Systematic Grid Research Articles

Land Suitability Assessment and Crop Water Requirements for Twenty Selected Crops in an Arid Land Environment

Expanding projects to reclaim marginal land is the most effective way to reduce land use pressures in densely populated areas, such as Egypt’s Nile Valley and Delta; however, this requires careful, sustainable land use planning. This study assessed the agricultural potential of the El-Dabaa area in the northern region of the Western Desert, Egypt. It focused on assessing land capability, evaluating crop suitability, mapping soil variability, and calculating crop water requirements for twenty different crops. In this work, we evaluated land capability using the modified Storie index model and assessed soil suitability using the land use suitability evaluation tool (LUSET). We also calculated crop water requirements (CWRs) utilizing the FAO-CROPWAT 8.0 model. Additionally, we employed ArcGIS 10.8 to create spatial variability maps of soil properties, land capability classes, and suitability classes. Using a systematic sampling grid, 100 soil profiles were excavated to represent the spatial variability of the soil in the study area, and the physicochemical parameters of the soil samples were analyzed. The results indicated that the study area is primarily characterized by flat to gently sloping surfaces with deep soils. Furthermore, there are no restrictions on soil salinity or alkalinity, no sodicity hazards, and low CaCO3 levels. On the other hand, the soils in the study area are coarse textured and have low levels of CEC and organic matter (OM), which are the major soil limiting factors. As a result, the land with fair capability (Grade 3) accounted for the vast majority of the study area (87.3%), covering 30599.4 ha. Land with poor capability (Grade 4) accounted for 6.5% of the total area, while non-agricultural land (Grade 5) accounted for less than 1%. These findings revealed that S2 and S3 are the dominant soil suitability classes for all the studied crops, indicating moderate and marginal soil suitabilities. Furthermore, there were only a few soil proportions classified as unsuitable (N class) for fruit crops, maize, and groundnuts. Among the crops studied, barley, wheat, sorghum, alfalfa, olives, citrus, potatoes, onions, tomatoes, sunflowers, safflowers, and soybeans are the most suitable for cultivation in the study area. The reference evapotranspiration (ETo) varied between 2.6 and 5.9 mm day−1, with higher rates observed in the summer months and lower rates in the winter months. Therefore, the increase in summer ETo rates and the decrease in winter ones result in higher CWRs during the summer season and lower ones during the winter season. The CWRs for the crops we studied ranged from 183.9 to 1644.8 mm season−1. These research findings suggest that the study area is suitable for cultivating a variety of crops. Crop production in the study area can be improved by adding organic matter to the soil, choosing drought-resistant crop varieties, employing effective irrigation systems, and implementing proper management practices. This study also provides valuable information for land managers to identify physical constraints and management needs for sustainable crop production. Furthermore, it offers valuable insights to aid investors, farmers, and governments in making informed decisions for agricultural development in the study region and similar arid and semiarid regions worldwide.

Pemetaan Sebaran Salinitas di Desa Tanjung Pakis Kabupaten Karawang Menggunakan QGIS

The coastal area has a high likelihood of seawater intrusion, leading to the formation of brackish water. Brackish water is unsafe for consumption due to its high salt content, which exceeds 0.5 ppt. The purpose of this study is to identify the distribution of salinity by testing the well water of the residents in Tanjung Pakis Village, Karawang Regency. The sample determination method used is Systematic Grid Sampling. The grid used measures 1,000 x 250 m. In this study, 3 grids were used, each at different distances from the nearest coastline. Three samples were taken from each grid, resulting in a total of 9 samples. The parameter tested is salinity, which represents the salt content in the water. The method used to measure the salinity parameter is direct reading using a refractometer. The results of the study show that the highest salinity parameter value reached 120 ppt in Grid I, while the lowest salinity parameter value was 30 ppt in Grid II. The mapping results indicate high salinity values in Grid I, represented by a darker color. In Grid II, the salinity value decreases, indicated by a lighter color. An increase in salinity occurs in Grid III, shown by a darker color compared to Grid II.

Posterior comparison of model dynamics in several hybrid turbulence model forms

Hybrid turbulence models that can accurately reproduce unsteady three-dimensional flow physics across the entire range of grid scales and turbulence dynamics from Reynolds-averaged Navier–Stokes (RANS), through large-eddy simulation (LES), down to direct numerical simulations (DNS) are of increasing interest to the turbulence modeling community. However, despite decades of research and development, the basic tasks of eliminating poor-performing hybrid RANS-LES models and accelerating adoption of superior models through well-designed validation and verification have yet to occur. As a step in this direction, in this work we evaluate thirteen different hybrid RANS-LES models via systematic grid refinement of decaying homogeneous isotropic turbulence. We further derive a novel mathematical framework for assessing the energy partitioning dynamics of each Hybrid RANS-LES model, wherein model-to-model variations in energy partitioning can be interpreted as different feedback mechanisms operating on a low-dimensional nonlinear dynamical system. We found that model forms similar to the flow simulation methodology—also often termed very-large eddy simulation—are dynamically inconsistent with DNS at all resolutions. Additionally, we found a strong dynamical similarity in the feedback mechanisms of all models related to detached eddy simulation and partially averaged Navier–Stokes that is inherent to their general model forms.

The advantage of afforestation using native tree species to enhance soil quality in degraded forest ecosystems

Different vegetation restoration methods have improved soil quality to varying degrees. This study, focused on the forest–grassland–desert transition zone in the Hebei–Inner Mongolia border region, and employed a systematic grid sampling method to establish fixed monitoring plots in the Saihanba Mechanized Forest Farm and the Ulan Buh Grassland. The differences in soil quality evolution across various vegetation restoration methods under the same climatic and soil historical conditions were analyzed, elucidating the roles of these vegetation restoration methods in degraded forest ecosystems, with the aim of providing a reference for ecological restoration under similar land conditions. This study used a grid method to establish sample points in the forest–grassland–desert transitional zone and assessed five methods of vegetation restoration sites: artificial forest composed of native species of Larix principis-rupprechtii (FL), artificial forest composed of exotic Pinus sylvestris var. mongolica (FP), natural secondary broad-leaved forest (FN), open grassland (GO), and enclosed grassland (GC). The differences in soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkaline hydrolysis nitrogen (AN), rapidly available phosphorus (AP) and rapidly available potassium (AK) among the different vegetation restoration sites were compared via variance analysis, and the soil quality index (SQI) was calculated to assess the soil quality at the sample points. The SOC, TN, and AN contents of forest soil were significantly greater than those of grassland, and the TN, TP, AN, AK, and SOC contents of FL, FN, and GC were significantly greater than those of FP and GO. Among them, the TN, TP, and SOC contents were the highest in the FL, reaching 2.74, 0.39, and 47.27 g kg−1, respectively. In terms of ecological stoichiometric characteristics, the average N:P ratio in the study area was 6.68, indicating a serious lack of N in the study area. Among the different types of restoration sites, the effect was stronger in the FP than in the FL, and the TN and AN contents were only 1.48 g kg−1 and 116.69 mg kg−1, respectively. The SQI in the FL was not significantly different from that in the FN or GC, but it was significantly greater than that in the FP and GO. These findings indicate that native tree species restoration in degraded forest ecosystems significantly improved soil quality, while the introduction of exotic tree species for afforestation had a minimal effect on improving soil quality.

Verification and validation of a coupled CFD–FEM approach with overset structured grid through free decay and regular wave tests

This paper presents a comprehensive verification and validation study applied to the hydrodynamic analysis of a semi-submersible Floating Offshore Wind Turbine (FOWT) platform. The numerical simulation is conducted using a coupled Computational Fluid Dynamics-Finite Element Method (CFD–FEM) approach. The CFD program incorporates an overset grid interpolation method, facilitating a more systematic grid refinement in the estimation of discretization uncertainties. A mooring analysis program, based on the FEM scheme, is integrated with the CFD program to simulate the dynamic responses of mooring system. The necessity of the nonlinear mooring model is demonstrated by comparing solutions with a comparative test using a linear mooring model. The verification study, based on a least-squares-based extrapolation method, quantifies the spatial and temporal discretization errors of the numerical model. The numerical solutions based on the baseline model are further validated against the model test measurements. A pitch free decay model test and a regular wave model test conducted within the OC5 project are utilized for the verification and validation study. Results from both tests demonstrate the reliability and accuracy of the coupled method.

Impacts of anthropogenic habitat modification on mammalian diversity in the Mau Forest Complex, Kenya

Habitat destruction is a major driver of biodiversity loss, especially in the tropics. We investigated changes in the diversity and occupancy of forest-dwelling mammals in response to habitat loss, alteration, and degradation at the Mau Forest Complex (MFC), southwestern Kenya. Using a systematic camera trapping grid, we estimated patterns of mammalian diversity, occupancy of small carnivores, and detection rates of functional mammalian groups across three habitat types (primary forests, secondary forests, and cultivated habitats). Between March 2019 and October 2021, 246 camera traps were deployed across 181 locations representing 5509 sampling days, resulting in 47,345 images of 52 mammalian species in nine mammalian orders. Alpha diversity differed among treatments, with primary forests having the highest diversity, and cultivated habitats the lowest, though results were not statistically significant. Beta diversity differed significantly among treatments, indicating differences in community composition as well as species richness. Overall occupancy for small carnivores was highest in primary forests and lowest in cultivated habitats, with some species specializing in primary forests while others were able to occupy more degraded habitats. Among functional groups, detection rates only differed significantly for large rodents, primates, and ungulates. Our results indicate that although functional groups and species may respond differently to habitat loss and alteration at the MFC, overall species diversity of specialist mammals was highest in the less-disturbed primary forests. Specialist, forest-dependent species are likely most affected by human activities, making them especially vulnerable to habitat loss at the MFC.

Post-Earthquake Liquefaction Vulnerability Mapping by Swedish Weight Sounding and Standard Penetration Test

On September 28, 2018, a 7.5-magnitude earthquake struck Palu City, Sigi Regency, and Donggala Regency in Central Sulawesi. It triggered liquefaction in different locations, including Balaroa, Petobo, Jono Oge, and Sibalaya; Typically, a significant number of studies conducted in the Balaroa region relied on a small amount of field test data to cover a rather large area. This research aims to map the liquefaction vulnerability by analyzing the data from both the Swedish Weight Sounding (SWS) and the Standard Penetration Test (SPT) in the Balaroa area. The SWS data was acquired through mapping using a systematic grid sampling method at ten different locations. The liquefaction potential was analyzed based on the N values by converting the SWS data (Nsw and Wsw) to Nvalues using the Inada equation (1960). Afterward, the analysis findings were verified by comparing them with the SPT data obtained from the same area. Based on the SWS and SPT data analysis results, all locations, including the adjacent areas, exhibited very high liquefaction vulnerability. In contrast, the SPT data indicated that the areas further from the spots exhibited low and very low liquefaction. The findings indicated that the occurrence of post-earthquake liquefaction in Balaroa and other regions within Palu City is prone to recurrence following earthquakes of specific magnitudes. Doi: 10.28991/CEJ-2024-010-07-09 Full Text: PDF

Optimizing Gene Expression Programming to Predict Shear Capacity in Corrugated Web Steel Beams

Corrugated web steel systems, such as corrugated web girders (CWG) and beams (CWSB), have the potential to influence the modern construction industry due to their unique properties, including enhanced shear strength and reduced necessity for transverse stiffeners. Nevertheless, the lack of a rapid and accurate design approach still limits its wide applications. Recently, gene expression programming (GEP) has been employed to predict the shear capacity of cold-formed steel channels, demonstrating superior predictive accuracy and compliance with established standards. This study applies GEP to predict the shear capacity of sinusoidal CWSBs and optimizes its predictive performance by employing a systematic grid search to explore combinations of chromosomes, head sizes, gene counts, and linking functions. The process involved testing 19 different parameter combinations and more than 60 developed models. The findings include the sensitivity of the model's performance to gene count and the critical role of the linking function. The optimal model in the study, GEP13, achieved R² of 0.95, an RMSE of 100.5, and an MAE of 86.6 in the testing dataset with 150 chromosomes, a head size of 12, and four genes using a multiplication linking function. Doi: 10.28991/CEJ-2024-010-05-02 Full Text: PDF

Unraveling biogeographical patterns and environmental drivers of soil fungal diversity at the French national scale

Abstract. The fungal kingdom is among the most diversified kingdoms on Earth, with estimations of up to 12 million species. However, it remains poorly understood, with only 150 000 fungal species currently described. Given the major ecological role of fungi in ecosystem functioning, these numbers stress the importance of investigating fungal diversity description across different ecosystem types. Here, we explored the spatial distribution of the soil fungal diversity on a broad geographical scale, using the French Soil Quality Monitoring Network that covers the whole French territory (2171 soils sampled along a systematic grid). Fungal alpha diversity was assessed directly from soil DNA using a meta-barcoding approach by targeting the 18S rDNA gene. The total accumulated fungal diversity across France included 136 219 operational taxonomic units (OTUs), i.e., about 1 % of worldwide soil fungal diversity (based on a maximum diversity estimate of 12 million) for a territory representing only 0.3 % of the terrestrial surface on Earth. Based on this dataset, the first extensive map of fungal alpha diversity was drawn and showed a heterogeneous and spatially structured distribution in large biogeographical patterns of 231 km radius for richness (Hill diversity of order 0) and smaller patterns of 36 km radius for dominant fungi (Hill diversity of order 2). As related to other environmental parameters, the spatial distribution of fungal diversity (Hill numbers based on different orders of diversity) was mainly influenced by local filters such as soil characteristics and land management and also by global filters such as climate conditions with various relative influences. Interestingly, cropped soils exhibited the highest pool of fungal diversity relative to forest and vineyard soils. To complement this, soil fungal OTU network interactions were calculated for the different land uses across France. They varied hugely and showed a loss of 75 % of the complexity in crop systems and grasslands compared to forests and up to 83 % in vineyard systems. Overall, our study revealed that a nationwide survey with a high spatial-resolution approach is relevant for deeply investigating the spatial distribution and determinism of soil fungal diversity. Our findings provide novel insights for a better understanding of soil fungal ecology across the 18S rDNA gene and upgrade biodiversity conservation policies by supplying representative repositories dedicated to soil fungi.

Accuracy and sensitivity of soil erosion estimation using 137Cs technology: A statistical perspective

Random spatial variation of the 137Cs inventory is the principal contributor to uncertainty in soil erosion estimation using 137Cs technology. A statistically sound sampling design is imperative for obtaining reliable soil erosion estimations. The objectives of this study are to: 1) characterize the effect of sample size on the estimates of mean inventories; 2) evaluate the sensitivity of the estimated soil redistribution to sample size using a new 137Cs model; 3) compare erosion detection limits among five conversion models; and 4) assess three spatial interpolation methods. Two small watersheds were sampled in an irregular grid design, and 30 samples were taken on a reference site. A moving window scheme was used to compute the 7-point and 11-point means of 137Cs inventories. The Welch’s modified t-test was used to test the mean estimates and to compute the detection limits for the replicated samples. Five models were selected to convert 137Cs inventories to soil redistribution rates. Since most single samples without replication yielded insignificant test results, the 137Cs technique cannot be used to estimate soil erosion at a single point due to random spatial variation. With 30 reference samples, 7 replicate samples on the sampling site provided reliable estimations of 137Cs inventories and soil redistribution. The spatial patterns of the estimated 137Cs inventories and soil redistribution became more regular and systematic as sample size increased, which agreed increasingly well with topography and surface hydrology. Replicate samples can be taken from each landform element or slope position where the erosion rate is expected to be uniform. Alternatively, a systematic grid sampling scheme can be used, and the nearest neighbors can be treated as replicates to calculate moving averages. The Type I and II errors with replicated samples showed that all sampling positions should be included in calculating watershed or area mean erosion rates even though the Type I errors indicated insignificant redistribution rates for some samples. Due to the lack of spatial autocorrelation, spline and inverse distance weighting methods performed better than kriging for interpolation. Detection limits and magnitudes of soil redistribution varied substantially with conversion models, depending on their sensitivities to changes in 137Cs inventories.

A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series

Accurate and fine-scale forest data are essential to improve natural resource management, particularly in the face of climate change. Here, we present SCANFI, the Spatialized CAnadian National Forest Inventory, which provides coherent, 30 m resolution 2020 wall-to-wall maps of forest attributes (land cover type, canopy height, crown closure, aboveground tree biomass, and main species composition). These maps were developed using the NFI photo-plot dataset, a systematic regular sample grid of photo-interpreted high-resolution imagery covering all of Canada's non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a multiresponse k-nearest neighbours and random forest imputation method. This tile-level approach revealed the importance of radiometric variables in predicting vegetation attributes, namely winter radiometry, as the large-scale climate gradients were controlled at the tile-level. SCANFI was validated with rigorous cross-validation analyses, which revealed robust model performance for structural attributes (biomass R2 = 0.76; crown closure R2 = 0.82; height R2 = 0.78) and tree species cover (e.g., Douglas fir R2 = 0.60). SCANFI attributes were also validated with several independent external products, ranging from ground plot-based tree species cover (e.g., black spruce R2 = 0.53) to satellite LiDAR height data products (e.g., crown closure R2 = 0.71). SCANFI total aboveground biomass trends also followed those published by other studies. The methodology presented herein can be used to map time series of these attributes, identify the original training points used to make any given prediction, as well as map additional variables associated with the NFI photo-plots that are challenging to map using traditional remote sensing approaches.

Numerical Study of a Model and Full-Scale Container Ship Sailing in Regular Head Waves

In the present study, the added resistance, heave, and pitch of the KRISO Container Ship (KCS) in waves, at both model scale and full scale, are predicted numerically in regular head waves, for four wavelengths and three wave heights. The ISIS-CFD viscous flow solver, implemented in the Fidelity Fine Marine software provided by CADENCE, was employed for the numerical simulations. The spatial discretization was based on the finite volume method using an unstructured grid. The unsteady Reynolds-averaged Navier–Stokes (RANS) equations were solved numerically, with the turbulence modeled by shear stress transport (k-ω) (SST). The free-surface capturing was based on the volume-of-fluid method. The computed solutions were validated through comparisons with towing test data available in the public domain. To predict the uncertainties in the numerical solution, a systematic grid convergence study based on the Richardson extrapolation method was performed for a single wave case on three different grid resolutions. Specific attention was given to the free-surface and wake flow in the propeller plane. The purpose was to compare the numerical results from the model- and full-scale tests to examine the scale’s effect on the ship’s performance in regular head waves. The comparison between the model scale and full scale showed obvious differences, less accentuated for the free-surface topology and clearly observed in terms of boundary layer formation in the propeller’s vicinity.

Prediction of major adverse cardiac events in the emergency department using an artificial neural network with a systematic grid search

BackgroundThe aim of our research was to design and evaluate an Artificial Neural Network (ANN) model using a systemic grid search for the early prediction of major adverse cardiac events (MACE) among patients presenting to the triage of an emergency department.MethodsThis is a single-center, cross-sectional study using electronic health records from January 2017 to December 2020. The research population consists of adults coming to our emergency department triage at Aga Khan University Hospital. The MACE during hospitalization was the main outcome. To enhance the architecture of an ANN using triage data, we used a systematic grid search strategy. Four hidden ANN layers were used, followed by an output layer. Following each hidden layer was back normalization and a dropout layer. MACE was predicted using three binary classifiers: ANN, Random Forests (RF), and logistic regression (LR). The overall accuracy, sensitivity, specificity, precision, and recall of these models were examined. Each model was evaluated using the receiver operating characteristic curve (ROC) and an F1-score with a 95% confidence interval.ResultsA total of 97,333 emergency department visits were recorded during the study period, with 33% of patients having cardiovascular symptoms. The mean age was 54.08 (19.18) years old. The MACE was observed in 23,052 (23.7%) of the patients, in-hospital (up to 30 days) mortality in 10,888 (11.2%) patients, and cardiac arrest in 5483 (5.6%) patients. The data used for training and validation were 77,866 and 19,467 in an 80:20 ratio, respectively. The AUC score for MACE with ANN was 0.97, which was greater than RF (0.96) and LR (0.96). Similarly, the precision-recall curve for MACE utilizing ANN was greater (0.94 vs. 0.93 for RF and 0.93 for LR). The sensitivity for MACE prediction using ANN, RF, and LR classifiers was 99.3%, 99.4%, and 99.2%, respectively, with the specificities being 94.5%, 94.2%, and 94.2%, respectively.ConclusionWhen triage data is used to predict MACE, death, and cardiac arrest, ANN with systemic grid search gives precise and valid outcomes and will benefit in predicting MACE in emergency rooms with limited resources that have to deal with a substantial number of patients.

Assessment of the DDES-γ Model for the Simulation of a Highly Loaded Turbine Cascade

Abstract The flow over the linear low-pressure turbine cascade MTU-T161 at Re = 90,000 is analyzed using delayed detached eddy simulations (DDES). At this operating point, the low Reynolds number and the high loading of the blade result in a separation bubble and a separation-induced transition of the flow over the suction side. The utilized DDES method is based on a vorticity-based formulation to calculate the subgrid length scales, and it incorporates the one-equation γ-transition model. The computational model of the MTU-T161 cascade consists of one blade passage, including the diverging viscous sidewalls. To reproduce realistic operating conditions and to mimic the experiments, synthetic turbulence is prescribed at the inlet of the computational domain. Several studies are performed to assess the accuracy and performance of the DDES one-equation γ-transition model against experimental data and a benchmark large eddy simulations (LES). The primary focus is on the prediction of the separation and the separation-induced transition mechanism. First of all, a systematic grid convergence study is conducted and grid criteria are derived in order to ensure a satisfactory agreement of the flow metrics, such as isentropic Mach number, friction coefficient distribution, and total pressure wake losses at mid-span with experimental data. Furthermore, a detailed analysis of the DDES model parameters, such as shielding function and subgrid length scale, is presented and the effect of these parameters on the prediction accuracy of the separation bubble region is analyzed. The analysis of the suction side boundary layer indicates that the turbulent kinetic energy should be resolved and modeled properly in order to represent the separation bubble correctly. In particular, the correct prediction of the separated shear layer above the separation bubble is of utmost importance. The results of the simulations reveal higher demands on grid resolution for such transitional flows than typically have been reported in the literature for turbulent boundary layers. This higher demand on grid resolution results in more expensive simulations than Reynolds-averaged Navier–Stokes (RANS). Nevertheless, DDES requires less computing time than wall-resolved LES. Additionally, the results of the transitional DDES model are compared to DDES without a transition model, an RANS eddy viscosity model, and a reference LES. The results show that the DDES approach needs to be coupled with a transition model, such as the one-equation γ-transition model, in order to capture the flow topology over a highly loaded turbine blade correctly. The benefit of the DDES one-equation γ-transition model becomes particularly evident when predicting the separated shear layer, the transition process, and the subsequent reattachment. The RANS eddy viscosity turbulence and transition models applied within our study are not able to predict the aforementioned mechanisms accurately. For highly loaded turbine blades in particular, the accurate prediction of flow separation and potential reattachment is crucial for the aerodynamic design of turbines, since large parts of the total pressure loss are generated in the separated region. For this reason, the DDES one-equation γ-transition model can be a good compromise in terms of predictive accuracy and computational costs.

SPATIAL DISTRIBUTION OF MALARIA AND LAND COVER PATTERNS IN OGONI LAND, RIVERS STATE, NIGERIA

The distribution of malaria is characterised by microgeographic variations determined by a range of factors, including the local environment. A study on the spatial distribution of malaria about land cover patterns was carried out by sampling Primary Health Centres in Ogoni Land. Nine Primary Healthcare Centres (PHCs) were selected across four local government areas (LGA) using Systematic Grid Point Sampling. Human blood samples were obtained from 318 consented individuals, and questionnaires were administered to obtain demographic data. Plasmodium species were identified through microscopy using thick and thin blood films. A geodatabase was created and imported into ArcGIS 10.7 to produce a thematic map of the study area. A cloud-free Landsat-8 Operational Land Imager (OLI) was employed for land cover analysis. Both supervised and unsupervised classifications of land cover were performed to generate the land cover classes. Pearson correlation was carried out to determine the significance between malaria distribution and land cover. Of the 318 individuals, 169 were infected with an overall prevalence of 53.1%.Only P. falciparum was identified and malaria distribution showed spatial variations. Across the PHCs sampled, the highest point prevalence was recorded in Model Primary Health Centre Koroma in Tai LGA whereas the lowest was recorded in MPHC Okwale in Khana LGA. Cumulatively, Kwawa PHC recorded the highest malaria prevalence whereas MPHC Bunu in Tai recorded the lowest prevalence. The highest prevalence was recorded in Khana LGA while the lowest was recorded in Eleme LGA. Land cover analysis revealed that Ogoni Land has a total land cover mass of 982.97km.2 Sparse vegetation dominated the study area (471.06km2) while dense vegetation covers a total mass of 213.1km2. Bivariate analysis showed a significant correlation between malaria prevalence and dense vegetation (p<0.05, 0.952). Dense vegetation played a significant role in malaria transmission in Ogoni Land. The study concludes that the presence of dense vegetation is associated with high malaria prevalence in the study area.

Data-driven approaches for identifying hyperparameters in multi-step retrosynthesis.

The multi-step retrosynthesis problem can be solved by a search algorithm, such as Monte Carlo tree search (MCTS). The performance of multistep retrosynthesis, as measured by a trade-off in search time and route solvability, therefore depends on the hyperparameters of the search algorithm. In this paper, we demonstrated the effect of three MCTS hyperparameters (number of iterations, tree depth, and tree width) on metrics such as Linear integrated speed-accuracy score (LISAS) and Inverse efficiency score which consider both route solvability and search time. This exploration was conducted by employing three data-driven approaches, namely a systematic grid search, Bayesian optimization over an ensemble of molecules to obtain static MCTS hyperparameters, and a machine learning approach to dynamically predict optimal MCTS hyperparameters given an input target molecule. With the obtained results on the internal dataset, we demonstrated that it is possible to identify a hyperparameter set which outperforms the current AiZynthFinder default setting. It appeared optimal across a variety of target input molecules, both on proprietary and public datasets. The settings identified with the in-house dataset reached a solvability of 93 % and median search time of 151 s for the in-house dataset, and a 74 % solvability and 114 s for the ChEMBL dataset. These numbers can be compared to the current default settings which solved 85 % and 73 % during a median time of 110s and 84 s, for in-house and ChEMBL, respectively.

The renewable energy: Tidal energy

This paper discusses the mechanism and principles of tidal energy. As a renewable energy, tidal energy generates electrical power using the potential difference of tides, in which the turbine functions crucially. Through systematic grid integration, energy generated can be efficiently collected and transmitted for practical use. Implementing tidal power programs will bring socioeconomic benefits, whereas environmental degradation and ecosystem imbalance can also be negatively affected.

Reduced Order Machine Learning Models for Accurate Prediction of CO2 Capture in Physical Solvents.

CO2 sorption in physical solvents is one of the promising approaches for carbon capture from highly concentrated CO2 streams at high pressures. Identifying an efficient solvent and evaluating its solubility data at different operating conditions are highly essential for effective capture, which generally involves expensive and time-consuming experimental procedures. This work presents a machine learning based ultrafast alternative for accurate prediction of CO2 solubility in physical solvents using their physical, thermodynamic, and structural properties data. First, a database is established with which several linear, nonlinear, and ensemble models were trained through a systematic cross-validation and grid search method and found that kernel ridge regression (KRR) is the optimum model. Second, the descriptors are ranked based on their complete decomposition contributions derived using principal component analysis. Further, optimum key descriptors (KDs) are evaluated through an iterative sequential addition method with the objective of maximizing the prediction accuracy of the reduced order KRR (r-KRR) model. Finally, the study resulted in the r-KRR model with nine KDs exhibiting the highest prediction accuracy with a minimum root-mean-square error (0.0023), mean absolute error (0.0016), and maximum R2 (0.999). Also, the validity of the database created and ML models developed is ensured through detailed statistical analysis.

Long Term of Soil Carbon Stock in No-Till System Affected by a Rolling Landscape in Southern Brazil

In the 1960s, a conservationist agricultural practice known as a “no-tillage system” was adopted. Several benefits such as soil erosion reduction and soil carbon sequestration, among others, could be ascribed to no-till systems. Therefore, it is important to evaluate the long-term sustainability of this agricultural system in different environments. This study has the objective to evaluate the soil organic carbon dynamic in a no-till system (40-year) and on a rolling landscape in Southern Brazil. A systematic grid with four transversal–longitudinal transects was used for soil sampling. Soil samples from 0–20, 20–40, and 40–60 cm depths were collected (16 trenches × 3 depths × 1 sample per soil layer = 48), and a forest nearby was used as control (4 trenches × 3 depths × 1 sample = 12). The soil at the forest site showed 20% more carbon stock than no-till at the 0–20 cm soil depth. However, the entire no-till soil profile (0–60 cm) showed similar soil carbon as forest soil. The soil carbon stock (0–20 cm) in no-till was depleted at a rate of 0.06 kg C m−2 year−1, summing up to a carbon loss of 2.43 kg C m−2. In addition, the non-uniform hillslope affected the soil carbon redistribution through the landscape, since the convex hillslope was more depleted in carbon by 37% (15.87 kg C m−2) when compared to the concave sector (25.27 kg C m−2). On average, the soil carbon loss in the subtropical agroecosystem was much lower than those reported in literature, as well as our initial expectations. In addition, the no-till system was capable of preserving soil carbon in the deepest soil layers. However, presently, the no-till system is losing more carbon in the topsoil at a rate greater than the soil carbon input.

Period-doubling bifurcations and islets of stability in two-degree-of-freedom Hamiltonian systems.

In this paper, we show that the destruction of the main Kolmogorov-Arnold-Moser (KAM) islands in two-degree-of-freedom Hamiltonian systems occurs through a cascade of period-doubling bifurcations. We calculate the corresponding Feigenbaum constant and the accumulation point of the period-doubling sequence. By means of a systematic grid search on exit basin diagrams, we find the existence of numerous very small KAM islands ("islets") for values below and above the aforementioned accumulation point. We study the bifurcations involving the formation of islets and we classify them in three different types. Finally, we show that the same types of islets appear in generic two-degree-of-freedom Hamiltonian systems and in area-preserving maps.