Since the emergence of power market, the target of power generating utilities has mainly switched from cost minimization to revenue maximization. They dispatch their power energy generation units in the uncertain environment of power market. As a result, multi-stage stochastic programming has been applied widely by many power generating agents as a suitable tool for dealing with self-scheduling strategies under uncertainty. However, dependence structure between stochastic variables has been almost ignored in the literature. Copula function is a new concept in the probability and statistics field which has the capability to represent the dependence structure among stochastic variables. However, Copula function has recently taken into account in power system studies by some articles. In this article, self-scheduling strategy of a generation utility owning thermal units is investigated while the dependence structure among stochastic load and market price variables is taking into account. We assume that the generation utility is a price-taker agent in a power market, and it also has to meet the load of a specific region as a retailer. The results indicates that as the stochastic dependence structure among load and price variables is considered in modeling load and price scenarios, the output of unit commitment problem changes so that the revenue of generation utility increases.

Independent random variables Y1, . . . , Yn belongs to the proportional reversed hazard rate (PRHR) model with proportionality parameters l1, ..., ln, if Yk ~ Glk (x), for k = 1, ..., n, where G is an absolutely continuous distribution function. In this paper we compare the smallest order statistics, the sample ranges and the ratios of the smallest and largest order statistics of two sets of independent random variables belonging to PRHR model, in the sense of (reversed) hazard rate order, likelihood ratio order and dispersive order, when the variables in one set have proportionality parameters l1, ..., ln and the variables in the other set are independent and identically distributed with common parameter `l=Snk =1 lk/n. We also compare the relative degree of dependence between the smallest and the largest order statistics of these samples whit respect to the monotone regression dependence order.

This paper reviews some recent results on stochastic orders and dependence among order statistics when the observations are independent and follow the proportional hazard rates model.

Efficiency in agricultural production is indicative of the efficiency level of farm households in their farming activities. Farmers in developing countries do not make use of all the potential technological resources, thus making inefficient decisions in their agricultural activities. Herein, technical efficiency in relation with the production of three types of rice crop (Boro, Aus and Aman) was evaluated, with some determinants of technical efficiency identified, in Bangladesh. It was attempted, throughout this study, to access the status of technical efficiency in rice production in Bangladesh for panel data while using the Stochastic Frontier Production Model with either of truncated normal or half-normal distributional assumptions. Both time-variant and time-invariant inefficiency effects models were estimated, one at a time. Collected data from agricultural sector pertaining to three main rice crops in Bangladesh for the period of 1980 to 2008 were made used of throughout the study. The results revealed that technical efficiency gradually increased over the reference period with the half normal distribution being found preferable to the truncated normal distribution as regards the technical inefficiency effects. The value of technical efficiency was found high for Boro rice while low for Aus in comparison with Aman rice in Bangladesh for both distributions in either of time-variant or invariant ones. It was observed that the most efficient rice production system has occurred for the case of Boro with a technical efficiency of 0.98. Year wise mean technical efficiency increased during the reference time periods.

Objective The prevention against tobacco dependence project (PAD) in Iran is an interventional project that is in progress in four phases with a focus on the prevention of tobacco use from childhood to adolescence. This study, as the second phase of this project, aims to evaluate the effectiveness of the PAD project by investigating the knowledge and attitudes of school students with and without education based on this project. Methods This is a quasi-experimental and quantitative study that was conducted in Varamin, Iran, in 2022. Using a proportional stratified sampling method, 200 school students who were PAD helpers were selected as the intervention group and 200 non-PAD students as the control group. The educational intervention was about prevention against tobacco products provided by using booklets, pamphlets, brochures, posters, educational videos, holding scientific, cultural and artistic competitions, providing consulting services, and recreational and training camps. A 57-item questionnaire was designed based on the World Health Organization questionnaires. The obtained data were analyzed in SPSS software, version 23 using t-test and Pearson’s correlation test. The significance level was set at 0.05. Results The mean score for the knowledge of the harms of tobacco in the PAD (24.41±0.32) and non-PAD (21.02±0.32) groups showed the significantly higher knowledge of the PAD group (P<0.05). Moreover, the mean score for the attitude variable in the PAD (18.156±0.31) and non-PAD (15.752±0.31) groups showed the significantly higher attitude of the PAD group (P<0.001).  Conclusion The PAD project has been effective in increasing awareness and changing the attitude of school students in Iran regarding tobacco smoking during the COVID-19 pandemic.

This paper explores an extension of the bivariate Schur-constant model, introducing an additional parameter to its associated Archimedean copula for greater flexibility. We analyze the dependence properties of the proposed model and illustrate our findings with several examples. Furthermore, provide a likelihood ratio test to compare the performance of the extended Archimedean copula with that of the traditional Archimedean subfamily. Two real-data analysis are also included.

Finding the location for plans like factories or warehouses for any organization is an important and strategic decision. Costs of transportation which are the main part of the price of the goods, is the function of the location of these projects. to find the optimum location of these projects, there have been various methods proposed which are usually defined (not random). In reality and in dealing with real world conditions, taking into account influential and effective parameters leads to unexpected results. In this research, while introducing these defined algorithms, it been tried to random location model based on the existing models. Regarding this, by studying the models taken in random location we tried to propose an efficient and effective model. For this purpose, by using randomized planning and randomized constrained planning, we turn random model to defined model, which is solvable by using the last algorithm or standard planning methods.