Sustainability and Corporate Social Responsibility in the Perspective of Social Economy Entities: A Bibliometric Study

We start from the conceptual interconnection between Sustainability and Corporate Social Responsibility, which, although continuously subject to controversy, both within the scope of its definition and in its performance, advocate, as the ultimate goal, human development and of the society in general, promoting the interests of the Communities on a permanent, long-term basis and without compromising the options of the future generations. In this context, we cannot fail to draw a parallel with the entities that constitute the Social Economy Sector. The activities they carry out are of economic and social nature and must be pursued in the general interest of their members, users and beneficiaries, thus in the general interest of the Community. These institutions also reveal concerns about the sustainability in all the dimensions involved (economic, social and environmental), in which the organizational performance is particularly important, as it becomes imperative to guarantee their continuity, fostering and promoting their social action. We will, therefore, start by framing what is meant by Sustainability, Corporate Social Responsibility and Social Economy, with a particular focus on the current requirements of stakeholders regarding the socially responsible behaviour of the institutions as these, in turn, will entail the adoption of more comprehensive management tools, also more efficient and transparent concerning all dimensions (economic, financial and social). It is within this framework that a project called “TFA—TheoFrameAccountability—Theoretical framework for the promotion of accountability in the social economy sector: The IPSS case” emerges, being promoted by the University of Aveiro, with the participation of National Confederation of Solidarity Institutions (CNIS—acronym in Portuguese), and the Polytechnic Institutes of Coimbra and Porto. This project aims to promote the accountability of the social economy sector (economic, financial and social aspects), in the Private Social Solidarity Institutions (IPSS—acronym in Portuguese), assisting them not only in fulfilling their legal obligations, but also facilitating the reporting of results of activities carried out in a more effective manner and promoting transfer of knowledge (for the IPSS and also for the academic community), thus contributing to the development and sustainability of these institutions. Thus, we develop an exploratory and descriptive analysis, of a quantitative-qualitative nature, in which the procedures of data collection determine the result of the search strategy by the defined descriptors. For this purpose, the analysis will focus on the following variables: number of articles published per year; methodologies used; theories of support; identification by sector/area of activity; countries of origin; more representative institutions; authors who publish more and journals with the largest number of publications. The main results indicate a growing concern about sustainability and a growing publication in this area. This paper presents a bibliometric study to evaluate the main trends of current research on sustainability and on corporate social responsibility, thus contributing to the construction of the theoretical basis underlying the “TFA—TheoFrameAccountability” project.info:eu-repo/semantics/publishedVersio

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Contraste de condutividade hidráulica em solos de texturas arenosa e argilosa encontrados nos tributários da margem esquerda do rio tijuco, município de Ituiutaba (MG) / DISTINCTION OF THE HYDRAULIC CONDUCTIVITY BETWEEN SAND AND CLAYEY TEXTURE SOILS FROM TRIBUTARY OF THE TIJUCO LEFT BORDER RIVER, ITUIUTABA, MINAS GERAIS STATE, BRAZIL

O objetivo principal deste trabalho é determinar as diferenças de condutividade hidráulica entre solos de textura arenosa e argilosa nos tributários da margem esquerda do Rio Tijuco, município de Ituiutaba/MG. Utilizou-se as técnicas de anéis concêntricos e open end hole para a determinação das condutividades hidráulicas na superfície do terreno e nas profundidades de 0,5, 1,0 e 1,5 m. Análise granulométrica indicou texturas franco-argilo-arenosa para os latossolos vermelhos, e argilosa para os nitossolos vermelhos férricos. Os valores de condutividade hidráulica de superfície nos pontos P1, P2, P3, P4, P5 e P6 variaram entre 1x10-4 a 9x10-5. A 0,5 m de profundidade os valores variaram entre 9x10-7 a 9x10-6; para a profundidade de 1,0 m, os valores estiveram entre 5x10-6 e 1x10-6, finalmente, os resultados para a profundidade de 1,5 m, os valores variaram entre 5x10-6 a 1x10-6. Análise dos resultados demonstra que a estrutura do solo exerce maior controle na condutividade hidráulica (respectiva eficiência de infiltração) de que a granulometria. Nesse caso, os nitossolos, mesmo sendo solos mais argilosos que os latossolos, de modo geral, apresentaram resultados maiores de condutividade hidráulica em função da alta densidade de fendas, típicas de expansão e contração de argilas de alta atividade