Mapping domestic workers’ organizing globally

Domestic workers face challenges for organizing, e.g. decentralization of the workforce, nature of the employment relationship. This article analyses, based on a multiple country-comparison, how domestic workers organize despite constrictions. We identify three forms of organizing: the trade union model and the association model (Shireen Ally). We propose, though, an additional third model, the ‘hybrid type’: domestic workers organize ‘amongst themselves’ in associations and at the same time these associations are linked to or integrated into trade unions, which provides representation, services and contact with other workers. Related to this finding, we see a trend of an ‘emerging trade unionism’. Which means that we tend to find more trade union-related forms of organizing than a decade ago. One explanatory factor is the “governance struggle” of winning the International Labour Organization’s Convention “Decent Work for Domestic Workers” in 2011, which led to an increased collaboration and trust-building between organized domestic workers and trade unions.

Penentuan Paket Promosi Pakaian PT. D&C Production dengan Menggunakan Algoritma FP-Growth

PT. D&C Production is a center for selling, foam mattresses, mattresses of all sizes and models, may types of car accessories and various kinds of women's and men's underwear. Problems regarding the decline in sales resulted in the accumulation of goods so that it became a loss. Data mining can be a solution to overcome these problems. This study will use the fp-growth algorithm to form an association model with the aim of helping companies increase their sales by creating underwear promotional packages. The data set that will be used to support this research is the sales transaction data set for the period April 2020 to December 2020. The results show that known rules have been obtained using the fp-growth algorithm, where the rules of this association can create strategies to increase clothing sales. In the form of five association rules that are ready to be used for making clothing promotional packages by meeting the support values and confidence values that have been set at the beginning, namely having a confidence value above 80% and a support value above 25%.

Exposure to Cadmium, Lead, Mercury and Arsenic and Uric Acid Levels: Results from NHANES 2007-2016

Background: Mechanisms underlying abnormal uric acid (UA) levels from exposure to heavy metals have not been not fully elucidated, especially in the context of mixtures.Objectives: To identify major heavy metals affected UA levels with a mixture exposure concept in the association model.Methods: 4794 adults from 2007-2016 National Health and Nutrition Examination Survey (NHANES) were involved. Serum UA (SUA) and SUA/SCr were used to estimate the UA levels, and cadmium (Cd), lead (Pb), mercury (Hg) and arsenic (As) in blood and/or urinary were evaluated in the study. We assessed the associations between heavy metals and UA levels using linear regression and Bayesian kernel machine regression (BKMR). Results: The median [P25, P75] SUA/SCr and SUA level were 6.22[5.27, 7.32] and 0.83[0.72, 0.98], respectively. There was no difference for SUA/SCr by gender, (men: 6.25[5.39, 7.29]; women: 6.17[5.17, 7.36], P=0.162), but men had higher SUA than women (men: 0.95[0.85, 1.05]; women: 0.72[0.64, 0.82], P<0.001). Blood Pb (βmen = 0.651 and βwomen =1.014) and urinary Cd (βmen = 0.252 and βwomen = 0.613) were positively associated with SUA/SCr, and urinary Pb (βmen = -0.462 and βwomen = -0.838) was inversely associated with SUA/SCr in multivariate linear regression analysis, but urinary As (βmen= 0.351) was positively associated with SUA/SCr only in men. BKMR showed that higher concentrations of exposure to a mixture of heavy metals was positively associated with higher UA levels, where Cd, Pb and urinary As contributed most to the overall mixture effect in men, while Pb and urinary Cd in women.Conclusions: Our study provided the first evidence that mixtures of metals are associated with the UA levels. Increased concentrations of metals, particularly blood Pb, urinary Cd and As (only in men) may increase the levels of UA.

A Relevant Customer Identification Algorithm Based on the Internet Financial Platform

In order to improve the intelligent search capabilities of Internet financial customers, this paper proposes a search algorithm for Internet financial data. The proposed algorithm calculates the customers corresponding to the two selected financial platforms based on the candidate customer set selected from the seed dataset and combined with the restored social relationship. Moreover, it also calculates the similarity of each field between the pairs. Furthermore, this article proposes an entity customer classification model based on logistic regression. Through the SNC model, threshold propagation, and random propagation, the model is transformed into an algorithm that identifies the associated customers, eliminates redundant customers, and realizes associated user identification. Experimental results verify that pruning increases the accuracy of identifying related customers by 8.44%. The average sampling accuracy of the entire customer association model is 79%, the lowest accuracy is 40%, and the highest is 1. From the sampling results, the overall recognition effect of the model reaches the expected goal.

Heat Capacity of Alcoholic (Isopropyl Alcohol) and Aqueous Alcoholic Solutions of Sodium Iodide Electrolyte Salt as a Function of Concentration

The paper considers using a high-sensitivity calorimeter with an isothermal jacket to measure heat capacities of electrolyte salt solutions at the temperature of 298.15 K, the salt being sodium iodide NaI dissolved in isopropyl alcohol and in mixtures of isopropyl alcohol with water containing 10, 20, and 40 % water by mass, at various molalities of the electrolyte salt. We processed the apparent heat capacity values computed for the electrolyte salt by means of the ion association model, which assumes that there exists an equilibrium between ions and ion pairs of the same type in a solution. The association constant values obtained make it possible to predict the heat capacity values not measured empirically, which lie within the margin of error of the experimental values. The investigation shows that the apparent heat capacity of the electrolyte salt as a function of concentration is adequately described by the ion association model in a wide range of solution molalities

Holistic Analytics of Digital Artifacts

Investigation of every crime scene with digital evidence is predominantly required in identifying almost all atomic files behind the scenes that have been intentionally scrubbed out. Apart from the data generated across digital devices and the use of diverse technology that slows down the traditional digital forensic investigation strategies. Dynamically scrutinizing the concealed or sparse metadata matches from the less frequent archives of evidence spread across heterogeneous sources and finding their association with other artifacts across the collection is still a horrendous task for the investigators. The effort of this article via unique pockets (UP), unique groups (UG), and unique association (UA) model is to address the exclusive challenges mixed up in identifying incoherent associations that are buried well within the meager metadata field-value pairs. Both the existing similarity models and proposed unique mapping models are verified by the unique metadata association model.

Application of Artificial Intelligence in the Establishment of an Association Model between Metabolic Syndrome, TCM Constitution, and the Guidance of Medicated Diet Care

Background. This study conducted exploratory research using artificial intelligence methods. The main purpose of this study is to establish an association model between metabolic syndrome and the TCM (traditional Chinese medicine) constitution using the characteristics of individual physical examination data and to provide guidance for medicated diet care. Methods. Basic demographic and laboratory data were collected from a regional hospital health examination database in northern Taiwan, and artificial intelligence algorithms, such as logistic regression, Bayesian network, and decision tree, were used to analyze and construct the association model between metabolic syndrome and the TCM constitution. Findings. It was found that the phlegm-dampness constitution (90.6%) accounts for the majority of TCM constitution classifications with a high risk of metabolic syndrome, and high cholesterol, blood glucose, and waist circumference were statistically significantly correlated with the phlegm-dampness constitution. This study also found that the age of patients with metabolic syndrome has been advanced, and shift work is one of the risk indicators. Therefore, based on the association model between metabolic syndrome and TCM constitution, in the future, metabolic syndrome can be predicted through the syndrome differentiation of the TCM constitution, and relevant medicated diet care schemes can be recommended for improvement. Conclusion. In order to increase the public’s knowledge and methods for mitigating metabolic syndrome, in the future, nursing staff can provide nonprescription medicated diet-related nursing guidance information via the prediction and assessment of the TCM constitution.

A possible model for estimating birth length of babies from common parental variables using a sample of families in Lagos, Nigeria

Background: Length at birth is important for evaluating childhood growth and development. It is of interest in Pediatrics because of its implications for perinatal and postnatal morbidity and mortality. Predicting birth length will be useful in an- ticipating and managing possible complications associated with pregnancy and birth of babies with abnormal birth length. Objective: The aim was to identify easily accessible parental determinants of baby’s birth length in Lagos, Nigeria, using a sample of patients attending a government hospital. Methods: Parental anthropometrics and other data were obtained from 250 couples by actual measurements, oral interviews and questionnaires. Baby’s birth length was measured immediately after delivery by qualified, a well-trained obstetric nurse, and association between parental and offspring parameters were assessed. Results: Weight gain, maternal weight, parity and mid-parental height were the significant parental explanatory variables of offspring birth length. They were the most suitable variables for a generated model for predicting babies’ birth length from parental variables in the study. Conclusion: A model that might be useful for predicting babies’ birth length from easily accessible parental variables was produced. This model may complement ultrasonographic data for predicting baby’s birth length with a view to achieving better perinatal and postnatal care. Keywords: Parental anthropometrics; birth length; association; model; correlation.