Potential Modeling Research Articles

Molecular Modeling and Simulation Analysis of Antimicrobial Photodynamic Therapy Potential for Control of COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can enter the host cells by binding the viral surface spike glycoprotein (SG) to angiotensin-converting enzyme 2. Since antiviral photodynamic therapy (aPDT) has been described as a new method for inhibiting viral infections, it is important to evaluate whether it can be used as a photoactivated disinfectant to control COVID-19. In this in silico study, SARS-CoV-2-SG was selected as a novel target for curcumin as a photosensitizer during aPDT to exploit its physicochemical properties, molecular modeling, hierarchical nature of protein structure, and functional analysis using several bioinformatics tools and biological databases. The results of a detailed computational investigation revealed that SARS-CoV-2-SG is most similar to 6VXX_A, with 100% query cover and identity. The predicted structure of SARS-CoV-2-SG displayed that it is a protein with a positive charge and random coil dominates other secondary structures located outside the viral cell. The protein-protein interaction network showed that SARS-CoV-2-SG interacted with ten potential interacting partners. In addition, primary screening of binding modes through molecular docking showed that curcumin desires to bind and interact with residues of SARS-CoV-2-SG as the main site to enhance the yield of aPDT. Overall, the computer simulation reveals that SARS-CoV-2-SG can be a suitable target site for interaction with curcumin during aPDT.

Individual level spatial-temporal modelling of exposure potential of livestock in the Cove Wash watershed, Arizona

ABSTRACT Personal exposure studies suffer from uncertainty issues, largely stemming from individual behaviour uncertainties. Built on spatial-temporal exposure analysis and methods, this study proposed a novel approach to spatial-temporal modelling that incorporated behaviour classifications taking into account uncertainties, to estimate individual livestock exposure potential. The new approach was applied in a community-based research project with a Tribal community in the southwest United States to address questions on potential livestock exposure to abandoned uranium mines (AUMs). The study aimed to 1) classify Global Positioning System (GPS) data from livestock into three behaviour subgroups – grazing, travelling or resting; 2) calculate the daily cumulative exposure potential for livestock; 3) assess the performance of the computational method with and without behaviour classifications. Using Lotek Litetrack GPS collars, we collected data at a 20-min-interval for two flocks of sheep and goats during the spring and summer of 2019. Analysis and modelling of GPS data demonstrated no significant difference in individual cumulative exposure potential within each flock when animal behaviours with probability/uncertainties were considered. However, when daily cumulative exposure potential was calculated without consideration of animal behaviour or probability/uncertainties, significant differences among animals within a herd were observed, which does not match animal grazing behaviours reported by livestock owners. These results suggest that the proposed method including behaviour subgroups with probability/uncertainties more closely resembled the observed grazing behaviours. Results from the research may be used for future intervention and policy-making on remediation efforts in communities where grazing livestock may encounter environmental contaminants.

Potential Distribution and Medicinal Uses of the Mexican Plant Cuphea aequipetala Cav. (Lythraceae)

Carrying out studies that lead us to obtain information on both the cultural and biological heritage of a locality, region, or country allows us to create appropriate strategies for the conservation of biocultural diversity. In this context, the objective of this study was to model the potential distribution of Cuphea aequipetala Cav. within the Mexican territory, to identify the main environmental variables that delimit its habitat, and to obtain information from traditional knowledge through the medicinal uses that the inhabitants of nearby communities give to the plant. Potential distribution modeling was performed with MaxEnt together with 19 bioclimatic variables of Worldclim plus three variables closely related to the habitat of the species. Data on its presence were obtained in situ within the Lagunas de Zempoala National Park and from iNaturalist. Information on the medicinal uses of the plant was obtained through semi-structured surveys with people who were in continuous contact with it. The potential range of C. equipetala Cav. was 3205.63 km2, which represents 0.16% of the Mexican territory. The altitude, precipitation in the driest period, average temperature of the warmest quarter, and average temperature of the driest quarter were the variables that had the greatest effects on the potential distribution (49%), and these factors mainly delimited the suitability of the habitat. C. aequipetala Cav. is still used in traditional medicine, mainly for conditions related to cancer, shocks, and inflammation. Finally, it was found that the potential distribution coincided with the states of the country where its medicinal use was reported. This information is important, since it constitutes the basis for performing actions targeting the conservation of this species of medicinal relevance. For example, potential distribution areas can be integrated into habitat restoration and conservation plans to prevent anthropogenic activities, such as felling, that directly affect the habitat. This information can also be used as a reference to promote the preservation of medicinal uses among the young population of the distribution areas.

Fusion of expert uncertain assessment in FMEA based on the negation of basic probability assignment and evidence distance

Failure mode and effects analysis (FMEA) has been widely used for potential risk modeling and management. Expert evaluation is used to model the risk priority number to determine the risk level of different failure modes. Dempster–Shafer (D–S) evidence theory is an effective method for uncertain information modeling and has been adopted to address the uncertainty in FMEA. How to deal with conflicting evidence from different experts is an open issue. At the same time, different professional backgrounds of experts may lead to different weights in modeling the evaluation. How to model the relative weight of an expert is an important problem. We propose an improved risk analysis method based on triangular fuzzy numbers, the negation of basic probability assignment (BPA) and the evidence distance in the frame of D–S evidence theory. First, we summarize and organize the expert’s risk analysis results. Then, we model the expert’s assessments based on the triangular fuzzy numbers as BPAs and calculate the negation of BPAs. Third, we model the weight of expert based on the evidence distance in the evidence theory. Finally, the Murphy’s combination rule is used to fuse the risk assessment results of different experts and calculate the new risk priority number (RPN). At the end of this paper, we apply the proposed method to analyze seventeen failure modes of aircraft turbine blades. The experimental results verify the rationality and effectiveness of this method.

Potential field interpretation and modelling in the deep-water Otway Basin

The NW–SE trending Otway Basin is a well-established hydrocarbon province with onshore and shallow-water offshore discoveries. However, the deep-water offshore is underexplored due to the sparsity of data in the past. New 2D seismic, gravity and magnetic data, acquired during the 2020 Otway Basin Seismic Program, were combined with legacy seismic data to improve the understanding of this part of the basin. The new data have been processed and integrated with existing open-file data to derive Moho and lithosphere–asthenosphere boundary surfaces. Additional wavelength filtering of the newly acquired magnetic and gravity data, using constraints from seismic data, provides valuable information on the geometry and the spatial extent of igneous rocks in the deep-water basin. While extrusive igneous rocks and related intrusive rocks in the Otway Basin can be inferred from seismic reflection data, the new magnetic and gravity data provide additional constraints that help to map their distribution across the basin.

Interaction between the Arctic and Non-Arctic States in Scientific Research and Practical Development of the Geopolitical Potential of the Arctic

Aim. Reveal the methodological role of ideas about the geopolitical potential of the state in making managerial decisions in the development of the space of the Russian Arctic. The article presents an overview of fundamental research and articles by foreign authors that addresses the problem of realizing the geopolitical potential of the Arctic.Tasks. Development of the principles of geopolitical forecasting based on mathematical modeling of the geopolitical potential of the state and systematization of developments on the spatial development of the Russian Arctic as part of Greater Eurasia.Methods. Mathematical modeling of geopolitical processes, medium-term forecasting of the spatial development of the Russian Arctic in the face of challenges from foreign states claiming its natural resources and territorial waters.Results. It confirms the need to include scientific developments in the methodological apparatus of geopolitical research to identify qualitative and quantitative definitions of the geopolitical potential of the state, which contributes to ensuring the national security of Russia and improving its spatial development.Conclusions. The transition to the sixth technological mode is not limited to a radical transformation of the country’s production complex, but also implies the need to create a unified infrastructure architecture for the Greater Eurasia (“smart” Eurasia), into which the Russian Arctic organically fits.

Inhibition of sodium conductance by cannabigerol contributes to a reduction of dorsal root ganglion neuron excitability.

Cannabigerol (CBG), a non-psychotropic phytocannabinoid and a precursor of ∆9 -tetrahydrocannabinol and cannabidiol, has been suggested to act as an analgesic. A previous study reported that CBG (10μM) blocks voltage-gated sodium (Nav ) currents in CNS neurons, although the underlying mechanism is not well understood. Genetic and functional studies have validated Nav 1.7 channels as an opportune target for analgesic drug development. The effects of CBG on Nav 1.7 channels, which may contribute to its analgesic properties, have not been previously investigated. To determine the effects of CBG on Nav channels, we used stably transfected HEK cells and primary dorsal root ganglion (DRG) neurons to characterize compound effects using experimental and computational techniques. These included patch-clamp, multielectrode array, and action potential modelling. CBG is a ~10-fold state-dependent Nav channel inhibitor (KI -KR : ~2-20 μM) with an average Hill-slope of ~2. We determined that, at lower concentrations, CBG predominantly blocks sodium Gmax and slows recovery from inactivation. However, as the concentration is increased, CBG also induces a hyperpolarizing shift in the half-voltage of inactivation. Our modelling and multielectrode array recordings suggest that CBG attenuates DRG excitability. Inhibition of Nav 1.7 channels in DRG neurons may underlie CBG-induced neuronal hypoexcitability. As most Nav 1.7 channels are inactivated at the resting membrane potential of DRG neurons, they are more likely to be inhibited by lower CBG concentrations, suggesting functional selectivity against Nav 1.7 channels, compared with other Nav channels (via Gmax block).

Integrating spatially explicit sensitivity and uncertainty analysis in a multi-criteria decision analysis-based groundwater potential zone model

This study presents a spatially explicit sensitivity and uncertainty analysis approach to a GIS-based multi-criteria groundwater potential zone model. The study addressed a deficiency in the way groundwater potential mapping results are typically presented using discrete class outputs without assessment of their certainty with respect to variations in criteria weighting, one of the main contributors to output uncertainty in GIS-based multi-criteria decision analysis studies. We argue, moderating groundwater potential mapping results with localised uncertainty levels will help to refine and prioritise groundwater exploration efforts. The approach also enables a better understanding of the underlying factors influencing uncertainty in model outputs, which can help to inform the calibration of input parameters to improve model performance. Although the procedures presented in this study have been applied to other types of multi-criteria evaluations, its integration in GIS-based groundwater potential modelling has received little attention. We provide a case study focused on a fractured rock environment surrounding the township of Hawker in South Australia where new groundwater resources are sought. Small incremental weight changes were applied one-at-a-time and automated as a task in ArcGIS Pro, built using the ArcPy Python module that interacts with spatial tools allowing geographical analysis. The approach is applicable to both continuous and discrete class-based mapping outputs and enabled a deeper understanding of model output behaviour with respect to criteria weighting alternatives. The case study findings demonstrate the potential value of the approach in mitigating uncertainty and improving confidence in locating sites with high groundwater potential.

Distribution data of the dormice species in Romania: a review

All four species of dormice present in Romania are listed as Vulnerable in the Romanian Red Book of Vertebrates and are protected by national laws. Even though they are important bioindicators of the condition of forest habitats, little is known about their distribution. We reviewed the distribution data for the Romanian dormice species: the Garden Dormouse (Eliomys quercinus), Hazel Dormouse (Muscardinus avellanarius), Edible Dormouse (Glis glis), and Forest Dormouse (Dryomys nitedula). Our sources include published scientific literature, online databases (such as GBIF, Observation.org and iNaturalist), museum collections, verified citizen’s data as well as original data. A database was organized in GeoPackage format in QGIS 3.16.0. Historical locations from old scientific papers were georeferenced and included in the database with all metadata found. Recent and original data locations were recorded using geographical coordinates. Our aim was to produce up to date distribution maps for the dormice species occuring in Romania that can be further used in potential habitat modelling for the study and protection of these species and their habitat. We found the most abundant observation were of Muscardinus avellanarius and the least recorded species was Eliomys quercinus, without any recent data.

Cu–Au exploration target generation in the eastern Carajás Mineral Province using random forest and multi-class index overlay mapping

The Carajás Mineral Province (CMP) located in the southeast of the Amazon craton, northern Brazil is one of the most important provinces of the world hosting a high number of large-tonnage Cu–Au deposits. This potential is due to overprinted mineralization events dated from the Neoarchean to Paleoproterozoic, including VMS, IOCG, and Cu–Au granite-related systems. The discovery of a new mineral deposit is linked to direct detection methods that have an elevated cost. Mineral potential modeling is a multi-source geoscience data that can help outline high prospective areas, containing new mineral exploration targets in the order to reduce the exploration risk. This work employed one knowledge-based method, multi-class index overlay, and one machine-learning algorithm, random forest, to produce two mineral potential maps in a district scale for Cu–Au deposits in the easternmost CMP (Parauapebas area) using a mineral system-based approach. This approach consists of translating the critical ore-forming processes of the mineral system components (source of fluids, metals, and ligands, source of energy, pathways, ore deposition gradient) into spatial data (evidence maps). For this study, ten evidence maps were created using the Spatial Data Analyst in GIS, then for the multi-class index overlay, each evidence map received a weight according to its relevance in the modeling, and integrated through the Image Analyst Tools. For the random forest algorithm, meanwhile, the model was trained four times using the Orange Data Mining software. The performance of the models was measured through the classification of accuracy, ROC curve, AUC values, predictive and success-rate curves. Both models achieve good performance with classification accuracy and AUC values of 80% and 0,805 for the multi-class index overlay and values of 76,4% and 0,812 for the random forest. Although the models have similar performance, the analysis of the curves showed that the random forest model was able to predict 82% of the locations of the known deposits, selecting about 25% of the total study area, while the multi-class index overlay predicted 72% of the location of the known deposits over 35% of the study area. Furthermore, both models predicted less than 10% of the study area as highly prospective helping prioritize areas for research and follow-up copper and gold exploration activities.

3D geological and potential field modelling of the buried alkaline-carbonatite Tajno massif (East European Craton, NE Poland)

Geological and geophysical data are used to model the 3D geometry of the Tajno alkaline massif intruded during the Early Carboniferous in NE Poland. The massif consists mainly of pyroxenite, mafic intrusive and volcanic rocks, and carbonatites containing rare earth elements (REE) and other important mineral resources. The deep structure of the massif, and its thickness, shape and internal structure, has been poorly known making it impossible to properly search for useful mineral resources. In order to better constrain the distribution, geometries and relationships between the main rock types, a 3D geological model of the Tajno massif has been developed. The input data comprise a set of geological cross-sections built on an updated subsurface geological map, and borehole, magnetic and gravity data. 3D Geomodeller software was applied to integrate geological data into a coherent and geologically feasible model of the massif using geostatistical analysis. The magnetic and gravity data were used to constrain the 3D geological modelling results. The final 3D model is thus compatible with the geological data, as well as with geophysical data. The most important conclusions obtained from the modelling are as follows: (i) a higher proportion of nepheline syenites or tuffs and pyroclastic breccia in relation to pyroxenites; and, (ii) a smaller proportion of chimney breccia relative to chimney-hosted tuffs and volcanic breccia than proposed in previous geological interpretation. These results are important for further studies on the evolution of the Tajno massif and its associated carbonatites.

Potential field modelling and U–Pb geochronology reveal the pluton emplacement dynamics of the Lower Devonian Tarnagulla Granodiorite, southeast Australia

The Tarnagulla Granodiorite in the western Lachlan Fold Belt is poorly exposed; however, magnetic data reveal its distinct U shape (in plan view) and patterns characterised by multiple nested cusp-shaped anomalies of contrasting magnetic intensity. We interpret this magnetic response to reflect incremental assembly of the pluton above a persistent magma point source that migrated laterally. Here, we use potential field data, geophysical modelling and high-precision zircon U–Pb dating to constrain the shape of the intrusion at depth and deduce the migration path of the magmatism epicentre. Potential field profile modelling across three traverses, constrained by petrophysics, resolves the subsurface geometry of the Tarnagulla Granodiorite. In cross-section, the intrusion is funnel-shaped, with a lower boundary that varies from ∼2 km depth in the north to ∼6 km depth in the southwest, reaching a maximum depth of ∼8 km, which may coincide with a magma feeder zone. New precise U–Pb geochronology provides ages that confirm the interpreted magnetic phases were emplaced sequentially along a clockwise intrusion path that progressed stepwise from the north to the southwest between 406.62 ± 0.27 Ma and 399.3 ± 1.1 Ma. We attribute migration of the epicentre of magmatism within the Tarnagulla Granodiorite to syn-intrusion (Early Devonian) reactivations of the underlying Tarnagulla Fault, with relative displacements between the fault hanging wall (which hosts the pluton) and footwall (which we interpret to have hosted a persistent point-source magma feeder zone during pluton emplacement). Our model explains the intrusion’s U-shaped magnetic response and reveals critical information about the paleo-stress and paleo-strain conditions during emplacement, characterised by three main displacement vectors and fault slip magnitudes. The geophysical characteristics, path of emplacement and mineralisation distribution all suggest that progressive fractional crystallisation contributed to the formation of an enriched final pulse of magma that resulted in Mo, Cu and W mineralisation hosted in the Moliagul Granodiorite. KEY POINTS High-precision U–Pb zircon geochronology reveal that the Tarnagulla Granodiorite was emplaced along a clockwise pathway from the northeast to the southwest between 406.62 ± 0.27 Ma and 399.3 ± 1.1 Ma. Active faulting during pluton emplacement is the suggested explanation for the Tarnagulla Granodiorites’ U-shaped magnetic response. Fractional crystallisation throughout pluton emplacement produced the varying internal geophysical response of the pluton as well as the enrichment of Mo and W at Mt Moliagul.

Combining high resolution input and stacking ensemble machine learning algorithms for developing robust groundwater potentiality models in Bisha watershed, Saudi Arabia

The present research aims to build a unique ensemble model based on a high-resolution groundwater potentiality model (GPM) by merging the random forest (RF) meta classifier-based stacking ensemble machine learning method with high-resolution groundwater conditioning factors in the Bisha watershed, Saudi Arabia. Using high-resolution satellite images and other secondary sources, twenty-one parameters were derived in this study. SVM, ANN, and LR meta-classifiers were used to create the new stacking ensemble machine learning method. RF meta classifiers were used to create the new stacking ensemble machine learning algorithm. Each of these three models was compared to the ensemble model separately. The GPMs were then confirmed using ROC curves, such as the empirical ROC and the binormal ROC, both parametric and non-parametric. Sensitivity analyses of GPM parameters were carried out using an RF-based approach. Predictions were made using six hybrid algorithms and a new hybrid model for the very high (1835–2149 km2) and high groundwater potential (3335–4585 km2) regions. The stacking model (ROCe-AUC: 0.856; ROCb-AUC: 0.921) beat other models based on ROC's area under the curve (AUC). GPM sensitivity study indicated that NDMI, NDVI, slope, distance to water bodies, and flow accumulation were the most sensitive parameters. This work will aid in improving the effectiveness of GPMs in developing sustainable groundwater management plans by utilizing DEM-derived parameters.

High-fidelity FE models for assessing progressive collapse robustness of RC ordinary moment frame (OMF) buildings

Most of the buildings in Saudi Arabia were constructed using reinforced concrete (RC) ordinary moment frames (OMFs), which, in most cases, had discontinuous bottom beam rebars across the beam-column joints. Consequently, they may be susceptible to the potential of progressive collapse owing to accidental loss of one (or more) exterior column(s) in an extreme event. Therefore, it is imperative to examine the progressive collapse robustness of RC OMF buildings. In this paper, 16 half-scale RC OMF assemblies (skeletal frames comprising of multi-span beams and two-story columns) were investigated using validated FE modeling for progressive collapse potential in the event of middle column removal. The analysis was carried out with the help of LS-DYNA software. It incorporated nonlinear constitutive modeling of concrete and steel rebars considering the effect of strain rate, and modeling of bond-slip behavior at the interaction of steel rebars with concrete. Out of the 16 assemblies, 12 specimens represented the actual detailing of an existing 8-story building. The studied variables included axial load on columns, percentage of continuous bottom beam rebars across the beam-column joint (with respect to the total number of bottom rebars), and number of beam spans. The performance of the 12 assemblies was evaluated in accordance with mode of failure and characteristics of load–displacement envelope. A simple approach was suggested to assess the progressive collapse risk level of RC OMFs. Five risk levels were proposed, ranging from very low to very high. Very high to high risk was predicted for the 12 studied assemblies. The last four assemblies of the analysis matrix were newly designed to reduce the progressive collapse risk to a moderate (or low) level.

Numerical modeling of rock surface zeta potential in the low salinity water-injection

Experimental measurements of zeta potential could be employed for the disjoining pressure calculation to determine wettability alteration during low salinity water injection. However, due to the instability of zeta potential measurements at high temperatures (∼>80ͦc) which are representative of hydrocarbon reservoir temperature, this approach has been rarely recommended for hydrocarbon reservoirs. To avoid the zeta potential measurement, a theoretical approach is presented in this study. A numerical model is developed which considers the involved equilibrations, including chemical and thermodynamic equilibriums. The model presents an in-house MATLAB platform that includes interface charge alteration which affects surfaces zeta potential for a given Crude Oil-Brine-Rock system (i.e., oil-brine and rock-brine interfaces). The proposed model conducts phase equilibrium calculation. It considers the effect of soluble components of each phase on the composition and thickness of the electrical double layer that determines the stability of the water film between oil and rock surfaces. Consequently, the zeta potential is related to pressure, temperature, oil and brine composition, and pH for the carbonate rocks. The model is validated using the data sets from the literature. Results show that the developed model correctly represents zeta potential trends arising from changes in brine composition.

Light-Induced Quantum Droplet Phases of Lattice Bosons in Multimode Cavities.

Multimode optical cavities can be used to implement interatomic interactions which are highly tunable in strength and range. For bosonic atoms trapped in an optical lattice we show that, for any finite range of the cavity-mediated interaction, quantum self-bound droplets dominate the ground state phase diagram. Their size and in turn density is not externally fixed but rather emerges from the competition between local repulsion and finite-range cavity-mediated attraction. We identify two different regimes of the phase diagram. In the strongly glued regime, the interaction range exceeds the droplet size and the physics resembles the one of the standard Bose-Hubbard model in a (self-consistent) external potential, where in the phase diagram two incompressible droplet phases with different filling are separated by one with a superfluid core. In the opposite weakly glued regime, we find instead direct first order transitions between the two incompressible phases, as well as pronounced metastability. The cavity field leaking out of the mirrors can be measured to distinguish between the various types of droplets.

Graph Neural News Recommendation with User Existing and Potential Interest Modeling

Personalized news recommendations can alleviate the information overload problem. To enable personalized recommendation, one critical step is to learn a comprehensive user representation to model her/his interests. Many existing works learn user representations from the historical clicked news articles, which reflect their existing interests. However, these approaches ignore users’ potential interests and pay less attention to news that may interest the users in the future. To address this problem, we propose a novel G raph neural news R ecommendation model with user E xisting and P otential interest modeling, named GREP. Different from existing works, GREP introduces three modules to jointly model users’ existing and potential interests: (1) Existing Interest Encoding module mines user historical clicked news and applies the multi-head self-attention mechanism to capture the relatedness among the news; (2) Potential Interest Encoding module leverages the graph neural network to explore the user potential interests on the knowledge graph; and (3) Bi-directional Interaction module dynamically builds a news-entity bipartite graph to further enrich two interest representations. Finally, GREP combines the existing and potential interest representations to represent the user and leverages a prediction layer to estimate the clicking probability of the candidate news. Experiments on two real-world large-scale datasets demonstrate the state-of-the-art performance of GREP.

Novel hybrid models to enhance the efficiency of groundwater potentiality model

The present study aimed to create novel hybrid models to produce groundwater potentiality models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning (EML) algorithms, such as random forest (RF), random subspace, dagging, bagging, naïve Bayes tree (NBT), and stacking, coupled with fuzzy logic (FL) models and a ROC-based weighting approach have been used for creating hybrid models integrated GWP. The GWP was then verified using both parametric and nonparametric receiver operating characteristic curves (ROC), such as the empirical ROC (eROC) and the binormal ROC curve (bROC). We conducted an RF-based sensitivity analysis to compute the relevancy of the conditioning variables for GWP modeling. The very high and high groundwater potential regions were predicted as 831–1200 km2 and 521–680 km2 areas based on six EML models. Based on the area under the curve of the ROC, the NBT (eROC: 0.892; bROC: 0.928) model outperforms rest of the models. Six GPMs were considered variables for the next step and turned into crisp fuzzy layers using the fuzzy membership function, and the ROC-based weighting approach. Subsequently four fuzzy logic operators were used to assimilate the crisp fuzzy layers, including AND, OR, GAMMA0.8, and GAMMA 0.9, as well as GAMMA0.9. Thus, we created four hybrid models using FL model. The results of the eROC and bROC curve showed that GAMMA 0.9 operator outperformed other fuzzy operators-based GPMs in terms of accuracy. According to the validation outcomes, four hybrid models outperformed six EML models in terms of performance. The present study will aid in enhancing the efficiency of GPMs in preparing viable planning for groundwater management.

Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density

Improvements in wind energy technology, reduced costs, and ambitious clean energy goals have led to projections of high wind contribution in coming years. Developing methodologies to design wind plants with a variety of siting constraints and turbine sizes helps enable high wind penetration, and gain a better understanding of how wind plants are sensitive to setback constraints and turbine design. In this paper, we present a two-step optimization method to simultaneously determine the optimal number of turbines and their locations in a wind plant domain divided into many small, discrete parcels. We present the optimized performance metrics of a wind plant optimized with different turbine sizes and ratings, and with different siting restrictions within the wind plant. Our results indicate that taller and larger turbines are more sensitive to increasing siting constraints. We also compare the optimal wind plant layouts and performance for wind plants optimized for minimum COE and maximum profit. Wind plants optimized for profit had 130%–190% of the capacity of plants optimized for COE, which demonstrates that the optimal results are greatly affected by the objective function, which should be carefully considered. Finally, in this paper we demonstrate the effect of increasing siting constraints on wind plant capacity density, and how the results change when different land areas are used to calculate capacity density. When using the entire wind plant boundary area to determine capacity density, increasing siting constraints decreases the capacity density. However, when we only use the available area (the area left after removing the siting constraints) to calculate the capacity density, increasing the siting constraints increases capacity density. This is a critical insight because of how capacity density is typically defined and used in research, and has important implications for assessment of technical potential and capacity expansion modeling, as well as future wind deployment potential.

Structure of Aqueous Scandium(III) Nitrate Solution by Large-Angle X-ray Scattering Combined with Empirical Potential Refinement Modeling, X-ray Absorption Fine Structure, and Discrete Variational Xα Calculations

Abstract Large-Angle X-ray scattering (LAXS) and Sc K X-ray absorption near-edge structure (XANES) measurements are made at room temperature on a 1 M (= mol dm−3) Sc(NO3)3 aqueous solution. The X-ray interference function is subjected to an empirical potential structure refinement (EPSR) modeling to extract the site-site pair correlation functions, the coordination number distribution, and the spatial density functions (three-dimensional structure). The LAXS analysis combined with EPSR reveals that Sc3+ is surrounded by six or seven water molecules and one oxygen atom of NO3− with an Sc3+-Ow (H2O) and Sc3+-ON (NO3−) distance of 2.14 Å. NO3− has a weak solvation shell with a broad distribution of coordination numbers with the highest population of nine at an N-Ow distance of 3.66 Å. Solvent water forms the tetrahedral network structure. The XANES spectrum is compared with simulated spectra on various coordination geometries of an ScO7 moiety with a discrete variational Xα (DV-Xα) molecular orbital (MO) method. A distorted monocapped trigonal prism structure of Sc3+ best reproduced the experimental pre-peak due to the Sc 1s → 3d transition in the XANES spectrum.