Search

The experiments where response of a treatment (direct effect) is affected by the treatment(s) applied in neighboring units, neighbor designs are used to balance the neighbor effects. Being the economical, minimal neighbor designs are preferred by the experimenters. Minimal circular neighbor designs could not be constructed for almost every case of v even, where v is number of treatments. For v even, minimal circular generalized neighbor designs are preferred. In this article, algorithms are developed to obtain minimal circular generalized neighbor designs in which (a) v/2 of the unordered pairs, and (b) 3v/2 of the unordered pairs, do not appear as neighbor whereas the remaining ones appear once. These algorithms are also coded with R-language. [ABSTRACT FROM AUTHOR]

The current research aimed to monitor and assess the heavy metal contamination in the surface water of 53 sampling sites along the selected rivers using principal component analysis and cluster analysis. For this purpose, both physiochemical parameters such as the temperature (T), the potential of hydrogen (pH), total dissolved solids (TDS) and electroconductivity (EC), and heavy metals such as iron (Fe), chromium (Cr), nickel (Ni), cadmium (Cd), lead (Pb) and arsenic (As) are analyzed as potential water contaminants. The average values of pH, TDS, EC and T are found at 7.75, 70.89 mg/L, 139.11 µs/cm and 20.29 °C, respectively, and heavy metals including Cr, Ni, Cd, Pb, As and Fe are observed at 0.04, 0.04, 0.04, 0.03, 0.001 and 0.04 mg/L, respectively. Moreover, it is found that in both rivers hazardous metals, including Cr (100%), Cd (92.30%), Pb (100%), Ni (100%) and Fe (91%), exceed the permissible limits of the WHO. [ABSTRACT FROM AUTHOR]

Drought is one of the disastrous natural hazards with complex seasonal and spatial patterns. Understanding the spatial patterns of drought and predicting the likelihood of inter-seasonal drought persistence can provide substantial operational guidelines for water resource management and agricultural production. This study examines drought persistence by identifying the spatial patterns of seasonal drought frequency and inter-seasonal drought persistence in the northeastern region of Pakistan. The Standardized Precipitation Index (SPI) with a three-month time scale is used to examine meteorological drought. Furthermore, Bayesian logistic regression is used to calculate the probability and odds ratios of drought occurrence in the current season, given the previous season's SPI values. For instance, at Balakot station, for the summer-to-autumn season, the value of the odds ratio is significant (6.78). It shows that one unit increase in SPI of the summer season will cause a 5.78 times to increase in odds of autumn drought occurrence. The average drought frequency varies from 37.3 to 89.1%, whereas the average inter-seasonal drought persistence varies from 21.9 to 91.7% in the study region. Results indicate that some areas in the study region, like Kakul and Garhi Dupatta, are more prone to drought and vulnerable to inter-seasonal drought persistence. Furthermore, the Bayesian logistic regression results reveal a negative relationship between spring drought occurrence and winter SPI, demonstrating that the overall study region is more prone to winter-to-spring drought persistence and less vulnerable to summer-to-autumn drought persistence. Overall study has concluded that the region's seasonal drought forecast is challenging due to uncertain drought persistence patterns. However, the Bayesian logistic regression model provides more accurate and precise regional seasonal drought forecasts. The outcome of the present study provides scientific evidence to develop early warning systems and manage seasonal crops in Pakistan. [ABSTRACT FROM AUTHOR]

The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan. [ABSTRACT FROM AUTHOR]

The conservation of the vulnerable Asiatic black bear (Ursus thibetanus) in Pakistan is challenged by retaliatory killing, driven by negative interactions between people and bears, such as livestock depredation. We distributed a questionnaire among 369 individuals in rural communities within the Mansehra District, Pakistan, where bear retaliatory killings are often reported. We focused on human–Asiatic black bear negative interactions, such as livestock depredations, crop damage, and human injuries occurring between 2015 and 2019. Although the number of livestock depredation events was small in absolute terms (an average of 11.5 livestock heads reported to be killed annually), it had a large estimated economic impact in terms of local economies ($1367 per year, or a total of $5.469 between 2015 and 2019). Such annual estimated costs account for 93% of the per capita gross domestic product for Pakistan, roughly equivalent to $55,853 for a US citizen. Additionally, 30 incidents between people and bears were registered, where 6% human were fatal. We recommend awareness campaigns on different mitigating interventions, and training on how to use them, as well as behaviors to reduce the risk of negative interactions. [ABSTRACT FROM AUTHOR]

Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakistan meteorological department observed these environmental parameters at more than 90 stations. The allocation of these monitoring stations is not made systematically correct. This leads to inaccurate predictions for unobserved locations. The study aims to propose a monitoring network by which these prediction errors of the environmental parameters can be minimized. The well-known prediction techniques named, model-based ordinary kriging and model-based universal kriging (UK) with the known Matheron variogram model are used for prediction purposes. We investigate the monitoring network of Pakistan for rainfall and focus on both the optimal deletion/addition of monitoring stations from/to this network. The two stochastic search algorithms, spatial simulated annealing, and genetic algorithm are used for optimization purposes. Furthermore, the minimization of the Average Kriging Variance (AKV) is taken as the interpolation accuracy measure. The spatial simulated annealing exhibits a lower AKV as compared to the Genetic algorithm when adding/removing the optimal/redundant locations from the monitoring network. [ABSTRACT FROM AUTHOR]

Based on different surveys, dairy farmers are concerned about extensive use of exogenous oxytocin in buffaloes, which is being held responsible for reproductive problems including irregular oestrous cycle and delayed ovulation. For these concerns, effects of oxytocin injection on postpartum follicular dynamics, postpartum oestrous interval (PEI), oestrous length, the interval from onset of estrus to ovulation and blood progesterone (P4) were studied in Nili‐Ravi buffaloes. For this purpose, 23 animals within 1 week after calving were randomly divided into three groups: without oxytocin (CON; n = 7), 10 i.u. oxytocin (LOW; n = 8), 30 i.u. oxytocin – (HIGH; n = 8) and used to record the PEI for the study period of 154 days. At subsequent estrus, three buffaloes from each group (not served) were selected randomly to monitor two cycles for 6 weeks. Transrectal ultrasonography was performed to evaluate follicular and corpus luteum (CL) development, and blood sampling was done for progesterone (P4) analysis. These results revealed that postpartum oestrous interval (PEI) decreased significantly in oxytocin‐treated groups. The number of small, medium and total follicles on the left ovary was significantly higher in the HIGH group. However, an overall number of small and total follicles on both right and left ovaries was significantly higher in CON and HIGH groups. On the other hand, there was no difference in the number of follicles on the right ovary among all treatment groups. The same was true for the size of pre‐ovulatory follicles, CL, P4 concentrations and oestrous cycle length. The intervals from onset of estrus to ovulation and from standing estrus to ovulation were increased considerably in the HIGH group. It is concluded that exogenous oxytocin administration resulted in the shortening of PEI but triggered a delay in ovulation. Moreover, a higher dose of oxytocin could stimulate the growth of small, medium, and total follicles in postpartum Nili‐Ravi buffaloes. [ABSTRACT FROM AUTHOR]

In this article, we propose Liu-type shrinkage estimators for the zero-inflated Poisson regression (ZIPR) model in the presence of multicollinearity. Our new approach is a remedy to the problem of inflated variances for the ML estimation technique—which is a standard approach to estimate these types of count data models. When the data are in the form of non-negative integers with a surplus of zeros it induces overdispersion in the dependent variable. Considerable multicollinearity is frequently observed, but usually disregarded, for these types of data sets. Based on a Monte Carlo study we illustrate that our proposed estimators exhibit better MSE and MAE than the usual ML estimator and some other Liu estimators in the presence of multicollinearity. To demonstrate the advantages and the empirical relevance of our improved estimators, maternal death data are analyzed and the results illustrate similar benefits as is demonstrated in our simulation study. [ABSTRACT FROM AUTHOR]

Toxoplasma infection is one of the most common human parasitic diseases. During 2018-2020, in the rural areas of three districts of Pakistan, we surveyed a total of 451 animals, belonging to different asymptomatic ruminant species, to determine the prevalence of Toxoplasma gondii antibodies. We used ELISA assay as well as recorded some associated risk factors contributing to its transmission. IgM antibodies were detected in 17% and IgG in 13.4% of ruminant samples with the highest percentage, 10% for IgM and 8.6% for IgG in sheep. A strong significant association was found between antibodies and different species (IgM, χ 2 = 29.280, P =.000 , and IgG, χ 2 = 22.580 , P =.000), respectively. Infection with T. gondii seems mainly associated with different geographic features and the presence of cats in the environment, low hygiene water systems and livestock that are mostly dependent on outdoor drinking and grazing. There was no significant association between IgM and age grouping ( χ 2 = 6.660 , P = 0.840 nor for IgG ( χ 2 = 8.136 , P = 0.43). The results of this study may be considered the starting point to promote the awareness about parasitic infections in ruminants in Pakistan in order to prevent this infection from further spreading. [ABSTRACT FROM AUTHOR]

This article introduces some Liu parameters in the linear regression model based on the work of Shukur, Månsson, and Sjölander. These methods of estimating the Liu parameter d increase the efficiency of Liu estimator. The comparison of proposed Liu parameters and available methods has done using Monte Carlo simulation and a real data set where the mean squared error, mean absolute error and interval estimation are considered as performance criterions. The simulation study shows that under certain conditions the proposed Liu parameters perform quite well as compared to the ordinary least squares estimator and other existing Liu parameters. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic (COVID-19) has impacted the usual global movement patterns, atmospheric pollutants, and climatic parameters. The current study sought to assess the impact of the COVID-19 lockdown on urban mobility, atmospheric pollutants, and Pakistan's climate. For the air pollution assessment, total column ozone (O3), sulphur dioxide (SO2), and tropospheric column nitrogen dioxide (NO2) data from the Ozone Monitoring Instrument (OMI), aerosol optical depth (AOD) data from the Multi-angle Imaging Spectroradiometer (MISR), and dust column mass density (PM2.5) data from the MERRA-2 satellite were used. Furthermore, these datasets are linked to climatic parameters (temperature, precipitation, wind speed). The Kruskal–Wallis H test (KWt) is used to compare medians among k groups (k > 2), and the Wilcoxon signed-rank sum test (WRST) is for analyzing the differences between the medians of two datasets. To make the analysis more effective, and to justify that the variations in air quality parameters are due to the COVID-19 pandemic, a Generalized Linear Model (GLM) was used. The findings revealed that the limitations on human mobility have lowered emissions, which has improved the air quality in Pakistan. The results of the study showed that the climatic parameters (precipitation, Tmax, Tmin, and Tmean) have a positive correlation and wind speed has a negative correlation with NO2 and AOD. This study found a significant decrease in air pollutants (NO2, SO2, O3, AOD) of 30–40% in Pakistan during the strict lockdown period. In this duration, the highest drop of about 28% in NO2 concentrations has been found in Karachi. Total column O3 did not show any reduction during the strict lockdown, but a minor decline was depicted as 0.38% in Lahore and 0.55% in Islamabad during the loosening lockdown. During strict lockdown, AOD was reduced up to 23% in Islamabad and 14.46% in Lahore. The results of KWt and WRST evident that all the mobility indices are significant (p < 0.05) in nature. The GLM justified that restraining human activities during the lockdown has decreased anthropogenic emissions and, as a result, improved air quality, particularly in metropolitan areas. [ABSTRACT FROM AUTHOR]