Background: Citrus is among the cold temperature (freezing) susceptible plants. The performance of storage and breeding of horticultural crops is mainly dependent on the correlation among the traits. Objectives: The present study was conducted to identify the physiological and biochemical traits of some citrus cultivars with the greatest influence on stress tolerance and determine the direct and indirect effects of these traits on the median Lethal Time (LT50) using integrated pathway analysis (i. e., Stepwise Regression and causality coefficient). Methods: Pathway analysis (i. e., Stepwise Regression and causality coefficient) based on additive models, which is among the robust statistical methods to describe the relationship among the traits, was used to investigate the physiological and biochemical charac, teristics of four citrus cultivars (i. e., Japanese mandarin, Thomson orange, Ruby Star grapefruit, and Lisbon lemon) at temperature levels (-8,-4, and +4°, C) in a completely randomized design with three replications in 2021. Results: The results showed that Japanese mandarin and Thomson orange cultivars had the highest stress tolerance (LT50). The results of the correlation coefficient demonstrated that the highest positive and significant correlations belonged to the LT50 with total flavonoid (0. 443**), chlorophyll a with chlorophyll b (0. 613**), carotenoid with chlorophyll a (0. 929**), chlorophyll b (0. 573**), and total chlorophyll (0. 849**), relative moisture content with malondialdehyde (0. 559**), glycine-betaine with catalase (0. 919**), hydrogen peroxide with total flavonoid (0. 405**), and catalase (0. 611**). Conclusions: The results of Stepwise Regression for all traits indicated the importance and critical role of total flavonoid, proline, and LT50 characteristics.

Inclusion of steel fibers to concrete progresses the flexural and tensile capacities of concrete. Consequently the shear capacity of concrete flexural members improve. Predicting the shear capacity of concrete beams containing steel fiber is an important issue not only in structural design but also to retrofitting of existing structures. Since there are several variables to assess the shear capacity of steel fiber reinforced concrete (SFRC) beams, presenting a suitable equation is a complicated task. The aim of the present paper is to evaluate an empirical formulae based Stepwise Regression (SR) method for shear capacity of SFRC beams. A series of reliable experimental data has been provided from literatures for model development. The obtained results based SR model were compared with experimental data in training and testing state. A practical formulae based SR method has been developed for shear capacity assessment of SFRC beams. Besides, several equations based models also presented to compare with the equation based SR model. The comparison showed the SR formulae gives the most exact accuracy than others in terms of shear capacity assessment of SFRC beams.

Hepatitis-related liver diseases are a leading cause of mortality and morbidity among people with HIV/ AIDS taking highly active antiretroviral therapy due to shared transmission routes. An estimated 2–4 million HIV-infected persons have chronic HBV co-infection, and 4–5 million have HCV co-infection worldwide and 14,000 new infections each day. The purpose of this study was to determine the prevalence and associated factors of HBV and HCV co-infection in HIV-positive patients. A cross-sectional study was conducted among 235 HIV/ AIDS patients seeking medical care at special clinics of two public hospitals in Lahore, Pakistan, from February 2018 to May 2018. A structured questionnaire was used to collect information on socio-demographic and clinical characteristics of HIV/ AIDS patients after obtaining their written informed consent. Chi-square, Fisher's exact, and two independent sample t-tests as appropriate were used to find the association between risk factors and HBV, HCV co-infection with HIV. Further, a forward Stepwise logistic Regression model was used to evaluate the predictors of HBV and HCV co-infection with HIV. P-value < 0.05 was regarded as significant. Of 235 HIV-positive patients, 9\% were co-infected with HBV, 41 were HCV co-infected, and 6\% had HBV-HCV triple infection. The highest prevalence of HBV (55\%), HCV co-infection (70\%), and HBV-HCV triple infection (85\%) were observed in intravenous drug users followed by heterosexual routes. Male, hypertensive, alcohol consumers, and smokers were statistically significantly associated with HBV co-infection $(P-value < 0.05)$. The factors include being male, never married, having $< 1$ year of HIV diagnosis, having $< 200$ CD4 counts (cell/mm3), presence of physical disability, having been infected through sexual routes, injecting drug user, alcohol consumer, and smoker were statistically significantly associated with HCV co-infection $(P-value < 0.05)$. Whereas the factors; heterosexual transmission, intravenous drug use, alcohol use, smoking, and presence of physical disability were statistically significantly associated with HBV, HCV triple infection $(P < 0.05)$. The adjusted odds ratio obtained by fitted logistic Regression model showed that HIV transmission routes (both hetero and homo) and never married had lesser odds of HCV co-infection whereas the person with HIV transmission through intravenous drug use, who smoke and aged more than 30 years, had greater odds of HCV co-infection. Co-infection with hepatitis B and C virus is common among this studied sample of HIV-infected patients. The study's finding reaffirms the need for routine baseline screening for this marker and as there is more chance of co-infection with these hepatitis viruses due to enhanced immunodeficiency by HIV and shared routes of transmission. It highlights the need for timely initiation of HAART. Furthermore, those found to be negative should be immunized with HBV and HCV vaccines to improve.

The project scheduling problem is known as a NP-hard problem in literature. In this research, a resource constrained project scheduling problem which is known as a NP-Hard problem is considered. This problem has attracted many researchers during recent years. The aim of this problem is to determine the optimal starting times of activities considering both precedence and available resources constraints such that the total project completion time is minimized. In this paper a combination of discount based pricing policy and project scheduling is proposed, whereas in classical models it is assumed that price of required resources is fixed. To solve the proposed model, a hybrid algorithm based on two algorithms, i.e. genetic algorithm and variable neighborhood search is proposed. In this method, genetic algorithm as a main framework and variable neighborhood search as a new operator are designed. Moreover, since the parameter values of evolutionary algorithms have great influences on algorithm efficiency, to set the parameters of proposed algorithm a new statistical approach based on Stepwise Regression technique is devised. Computational results show the good performance of proposed approach with regard to the other methods.

In the current study two methods are evaluated for predicting the compressive strength of concrete containing metakaolin. Adaptive neuro-fuzzy inference system (ANFIS) model and Stepwise Regression (SR) model are developed as a reliable modeling method for simulating and predicting the compressive strength of concrete containing metakaolin at the different ages. The required data in training and testing state obtained from a reliable data base. Then, a comparison has been made between proposed ANFIS model and SR model to have an idea about the predictive power of these methods.

Characterization of physical and chemical soil criteria is a key step in understanding the source of spatial variability in the productivity across agricultural fields (21). Crop yield variability can be caused by many factors, including spatial variability of soil texture, crop management, soil physical and chemical properties and nutrient availability (45). Understanding the spatial variability of soil physical and chemical characteristics is essential for crop management, as it is directly contributing to variability in growth and yield of crop (38 & 14). Hence, understanding their spatial variability across agricultural fields is essential in optimizing the application of agricultural inputs and crop yield and it could help significantly in managing the spatial variability in the productivity of soil agroecosystems (30 & 14). Therefore, the objectives of this study were: (i) evaluate the effect of soil physical and chemical criteria on yield indices of wheat and (ii) to investigate the correlation between physical and chmical soil properties and wheat yield. Materials and Methods Samplings were performed based on random-systematic method from 30 fields in Khorasan-e Razavi province during 2017 and 2018. Studied characteristics were texture, organic matter (OM), organic carbon (OC), total nitrogen (TN), available P, available K, pH and C: N ratio of soil and seed yield, biological yield, straw yield, 1000-seed weight and harvest index (HI) of wheat. Multiple Regression model was used to identify the relationship between soil variables (independent variables) and wheat yield indices (dependent variables). In addition, determining the most important factors of soil physical and chemical properties which have on wheat yield criteria was done by Stepwise Regression analysis. Results and discussion The results revealed showed that the mean values of seed yield, straw yield, biological yield, 1000-seed weight and HI of wheat were observed with 3588. 47 kg. ha-1, 7362. 80 kg. ha-1, 10951. 27 kg. ha-1, 35. 40 g and 48. 56%, respectively. The highest and the lowest standard errors were computed for biological yield (198. 40) and 1000-seed yield (0. 74), respectively. Also, The effect of soil textures was significant (p≤ 0. 05) on soil chemical criteria and wheat yield. The maximum OM, OC, TN, available P, available K and pH were observed for sandy clay with 1. 86%, 1. 09%, 0. 18%, 166. 20 ppm, 0. 05 ppm and 7. 37, respectively. The maximum seed yield and biological yield were related for clay soil (with 4313. 83 and 11924. 86 kg. ha-1, respectively). The highest correlation coefficients were computed for OM (r=0. 935**) and OC (r=0. 933**) with 1000-seed weight. The most important factors influencing wheat yield by using step by step Regression were OM, available P, TN and available K, respectively. Conclusion Longterm sustainability of agroecosystems depends on soil quality and its fertility. Poor soil management practices can lead to degraded soil and environmental quality and reduction in crop yields. Results suggest that novel management approaches are needed to maintain the longterm sustainability of soil resources and crop yields without seriously degrading the environment that this will help in reducing the cost of fertilization and improving soil and environmental quality without altering crop yields

Background: This paper compared the QSAR modeling of anti-cancer activity of compounds 1, 4-Dihydro-4-oxo-1-(2-thiazolyl)-1, 8-naphthyridines and its derivatives using Stepwise multiple linear Regression (S-MLR) and combined genetic algorithm-multiple linear Regression methods (GA-MLR(. Materials and methods: A set of 100 compounds with certain anticancer activity were selected from literature. All molecules were “ cleaned up” and the Allinger’ s MM2 force field was used for energy minimization, the semi-empirical quantum method Austin method 1 (AM1) was used for geometry optimization using the Polak-Ribiere algorithm. A large number of theoretical descriptors for each molecule were calculated using Dragon software. In order to select the best set of descriptors for QSAR modeling, GA-MLR and Stepwise-MLR as two variable selection methods were used. First the random sampling of the training sets (80% of data) were randomly taken 20 times, and the remaining molecules (20 percent of the data) were used as prediction set for external validation. Among the random samples, one of the samples with high Q2CV, Q2cal, Q2test was selected as the best train and test set. Using this train set, QSAR modeling performed using GA-MLR and Stepwise-MLR methods. Results: QSAR models by GA-MLR modeling had larger validated squared correlation coefficient than the obtained models by S-MLR. Conclusion: According to the results, it could be concluded that the activity of similar compounds will be predictable by the obtained model.