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Kim SA, Bae SM. Impacts of Perceived Stress, Neglect, Victim and Respect for Human Rights on Depression of Adolescents. Child Psychiatry Hum Dev 2023:10.1007/s10578-023-01491-3. [PMID: 36645536 DOI: 10.1007/s10578-023-01491-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/06/2023] [Indexed: 01/17/2023]
Abstract
The purpose of this study was to identify the impacts of perceived stress, neglect, online and offline violence, and respect for human rights on depression. To fulfill the purpose of the study, the data of 6277 middle and high school students (M = 15.64, SD = 1.69) from the Survey on the Human Rights of Children and Youth (2018) were used. The main results of the hierarchical multiple regression analysis are as follows. First, gender and age had significant impacts on depression. Second, neglect, perceived stress, and online violence were positively related to depression, whereas offline violence showed no relationship with depression. Third, respect for human rights, which is the final stage of the hierarchical multiple regression analysis, was negatively associated with depression. This study contributed to the research by verifying that perceived respect for human rights is a protective factor against depression.
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Affiliation(s)
- Sung A Kim
- Department of Psychology, Graduate School, Dankook University, Cheonan, Republic of Korea
| | - Sung Man Bae
- Department of Psychology, Graduate School, Dankook University, Cheonan, Republic of Korea.
- Department of Psychology and Psychotherapy, College of Health Science, Dankook University, 119 Dandae-Ro, Dongnam-Gu, Cheonan, Chungnam, Republic of Korea.
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Sott MK, Nascimento LDS, Foguesatto CR, Furstenau LB, Faccin K, Zawislak PA, Mellado B, Kong JD, Bragazzi NL. A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure. SENSORS 2021; 21:s21237889. [PMID: 34883903 PMCID: PMC8659853 DOI: 10.3390/s21237889] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/21/2022]
Abstract
The agriculture sector is one of the backbones of many countries’ economies. Its processes have been changing to enable technology adoption to increase productivity, quality, and sustainable development. In this research, we present a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, we used 4694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis of the literature using SciMAT software with the support of the PICOC protocol. Our findings presented 22 strategic themes related to Digital Agriculture, such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAV) and Climate-smart Agriculture (CSA), among others. The thematic network structure of the nine most important clusters (motor themes) was presented and an in-depth discussion was performed. The thematic evolution map provides a broad perspective of how the field has evolved over time from 1994 to 2020. In addition, our results discuss the main challenges and opportunities for research and practice in the field of study. Our findings provide a comprehensive overview of the main themes related to Digital Agriculture. These results show the main subjects analyzed on this topic and provide a basis for insights for future research.
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Affiliation(s)
- Michele Kremer Sott
- Business School, Unisinos University, Porto Alegre 91330-002, RS, Brazil; (C.R.F.); (K.F.)
- Correspondence: (M.K.S.); (N.L.B.)
| | - Leandro da Silva Nascimento
- School of Management, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil; (L.d.S.N.); (P.A.Z.)
| | | | - Leonardo B. Furstenau
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil;
| | - Kadígia Faccin
- Business School, Unisinos University, Porto Alegre 91330-002, RS, Brazil; (C.R.F.); (K.F.)
| | - Paulo Antônio Zawislak
- School of Management, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil; (L.d.S.N.); (P.A.Z.)
| | - Bruce Mellado
- School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa;
| | - Jude Dzevela Kong
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
| | - Nicola Luigi Bragazzi
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
- Correspondence: (M.K.S.); (N.L.B.)
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Dias JL, Sott MK, Ferrão CC, Furtado JC, Moraes JAR. Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2021; 39:1331-1340. [PMID: 34525881 DOI: 10.1177/0734242x211042276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.
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Affiliation(s)
- Janaína Lopes Dias
- Department of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul, Brazil
| | | | | | - João Carlos Furtado
- Department of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul, Brazil
| | - Jorge André Ribas Moraes
- Department of Environmental Technology, University of Santa Cruz do Sul, Santa Cruz do Sul, Brazil
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Kolling ML, Furstenau LB, Sott MK, Rabaioli B, Ulmi PH, Bragazzi NL, Tedesco LPC. Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18063099. [PMID: 33802880 PMCID: PMC8002654 DOI: 10.3390/ijerph18063099] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/12/2021] [Accepted: 03/15/2021] [Indexed: 12/15/2022]
Abstract
In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.
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Affiliation(s)
- Maikel Luis Kolling
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
| | - Leonardo B. Furstenau
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil;
| | - Michele Kremer Sott
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
| | - Bruna Rabaioli
- Department of Medicine, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
| | - Pedro Henrique Ulmi
- Department of Computer Science, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
- Correspondence: (N.L.B.); (L.P.C.T.)
| | - Leonel Pablo Carvalho Tedesco
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
- Department of Computer Science, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
- Correspondence: (N.L.B.); (L.P.C.T.)
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