Using a stress and social support framework, this study explored the trajectory of depression in 388 married Arab immigrant women. The women provided three panels of data approximately 18 months apart. Depression at Time 3 was regressed on Time 1 depression, socio-demographic variables, and rate of change over time in stress and social support. The regression model was significant and accounted for 41.16% of the variation in Time 3 depression scores. Time 1 depression, English reading ability, husband’s employment status, and changes over time in immigration demands, daily hassles, and social support from friends were associated with Time 3 Depression.

Purpose Since December 2019, the coronavirus disease 2019 (COVID-19) epidemic has swept the world, causing widespread burden and increasingly hospitalizations. Researchers from around the world have tried to study the virus and its effect with more precision in various fields. The purpose of this study is to identify levels of anxiety and depression with regard to precautionary for prevention of COVID-19, and to identify the relationship between demographic variables and both depression and anxiety. Methods This was a descriptive cross-sectional study; data were collected by questionnaire via a mobile phone application in the Kurdistan Region of Iraq from 25 March, 2020 to 5 April, 2020. The sample size was 894 after deleting 20 cases because of duplication. The questionnaire consists of three main parts; part one is related to the sociodemographic characteristics of participants, the second and third parts consist of items related to depression and anxiety about COVID-19 using a 5-point Likert Scale (1 = of course no, 2 = no , 3 = normal for me, 4 = yes, and 5 = of course yes). Data was analyzed using SPSS V.25. Results The majority of the participants were from Erbil (58.8%), most of them were male (58.4%); nearly 21.2% preferred quarantine and 41.7% chose curfew as a best way to to avoid being infected by COVID-19. Most of the participants had depression because of people's lack of knowledge about how to protect themselves from the virus (88.14%), while the majority of them had anxiety concerning shopping and contact with infected people (97%) and financial problems (97%). Females had higher rates of COVID-19 depression than did males. There was a significant correlation between age and home setting and anxiety, and a significant association between marital status and level of education and depression. There was no significant association between other variables and depression and anxiety Conclusion The findings of the study indicated that the majority of participants were depressed and had anxiety about COVID-19. There was a significant association between gender and depression and anxiety, while there was no significant association between occupation and income, and depression and anxiety.

The process of producing electricity from sources of energy is known as electricity production. Electric also isn't freely accessible in environment, thus it should be "manufactured" (i.e., converting another kinds of energy to electrical energy) by utilities with in electricity industry (transportation, distributing, and so on).Moreover, the objective of this study is to compared of Brown’s as well as Holt’s Double Exponential Smoothing also build a best forecasting time series model among two smoothing model forecasting, as well as focuses on optimizing characteristics to use the golden section technique.  This exponential smoothing approach has been one of the time series forecasting methods that would be used to forecast (Generation Electrical) with in Kurdistan area. The issue that arises with this technique is determining the appropriate parameters to reduce predict inaccuracy. In addition, Data used in this paper are (Generation Electrical) in Kurdistan region for (132) months from 2010 to 2020. The study revealed that such data is trending modeled, indicating that a double exponential smoothing (DES) approach from Brown & Holt can be used with the (Stratigraphic & Minitab) software. There are the same results but the Result of analysis more depend on the R-program. The difference among the forecast findings acquired with optimum parameters as well as the assaying data was utilized to assess the feasibility of the forecast by completing normality and randomness tests. Ultimately, the outcomes of parameterization show that the optimal value of α that in DES Brown is (0.22) as well as the optimal MAPE is 9.23616 percent, whereas in DES Holt the optimal is (0.95) as well as the optimal β is (0.05) via the optimal MAPE of 8.08586 percent. This MAPE of a DES Brown technique is greater than the MAPE of a DES Holt approach. Feasibility experiments revealed that both approaches are capable of predicting. Depending on the value of MAPE as well as evaluation process, DES Holt's was recognized as the main prediction model.

Nonparametric kernel estimators are mostly used in a variety of statistical research fields. Nadaraya-Watson kernel estimator (NWK) is one of the most important nonparametric kernel estimator that is often used in regression models with a fixed bandwidth. In this article, we consider the four new Proposed Adaptive Nadaraya-Watson Kernel Regression Estimators (Interquartile Range, Standard Deviation, Mean Absolute Devotion, and Median Absolute Deviation) rather than (Fixed Bandwidth, Adaptive Geometric, Adaptive Mean, Adaptive Range, and Adaptive Median). The outcomes in both simulation and actual data in Leukemia Cancer show that the four new ANW Kernel Estimators (Interquartile Range, Standard Deviation, Mean Absolute devotion, and Median Absolute Deviation) is more effective than the kernel estimations with fixed bandwidth in previous studies using Mean Square Error (MSE) Criterion.

It is clear that the COVID-19 virus has affected human life and activities all over the world. The agricultural sector is not excluded from the affection of course. The importance of this issue encouraged us to carry out this statistical study aiming to show the impact of COVID-19 on the agricultural supply chain (Vegetative) production in Halabja. Three forms of the self-administered questionnaire (SAQ) were used to collect data from three targets; farmers, wholesalers, and retailers using the nonprobability sampling method. Descriptive analysis was used to show frequencies, percentages, and mean scores, while Independent Sample T-Test was used to find the relationship between variables using SPSS software V.26. Results show that there is no statistically significant relationship between characteristics of production and residence. Lockdown had a major impact on the plant production and its supplying chain in the following sequence: fewer products were delivered to the wholesaling markets by farmers, retailers bought less so consumers got fewer products.