When it comes to solving dynamic programming challenges, it is essential to have a well-structured decision theory. As a result, the decision-makers must operate in a dynamically complicated environment where appropriate and rapid reaction in a cooperative way is the fundamental key to effectively completing the task. We express a theory of decision modeling and axiomatizing a decision-making process. The payoffs and probabilities are represented with simplified neutrosophic sets. We therefore, provide the theory of choice with the implementation of simplified neutrosophic sets. By exploiting the idea of pure strategy, we introduce two steps: in the first step, for each attractive point, some particular event is selected that can bring about a relatively neutrosophic upper payoff with a relatively neutrosophic upper probability or a relatively neutrosophic lower payoff with a relatively neutrosophic upper probability. A decision-maker selects the most favored attractive point in the second stage, based on the focus on all attractive points. Neutrosophic focus theory has been introduced to improve overall performance with more flexibility in complex decision-making. The approach suggested in this work has been implemented in a real-life example to determine its effectiveness. The proposed method is shown to be the most useful for ranking scenarios and addressing dynamic programming problems in decision-making

Plant development can be defined as a programmed qualitative change in plant form, which leads plant to maturity, and researchers call it as phasic development or phenology. Recognizing the timing of occurring each development stage is necessary for managing system in order to yield increment. The timing of occurring development stages depend on climate, genotype specifications and sowing date then determination of these times in different regions is difficult and it is only possible through the using of crop simulation models which can predict the timing of occurrence each development stage by integrating effective factors. The model was constructed based on linear equation of plant temperature response. In order to model evaluation two experiments were carried out in agricultural and natural resources research center of Khuzistan in 2003-2004 and 2004-2005 cropping years. Wheat development stages were determined based on Kirby and Appleyard’s scale by stereoscopic microscope and required GDD for each development stage as well. The constructed model was calibrated and run for simulation. Comparison of simulated and observed data showed that the model can strongly predict wheat development stages.

Today, one of the most widely used natural language representation methods is meaning representation for text processing in different systems. Meaning representation methods have had many applications in the field of natural language processing in recent years, including automatic text summarization systems and question and answer systems, etc. Many text expressions may be different in terms of grammatical structure, but they are the same in terms of meaning, so how to apply a single and uniform meaning to them is one of the challenges of text processing. The main goal of this paper is to design an Integrated Semantic Representation (ISR) model for natural language. The proposed model, while maintaining its simplicity in annotation and understanding, does not depend on specific natural language features or on syntactic and lexical structure. In this regard, several examples in both English and Persian language, which have different in terms of written and grammatical structure, are presented in this paper. Moreover, by using the proposed model, the same representation is provided for texts with the same meaning and different grammatical structure. The proposed model is designed in graph and list format is recommended to annotate its corpuses. One of the main features of this model is that it can represent semantic relations at both sentence-level and document-level and is able to represent complex and important linguistic phenomena such as aspect, tense, and quantification. The simplicity of the proposed model helps to avoid making language processing slow or complicated in various applications, and the preparation of structures based on this model for different natural languages will not be too complicated, so that it can be used both for natural languages with low resources and for those with various resources. Further, features of the proposed model are compared with one of the most important related works.

Purpose: Resilience in start-ups is a challenging issue that has caught the attention of policymakers and business owners. The present study is an attempt to provide a model for designing the resilience model of start-up businesses as a path to the future of start-up businesses. Method: This research is practical in terms of purpose and from the perspective of data collection is a qualitative research that has been done by content analysis method. The data collection method is semi-structured interview. Interviews with participants continued until they reached the theoretical saturation point, which was achieved by conducting 15 interviews. Participants were selected by snowball and purposeful method and data analysis was done through three coding steps (open, axial, selective) with MAXQDA2020 software. Findings: Based on the research results, the components of "cognitive resilience, financial resilience, adaptive resilience" as well as the effective organizational factors of "precedent, drivers and boys and resilience consequences" were identified. In this research, foresight and futures studies were identified as the drivers of resilience of start-up businesses. Conclusion: Resilience enables businesses to respond effectively to the dynamic conditions of their environment and maximize their competencies through strategic foresight and as a result gain more value than their competitors in the field of business. In this regard, the integration of foresight in the process of business resilience can be suggested as a facilitator of drivers.

Background and Aim: Job enrichment is a method that has been used to increase employee satisfaction and work motivation. The Hackman and Oldham job characteristics model (1976) has served as the foundation for many job enrichment efforts. In particular, a considerable amount of research has been devoted to the study of the job characteristics-job satisfaction relations. Te purpose of this study was to statistically determine the relation between job characteristics and job satisfaction among employees of hospitals affiliated to Tehran University of Medical Sciences (TUMS) in 2007. Materials and Methods: This study was a cross-sectional one, including 6 TUMS-affiliated hospitals-Emam, Shariati, Baharlo, Ziaeian, Madaen and Azadi. The sample size was 400, consisting of 200 administrative employees (50%), 151 nurses (38%) and 49 physicians (12%). The random sampling and stratified proportional sampling methods were used to select the hospitals and interviewees, respectively. Two questionnaires were developed and used to identify job characteristics and job satisfaction: 1) job diagnostics surveys (JDS) and 2) Hertzberg's job satisfaction questionnaire. The reliability of the questionnaires was ascertained by test-retest using the Pearson correlation coefficient (r = 0.88). Linear-by-linear association and ordinal logistics regression analysis were used for analyzing the data gathered.Results:The relations between the Motivational Potential Score (MPS) and job characteristics (except for feedback) with job satisfaction were statistically significant (p<0.05). Task identity was the most effective determinant of job satisfaction. Conclusion: It appears that all the job characteristics (except feedback) are important determinants of job satisfaction of the hospital employees. Although the work environment is in need of restructuring, it is important to note, from a positive perspective, that the job characteristics discussed in this study are readily amenable to change job satisfaction at the organizational level.

In this ‎paper‎‎, ‎we study the historical background of class field theory‎. ‎First‎, ‎we investigate Kummer's theory‎, ‎which is one of the algebraic backgrounds of class field theory‎. ‎Then we turn to Comer's youthful dream that led to the question of finding all Abelian extensions of number fields‎. ‎After this‎, ‎we state Takagi's theorems and finally study Hilbert's problems‎. ‎By examining the historical course of the above‎, ‎we are trying to present a better picture of the historical background of class field theory‎, ‎which is one of the basic foundations of mathematics.

Alexander Grothendieck is undoubtedly one of the most influential mathematicians of the 20th century who revolutionized modern algebraic geometry‎. ‎This article intends to study the story of one of Grothendieck's first results in another field of mathematics‎, ‎namely functional analysis‎, ‎which has recently been discovered to have a surprising connection with model theory‎. ‎We will show that the fundamental theorem of stability theory‎, ‎which was proved by Shelah in the 1970s‎, ‎is in fact a consequence of Grotendieck's double limit theorem‎, ‎which was proved in his doctoral thesis in the 1950s‎. ‎We will also discuss the model-theoretic meaning of Grothendieck's theorem and reinforce this theorem for generically stable types‎. ‎Finally‎, ‎we will discuss some possible applications and results in future work‎.