Factory modelling: data guidance for analysing production, utility and building architecture systems

Work on energy and resource reduction in factories is dependent on the availability of data. Typically, available sources are incomplete or inappropriate for direct use and manipulation is required. Identifying new improvement opportunities through simulation across factory production, utility and building architecture domains requires analysis of model feasibility, particularly in terms of system data composition, input resolution and simulation result fidelity. This paper reviews literature on developing appropriate model data for assessing energy and material flows at factory level. Gaps are found in guidance for analysis and integration of resource-flows across system boundaries. The process for how data was prepared, input and iteratively developed alongside conceptual and simulation models is described. The case of a large-scale UK manufacturer is presented alongside discussions on challenges associated with factory level modelling, and the insights gained from understanding the effect of data clarity on system performance

The forcast of slag addition during the ladle furnace (LF) refining process based on LWOA-TSVR

LF refining slag addition is an important factor affecting the end steel composition of the refining process. In order to better control the end steel composition and improve the production efficiency, this paper uses an improved whale optimization algorithm to optimize twin support vector machines to establish a LF refining slag addition prediction model. The LWOA-TSVR model is trained and tested by historical data, and the model has a strong generalization capability and high accuracy. Applying the model to the industrial production process, it was verified that the model has high prediction accuracy and can provide guidance for the actual refining production process of LF refining slag addition, which is important for the control of the end steel composition

Composing in a global-local context : careers, mobility, skills.

When composition students look to their teachers for vocational guidance, both groups should acknowledge that the contexts of such terms as career, mobility, and skills have radically changed. In particular, the economy now links the global with the local, and capitalism has shifted from the fordist model, dominant through much of the twentieth century, to a newer, “fast” model

Evaluation of pollution in graphite materials irradiated in a nuclear reactor

The paper provides guidance on the reprocessing of irradiated graphite in a lowtemperature plasma. The composition and properties of irradiated graphite of water- graphite reactors are considered. An assessment of the state of the graphitic material with long-term exposure in a nuclear reactor is given. These researches are necessary in the future to build a model of decontamination process for irradiated graphite

Analysis of rolling group therapy data using conditionally autoregressive priors

Group therapy is a central treatment modality for behavioral health disorders such as alcohol and other drug use (AOD) and depression. Group therapy is often delivered under a rolling (or open) admissions policy, where new clients are continuously enrolled into a group as space permits. Rolling admissions policies result in a complex correlation structure among client outcomes. Despite the ubiquity of rolling admissions in practice, little guidance on the analysis of such data is available. We discuss the limitations of previously proposed approaches in the context of a study that delivered group cognitive behavioral therapy for depression to clients in residential substance abuse treatment. We improve upon previous rolling group analytic approaches by fully modeling the interrelatedness of client depressive symptom scores using a hierarchical Bayesian model that assumes a conditionally autoregressive prior for session-level random effects. We demonstrate improved performance using our method for estimating the variance of model parameters and the enhanced ability to learn about the complex correlation structure among participants in rolling therapy groups. Our approach broadly applies to any group therapy setting where groups have changing client composition. It will lead to more efficient analyses of client-level data and improve the group therapy research community's ability to understand how the dynamics of rolling groups lead to client outcomes.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS434 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Finding an Effective Classification Technique to Develop a Software Team Composition Model

Ineffective software team composition has become recognized as a prominent aspect of software project failures. Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection. It is also believed that the technique/s used while developing a model can impact the overall results. Thus, this study aims to: 1) discover an effective classification technique to solve the problem, and 2) develop a model for composition of the software development team. The model developed was composed of three predictors: team role, personality types, and gender variables; it also contained one outcome: team performance variable. The techniques used for model development were logistic regression, decision tree, and Rough Sets Theory (RST). Higher prediction accuracy and reduced pattern complexity were the two parameters for selecting the effective technique. Based on the results, the Johnson Algorithm (JA) of RST appeared to be an effective technique for a team composition model. The study has proposed a set of 24 decision rules for finding effective team members. These rules involve gender classification to highlight the appropriate personality profile for software developers. In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models

The biodiversity audit approach challenges regional priorities and identifies a mismatch in conservation

1. Despite a strong uptake of evidence-based approaches, conservation often proceeds from a grossly incomplete understanding of species priorities. To optimize conservation impact within a biogeographical region, quantitative knowledge is needed of the species present, which should be prioritized, and the management interventions these require. The next challenge is to avoid a proliferation of competing species plans, or conversely, a lack of detail within generic habitat-based approaches. 2. We present a methodology for biodiversity auditing. We quantified regional biodiversity by systematically collating available species records, allowing objective prioritization. We collated autecological information to integrate multiple species into management guilds with shared requirements, providing evidence-based guidance for regional conservation. 3. For two regions of Eastern England, Breckland (2300 km^2 ) and The Broads (2000 km^2 ), we collated 083 and 15-million records, respectively. Numbers of species (12 845 and 11 067) and priority species (rare, threatened, designated or regionally restricted: 2097 and 1519, respectively) were orders of magnitude greater than previously recognized. Regional specialists, with a UK range largely or entirely restricted to the region, were poorly recognized posing a risk of regional homogenization. 4. A large body of autecological information existed for priority species and collating this allowed us to define cross-taxa management guilds. Numbers of priority species requiring different combinations of ecological processes and conditions were not matched by current conservation practice in Breckland. For example, the current agri-environment agreements for designated grass heaths potentially catered for only 15% of the 542 priority species and 21% of 47 regional specialists that could potentially benefit from evidence-based management. A focus on vegetation composition rather than the ecological requirements of priority species underpinned this failure. 5. Synthesis and applications. The biodiversity audit approach provides an objective model for prioritization and cost-effective conservation, applicable to regions of Europe where biodiversity has been well characterized. By using this approach to collate available information, management guilds with similar requirements can be defined across taxa, providing evidence-based guidance for regional conservatio

Prediction of alloy addition in ladle furnace (LF) based on LWOA-SCN

The amount of alloy added during the LF refining process affects the hit rate of steel composition control. Therefore, improving the accuracy of the alloy addition amount can help improve efficiency and reduce production costs. To address the existing problem of inaccurate alloy addition in the refining process, the group established an alloy addition prediction model based on an improved whale swarm optimization algorithm and stochastic configuration network (LWOA-SCN) with the historical smelting data of a steel mill. The model can effectively improve the prediction accuracy and convergence speed of the model. The research results show that the model is more advantageous in improving the hit rate of alloy addition, which provides theoretical guidance for practical production

Prediction of alloy addition in ladle furnace (LF) based on LWOA-SCN

The amount of alloy added during the LF refining process affects the hit rate of steel composition control. Therefore, improving the accuracy of the alloy addition amount can help improve efficiency and reduce production costs. To address the existing problem of inaccurate alloy addition in the refining process, the group established an alloy addition prediction model based on an improved whale swarm optimization algorithm and stochastic configuration network (LWOA-SCN) with the historical smelting data of a steel mill. The model can effectively improve the prediction accuracy and convergence speed of the model. The research results show that the model is more advantageous in improving the hit rate of alloy addition, which provides theoretical guidance for practical production