Accurate measurements of the B8 neutrino spectrum are important for the interpretation of solar neutrino data. Experimentally, the B8 neutrino spectrum can be obtained from the measurement of the $\beta${}-delayed $\alpha${} spectrum. We report on an $\alpha${}-$\alpha${} coincidence measurement performed at the IGISOL facility in Jyv\"askyl\"a, Finland. Our measurement allows extensive cross-checks to be performed and gives a more intense neutrino spectrum at high energies compared to the present standard. The deviation reaches 4% at the end point of the spectrum. Below 11~MeV, the deviation is less than 1%.

The Advanced Time-Delayed $\beta \gamma \gamma$(t) method was used to measure lifetimes of selected high-spin states in ${}^{122}\mathrm{Cd}$ populated from ${\beta }^{-}$ decay of the $J^{\pi }=(9^{-})$ isomer in ${}^{122}\mathrm{Ag}$. From the $\gamma \gamma$ coincidences, a new energy level was established at 2616.6 keV with a suggested spin-parity assignment of $8^{-}$. Lifetimes were determined for the high-spin states at 2616.6 and 2502.7 keV as $T_{1/2}=1.35(29)$ ns and 0.24(6) ns, respectively. The transition rates for γ rays de-exciting the $7^{-}$ states in the $N=74$ isotones of ${}^{122}\mathrm{Cd}$, ${}^{124}\mathrm{Sn}$, and ${}^{126}\mathrm{Te}$ were found to be very similar.

Rapid population growth in North-East India has led to urban sprawl, agricultural expansion, and industrialization, resulting in significant deforestation. Numerous studies have utilized Forest Canopy Density (FCD) coupled with remote sensing and GIS to assess forest cover changes. This study evaluates how FCD changed from 2000 to 2020, highlighting deforestation patterns in North-East India. Machine learning models, including Binary Logistic Regression (BLR), Random Forest (RF), REP-Tree, and XGBoost Regression (XGBR), were used to identify areas at risk of deforestation. Influencing factors considered were forest density, barren land, agricultural land, urban areas, distance to roads, and topographical characteristics. Results indicated that proximity to roads notably increases deforestation risk. Higher densities of agricultural and urban land also contribute to greater deforestation rates, whereas increased forest and barren land densities reduce this risk. Among the tested models, Random Forest showed superior performance with a high True Positive Rate (TPR) and low False Positive Rate (FPR), effectively identifying high-risk deforestation zones. Analysis also revealed a concerning shift from high-density to low-density forests, signifying substantial forest cover loss and potential threats to biodiversity and ecosystem services. The findings emphasize the need for integrated land-use planning and targeted conservation, specifically addressing road proximity, to effectively combat deforestation in North-East India and similar regions.

Soil nutrients are the key factors that influence crop productivity and plant growth. The present study gives information about the soil nutrient status of Jabalpur Block in Jabalpur District, Madhya Pradesh. 30 soil samples were collected from the field at a depth of 0-15 cm. Soil samples were collected using grid sampling techniques and the location of soil sampling sites was determined using GPS. Soil samples were further analysed to determine the soil nutrient index (SNI) using the soil chemical properties such as pH, OC, EC, and soil macronutrients i.e. N, P, K and Sulphur (S), etc. The nutrients are further categorized as low, medium, and high as per the soil nutrient index (NI). The spatial variation of soil nutrient parameters was represented by using the interpolation method in the ArcGIS environment. Based on the fertility measurement, the pH of the Jabalpur block ranges from 7.1 to 7.9, which is indicative of Neutral to alkaline nature, and organic carbon content ranges from 0.7 % to 1.78 %, indicating that the amount of SOC is medium to high along with available N ranging from (90kg/ha to 320kg/ha), available P with (20.16kg/ha to 47.04kg/ha) and available K of (29.12 kg/ha to 118kg/ha). The fertility status of Jabalpur block in this study is primarily constrained by soil pH, as well as N, P, K and S. Therefore, to enrich the soil fertility status of Jabalpur block, proper nutrient management practices, application of different organic manures, suitable cropping pattern and sustainable use of agricultural land are very essential.

Land degradation is a pervasive environmental and “economic challenge of the present time, especially in developing countries. Soil erosion caused by water is considered as one of the major types of land degradation processes. Soil loss estimation and detection of soil erosion-prone areas are the most important for agricultural planning and various other land management plannings in the recent era. The amount of annual average soil loss was calculated by using the Revised Universal Soil Loss Equation (RUSLE) model. This model was popularized for identifying soil loss zone areas or zones and soil erosion risk areas. This study provided a creditable prediction of soil erosion and the probable soil erosion severity zones of Jalpaiguri Sadar Block in Jalpaiguri District, West Bengal. GIS Environment was used to create the raster layer in the RUSLE factors. RUSLE data layers including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practice (P) factors were calculated to account for the average annual soil loss of the study area. The results of this work show that the range of soil erosion is >100.00 tons hac-1year-1 to <25.00 tons hac-1year-1 while the amount of soil erosion is more in the upper part of the Jalpaiguri Sadar block, mainly along the right bank of the Tista river and the bank of the Karala river, because the soil texture is very loose in nature and the area also receive heavy rainfall. Therefore, the high level of risk of soil erosion can be checked through the processes of various soil conservation techniques.