Potential Maps Research Articles

MEASUREMENT OF WATER DISCHARGE OF SAKARTEMEN RIVER TO IDENTIFY POTENTIAL OF MICRO HYDRO POWER PLANT IN FAKFAK REGENCY

Fakfak Regency, West Papua Province is one of the oldest regencies in Papua. However, electricity problems are still a scourge that has not yet been resolved properly. So far, the mainstay of power plants in Fakfak Regency is the Diesel Power Plant (PLTD) which is highly dependent on the supply of Fuel Oil (BBM) and the reliability of the Diesel Engine. In fact, Fuel Oil (BBM) is one of the natural resources that cannot be renewed. For this reason, it is necessary to conduct a study on New Renewable Energy (EBT) that has the potential to be developed in Fakfak Regency. There are several rivers that have the potential to be developed as Micro Hydro Power Plants (PLTMH) in Fakfak Regency. The existence of Micro Hydro Power Plants (PLTMH) in Fakfak Regency is one of the real solutions to the power outages that still often occur in Fakfak Regency. Through this study, it is hoped that there will be a mapping of the energy potential that can be produced by several rivers in Fakfak Regency if used as Micro Hydro Power Plants (PLTMH). In addition, the potential of Micro Hydro Power Plant (PLTMH) will support the transition of power plants in Fakfak Regency. Another objective is to support environmental conservation efforts (nature and culture) and increase community participation in the management of existing natural resources, thus providing economic benefits to the local community. The research was conducted through surveys and direct measurements where the measurement results will be calculated to determine the potential river flow discharge and the potential for power generation. The results of the measurements and calculations obtained an average discharge of the Sakartemen River of 3.1m3/second which based on the measurement of the effective fall height (Heff) of the flow was obtained at 4 m. With these results, it can be estimated that the electrical power generated is 104,260 kW.

Targeting high-potential mineral prospects in the Ezzhiliga region, Moroccan central massif, using spectral data from the ASTER sensor

This research paper introduces a comprehensive methodology for assessing hydrothermal alteration zones and structural complexity in the Ezzhiliga region, situated in the Hercynian Central Massif of Morocco. The approach utilizes ASTER imagery as the primary data source. The main objectives were to identify and analyze these geological features and then create a potential map for mineral exploration by integrating fuzzy logic and fractal concentration area analysis. Band ratios (RBD) and principal component analysis (PCA) were employed to detect and map argillic, phyllic, propylitic, and iron oxide alteration zones. Additionally, structural lineaments were extracted from the PC1 imagery to understand the structural pattern of the survey area. The incorporation of hydrothermal alteration zones and structural lineaments was achieved through the application of a fuzzy logic model, resulting in the generation of a mineral favorability map. The fuzzy logic model was customized to combine hydrothermal alteration and lineaments density maps, effectively eliminating false spectral anomalies induced by various interference factors. This map was then analyzed using the fractal concentration-area (C-A) model, which separated the anomaly from the geological background and generated a final mineral potential map. Analysis of fractal concentration-area was employed to define thresholds with greater precision, enhancing the reliability of mineral prospectivity assessments. Furthermore, laboratory analyses were performed to verify the outcomes of the mineral potential map. The obtained results revealed a significant affinity with the field data and indicated that the highly prospective zones are perfectly limited in spatial extent and generally associated with the contact of the Zaër granitic pluton with the metamorphic host rock, except for the anomaly identified to the southeast of the study area, along a major NE-SW trending fault.

Application of machine learning and fuzzy AHP for identification of suitable groundwater potential zones using field based hydrogeophysical and soil hydraulic factors in a complex hydrogeological terrain

The eastern section of West Bengal grapples with limited surface water availability in its hard rock terrain, compounded by a semi-arid climate, variable rainfall, and a plateau topography, prompting communities to adapt groundwater water-use practices, leading to unsustainable extraction and misuse. Thus, the novel objective of the present research was to produce groundwater potential maps by comparing machine learning techniques with a Fuzzy MCDM model using specific field-based conditioning factors. In the first step, 285 wells were identified, of which 70 percent were used for training and 30 percent for the validation of the models. Secondly, field-based conditioning factors including, longitudinal conductance (SC), longitudinal resistance (ρl), transverse resistance (TR), coefficient of electrical anisotropy (λ), resistivity of formation (ρm), fracture porosity (φf), reflection coefficients (r), hydraulic conductivity (K), transmissivity(Tr), bulk density, porosity, permeability, soil moisture content and water holding capacity were used to analyze the association between these conditioning factors and groundwater occurrences. In the following steps, the XGBoost, Random Forest, and Naïve Bayes models were executed using the training dataset, and factor weights were calculated using Fuzzy Analytical Hierarchy Process of Extent analysis method. To validate and compare the performance of four models, ROC curves, AUCs, MCAs, and correlation plots were used. In general, all four models were successful in evaluating the potential of groundwater occurrences. The predictive capability of the XGBoost techniques with the highest AUC values (0.79) and the highest correlation value (0.78) is superior to those of other machine learning and MCDM models. Geophysical survey revealed that transmissivity and hydraulic conductivity of the aquifer of the river basin range from 1.55 to 440.11 m/day and 10.15–2253 m2/day, indicating a moderate to good hydrodynamic potential. Planners and engineers can use such groundwater potential maps to manage water resources effectively.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density(DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future.

Remote sensing and aeromagnetic mapping for unveiling mineralization potential: Nuqrah Area, Saudi Arabia

Lately, Saudi Arabia has been developing its mineral exploration. However, comprehensive studies of the collected data are not accessible. Thus, the purpose of this research is to identify and map the hydrothermal alteration zones and structural lineaments that regulate the mineral occurrences in the Nuqrah region of the Kingdom of Saudi Arabia using remote sensing and aeromagnetic data. To achieve the desired goal of the study, ASTER remote sensing data were employed, and they were processed in several ways, including principal component analysis, band ratio, and false color composites to reveal the zones of alteration and structure lineaments. In addition, aeromagnetic data was employed to map the lineaments controlling the mineralization. These datasets were integrated using GIS tools to generate a new mineralization potential map of the Nuqrah area, which was classified into three classes: low, moderate, and high probability mineralization. The results showed thirteen intriguing anomalies (high potential mineralization) dispersed over the research area to be prospected. Additionally, techniques such as residual, regional, first vertical derivative, and tilt derivative were utilized to detect the potential mineral-related geologic structures. The results were validated by plotting known mineralization sites on our maps. Six significant faulting trends have been found, according to the lineament map and rose diagrams from remote sensing: NE–SW, WNW–ESE, N–S, ENE–WSW, NNE–SSW, NW–SE, and E–W. The research region is most affected by the NW–SE, ENE–WSW, E–W, and N–S trends, which are organized in decreasing order of magnitude, according to the rose diagram of the aeromagnetic maps. The applied approach can be employed to map potential mineral deposits in Saudi Arabia and similar zones around the globe.

An Integrative Computational Approach for Design and Evaluation of Novel [1,2,4]triazolo[3,4-b][1,3,4]thiadiazole Analogues as Dual-Action Anti-Retroviral and Anti-Bacterial Agents: Insights into Rational Drug Design Strategies

The development of effective antiretroviral inhibitors (HIV-1 Reverse Transcriptase) and glucosamine-6-phosphate (GlcN6P) agents represent a significant challenge for combating viral infections and related diseases. In this study, we employed a multidisciplinary approach to design and evaluate a series of novel triazole analogs as potential candidates for anti-retroviral and glucosamine-6-phosphate agents. Using molecular docking, we explored the binding interactions between the RSA3 analog and the target proteins implicated in viral replication and glucosamine metabolism. Density functional theory (DFT) employing the 6-311++G(d,p) basis set was utilized for computational analysis, with a subsequent examination of the electronic parameters. The HOMO and LUMO orbitals as well as the molecular electrostatic potential map were investigated using DFT to characterize the reactivity of our molecule. Molecular dynamics simulations were performed to investigate the stability and dynamic behavior of the ligand-protein complex (RSA3) over time (300 ns). Furthermore, we employed MM-GBSA calculations of the compound RSA3 (molecular mechanics generalized band surface area) to estimate the binding free energies (−71.30 ± 7.26 kcal/mol and −67.86 ± 7.09 kcal/mol, respectively) and identified the most promising compounds with high affinity for the target proteins. Additionally, predictive absorption, distribution, metabolism, excretion and toxicity (ADMET) studies of compound RSA3 were conducted to assess its drug-like properties and potential safety profiles of the selected compound. Our results revealed that RSA3 has favorable binding interactions, is a stable complex and has significant binding affinity for target proteins. Moreover, predictive ADMET studies indicated that RSA3 possesses desirable drug-like properties, suggesting its potential for further investigation as an anti-retroviral agent and glucosamine-6-phosphate agent. Overall, this integrative computational approach combining molecular docking, DFT analysis, dynamics simulations, MM-GBSA and predictive ADMET studies provides valuable insights into the design and evaluation of novel triazole analogs with potential therapeutic applications, highlighting the importance of rational drug design strategies in the field of antiretroviral and glucosamine research.

Pendampingan Revitalisasi Bumdes Amanah Mandiri Desa Jatimukti, Kabupaten Sumedang, Provinsi Jawa Barat

This article discusses the revitalization of the Village-Owned Enterprises (BUMDes) Amanah Mandiri in Jatimukti Village, Jatinangor Subdistrict, Sumedang Regency. The aim of this community service was to reactivate the previously inactive BUMDes through a series of activities held from June 3 to 6, 2024, including participatory FGDs, education on BUMDes and village potential mapping, and simulation and assistance in organic fertilizer production. Methods employed included FGDs, training sessions, simulations, and participatory dialogues with various stakeholders. This community service was collaboratively conducted by involving agricultural advisors from Sumedang Regency. The main findings indicate that integrated participatory approaches and education effectively stimulate interest and economic activities within BUMDes, particularly through innovative organic fertilizer products. The conclusion drawn from this study is that with the right approach, BUMDes can serve as the backbone of village economies, enhance community welfare, and reduce farmers' dependency on chemical fertilizers.

Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India

Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies.In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

Diorganotin(IV) derivatives of ONO tridentate Schiff bases: Synthesis, spectroscopic characterization, DFT studies, CT-DNA binding, and plasmid-DNA cleavage studies

Two Schiff bases 2-amino-N'-(5‑bromo-2-hydroxybenzylidene)-3-(4-hydroxyphenyl)propanehydrazide (H2L1) and 2-amino-N'-(5‑chloro-2-hydroxybenzylidene)-3-(4-hydroxyphenyl)propanehydrazide (H2L2) and their six diorganotin(IV) complexes (R = C4H9 (1, 4), CH3 (2, 5), and C8H17 (3, 6)) have been synthesized. Schiff bases (H2L1–2) and their R2SnL1–2 complexes (1–6) have been characterized by CHNS analysis, IR spectroscopy, 1H, 13C, 119Sn NMR, and ES-MS. Schiff bases (H2L1–2) act as a dianionic tridentate ligand coordinated to Sn through phenolic and enolic oxygen and azomethine nitrogen in R2SnL1–2 complexes (1–6). A density functional theory (DFT)-based quantum chemical calculation validated the structures of R2SnL1 (1–3) and R2SnL2 (4–6) at the B3LYP/6‐311G(df,pd)/Def2‐SVP(Sn) and B3LYP/6‐31G(d,p)/Def2‐SVP(Sn) levels of theory, respectively. At these levels of theories, comprehensive electronic structure calculations determined atomic charges at selected atoms. Additionally, a molecular electrostatic potential (MEP) map identified electrostatic potential-varying sites on the molecule's surface, and MEP maps used to identify regions where other molecules might interact or react with the molecules on them. Furthermore, a conceptual-DFT-based global reactivity descriptor determined the stability and reactivity behaviour of R2SnL1–2 complexes (1–6). H2L1–2 and R2SnL1–2 complexes (1–6) have binding constants in the range ∼105 M-1 with CT-DNA, as revealed by UV–visible and fluorescence spectral titrations. UV-visible, fluorescence spectroscopy, and CD studies suggested that H2L1–2 and their R2SnL1–2 complexes (1–6) interact with CT-DNA electrostatically or groove-bound. DNA cleavage studies reveal that H2L1–2 and their R2SnL1–2 complexes (1–6) have ability to cleave the plasmid-DNA from its supercoiled form (SC, I) to nicked form (NC, II). However, n-Bu2SnL2 (4) and n-Oct2SnL2 complex (6) have higher cleavage activity than Schiff bases and other R2SnL1–2 complexes (1–6).

Enhancing training performance of convolutional neural network algorithm through an autoencoder-based unsupervised labeling framework for mineral exploration targeting

Convolutional neural networks (CNNs), a prominent deep learning approach, have garnered significant interest in the field of mineral potential mapping (MPM) due to their capability to capture and learn spatial features that traditional algorithms tend to overlook. The effectiveness of CNNs is closely tied to the quantity of training data available, thereby impacting the outcomes of MPM. Moreover, uncertainties arising from delineation of negative samples can compromise the reliability of MPM assessments. To deal with these challenges, we propose the utilization of an autoencoder-based anomaly detection technique for the purpose of annotating locations in an unsupervised manner. Subsequently, a CNN is trained using the unsupervised annotated data to generate a Fe prospectivity model within a specific region in Iran. To validate the effectiveness of the proposed method, we first perform an MPM with insufficient positive training samples that extract from the location of known occurrences. We then execute another MPM on a set of samples labeled based on the reconstruction error of an autoencoder network. When comparing the two prospectivity models, namely using augmented data or inadequate samples, it is evident that modeling with augmented data outperforms the MPM model trained with insufficient samples. This confirms the effectiveness of the adopted approach and shows that the unsupervised labeling technique proposed in this work can significantly improve the performance of the CNN in MPM.

COMPETITIVENESS OF SOUTH AFRICAN ORANGES IN ASIAN STRATEGIC MARKETS: THE CASE OF MAIN COMPETITORS

This study employed various descriptive, conceptual, and empirical models to measure South Africa’s competitiveness in the Asian strategic markets concerning its competitors from 2003 to 2022. The competitiveness measurements included relative comparative advantage, the net export index, relative trade advantage, and constant market share analysis. The data were sourced from official databases such as the World Bank, FAO, ITC, export potential map, and World Integrated Trade Solutions (WITS). The South African competitiveness of oranges in the specified strategic markets realized a positive competitive effect of 2.46%, indicating a gain in the market due to increased competitiveness of the fresh food sector in the world market. The RCA values were above 1, with most of the values closer to 20 indicating that South Africa has a good comparative advantage in the production of oranges. Egypt and Greece were the competitors, with average values of 16.9 and 19.7, respectively. The South African orange industry has positive export performance, with values close to 100, which indicates that the country is a net exporter of oranges and is experiencing a sustained comparative advantage. The RTA index values were mostly positive.

A simple method to map pollination ecosystem services potential in urban lawns

Urban areas have detrimental impacts on the ecosystems. Nevertheless, they still supply many ecosystem services (ES), such as Pollination, in different urban green spaces (UGS). Lawns are among the most degraded UGS due to very high human impact. Still, flowers such as Dandelions (Taraxacum officinalis) live in these spaces. These flowers are considered a suitable habitat for pollinators. In this work, we develop a methodology to map Pollination ES potential in urban lawns using an Unmanned Aerial Vehicle. A detailed protocol was developed using high-resolution images, consisting of orthomosaic creation, flower vectorisation, field validation, and finally, Pollination ES potential mapping using Kernel and Point Density. This method can be applied to urban lawns and grasslands in Spring and Summer.•A novel method was developed to map pollination potential in lawns.•Dandelions (Taraxacum officinale) were mapped using UAV high-resolution images.•The method is helpful to identify areas with pollination potential in urban lawns.

Geo-electric mapping of groundwater potential in Elebele, Ogbia Local Government Area, Bayelsa State, Nigeria

Geo-electric mapping of groundwater potential in Elebele, Ogbia Local Government Area, Bayelsa State, Nigeria

Biochar modified by ammonium pyrrolidine dithiocarbamate for high selective adsorption of copper in wastewater

Biochar application in wastewater purification is extremely appealing. Although heteropolysaccharides like Gum Seyal biochar (BC GS500) display great surface area and porosity, the adsorption capacity for Cu (II) was relatively low due to the week-binding surface functional groups. In this work, an innovative modification with the chelating agent ammonium pyrrolidine dithiocarbamate (APDC) capable of selectively and effectively adsorbing Cu (II) was conducted. Gum Seyal biochar was grafted with pyrrolidine dithiocarbamate (C5H8NS2–), amine (–NH2), and imine (–NH) groups using APDC based on the “hard and soft acids and bases theory” (HSAB). Batch studies, along with characterization (FITR, XPS, and SEM-EDS), molecular electrostatic potential (MEP) mapping, and density functional theory (DFT) calculations, were implemented to investigate the adsorption performance and mechanism responsible for selectively adsorptive Cu (II) removal. It was found that Cu (II) adsorption by modified biochar occurred endothermically and spontaneously. The adsorption data at pH 5 matched the pseudo-second-order kinetic model and the Langmuir isotherm (211.24 mg/g). DFT calculations show that modified biochar selectively binds Cu (II) superior to common ions such as Mg (II) and Ca (II) through a unique chelating mechanism. Their performance in real electroplating wastewater was investigated, and the production cost was estimated. It was concluded that the modified biochar (BC APDC) could have high applicable potential for Cu (II)-laden wastewater purification.

An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping

Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed a Phenology domain Optical-SAR feature inTegration method to Automatically generate single (SC-Rice) and double cropping Rice (DC-Rice) sample (POSTAR) for efficient and refined rice mapping. POSTAR includes three major steps: (1) generating a potential rice map using a phenology- and object-based classification method with optical data (Sentinel-2 MSI) to select candidate rice samples; (2) employing K-means to identify SC- and DC-Rice candidate samples according to unique SAR-based (Sentinel-1 SAR) phenological features; (3) implementing a two-step refinement strategy to filter high-confidence SC- and DC-Rice samples, maintaining a balance between intraclass phenological variance and sample purity. Test areas selected for validation include the Dongting Lake plain and Poyang Lake plain in South China, as well as Fujin county located in the Sanjiang plain of North China. POSTAR proved effective in producing reliable SC- and DC-Rice samples, achieving a high spectral correlation similarity (>0.85) and low dynamic time wrapping distance (<8.5) with field samples. Applying POSTAR-derived samples to random forest classifier yielded an overall accuracy of 89.6%, with F1 score of 0.899 for SC-Rice and 0.938 for DC-Rice in the Dongting Lake plain. Owing to the incorporation of knowledge-based optical and SAR phenological features, POSTAR exhibited strong spatial transferability, achieving an overall accuracy of 96.0% in the Poyang Lake plain and 97.8% in the Fujin county. These results demonstrated the effectiveness of the POSTAR method in accurately mapping rice cropping patterns without extensive field visits, providing valuable insights for crop monitoring in large, diverse, and cloudy regions through the integration of optical and SAR data.

Affordances of Electrostatic Potential Maps in Promoting Use of Electronic Features and Causal Reasoning in Organic Chemistry

Affordances of Electrostatic Potential Maps in Promoting Use of Electronic Features and Causal Reasoning in Organic Chemistry

A GIS-Based Approach for Assessing Urban Development Potential in Federal Capital Territory Nigeria

Urban development potential assessment is essential for guiding the spatial expansion of cities, optimizing land use, and ensuring sustainable urban growth. In the context of rapidly urbanizing regions, such as Nigeria, understanding development potential helps mitigate the challenges of urban sprawl, infrastructure strain, and environmental degradation, hence, this study is aimed at a GIS based approach for assessing urban development potential in federal capital territory Nigeria. The objectives of the study are to: identify and categorize the key factors influencing urban development in the Federal Capital Territory, Nigeria; assess the reliability of the identified factors using consistency index and sensitivity analysis; determine the potential areas for urban development in the Federal Capital Territory, Nigeria using analytical hierarchical process; and produce an urban development potential map defining the extent of urban development potential across the urban landscape of the Federal Capital Territory, Nigeria. The study employed the assessment of relative priorities in modelling and mapping urban development potential in Federal Capital Territory Nigeria. This assessment was based on a comprehensive set of criteria, including slope, elevation, proximity to road network, population density and proximity to developed areas. The Analytical Hierarchical Process was utilized to compare and establish the relative importance of these criteria by means of matrix comparisons. Subsequently, these criteria were assigned relative weights. Weighted Overlay technique was applied to generate the final development potential map. The study revealed that Abuja Municipal Area had the highest potential development area, covering an area 851.40 km2, with Bwari and Kuje Area Councils as having the second and third highest development potential with an area coverage of 312.40 km2 and 279.69 km2 respectively. Gwagwalada and Kwali Area Councils were ranked fourth and fifth with a developable area coverage of 180.20 km2 and 127.60 km2 respectively, while Abaji Area Council was ranked the least with developable area coverage of 40.27 km2. The study successfully integrated an approach to investigate development potential within the Federal Capital Territory. This evaluation approach, is hereby recommended to be used as tool for planning and decision making in urban development in the study area.

Theoretical interpretation of electron delocalization potential of the substituent in o-hydroxynaphthalidene-p-substituted anilines for prospective molecular electronics applications

The delocalization phenomenon plays a vital role in the electron transport properties of a molecule, which is essential for developing molecular-based electronics. The current work examined the delocalization potential of five theoretically designed low molecular weight anils, o-hyroxynaphthalidene-p-substitutedanilines(NP-X) with the substituent X(X = -CH3, -NO2, -OCH3, and –Cl) being placed in phenyl ring at the p- position to its functional moiety, azomethine linkage and compared with the compound without substitution(X = H). The geometric optimization through DFT with B3LYP/6-311G (d, p) level basis set indicated the anils to be coplanar. Theoretical FTIR could discriminate the aromatic and aliphatic C-H vibrational frequencies and confirm the vectorial flow of the electrons when the o-OH group and p-NO2 group were placed at the two extreme ends of anil, further supported by Natural Bond Orbital analysis. Atomic charge analysis indicated the significant effect on the charge of azomethine carbon atom. It followed the order: OCH3 < CH3 < H < Cl < NO2 parallel with the σp values of the corresponding substituent. The molecular electrostatic potential mapping identified azomethine nitrogen as an electron-rich centre. The global and local reactivity parameters indicated the highest tendency of NO2 substituted compound to accept electrons among the other compounds. The HOMO–LUMO energy gap of the NP-X predicted from Natural Localized Molecular Orbital analysis was found to be NP-H: 4.02 eV, NP-CH3: 3.94 eV, NP-NO2: 3.69 eV, NP-OCH3: 4.18 eV, NP-Cl: 4.10 eV. The overall studies corroborated the optimized delocalization in NP-NO2 with the lowest HUMO-LUMO gap, which would be better suited for developing molecular-based electronic devices than others. The importance of the electron delocalization phenomenon in searching for a molecular probe to develop molecular electronics device applications has been discussed.Graphical

Potential biodiversity maps of Geadephaga (Coleoptera) from subantarctic forests: Relating diversity patterns and conservation hotspots with forest integrity

Abstract Potential biodiversity maps (PBMs) allow the identification of areas with different potential for conservation, to support political decisions about the management and protection of biodiversity. As these maps are seldom constructed for inconspicuous species, we proposed to develop PBMs for species belonging to the Geadephaga (Coleoptera), which is a group of beetles that contributes as predators for pest suppression and other ecosystem services in forest ecosystems. Given that human activities are reducing forest integrity, we consider that it is crucial to recognize how diversity patterns of Geadephaga are related to degraded forests. We developed these maps for the Geadephaga associated with subantarctic forests considering diversity measures of species richness, specificity, and rarity to establish spatial relationships between each diversity measure and different levels of forest integrity, and to identify potential hotspots and suggest conservation priorities. Results showed a latitudinal pattern of decrease in scores on richness and specificity from north to south, but a patchy pattern of species rarity across the region. Outcomes also show that areas with high scores of diversity measures are overlapped with degraded forest, and that hotspots have a low spatial overlap between them. In this work, we provide for the first time regional PBMs at a relatively high spatial resolution of three different diversity measures for Geadephaga that inhabit subantarctic forest. These maps constitute tools that allow not only to recognize potential diversity patterns of these insects, but also to offer valuable information to be used in conservation decision‐making.

A multi-disciplinary approach for uranium exploration using remote sensing and airborne gamma-ray spectrometry data in the Gebel Duwi area, Central Eastern Desert, Egypt

Uranium exploration plays a pivotal role in meeting global energy demands and advancing nuclear technology. This study presents a comprehensive approach to uranium exploration in the Gebel Duwi area of the Central Eastern Desert of Egypt, utilizing remote sensing and airborne gamma-ray spectrometric data. Multispectral remote sensing techniques, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Band Ratioing (BR), are employed to identify lithological units and hydrothermal alteration zones associated with uranium deposition, such as iron oxides, argillic, propylitic, and phyllic alterations. Additionally, airborne gamma-ray spectrometry data provide insights into the spatial distribution of radioelements, including uranium (eU), thorium (eTh), and potassium (K), as well as radioelement ratios (eU/eTh, eU/K, and eTh/K). The uranium migration index map (eU-(eTh/3.5)) and the F-parameter map (K*(eU/eTh)) have been generated to investigate the movement of uranium within various geological zones and characterize anomalous uranium concentrations. Statistical analyses, including mean (X), standard deviation (S), and coefficient of variability (C.V.), are conducted to identify uranium-rich zones. The integration of these datasets enables the generation of a uranium potential map highlighting areas of elevated concentrations indicative of uranium mineralization. Field observations and mineralogical analyses of collected samples validate our findings, confirming the presence of minerals associated with uranium mineralization in mapped high-potential areas. The significance of minerals like Fe-Chlorite, Fe-Mg-Chlorite, ferrihydrite, goethite, calcite, muscovite, dolomite, actinolite, vermiculite, and gypsum in indicating potential uranium mineralization processes underscores the importance of our results.