Inverse Distance Weighting Method Research Articles

التحليل المكاني والزماني لتباين التهاطل المطري والتنبؤ به في منطقة القصيم (2017-1987)

هدفت هذه الدراسة إلى تحليل مؤشرات التوزيع المكاني والزماني لقياسات الأمطار اليومية والشهرية والموسمية. إنه يبحث عن إمكانية استقرار أو تغيير خصائص الترسيب باستخدام بيانات الحاضر. كما أنها تعمل مع أهم العوامل التي تؤثر على هطول الأمطار ، علاوة على العديد من الأساليب الإحصائية المطبقة في هذه الدراسة. استخدام طريقة الوزن العكسي للمسافة الموزونة (IDW) لتوليد سطح الاستيفاء بالمطر الذي توافق عليه الأداة في برامج أنظمة المعلومات الجغرافية. المطر عنصر مهم في العديد من الأنشطة الاقتصادية. لذلك فإن أهمية التنبؤ بالتوزيع المكاني لهطول الأمطار من مصادر مياه الأمطار. تقدم هذه الدراسة تحليلاً للتباين الزماني المكاني للأمطار اليومية والشهرية والموسمية في منطقة القصيم ، بناءً على بيانات سبع محطات أرصاد جوية ، وهي بريدة ، وعنيزة ، والرصة ، والهيئة العامة للأرصاد وحماية البيئة ، وكذلك الأمير. محطة مطار نايف بين (2017-1987) والتي تضمنت 31 سنة لدقة النتائج. توصلت الدراسة إلى عدة توصيات يمكن الاستفادة منها في المجالات الجغرافية سواء كانت بيئية أو بشرية تتعلق بالموارد المائية والسيول ومشاريع تصريف مياه الأمطار والمناطق الحضرية لتلافي الكوارث.

Country-Wide qPCR Based Assessment of Plasmodiophora brassicae Spread in Agricultural Soils and Recommendations for the Cultivation of Brassicaceae Crops in Poland.

Clubroot is a damaging disease of oilseed rape and vegetable brassicas worldwide, caused by the soil-borne protist Plasmodiophora brassicae Wor. Due to the long life of resting spores, the assessment of the pathogen abundance in agricultural fields can serve as a guideline for disease control at the country-wide level or the regional scale. Between 2013 and 2019, we collected 431 soil samples from fields cultivated with Brassicaceae crops throughout 16 provinces of Poland. The samples were subjected to qPCR based analysis of P. brassicae DNA concentration. From these data, the spore loads and gene copies g−1 soil were calculated and used to produce an assessment of the current clubroot risk potential at a country-wide and regional scale. The country-wide map, showing the spread of the pathogen in agricultural soils, was made using ArcGis software package implementing the interpolation with the Inverse Distance Weight method. The calculation of gene copies specific to P. brassicae helped to formulate the recommendations for farmers in respect to the cultivation guidelines. It showed a high risk of yield losses in defined regions of north, south-west and central Poland and an urgent need to undertake intensive preventative measures.

Evaluation of DEM interpolation techniques for characterizing terrain roughness

The emergence of modern geospatial techniques has brought a considerable change in data capturing, processing, and visualization tools. Digital elevation models (DEMs) are a 3D mathematical representation of the earth’s surface. Due to enormous advancement in geographic information system (GIS) tools and technologies, DEMs have a wide range of applications in natural resources management, environmental science, and engineering. In general, primary topographic variables extracted from DEMs are frequently less useful than secondary ones, e.g., surface roughness. Therefore, the precise values of secondary terrain features need to be derived from base DEMs. Generally, topographic roughness is an essential geomorphological variable that is a vital issue in geoscience. This research presents a comparison of deterministic interpolation methods, inverse distance weighted (IDW) and a natural neighbor (NN), with geostatistical technique, ordinary kriging (OK), to investigate the influence of generated DEM on quantifying terrain roughness relied on synthetic data and applying the zonal statistics tool. At the same time, a quantitative and qualitative approach is adopted to assessing the results through statistical methods and visual representations. The findings indicated that roughness values' behavior is broadly related to the quality of the built DEM. The NN approach yielded the greatest DEM accuracy, standard deviation, from a quantitative perspective (±0.930 m) than IDW (±3.748 m) and OK (SD = ±5.544 m) methods.

Spatial distribution of soil salinity: a GIS-based assessment at the Hardap irrigation scheme, Namibia

Salinization is a worldwide problem that affects the physical and chemical properties of soils. It is the most common and widely recognized form of land degradation, particularly in arid and semi-arid regions, where evaporation surpasses precipitation. Namibia aims to grow the agricultural sector for food independence, economic growth and stability. As a result, it is of great importance to manage and protect crop farms from salinization in the country. This study assessed the spatial distribution of soil salinity at the Hardap Irrigation Scheme, one of the two largest and oldest schemes situated in a hot desert climate in Namibia. The grab sampling method was used to collect 59 surface soil samples from randomly selected sites in the study area. The sampling sites included both irrigated and non-irrigated areas. Samples were subsequently analysed in the laboratory for electrical conductivity (EC), using saturated paste EC1:5 method. Resultant measurements were then grouped into five salinity classes. The point data were interpolated using the Inverse Distance Weighted (IDW) method in ArcGIS. Salinity levels in the study area ranged between 0.004 and 1.97 dS/m ± 0.557 dS/m. Although high salinity levels were recorded, non- and slightly saline classes cover just over two-thirds of the study area. Soil salinity in the study area is thus generally low, but the hazard exists. The low salinity level in the study area may be due to hydropedological factors, the prevailing management system or both. Regular monitoring of soil salinity is recommended, especially when new management system is introduced.

Evaluation of corrosive behaviour and scaling potential of shallow water aquifer using corrosion indices and geospatial approaches in regions of the Yamuna river basin

The research was conducted in the district of Mathura, Uttar Pradesh, India, and samples were obtained and analysed from 65 locations in different parts of the Mathura region in June 2016. Using the obtained water quality parameters of the study area, different types of indices including Ryznar index (RI), Langelier saturation index (LSI), Aggressive index (AI), Larson–Skold index (LS), Puckorius index (PI) and chloride sulfate mass ratio (CSMR) has been assessed and compared. The spatial distribution maps were prepared using the Inverse Distance Weight method and reveal that more than 75% of the total samples lie in the category of LI < 0, while 23% of samples indicate the water to be supersaturated with scaling forming tendency. However, 83% of the total samples point towards moderate corrosive behavior and the Ryznar index of these samples lies between limit of 6.8–8.5. The Ryznar index value of 11% of the total samples is found to be more than 8.5 thus indicating a rigorous corrosive tendency towards the water. The Puckorius index value of less than 6 has been assessed in 20% of the total samples thus conforming to the tendency of water towards scaling. The Puckorius index value between 6 and 7 has been observed in 46% of the total samples analyzed in the present study, thus indicating the water to be little corrosive in nature. The chloride sulfate mass ratio value is found to be greater than 0.5, which is observed in more than 90% of the samples of the study area, thus conforming to the corrosiveness of water towards the distribution system. Most of the indexes show that the corrosion trends in water samples are very high, so that domestic and industrial water quality and distribution systems may have more deteriorated in the future.

Optimal location of yield with the cheapest water footprint of the crop using multiple regression and artificial neural network models in GIS

Iran is located in an arid and semi-arid region of the world; however, more than 90% of Iran’s total water is allocated to agricultural sector. Owing to the climatic limitations of the country, agricultural water management has become one of the country’s greatest concerns. Therefore, the present study has attempted to identify optimal locations in terms of high yield and low total water footprint (WF) (m3/$) in the northwest and southeast provinces of the country to produce main cereals such as wheat, barley, and maize. In this regard, inverse distance weighting (IDW) method was first employed 150 sample coordinates to prepare interpolation maps of evapotranspiration (ETc) (mm), irrigation requirements (IR) (mm), nitrogen application (kg/ha), and effective precipitation (Peff) (mm). Subsequently, green WF, blue WF, gray WF, total WF (TWF) (m3/ton), and economical total WF (ETWF) (m3/$) maps were prepared for each crop. Multiple linear regression (MLR) was then applied to determine the correlation between ETWF (m3/$) and other parameters. ETWF (m3/$) was later predicted using multiple linear regression (MLR), in conjunction with artificial neural networks (ANNs) including multilayer perceptron (MLP) and radial basis function (RBF). Findings showed that IDW can be applied with a relatively low error so that an interpolation map for each parameter can be obtained and the changes can be examined. The results of MLR indicated that for assessing the yield of barley and wheat the TWF possesses and for assessing the yield of maize, the IR model had the highest rate of regression. The results of ANN indicated that the RBF (R2 = 0.901), the RBF (R2 = 0.882), and the MLP (R2 = 0.947) were the most appropriate models for the prediction and evaluation of ETWF and yield with respect to measured estimates of ETWF (m3/$) and yield of wheat, barley, and maize (in order of appropriability).

Development of High Resolution and Homogenized Gridded Land Surface Air Temperature Data: A Case Study Over Pan-East Asia

The Land Surface Air Temperature (LSAT) climatology during the period of 1961-1990 and the anomalies (relative to the 1961-1990 climatology) have been developed over Pan-East Asian region at a (monthly) 0.5°×0.5° resolution. The development of these LSAT data sets are based on the recently released C-LSAT station datasets and the high resolution Digital Elevation Model (DEM), and interpolated by the Thin Plate Spline (TPS) method (through ANUSPLIN software) and the Adjusted Inverse Distance Weighting (AIDW) method. Then they are combined into the high resolution gridded LSAT datasets (including the monthly mean, maximum, and minimum temperature). Considering the mean LSAT for example, the Cross Validation (CV) of the datasets indicates that the regional average of the Root Mean Square Error (RMSE) for the climatology is about 0.62℃, and the average RMSE and Mean Absolute Error (MAE) for the anomalies are between 0.47 - 0.90 ℃ and 0.32 - 0.63 ℃ during the study period. The analysis also demonstrate that the gridded anomalies describe the spatial pattern fairly well for the coldest (1912, 1969) and the warmest (1948, 2007) years during the first and second half of the 20th century. Further analysis reveals that the high resolution dataset also performs well in the estimation of long-term LSAT change trend. Thus it can be concluded that this newly constructed datasets is a useful tool for regional climate monitoring, climate change research as well as climate model verification.

Exposure to ambient PM2.5 and cognitive function in older adults in Mexico

Background: Recent observational studies have shown that chronic exposure to fine particulate matter (PM2.5) is associated with a poorer cognitive function in older adults. However, evidence of the association with cognitive test performance has not always been consistent. Furthermore, most of the available studies are cross-sectional and have been carried out in developed countries. Objective: To evaluate the association between exposure to ambient PM2.5 and cognitive function in adults participating in the Mexican Health and Aging Study (MHAS), in 2012 and 2015. Methods: Measures of cognitive function, sociodemographic and health data were obtained for each MHAS participant in 2012 and 2015 waves. A total of 1544 for 2012 and 693 for 2015 adults aged 50 years and older, who had exposure and cognitive test data, composed the preliminary analytical samples. Cognitive function was assessed using a modified version of the screening portion of the Cross-Cultural Cognitive Examination (CCCE), including tests of orientation, verbal learning memory, verbal recall memory, visual scanning, visuospatial abilities, visual memory, verbal fluency, and numeracy. Long term exposures to PM2.5 were estimated by a spatial analysis using concentrations from monitors and the Inverse Distance Weighting (IDW) method, 1, 2 and 5 years preceding cognitive testing of each participant. Association between exposure to PM2.5 and cognitive function was estimated using linear regression models. Preliminary Results: Adjusted linear regression models for 2012, showed statistically significant lower scores in 2nd and 3rd PM2.5 exposure tertiles, compared with lower exposure tertiles, for verbal learning memory (-0.175 to -0.184), visual scanning (-2.26 to -2.64) and visual memory (-0.23 to -0.39) tests. Conclusions: Environmental exposure to PM2.5 seems to affect neurocognitive functioning in Mexican adults 50 years and older, a complementary analysis will be performed and presented.

Prenatal exposure to particulate matter and ADHD in children: a prospective cohort study

Background/Aim Environmental factors may play a role in the development of attention-deficit/hyperactivity disorder (ADHD) and some studies have shown that ambient air pollution is associated with the ADHD. This study was designed to investigate the impact of prenatal particulate matter (PM10) exposure on ADHD among children over 60 months of age in Korea by using MOCEH birth cohort study’s data.Methods This study is a part of the Mothers and Children's Environmental Health (MOCEH) study, a Korean multi-center prospective birth cohort study initiated in 2006. After the delivery, children were followed up at 60, 72, 84 and 96 months of age. The average exposure level of particulate matter (PM10) were estimated using the inverse distance weighting (IDW) method from the prenatal period. ADHD behaviour were assessed using the K-ARS, K-CBCL/1.5-5 and 6-18 at each follow-up period. Generalized Linear Models (GLM) were used to explore the association between PM10 and ADHD.Results The mean PM10 concentrations during total pregnancy was 54.1μg/m3 with a range from 24.2 to 85.7μg/m3. In the GLM model, exposure to PM10 at first trimester and during pregnancy showed significant positive association with 72 months K-ARS score (β=0.06, 95% CI: 0.01,0.11 for first trimester, β=0.10, 95% CI: 0.01,0.19 for total pregnancy exposure). Furthermore, exposure to PM10 at first trimester and during total pregnancy showed significant association with 60, 72, and 84 months K-CBCL score, especially with total problems, attention problems and aggressive behaviour (β=0.26, 95% CI: 0.07,0.44 between first trimester and total problem score at 60 months, β=0.10, 95% CI: 0.05,0.15 between first trimester and attention problems at 72 months).Conclusions In conclusions, our results have demonstrated that the prenatal PM10 concentration was significantly associated with ADHD behaviour in Korean children. Further studies regarding other air pollutants’ effects to the neurodevelopment in children are recommended.

Temperature and hand, foot and mouth disease in California, 2005-2013, an exploratory analysis

Background: Researchers have found that temperature has been associated with hand, foot and mouth disease (HFMD) in Asia. However, few have explored the relationship in the United States, particularly California where HFMD cases have increased recently.Methods: We collected weekly counts of HFMD emergency department (ED) visits in California from 2005 to 2013 and calculated weekly temperature for each of the state’s sixteen climate zones using an inverse distance-weighting method for weekly lags 0 to 2. For each season, we conducted a time-series analysis using Poisson regression, adjusting for weekly average relative humidity, average number of HFMD cases in previous weeks and long-term temporal trends. We combined season-specific climate zone estimates to produce an overall estimate for California.Results: We found an increased risk of ED visits for HFMD per 1°F increase in temperature during the same week by 2.00% (95% confidence intervals: 1.15, 2.86) and 2.35% (1.38, 3.33) during the warm and cold season, respectively. The coastal region showed a higher association during the cold season [3.18% (1.99, 4.39)] than the warm season [1.64% (0.47, 2.82)] for weekly lag 0, which may be attributed to the relatively mild winter in the coastal region. Additionally, during the warm season, only weekly lag 0 showed a positive association while weekly lags 0, 1 and 2 remained elevated during the cold season in the all California analysis.Conclusions: Our findings indicated an association between temperature and ED visits for HFMD in California, which varied both seasonally and regionally. The elevated associations for later lags observed in the cold season could indicate a more favorable environment for HFMD pathogens to survive on surfaces and in the air. Because HFMD is a disease that mainly affects children, a group that is already vulnerable to higher temperature, health care providers should be especially aware.

Construction of Spatial local C-vine model and application of air temperature interpolation

Based on the daily average temperature of 73 meteorological stations and the geographical location information of each station from 1980 to 2010 in Yunnan Province, spatial interpolation method of temperature data suitable for Yunnan province was explored. Based on the definition of spatial local C-Vine, the full model, horizontal distance model and vertical distance model were constructed. Using maximum likelihood, Akaike information criterion (AIC)and Bayesian information criterion (BIC) select the three models to obtain full model as the optimal model. 6 stations in the region were randomly selected and temperature interpolation was made using full model, Ordinary Kriging and Inverse Distance Weighting method. The results showed that full model based on Spatial local C-vine had better prediction accuracy.

APPLICATION OF AIR QUALITY INDEX AND INVERSE DISTANCE WEIGHTING FOR MAPPING THE DISTRIBUTION OF AIR POLLUTION AT SEVERAL URBAN DISTRICTS OF HANOI CITY

The air pollution level can be assessed using air quality index - AQI calculated from the concentration of some gases and particle matters which are measured at ambient air quality monitoring stations. The calculated AQI values are characterized by temporal continuity but spatial discontinuity. However, AQI values of each monitoring station is interpolated by the IDW (Inverse Distance Weighting) method in GIS which helps us to assess the air quality at a detailed and specific level for every location in the study area by establishing distribution maps of air pollution. The interpolation of AQI values for zoning air quality in several urban districts of Hanoi during the Winter (October, November, December 2019) shows that in general, the areas with a very bad level of air quality occupied an important surface in the Northwest of urban districts (on the territory of Bac Tu Liem, Ba Dinh, Tay Ho, Cau Giay) for last 3 months of the year. The areas with a bad level of air quality occupied a large surface in the Southeast in October and December, but its surface became narrow in November. But in November, areas having a bad level of air quality were expanded to the Southeast while they occupied only a small surface at the center of the study area in October and December. Although the distribution of each level vary in terms of coverage, their common pattern has been conserved during three months of Winter. The distribution map of air quality provides the complete picture of the air pollution situation and it helps to adequately evaluate this issue in the urban districts of Hanoi city.

Spatial modelling of PM2.5 concentrations in Tehran using Kriging and inverse distance weighting (IDW) methods

Introduction: Estimating air pollution levels in areas with no measurements is a major concern in health-related studies. Therefore, the aim of this study was to investigate the amount of exposure to particulate matter below 2.5 µ (PM2.5) in the metropolis of Tehran. Materials and methods: The hourly concentrations of PM2.5 during 2017-2018 period were acquired from the Department of Environment (DOE) and Air Quality Control Company of Tehran (AQCC). The hourly concentrations were validated and 24-h concentrations were calculated. Inverse distance weighting (IDW), Universal Kriging, and Ordinary Kriging were used to spa- tially model the PM2.5 over Tehran metropolis area. Root Mean Square Error (RMSE) and Mean Error (ME) were used to measure and control for the ac- curacy of the methods. Results: The results of this study showed that RMSE and MENA values in Kriging method was less than the IDW, which indicates that the Kriging was the best method to estimate PM2.5 concentrations. According to the final map, the highest annual concentrations of PM2.5 were observed in the southern and southwestern areas of Tehran (districts 10, 15, 16, 17, and 18). The lowest exposure to PM2.5 was found to be in districts 1, 2, 3, 6, and 8. Conclusion: It can be concluded that Kriging method can predict spatial vari- ations of PM2.5 more accurately than IDW method.

Investigation of relationship between particulate matter (PM2.5) and meteorological parameters in Isfahan, Iran

Introduction: Estimating air pollution levels in areas with no measurements is a major concern in health-related studies. Therefore, the aim of this study was to investigate the amount of exposure to particulate matter below 2.5 µ (PM2.5) in the metropolis of Tehran. Materials and methods: The hourly concentrations of PM2.5 during 2017- 2018 period were acquired from the Department of Environment (DOE) and Air Quality Control Company of Tehran (AQCC). The hourly concentrations were validated and 24-h concentrations were calculated. Inverse distance weighting (IDW), Universal Kriging, and Ordinary Kriging were used to spatially model the PM2.5 over Tehran metropolis area. Root Mean Square Error (RMSE) and Mean Error (ME) were used to measure and control for the accuracy of the methods. Results: The results of this study showed that RMSE and MENA values in Kriging method was less than the IDW, which indicates that the Kriging was the best method to estimate PM2.5 concentrations. According to the final map, the highest annual concentrations of PM2.5 were observed in the southern and southwestern areas of Tehran (districts 10, 15, 16, 17, and 18). The lowest exposure to PM2.5 was found to be in districts 1, 2, 3, 6, and 8. Conclusion: It can be concluded that Kriging method can predict spatial variations of PM2.5 more accurately than IDW method.

Application of Inverse Distance Weighting in Mapping Some of Soil Chemical Properties in Ayn Hizam, Qaryat Batth and Taknis

Determining variabilities of soil properties is important for ecological modelling, environmental predictions, precise agriculture, and management of natural resources. This study was aimed to examine Inverse distance weight (IDW) to predict the spatial variability of Exchangeable Sodium Percentage (ESP), Calcium Carbonate Percentage (% CaCO3) soil pH, Electrical conductivity and % Gypsum . The study area selected for this work consists of Ayn Hizam, Qaryat- Batth and Taknis. Data for 220 randomly distributed representing soil profiles were encoded in spreadsheets, 198 of them were used for predicting the spatial variability in the GIS environment for ESP, % CaCO3, soil pH, Electrical conductivity and % Gypsum. The rest of Data (i.e. 22 representative soil profiles) were utilized to evaluate the maps produced using Kriging or IDW methods. The results showed that using IDW method was trustable because the values of RMSE and R2 for all the IDW maps were within the acceptable range. The study suggested adopting the Geostatistical methods for studying spatial prediction for different soil proprieties. In addition, the study recommended updating soil data for the study area.

237P Breast cancer risk in patients with polycystic ovary syndrome: A Mendelian randomization analysis

The association between polycystic ovary syndrome (PCOS) and breast cancer remains inconclusive. Conventional observational studies are susceptible to inverse causality and potential confounders. With a Mendelian randomization (MR) approach, we aimed to investigate the causal relationship between genetically predicted PCOS and breast cancer risk. Our study included 11 PCOS-associated single nucleotide polymorphisms as instrumental variables identified by the latest genome-wide association study. Individual-level genetic summary data of participants were obtained from the Breast Cancer Association Consortium, with a total of 122 977 cases and 105 974 controls. The Inverse-weighted method was applied to estimate the causality between genetically predicted PCOS and breast cancer risk. To further evaluate the pleiotropy, the weighted median and MR-Egger regression method were implemented as well. Our study demonstrated that genetically predicted PCOS was causally associated with an increased risk of overall breast cancer (odds ratio (OR) = 1.07; 95% confidence interval (CI) = 1.02-1.12, p = 0.005). The subgroup analyses according to immunohistochemical type further illustrated that genetically predicted PCOS was associated with an increased risk of estrogen receptor (ER)-positive breast cancer (OR = 1.09; 95% CI = 1.03-1.15, p = 0.002), while no causality was observed for ER-negative breast cancer (OR = 1.02; 95% CI = 0.96-1.09, p = 0.463). Additionally, no horizontal pleiotropy was found in our study.Table 237PMendelian randomization estimates of the causality between PCOS and breast cancer.OutcomeIVW methodMR-EggerWeighted median methodOR (95% CI)P-valueOR (95% CI)P-valueOR (95% CI)P-valueBreast cancer overall1.0667 (1.0200, 1.1155)0.00471.0450 (0.8351, 1.3077)0.70931.0742 (1.0226, 1.1283)0.0043ER-positive breast cancer1.0881 (1.0318, 1.1474)0.00181.0901 (0.8352, 1.4227)0.54141.0992 (1.0346, 1.1678)0.0022ER-negative breast cancer1.0242 (0.9609, 1.0917)0.46280.8817 (0.6507, 1.1947)0.43761.0063 (0.9275, 1.0919)0.8793 Open table in a new tab Our findings indicated that PCOS was likely to be a causal factor in the development of ER-positive breast cancer, providing a better understanding for the etiology of breast cancer and the prevention of breast cancer.

Applying the Inverse Distance Weighting (IDW) Method in the Designation of Character Areas and the Physical Conservation of the Historic Urban Landscape (Case Study: Lar Old City, Fars, Iran)

Applying the Inverse Distance Weighting (IDW) Method in the Designation of Character Areas and the Physical Conservation of the Historic Urban Landscape (Case Study: Lar Old City, Fars, Iran)

Habitual coffee intake and risk for nonalcoholic fatty liver disease: a two-sample Mendelian randomization study.

Epidemiological studies support a protective role of habitual coffee and caffeine consumption against the risk of non-alcoholic fatty liver disease (NAFLD). We aimed to investigate the causal relationship between coffee intake and the risk of NAFLD. We performed a two-sample Mendelian randomization (MR) analysis using SNPs associated with habitual coffee intake in a published genome-wide association study (GWAS) as genetic instruments and summary-level data from a published GWAS of NAFLD (1122 cases and 399,900 healthy controls) in the UK Biobank. The causal relationship was estimated with the inverse weighted method using a 4-SNP and 6-SNP instrument based on the single largest non-UK Biobank GWAS (n = 91,462) and meta-analysis (n = 121,524) of GWAS data on habitual coffee intake, respectively. To maximize power, we also used up to 77 SNPs associated with coffee intake at a liberal significance level (p ≤ 1e-4) as instruments. We observed a non-significant trend towards a causal protective effect of coffee intake on NAFLD based upon either the 4-SNP (OR: 0.76; 95% CI 0.51, 1.14, p = 0.19) or 6-SNP genetic instruments (OR: 0.77; 95% CI 0.48, 1.25, p = 0.29). The result also remains non-significant when using the more liberal 77-SNP instrument. Our findings do not support a causal relationship between coffee intake and NAFLD risk. However, despite the largest-to-date sample size, the power of this study may be limited by the non-specificity and moderate effect size of the genetic alleles on coffee intake.

Spatial variability of soil Cd content based on IDW and RBF in Fujiang River, Mianyang, China

Establishing the spatial distribution of soil properties in agro-ecosystems is critical to increasing the productivity of intensive tillage soils and reducing environmental pressure. Thus, a comprehensive understanding of soil’s heavy metal distribution characteristics is important for optimal management. The goal of this study was to determine the most accurate soil Cd interpolation method for the study area and analyze the variation and spatial distribution of Cd content with depth. In this work, we employed two common spatial interpolation methods—inverse distance weighting (IDW) and radial basis function (RBF)—in conjunction with a geographic information system (GIS) to interpolate sampling data of the heavy metal cadmium (Cd) throughout Santai County; a locale in the lower part of Fujiang River, Mianyang, in the northeast of Sichuan Province. The results of our investigation are as follows. (1) At depths of 0–50 cm, the Cd content decreased with depth while the coefficient of variation (Cv) increased with depth. The exponential model and the Gauss model adequately described the Cd content’s spatial autocorrelation and the determination coefficient (R2) varied within 0.747–0.962, indicating that the model had high precision. (2) With respect to the accuracy of the IDW and RBF methods, IDW was more accurate than the RBF in the 0–20 cm soil layer, yet the reverse was found in the 20–50 cm soil layer. (3) Three-dimensional spatial distribution maps—constructed using the RBF and IDW methods at different soil depths—showed that the RBF method provided a greater smoothing effect than the IDW method. The spatial variability of soil Cd content shows that: (1) the Cd content’s spatial autocorrelation was strong in the 0–50 cm soil layer. (2) The IDW method is suitable for areas that are greatly influenced by human or farming activities while the RBF method is appropriate for areas that are greatly influenced by internal soil factors. The RBF is superior for evaluating the spatial distribution of soil Cd content in this area.

On histogram-based regression and classification with incomplete data

We consider the problem of nonparametric regression with possibly incomplete covariate vectors. The proposed estimators, which are based on histogram methods, are fully nonparametric and straightforward to implement. The presence of incomplete covariates is handled by an inverse weighting method, where the weights are estimates of the conditional probabilities of having incomplete covariate vectors. We also derive various exponential bounds on the $$L_1$$ norms of our estimators, which can be used to establish strong consistency results for the corresponding, closely related, problem of nonparametric classification with missing covariates. As the main focus and application of our results, we consider the problem of pattern recognition and statistical classification in the presence of incomplete covariates and propose histogram classifiers that are asymptotically optimal.