Evolution Analysis of Free Fatty Acids and Aroma-Active Compounds during Tallow Oxidation

To explore the role of fatty acids as flavor precursors in the flavor of oxidized tallow, the volatile flavor compounds and free fatty acid (FFAs) in the four oxidization stages of tallow were analyzed via gas chromatography (GC)–mass spectrometry (MS), the aroma characteristics of them were analyzed by GC–olfactory (GC-O) method combined with sensory analysis and partial least-squares regression (PLSR) analysis. 12 common FFAs and 35 key aroma-active compounds were obtained. Combined with the results of odor activity value (OAV) and FD factor, benzaldehyde was found to be an important component in unoxidized tallow. (E,E)-2,4-Heptadienal, (E,E)-2,4-decadienal, (E)-2-nonenal, octanal, hexanoic acid, hexanal and (E)-2-heptenal were the key compounds involved in the tallow flavor oxidation. The changes in FFAs and volatile flavor compounds during oxidation and the metabolic evolution of key aroma-active compounds are systematically summarized in this study. The paper also provides considerable guidance in oxidation control and meat flavor product development.

A Systematic Review of Attributes and Techniques for Open Source Software Evolution Analysis

Many studies have been conducted to understand the evolution process of Open Source Software (OSS). The researchers have used various techniques for understanding the OSS evolution process from different perspectives. This chapter reports a meta-data analysis of the systematic literature review on the topic in order to understand its current state and to identify opportunities for the future. This research identified 190 studies, selected against a set of questions, for discussion. It categorizes the research studies into nine categories. Based on the results obtained from the systematic review, there is evidence of a shift in the metrics and methods for OSS evolution analysis over the period of time. The results suggest that there is a lack of a uniform approach to analyzing and interpreting the results. There is need of more empirical work using a standard set of techniques and attributes to verify the phenomenon governing the OSS projects. This will help to advance the field and establish a theory of software evolution.

ANÁLISE DA EVOLUÇÃO DO USO E OCUPAÇÃO DAS TERRAS NO ARRONDISSEMENT DE ARCAHAIE-HAITI (1987, 1997 E 2018)

Atualmente, utiliza-se os recursos da natureza de forma exploratória, sem a projeção dos impactos que essa atitude pode ocasionar, gerando problemas de caráter social e/ou ambiental, podendo apresentar seus efeitos rapidamente ou em grande escala de tempo. No Haiti, constata-se que a região possui incapacidade de enfrentar qualquer problema de caráter ambiental, essa situação está relacionada diretamente com as atividades antrópicas. Nesse sentido, a presente pesquisa teve como objetivo analisar a evolução temporal do uso e ocupação da terra do Arrondissement de Arcahaie, localizada a Oeste do Haiti. O Arrondissement é uma divisão administrativa do território haitiano que é composta por vários municípios. O método utilizado foi uma fotointerpretação sobre o recorte das Imagens Landsat 5 e Landsat 8, referente a área de estudo. Para o mapeamento foi utilizada a classificação supervisionada. As classes de uso e ocupação definidas foram: pastagem, cobertura vegetal, solo exposto, área cultivada, área urbana, mata natural. Os resultados mais significativos mostram mudanças na dinâmica de uso e ocupação da terra no Arrondissement de Arcahaie, durante 31 anos e consequentemente, podendo causar danos, muitas vezes, irreversíveis ao meio ambiente e comprometer a qualidade de vida da população. EVOLUTION ANALYSIS OF THE LAND USE AND OCCUPATION IN ARRONDISSEMENT OF ARCAHAIE-HAITI (1987, 1997 AND 2018)A B S T R A C TCurrently, the nature resources are used in an exploratory way, without the projection of the impacts that this attitude can cause, generating social and / or environmental problems, can present their effects quickly or in a large scale of time. In Haiti, it appears that their inability to face any environmental problem is directly related to anthropic activities. In this sense, the present research had as main objective to analyze the temporal evolution of the use and occupation of the land of the Arrondissement of Arcahaie, located to the West of Haiti. The Arrondissement is an administrative division of Haitian territory that decomposes several municipalities. The method used was a photointerpretation on the clipping of the Landsat 5 and Landsat 8 images, referring to the study area. Supervised classification was used for the mapping. The classes of use and occupation defined were pasture, vegetation cover, exposed soil, cultivated area, urban area, natural forest. The most significant results show changes in the dynamics of use and occupation in the Arrondissement of Arcahaie during the last 31 years and, consequently, can cause damage, often irreversible to the environment and compromise the quality of life of the population.Key words: Environmental impacts, Natural resources, Arrondissement of Arcahaie

Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets

This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions between users on topics related to the pandemic. The third contribution is a sentiment analysis, which explores the evolution of emotions throughout time, while the fourth one is an association rule mining task. The indicators determined by statistics and the results obtained from sentiment analysis and association rule mining are eloquent. For instance, signs of an upcoming worldwide economic crisis were clearly detected at an early stage in this study. Overall results are promising and can be exploited in the prediction of the aftermath of COVID-19 and similar crisis in the future.

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.