by hydrologists, water resources planners, foresters, and engineers, as well. Therefore, this work was aimed to estimate the curve number using CN-values through several methods viz. SCS, Sobhani (1975), Hawkins et al. (1985), Chow et al. (1988), Neitsch et al. (2002) and Mishra et al. (2008) in Bar Watershed, Iran. According to the results, the Neitsch formula showed the best performance for estimating the Curve Number in situation with low (CNI) and high (CNIII) antecedent moisture conditions. However, the weakest performance was related to Mishra (2008) in CNI and CNIII-conversions. The weakest performance was resulted from the exponential form of the Neitsch et al. formula and the variable meteorological conditions of the Bar Watershed over the year.

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There is different methods for simulating river flow. Some of thesemethods such as the process based hydrological models need multiple input data and high expertise about the hydrologic process. But some of the methods such as the regression based and artificial inteligens modelsare applicable even in data scarce conditions. This capability can improve efficiency of the hydrologic modeling in ungauged watersheds in developing countries. This study attempted to investigate the capability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for simulating the monthly river flow in three hydrometric stations of Pole-Almas, Nir, and Lai; which have different rate of river flow. The simulations are conducted using three input data including the precipitation, temperature, and the average monthly hydrograph (AMH). The study area islocated in the Gharasu Watershed, Ardabil Province, Iran. For this aim, six groupsof input data (M₁, M₂, … M₆) were defined based on different combinations of the above-mentioned input data. Theconducted simulations in Pole-Almas and Nir stations have presented an acceptable results; but in Lai station it was very poor. This different behavoirs was referred to the lower volume of flow and consequently irregularity and variability of flow in Lai station, which cause the decrease of accuracy in the simulation. The AMH parameter had an important role in increasing the accuracy of the simulations in Pole-Almas and Nir stations. The findings of this study showed that ANFIS is an efficient tool for river flow simulation; but in application of ANFIS, the selection and utilization of relevant and efficient input data will have a determinativerole in achieving to a successful modeling.

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The geometric distribution of states duration is one of the main performance limiting assumptions of hidden Markov modeling of speech signals. Stochastic segment models, generally, and segmental HMM, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. In this paper, a new duration modeling approach is presented. The main idea of the model is to consider the effect of adjacent segments on the probability density function estimation and evaluation of each acoustic segment. This idea not only makes the model robust against segmentation errors, but also it models gradual change from one segment to the next one with a minimum set of parameters. The proposed idea is analytically formulated and tested on a TIMIT based context independent phoneme classification system. During the test procedure, the phoneme classification of different phoneme classes was performed by applying various proposed recognition algorithms. The system was optimized and the results have been compared with a continuous density hidden Markov model (CDHMM) with similar computational complexity. The results show slight improvement in phoneme recognition rate in comparison with standard continuous density hidden Markov model. This indicates improved compatibility of the proposed model with the speech nature.

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Wetlands as a situ for the growth of native plants, as a habitat for certain species of fish and aquatic birds, and because of their potential economic, cultural and recreational services, are valuable heritage so their protection and conservation is very essential. Mostly due to the absence of wetlands services’ valuation, lack of special regulations, and lack of guarantee for these properties, resources and services of wetlands are not utilized appropriately, and destructed and evacuated in a free and unrestricted fashion, leading to inefficiency in use. The purpose of this study is the economic valuation of Gavkhony wetland ecosystem attributes, estimation of implicit price for attributes, impact assessment of socio-economic variables such as age, marriage, indigenous, family size and education on willingness to pay (WTP), and analyzing welfare and compensation variation due to variation of hypothetical policy. The approach being used is choice experiment that is a subset of choice modeling procedure and stated preference method. Data were collected from six different choice experiments provided in the questionnaires, which were filled out by 500 randomly selected households in Isfahan and Varzaneh cities in the spring and summer of 2013. Each questionnaire contained 72 hypothetical policies, 36 choice sets, 2442 observations and 7327 rows of data. Nested Logitech models and Hausman-MacFadden test were used in order to estimate the visitors’ WTP for improving attribute levels for Gavkhony wetland. This procedure was used on the basis of multinomial discrete choice analysis of preferences, Lancaster’s theory of value and the theory of random utility function. The Hausman-MacFadden test results showed that cross-elasticity between the first and third options was the same. Thus, these two options were placed in the second nest. The results further showed that the visitors had WTP for preserving forest diversity and vegetation of wetlands and its surrounding; preserve of natural habitats and organisms life of wetland (bird, fish and animals); wetland hygiene (preventing industrial and domestic effluent, and water salinity); and increasing the water surface (increasing wetland water inlet). The values estimated for these four aspects correspondingly were 8636, 12584, 11553 and 4740 Rials. Some socio-economic variables such as gender, marriage, age, family expenditure, education and being native had a positive impact on the visitors’ WTP. The surplus welfareresults showed that in 72 hypothetical policies, option 1 had the most positive welfare, and option 5 had the most negative welfare for the users of Govkhony wetland. The surplus welfare results based on WTP estimation provide important tools for policy making.

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Abstract Various researches indicates that family businesses are short-timed and faced with challenges to their survival. Succession planning is one of the main challenges have posed by these companies. This paper reviews the process of succession planning and main factors affecting it. Therefore two independent variables (leadership style and employee maturity) are considered and hypothesized that they have direct relationship with the succession process in the family businesses. The statistical sample was included 45 family businesses working in Khorasan-e-Razavi and hypothesis have been tested using structural equation modeling and all of them were confirmed. Responders to the questionnaire were selected among senior managers and high talented employees who has the chance of being senior managers.  It has been suggested that senior executives in family businesses, firstly choose their leadership style in accordance with the maturity level of their employees and more importantly do the succession planning process.          

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Nowadays, customer is known as the organizations’ most important source of information. The competitive advantage was lately obtained by innovation of product and creating a brand, but in twenty first century, companies are facing more interactions creating a competitive advantage which is obtained by gathering customer knowledge. This study intends to determine a structural model by the application of interpretive structural modeling (ISM). According to the interpretive structural modeling findings, twenty five important and effective factors on the process of implementing the customer knowledge management in the bank have been identified. The structural model analysis showed that following variables of the superior management commitment, the middle managers, organizational culture, financial resources and information technology are the main factors, and act like the foundation of the model. Any type of change on other factors which have high levels of guidance and dependency, could affect the system and system outcome could also change these variants again. The final result of factor in model expresses that executing and implementing the customer knowledge management process is effective at achieving factors such as customers' satisfaction, increase of service quality and keeping customers as a valuable asset and finally obtaining a competitive advantage.    

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Nowadays, many organizations deal with increasing competition and environmental uncertainty, which caused by innovations in technologies and changes in customer needs. Considering this condition and existing environment, old and traditional supply chain has lost its efficiency. One efficient method in this regard is leagility. Thus, to model the leagility of supply chain, different researches was reviewed in literature. Results of these researches and experts interviews lead to 15 critical success factors of leagilty in supply chain. These factors were ask by an ISM and DEMATEL techniques questionnaires and experts were asked to set the relationships. Acquired results were analyzed by these two techniques. Obtained maps and relations showed that for both techniques using IT, management, employee training, designing supply chain network, process standardization and demand and supply management are the basics of leagilty in supply chain. This model would help supply chain managers with strategic planning to make improvement in leagility supply chain.

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Ethics is the most important discussion of each religion. To build a society based on Islamic ​​and ethical values, it is essential for ethics to be considered by society. One of the most important issues in modern organizations management is institutionalizing ethics in organizations. This study is conducted to identify factors influencing the institutionalization of organizational ethics in organizations. To satisfy this purpose, existing literature were reviewed and an interpretive structural modeling were applied to define the relationships between affecting factors of institutionalizing ethics in organizations. To define these relations, opinions of 16 experts at Islamic Management and having at least 5 years work experience as managers at different Public organizations were used. These expert were chose using Snow ball sampling. The results show that the model includes seven factors: culture, code of ethics, selective system, educational system, performance evaluation system, management support and individuals' psychological contract. Creating a code of ethics have a fundamental role because of high driving power and low dependency. Other factors of selective system, educational system, management support and individuals' psychological contract to be high driving power of other factors and are also less dependent on other factors. The performance evaluation system is at the third level and culture is located at the fourth since it has lowest driving power and highest dependency.

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Stripe rust cause by Pucciniastriiformis f. sp. tritici is one of the most important diseases of wheat and can cause severe yield loss in many wheat growing regions of the world including Iran. To determine yield loss caused by this disease and evaluate the effect of some chemical components on reduction of yield loss in south of Iran, field experiments were carried out in split plot design with three replications at Ahvaz research station during 2014-2015. Three cultivars; Chamran, Virinak and Boolani, were used and artificial inoculation was performed using an isolate which was collected from south of Iran and designated as Yr27 race variant. Meanwhile the effects of propiconazole and some herbicides on yield loss reduction were studied. In this study, grain yield and area under disease progress curve (AUDPC) were measured. Statistical analysis showed that the level of the yield reduction was significantly different in the three studied cultivars and different treatments. Propiconazole could control the disease significantly. The highest yield loss was observed for cv. Boolani in both with (9%) and without (54%) fungicide treatments. Combined application of propiconazole and herbicides significantly reduced yield loss compared with using them separately. The results of crop loss modeling using integral and multiple point regression models showed that the integral model (L = 0.017AUDPC-17.831) could explain more than 69% of AUDPC variations in relation to crop loss in all cultivars. In multiple point models, disease severity at various dates was considered as independent variable and crop loss percentage as dependent variable. This model with the highest coefficient of determination had the best fitness for crop loss estimation. The results showed that the disease severity at GS39, GS45, GS50 and GS60 stages (Zadok's scale) were more important for crop loss prediction than those in other phenological stages.