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Solutions of the twin plane jets HF chemical laser flow based on a turbulent kinetic theory, due to a modified Green’s function method, are presented. The calculated results of probability density function (PDF) of various chemical species in velocity space, and mass fraction concentration distributions of various reactants and products in the flow field, are revealed and discussed in this analysis. The transport phenomena of different pumping rate, collisional deactivation rate, and radiative deactivation rate in the interaction between the twin plane jets HF chemical laser show that the properties of species mass fraction concentrations, collisional reaction rate, and radiative incident intensity are the dominant factors. The present study provides the fundamentals for theoretical understanding of twin plane jets HF chemical laser and further application to multiple‐jet HF chemical laser analysis.

A citation analysis was conducted on publications of the faculty from the University of Florida (UF), Department of Chemical Engineering. The analysis was undertaken to gain a better understanding of the research programs in chemical engineering and to examine how the library aligns its research support through resources and journals to the needs of the department’s faculty. The analysis focused on where the faculty published, their most frequently cited resources, and what growth patterns were evident. The paper aims to discuss these issues.

Five years of publications (2011-2015) by the UF chemical engineering faculty were included in the analysis. Web of Science was used to compile a list of the articles published in peer-reviewed journals as well as the citations.

Faculty were found to have published 279 articles in the last five years, with 27 percent of those articles shared by five journals, and with an average impact factor of 3.459. Applied Physics Letters was the most cited journal and Advanced Materials had the highest impact factor of 17.493. The library owned 95 percent of the publications in which faculty published their work and at least 82 percent of the resources cited in their publications. The pattern of the publications was not consistent and the most articles in one year (69) were published in 2013.

Since this study focused on the current patterns regarding where the faculty are publishing and citing, it should be a fairly accurate indicator of their future needs. Therefore, the study impacts decisions regarding future directions by enabling the library to maintain a high-quality collection for the faculty.

Biomonitoring uses biological responses to assess environmental changes, which are frequently due to anthropogenic pollution. Biomonitoring can be carried out at different levels of complexity: population, species or suborganismic responses may be used as indicators for exposure to pollutants. In the case of tests with whole organisms, their quality is often impaired by the heterogeneity of living organisms, slow responses and the fact that little information can be provided on the pollutants. Although suborganismic tests can provide more specific and fast responses, structure and concentration of pollutants are only revealed by chemical analysis. A novel approach to biomonitoring is introduced, which applies tight coupling of bioassays and chemical analysis and is defined as bioresponse‐linked instrumental analysis. It combines biomolecular recognition, initiating a biological effect and chemical analysis.

The purpose of this paper is to propose a process management framework for Chemical Waste Treatment Laboratories (CWTL) that can be used as a management tool to identify and manage critical process.

Proposition of a generic classification for categories of chemical waste; use of the ABC analysis as a tool for analysis of priority in relation to the inputs of an CWTL; use of the process matrix (variety vs volume) to identify the key resources required to perform the activities of a CWTL; and use of mapping process techniques to map the processes defined and calculate times.

The proposed framework was applied to a CWTL at University of São Paulo, Brazil, and showed that the high variability of demand is a significant factor in the management of this laboratory, requiring processes that are flexible to meet this demand. The results showed that the applicability of the production and operations management theories within the scope of process management of CWTLs, proved to be useful tools for improving the treatment efficiency of chemical waste in these facilities.

The novelty of this work is in the fact of using production and operations management tools in the management of CWTLs to propose diagnoses to improve the management of their processes. The proposition of a comprehensive classification for chemical wastes generated in CWTLs is also highlighted.

The purpose of this paper is to describe a multi‐robot solution to the problem of chemical source localization, in which a team of inexpensive, simple vehicles with short‐range, low‐power sensing, communication, and processing capabilities trace a chemical plume to its source emitter

The source localization problem is analyzed using computational fluid dynamics simulations of airborne chemical plumes. The analysis is divided into two parts consisting of two large experiments each: the first part focuses on the issues of collaborative control, and the second part demonstrates how task performance is affected by the number of collaborating robots. Each experiment tests a key aspect of the problem, e.g. effects of obstacles, and defines performance metrics that help capture important characteristics of each solution.

The new empirical simulations confirmed previous theoretical predictions: a physics‐based approach is more effective than the biologically inspired methods in meeting the objectives of the plume‐tracing mission. This gain in performance is consistent across a variety of plume and environmental conditions. This work shows that high success rate can be achieved by robots using strictly local information and a fully decentralized, fault‐tolerant, and reactive control algorithm.

This is the first paper to compare a physics‐based approach against the leading alternatives for chemical plume tracing under a wide variety of fluid conditions and performance metrics. This is also the first presentation of the algorithms showing the specific mechanisms employed to achieve superior performance, including the underlying fluid and other physics principles and their numerical implementation, and the mechanisms that allow the practitioner to duplicate the outstanding performance of this approach under conditions of many robots navigating through obstacle‐dense environments.

The complexity in determining the quality of a credence good like wine increases due to the lack of mandatory ingredient labeling. This has generated a significant information asymmetry in the wine market, leading consumers to delegate their purchase decisions to expert rankings and wine guides. This paper explores whether expert assessments reduce the information asymmetry caused by the absence of ingredient labeling in the wine market.

By employing analysis of variance (ANOVA) in a sample of 304 wines included in the Wine Guide of the Spanish Consumers Organization (OCU), this paper assesses the extent to which expert assessments based on sensory evaluations converge with the objective cues provided by laboratory analysis in wine quality evaluations.

Results reveal a mismatch between expert assessments and laboratory analyses. Chemical aspects such as SO₂ levels or volatile acidity, sensorial factors such as intensity and persistence, and extrinsic variables such as the region of origin or wine type play an important role in the quality ranking of wines.

These findings call for the inclusion of objective intrinsic cues in expert sensory assessments to provide consumers reliable information about wines and to resolve the apparent dissonances in wine quality assessments.

The purpose of this paper is to make a lean assessment of a chemical analyst training laboratory in a higher education institution and identify the main types of waste on a daily basis and understand the lean maturity of the laboratory and establish priority areas of intervention to make the laboratory leanest.

A single descriptive case study methodology was used to carry out the lean laboratory evaluation. The lean manufacturing waste terminology was adapted to a lean analytical laboratory environment, and a lean waste assessment step-by-step procedure was developed to reach the study goal.

Three types of waste (i.e. transport, waiting and defects) were the main contributors of the problem. The Pareto analysis results showed that 37.5% of the different types of waste contributed to almost 51.4% of the problems. The case study allowed on diagnosing wastes, understanding the lean maturity in a teaching laboratory setting and priority areas of intervention

Some data collection methods were used, and tools were developed to answer the research questions. A waste measurement instrument was created to evaluate lean waste in a chemical analytical laboratory, and a lean classification scheme was built to understand the lean maturity of the laboratory. The lessons learnt of the lean assessment in a teaching laboratory and the developed tools will be helpful for future research and for practitioners in a teaching chemical analytical laboratory setting.

The number of lean assessment studies in teaching laboratories is not very significant, and this work contributes to overcome this gap illustrating the lean waste assessment foundation with a step-by-step procedure and tools used in a teaching laboratory to perform a lean assessment and identify opportunities for improvement. A generic roadmap to lean laboratory waste assessment and continuous improvement is proposed with the key elements to take into consideration.

Introduces some simple statistics employed in analysing chemical analysis data. Describes measures of precision and accuracy and how the use of confidence intervals and repeatability can guide validation of data.

The purpose of this paper is to substitute wheat flour by passion fruit peel flour in Brazilian honey bread (pão de mel), with evaluation of the breads’ sensory features, chemical composition and physical properties.

Honey breads with wheat flour (standard) and with replacement of this ingredient by 10 to 50 per cent passion fruit peel flour were produced. Two sensory tests were applied, to identify how different formulations with passion fruit peel flour were when compared to the standard formulation, and also the acceptability of the products. The selected honey breads, through sensory results, were evaluated regarding to chemical composition and physical properties.

Formulations with 10 and 20 per cent substitution were the least different to formulations with only wheat flour, and were as acceptable as the wheat flour sample. Physical and chemical characteristics of breads with 10 and 20 per cent passion fruit peel flour were similar; however, honey bread with 20 per cent passion fruit peel flour had higher fibre content, ash quantity and hardness value; a lower specific volume; and a different colour from the wheat flour sample.

This study offers an opportunity to food industries through utilization of an agro-industrial by-product on the formulation of a Brazilian traditional product.

This study presents the feasibility of using an agro-industrial by-product to Brazilian honey bread, enhancing the nutritive value of this product and reducing the impact of passion fruit peel flour on the environment.

In computational chemistry, the chemical bond energy (pKa) is essential, but most pKa-related data are submerged in scientific papers, with only a few data that have been extracted by domain experts manually. The loss of scientific data does not contribute to in-depth and innovative scientific data analysis. To address this problem, this study aims to utilize natural language processing methods to extract pKa-related scientific data in chemical papers.

Based on the previous Bert-CRF model combined with dictionaries and rules to resolve the problem of a large number of unknown words of professional vocabulary, in this paper, the authors proposed an end-to-end Bert-CRF model with inputting constructed domain wordpiece tokens using text mining methods. The authors use standard high-frequency string extraction techniques to construct domain wordpiece tokens for specific domains. And in the subsequent deep learning work, domain features are added to the input.

The experiments show that the end-to-end Bert-CRF model could have a relatively good result and can be easily transferred to other domains because it reduces the requirements for experts by using automatic high-frequency wordpiece tokens extraction techniques to construct the domain wordpiece tokenization rules and then input domain features to the Bert model.

By decomposing lots of unknown words with domain feature-based wordpiece tokens, the authors manage to resolve the problem of a large amount of professional vocabulary and achieve a relatively ideal extraction result compared to the baseline model. The end-to-end model explores low-cost migration for entity and relation extraction in professional fields, reducing the requirements for experts.