Boosted Regression Tree Models Research Articles

A tree-based automated machine learning approach of the obstructed view factor of thermal radiation in nuclear pebble beds

For the nuclear pebble bed of the high temperature gas-cooled reactor (HTGR), a tree-based automated machine learning approach is developed to discuss the complicated thermal radiation behaviors. The AutoML model for calculating the obstructed view factor between any two particles in the pebble bed includes the gradient boosting regression tree model with the fine-tuned hyperparameters and the analytical base model. Using the discrete packing of the HTR-PM nuclear reactor and the leaped Halton sequence, a large dataset of the view factor under different conditions is generated to train the AutoML model. On the same platform, numerical results show that AutoML model is approximately 5 × 105 times faster than the traditional approach. The Pareto front of the AutoML model indicates that the mean squared error decreases with the model complexity until it reaches the optimal solution. Then, the trained AutoML model is applied to the engineering pebble bed. It is shown that the average radiation exchange factor near the wall is lower than that in the bulk region. In addition, the packing height is also an important parameter for evaluating the radiative behaviors. In particular, when the height of the nuclear pebble bed is less than 20 times particle diameter, it is necessary to consider the contribution of the packing height to the radiation effective thermal conductivity.

Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning

Abstract Understanding the unique properties of perovskite materials is crucial in advancing solar energy technologies. Factors like heat of formation and bandgap significantly influence the light absorption capability and stability of perovskite solar cells. However, it is time-consuming and labor-intensive to obtain the properties of perovskites using traditional experimental or high-throughput computational methods. As a prospective method, machine learning can find regularities in the given training data and give accurate prediction results. In this article, we use deep learning models based on attention mechanisms and elemental features to predict the heat of formation and bandgap of perovskite materials. Random Forest and Gradient Boosted Regression Tree models have also been used for interpretable predictions of properties. The compositionally restricted attention-based network was improved by introducing a densely connected network and optimizing the network structure to increase data processing capabilities. The experiment results show that the mean absolute errors of the heat of formation and bandgap on the test sets are decreased by 5.77% and 3.37% respectively. The optimized model also shows better performance when used for classification tasks. In addition, we use the Gradient Boosting Regression Tree model and the Shapley Additive Explanations tool to conduct an interpretable analysis, explaining the impact of different features on the predictions of the properties.

The Use of Boosted Regression Trees to Predict the Occurrence and Quantity of Staphylococcus aureus in Recreational Marine Waterways

Microbial monitoring in marine recreational waterways often overlooks environmental variables associated with pathogen occurrence. This study employs a predictive boosted regression trees (BRT) model to predict Staphylococcus aureus abundance in the Tampa Bay estuary and identify related environmental variables associated with the microbial pathogen’s occurrence. We provide evidence that the BRT model’s adaptability and ability to capture complex interactions among predictors make it invaluable for research on microbial indicator research. Over 18 months, water samples from 7 recreational sites underwent microbial quantitation and S. aureus isolation, followed by genetic validation. BRT analysis of S. aureus occurrence and environmental variables revealed month, precipitation, salinity, site, temperature, and year as relevant predictors. In addition, the BRT model accurately predicted S. aureus occurrence, setting a precedent for pathogen–environment research. The approach described here is novel and informs proactive management strategies and community health initiatives in marine recreational waterways.

Temporal–Spatial Characteristics and Influencing Factors of Forest Fires in the Tropic of Cancer (Yunnan Section)

Forest fires often cause many casualties and property losses, and it is important to explore the time and space laws of forest fires and the influencing factors. The present study used the cities (prefectures) crossed by the Tropic of Cancer (Yunnan section) as the study area. Based on burned land data, a combination of natural factors, such as climate, topography, vegetation, and human activities, such as distance from settlements and population density, a binary logistic regression model, and a boosted regression tree model, were used to analyze the temporal–spatial characteristics and influencing factors of forest fires in 2000 to 2020. The following results were obtained: (1) During 2000–2020, the overall forest fire area in the study area showed a trend of fluctuating decline. The high incidence period of forest fires occurred in 2010. After 2010, the forest fire area in the study area was greatly reduced. (2) The forest fire area in the study area was greater in the east and less in the west. The forest fire areas in Wenshan Prefecture and Honghe Prefecture in the east were larger, accounting for 68%, and the forest fire areas in Pu’er City, Lincang City, and Yuxi City in the west were smaller, accounting for only 32%. (3) The contribution rate of the average precipitation and average temperature factors ranked in the top two in the two driving force analysis models, which indicated that precipitation and temperature had a significant effect on the incidence of forest fires in the study area.

Prediction of microbiological non-compliances using a Boosted Regression Trees model: application on the drinking water distribution system of a whole country

Abstract Universal access to safe drinking water is a fundamental human right and a requirement for a healthy life. Therefore, monitoring the quality of the supplied water is of utmost importance. To achieve this goal, there is a need to develop tools that support monitoring activities and improve efficiency. Forecasting models enable the prediction of pollution levels and facilitate the implementation of action plans. In this study, the Boosted Regression Trees method was employed to investigate the variables influencing water quality failures (WQFs) due to microbial contamination at the delivery point. The dataset used was obtained from localities across the country's distribution systems. The variables under consideration included physicochemical parameters such as pH, turbidity (NTU), and free chlorine (mg L−1), along with contextual parameters like the year, season, geographic location, and locality population. Indicators of microbial contamination assessed were the presence of total coliforms, Escherichia coli, and Pseudomonas aeruginosa. The most significant variables were geographic location, free chlorine content, and the population of the locality. The model achieved an AUC value of 0.77 and provided adequate predictions in the conducted tests. It enables the exploration of key factors affecting microbiological water quality, allowing for informed action to reduce associated risks.

How Do Ecological Restoration Projects Affect Trade-Offs and Synergies between Ecosystem Services?

Scientific ecosystem management requires the clarification of the synergic and trade-off relationship between ecosystem services, particularly in the environmentally delicate Loess Plateau region. Previous studies have indirectly deduced that ecological restoration projects affect ESRs by analyzing their impacts on ecosystem services, but there is no direct evidence from the existing research to show whether and to what extent different ecological restoration projects have an impact on trade-off synergies, which weakens the explanatory strength of ecological restoration projects as an important factor affecting ESRs. In this study, based on the spatial mapping of three pairs of relationships between three typical ESs in Fugu County, Shaanxi Province, and the relative contribution of each ecological restoration projects, as well as Ecosystem services and the relationship between them, were explored through the boosted regression tree modeling (BRT). This study proved that different ecological restoration projects have different impacts on ESRs. The results indicated that the three pairs of ESRs obtained among the three ecosystem services in Fugu County could be categorized into two types. The relationship between carbon storage and soil conservation and the relationship between carbon storage and water conservation CS–WC were spatially predominantly trade-offs, and their spatial distributions were highly similar. Various ecological restoration projects have varying effects on ESRs. The connection between ecological restoration projects and ESRs involves a nonlinear transformation, and the change varies from project to project. Based on the above findings, this study further explores the influence process of various types of ecological restoration projects on ESRs, and provides scientific support for optimizing ecosystem management and comprehensive management of the region.

Warming waters lead to increased habitat suitability for juvenile bull sharks (Carcharhinus leucas)

Coastal ecosystems are highly vulnerable to the impacts of climate change and other stressors, including urbanization and overfishing. Consequently, distributions of coastal fish have begun to change, particularly in response to increasing temperatures linked to climate change. However, few studies have evaluated how natural and anthropogenic disturbances can alter species distributions in conjunction with geophysical habitat alterations, such as changes to land use and land cover (LU/LC). Here, we examine the spatiotemporal changes in the distribution of juvenile bull sharks (Carcharhinus leucas) using a multi-decadal fishery-independent survey of coastal Alabama. Using a boosted regression tree (BRT) modeling framework, we assess the covariance of environmental conditions (sea surface temperature, depth, salinity, dissolved oxygen, riverine discharge, Chl-a) as well as historic changes to LU/LC to the distribution of bull sharks. Species distribution models resultant from BRTs for early (2003–2005) and recent (2018–2020) monitoring periods indicated a mean increase in habitat suitability (i.e., probability of capture) for juvenile bull sharks from 0.028 to 0.082, concomitant with substantial increases in mean annual temperature (0.058°C/yr), Chl-a (2.32 mg/m3), and urbanization (increased LU/LC) since 2000. These results align with observed five-fold increases in the relative abundance of juvenile bull sharks across the study period and demonstrate the impacts of changing environmental conditions on their distribution and relative abundance. As climate change persists, coastal communities will continue to change, altering the structure of ecological communities and the success of nearshore fisheries.

Hybrid Metaheuristic Optimization Algorithms with Least-Squares Support Vector Machine and Boosted Regression Tree Models for Prediction of Air-Blast Due to Mine Blasting

Hybrid Metaheuristic Optimization Algorithms with Least-Squares Support Vector Machine and Boosted Regression Tree Models for Prediction of Air-Blast Due to Mine Blasting

Selection of best wavelet functions and decomposition levels for coupling with soft computing methods in estimating ETo in coastal and island regions

ABSTRACT Mass transfer-based models are among the extensively applied reference evapotranspiration (ET o ) estimation approaches that have been proven to provide reliable estimates under certain climatic and geographical conditions. A major issue with such approaches is the high variations in wind speed time series, which is a governing meteorological parameter in mass transfer-based models. few studies have focused on applying wavelet transform as a robust pre-processing technique for ETo estimation. A major question with coupling wavelet transforms with soft computing methods would be the proper choice of the wavelet function, vanishing moments and the decomposition levels. The present study aims at assessing different wavelet functions coupled with boosted regression tree (BT) models, i.e. wavelet-BT (WBT), in estimating ET o values (through mass transfer parameters) in both coastal and island regions, where it is supposed that wind speed variations are affected by sea–inland interactions and so has the sharp variations (unlike ET o ). The obtained results showed that the coupled WBT model could improve the single BT model results to a great extent, and meanwhile, the Daubechies wavelet function with six vanishing moments provided the most accurate results in all the locations. Further, the best simulation outcomes with each wavelet function were obtained when the maximum possible decomposition level was used with each function.

Assessing vegetation resilience and vulnerability to drought events in Central Asia

Extreme drought events in Central Asia are becoming increasingly frequent, causing further harm to the vegetation in this region. It is essential to comprehend how vegetation continually responds to extreme drought events, focusing on both resistance and recovery aspects. To address this, based on the run theory, the study quantified drought characteristics for drought events affecting vegetation. Then, vegetation resistance and resilience to drought events were assessed by adopting a ‘resistance–resilience’ framework. Further analysis evaluated vegetation vulnerability to drought events by considering both vegetation resistance and resilience. The impact of different drought features on vegetation vulnerability to drought events was quantified using the boosted regression tree (BRT) model. The results showed that rainfed croplands in Northern Kazakhstan exhibited low resistance to drought events, while sparse vegetation in southern Central Asia displayed higher resistance compared to the other land covers. Moreover, natural vegetation demonstrated a longer resistance time compared to croplands. However, the opposite pattern was observed for vegetation resilience, with croplands and sparse vegetation showing high and low resilience, respectively. Sparse vegetation had a longer recovery time compared to croplands. Notably, the Amu Darya delta exhibited vegetation with low resistance and resilience. Furthermore, the study revealed that vegetation resilience in arid areas was more sensitive to dryness compared to humid areas. Regarding vegetation vulnerability to drought events, vegetation in southern region experienced low vulnerability. In contrast, croplands and grasslands around the Aral Sea showed high vulnerability. Most vegetation in the semi-arid zone was more susceptible to the negative impacts of drought events, particularly in croplands and grasslands, compared to other climate zones. The BRT model revealed that drought duration was found to be the primary factor influencing vegetation vulnerability to drought events, accounting for approximately 40% of the explanation in the model. The influences of drought duration on vegetation vulnerability intensified from forests (28.17%) to sparse vegetation (55.87%). The number of drought events contributed only approximately 5% of the explanation. With an understanding of vegetation resilience and vulnerability, policymakers and land managers can make informed decisions and implement measures to mitigate the negative effects of drought, protect fragile vegetation, and promote sustainable management practices in Central Asia.

++Spatiotemporal dynamics and driving factors of green-blue space in High-Density Cities: Evidence from central Nanjing

Urban green–blue space (UGBS) plays a crucial role in Chinese cities, occupying more than half of the built-up areas. Understanding the spatiotemporal dynamics and driving factors of UGBS in central Nanjing during rapid urbanization is important for studying UGBS evolution in high-density urban-area environments, providing a fundamental basis for related research and policy formulation. This study utilized remote sensing images to estimate the extent of UGBS from 2000 to 2020 and analyzed the evolution using the intensity of area change, a land transfer matrix, the multi-order adjacency index, and a boosted regression tree model. This study revealed the spatiotemporal dynamics and driving factors of UGBSs in central Nanjing from 2000 to 2020: (1) between 2000 and 2020, there was a 4.0% net increase in the total area of UGBSs in central Nanjing. (2) UGBS primarily showed marginal-type expansion, accompanied by proximity-type expansion. Spatially, new green–blue patches tended to cluster around large pre-existing natural green–blue patches. Furthermore, the expansion of UGBS shifted from the urban periphery to the urban interior. (3) Natural factors exerted a greater influence (88.3%) than socioeconomic factors (11.7%) on the expansion of UGBS. Average annual radiation (43.4%), elevation (16.5%), slope (15%), aspect (13.4%), and population density (2.8%) were the primary driving factors. Our results contribute to understanding changes in UGBS and the driving factors behind these changes, thereby providing support for the future planning, protection, and sustainable development of UGBS.

Modelling spatial distributions of biogenic habitat‐forming taxa to inform marine spatial planning

Abstract Biogenic habitats are foundational habitats for species assemblages and drive a range of ecosystem functions. The Hauraki Gulf/Tiikapa Moana is the most intensively used coastal area in Aotearoa/New Zealand, and decades of commercial fishing, sedimentation and industrialization have degraded biogenic habitats in the Gulf. In response, the marine spatial plan ‘Sea Change’ includes proposals to create new and extend existing marine protected areas (MPAs) and restrict the area open to mobile bottom‐impact fishing methods to conserve and help the recovery of biogenic habitats. To assess the benefits of different spatial planning scenarios for biogenic habitats, information on their spatial distribution is needed, but data limitations are a significant challenge. Here, an approach is detailed that maximized the information extracted from limited species occurrence data, by incorporating expert knowledge to develop and evaluate models of biogenic habitat‐forming taxa. Ensemble habitat suitability models (using boosted regression tree and random forest models) were created for 20 biogenic habitat groups. Using withheld data for validation, area under curve (AUC) scores ranged from 0.58 to 0.95 and true skill statistics (TSS) ranged from 0.37 to 0.84, though three models were further evaluated as insufficient representations of known ecological habitats by expert assessors. Models produced here provide substantially increased information to inform progress on implementation of the Sea Change plan. Two stakeholder processes have been held, resulting in the development of spatial plans for bottom trawl mitigation that are currently under public consultation. Systematic surveys that gather abundance and high‐resolution environmental data should be a priority to improve the utility of predictive models and inform future management processes in the Hauraki Gulf, and funding has been allocated to support additional data collection to determine the effectiveness of spatial management measures in the Hauraki Gulf.

Spatial pattern of urban forest diversity and its potential drivers in a snow climate city, Northeast China

Urban forest diversity is the cornerstone of providing ecosystem services and improving urban environment. However, the spatial pattern of urban forest (UF) diversity and its driving mechanisms are still not well understood. In our study, using 330 UF sample plots and the corresponding plot-based standard deviation values of remote sensing parameters with different spatial resolutions, we developed prediction models for the spatial pattern of UF diversity, and the driving factors of UF diversity were further explored in our study. Our findings indicated that the 3-m spatial resolution of remote sensing was optimal for predicting UF diversity. Both excessively high and excessively low resolutions can increase misclassification errors. In addition, among the three prediction models, the boosted regression trees (BRT) model exhibited greater robustness than the support vector machine (SVM) and random forest (RF) models for predicting UF diversity. UF species were abundant, with 112 tree species in Changchun City. Spatially, the UF diversity showed a significant gradient decreasing trend from suburban areas (outer rings) to downtown areas. We also observed higher diversity in the older communities with high housing prices, supporting the “legacy effect and luxury effect”. Additionally, our research revealed that socioeconomic factors, landscape cover, and patterns collectively explained 41.25% of the UF diversity variation, wherein socioeconomic factors contributed the most variation (17.33%). We further found that UF diversity does not increase more with increasing house prices when the house prices are higher than ¥ 12,000/m2. The higher patch density reduced UF diversity, while a higher UF landscape shape index (LSI) promoted diversity. The LSI threshold, which significantly impacted UF diversity, was about 12. Our research showed that high-resolution remote sensing offers a rapid, cost-effective approach to acquire UF diversity patterns, which could enhance urban forest diversity and contribute to sustainable urban development.

Seasonality, distribution, and harvest trends of Crevalle Jack in Texas

AbstractObjectiveCrevalle Jack Caranx hippos are important coastal predators and support an increasingly popular catch‐and‐release sport fishery. However, population declines have recently been perceived by stakeholders in parts of the species' range. Here, we aimed to provide distribution and harvest trends for Crevalle Jack in the northwestern Gulf of Mexico.MethodsLong‐term fishery‐independent and fishery‐dependent data were collected by the Texas Parks and Wildlife Department from 1983 to 2021. Bag seine, bay trawl, and gill‐net samples were collected in conjunction with angler creel surveys. Crevalle Jack that were observed in fishery‐independent sampling were used to assess patterns in both juvenile and adult relative abundance, distribution, and seasonality. In addition, boosted regression tree models were used to highlight important predictors of Crevalle Jack presence. Crevalle Jack harvest observed during angler surveys was used to describe the fishery, including differences in inshore and offshore catch patterns. Relative abundance of both juveniles and adults was variable across years but displayed no trend throughout the time series.ResultCrevalle Jack of all sizes were found within inshore waters, with subadults and large adults particularly reliant on estuarine systems. Recruitment peaked in early summer, with juveniles occupying all major estuaries along the coast. Shifts in recruitment were evident in bag seines, as the mean date of catch trended almost 2 weeks earlier over the study period. The Crevalle Jack fishery was spread across inshore and offshore waters, with significantly larger fish harvested offshore during summer. The majority of anglers who harvested Crevalle Jack stated that they were specifically targeting other species, thus supporting the notion that the targeted fishery is mainly catch and release.ConclusionAs Crevalle Jack declines have been noted elsewhere in the species' range, these results could help to inform future management decisions in Texas.

Unveiling temporal variation in dissolved organic matter (DOM) by high-frequency spectroscopic measurements in a shallow eutrophic lake

Dissolved organic matter (DOM) plays an important role in biogeochemistry of lake ecosystems. Studies measuring DOM at short intervals in lakes are still rare, thus its short-term dynamics are largely unknown. We investigated DOM temporal variation in large and shallow eutrophic Lake Võrtsjärv (Estonia) during one growing season (May–September 2016) using field-deployable spectrometer In Situ Spectral Analyzer (GO Systemelektronik GmbH) to measure absorbance spectra (wavelength range 200–708 nm) at a 2-hour interval coupled with monthly discrete water sampling. Collected spectra were analyzed together with some in-lake variables, lake metabolic rates, and meteorological and hydrological data using Boosted Regression Tree (BRT) and Random Forest (RF) models. Different spectral parameters were used to assess total and allochthonous DOM quantity, and relative share of autochthonous DOM. All parameters, i.e., DOM quantity and quality, varied on a large scale. For example, dissolved organic carbon (DOC) concentrations ranged from 12.0 to 17.3 mg L-1. High levels of DOM were mainly of allochthonous origin; strong relationship with inflow indicated the same. Relative share of autochthonous DOM increased with rising air temperature as primary production rose in warm water; however, we did not find any direct relationships with gross primary production. RF and BRT models explained up to 38% and 63% of DOM temporal variability, respectively. Our results showed that monthly water samples did not capture large variation in DOM. Therefore, high-frequency measurements using in situ spectrometer improve temporal representativeness of DOM monitoring in lakes compared to traditional sampling methods.

Importance of forest stand structures for gross rainfall partitioning on China’s Loess Plateau

Large-scale ecological restoration in the arid and semi-arid Loess Plateau region of northern China is challenged by the intensifying water stress due to the changes in precipitation regimes. Although the importance of Gross Precipitation Partitioning (GRP) has been well recognized in forest hydrology, especially in dry areas, the processes are not understood because site- or region-specific factors complicate the accurate quantifications. This study integrates meta-analysis and field observation to compare patterns of GRP in forest stands that had different origins (plantations vs. native forests) and tree species (deciduous vs. evergreen) compositions. We aimed at quantifying the dominant biotic and abiotic factors controlling each of the GRP components to form empirical relationships between the GRP components and the influencing factors via the Boosted Regression Trees (BRT) model. We found a convergence in the GRP components among the examined stands that have diverse tree species and origins. Our results indicated that, unlike stem flow (SF) and canopy interception (Ic), the throughfall (TF) did not differ between natural and plantation stands. Meanwhile, from the perspective of species composition, the broadleaf forest stands had significantly higher SF rates than the coniferous stands. We found that stand structures exerted limited influence on TF, but significantly affected SF and Ic. We hypothesized that the residence time of the GRP components was important in explaining the matrix of influencing factors. The cumulative influences of stand structure factors played a more important role than species in most conditions. Based on the most influential factors (cumulative explanatory power > 70 %) selected from the BRT model, we successfully built a general formula to represent the GRP processes across the diverse forest stands in the Loess Plateau. Our study provides insights into the underlying mechanism of GRP for developing more realistic forest hydrological models. An improved understanding of GRP and better models are important for guiding large scale reforestation efforts that account for hydrological processes across stand species and origins.

Nitrous oxide emissions are driven by environmental conditions rather than nitrogen application methods in a perennial hayfield.

Agricultural best management practices (BMPs) intended to solve one environmental challenge may have unintended climate impacts. For example, manure injection is often promoted for its potential to reduce runoff and nitrogen (N) loss as NH3 , but the practice has been shown to increase N2 O, a powerful greenhouse gas, compared to surface application. Urease inhibitor application with N fertilizer is another BMP that can enhance N retention by reducing NH3 emissions, but its impact on N2 O emissions is mixed. Thus, we measured N2 O, CO2 , soil mineral N availability, soil moisture, soil temperature, and yield in a 2-year perennial hayfield trial with four fertilization treatments (manure injection, manure broadcast, synthetic urea, and control) applied with or without a urease inhibitor in Alburgh, VT. We used linear models to examine treatment effects on daily and cumulative N2 O emissions and a boosted regression tree (BRT) model to identify the most important drivers of daily N2 O fluxes in our trial. While fertilization type had a significant impact on N2 O fluxes (p<0.05), our treatments explained an unexpectedly small amount of the variation in emissions (R2 =0.042), and urease inhibitor had no effect. Instead, soil moisture was the most important predictor of daily N2 O fluxes (39.7% relative influence in BRT model), followed by CO2 fluxes, soil inorganic N, and soil temperature. Soil moisture and temperature interacted to produce the largest daily N2 O fluxes when both were relatively high, suggesting that injecting manure during dry periods or during wet but cool periods could reduce its climate impacts.

Understanding transmission risk and predicting environmental suitability for Mayaro Virus in Central and South America.

Mayaro virus (MAYV) is a mosquito-borne Alphavirus that is widespread in South America. MAYV infection often presents with non-specific febrile symptoms but may progress to debilitating chronic arthritis or arthralgia. Despite the pandemic threat of MAYV, its true distribution remains unknown. The objective of this study was to clarify the geographic distribution of MAYV using an established risk mapping framework. This consisted of generating evidence consensus scores for MAYV presence, modeling the potential distribution of MAYV in select countries across Central and South America, and estimating the population residing in areas suitable for MAYV transmission. We compiled a georeferenced compendium of MAYV occurrence in humans, animals, and arthropods. Based on an established evidence consensus framework, we integrated multiple information sources to assess the total evidence supporting ongoing transmission of MAYV within each country in our study region. We then developed high resolution maps of the disease's estimated distribution using a boosted regression tree approach. Models were developed using nine climatic and environmental covariates that are related to the MAYV transmission cycle. Using the output of our boosted regression tree models, we estimated the total population living in regions suitable for MAYV transmission. The evidence consensus scores revealed high or very high evidence of MAYV transmission in several countries including Brazil (especially the states of Mato Grosso and Goiás), Venezuela, Peru, Trinidad and Tobago, and French Guiana. According to the boosted regression tree models, a substantial region of South America is suitable for MAYV transmission, including north and central Brazil, French Guiana, and Suriname. Some regions (e.g., Guyana) with only moderate evidence of known transmission were identified as highly suitable for MAYV. We estimate that approximately 58.9 million people (95% CI: 21.4-100.4) in Central and South America live in areas that may be suitable for MAYV transmission, including 46.2 million people (95% CI: 17.6-68.9) in Brazil. Our results may assist in prioritizing high-risk areas for vector control, human disease surveillance and ecological studies.

Global Distribution and Prediction of Transmission-Risk of Visceral Leishmaniasis

Backgrounds: Visceral leishmaniasis (VL) is a vector-borne disease posing increasing public health concerns. Although efforts to eliminate VL are underway, its global risk distribution remains unclear, because of changes in transmission risk. Methods: Worldwide province-level data on VL incidence were obtained from multiple sources. We analyzed the global evolution features of VL and estimated its ecological niches with boosted regression tree models. A risk map was generated to analyze regions with high VL risk. Results: A total of 558 articles and 2,694 records from 310 provinces were included. The model indicated that precipitation in the warmest quarter and poor socio-demographic index were the most significant risk factors affecting VL distribution. The risk map indicated that Brazil, Iran, Ethiopia, and India were the regions with the highest probability of infection. We estimated that 5.3 billion people live in regions at risk of VL. Conclusions: VL is undergoing geographic expansion. Our study increases understanding of VL’s global-risk distribution and how risk factors influencing the disease transmission. Our findings may aid in promoting disease control in future surveillance activities.

Experimental and ensemble machine learning analyses of heat transfer, friction factor and thermal performance factor of rGO/water nanofluids in a tube

This paper explains about the experimental evaluation of thermophysical properties, heat transfer coefficient, Nusselt number, friction factor, and thermal performance factor of water-based reduced graphene oxide (rGO) nanofluids passes through a tube under turbulent flow conditions. The rGO nanoparticles in this study was prepared based on the Hummer's procedure. The thermophysical properties were estimated at different volume loadings from 0.5 % to 2.0 % and at different temperatures from 20 °C to 60 °C, respectively, similarly, the heat transfer and friction factor experiments were conducted in the Reynolds number ranging from 1800 to 21,000. The obtained data of heat transfer coefficient, Nusselt number, friction factor and thermal performance factor was predicted based on the advanced machine learning algorithms of boosted regression tree (BRT), and extreme gradient boosting (XGB), respectively. Results indicated that, at 2.0 % vol. of rGO/water nanofluid the thermal conductivity is raised by 26.95 % at a temperature of 60 °C, viscosity is raised by 63.29 % at a temperature of 20 °C, over base fluid. Moreover, the results are further shown that, at 2.0 % vol. of nanofluid the Nusselt number, heat transfer coefficient, factor in friction, drop in pressure, and pumping power raise of 34.80 %, 44.19 %, 16.73 %, 10.48 %, and 4.55 % at a Reynolds number of 13,705, against water. The correlation coefficient (R2) results predicted from BRT and XGB models for Nusselt number data is 0.9831 and 0.994, respectively. It is understood that the XBG algorithm predicts very accurate values compared to BRT algorithm with a less mean square error. The overall performance of the system was raised by 1.28 -folds than water. Using the experimental data, new Nusselt number and friction factor correlations were proposed.