The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. NonParametric Mann–Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann–Kendall tests (pre-whitening, trend-free pre-whitening, bias-corrected pre-whitening, and two variants of variance correction approaches) were applied. A significant increasing summer rainfall trend is observed in six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast monsoon season. To identify the trend change points in the time series, distribution−free cumulative sum test, and sequential Mann–Kendall tests were applied. Two open−source library packages were developed in R language namely, ”modifiedmk” and ”trendchange” to implement the statistical tests mentioned in this paper. The study results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region.

Rainfall and temperature have been extensively considered as an initial point toward the apprehension of climate change progressions, establishing one of the important constituents of the hydrological cycle. The purpose of the present study is to examine the variability of rainfall and temperature for better understanding of the hydrological environment of the river basin located in northern Tamil Nadu. Mann-Kendall and Sen's slope tests were employed for monthly, seasonal, and annual temperatures, and also for annual maximum daily rainfall, seasonal, and annual rainfall statistics. A change point detection test was applied for annual maximum and minimum, mean temperature, and annual precipitation series. The results revealed that all the monthly, seasonal, and annual maximum, minimum, and mean temperatures have a significant greater rising trend. The magnitude of increasing trends in NEM (northeast monsoon) and SWM (southwest monsoon) are greater than that of summer and winter seasons for almost all the rain gauge stations. The maximum temperature and minimum temperature change points are identified in the years 1985 and 2001 and 1987 and 2013, respectively. From the mean annual temperature, it is seen that the change point is present in 1983-88 and 2000-04 at 100% confidence interval.

In the present global environment, liberalization of international trade and the intense international competition, it has become more important for multinational corporations (MNCs) to internationalize their business. In the course of the internationalization, it is imperative that MNCs need to offer their employees the possibility of working abroad (called expatriation). However, studies have shown that when expatriates return to the home organization, called repatriation, it is related to many problems and these problems are not always taken seriously. Therefore, this article describes several repatriation processes undertaken by Indian Information Technology (IT) MNCs and how effective they are at lowering repatriates' turnover intentions. To fulfill this objective, first, the article reviews the literature on turnover intention among repatriates and then an empirical quantitative study is developed with a sample of 292 repatriates who have recently returned to India. The results indicated that the surveyed repatriates believed that perceived support during international assignment and upon return from assignment are two most important variables to increase the repatriate's retention and lack of it thereof was likely to generate unfavorable attitudes toward the company and higher turnover intention.

Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the south-west monsoon season and at two stations in the north-east monsoon season. To identify the trend change-points in the time series, distribution-free Cumulative SUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, ‘modifiedmk’ and ‘trendchange’ to implement the statistical tests mentioned in this paper. The study will benefit water resource management, drought mitigation, socio-economic development and sustainable agricultural planning in the region.

The internet of things (IoT), an emerging technological marvel, consists of a group of physical objects such as vehicles, machines and sensors to monitor and transfer data over the internet with much less human to machine interaction. It relies on a host of technologies like application programming interfaces (API), which in turn, help the devices to get connected with the internet. Efficient irrigation tank management requires a strong database on continuous water level dynamics for irrigation decision-making. Real-time tank water level monitoring is possible through an IoT device by integrating sensors and microcontroller that can send the water level data to the cloud. Google sheet is used to store the water level data that can be viewed using web application as well as mobile application. The contour map of the study tank is used to develop the stage (water level) vs volume curve. The volume of water present in the tank at any time can be arrived at for any tank water level using the above curve. The developed device can provide real-time continuous water level data with low cost and simple infrastructure, thus aiding tank water management.