Machine learning-based prediction of a BOS reactor performance from operating parameters

A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors. View Full-Tex

Substance and materiality? The archaeology of Talensi medicine shrines and medicinal practices

This is the final version of the article. Available from Taylor & Francis via the DOI in this record.Talensi materia medica is varied, encompassing plant, mineral, and animal substances. Healing, medicines, and medicinal practices and knowledge can be shrine-based and linked with ritual practices. This is explored utilising ethnographic data and from an archaeological perspective with reference to future possibilities for research both on Talensi medicine and, by implication, more generally through considering the archaeology of Talensi medicine preparation, use, storage, spread, and disposal. It is suggested that configuring the archaeology of medicine shrines and practices more broadly in terms of health would increase archaeological visibility and research potential.The author is grateful to the Wellcome Trust for funding the research

Economic impacts of China's accession to the World Trade Organization

Ianchovichina and Martin present estimates of the impact of accession by China and Chinese Taipei to the World Trade Organization. China is estimated to be the biggest beneficiary, followed by Chinese Taipei and their major trading partners. Accession will boost the labor-intensive manufacturing sectors in China, especially the textiles and apparel sector that will benefit directly from the removal of quotas on textiles and apparel exports to North America and Western Europe. Consequently, developing economies competing with China in third markets may suffer relatively small losses. China has already benefited from the reforms undertaken between 1995 and 2001 (US$31 billion) and trade reforms after accession will lead to additional gains of around$US10 billion. Accession will have important distributional consequencesfor China, with wages of skilled workers and unskilled nonfarm workers rising in real terms and relative to farm incomes. Reduction in agricultural protection may hurt some farmers. Possible policy changes considered to offset these impacts include reductions in barriers to labor mobility and improvements in rural education. The authors estimate that the removal of the hukou system would raise farm wages and allow 28 million workers to migrate to nonfarm jobs. If, in addition, there is an increase in education spending that results in a percentage point increase in the annual skilled labor growth rate, approximately 32 million farm workers would leave their job for jobs in the nonfarm sectors. These policies would not only facilitate the evolution of China's economy toward high-technology manufacturing and services, they have the potential to much more than offset any negative impacts of accession on rural wages and rural incomes generally.Labor Policies,Economic Theory&Research,Trade Policy,Environmental Economics&Policies,Banks&Banking Reform,Environmental Economics&Policies,World Trade Organization,TF054105-DONOR FUNDED OPERATION ADMINISTRATION FEE INCOME AND EXPENSE ACCOUNT,Economic Theory&Research,Trade and Regional Integration

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers

A Note on Variation of Active Principles in Indian Medicinal Plants and TIM Formulations

The active principles or constituents (phytochemicals) in medicinal plants are chemical compounds known as secondary plant products. Some secondary products discourage herbivores; others inhibit bacterial or fungal pathogens. Active principles in medicinal plants may affect health, but are not-essential nutrients as our diet does not require them to sustain life in the same way as vitamins and minerals

Competitions of professional skills as a tool of professional development young workers in an industrial plant

На основе анализа статистических данных о молодых работниках – призерах конкурсов профессионального мастерства – проведен анализ карьерных траекторий рабочих промышленного предприятия и их образовательного уровня.Based on the analysis of statistical data of young workers – winners of competitions of professional skills – of the analysis of the career trajectories of workers of industrial enterprises and their level of education

Neural network fault diagnosis of a trolling motor based on feature reduction techniques for an unmanned surface vehicle

This article presents a novel approach to the diagnosis of unbalanced faults in a trolling motor under stationary operating conditions. The trolling motor being typically of that used as the propulsion system for an unmanned surface vehicle, the diagnosis approach is based on the use of discrete wavelet transforms as a feature extraction tool and a time-delayed neural network for fault classification. The time-delayed neural network classifies between healthy and faulty conditions of the trolling motor by analysing the stator current and vibration. To overcome feature redundancy, which affects diagnosis accuracy, several feature reduction methods have been tested, and the orthogonal fuzzy neighbourhood discriminant analysis approach is found to be the most effective method. Four faulty conditions were analysed under laboratory conditions, where one of the blades causing damage to the trolling motor is cut into 10%, 25%, half and then into full to simulate the effects of propeller blades being damaged partly or fully. The results obtained from the real-time simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and accurately

Sodium ion interaction with psyllium husk (Plantago sp.)

The nature of and factors effecting sodium interactions with psyllium were investigated in vitro. In a batch extraction system, psyllium mucilage gel retained at least 50% of sodium across a range of concentrations (5–300 mg sodium per g psyllium) and pH (2–10) environments. FTIR and Na NMR analyses of psyllium gels indicated that binding was complex with non-specific multi-site interactions. The potential use of psyllium husk as a binding agent for the reduction of bioavailable sodium was therefore evaluated. The binding of sodium at physiologically relevant conditions (pH 1.2 (stomach) and 6.8 (intestine)) was studied in a gastrointestinal tract (GIT) pH simulated model. Results show consistently high sodium retention (∼50%) across the GIT model and less than 20% loss of bound sodium under the simulated intestinal pH conditions after repeated washings