Receipt, 1861

https://egrove.olemiss.edu/aldrichcorr_d/1013/thumbnail.jp

Paraffin ingestion - the problem

Paraffin ingestion is the commonest cause of accidental childhood poisoning in South Africa. Children from the lower socio-economic group are affected most. They drink paraffin in the summer months from bottles or intermediate containers, mistaking it for water or colddrink.The children are predominantly male with a mean age of 24 months. The clinical picture is one of respiratory distress with a hospital case fatality rate of 0,74%. The use of paraffin as a source of household energy in South Africa is on the increase. Based on a modernisation index it would seem that this trend will continue into the next century. It can therefore be expected that the number of cases of paraffin ingestion will steadily increase if no active steps are taken to address the problem.Prevention should entail a wide spectrum of measures, the basis of which should be a child-resistant container. An effective durable, low-cost child-resistant container which is easy to pour from should be made available by petroleum companies and/or entrepreneurs and distributed through their network. This should be combined with health education on the danger of paraffin. Health care workers and administrators should be made more aware of the problem and become involved in health education and prevention.Further research should be undertaken on the effect a change in the colour of paraffin and the use of childresistant caps would have on the incidence of paraffin ingestion in South Africa

The impact of child-resistant containers on the incidence of paraffin (kerosene) ingestion in children

The commonest cause of accidental poisoning in the South African black paediatric population is paraffin ingestion. In this intervention study a specifically designed child-resistant container (CRG) was introduced to evaluate whether its use would decrease the incidence ofparaffin ingestion. CRCs were distributed to 20 000 households in the study area (Gelukspan district). No CRCs were distributed in the control area (Lehunutshe district). Health education about paraffin poisoning prevention was given in both the control and the study areas. The monthly incidence rates of paraffin ingestion were monitored during the 14-month intervention period after the distribution and were compared with the pre-intervention incidence rates in the study and control areas.The main finding was that the incidence of paraffin ingestion dropped by 47% in the study area during the intervention period. The circumstances surrounding the cases of paraffin ingestion that still occurred in the study and control areas were investigated by means of a questionnaire. We recommend that paraffin be sold in CRCs, and suggestions are made for improving health education to prevent paraffin poisoning

Ensemble decision tree models using RUSBoost for estimating risk of iron failure in drinking water distribution systems

Safe, trusted drinking water is fundamental to society. Discolouration is a key aesthetic indicator visible to customers. Investigations to understand discolouration and iron failures in water supply systems require assessment of large quantities of disparate, inconsistent, multidimensional data from multiple corporate systems. A comprehensive data matrix was assembled for a seven year period across the whole of a UK water company (serving three million people). From this a novel data driven tool for assessment of iron risk was developed based on a yearly update and ranking procedure, for a subset of the best quality data. To avoid a ‘black box’ output, and provide an element of explanatory (human readable) interpretation, classification decision trees were utilised. Due to the very limited number of iron failures, results from many weak learners were melded into one high-quality ensemble predictor using the RUSBoost algorithm which is designed for class imbalance. Results, exploring simplicity vs predictive power, indicate enough discrimination between variable relationships in the matrix to produce ensemble decision tree classification models with good accuracy for iron failure estimation at District Management Area (DMA) scale. Two model variants were explored: ‘Nowcast’ (situation at end of calendar year) and ‘Futurecast’ (predict end of next year situation from this year’s data). The Nowcast 2014 model achieved 100% True Positive Rate (TPR) and 95.3% True Negative Rate (TNR), with 3.3% of DMAs classified High Risk for un-sampled instances. The Futurecast 2014 achieved 60.5% TPR and 75.9% TNR, with 25.7% of DMAs classified High Risk for un-sampled instances. The output can be used to focus preventive measures to improve iron compliance

Ensemble decision tree models using RUSBoost for estimating risk of iron failure in drinking water distribution systems

Safe, trusted drinking water is fundamental to society. Discolouration is a key aesthetic indicator visible to customers. Investigations to understand discolouration and iron failures in water supply systems require assessment of large quantities of disparate, inconsistent, multidimensional data from multiple corporate systems. A comprehensive data matrix was assembled for a seven year period across the whole of a UK water company (serving three million people). From this a novel data driven tool for assessment of iron risk was developed based on a yearly update and ranking procedure, for a subset of the best quality data. To avoid a ‘black box’ output, and provide an element of explanatory (human readable) interpretation, classification decision trees were utilised. Due to the very limited number of iron failures, results from many weak learners were melded into one high-quality ensemble predictor using the RUSBoost algorithm which is designed for class imbalance. Results, exploring simplicity vs predictive power, indicate enough discrimination between variable relationships in the matrix to produce ensemble decision tree classification models with good accuracy for iron failure estimation at District Management Area (DMA) scale. Two model variants were explored: ‘Nowcast’ (situation at end of calendar year) and ‘Futurecast’ (predict end of next year situation from this year’s data). The Nowcast 2014 model achieved 100% True Positive Rate (TPR) and 95.3% True Negative Rate (TNR), with 3.3% of DMAs classified High Risk for un-sampled instances. The Futurecast 2014 achieved 60.5% TPR and 75.9% TNR, with 25.7% of DMAs classified High Risk for un-sampled instances. The output can be used to focus preventive measures to improve iron compliance

Spin-dependent structure functions g^1\hat g_1 and g^2\hat g_2 for inclusive spin-half baryon production in electron-positron annihilation

Two spin-dependent structure functions $\hat g_1$ and $\hat g_2$ for the inclusive spin-half baryon production in electron-positron annihilation are studied in the context of QCD factorization as well as in the naive quark parton model. As a result, it is found that the sum of $\hat g_1$ and $\hat g_2$ is related to $\hat h_1$ and $\hat g_T$, two quark fragmentation functions defined by Jaffe and Ji. In connection with the measurement of quark fragmentation functions, the possible phenomenological consequences are discussed.Comment: RevTex, four Ps figures, to appear in Phys. Rev.

Social Network and Sentiment Analysis of the #Nutrition Discourse on Twitter

Social media platforms allow people to share information, connect, and build networks at an unprecedented scale with positive and negative consequences. Social network analysis (SNA) applies mathematical network and graph theory to visualise information transfer as relational networks of connected nodes. Measuring node connectivity (centrality) permits the identification of ‘influencers’. SNA has been applied to analyse the spread of misinformation on Twitter (1), but to date, no research has examined nutrition networks. Therefore, this study examined the #Nutrition conversations on Twitter utilising SNA and linguistic analyses. English language tweets including ‘#Nutrition’ on 1–21 March 2023 were collected using the SNA tool, NodeXL Pro (Network Overview for Discovery and Exploration in Excel) (2). SNA is a multistep process that calculates graph metrics and develops a network graph to measure the relationships between users. SNA also identifies semantically related words, hashtags, and word pairs and identifies the sentiment of words used, as measured against the Opinion Lexicon (2). The #Nutrition network included 17,129 vertices (users) with 26,809 unique edges (connections); edges with duplicates were merged. The network density was low, suggesting that most users communicate heavily with a small number of users. The average geodesic distance between any two users was 5.26, revealing a dispersed online discussion. SNA identified the top 10 influencers in this network, measured by high betweenness centrality (23,375,543–5,207,998). Influential users were from a mix of accounts including personal, online blogs, and government organisations. High betweenness centrality identified the users with the greatest influence, acting as bridges between network groups and therefore amplifying #Nutrition messages. Sentiment analysis found the discourse was more positive (0.047, 22,218 words) than negative (0.015, 6795 words). Semantic analysis calculated the total words, 468,191, and identified the most frequently used words in the tweets: #nutrition, #health, food, more, nutrition, health, #diet, #healthylifestlye, #fitness, and #food. Social network analysis shows the discourse on Twitter relating to #Nutrition is dispersed without clear polarising views. Semantic analysis showed that ‘health’ was the main topic discussed in relation to nutrition in this network and was most frequently associated with #Nutrition. The narrative was positively framed, as identified through sentiment analysis

Null Deformed Domain Wall

We study null 1/4 BPS deformations of flat domain wall solutions (NDDW) in N=2, d=5 gauged supergravity with hypermultiplets and vector multiplets coupled. These are uncharged time-dependent configurations and contain as special case, 1/2 supersymmetric flat domain walls (DW), as well as 1/2 BPS null solutions of the ungauged supergravity. Combining our analysis with the classification method initiated by Gauntlett et al., we prove that all the possible deformations of the DW have origin in the hypermultiplet sector or/and are null. Here, we classify all the null deformations: we show that they naturally organize themselves into "gauging" (v-deformation) and "non gauging" (u-deformation). They have different properties: only in presence of v-deformation is the solution supported by a time-dependent scalar potential. Furthermore we show that the number of possible deformations equals the number of matter multiplets coupled. We discuss the general procedure for constructing explicit solutions, stressing the crucial role taken by the integrability conditions of the scalars as spacetime functions. Two analytical solutions are presented. Finally, we comment on the holographic applications of the NDDW, in relation to the recently proposed time-dependent AdS/CFT.Comment: 38 pages; minor changes, references added; text revised, minor changes, final version published in JHE

Phase Transition in the 1d Random Field ising model with long range interaction

We study the one dimensional Ising model with ferromagnetic, long range interaction which decays as |i-j|^{-2+a}, 1/2< a<1, in the presence of an external random filed. we assume that the random field is given by a collection of independent identically distributed random variables, subgaussian with mean zero. We show that for temperature and strength of the randomness (variance) small enough with P=1 with respect to the distribution of the random fields there are at least two distinct extremal Gibbs measures