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The Industrial Revolution in World History

John Ziman, Real Science: What It Is, and What It Means, Cambridge: Cambridge University Press, 2000, 399 pp. Ziman is one of the most senior British contributors to science and society studies, and is well known for his earlier classics, Public Knowledge (1968) and Reliable Knowledge (1978). Real Science is a thorough and provocative treatise, arguably his most informative to date. The recent "Sokal hoax" revisited the juxtaposition of earlier generations about the rational foundations of science promoted by Nagel and Hempel versus the non-rational or constructionist foundations suggested by Kuhn and Feyerabend. Without referring to the particulars of the Sokal case, Ziman advances a more realistic characterization of the social nature of science required by the "science wars" in which he says "sociology has superseded philosophy at the theoretical core of 'science studies."' Ziman offers a naturalistic account of science. It is a set of interrelated communities governed by normative structures that check the originality and often-transient irrationality of specific innovations with sets of collective practices that subject novel claims to critical evaluation. Sound familiar? The close tie between the cognitive elements that make up the content of science and the collective process of creating and evaluating knowledge is pure Mertonian, and it is one of the core suppositions of the book. Ziman offers a more exhaustive analysis than Merton of the kinds of knowledge produced by research and the range of work falling along a broad continuum from work based solely on curiosity, "pure science," and research mandated by R&D policies. Often the key elements of the scientific ethos -- Communalism or public knowledge, Universalism, Detachment, Originality, and Scepticism (which culminate in 'CUDOS,' i.e. the reward structure) can go off the rails under the new conditions of knowledge production. The academic mode of production which characterized the British and German universities from 1850 to 1950 -- individual male scientists pursing their own projects, working in relative isolation with little external funding, teaching in tenured academic positions to support their research -- has increasingly been replaced by "post-academic science." Here, teams of increasingly specialized experts, men and women, often work in diverse labs on renewable 5 year contracts and keep in contact with a network over the internet, pursuing research questions set by private interests and national policies, working under tightly scrutinized spending regimes, often resulting in the acquisition of proprietary knowledge, and in a constant search for further funds to support postdocs and on-going projects. The post-academic scientist becomes bureaucrat-administrator, and is increasingly liable to be working in a non-academic setting. In Canada there is evidence from two cases that commercial interests have eroded some traditional academic values, including academic freedom. Dr. Nancy Olivieri was fired from her lab position in 1997 at the Hospital for Sick Children, a division of the University of Toronto, after blowing the whistle on potentially dangerous side effects found in preliminary tests of a drug the research on which had been funded by Apotex Ltd. Her actions were directly contrary to a contract signed by Olivieri giving the company control over release of results. Such contracts directly limit academic freedom but have become common as universities rely increasingly on the private sector to staff the universities' research capabilities. It was only after the CAUT intervened with the assistance of international medical researchers that the university supported her demands for protection of academic freedom, re-instated her and assisted in her $150,000 legal fees (see Turk 2000). …

Real Science: What It Is, and What It Means. (Book Reviews/Comptes Rendus)

Smart manufacturing, which is increasingly popular worldwide, is aided by time-series forecasting. As the volume of historical data increases, powerful forecasting techniques that reveal unknown relationships between past and future values are required to provide accurate forecasts of production and sales. Thus, in this article, a composite gate recurrent unit (GRU)–Prophet model with an attention mechanism was constructed to predict sales volume. In this composite model, Prophet model and GRU model with attention mechanism were used to capture linear and nonlinear features, respectively. The composite model was experimentally determined to be more applicable and to provide more accurate predictions than did recurrent neural network, long short-term memory, gate recurrent unit, Prophet, and autoregressive integrated moving average models. This article's composite model is suited to rapid changes in market demand and helps enterprises be more competitive in the field of smart manufacturing.

Clothing Sale Forecasting by a Composite GRU–Prophet Model With an Attention Mechanism

The objective of this study is to zone the tubular well density and groundwater quality in the urban area of the city of Lencois, Bahia, through geoprocessing techniques in the GIS platform. Lencois, is a city of Chapada Diamantina, in the Brazilian Northeast that has in its territory tourism as main socioeconomic activities, receiving annually a high amount of visitors. The methodology used is based on data tabulation, kernel estimator runs for densities, and the Inverse Distance Weighted (IDW) algorithm for interpolation of nitrate and chloride contents. Both processes were executed in the ArcGis 10 software. The results showed the presence of a large zone with high density of wells in the central-east sector of the urban area of Lencois, which presents high flow dynamics (vehicles, people), above to greater concentration of facilities such as hotels, inns, shops and restaurants. Notably, this sector has the greatest demand for water and needs to be monitored by competent institutions that seek to maintain a balance in the use of water resources, above to supply, even in periods of water stress. Inside this zone also traces of anthropic contamination occur, indicated by higher values of nitrate and chloride concentrations. This suggests that above to the pressure exerted by the high demand for water, there is inefficient management of wastewater and rainwater in this area of the urban area, compromising the quality of groundwater.

https://periodicos.ufpe.br/revistas/jhrs/article/download/22763/18892

Zoning of the use and quality of groundwater as a subsidy for the management of water resources: The case of the urban area of the municipality of Lençóis, Bahia, Northeastern Brazil

The electrical power usage forecasting is the basis for energy investment planning and plays an important role in developing institutions and agencies. The combination of computational mathematical concepts and computer technology has widely used for forecasting electric power usage while those methods proved very powerful to predict the electric power usage in the future. There are two main roots in logic and reasoning in the philosophy of science and mathematics which are the basis of all computational activities. One of them is a heuristic approach that has widely applied in various studies in the areas of predictive problems, selection, and search problems. In this study, the prediction of electric power usage for each category carried out by applying the concept of a heuristic network. Time series data modeling is done using a weighted network. The values of each network weighting are obtained using a heuristic approach. The purpose of this study is to simultaneously predict two categories of electricity use by implementing a heuristic network. The results of the study show that the MISO (Multi Input Single Output) Heuristic Network can be stated to be significant enough to carry out the activity of predicting two categories of time series data simultaneously. Furthermore, the results of this study obtained concluded that the parameter that has the most dominant influence on training results is the number of model orders.

Electrical Power Usage Prediction using A Multi Input Single Output Heuristic Network

Prediction of electricity sales becomes important for State Electricity Company of Indonesia (PLN) in order to estimate the Statement of Profit and Loss in next year. To obtain good predictive results may require many variables and data availability. There are many available methods that do not require so many variables to get predicted results with a good approximation. The aim of this study was to predict electricity sales by using an interpolation method called IDW (Inverse Distance Weighting). Several data samples are mapped into Cartesian coordinates. The data samples used are power connected to the household (X-axis), to industry (Y-axis), and electricity sales (Z value). Firstly, the sampled data clustered by using SOM algorithm. The Z value in each cluster is predicted by using the IDW method. The prediction results of IDW method are then optimized using ANN-BP (Artificial Neural Network Back Propagation). The trained net structure is then used to predict the electricity sale in next year.

/pdf/prediction-of-electricity-sales-using-neural-based-inverse-ehrdgep4lz.pdf

Prediction of electricity sales using neural based inverse distance weighting method

Experience shows that teaching Power Electronics subjects is not easy, even to undergraduate students. This phenomenon might stem from the fact that Power Electronics is an engineering discipline that closely related to other disciplines. Some experts stipulate that Power Electronics is a multidisciplinary subject while for some other it is an interdisciplinary subject. In students point of view, it does not matter what side Power Electronics reside, they always see it as a difficult subject. Most of the time engineering technology (TVET) student might consider this subject more as a burden. This does not come as a surprise; sampled Indonesian students score lower in PISA (Programme for International Student Assessment), a periodic student testing conducted by OECD (Economic Co-operation and Development). The score outline the lack of HOTS (Higher Order Thinking Skills) from many Indonesian students. The inadequacy in that skills hampers the future development of many students, especially in a subject such as Power Electronics that require some degree of mathematical prowess and deeper thinking. It gets worse with the influence of technology on student attention span, and they cannot focus on a more extended period as their predecessor was. This kind of deficiency threat the very survival of the nation as we enter The Industrial Revolution 4.0 where many current human jobs will be replaced by network connected automation. We need to address this problem adequately. The massive national interest in robotics may be one of the answers to this problem. However, due to the expensiveness of this solution, it needs to be adapted to the available budget. The literature study that used as a foundation of ongoing effort to incorporate a simple robotic experiment in Power Electronics teaching is presented. The study then followed by a discussion about the current state of the effort.

https://iopscience.iop.org/article/10.1088/1742-6596/1244/1/012002/pdf

Bustani

Papers

Prediction of electricity sales using neural based inverse distance weighting method

Teaching Power Electronics as a Multidisciplinary Subject with Small and Inexpensive Robotic Arm