The Jaintiapur-Jaflong region, strategically positioned between the subsiding Surma Basin to the south and the uplifting Shillong Massif to the north, presents a unique geological setting. This study employed geological clinometers and other field methods to ascertain the geological characteristics of the area. The regional strike was determined to be N66˚W, with a dip direction of S24˚W and a dip angle of 42.25˚. Through extensive field investigations, including geological mapping, stratigraphic logging, rock sampling, fossil analysis, and structural analysis, complemented by Global Positioning System (GPS), photography, remote sensing, and Geographic Information System (GIS) technologies, seven lithostratigraphic units were identified. These include the variegated color sandstone, mottled clay, yellowish to reddish-grey sandstone, sandy shale with intercalated silty shale, pinkish sandstone, bluish to blackish-grey shale, and limestone units, corresponding sequentially to Dupi Tila, Girujan Clay, Tipam Sandstone, Surma Group, Jenum Shale Fm, Kopili Shale, and Sylhet Limestone Fm, respectively. Five critical geological contact boundaries were delineated, with notable boundaries identified at the Dupigaon-Sari River Section, the Lalakhal-Tetulghat Section, the Nayagang-Gourishankar Section, and between the Barail and Jaintia groups at the Tamabil-Jaflong Highway Road Cut Section. These findings elucidate the geological contacts and stratigraphic units, providing significant implications for paleoenvironmental reconstruction, resource potential assessment, and stratigraphic correlation, thus enhancing the understanding of regional geological history and laying a foundation for future research endeavors.

Rainfall is crucial for agricultural practices, and climate change has significantly altered rainfall patterns. Understanding the dynamic nature of rainfall in the context of climate change through Machine Learning (ML) and Deep Learning (DL) algorithms is essential for ensuring food security. ML techniques provide tools for processing large-scale data to extract meaningful insights. This study aims to compare the performance of a ML algorithm, Random Forest (RF), with a DL algorithm, Long Short-Term Memory (LSTM), in predicting rainfall in six state capitals in Southwest Nigeria: Osogbo, Ikeja, Ibadan, Akure, Ado-Ekiti, and Abeokuta. The dataset for this study was sourced from the HelioClim website archive, which offers high-quality solar radiation and meteorological data derived from satellite measurements. This archive is known for its accuracy and reliability, providing extensive and consistent historical datasets for various applications. The monthly rainfall data spanning from January 1, 1980, to December 31, 2022, were used, with 80% allocated for training and 20% for validation. As the data are time series, each model was constructed using a look-back period of five months, meaning the past five months' rainfall data served as input features. The performance of these algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results indicated that the RF algorithm yielded the lowest MSE, RMSE, and MAE across all selected cities in Southwest Nigeria. This study demonstrated the superiority of RF regression over LSTM in predicting rainfall in these regions, providing a valuable tool for agricultural planning and climate adaptation strategies.

The utilization of remote sensing (RS) techniques plays a crucial role in the efficient planning and monitoring of afforestation projects within constrained timeframes. This study evaluates the progress of the Billion Trees Afforestation Project (BTAP) in Dera Ismail Khan (DIK), Khyber Pakhtunkhwa, Pakistan, using RS technology. Geographical positioning systems were employed to delineate the boundaries of the plantation areas, and two temporal Sentinel-2 images from 2016 (the commencement of the plantation) and 2018 were analyzed to calculate the normalized difference vegetation index (NDVI). The results revealed that the survival rate of plantations varied between 37.39% and 85.15%, while the area of unstocked regions ranged from 14.84% to 62.60%. Overall, in 2016, the survival rate was determined to be 61.28%, with 38.72% of the area remaining unstocked. The NDVI values in 2016 ranged from -1 to -0.43, whereas in 2018, they spanned from -0.43 to 0.80, indicating significant progress in plantation growth and a substantial reduction in unstocked areas. The RS-based assessment proved to be highly effective, suggesting its adoption for the rapid detection and evaluation of plantation efforts. It is recommended to use high-resolution satellite images and drone technology to enhance accuracy further. Additionally, measures such as the establishment of closures, pit sowing, appropriate site and species selection, and effective soil and water conservation techniques are essential to maximizing the survival rate of plantations.

The utilization of oil-based drilling fluids is a significant technical approach for drilling in ultra-deep, unconventional, and other complex hydrocarbon reservoirs. However, these fluids present notable disadvantages, including high preparation costs and environmental pollution. There is an urgent need to develop an eco-friendly, high-performance water-based drilling fluid system suitable for complex geological conditions to support the exploration and development of oil and gas under deep, challenging, and unconventional conditions. Addressing the current issue where polymer filtrate reducers cannot simultaneously achieve temperature resistance, salt resistance, and environmental performance, a novel organic/inorganic composite micro-nano filtrate reducer (MNFR) was developed using inverse emulsion polymerization. The MNFR has a D₅₀ particle size of 1.313μm, withstands temperatures up to 200℃, resists saturated NaCl brine, and exhibits an EC₅₀ biotoxicity value of 86700 mg/L. Furthermore, a high-temperature-resistant (up to 200℃) eco-friendly high-performance drilling fluid system (HBHP) was constructed, demonstrating excellent rheological and filtration properties, with a high temperature and high pressure (HTHP) filtration volume of only 7.6mL and an EC₅₀ biotoxicity value of 54300mg/L. It also shows outstanding plugging, anti-collapse, and hydration inhibition properties. The HBHP system has been applied in three wells in the Shengli oilfield, with no complex situations related to wellbore stability occurring during field operations, thus providing technical support for the green development of complex hydrocarbon reservoirs such as deep, ultra-deep, offshore deepwater, and unconventional formations.

According to data from the National Disaster Management Agency (BNPB), 629 landslides occurred in 2022, resulting in 318 fatalities, 459 displaced individuals, and extensive damage to 892 buildings and public facilities. To mitigate the impacts of such events, an early warning system for landslides based on Long Range Wide Area Network (LoRaWAN) was developed, enabling more effective monitoring and response in high-risk areas. This system integrates LoRaWAN technology with a suite of sensors, including a soil moisture sensor to track moisture levels, a Global Position System (GPS) sensor to provide location data, and an accelerometer to detect tilt and acceleration changes. Sensor data were transmitted to a gateway and monitored in real time via the Blynk application. Furthermore, the relationship between Spreading Factor (SF) values, transmission distance, Time on Air (ToA), and Packet Delivery Ratio (PDR) was examined to optimize system performance. The results indicate that SF 12 provides the most reliable performance in the context of early landslide detection. Data transmission in both emergency and scheduled modes was successfully achieved, with seamless integration of the gateway and Blynk platform. This research presents a robust framework for improving disaster mitigation efforts through early detection and monitoring systems.