Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques

Concentrations of four heavy metals (Cr, Cu, Ni, and Zn) were measured at 1,082 sampling sites in Changhua county of central Taiwan. A hazard zone is defined in the study as a place where the content of each heavy metal exceeds the corresponding control standard. This study examines the use of spatial analysis for identifying multiple soil pollution hotspots in the study area. In a preliminary investigation, kernel density estimation (KDE) was a technique used for hotspot analysis of soil pollution from a set of observed occurrences of hazards. In addition, the study estimates the hazardous probability of each heavy metal using geostatistical techniques such as the sequential indicator simulation (SIS) and indicator kriging (IK). Results show that there are multiple hotspots for these four heavy metals and they are strongly correlated to the locations of industrial plants and irrigation systems in the study area. Moreover, the pollution hotspots detected using the KDE are the almost same to those estimated using IK or SIS. Soil pollution hotspots and polluted sampling densities are clearly defined using the KDE approach based on contaminated point data. Furthermore, the risk of hazards is explored by these techniques such as KDE and geostatistical approaches and the hotspot areas are captured without requiring exhaustive sampling anywhere

Precipitation controls on nutrient budgets in subtropical and tropical forests and the implications under changing climate

Biological, geological and hydrological drivers collectively control forest biogeochemical cycling. However, based on a close examination of recent literature, we argue that the role of hydrological control particularly precipitation on nutrient budgets is significantly underestimated in subtropical and tropical forests, hindering our predictions of future forest nutrient status under a changing climate in these systems. To test this hypothesis, we analyzed two decades of monthly nutrient input and output data in precipitation and streamwater from a subtropical forested watershed in Taiwan, one of the few sites that has long-term nutrient input-output data in the tropics and subtropics. The results showed that monthly input and output of all ions and budgets (output – input) of most ions were positively correlated with precipitation quantity and there was a surprisingly greater net ion export during the wet growing season, indicating strong precipitation control on the nutrient budget. The strong precipitation control is also supported by the divergence of acidic precipitation and near neutral acidity of streamwater, with the former being independent from precipitation quantity but the latter being positively related to precipitation quantity. An additional synthesis of annual precipitation quantity and nutrient budgets of 32 forests across the globe showed a strong correlation between precipitation quantity and nutrient output-input budget, indicating that strong precipitation control is ubiquitous at the global scale and is particularly important in the humid tropical and subtropical forests. Our results imply that climate change could directly affect ecosystem nutrient cycling in the tropics through changes in precipitation pattern and amount

MENTOR: Multilingual tExt detectioN TOward leaRning by analogy

Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task. For instance, delivery robots in multilingual cities need to be capable of doing multilingual text detection so that the robots can read traffic signs and road markings. Moreover, the target languages change from region to region, implying the need of efficiently re-training the models to recognize the novel/new languages. However, collecting and labeling training data for novel languages are cumbersome, and the efforts to re-train an existing/trained text detector are considerable. Even worse, such a routine would repeat whenever a novel language appears. This motivates us to propose a new problem setting for tackling the aforementioned challenges in a more efficient way: "We ask for a generalizable multilingual text detection framework to detect and identify both seen and unseen language regions inside scene images without the requirement of collecting supervised training data for unseen languages as well as model re-training". To this end, we propose "MENTOR", the first work to realize a learning strategy between zero-shot learning and few-shot learning for multilingual scene text detection.Comment: 8 pages, 4 figures, published to IROS 202

Epidemiology of acute otitis media among young children: A multiple database study in Taiwan

Background/PurposeAcute otitis media (AOM) is a common complication of upper respiratory tract infection (URTI) among children. The purpose of this study was to evaluate the epidemiology of AOM among young children in Taiwan, including the age incidence and seasonality by combining multiple databases.MethodsTwo country-based questionnaire survey studies had been conducted to evaluate the experience of otitis media (OM) among young children: one in 2007 and the other between 2005 and 2010. The number of OM cases (5% of population younger than 7 years) in 2005 and annual visiting rates for URTI from 2005 to 2010 obtained from the National Health Insurance Research Database of Taiwan were collected and comprised the third database. The fourth database comprised ambulatory visits of children with OM to a medical center in central Taiwan between 2005 and 2010.ResultsData from a total of 1099 questionnaires were entered into Database I in 2007, and data from 9705 questionnaires between 2005 and 2010 comprised Database II. There were 86,702 children (younger than 7 years, representing 5% of the whole population for this age group) retrieved from Database III in 2007, and 5,904 cases of OM in children between 2005 and 2010 in a hospital. In Database I, 7.46% children experienced at least one episode of AOM compared with 9.21% in Database II for children aged 5 years and younger. In Database III, 13.2% children younger than 7 years had AOM in 2005. The peak season of AOM among children was from March to May (Databases III and IV).ConclusionAOM was thought to be a very common disease among children; however, this comparative analysis showed that the overall prevalence of AOM among children younger than 5 years was only 20%, much lower than in other countries. AOM was more prevalent during the spring season, and still was similarly common after age 2 years

DC-SIGN (CD209) Promoter −336 A/G (rs4804803) Polymorphism Associated with Susceptibility of Kawasaki Disease

Kawasaki disease (KD) is characterized by systemic vasculitis of unknown etiology. High-dose intravenous immunoglobulin (IVIG) is the most effective therapy for KD to reduce the prevalence of coronary artery lesion (CAL) formation. Recently, the α2, 6 sialylated IgG was reported to interact with a lectin receptor, specific intracellular adhesion molecule-3 grabbing nonintegrin homolog-related 1 (SIGN-R1) in mice and dendritic cell-specific intercellular adhesion molecule-3 grabbing nonintegrin (DC-SIGN) in human, and to trigger an anti-inflammatory cascade. This study was conducted to investigate whether the polymorphism of DC-SIGN (CD209) promoter −336 A/G (rs4804803) is responsible for susceptibility and CAL formation in KD patients using Custom TaqMan SNP Genotyping Assays. A total of 521 subjects (278 KD patients and 243 controls) were investigated to identify an SNP of rs4804803, and they were studied and showed a significant association between the genotypes and allele frequency of rs4804803 in control subjects and KD patients (P = 0.004 under the dominant model). However, the promoter variant of DC-SIGN gene was not associated with the occurrence of IVIG resistance, CAL formation in KD. The G allele of DC-SIGN promoter −336 (rs4804803) is a risk allele in the development of KD