An Indirect Colorimetric Method for Potassium Determination in Soil Using a Paper Device and Smartphone

This study presents a simple method for determination of potassium in microliter scale using a paper device together with a smartphone. The method begins with the ion-pair extraction of dibenzo-18-crown-6-K+ complex into dichloromethane with an excess amount of calmagite. The aqueous phase containing the remaining calmagite is transferred to the paper device, where a smartphone is used to capture the color and convert to RGB value. The linear detection range was found to cover potassium concentrations from 20 mg L-1 to 120 mg L-1. The detection and quantification are 5.41 mg L-1 and 18.03 mg L-1, respectively. Potassium detection was carried out in a variety of actual soil samples, and the results were validated against spectrophotometric results using a paired t-test, which indicated high accuracy. The proposed method is simple, fast, and inexpensive, and it requires no complicated equipment, making it ideally suited for detection of potassium in soil

Potential Adsorption of Leonardite for Heavy Metal Ions Removal from Aqueous Solution

ABSTRACT The potential of leonardite as an adsorbent for the removal of heavy metal ions, Cu(II) and Mn(II), from aqueous solutions was investigated. The adsorption parameters included pH and contact time were optimized in the batch treatment process. The adsorption kinetics of leonardite for heavy metal ions were followed the pseudo-second-order model. Adsorption process for removal of Cu(II) has fitted to the pseudo-second-order kinetic model, while that of Mn(II) has followed the pseudo-first-order. The adsorption of leonardite for both metals was fitted well with Langmuir and Freundlich isotherms. The maximum adsorption capacity was 41.67 and 11.57 mg g-1 for Cu(II) and Mn(II), respectively. The results indicated that leonardite waste materials could be employed as a promising adsorbent for removal of Cu(II) and Mn(II) ions from the industrial wastewater treatment process.

Fine and ultrafine particles from indoor sources – Effects on healthy humans in a controlled exposure study and on lung epithelial cells in vitro

In recent years increasing concern has been expressed about the potential adverse health effects of particles from indoor sources. The aims of the EPIA project were: (1) to characterize potentially relevant indoor sources of (ultra)fine particles with respect to their emission levels and composition and (2) to investigate their adverse health effects. We investigated the effects of emissions from candle burning (CB), toasting of bread (TB) and sausage frying (FS) in a randomized, cross-over sham-controlled exposure study in healthy adults as well as in vitro in A549 human lung epithelial cells. Participants were exposed for 2 h to each of these sources at two different exposure levels, and examined before, during and after the exposures at defined time-intervals. We found transient associations between exposures and several respiratory and cardiovascular effects as well as inflammatory changes (e.g. lung function, blood pressure, arterial stiffness, interleukin-8 in nasal lavage/blood). Specific effects were found to depend strongly on the emission source and the selected exposure metric (e.g. size-specific particle mass concentration, size-specific particle number concentration, lung deposited surface area concentration). Evaluation of PM2.5 samples in the A549 cells, revealed an increased interleukin-8 release and DNA strand breakage induction for toasting, whereas candle burning only resulted in DNA damage. The results from our project demonstrate that elevated concentrations from certain indoor emission sources may lead to changes in the lung and cardiovascular systems as well as possibly induce inflammation

A novel feature selection-based sequential ensemble learning method for class noise detection in high-dimensional data

© 2018, Springer Nature Switzerland AG. Most of the irrelevant or noise features in high-dimensional data present significant challenges to high-dimensional mislabeled instances detection methods based on feature selection. Traditional methods often perform the two dependent step: The first step, searching for the relevant subspace, and the second step, using the feature subspace which obtained in the previous step training model. However, Feature subspace that are not related to noise scores and influence detection performance. In this paper, we propose a novel sequential ensemble method SENF that aggregate the above two phases, our method learns the sequential ensembles to obtain refine feature subspace and improve detection accuracy by iterative sparse modeling with noise scores as the regression target attribute. Through extensive experiments on 8 real-world high-dimensional datasets from the UCI machine learning repository [3], we show that SENF performs significantly better or at least similar to the individual baselines as well as the existing state-of-the-art label noise detection method

Development of intermediate layer systems for direct deposition of thin film solar cells onto low cost steel substrates

The functionalisation of low-cost steel over large areas with low cost intermediate layers (ILs) for utilisation as substrates in thin film solar modules is reported. Three approaches for the deposition of ILs are demonstrated and evaluated; a thick SiOx sol–gel based on a one-step acidic catalysis applied by spray technique, a commercial screen-printable dielectric ink, and an epoxy-based material (SU8) deposited by screen printing or bar coating. These ILs demonstrated the properties of surface levelling (quantified by mechanical profilometry), electric insulation (tested using breakdown voltage and leakage current) and acted as an anti-diffusion barrier (demonstrated with glow discharge mass spectrometry). Moreover, the performances of amorphous silicon (a-Si:H) and organic photovoltaic (OPV) thin film solar cells grown on carbon and stainless steels (a-Si:H: 5.53% and OPV: 2.40%) show similar performances as those obtained using a reference glass substrate (a-Si:H: 5.51% and OPV: 2.90%). Finally, a cost analysis taking into account both the SiOx sol–gel and the dielectric ink IL was reported to demonstrate the economic feasibility of the steel/IL prototypes

ปัจจัยที่มีความสัมพันธ์กับการปฏิบัติงานตามหลักเกณฑ์มาตรฐานสุขาภิบาลอาหารของผู้สัมผัส อาหารในโรงอาหาร กระทรวงสาธารณสุข จังหวัดนนทบุรี ในสถานการณ์ระบาดของโรคติดเชื้อไวรัสโคโรนา 2019

วิทยานิพนธ์ (คศ.ม.) -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2564The objectives of this research were to 1 ) study the personal factors of food handlers in the canteen of the Ministry of Public Health, Nonthaburi Province in the epidemic situation of coronavirus disease 2019. 2) Study the operations according to the food sanitation standards of food handlers. 3) Study the factors related to the performance of food handlers according to food sanitation standards. 4) Guidelines for solving problems in the work of food handlers according to food sanitation standards, which the research area is in the canteen of the Ministry of Public Health, Nonthaburi Province in the situation of the epidemic of coronavirus disease 2019. The research used a mixed method research consisting of a quantitative research using a sample of100 food handlers per restaurant and a qualitative research by interviewing 3 groups of food handlers. The statistics used in the data analysis were: frequency, percentage, mean, chi-square and Pearson correlation. The results of the research showed that 1) the personal factors of the majority of food handlers 68.00 percent are female, 37.00 percent aged between 41 - 50 years,36.00 percent graduated from high school, 42.00 percent with average monthly income between 10,000 - 20,000 baht, 97.00 percent are Buddhists and most 27 percent are food handlers from rice and curry shops. 2) The food sanitation practices of food handlers in the coronavirus disease 2019 epidemic situation, found that 65.00 percent of the workers performed according to sanitary principles at a very good level. 3)Factors related to the performance in accordance with food sanitation standards of food handlers in the epidemic situation of coronavirus disease 2019. The results of the study revealed that as for the personal factors of the restaurant type, the leading factories the general knowledge of the coronavirus disease 2019 epidemic situation and knowledge of food sanitation in the epidemic situation of coronavirus disease 2019with contributing factors including the support of equipment to clean or prevent coronavirus disease 2019 from government agencies. Food handlers' agencies courage participation in sanitation practices. They have also received training or advice from staff on how to behave in the midst of the coronavirus disease 2019 outbreak, there was a statistically significant correlation at the .05 level. As for personal factors in terms of gender, age, education, income, and religion, there are contributing factors such as the agency of food handlers campaigning or announcing measures to prevent the spread of the coronavirus disease 2019 and had been trained in food sanitation. There are additional factors such as having received food sanitation assessment during the coronavirus disease 2019 epidemic situation, received a certificate for clean and safe restaurants to prevent COVIC-19, and 4) guidelines for solving problems in working in accordance with the rules of food sanitation standards of food handlers in the canteen of the Ministry of Public Health, Nonthaburi Province in the situation of the coronavirus disease 2019 epidemic, found that workshop sanitation training should be organized, performance and personal hygiene assessments should be carried out. For public relations work, there should be more communication channels, additional measures on the part of service users and cleanliness, including support for protective and cleaning equipment.Rajamangala University of Technology Phra Nakho

Towards Breast Cancer Survivability Prediction Models in Thai Hospital Information Systems

Finding suitable ways to develop models for predicting unknown data classes is a challenging task in data mining and machine learning. The improvement of the quality of data sets and combining AdaBoost with a weak learner is an important contribution to the development of these prediction models. The objectives of this thesis are to build accurate, stable and effective breast cancer survivability prediction models using breast cancer data obtained from the Srinagarind Hospital in Thailand. To achieve these objectives, five approaches were proposed including: 1) £-means and RELIEF to improve accuracy and stability of prediction models generated from AdaBoost algorithms; 2) C-Support Vector Classification Filtering (CSVCF) to identify and eliminate outliers; 3) a combination of C-SVCF and oversampling approaches to handle both outliers and imbalanced data problems; 4) a hybrid AdaBoost and Random Forests to build stronger prediction models; and 5) C4.5 to form breast cancer survivability decision trees and rules. To illustrate capability, performance and effectiveness of these approaches, extensive experimental studies have been conducted using W E K A version 3.5.6, AdaBoost M A T L A B Toolbox, L I B S V M and C4.5 program

AdaBoost algorithm with random forests for predicting breast cancer survivability

In this paper we propose a combination of the AdaBoost and random forests algorithms for constructing a breast cancer survivability prediction model. We use random forests as a weak learner of AdaBoost for selecting the high weight instances during the boosting process to improve accuracy, stability and to reduce overfitting problems. The capability of this hybrid method is evaluated using basic performance measurements (e.g., accuracy, sensitivity, and specificity), Receiver Operating Characteristic (ROC) curve and Area Under the receiver operating characteristic Curve (AUC). Experimental results indicate that the proposed method outperforms a single classifier and other combined classifiers for the breast cancer survivability prediction. © 2008 IEEE