Search results for: Elen A. Perpetuo

Authors: Marcela dos P. G. Baltazar, Louise H. Gracioso, Luciana J. Gimenes, Bruno Karolski, Ingrid Avanzi, Elen A. Perpetuo

Abstract:

In this work, bacterial strains were isolated from environmental samples from a copper mine and three of them presented potential for bioremediation of copper. All the strains were identified by mass spectrometry (MALDI-TOF-Biotyper) and grown in three diferent media supplemented with 100 ppm of copper chloride in flasks of 500mL and it was incubated at 28 °C and 180 rpm. Periodically, samples were taken and monitored for cellular growth and copper biosorption by spectrophotometer UV-Vis (600 nm) and Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), respectively. At the end of exponential phase of cellular growth, the biomass was utilized to construct a correlation curve between absorbance and dry mass of the cells. Among the three isolates with potential for biorremediation, 1 strain exhibit capacity the most for bioremediation of effluents contaminated by copper being identified as Rhodococcus erythropolis.

Keywords: bioprocess, bioremediation, biosorption, copper

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Authors: Ingrid R. Avanzi, Marcela dos P. G. Baltazar, Louise H. Gracioso, Luciana J. Gimenes, Bruno Karolski, Elen A. Perpetuo, Claudio Auguto Oller do Nascimento

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In this study was developed a natural process using a biological system for the uptake of Copper and possible removal of copper from wastewater by dead biomass of the strain Burkholderia cenocepacia. Dead and live biomass of Burkholderia cenocepacia was used to analyze the equilibrium and kinetics of copper biosorption by this strain in function of the pH. Living biomass exhibited the highest biosorption capacity of copper, 50 mg g−1, which was achieved within 5 hours of contact, at pH 7.0, temperature of 30°C, and agitation speed of 150 rpm. The dead biomass of Burkholderia cenocepacia may be considered an efficiently bioprocess, being fast and low-cost to production of copper and also a probably nano-adsorbent of this metal ion in wastewater in bioremediation process. In this study was developed a natural process using a biological system for the uptake of Copper and possible removal of copper from wastewater by dead biomass of the strain Burkholderia cenocepacia. Dead and live biomass of Burkholderia cenocepacia was used to analyze the equilibrium and kinetics of copper biosorption by this strain in function of the pH. Living biomass exhibited the highest biosorption capacity of copper, 50 mg g−1, which was achieved within 5 hours of contact, at pH 7.0, temperature of 30°C, and agitation speed of 150 rpm. The dead biomass of Burkholderia cenocepacia may be considered an efficiently bioprocess, being fast and low-cost to production of copper and also a probably nano-adsorbent of this metal ion in wastewater in bioremediation process.

Keywords: biosorption, dead biomass, biotechnology, copper recovery

Procedia PDF Downloads 337

Authors: Louise H. Gracioso, Marcela P.G. Baltazar, Ingrid R. Avanzi, Bruno Karolski, Luciana J. Gimenes, Claudio O. Nascimento, Elen A. Perpetuo

Abstract:

Introduction: Higher copper concentrations promote a selection pressure on organisms such as plants, fungi and bacteria, which allows surviving only the resistant organisms to the contaminated site. This selective pressure keeps only the organisms most resistant to a specific condition and subsequently increases their bioremediation potential. Despite the bacteria importance for biosphere maintenance, it is estimated that only a small fraction living microbial species has been described and characterized. Due to the molecular biology development, tools based on analysis 16S ribosomal RNA or another specific gene are making a new scenario for the characterization studies and identification of microorganisms in the environment. News identification of microorganisms methods have also emerged like Biotyper (MALDI / TOF), this method mass spectrometry is subject to the recognition of spectroscopic patterns of conserved and features proteins for different microbial species. In view of this, this study aimed to isolate bacteria resistant to copper present in a Copper Processing Area (Sossego Mine, Canaan, PA) and identifies them in two different methods: Recent (spectrometry mass) and conventional. This work aimed to use them for a future bioremediation of this Mining. Material and Methods: Samples were collected at fifteen different sites of five periods of times. Microorganisms were isolated from mining wastes by culture enrichment technique; this procedure was repeated 4 times. The isolates were inoculated into MJS medium containing different concentrations of chloride copper (1mM, 2.5mM, 5mM, 7.5mM and 10 mM) and incubated in plates for 72 h at 28 ºC. These isolates were subjected to mass spectrometry identification methods (Biotyper – MALDI/TOF) and 16S gene sequencing. Results: A total of 105 strains were isolated in this area, bacterial identification by mass spectrometry method (MALDI/TOF) achieved 74% agreement with the conventional identification method (16S), 31% have been unsuccessful in MALDI-TOF and 2% did not obtain identification sequence the 16S. These results show that Biotyper can be a very useful tool in the identification of bacteria isolated from environmental samples, since it has a better value for money (cheap and simple sample preparation and MALDI plates are reusable). Furthermore, this technique is more rentable because it saves time and has a high performance (the mass spectra are compared to the database and it takes less than 2 minutes per sample).

Keywords: copper mining area, bioremediation, microorganisms, identification, MALDI/TOF, RNA 16S

Procedia PDF Downloads 375

Authors: Elen Puspitasari, Yeye Susilowati, Wahyu Meiranto

Abstract:

This study uses an experiment to examine the effect of motivational postures and peer’s reporting behavior on taxpayer compliance decisions. The motivational postures of taxpayers placed as a concomitant variable. Taxpayers were randomly assigned to alternate peer reporting behavior as an experimental variable with two level treatments and then responded to tax reporting scenarios. A motivational posture was measured with 29 statements developed by Braithwaite. Therefore, this experimental research uses Quasi-Experimental Design Between-Subject with Covariate with random assignment method. The subject in this experiment is the taxpayers who has a tax ID and have experience in reporting their tax revenue. The most important is that they earn income from their own business. The analysis technique used was Analysis of Covariate. The results showed that the posture of motivation as concomitant factors does not affect tax compliance decisions. Furthermore, this study proves that peer reporting behavior will determine the decisions of tax compliance. The findings in this study are intended to provide some practical implications for improving tax compliance.

Keywords: motivational postures, concomitant, tax compliance decisions, peer reporting behavior

Procedia PDF Downloads 273

Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

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Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: energy production, meteorological data, irradiance decomposition, solar photovoltaic system

Procedia PDF Downloads 141

Authors: Elen Gasparyan, Naira Hakobyan

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One of the leading factors determining the effectiveness of work in a modern organization is the motivation of its employees. In the scientific psychological literature, this phenomenon is understood mainly as constructive forms of motivation and the search for ways to increase it. At the same time, the motivation of employees can sometimes lead to a decrease in the productivity of the organization, i.e., destructive motivation is usually not considered from the point of view of various motivational theories. This article provides an analysis of various forms of destructive motivation of employees. These forms include formalism in labor behavior, inadequate assessment of the work done, and an imbalance of personal and organizational interests. The destructive motivation of personnel has certain negative consequences both for the employees themselves and for the entire organization - it leads to a decrease in the rate of production and the quality of products or services, increased conflict in the behavior of employees, etc. Currently, there is an increase in scientific interest in the study of destructive motivation. The subject of psychological research is not only modern socio-psychological processes but also the achievements of scientific thought in the field of theories of motivation and management. This article examines the theoretical approaches of J. S. Adams and Porter-Lawler, provides an analysis of theoretical concepts, and emphasizes the main characteristics of the destructiveness of motivation. Destructive work motivation is presented at the macro, meso, and micro levels. These levels express various directions of development of motivation stimuli, such as social, organizational, and personal ones. At the macro level, the most important characteristics of destructive motivation are the high-income gap between employers and employees, а high degree of unemployment, weak social protection of workers, non-compliance by employers with labor legislation, and emergencies. At the organizational level, the main characteristics are decreasing the diversity of work and insufficient work conditions. At the personal level, the main characteristic of destructive motivation is a discrepancy between personal and organizational interests. A comparative analysis of the theoretical and methodological foundations of the study of motivation makes it possible to identify not only the main characteristics of destructive motivation but also to determine the contours of psychological counseling to reduce destructiveness in the behavior of employees.

Keywords: destructive, motivation, organization, behavior

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 80

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 69