Inverse Weighting Research Articles

Automated Fixed System Specifically Designed for Agrochemical Applications in Protected Crops

Protected crops are intensive production systems characterized by high vegetation density, high temperatures, and high moisture, making them favorable environments for the development of pests and diseases. Consequently, these systems often require several interventions with agrochemicals to maintain profitable yields and high produce quality. However, the application of plant protection products (PPPs) in such systems is not efficient and poses environmental concerns. This study aims at analysing spray behaviour, particularly in terms of foliar deposition and losses to the ground according to spraying equipment and foliage height, focusing on a specifically designed and developed system for agrochemical application in protected crops, and comparing it with a commonly used spraying system, namely, the cannon sprayer. Such a system consists in a fixed net of tubing and anti-drip nozzles positioned at the top of the greenhouse’s apex, connected to a pneumatic sprayer ‘Special Serre 2000’ outside the greenhouse. Findings revealed a significant effect of the spraying system (Kruskal–Wallis χ2 = 12.239, df = 1, and p-value = 0.0004681) on normalized foliar deposition, with higher values obtained using the fixed spraying system. In addition, a simulation of the spatial distribution based on the principle of inverse distance weighting (IDW) was performed for qualitative spray assessment, confirming the heterogeneity of foliar deposition over the greenhouse with both of the used equipment. In addition, losses to the ground were affected by both spraying equipment and captor position.

Long-term clinical outcomes in patients between the age of 50-70 years receiving biological versus mechanical aortic valve prostheses.

The last two decades have seen an incremental use of biological over mechanical prostheses. However, while short-term clinical outcomes are largely equivalent, there is still controversy about long-term outcomes. All patients between the ages of 50-70 years undergoing elective/urgent isolated aortic valve replacement at our institute between 1996 to 2023 were included. Trends, early and long term outcomes were investigated. A total of 1708 (61% male) patients with a median age of 63.60 (IQR : 58.28,67.0) years were included of which 1191 (69.7%) received a biological prosthesis.After inverse propensity score weighting, there were no short-term differences when comparing patients receiving biological and mechanical valves. However, patients who received mechanical prostheses had better long-term survival (p < 0.001). Sub-group analysis revealed that patients with biological size 19 mm prosthesis had the worse long-term survival. Patients with a size 21 mm mechanical prosthesis had better survival compared to both size 19 mm (HR: 0.25, 95% CI: 0.17,0.37, p < 0.001), 21 mm (HR: 0.33, 95% CI: 0.23,0.48, p < 0.001) and 23 mm (HR: 0.40, 95% CI: 0.27,0.60, p = <0.001) biological prosthesis.Additionally, patients with severe patient-prosthesis mismatch exhibited the lowest survival rate compared to those with moderate or no (HR 1.56, 95% CI 1.21, 2.00, p < 0.001). Patients aged between 50-70 years with a mechanical aortic prosthesis had better long-term survival compared to those with a biological prosthesis. Our study underscores the need for a critical re-evaluation of prosthesis selection strategies in this age group.

Improved GIS Techniques and a Novel Physico-Chemical Risk Index for Surface Water Quality Assessment: A Case Study of the Al-Garraf Oil Field

Petroleum hydrocarbons and waste streams have polluted the environment, harmed human health, affected socioeconomic conditions, and impacted communities in oil-producing countries. The aim of the study is to identify hotspots of contaminated water, create spatial risk maps of pollution from the petroleum industry and develop a novel index to estimate ecosystem pollution named physico-chemical risk index (PRI). Ten sites in the Al-Gharraf oil field were analyzed for their water quality. The highest results of the six-month sample analysis were PH (8.7), DO (11.5mg/L), turbidity (70.3 NTU), temperature (34 C), BOD&lt;sub&gt;5&lt;/sub&gt; (37.8 mg/L), COD (101 mg/L) and TSS (109 mg/L). To achieve the above objectives, different methods and techniques were used; one of them is inverse distance weighting(IDW) with GIS to create geographical maps of the measured parameters. The IDW method was used to accurately map the distribution of ecosystem parameters of the oil field. The PRI was performed to compare the threshold values for pollutant elements with the contamination of the site. The threshold value for contaminated water in Garraf oil field is 24.328 and is determined by the PRI index. The analysis is carried out at regular intervals and compared with the threshold values. This work has created an important database for the oil industry that should be used to monitor ecosystems.

Number of children and dementia risk: a causal mediation analysis using data from the HUNT study linked with national registries in Norway

BackgroundChildlessness, as well as having a high number of children, has been reported to be associated with an elevated risk of dementia compared to having 2–3 children. The mechanisms underlying these relationships are not well understood and may be mediated by different midlife risk factors. We examined the mediating role of various factors on the relationship between the number of children and dementia risk. These factors include socioeconomic factors (e.g., occupational complexity), psychosocial (e.g.., social activities, loneliness, life satisfaction), lifestyle (e.g., smoking, physical inactivity, alcohol intake), and chronic diseases (e.g., obesity, diabetes, depression, hearing impairment and hypertension).MethodsUsing a historic cohort design, we included 9,745 participants born between 1931–48, with a mean age of 78.2 (SD = 6.4) years at the time of cognitive testing in the HUNT4 70 + sub-study (2017–2019). Further measures were obtained through data linkage between information from Statistics Norway and the HUNT1(1984–86), and HUNT2 (1995–97) Surveys. Causal mediation analyses using an inverse odd weighting approach were conducted to decompose the total effect of the number of children (0, 1, or 4 + children vs. 2–3) on the risk of dementia at age 70 + years into direct and indirect effects with mediators assessed at a mean age of 50.7 (SD = 6.4) years. The analyses were adjusted for age, sex, marital status at age 25 years, educational status, and religion assessed during HUNT3 (2006–2008).ResultsOverall, 15.7% were diagnosed with dementia. The proportions with dementia by the number of children were 22.3% among those with no children, 21.4% for those with one child, 13% for those with 2–3 children (specifically, 12.6% for those with 2 children and 13.4% for those with 3 children), and 19.9% for those with 4 + children. Compared to the reference group of individuals with 2–3 children, the dementia risk was higher among the groups with no children (relative risk (RR): 1.30, 95% confidence interval (CI) (1.12, 1.51)), those with one child (RR: 1.30, 95% CI (1.14, 1.47)) and those with 4 + children (RR: 1.12, 95% CI (1.01, 1.24)). The elevated risks of dementia were not mediated by the socioeconomic, psychosocial, lifestyle, or chronic diseases related factors that we tested. Sex-stratified analysis showed higher dementia risk for men without children and women with one or 4 + children compared to those with 2–3 children, with similar patterns across sexes. None of the mediators contributed to mediation in either group. None of the mediators appeared to contribute through mediation in either group.ConclusionsOur findings suggest that the number of children—specifically being childless, having one child, or having four or more children—may influence the risk of dementia. These relationships were not mediated by psychosocial, lifestyle, and socioeconomic factors, or markers of chronic diseases in adulthood considered in this study.

Evaluating solar power plant sites using integrated GIS and MCDM methods: a case study in Kermanshah Province

This study utilizes an integrated Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach to perform Solar Power Plant Site Selection (SPPSS) in Kermanshah Province, Iran. It introduces a novel group weighting method, the Dempster-based Best-Worst Method (DBWM), which combines weights vectors derived from experts’ opinions. The study also conducts a comprehensive sensitivity analysis comparing four GIS-based models for SPPSS. Findings indicate that the Inverse Distance Weighted (IDW) method is the most precise for interpolation, which was subsequently applied in the analysis. Results demonstrate that the GIS-based DBWM-Technique for Order Preference by Similarity to Ideal Solution (GIS-based DBWM-TOPSIS) model is the most stable, identifying slope as the primary criterion for SPPSS. Based on this model, 3% of the area is classified as very low suitability, 9% as low, 24% as moderate, 33% as high, and 31% as very high suitability. The study highlights the substantial impact of selecting appropriate spatial analysis techniques and uses normalization to standardize input criteria with varied units and ranges, enhancing comparability within the MCDM process. Eslamabad-e Gharb, Kangavar, and Gilan-e Gharb counties emerged as the most suitable locations for solar power plant (SPP) development.

Preparing Distribution Maps of Available Phosphorus in Soils Using Various GIS-based Spatial Interpolation Methods

The study, was conducted from January, 2022 to December, 2024 at Jawaharlal Nehru Krishi Vishwa Vidhyalaya, Jabalpur, Madhya Pradesh (482004), India. We have collected two thousand two hundred sixteen (2216) Global Positioning System (GPS) based soil samples from depth of 0–15 cm from farmer's field of the Kymore Plateau and Satpura Hills zone of Madhya Pradesh, India. Laboratory analysis showed that the available P content ranged from 1.11 to 117.7 kg ha⁻¹ (mean: 10.64 kg ha⁻¹). Significant correlations were found between pH, electrical conductivity (EC), organic carbon (OC), and P (r = 0.073). The study aimed to identify the most suitable interpolation method for mapping available P in soils, employing three geo-statistical (Ordinary Kriging, Simple Kriging, and Empirical Bayesian Kriging) and three deterministic methods (Radial Basis Function, Local Polynomial Interpolation, and Inverse Distance Weighting), as well as two barrier-based methods (Kernel Smoothing and Diffusion Kernel). Geo-statistical results indicated OK (Box-Cox) spatial interpolation method for estimating available P distribution in soils that followed exponential model with ranges of 3652.22 meters, nugget values of 16.90 and a N/S ratio of 0.47 which showed moderate spatial dependency. Among the methods tested, Empirical Bayesian Kriging (EBK) provided the most accurate estimates of Olsen P distribution, followed by Ordinary Kriging (OK) and Simple Kriging with Box-Cox transformation.

Derivation of a multivariate longitudinal causal effects model

This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the proposed method, we re-define the weights as a product of inverse weights at each time point, accounting for time-varying confounders and treatment exposures and possible correlation between and within (serial) the multiple outcomes. The proposed method is evaluated by simulation studies and with an application to estimate the effect of HIV positivity awareness on condom use and multiple sexual partners using the Malawi Longitudinal Study of Families and Health (MLSFH) data. The simulation study shows that the joint MSM-IPTW performs well with coverage within the expected 95% level for a large sample size (n = 1000) and moderate to strong between and within outcome correlation strength ( ρ j = 0.3 , 0.75, ρ k = 0.4 , 0.8) when the effects are similar. The joint MSM-IPTW performed relatively the same as the adjusted standard joint model when the treatment effect estimate was the same for the outcomes. In the application, HIV positivity awareness increased the usage of condoms and did not affect the number of sexual partners. We recommend using the proposed MSM-IPTWs to correctly control for time-varying treatment and confounders when estimating causal effects for longitudinal observational studies with multiple outcomes.

Reproductive phenology and pollination biology of the wild pineapple Ananas bracteatus (Bromeliaceae): an analysis based on field and herbarium records

ABSTRACT Ananas Mill., notably known for pineapple cultivation, is one of the most recognized genera of the Bromeliaceae. Despite its agricultural significance, the phenology of wild Ananas species remains poorly understood, leading to a problematic issue because understanding their reproductive cycle is necessary to better predict the effects of climate change on their populations. This study investigated the reproductive phenology, pollination, and floral biology of Ananas bracteatus (Lindl.) Schult. & Schult.f., an endemic species from the eastern coast of South America, through observations in an Atlantic Forest fragment on Bahia’s northern coast. We estimated its flowering and fruiting periods by interpolation using Inverse Distance Weighting (IDW) based on herbarium collections, using the speciesLink database. Ananas bracteatus exhibited a highly seasonal flowering, and continuous fruiting throughout the year, primarily influenced by temperature, and related to the long maturation period of its infructescence. IDW proved highly effective showing a high probability of A. bracteatus blooming between August and December and fruiting throughout the year. The flowers remain viable throughout anthesis and pollination is conducted by two efficient pollinators: Chionomesa frimbriata Gmelin, 1788 and Eupetomena macroura Gmelin, 1788. The application of innovative methodologies, such as IDW, can address research gaps in biodiverse regions like Brazil’s Atlantic Forest, where long-term phenological studies, vital for the conservation and management of species, are often limited and scarce.

Accommodating informative visit times for analysing irregular longitudinal data: a sensitivity analysis approach with balancing weights estimators

Abstract Irregular longitudinal data with informative visit times arise when patients’ visits are partly driven by concurrent disease outcomes. However, existing methods such as inverse intensity weighting (IIW), often overlook or have not adequately assessed the influence of informative visit times on estimation and inference. Based on novel balancing weights estimators, we propose a new sensitivity analysis approach to addressing informative visit times within the IIW framework. The balancing weights are obtained by balancing observed history variable distributions over time and including a selection function with specified sensitivity parameters to characterize the additional influence of the concurrent outcome on the visit process. A calibration procedure is proposed to anchor the range of the sensitivity parameters to the amount of variation in the visit process that could be additionally explained by the concurrent outcome given the observed history and time. Simulations demonstrate that our balancing weights estimators outperform existing weighted estimators for robustness and efficiency. We provide an R Markdown tutorial of the proposed methods and apply them to analyse data from a clinic-based cohort of psoriatic arthritis.

Groundwater quality mapping based on the Wilcox Classification method for agricultural purposes: Qazvin Plain aquifer case

Water quality is an essential component in managing surface and groundwater resources and for various uses; it is considered a necessary principle in planning. This study aims to map the groundwater quality of the Qazvin Plain aquifer in Iran for agricultural use based on the Wilcox classification method. For this purpose, the parameters of electrical conductivity (EC) and sodium adsorption ratio (SAR) of wells in the years 2015-2018 have been used. For interpolation, the inverse distance weighting (IDW) method with optimal power and Kriging geostatistical techniques were used based on spherical, exponential, and Gaussian semicircle algorithms. Electrical conductivity and SAR maps were drawn in the GIS platform after selecting the best interpolation method due to minor errors. The Wilcox method was used to classify the water quality of the studied wells. In most cases, the IDW method with optimal power was selected as the superior interpolation method. During the study, the results obtained from the water quality maps showed that the level attributed to the "high salinity" water class increased from 25.98 to 36.44, and the level attributed to the "slightly salty" water class decreased from 12.54 to 3.14. Finally, the results showed that the quality of underground water for agricultural purpose in the Qazvin Plain aquifer became more unfavourable during the studied period

Carcinogenic and non-carcinogenic risk estimation of indoor TVOCs, RSPM, FPM and SFPM on young women dwellers: a case study from the capital city of Uttar Pradesh, India

Total volatile organic compounds (TVOCs), respiratory suspended particulate matter (RSPM-PM10), fine particulate matter (FPM-PM2.5) and sub-fine particulate matter (SFPM-PM1) have been found to exert negative impact on the women health. This study was conducted to see the effect of indoor RSPM, FPM, SFPM and TVOCs on women health predominantly on young women dwellers (specifically categorized into pre-teenagers i.e., 8–12 years, teenagers i.e., 13–19 years and post-teenagers i.e., 20–21 years). Indoor monitoring was conducted from November 2022 to February 2023 in six different urban households of Lucknow, capital city of Uttar Pradesh state of India. Envirotech APM 550 for RSPM and FPM, APM 577 for SFPM and portable sensor-based instrument (BR-SMART) were used to measure TVOCs. The highest average indoor concentrations was found to be 250.1 ± 14.11 µg/m3 (PM10) at Rajajipuram, 140.62 ± 19.71 µg/m3 (PM2.5) at Indranagar, 27.60 ± 1.87 µg/m3 (PM1) and 934 ± 70.41 µg/m3 (TVOCs) at Kaiserbagh.Health risk assessment was also determined using average daily dose (ADD), excess lifetime cancer risk (ELCR) and hazard quotient (HQ) for carcinogenic and non-carcinogenic risk. ELCR values for PM1 and PM2.5 surpassed the permissible limit in every house and HQ values also exceeded the minimum allowable value for 20–21 year age group at all of the locations, indicating substantial health risk from exposure. International Committee of Radiological Protection Model (ICRP) and Multiple Path Particle Dosimetry (MPPD) model were used to see the regional deposition of PMs on the young women dwellers. To elucidate the spatial dynamics of these pollutants, the Inverse Distance Weighting (IDW) interpolation technique was employed. Additionally, site-specific analysis of PM mass ratios (PM2.5/PM10, PM1/PM2.5 and PM1/PM10) elucidated the particle size distribution and their sources, significantly enhancing the scientific understanding of aerosol dynamics in these urban settings.

Detecting high-risk road segments using kriging and inverse distance weighted methods

The detection of high-risk road segments is essential to set priorities in road rehabilitation projects. The ArcGIS tool has been used in previous studies as an alternative tool to identify high-risk segments. However, most tools use crash frequency as a safety performance measure. In this study, the aim is to investigate how ArcGIS tools, specifically kriging and inverse distance weighted (IDW) tools, can be used to find high-risk road segments using crash rate and equivalent property damage only (EPDO) metrics. A road section with a length of 85 km was selected for applying the investigated tools. Crash data with their details were collected from the official police station for 3 years. Kriging and IDW tools were processed by selecting five clusters, from the high-risk to the no-risk levels. The ranks of the segments have been compared with those resulting from the K-mean method using the t-paired test. The test results indicate that kriging and IDW yield uncorrelated segment ranks compared to the K-mean rank. In addition, using these tools produced false positives and false negative cases. According to the K-mean results, 37.8 km is diagnosed as the top highest risk segment, subjected to further crash data analysis to identify the risk factors and recommend suitable countermeasures.

Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·m−3, 162.3 Bq·m−3, and 150.8 Bq·m−3, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·m−3. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment.

A comparative study of interpolation methods for the development of ore distribution maps

Mining projects require precise knowledge about tonnage and quality of ore reserves for planning and decision-making. This is hard to establish as exploration operations, which are costly, time-consuming and require an accurate description of the deposit. Geologists use deterministic and geostatistical methods as interpolation tools to generate a continuous (or prediction) ore distribution map from known sampled data. A comparative study between four different geostatistical interpolation techniques: Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK) and Empirical Bayesian Kriging (EBK); and four deterministic techniques: Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), Radial Basis Function (RBF), and Local Polynomial Interpolation (LPI) were applied. The 95% confidence level was measured and the effectiveness of each interpolation method was assessed through cross-validation for the construction of the corresponding maps displaying the distribution of ore on the surface with the use of GIS. The output methods are ranked to perform a comprehensive analysis using the error statistics methods: Mean Error (ME), Root Mean Square Error (RMSE), Mean Standardized Error (MSE) and Root Mean Square Standardized Error (RMSSE). The results show that GPI, EBK and Kriging methods perform the best results while IDW has good statistical analysis factors but it came in the tail of the rank. Therefore, there is no appropriate interpolation method accurate for all cases, each method must be statistically evaluated before each application and essentially based on real data.

APPLICATION OF THE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) METHOD IN FORECASTING THE CONSUMER PRICE INDEX IN FIVE CITIES OF SOUTH SULAWESI PROVINCE

Changes in the Consumer Price Index (CPI) over time reflect the rate of increase (inflation) or decrease (deflation) of goods and services for daily household needs. The CPI and inflation serve as barometers for economic growth stability, as controlled inflation can increase people's purchasing power over time. According to the Central Statistics Agency (2023), in December, the year-on-year (y-o-y) inflation for five cities in South Sulawesi (Bulukumba, Watampone, Makassar, Parepare, and Palopo) was 2.81 percent, with a CPI of 117.35. Of the five cities, the highest y-o-y inflation occurred in Makassar at 2.89 percent, with a CPI of 117.49, while the lowest y-o-y inflation occurred in Palopo at 2.21 percent, with a CPI of 115.60. CPI forecasting is one way to predict future inflation values. This study aims to develop the best GSTAR model for forecasting CPI data for five cities in South Sulawesi, a topic that has not been extensively covered in previous research. The goal is to provide valuable information for maintaining CPI stability in South Sulawesi and to support the formulation of better economic policies. The study focuses on five cities within South Sulawesi, where direct relationships between cities are possible, allowing the spatial model to be limited to the first-order. The data used in this study consists of monthly CPI data from January 2014 to March 2023. The location weights used in the model include uniform weights, inverse distances, and normalized cross-correlations. The model development steps include testing for data stationarity, determining the space-time sequence, calculating location weights, estimating parameters, testing model adequacy, comparing Root Mean Square Error (RMSE), and selecting the best model for forecasting. The best GSTAR model found is GSTAR (1;1)-I(2) with inverse distance weighting, which yielded the smallest RMSE value. The results show that the forecasted values closely match the actual values for each city from March to September 2023.

Visualization microclimate scenarios of Classical Chinese Garden in Suzhou, China

The intensification of urbanization worldwide, particularly in China, has led to significant challenges in maintaining sustainable urban environments, primarily due to the Urban Heat Island (UHI) effect. This effect exacerbates urban thermal stress, leading to increased energy consumption, poor air quality, and heightened health risks. In response, urban green spaces are recognized for their role in ameliorating urban heat and enhancing environmental resilience. This paper has studied the microclimate regulation effects of three representative classical gardens in Suzhou—the Humble Administrator’s Garden, the Lingering Garden and the Canglang Pavilion. It aims to explore the specific impacts of water bodies, vegetation and architectural features on the air temperature and relative humidity within the gardens. With the help of Geographic Information System (GIS) technology and the Inverse Distance Weighted (IDW) spatial interpolation method, this study has analyzed the microclimate regulation mechanisms in the designs of these traditional gardens. The results show that water bodies and lush vegetation have significant effects on reducing temperature and increasing humidity, while the architectural structures and rocks have affected the distribution and retention of heat to some extent. These findings not only enrich our understanding of the role of the design principles of classical gardens in climate adaptability but also provide important theoretical basis and practical guidance for the design of modern urban parks and the planning of sustainable urban environments. In addition, the study highlights GIS-based spatial interpolation as a valuable tool for visualizing and optimizing thermal comfort in urban landscapes, providing insights for developing resilient urban green spaces.

Inpatient Hospitalizations for COVID-19 Among Patients With Prader-Willi Syndrome: A National Inpatient Sample Analysis.

Prader-Willi syndrome (PWS) is a genetic disorder associated with baseline respiratory impairment caused by multiple contributing etiologies. While this may be expected to increase the risk of severe COVID-19 infections in PWS patients, survey studies have suggested paradoxically low disease severity. To better characterize the course of COVID-19 infection in patients with PWS, this study analyses the outcomes of hospitalizations for COVID-19 among patients with and without PWS. The National Inpatient Sample, an all-payors administrative claims database of hospitalizations in the United States, was queried for patients with a coded diagnosis COVID-19 in 2020 and 2021. Hospitalizations for patients with PWS compared to those for patients without PWS using Augmented Inverse Propensity Weighting (AIPW). There were 295 (95% CI: 228-362) COVID-19 hospitalizations for individuals with PWS and 4,112,400 (95% CI: 4,051,497-4,173,303) for individuals without PWS. PWS patients had a median age of 33 years compared to 63 for those without PWS. Individuals with PWS had higher baseline rates of obesity (47.5% vs. 28.4%). AIPW models show that PWS diagnosis is associated with increased hospital length of stay by 7.43 days, hospital charges by $80,126, and the odds of mechanical ventilation and in-hospital death (odds ratios of 1.79 and 1.67, respectively). PWS patients hospitalized with COVID-19 experienced longer hospital stays, higher charges, and increased risk of mechanical ventilation and death. These results suggest that PWS should be considered a risk factor for severe COVID-19, warranting continued protective measures and vaccination efforts. Further research is needed to validate coding for PWS and assess the impact of evolving COVID-19 variants and population immunity on this vulnerable population.

Spatiotemporal distribution of global peatland area during the Holocene

Peatlands are a key component of terrestrial ecosystems, and their development has an important impact on global carbon cycle and climate change. However, the long-term evolution of global peatlands remains uncertain, particularly their spatial distribution. We compiled 4700 basal peatland data during Holocene, and 669 pollen data of Sphagnum with basal and end ages, to allow a more robust reconstruction of the spatial distribution of peatlands. Using buffer analysis (BA) and inverse distance weighted (IDW) interpolation of peat data, we reconstructed spatiotemporal changes in global peatland area at a spatial resolution of 0.5° × 0.5° for every 1,000 years period during Holocene. The results show that peatland area have expanded substantially in North America, Europe, and Western Siberia during early-Holocene, and increased rapidly from 2.18(0.32) Mkm2 to 4.03(3.08) Mkm2 during 12-6 ka BP, then slowly to 4.15(4.23) Mkm2 after 6 ka BP according to BA (IDW) methods. The database will be useful for analyzing the global/regional terrestrial carbon cycle and climate change during Holocene, especially for modeling peatland methane emissions.

Spatiotemporal Interpolation of Actual Evapotranspiration Across Turkey Using the Australian National University Spline Model: Insights into Its Relationship with Vegetation Cover

Accurate and precise prediction of actual evapotranspiration (AET) on a large scale is a fundamental issue in natural sciences such as forestry (especially in species selection and planning), hydrology, and agriculture. With the estimation of AET, controlling dams, agriculture, and irrigation and providing potable and utility water supply for industry would be possible. Gathering reliable AET data is possible only with a sufficient weather station network, which is rarely established in many countries like Turkey. Therefore, climate models must be developed for reliable AET data, especially in countries with complex terrains. This study aimed to generate spatiotemporal AET surfaces using the Australian National University spline (ANUSPLIN) model and compare the results with the maps generated by the inverse distance weighting (IDW) and co-kriging (KRG) interpolation techniques. Findings from the interpolated surfaces were validated in three ways: (1) some diagnostics from the surface fitting model include measures such as signal, mean, root mean square predictive error, root mean square error estimate, root mean square residual of the spline, and the estimated standard deviation of noise in the spline; (2) a comparison of common error statistics between the interpolated surfaces and withheld climate data; and (3) evaluation by comparing model results with other interpolation methods using metrics such as mean absolute error, mean error, root mean square error, and adjusted R2 (R2adj). The correlation between AET and normalized difference vegetation index (NDVI) was also evaluated. ANUSPLIN outperformed the other techniques, accounting for 73 to 94% (RMSE: 3.7 to 26.1%) of the seasonal variation in AET with an annual value of 83% (RMSE: 10.0%). The correlation coefficient between observed and predicted AET based on NDVI ranged from 0.49 to 0.71 for point-based and 0.62 to 0.83 for polygon-based data. The generated maps at a spatial resolution of 0.005° × 0.005° could provide valuable insights to researchers and practitioners in the natural resources management domain.

Rehabilitation at Home Using Mobile Health for Older Adults Hospitalized for Ischemic Heart Disease

Among older adults with ischemic heart disease, participation in traditional ambulatory cardiac rehabilitation (CR) remains low. While mobile health CR (mHealth-CR) provides a novel opportunity to deliver care, age-specific impairments to technology use may limit uptake, and efficacy data are currently lacking. To test whether mHealth-CR improves functional capacity in older adults. The RESILIENT phase 2, multicenter, randomized clinical trial recruited patients aged 65 years or older with ischemic heart disease (defined as a hospital visit for myocardial infarction or coronary revascularization) from 5 academic hospitals in New York, Connecticut, and Massachusetts between January 9, 2020, and April 22, 2024. Participants were randomized 3:1 to mHealth-CR or usual care. mHealth-CR consisted of commercially available software delivered on a tablet computer, coupled with remote monitoring and weekly exercise therapist telephone calls, delivered over a 3-month duration. As RESILIENT was a trial conducted in a routine care setting to inform decision-making, participants in both arms were also allowed to receive traditional CR at their cardiologist's discretion. The primary outcome was change from baseline to 3 months in functional capacity, measured by 6-minute walk distance (6MWD). Secondary outcomes were health status (12-Item Short Form Health Survey [SF-12]), residual angina, and impairment in activities of daily living. A total of 400 participants (median age, 71.0 years [range, 65.0-91.0 years]; 291 [72.8%] male) were randomized to mHealth-CR (n = 298) or usual care (n = 102) and included in the intention-to-treat analysis. Of those, 356 participants (89.0%) returned in person for 6MWD assessment at 3 months. For the primary outcome, there was no adjusted difference in 6MWD between participants receiving mHealth-CR vs usual care (15.6 m; 95% CI, -0.3 to 31.5 m; P = .06). Among subgroups, there was an improvement in 6MWD among women (36.6 m; 95% CI, 8.7-64.4 m). There were no differences in any secondary outcomes between groups (eg, adjusted difference in SF-12 physical component scores at 3 months: -1.9 points; 95% CI, -3.9 to 0.2 points). Based on inverse propensity score weighting, there was no effect of mHealth-CR on 6MWD among those who did not attend traditional CR (25.7 m; 95% CI, -8.7 to 60.2 m). In this randomized clinical trial of mHealth-CR vs usual care, mHealth-CR did not significantly increase 6MWD or result in improvements in secondary outcomes. The findings suggest the older adult population may require more age-tailored mHealth strategies to effectively improve outcomes. ClinicalTrials.gov Identifier: NCT03978130.