Land use and land cover alterations have profound implications for environmental sustainability and socio-economic development, particularly in regions experiencing rapid urbanization and population growth. This research investigates the dynamics of land use and land cover (LULC) in Kurigram District, Bangladesh, over a period of three decades from 1994 to 2024. The objective of this research is to assess changes in land use land cover patterns, with a particular emphasis on urbanization, agricultural practices, forest cover, and water bodies, and their implications for environmental management and regional development. Utilizing four sets of Landsat satellite imagery from 1994, 2004, 2014, and 2024, the research employs a combination of unsupervised classification, NDVI, and MNDWI techniques to map as well as analyze LULC dynamics. Findings indicate significant modifications in LULC dynamics between 1994 to 2024. Water bodies experienced a gradual decline, losing approximately 8,198.66 hectares over the study period, yet maintaining a consistent percentage of total land cover. Forest area fluctuated, peaking in 2014 before declining in 2024, while agricultural land showed notable variations, ranging from 39.69% in 1994 to 68.77% in 2024. NDVI and MNDWI analyses show declining water bodies (10.72% to 7.81% by 2024), fluctuating mixed land cover (30.32% to 26.27%), and dynamic agricultural land changes (39.69% to 68.77%). MNDWI reveals stable water areas (7.50% to 10.57%) and minor land fluctuations (89.43% to 92.50%), indicating terrestrial dominance over time. For accuracy assessment, the study compared a 2024 classified image with 40 ground truth data points, achieving a 90% overall classification accuracy. Utilizing Kappa Coefficient, the study found a substantial agreement (0.8703) between classified and referenced data, affirming 64% to 81% accuracy in the unsupervised classification, ensuring reliable LULC mapping results. The findings emphasize the importance of monitoring and addressing LULC changes to ensure environmental sustainability and socio-economic resilience, environmental management, urban planning in rapidly developing areas like Kurigram District.

Geological mapping of basement rocks in Kaduna Polytechnic Main Campus and its Environs was carried out. The study area is bounded by latitude 10o30’0.00”N to 10036’0.00”N and longitude 007024’0.00”E to 007030’0.00”E of sheet 123 Kaduna. The study aims at identification and classification of the rock units within the study area. Results of the Geological Mapping within the study area reveals that the study area falls within the Migmatite Gneiss Complex of Nigeria. The result also shows that there are various structural features prevalent within the study area namely; fractures, joints, faults and intrusion of quartz veins. Rose diagram generated shows structural trend of mineralization along northwest to southeast (NW-SE) direction. Petrographic studies on the rock samples indicates that the study area is predominantly underlain by the Migmatite Gneiss Complex with Medium to coarse grain Migmatitic Gneisses and Amphibolite which is part of the Older Granite suite of the basement complex whose mineralogy, color, relative time of emplacement, and texture vary greatly. The results of petrographic analysis of Migmatitic Gneiss shows the modal mineralogical composition of Medium to coarse grain granite reveals a percentage ratios of 40% Orthoclase feldspar: 40% Quartz: 20% Biotite whereas Amphibilite has 40% Biotite: 30% Quartz and 30% Orthoclase feldspar. The rock aggregate in the study can provide good construction materials such as in the construction of roads, bridges, houses etc. The Geophysical investigation indicate the presence of shallow layer of disturbed soil as the top lithology (< 320 m/s) underlain by basement rocks with high seismic velocities of > 2139 m/s.

The polymorphism of alite (C3S) has been subjected to examination through various factors, such as the burning process and cooling. This study is centered on investigating the impact of grinding on alite polymorphism. To determine the chemical composition, X-ray fluorescence (XRF) analysis was employed. The mineralogical characterization was conducted utilizing X-ray powder diffraction (XRD), complemented by the Rietveld method. Morphological assessments of the ground clinker were carried out using scanning electron microscopy (SEM) in conjunction with EDS microanalysis. XRD analysis, performed on both gray and white cement as well as clinker, has revealed that mechanical treatment (grinding), aside from reducing particle size, actively induces the transformation of alite polymorphism from monoclinic M1 to monoclinic M3.

This research aims to geospatially model crop yield in a FUTA farm to enhance productivity and management practices. Primary and secondary data were collected for seven planting seasons (2014-2021), including maize seeds, NPK fertilizers, urea used, harvesting dates, and yield measurements. GNSS observation was used to determine planting boundaries, while satellite imagery and climate records were used for secondary data. The study examined the vegetation indices (NDVI and GCI) of the farm between 2012 and 2022. Then, Artificial Neural Network (ANN) was used to model crop yield in the study area using the primary and secondary data and the NDVI, and GCI values. Result showed that Vegetative indices (NDVI and GCI) showed an increase between 2014 and 2016, while between 2017 and 2019, there was a decrease. In 2021, there was an increase in vegetative indices values, indicating healthier crops. The decline in crop yield between 2017 and 2019 was not coincidental, but it is believed that the decline in crop health is responsible for the corresponding reduction. The ANN model had a regression coefficient of 0.73282, and the coefficient of determination was 0.5176. The maximum and minimum crop yield values were 24.7 and 25.26 in 2016, and 5 and 4.01 in 2018, respectively. It was observed from that the minimum value of difference is -9.883708757 while the maximum value of difference is 1.451557122. The root means square error (RMSE) and the Mean Absolute Error (MAE) are 0.4296 and 0.2947, respectively. Modelled crop yield values were close to actual yield values, except for 2017 when a large difference was observed due to herdsmen invasion into the school farm. Since, the model showed close correlations with actual yield values, making it a recommended model for predicting crop yield in the study area.

The Makran Subduction Zone, straddling the nothern Arabian Sea along the borders of Pakistan and Iran, represents one of the most seismically complex regions on Earth, where the Arabian, Eurasian, and Indian plates converge. This study delves into the seismic intricacies of Eastern Makran, an area delineated by a labyrinth of tectonic demarcations including the Zendan, Jiroft, and Ornach-Nal faults, to uncover the underpinnings of its seismicity through an analysis of focal mechanism solutions (FMS) for earthquakes occurring between 1990 and 2019. Utilizing the Kikuchi and Kanamori method for modeling teleseismic P-waves and their surface reflections, this research filters through the data, discarding those compromised by noise, to present a clear picture of seismic activity ranging in magnitude from 4.0 to 7.8 Mw. Contrary to the expected prevalence of major earthquakes, findings reveal a rarity of such events in Eastern Makran, suggesting a nuanced interaction between the Indian and Eurasian plates marked by anticlockwise rotation. This rotation potentially fosters the isolation of microplates, hinting at a dynamic interplay of tectonic forces. Our comprehensive 30-year perspective provides new insights into the focal depths and fault plane solutions, contributing to a better understanding of the seismic behavior and tectonic mechanisms governing the Eastern Makran Subduction Zone.

One of the most used geophysical tools for characterization of the near surface is electrical resistivity tomography (ERT). The 2D resistivity survey was conducted along ten (10) buildings in Federal University Lokoja (Fulokoja), Adonkolo Campus. Dipole-dipole electrode configuration was conducted for data collection. Enhancing of measured field and calculated apparent resistivity details/data and interpretation of Electrical Resistivity Tomography (ERT) were generated using RES2DINV software which reveal a change in soil resistivity and type. From the result it is observed that the soil is made up of weathered migmatite rock which exist in layers of low resistivity, intermediate resistivity and high resistivity zone. The low resistivity zone is the uncompacted soil which provide an unstable foundation for the building while intermediate and high resistivity zone representing the fairly compacted to compacted soil which provide stable foundations for the buildings. The presence of clayey materials and oversaturated soil around the central part of the campus is the cause of the uneven ground settlement underneath these buildings causing the apparent cracks on some superstructures. The foundation of structures and buildings should be strengthened by mixing of the soil with gravels to enhance stability. Proper electrical resistivity survey and soil penetration test on the soil should be carried out prior to construction of buildings in the campus.

Earthquake early warning systems have become vital for minimizing damage from seismic events. However, their automated detection capabilities need strengthening to provide real-time alerts. Current algorithms have limitations in identification of P-waves and magnitude estimation, impacting warning lead times. Additionally, existing single-algorithm dependent systems are prone to errors. There is a need for standardized practices to optimally select and combine algorithms. Machine learning and artificial intelligence show promise to make detection more robust. Models trained on diverse seismological data can learn complex patterns to detect emergent P-waves earlier and refine magnitude assessment. However, research exploring such data driven approaches within early warning systems is limited. This aims to address this research gap and strengthen automated detection capabilities. It proposes a machine learning model integrating multiple existing algorithms using a novel prioritization framework. Performance is evaluated on real earthquake datasets through simulations vis-à-vis single algorithms. By developing an optimized multi-algorithm framework, hence it seeks to improve warning lead times and reliability. The model is designed considering operational requirements of early warning systems. Comparison of results with past methods helps evaluate contributions to the field. Overall, the research strives to enhance seismic hazard mitigation through more efficient automated detection in early warning networks.

This research investigates and report zircon U-pb age of a migmatite-gneiss in Idanre, southwestern Nigeria and the petrological implications of this U-Pb age. Field geology reveals Idanre migmatite isa diatexite withnebulitic structures and the gneiss,a metatexite with perverse deformation. The migmatite-gneiss which forms the country rockis a low-lying geomorphologic unitintruded by granite plutons and batholiths. Zircon grains recovered from the migmatite-gneiss have variable sizes and morphology. Geochemicalinvestigation of the zircon revealed Uranium contents range between 80.838 to 116.736 ppm, Th contents between 25.647 ppm and 38.227 ppm while Th/U ratio falls between 0.299 and 0.365. 206Pb/207Pb age of the migmatite-gneiss ranges from 1051 to 1082 Ma with weighted average 1065.1 ± 7.1 Ma (MSWD = 0.28).The age of migmatite-gneiss fallsoutside the Archean-Early Proterozoic (3200-1600 Ma) age bracket reported for migmatite-gneissin other parts of Nigeria.Even though, the 1.065 Ga age of the migmatite-gneissis comparableto granite-gneiss from central Zambezi which crystallized around 1005–1018 million years ago and Phyllite from the Maru schist belt of north central Nigeria which yielded 1110 Ma.However,the migmatite-gneiss age is uncommonin neighbouring Pan-African belt, it is older than the Bayudian event (~0.92 Ga) of the Bayuda Desert of Sudan. This suggests the Idanre migmatite-gneiss probably represents anatectic product of yet another ancient Archean protolith.

Bathymetric information is vital for navigational safety and is utilized for many more activities. Remote sensing data and satellite images are widely used these days to determine shallow coastal areas’ bathymetry at a low cost. This study reviewed different methods for satellite-derived bathymetry and selected the ratio transform method to apply to Landsat 8 imagery. Two images covered the northern coastal region of the Bay of Bengal. They were processed using a ratio-based algorithm calibrated with reference data. A hydrographic chart from the Bangladesh Navy depicted a general overview of the study area. Another chart from the BIWTA, along with GEBCO gridded bathymetry data, was used as reference data for the study. The ArcGIS and ENVI image processing software was used to process and analyze satellite imagery. The correlation between the satellite-derived bathymetry and reference data was also studied at the end of the study. The mean absolute deviation, mean squared error, and root mean square error were also evaluated. Both algorithms were able to extract bathymetry up to a depth of 12 meters with minimal errors. Satellite Derived Bathymetry (SDB) from the Central Coast resulted in an R-squared value of 0.82 with a Mean Absolute Deviation (MAD) of 0.89. SDB from the Western Coast had R2 of 0.81 and MAD of 1.16. After interpolating the algorithm results, contour lines were also generated to visualize the bathymetry. The deviations and irregularities in the contours resulted due to the high turbidity of the coastal waters of the Bay of Bengal.

Geo-electrostratigraphic assessments of flood-laden lithological units within a coastal region in Southern Nigeria were undertaken with the use of geophysical surveys. The region has challenges of extreme flooding, especially during periods of high precipitation, leading to intensive water-logging and its associated problems. Integrated surveys employing vertical electrical soundings and electrical resistivity tomography were undertaken at twenty locations with the use of Schlumberger and Wenner array configurations. Hydro-geophysical findings indicate that the region comprises comprises four of four geo-electric layers: motley topsoil, sandy clay, fine sand, and coarse sand. Interpretation of the geo-stratigraphic data delineated degrees of geo-anisotropy based on measures of the longitudinal conductance, transverse resistance, longitudinal conductivity, transverse conductivity, anisotropic co-efficient, and the reflection coefficient. Via use of Dar-Zarrouk parameters, the water retention capacity assessments show that the region has a generally good retentive capacity, leading to an increased susceptibility to extreme flooding. Results obtained from 2D resistivity tomography surveys corroborated the results obtained from vertical electrical sounding data interpretations. The results showed that area is highly anisotropic, and that the presence of argillaceous sequences intercalated with arenaceous stratigraphic units is the leading cause of the high water retentive capacity of the region, leading to flooding during periods of high precipitation.