Potential Maps Research Articles

Synthesis and density functional theory study of functionalized Oxazolidine-2-ones using a novel MnFe2O4@SiO2-SiO3H magnetic nanocatalyst

This study introduces MnFe2O4@SiO2-SiO3H as a novel magnetic catalyst and thoroughly investigates its structure, catalytic activity, and reusability. The synthesis of the magnetic catalyst was meticulously characterized using an array of analytical techniques. Utilizing MnFe2O4@SiO2-SiO3H, the synthesis of functionalized oxazolidine-2-ones were performed, versatile compounds widely employed in chiral auxiliaries, protecting groups, and medicinal chemistry. Remarkably, the two-step process from chalcones demonstrated one of the shortest reported pathways, highlighting the efficiency of our novel nanocatalyst. To elucidate the stability and reactivity of the synthesized products, we employed Density Functional Theory (DFT) calculations, including molecular electrostatic potential (MEP) mapping and reactivity indices such as electronegativity, electrophilic index, softness, and hardness, as well as frontier molecular orbitals (HOMO-LUMO). Furthermore, our investigations extended to the recycling capabilities of the nanocatalyst. Through a comprehensive evaluation of at least five reaction cycles, MnFe2O4@SiO2-SiO3H showcased a remarkable retention of activity (97–92 %), reaffirming its reusability and long-term potential. Our research presents MnFe2O4@SiO2-SiO3H as a highly effective and recoverable nanomagnetic catalyst for organic reactions, with demonstrated applications in synthesizing functionalized oxazolidine-2-ones. As such, our findings offer a promising alternative to traditional methods, presenting new opportunities in catalysis and materials science.

Optimizing Solar Potential: Site Potential Selection for Floating Photovoltaics in the Sepaku Semoi Dam Reservoir

Green energy is considered an essential solution to address the world’s energy scarcity. However, there are several technologies available for producing green energy, including photovoltaics, with a specific focus on floating photovoltaics. The application of photovoltaics on water surfaces can enhance the cooling effect of solar modules, thereby achieving optimal temperatures. The implementation of floating photovoltaics should be approached with careful consideration, for instance, hydrodynamic aspects, reservoir water level fluctuations, forest shadow effects, and safety distance from Dam Facilities. Furthermore, in light of the relocation of the Indonesian National Capital City from Jakarta to Ibu Kota Nusantara (IKN), the city’s green energy options must be thoroughly studied and researched. The Sepaku Semoi Dam is located in Penajam Paser Utara, Kalimantan Timur, and is designated as a flood control and raw rater supplier for IKN. In this research, hydrodynamic simulations using MIKE 21 were conducted to determine water surface velocities. These results were then combined with reservoir depth and the location of areas unaffected by forest shadows. The outcome of this analysis revealed that 15.2% of the water surface area in the Sepaku Semoi Dam Reservoir has the potential for installing floating photovoltaics. With an energy density of floating photovoltaic around 100 MW/km2, Sepaku Semoi Dam Reservoir would produce 50.83 MW. As a result, this research has generated a Potential Location Map for Floating Photovoltaics in Sepaku Semoi Reservoir Dam Reservoir.

Mapping of groundwater potential zones of Khordha District using GIS and AHP approaches

The most dependable source of fresh water is groundwater. Groundwater supplies are severely threatened by a number of factors, including urbanization, industrialization, and population growth. The amount, quality and variables affecting groundwater supplies are significantly impacted by climate variability. The fall in groundwater levels is often exacerbated by poor quality surface water resources and unreliable monsoons. Therefore, in order to supplement the groundwater supply, it is important to locate and define the groundwater potential zone (GPZ). The analysis is conducted for the Khordha district, where groundwater rather is a primary source for agricultural uses. In order to determine the possible groundwater zones, many factors, including geomorphology, geology, elevation, slope, precipitation, soil type, soil texture, drainage density (DD), lineament density (LD), Land use/Land cover (LULC), and lineament density (LD), are constructed as separate layers in the geographical information system (GIS) backdrop. The multi-criteria decision-making technique and the Analytic Hierarchy Process (AHP), which enable pairwise evaluation of criteria impacting the potential zone, were utilized to establish the weights for the different layers and after that, the weighted overlay analysis (WOA) tool in ArcGIS10.8 was used to produce the final groundwater potential map. The output map of specified region was delineated into five new classes-very good, good, moderate, poor, and very poor of which 12% (325.1745 km2) falls under ‘very low’, 22% (603.9765 km2) under ‘low’, 26% (700.7715 km2) under ‘moderate’, 26% (694.2591 km2) under ‘high’, 14% (376.7553 km2) under ‘very high’. Approximately 1395 km2 area concerning 52% of study region, falls under ‘high’ and ‘very high’ categories of GPZ. Validation of the generated GPWZ map was done with data acquired from Central groundwater board. The accuracy assessment was done by kappa coefficient error matrix, and based on overall accuracy, the obtained map was 81.538% accurate to field value. As dependable results were produced with the proposed methodology, future management plans incorporating natural and artificial recharge practices can be created in these locations with effectiveness.

Remote sensing and geospatial approach: Optimizing groundwater exploration in semi-arid region, Nepal

Groundwater is the fundamental component of the hydrological system that acts as a major factor in comprehensions of the physical processes in both the land surface and the atmosphere. Determining groundwater, which directly affects the agricultural productivity of semi-arid mountainous regions, is crucial. Mountain ecosystems, once abundant with flowing water, now face immense pressure from a changing climate, evident in the drying of springs and the diminishing flow of groundwater. Ensuring a steady flow of water, fair access to it, and responsible use are the cornerstones of a secure future for mountain communities. This study aims to assess the groundwater potential zones using remote sensing and a geospatial approach in the Mustang Valley's rural municipalities (Thasang and Gharapjhong). Nine factors were assigned to assess the groundwater potential map: slope, drainage density, lineament density, geology, soil, land use/land cover, rainfall, aspect, and soil moisture. The Bayesian weights of evidence model was used to delineate the groundwater potential zone. The study categorized groundwater potential across the region, creating five zones: very high, high, moderate, low, and very low. These zones covered 0.6 %, 12.4 %, 51.2 %, 35.5 %, and 0.3 % of the study area. The accuracy of the groundwater potential map was assessed by comparing its predictions with the actual locations of springs, using the area under the curve metric. The receiver operating characteristics curve analysis yielded an area under the curve of 0.7226, indicating a 72.26 % accuracy in predicting the presence of groundwater. The findings of this paper contribute to a better understanding of groundwater potential zones, which can support policymakers and planners for hydrological, meteorological, and crop planning applications in this climatically vulnerable region.

Delineation of Groundwater Potential Zones Using Remote Sensing and Gis Techniques of Phewa Watershed (Nepal)

Groundwater is the primary source of water in the study region, both for domestic and agricultural needs. The main objective of this research is to identify the groundwater potential zones via remote sensing (RS) and Geographic information system (GIS), which is essential in evaluating, preserving, and monitoring various groundwater-related development programs. Man-made pressures include over-exploitation of groundwater and climate changes have led to a strain on groundwater resources. As the use of consumable water increases for human consumption, agriculture, industrial growth, municipal, and pharmaceutical needs to evaluate the groundwater potential and aquifer productivity also increase. Groundwater inspections have been historically done by field survey method, which is inefficient or not practical in terms of time and resources. Arc GIS software is utilized to manipulate datasets. The LULC map of the study area will be developed using Landsat 8 satellite data. A soil map and rainfall map will be created using the data sources from USGS, a Parent soil map will creates using the data from NARC and slope map, lineament density and drainage density from ASTER from earth data. Seven thematic maps will be applied, each with appropriate weight and rank assigned based on its characteristics and connection with groundwater. All the thematic layers combine into a GIS domain, and weight values are put to the attribute table for each polygon. The groundwater potential zone map will be classified into different zones, i.e., very poor, poor, moderate, good, and very good. These results will help hydrogeologists, decision-makers, planners, and local authorities formulate better groundwater resource planning in the Phewa watershed area.

PROTECTIVE CAPACITY ASSESSMENT OF A SHALLOW AQUIFERAT THE EASTERN PART OF ADO-EKITI, SOUTHWESTERN NIGERIA

Vertical Electrical Soundings (VES) survey has been undertaken at the eastern part of Ado-Ekiti, southwestern Nigeria to assessthe aquifer protective capacity of the overburden units of the area. The Schlumberger electrode array was adopted with a maximumcurrent electrode spread (AB) of 300 m.Thirty-six(36) VES points wasoccupied and processed through a partial curve matching technique and 1-D forward modelingcomputer-assistedsoftware.The interpretation revealed H, A, KH and HA-type curves and threemajor geoelectric layers overlying the resistive basement. The groundwater potential and overburden protective capacity maps were prepared. The groundwater in the area was categorized into high, medium and low groundwater potential zones. About 80% of thestudy area falls within the moderate/low groundwater potential zone while the remaining 20% constituted the high groundwater potential zone (Agric Olope area). Hence, thegroundwater potential rating of other areas is considered to be low. The values of longitudinal conductance (ranging from 0.01 to 2.01 mhos) of the area enabled the overburden units to be rated into good, moderate and weakprotective capacity. About 75% of the area falls within the good/moderate rating while 25% constitutes the weak/poorprotective capacity rating (Agric Olope area), suggesting that the groundwater around Agric Olope isvulnerable to contamination.

Thermal, X-ray spectroscopy, morphological, density functional theory and molecular modeling studies on yttrium(III), germanium(IV), tungsten(VI), and silicon penicillinate antibiotic complexes

This manuscript elucidates the thermal stability analysis of four distinct penicillinate complexes, comprising yttrium(III), germanium(IV), tungsten(VI), and silicon, over a temperature ranging from 25 to 800°C. Thermal data revealed the high thermal stability nature of the decomposition steps. According to the spectroscopic measurements, the chemical formula of penicillinate complexes were [Y(Pin)2].Cl.6H2O complex (1), [Ge(Pin)2].2Cl.2H2O complex (2), [W(Pin)2].4Cl complex (3), and [Si(Pin)2].2Cl.2H2O complex (4). The powder XRD pattern revealed crystalline to polycrystalline natures. The synthesized penicillinate complexes were subjected to theoretical calculations utilizing density functional theory (DFT) calculations, employing the lanL2DZ/6-311G++ level of theory. The optimized geometry of each penicillinate complex was discerned, and a comprehensive evaluation of various properties, including the HOMO→LUMO electronic energy gap, molecular electrostatic potential map, and additional physical parameters, was conducted and validated against experimental findings. Molecular docking tool was used to explore the anticancer activity of the synthesized penicillinate complexes in comparison with penicillin potassium drug (Pin). For the computational investigation, two kinases — CSF1R (PDB ID: 7MFC) and MEK2 (PDB ID: 1S9I) associated with breast cancer progression and estrogen-dependent breast cancer were utilized. This comprehensive analysis provides a deeper understanding of the synthesized penicillinate complexes and their potential applications. KEY WORDS: Penicillinate complexes, TGA-DrTGA, TEM, XRD, DFT/TD-DFT, Molecular docking Bull. Chem. Soc. Ethiop. 2024, 38(4), 973-988. DOI: https://dx.doi.org/10.4314/bcse.v38i4.13

Theoretical Study of Radical Scavenging Antioxidant Activity of Coumarin Derivatives in Licorice Using DFT

Nowdays, much research is focused on exploring antioxidant activity based on natural compounds. Licorice, a traditional natural product rich in antioxidant components, has been widely used in clinical medicine. However, there is a lack of theoretical studies on the active components in licorice. In this context, the antioxidant activity of several common active components of coumarin derivatives in licorice was investigated using a density flooding theory (DFT) approach. The free radical scavenging ability of the compounds was achieved by hydrogen atom transfer (HAT), sequential electron transfer proton transfer (SET-PT) and sequential proton loss electron transfer (SPLET) mechanisms. The effect of solvent on antioxidant activity was also investigated. It was found that in the gas and benzene phases, the compounds studied preferred to undergo the HAT mechanism, while SPLET was favoured in polar solvents. In addition to the above mechanism, the HOMO and LUMO energies of the compounds as well as molecular descriptors and electrostatic potential maps were calculated and applied to predict the antioxidant activity of the compounds. The above studies allow a better understanding of the mechanism of free radical scavenging activity of coumarin derivatives from licorice.

Conditional generative adversarial network driven variable-duration single-lead to 12-lead electrocardiogram reconstruction

The standard 12-lead ECG can represent the cardiac status from twelve different perspectives, while the single-lead ECG just involves a single view, so it is challenging for the single-lead ECG to provide information as rich as the 12-lead ECG. Therefore, this study adopts the improved generative adversarial network (GAN) framework to model the potential mapping relation between single-lead and 12-lead ECG. Taking a heartbeat (1024 points) in lead I as input, in which the R-peak aligning strategy will be used for variable-duration reconstruction. The average correlation coefficients(CC) between the generated and the real signals are 0.742 at the heartbeat level and 0.744 at the segment level, and the CC in lead aVR could be up to 0.91 at the segment level. This study demonstrates that the generated 12-lead ECG signals effectively retain pathological features, thereby providing valuable multi-scale diagnostic information. To assess the efficacy of the diagnostic information derived from the generated signals, this study employs the arrhythmia classifiers for both single-lead and 12-lead ECG, achieving accuracy of 0.71 and 0.81, and macro-F1 scores of 0.66 and 0.77. For the generated 12-lead ECG, the accuracy and macro-F1 for arrhythmia classification could be 0.74 and 0.70, successfully bridging the gap between the single-lead ECG and the 12-lead ECG. In addition, the supplement experiments for other cases have been shown and compared. The source code is available at https://github.com/Zehui-Zhan/12-lead-reconstruction.git.

Assessment of groundwater potential zone mapping for semi-arid environment areas using AHP and MIF techniques

AbstractGroundwater resources are essential for drinking water, irrigation, and the economy mainly in semiarid environments where rainfall is limited. Currently, unpredictable rainfall due to climate change and pollution on the Earth’s surface directly affects groundwater resources. In this area, most people depend on groundwater resources for irrigation and drinking purposes, and every summer, most of the area depends on groundwater in a semiarid environment. Hence, we selected two popular methods, the analytical hierarchy process (AHP) and multiple influence factor (MIF) methods, which can be applied to map groundwater potential zones. Nine thematic layers, such as land use and land cover (LULC), geomorphology, soil, drainage density, slope, lineament density, elevation, groundwater level, and geology maps, were selected for this study using remote sensing and geographic information system (GIS) techniques. These layers are integrated in ArcGIS 10.5 software with the help of the AHP and MIF methods. The map of the groundwater potential zones in the study area revealed four classes, i.e., poor, moderate, good, and very good, based on the AHP and MF methods. The groundwater potential zone area is 241.50 (ha) Poor, 285.64 (ha) moderate, 408.31 (ha) good, and 92.75 (ha) very good using the AHP method. Similarly, the MIF method revealed that the groundwater potential classes were divided into four classes: 351.29 (ha) poor, 511.18 (ha), moderate, 123.95 (ha) good, and 41.78 (ha) very good. The results were compared to determine which methods are best for planning water and land resource development in specific areas that have basaltic rock and drought conditions. Both groundwater potential zone maps were validated with water yield data. The receiver operating characteristic (ROC) curve and area under the curve (AUC) model results are found to be 0.80 (good) and 0.93 (excellent) using the MIF and AHP methods, respectively; hence, the AHP method is best for delineation of groundwater potential zone maps and groundwater resource planning. The present study’s framework and the results will be valuable for improving the efficiency of irrigation, conserving rainwater and maintaining the ecosystem in India.

Mapping almond stem water potential using machine learning and multispectral imagery

AbstractAlmonds are a major crop in California which produces 80% of all the world’s almonds. Widespread drought and strict groundwater regulations pose significant challenges to growers. Irrigation regimes based on observed crop water status can help to optimize water use efficiency, but consistent and accurate measurement of water status can prove challenging. In almonds, crop water status is best represented by midday stem water potential measured using a pressure chamber, which despite its accuracy is impractical for growers to measure on a regular basis. This study aimed to use machine learning (ML) models to predict stem water potential in an almond orchard based on canopy spectral reflectance, soil moisture, and daily evapotranspiration. Both artificial neural network and random forest models were trained and used to produce high-resolution spatial maps of stem water potential covering the entire orchard. Also, for each ML model type, one model was trained to predict raw stem water potential values, while another was trained to predict baseline-adjusted values. Together, all models resulted in an average coefficient of correlation of R2 = 0.73 and an average root mean squared error (RMSE) of 2.5 bars. Prediction accuracy decreased significantly when models were expanded to spatial maps (R2 = 0.33, RMSE = 3.31 [avg]). These results indicate that both artificial neural networks and random forest frameworks can be used to predict stem water potential, but both approaches were unable to fully account for the spatial variability observed throughout the orchard. Overall, the most accurate maps were produced by the random forest model (raw stem water potential R2 = 0.47, RMSE = 2.71). The ability to predict stem water potential spatially can aid in the implementation of variable rate irrigation. Future studies should attempt to train similar models with larger datasets and develop a simpler faster workflow for producing stress predictions from field measurements.

Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1

Abstract. Isoprene, a key biogenic volatile organic compound, plays a pivotal role in atmospheric chemistry. Due to its high reactivity, this compound contributes significantly to the production of tropospheric ozone in polluted areas and to the formation of secondary organic aerosols. The assessment of biogenic emissions is of great importance for regional and global air quality evaluation. In this study, we implemented the biogenic emission model MEGANv2.1 (Model of Emissions of Gases and Aerosols from Nature, version 2.1) in the surface model SURFEXv8.1 (SURface EXternalisée in French, version 8.1). This coupling aims to improve the estimation of biogenic emissions using the detailed vegetation-type-dependent treatment included in the SURFEX vegetation ISBA (Interaction between Soil Biosphere and Atmosphere) scheme. This scheme provides vegetation-dependent parameters such as leaf area index and soil moisture to MEGAN. This approach enables a more accurate estimation of biogenic fluxes compared to the stand-alone MEGAN model, which relies on average input values for all vegetation types. The present study focuses on the assessment of the SURFEX–MEGAN model isoprene emissions. An evaluation of the coupled SURFEX–MEGAN model results was carried out by conducting a global isoprene emission simulation in 2019 and by comparing the simulation results with other MEGAN-based isoprene inventories. The coupled model estimates a total global isoprene emission of 443 Tg in 2019. The estimated isoprene is within the range of results obtained with other MEGAN-based isoprene inventories, ranging from 311 to 637 Tg. The spatial distribution of SURFEX–MEGAN isoprene is consistent with other studies, with some differences located in low-isoprene-emission regions. Several sensitivity tests were conducted to quantify the impact of different model inputs and configurations on isoprene emissions. Using different meteorological forcings resulted in a ±5 % change in isoprene emissions using MERRA (Modern-Era Retrospective analysis for Research and Applications) and IFS (Integrated Forecasting System) compared with ERA5. The impact of using different emission factor data was also investigated. The use of PFT (plant functional type) spatial coverage and PFT-dependent emission potential data resulted in a 12 % reduction compared to using the isoprene emission potential gridded map. A significant reduction of around 38 % in global isoprene emissions was observed in the third sensitivity analysis, which applied a parameterization of soil moisture deficit, particularly in certain regions of Australia, Africa, and South America. The significance of coupling the SURFEX and MEGAN models lies particularly in the ability of the coupled model to be forced with meteorological data from any period. This means, for instance, that this system can be used to predict biogenic emissions in the future. This aspect of our work is significant given the changes that biogenic organic compounds are expected to undergo as a result of changes in their climatic factors.

Role of climate change on Oriental spruce (Picea orientalis L.): Modeling and mapping

Global climate change is a process with dramatic consequences for ecosystems, and changes that may occur in the potential distribution of plant communities especially draw attention. This study aimed to reveal the potential distribution modeling and mapping of the Oriental spruce (Picea orientalis L.), distributed in a limited area, using current and future (year 2100) climate scenarios in Turkey. The maximum entropy method for potential distribution and Chelsa V2.1 technical specification IPSL-CM6A-LR scenarios (SSP126-SSP370-SSP585) were preferred to reveal the effect of climate change. Results for the current were in the “excellent” category with training and test data AUC 0.981 and 0.977, respectively. The variables contributing to the model were the precipitation amount of the driest month, mean diurnal air temperature range, annual precipitation amount, and mean annual air temperature. Variables contributing to the current model were analysed using the SSP126, SSP370, and SSP585 scenarios of the year 2100. It was assessed that the potential distribution for 2100 decreases according to SSP126, was fragmented according to SSP370, and decreased according to the SSP585 scenario. As a result, the authors determined that the high potential distribution is reduced 61% when the current mapping of Oriental spruce is compared with the SSP585 mapping.

Exploring the adsorption of five emerging pollutants on activated carbon: A theoretical approach

The identification and management of contaminants of emerging concern (CECs) in water systems is crucial for protecting public and environmental health. This paper reports a theoretical approach to studying the adsorption of five CECs: Atrazine (ATZ), Caffeine (CAF), Carbamazepine (CBZ), Sulfamethoxazole (SMX), and Ibuprofen (IBU) - onto Activated Carbon (AC). A set of computational methods, including electrostatic molecular potential maps, conceptual density functional theory, Fukui functions, thermodynamic analysis, and tight-binding molecular dynamics simulations, were employed to analyze the electronic/energetic interactions and mechanisms involved in the adsorption of CECs on AC. The theoretical methodology offered valuable predictions on reactivity sites, stability, and binding mechanisms. Results showed that adsorption primarily occurred through non-covalent interactions like π-π electron donor-acceptor interactions, van der Waals forces, and hydrophobic interactions. Thermodynamic properties suggested the adsorption process was spontaneous and exothermic. However, for the AC/SMX system, the Gibbs free energy reveals that adsorption may be unfavorably compared to the other study systems. Molecular dynamics simulations validated the kinetic stability in the following order CAF (0.13 Å)>CBZ (0.23 Å)>ATZ (0.75 Å)> IBU (1.28 Å)>SMX (1.54 Å). This exploratory theoretical study provides a deep understanding of the interactions between AC and five CECs, aiding in the rational design and optimization of AC-based treatment systems for environmental and industrial applications.

Geological mapping and mineral exploration in Francistown region, north east Botswana from integration of ASTER TIR and aeromagnetic data

To map the region's geology and mineral exploration, this study looked at how remote sensing satellite imaging from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Thermal Infrared (TIR) sensor and aircraft geophysics may be used. The Francistown region forms part of the Zimbabwe craton, northeast of Botswana. It comprises felsic rock types, including granite and dry river sand, mafic-ultramafic rocks, and gneiss country rock. Based on their spectral emissivity responses, mafic-ultramafic units and quartz-rich units were discovered. These units are important targets for the discovery of mineral resources. The research region can be described as convoluted, fractured, intruded, metamorphosed, and ultimately worn from a geological and geophysical standpoint. Several remote sensing and aeromagnetic approaches were employed. The false color composite (FCC) and mineral indices using ASTER TIR data provided helpful information for delineating the area's geology and concentration of major-rock-forming minerals. The aeromagnetic data extracted lineaments well, and the Center for Exploration Target (CET) grid analysis system was very helpful. The CET Porphyry Analysis technique was applied in mapping near-circular porphyry features, which are good indicators of possible sites with high mineralization potential. The analysis of the lineament map shows that mineral deposits are mostly along the NE-SW direction. The results from the ASTER TIR and aeromagnetic enhancements indicate the occurrence of mafic-ultramafic rocks and felsic-rich units, which have been documented to host mineralization in the region. Porphyry structures are also mapped successfully along the general strike of the geological structure in the study area. The combination of these methods proved their success, and they can be used further in other regions of high mineral potential and lithological mapping for their cost efficiency and reliability.

The influence of catalyst structure on acidic strength of –SO3H groups for alkyl levulinate synthesis from biomass–derived feedstocks using sulfonic acid functionalized flexible MOF catalyst

A Brönsted acidic BUT−8(Cr)–SO3H metal–organic framework was used for the synthesis of ethyl levulinate from two different biomass–derived platform chemicals viz. furfuryl alcohol and levulinic acid. The catalyst was well characterized using various methods such as PXRD, FTIR spectroscopy, acid–base titration, NH3–TPD, TGA–DTA, SEM, TEM as well as the computational density functional theory (DFT) method. The performance of BUT–8(Cr)–SO3H catalysts was found to be superior to other catalysts (HZSM–5, H–Beta, and amberlyst –15) taken in this study. The sulfonic acid moieties bound to the aromatic naphthalene structure of BUT–8(Cr)–SO3H MOF were found to have a much stronger influence on the catalytic performance than the sulfonic acid moieties bound to the styrene framework of amberlyst–15. The key discoveries regarding the Brönsted acidic characteristics were computed via DFT incorporating the hydrogen removal energy, molecular electrostatic potential maps, and stability of the conjugate base of –SO3H moieties. The trend in the DFT–derived values was in the following order: BUT–8(Cr)–SO3H > Linker–naph –SO3H > Amberlyst–15. The results also indicate the influence of Cr metal on MOF structure to increase the acidic strength of the –SO3H group. The reaction optimization using DOE, recyclability, leaching study, and substrate scope study for both pathways using BUT–8(Cr)–SO3H were obtained.

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

AbstractDriving in an off‐road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human‐like cognition and cost evaluation for off‐road driving. First, we construct a multilayer map describing different features of off‐road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network‐long short‐term memory network to learn the trajectories planned by human drivers in various off‐road scenarios. Then, based on human‐like generated trajectories in different environments, we design a primitive‐based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off‐road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off‐road environment with complex terrain and varying road conditions. The experimental results show that the proposed human‐like motion planner has excellent adaptability to different off‐road conditions. It shows real‐time operation, greater stability, and more human‐like planning ability in diverse and challenging scenarios.

Assessment of the geothermal potential zone of India utilizing GIS-based multi-criteria decision analysis technique

India, with 80 % energy demand reliant on fossil fuels, is the world's third-largest greenhouse gas emitter, necessitating a shift from fossil fuels to renewable energy. While solar and wind production has grown, geothermal energy remains untapped despite enormous prospects in the country. In this regard, India's first regional-scale geothermal potential (GP) map has been prepared using a GIS based on a multi-criteria decision analysis technique. Five thematic layers (geology, fault, heat flow, thermal spring, and earthquake epicenter) have been used to calculate GP index, which is divided into high, moderate, and low categories. The result shows that most geothermal drilling sites are distributed in the high GP category zone, showing high efficacy. Further, a joint study of the geothermal surface and reservoir temperature with the present study shows that the northern and western parts of India have significant potential for geothermal exploration. Our result also reveals that the projected CO2 emissions are approximately 2.6 gigatons (which is lower than other energy sources) for an output of 10 GW using geothermal energy in the next 75 years. This study can enhance geothermal prospect identification, cutting exploration costs, and site selection, and contributing to multiple UN Sustainable Development Goals.

Demarcation of groundwater potential zones in Ponnai sub-basin, Southern India using remote sensing, GIS and fuzzy-AHP approaches

ABSTRACT Groundwater is an essential source for household, irrigation and industry needs. The amount of groundwater and its quality are rapidly declining because of urbanization and industrialization. Groundwater resources in hard rock regions should be handled properly to maximize their utilization. This work aims to demarcate the Groundwater Potential Zones (GWPZ) in a part of the Palar River Basin (Ponnai sub-basin) by combining Remote Sensing (RS), Geographic Information System (GIS) and Fuzzy-Analytical Hierarchy Process (FAHP). The factors which influence the groundwater like geology, land use/land cover (LULC), geomorphology, slope, soil, rainfall, lineament density (LD), drainage density (DD), normalized difference vegetation index (NDVI), topographic wetness index (TWI) and topographic position index (TPI) are considered for mapping of GWPZ. The weightage is allocated to these parameters on the basis of the FAHP technique and the obtained groundwater potential map was classified as, very poor (13.5%), poor (21.76%), moderate (31.5%), good (20.66%) and very good (8.54%). The GWPZ map was validated with the actual well yield of the Ponnai sub-basin. The research work will serve as a guideline for the effective use of groundwater and long-term sustainable management of the aquifer in the Ponnai sub-basin.

Synthesis, in vitro Anti-HIV-1RT evaluation, molecular modeling, DFT and acute oral toxicity studies of some benzotriazole derivatives

This study synthesized and evaluated a series of benzotriazole derivatives denoted 3(a-j) and 6(a-j) for their anti-HIV-1 RT activities compared to the standard drug efavirenz. Notably, compound 3 h, followed closely by 6 h, exhibited significant anti-HIV-1 RT efficacy relative to the standard drug. In vivo oral toxicity studies were conducted for the most active compound 3 h, confirming its nontoxic nature to ascertain the safety profile. By employing molecular docking techniques, we explored the potential interactions between the synthesized compounds (ligands) and a target biomolecule (protein)(PDB ID 1RT2) at the molecular level. We undertook the molecular dynamics study of 3 h, the most active compound, within the active binding pocket of the cocrystallized structure of HIV-1 RT (PDB ID 1RT2). We aimed to learn more about how biomolecular systems behave, interact, and change at the atomic or molecular level over time. Finally, the DFT-derived HOMO and LUMO orbitals, as well as analysis of the molecular electrostatic potential map, aid in discerning the reactivity characteristics of our molecule.