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Background: Standardized methods for reporting surgical quality have been described for all the major urological procedures apart from radical nephroureterectomy (RNU). Objective: To propose a tetrafecta criterion for assessing the quality of RNU based on a consensus panel within the Young Association of Urology (YAU) Urothelial Group, and to test the impact of this tetrafecta in a multicenter, large contemporary cohort of patients treated with RNU for upper tract urothelial carcinoma (UTUC). Design, setting, and participants: This was a retrospective analysis of 1765 patients with UTUC treated between 2000 and 2021. Outcome measurements and statistical analysis: We interviewed the YAU Urothelial Group to propose and score a list of items to be included in the “RNU-fecta.” A ranking was generated for the criteria with the highest sum score. These criteria were applied to a large multicenter cohort of patients. Kaplan-Meier curves were built to evaluate differences in overall survival (OS) rates between groups, and a multivariable logistic regression model was used to find the predictors of achieving the RNU tetrafecta. Results and limitations: The criteria with the highest score included three surgical items such as negative soft tissue surgical margins, bladder cuff excision, lymph node dissection according to guideline recommendations, and one oncological item defined by the absence of any recurrence in ≤12 mo. These items formed the RNU tetrafecta. Within a median follow-up of 30 mo, 52.6% of patients achieved the RNU tetrafecta. The 5-yr OS rates were significantly higher for patients achieving tetrafecta than for their counterparts (76% vs 51%). Younger age, lower body mass index, and robotic approach were found to be independent predictors of tetrafecta achievement. Conversely, a higher Eastern Cooperative Oncology Group score, higher clinical stage, and bladder cancer history were inversely associated with tetrafecta. Conclusions: Herein, we present a “tetrafecta” composite endpoint that may serve as a potential tool to assess the overall quality of the RNU procedure. Pending external validation, this tool could allow a comparison between surgical series and may be useful for assessing the learning curve of the procedure as well as for evaluating the impact of new technologies in the field. Patient summary: In this study, a tetrafecta criterion was developed for assessing the surgical quality of radical nephroureterectomy in patients with upper tract urothelial carcinoma. Patients who achieved tetrafecta had higher 5-yr overall survival rates than those who did not.

Land suitability analysis (LSA) is the progression of influence if a certain piece of land is suitable for a specific use. Land suitability study for crops is an essential contemporary step in identifying appropriate and sustainable land use strategies to enhance the land's potential. LSA for agriculture is one of the most capable models for exposing the apprehension for arable land and forecasting the allocation for long-term growth in semi-arid regions. As a result, the goal of this research is to provide a conceptual process for LSA that will aid in increasing green cover to address environmental challenges that prevent groundwater recharge. Several thematic layers like soils, geology, slope, geomorphology, Drainage Density (DD), Landuse landcover (LULC), Soil pH, soil nitrogen, soil phosphorous, and soil potassium are derived by using collateral data, and satellite images involved in LSA analysis. The Multicriteria Decision Making (MCDM), with Analytical Hierarchy Process (AHP) based pairwise comparison matrix, was applied to estimate LSA for agriculture. The outcomes revealed, around 80.53 sq.km (26.55%) is highly suitable, 87.38 sq.km (28.81%) is moderately suitable, 82.34 sq.km (27.15%) is marginally suitable, and 53.07 sq.km (17.50%) is not suitable for agriculture in the study region. The decision-making AHP tool combined with GIS presents a unique technique, and the findings from this work might be valuable in identifying viable agricultural lands in diverse regions of the world.

Surface water monitoring is a necessary component of researching ecological and hydrological processes. Recent improvements in satellite-based remote sensors have ushered in a new era in the area of surface water monitoring. The assessment and mapping of water resources in the semi-arid regions are one of the challenging tasks for the research domain of hydrogeologists and decision-makers. In this study, an inventory of existing ancillary data and remotely sensed data, i.e., Landsat 7 ETM+, Landsat 8 OLI/TIRS, and DEM data used for monitoring and mapping the surface water resources in the semi-arid regions of south India. For monitoring and mapping the water resources, twenty years of land cover changes were assessed using the Modification of Normalized Difference Water Index (MNDWI) techniques. Each MNDWI land cover has been classified into five categories viz., fallow land, vegetation land, developed land, moisture soils, and surface water bodies, based on the pixel values varying from +1 to -1. The results of the research reveal that the surface water bodies vary from year to year over the past two decades. The fallow land (36.20 %), vegetation lands (35.37 %) are decreased, and developed land (22.56 %), moisture soils (4.81 %), and surface water bodies (1.06 %) are increased over the past two decades from 2000 to 2020. Most land cover categories differed statistically from one another, however, when defining vegetation with varying water content, there were some commonalities. MNDWI is found to be efficient in identifying water bodies.

This research aims to find a relation between measured Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) for assessing land degradation and desertification in semi-arid regions using LANDSAT 8. In the twenty-first century, soil loss has been a serious environmental problem due to anthropogenic pressure and climate change. Monitoring and assessing land degradation vulnerability is becoming increasingly important for sustainable land resource management, especially in semi-arid ecosystems. The purpose of this research is to retrieve the LST and examine the relationship between LST and NDVI, in addition to LST and Landsat 8 bands. The association between LST and Landsat 8 bands is found to be positive, as well as the regression coefficient from NDVI to LST is also found to be strong Positive. The resultant of the NDVI is further classified into five categories based on the pixel values, and they are reclassified into five categories for land cover assessment, they are namely waterbodies, degraded lands, desertified lands, vegetation, and built-up lands. The results reveal that the desertified lands are occupying an area of 13.62%, degraded lands occupy 48.91%, built-up lands occupy 8.39%, vegetation lands occupy 27.34%, and water bodies occupy 1.71% in the study region. These research findings can be used to optimize the technique for future research in the field of degradation assessment, thermal comfort zone assessment, and sustainable agricultural growth.

Present dataset aims at the inventory data on preparation of Desertification Status Maps (DSM) for the first time in semi-arid region of Anantapur district in the state of Andhra Pradesh, South India by applying Normalized Difference Vegetation Index (NDVI) with acquired Remote Sensing (RS) satellite imageries and processed in ERDAS Imagine and ArcGIS software's. The NDVI has been classified into five such as water body, vegetation, fallow land, degradation land, and desertified land. Further, degradation land has been decreased to 4.87% which lead to desertification in the study region. The current research data will be resourceful to the environmental scientists and planning agencies who can utilize optimum for sustainable development and good governance in land degradation, desertification, and conserve land resources.

The data deals with the functions that automatically extracted lineaments from the Cartosat, ASTER and SRTM of Digital Elevation Model (DEM) of different spatial resolutions, in the software ArcGIS 10.4. The extracted lineaments result shows the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) DEM gives the lowest number of lineaments reflects Cartosat and SRTM (Shuttle Radar Topography Mission) DEM shows a medium number of lineaments. Cartosat DEM is most appropriate for extraction of contours precisely rather than ASTER and SRTM. This study reveals the Cartosat DEM data is best to use extraction of lineaments in the Indian provinces, offers at most comprehensive geological structural info amongst all the data sets. The extracted lineaments lengths and densities are determined by the statistical method. Based on the data generated lineament density and rose diagram. Cartosat DEM data are the best suited for studying very small areas as through geological and structural information can be mined by using this data. Abbreviations: DEM Digital Elevation Model, SRTM Shuttle Radar Topography Mission, ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer, Keywords: ASTER-DEM, SRTM, Cartosat DEM, Lineaments, Rose diagram, Extraction