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作者： Jarvis, Shannon Trent University (Canada)

学位级别：M.S., Master of Science/Master of Surgery

This thesis explores the shot-term relationship between exposure to ambient air pollution and human health through metrics such as mortality and hospitalization in Canada. We begin by detailing the organization and interpolation of air pollution data from its partially quality-controlled source form. Analyses of seasonal, regional and temporal trends of all major components of PM2.5, was performed, showing a seasonal variation across most regions and validating the dataset. A one-pollutant statistical generalized additive Model was applied to the data, estimating the health risk associated with exposure to thirteen different components of PM2.5. The selected components were based on those that compromised the majority of the mass and included: sulphate, nitrate, zinc, silicon, iron, nickel, vanadium, potassium, organic carbon, organic matter, elemental carbon, total carbon. Trends based on annual estimates of the association for PM2.5, and its constituents,were compared, showing that carbonaceous compounds, sulphate and nitrate had similar estimates of association. Many estimates, as is common in population ecologic epidemiology, had association estimates statistically indistinguishable from zero,but with clear features of interest, including evident differences between cold and warm season associations in Canada's temperate climate. A method to model two correlated pollutants (in this case, PM2.5 and O3) was developed using thin plate splines. In this approach, the location of the response surface (after accounting for the temperature, a smooth function of time and day of week) that corresponds to the average pollutant concentration and the average plus one unit was used as the estimate of the joint contribution of pollutants due to a unit increase. The estimates from the thin plate spline (TPS) approach were compared to the single pollutant models, with large increases and decreases in PM2.5 and O3 being captured in the TPS estimates. However, this approach indica

关键词： Air pollution Environmental epidemiology generalized additive models Human health Multivariate models Thin plate splines

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Analysis of the effect of meteorological factors on hemorrhagic fever with renal syndrome in Taizhou City, China, 2008-2020

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BMC PUBLIC HEALTH 2022年第1期22卷 1097-1097页

作者： Zhang, Rong Zhang, Ning Sun, Wanwan Lin, Haijiang Liu, Ying Zhang, Tao Tao, Mingyong Sun, Jimin Ling, Feng Wang, Zhen Zhejiang Prov Ctr Dis Control & Prevent Key Lab Vaccine Prevent & Control Infect Dis Zhej Hangzhou 310051 Zhejiang Peoples R China Puyan St Community Hlth Serv Ctr Binjiang Dist Hangzhou 310013 Zhejiang Peoples R China Taizhou City Ctr Dis Control & Prevent Taizhou 318000 Zhejiang Peoples R China Ningbo Univ Sch Med Ningbo 315000 Zhejiang Peoples R China

Background Hemorrhagic fever with renal syndrome (HFRS) is endemic in Zhejiang Province, China, while few studies have concentrated on the influence of meteorological factors on HFRS incidence in the area. Methods Data on HFRS and meteorological factors from January 1, 2008 to December 31, 2020 in Taizhou City, Zhejiang Province were collected. Multivariate analysis was conducted to the relationship between meteorological factors including minimum temperatures, relative humidity, and cumulative rainfall with HFRS. Results The HFRS incidence peaked in November and December and it was negatively correlated with average and highest average temperatures. Compared with median of meteorological factors, the relative risks (RR) of weekly average temperature at 12 celcius, weekly highest temperature at 18 celciusrelative humidity at 40%, and cumulative rainfall at 240 mm were most significant and RRs were 1.41 (95% CI: 1.09-1.82), 1.32 (95% CI: 1.05-1.66), 2.18 (95% CI: 1.16-4.07), and 1.91 (95% CI: 1.16-2.73), respectively. Average temperature, precipitation, relative humidity had interactions on HFRS and the risk of HFRS occurrence increased with the decrease of average temperature and the increase of precipitation. Conclusion Our study results are indicative of the association of environmental factors with the HFRS incidence, probable recommendation could be use of environmental factors as early warning signals for initiating the control measure and response.

关键词： Hemorrhagic fever with renal syndrome Distributed lag non-linear generalized additive models Lag effect Interactive effect

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Temperature extremes and human health in Cyprus: Investigating the impact of heat and cold waves

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ENVIRONMENT INTERNATIONAL 2025年 199卷

作者： Kekkou, Fragkeskos Economou, Theo Lazoglou, Georgia Anagnostopoulou, Christina Cyprus Inst Climate & Atmosphere Res Ctr CARE C CY-2121 Aglantzia Cyprus Univ Exeter Dept Math & Stat Exeter England Aristotle Univ Thessaloniki Fac Sci Sch Geol Dept Meteorol & Climatol Thessaloniki 54124 Greece

In Mediterranean countries like Cyprus, extreme temperatures and especially heatwaves during summer months are increasingly common, leading to heightened bio-climatic stress. While coldwaves are less frequent and understudied in this region, they still pose significant health risks. This study utilizes temperature data from ERA5-Land in conjunction with mortality and hospitalization data from Cyprus. Employing statistical machine learning methods such as Distributed Lag models (DLMs) and generalized additive models (GAMs), mortality and hospitalization risks for major health concerns in Cyprus, along with attributable deaths and hospital admissions associated with temperature fluctuations and extreme events over two seasons, were estimated. This study also examines temperature trends over the past four decades in Cyprus, noting a significant increase in both maximum and minimum temperatures as well as an increase in extreme high-temperature days and a decrease in extreme low-temperature days. For mortality, elevated risks were identified at very high temperatures, peaking at shorter lags with up to a 7.5% above-average risk, which further increased to 14.3% during heatwave events in the warm season. Conversely, mortality risks at very low temperatures during the cold season reached 4.2% particularly for longer lag days. Hospitalization risks were notably higher during periods of cold temperatures. Importantly, our findings confirm that cold-related mortality exceeds heat-related mortality in absolute terms. However, during heatwave days, the rate of increase in attributable deaths is five times higher compared to typical days in the warm season, whereas for cold days it is about twice as high as on non-cold days.

关键词： Heatwave Coldwave generalized additive models Mortality Hospitalizations Cause-specific Wave-synergy

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Stepping up to the thermogradient plate: a data framework for predicting seed germination under climate change

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ANNALS OF BOTANY 2022年第7期129卷 787-794页

作者： Collette, Justin C. Sommerville, Karen D. Lyons, Mitchell B. Offord, Catherine A. Errington, Graeme Newby, Zoe-Joy von Richter, Lotte Emery, Nathan J. Australian Inst Bot Sci Australian Bot Garden Australian PlantBank Mt Annan NSW 2567 Australia Sch Biol Earth & Environm Sci Ctr Ecosyst Sci Sydney NSW 2052 Australia

Background and Aims Seed germination is strongly influenced by environmental temperatures. With global temperatures predicted to rise, the timing of germination for thousands of plant species could change, leading to potential decreases in fitness and ecosystem-wide impacts. The thermogradient plate (TGP) is a powerful but underutilized research tool that tests germination under a broad range of constant and alternating temperatures, giving researchers the ability to predict germination characteristics using current and future climates. Previously, limitations surrounding experimental design and data analysis methods have discouraged its use in seed biology research. Methods Here, we have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature. We illustrate this analysis framework using three example species: Wollemia nobilis, Callitris baileyi and Alectryon subdentatus. The script generates >40 germination indices including germination rates and final germination across each cell of the TGP. These indices are then used to populate generalized additive models and predict germination under current and future monthly maximum and minimum temperatures anywhere on the globe. Key Results In our study species, modelled data were highly correlated with observed data, allowing confident predictions of monthly germination patterns for current and future climates. Wollemia nobilis germinated across a broad range of temperatures and was relatively unaffected by predicted future temperatures. In contrast, C. baileyi and A. subdentatus showed strong seasonal temperature responses, and the timing for peak germination was predicted to shift seasonally under future temperatures. Conclusions Our experimental workflow is a leap forward in the analysis of TGP experiments, increasing its many potential benefits, thereby improving research predictions and providing substantial information to inform management and conservation of plan

关键词： Alectryon subdentatus Callitris baileyi climate change generalized additive models germination niche seed testing temperature Wollemia nobilis

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The habitat preference of commercial tuna species based on a daily environmental database approach in the tropical region of the Eastern Indian Ocean off Java-Bali waters

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DEEP-SEA RESEARCH PART II-TOPICAL STUDIES IN OCEANOGRAPHY 2024年 216卷

作者： Setiawati, Martiwi Diah Rachman, Herlambang Aulia As-syakur, Abd Rahman Setiawan, Riza Yuliratno Syahailatua, Augy Wouthuyzen, Sam Natl Res Innovat Agcy BRIN Res Ctr Oceanog Jakarta 14430 Indonesia United Nations Univ Inst Adv Study Sustainabil UNU IAS Tokyo 1508925 Japan Trunojoyo Univ Dept Marine Sci Madura Bangkalan 69162 Indonesia Udayana Univ Fac Marine & Fisheries Marine Sci Dept Bukit Jimbaran Campus Bali 80361 Indonesia Gadjah Mada Univ Fac Agr Dept Fisheries Yogyakarta 55281 Indonesia

This paper explores the habitat characteristics of commercial tuna species based on daily oceanography parameters in the Eastern Indian Ocean Off Java-Bali Waters. Moreover, more research is needed combining the daily spatial distribution of oceanographic variables of surface and sub-surface data to analyse the habitat characteristics of large pelagic fish, including tuna species. In this study, we used five main daily oceanography parameters: sea surface temperature (SST), sea surface chlorophyll (CHLa), sea surface height (SSH), dissolved oxygen at 100m (DO100), the temperature at 100m (temp100) and the combination of the catch of yellowfin (YFT), albacore (ALT) and bigeye (BET) tuna that use long lines. To analyse the relationship between the environmental database and tuna catch, we utilized generalized additive models (GAMs) from univariate variables until the combination of all variables. The result stated that all the variables influence the existence of all tuna species with P-values <0.001. Temperature is the most critical predictor variable, SST is the most vital predictor for BET and YFT tuna, and temp100 is the most critical for ALT. The second most essential variables were DO100 for BET, Temp 100 for YFT, and SSH for ALT. Moreover, BET and YFT prefer to stay at a lower temp100, and ALT tuna remains at a higher temp100. However, all of them avoid an SST higher than 29 degrees C. Further assessment of the long-term SST trend specific to tuna species is required to fully account for the effects of global warming on the oceans.

关键词： Bigeye Yellowfin Albacore generalized additive models Habitat suitability index Indonesian seas Indonesian seas

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COVID-19, social distancing and guests' preferences: impact on peer-to-peer accommodation pricing

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CURRENT ISSUES IN TOURISM 2022年第16期25卷 2571-2577页

作者： Hidalgo, Alberto Riccaboni, Massimo Rungi, Armando Velazquez, Francisco J. IMT Sch Adv Studies Lucca Italy Univ Complutense Madrid Dept Appl & Struct Econ & Hist Madrid Spain

This paper investigates how guests' preferences in peer-to-peer (P2P) accommodations changed during the COVID-19 summer season. Specifically, we test the importance of attributes that better allow for preserving the social distancing. To this end, we adopt a semi-parametric hedonic pricing model. We take the city of Madrid as a compelling case study of an important tourist destination severely hit by the crisis. We show that guests' marginal willingness to pay for social distancing characteristics has changed from August 2019 to August 2020. In particular, we find that whereas listings with kitchen amenities increase 15.2 percentage points their premium price in August 2020 with respect to the previous year, the marginal willingness to pay for size-related characteristics decreased by 2.7 percentage points. Results are robust to sample and time composition. This study provides meaningful findings of a shift in guests' tastes towards social distancing attributes on P2P accommodations.

关键词： COVID-19 pandemic consumer behaviour peer-to-peer accommodation hedonic modelling generalized additive models

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Effective Modeling of CO2 Emissions for Light-Duty Vehicles: Linear and Non-Linear models with Feature Selection

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ENERGIES 2024年第7期17卷 1655页

作者： Vu, Hang Thi Thanh Ko, Jeonghan Ajou Univ Dept Ind Engn 206 Worldcup ro Suwon 16499 South Korea

Predictive modeling is important for assessing and reducing energy consumption and CO2 emissions of light-duty vehicles (LDVs). However, LDV emission datasets have not been fully analyzed, and the rich features of the data pose challenges in prediction. This study aims to conduct a comprehensive analysis of the CO2 emission data for LDVs and investigate key prediction model characteristics for the data. Vehicle features in the data are analyzed for their correlations and impact on emissions and fuel consumption. Linear and non-linear models with feature selection are assessed for accuracy and consistency in prediction. The main behaviors of the predictive models are analyzed with respect to vehicle data. The results show that the linear models can achieve good prediction performance comparable to that of nonlinear models and provide superior interpretability and reliability. The non-linear generalized additive models exhibit enhanced accuracy but display varying performance with model and parameter choices. The results verify the strong impact of fuel consumption and powertrain attributes on emissions and their substantial influence on the prediction models. The paper uncovers crucial relationships between vehicle features and CO2 emissions from LDVs. These findings provide insights for model and parameter selections for effective and reliable prediction of vehicle emissions and fuel consumption.

关键词： CO2 emission fuel consumption predictive modeling linear regression non-linear generalized additive models sustainability

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Improving diagnostics and prognostics of implantable cardioverter defibrillator batteries with interpretable machine learning models

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JOURNAL OF POWER SOURCES 2024年 610卷

作者： Galuppini, Giacomo Liang, Qiaohao Tamirisa, Prabhakar A. Lemmerman, Jeffrey A. Sullivan, Melani G. Mazack, Michael J. M. Gomadam, Partha M. Bazant, Martin Z. Braatz, Richard D. MIT Cambridge MA 02139 USA Medtron Energy & Component Ctr Brooklyn Ctr MN USA Univ Pavia Pavia PV Italy

Medtronic Implantable Cardioverter Defibrillators (ICDs) and Cardiac Resynchronization Therapy Defibrillators (CRT-Ds) rely on high-energy density, lithium batteries, which are manufactured with a special lithium/carbon monofluoride (CFx)-silver F x )-silver vanadium oxide (SVO) hybrid cathode design. Consistently high battery performance is crucial for this application, since poor performance may result in ineffective patient treatment, whereas early replacement may involve surgery and increase in maintenance costs. To evaluate performance, batteries are tested, both at the time of production and post-production, through periodic sampling carried out over multiple years. This considerable amount of experimental data is exploited for the first time in this work to develop a data-driven, machine learning approach, relying on generalized additive models (GAMs) to predict battery performance, based on production data. GAMs combine prediction accuracy, which enables evaluation of battery performance immediately after production, with model interpretability, which provides clues on how to further improve battery design and production. Model interpretation allows to identify key features from the battery production data that offer physical insights to support future battery development, and foster the development of physics-based model for hybrid cathode batteries. The proposed approach is validated on 21 different datasets, targeting several performance-related features, and delivers consistently high prediction accuracy on test data.

关键词： Batteries Defibrillators Machine learning generalized additive models Diagnostics Prognostics

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Mapping soil organic carbon density via geographically weighted regression with smooth terms: A case study in Shanxi Province

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ECOLOGICAL INDICATORS 2024年 166卷

作者： Zheng, Yutong Zhao, Xiaonan Li, Xiangyu Chen, Hongyu Li, Changcheng Zhang, Chutian Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Peoples R China

Soil, as the largest organic carbon reservoir in terrestrial ecosystems, plays a crucial role in the global carbon cycle. Accurately predicting the spatial distribution of soil organic carbon density (SOCD) is highly important for estimating organic carbon stocks and guiding agricultural production. Due to the complex relationship between the spatial distribution of SOCD and environmental factors, its relationship with a specific region is not exclusively linear or nonlinear;both types of relationships may coexist simultaneously. In this study, a new model, geographically weighted regression with smooth terms (SGWR), was proposed. This model can simultaneously consider both global spatial nonlinear relationships and local spatial nonstationary relationships. It was utilized to predict SOCD in Shanxi Province and was compared to multiple linear regression (MLR), generalized additive models (GAM) and geographically weighted regression (GWR) in terms of spatial prediction performance. The results showed that SGWR was highly competitive in predicting the spatial distribution of SOCD in Shanxi Province. Of factors influencing SOCD in Shanxi Province, elevation and slope were considered local linear variables, while the normalized difference vegetation index (NDVI), gross primary productivity (GPP), annual precipitation (AP), and soil moisture (SM) were considered global nonlinear variables. Among these factors, the NDVI and GPP had the most significant impacts on the distribution of SOCD, while elevation and SM had the least significant impacts on it. The results imply that integrating global linear, local linear, and global nonlinear relationships concurrently, as implemented in SGWR, improves predictive accuracy compared to approaches that solely address either local or global linear relationships based on environmental variable changes in SGWR. The results of this study indicate that the SGWR model is potentially a competitive method for digital soil mapping.

关键词： Soil organic carbon density Nonlinear relationship Spatial nonstationary relationship generalized additive models Geographically weighted regression model with smooth terms

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A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants

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INTERNATIONAL JOURNAL OF FORECASTING 2022年第1期38卷 321-338页

作者： Posch, Konstantin Truden, Christian Hungerlaender, Philipp Pilz, Juergen Alpen Adria Univ Klagenfurt Dept Math Klagenfurt Austria Alpen Adria Univ Klagenfurt Dept Stat Klagenfurt Austria

Accurate demand forecasting is one of the key aspects for successfully managing restaurants and staff canteens. In particular, properly predicting future sales of menu items allows for a precise ordering of food stock. From an environmental point of view, this ensures a low level of pre-consumer food waste, while from the managerial point of view, this is critical to the profitability of the restaurant. Hence, we are interested in predicting future values of the daily sold quantities of given menu items. The corresponding time series show multiple strong seasonalities, trend changes, data gaps, and outliers. We propose a forecasting approach that is solely based on the data retrieved from point-of-sale systems and allows for a straightforward human interpretation. Therefore, we propose two generalized additive models for predicting future sales. In an extensive evaluation, we consider two data sets consisting of multiple time series collected at a casual restaurant and a large staff canteen and covering a period of 20 months. We show that the proposed models fit the features of the considered restaurant data. Moreover, we compare the predictive performance of our method against the performance of other well-established forecasting approaches. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Sales prediction Time series generalized additive models Bayesian methods Shrinkage priors

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