Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD) through machine learning techniques has attracted substantial attention from psychiatric and artificial intelligence communities. These approaches mostly rely on various biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data. In this paper, we provide an updated overview of existing machine learning-based methods for bipolar disorder (BD) diagnosis using MRI and EEG data. Method: This study is a short non-systematic review with the aim of describing the current situation in automatic diagnosis of BD using machine learning methods. Therefore, an appropriate literature search was conducted via relevant keywords for original EEG/MRI studies on distinguishing BD from other conditions, particularly from healthy peers, in PubMed, Web of Science, and Google Scholar databases. Results: We reviewed 26 studies, including 10 EEG studies and 16 MRI studies (including structural and functional MRI), that used traditional machine learning methods and deep learning algorithms to automatically detect BD. The reported accuracies for EEG studies is about 90%, while the reported accuracies for MRI studies remains below the minimum level for clinical relevance, i.e. about 80% of the classification outcome for traditional machine learning methods. However, deep learning techniques have generally achieved accuracies higher than 95%. Conclusion: Research utilizing machine learning applied to EEG signals and brain images has provided proof of concept for how this innovative technique can help psychiatrists distinguish BD patients from healthy people. However, the results have been somewhat contradictory and we must keep away from excessive optimistic interpretations of the findings. Much progress is still needed to reach the level of clinical practice in this field.
Man's attitudes about the environment have generated irreversible damage to the planet, emerging as an alternative to this problem Environmental Education, which aims to reorient social awareness towards a friendly and thoughtful culture. Through environmental education, we seek to make people aware of the problems of the natural and social environment from their school education in childhood to generate values, new attitudes, behaviors, and beliefs aimed at caring for the environment and learning new relationships between people. Likewise, to carry out these environmental education strategies, it is important to know some specific parameters, such as biological diversity and conservation, in addition to the conservation policies carried out by each nation. In this sense, in this work a bibliometric study was carried out based on high-impact scientific production and stipulated by ScienceDirect related to Environmental Education during a period of the last 20 years. The results were grouped into five clusters: "Environmental Education" OR "Education for Sustainable Development" OR "Education for Sustainability" OR "Education for Climate Change" OR "Eco citizenship”. The union of all these clusters are connected and intertwined with each other. Them in a dependent way, which is a consequence of the study carried out.
By December 2019, multiple cases of unexplained pneumonia were reported in some hospitals in the city of Wuhan, China. Since then, it had been confirmed that it corresponded to an acute respiratory infection caused by a new coronavirus that spread quickly, becoming pandemic in a very short time. On the other hand, this pandemic forced confinement for months, something unprecedented. In that time, millions of people went online for entertainment, education, etc. Consequently, the use of the Internet increased, bringing, on the one hand, online education, and entertainment on the Internet, ensuring social distancing; and on the other hand, it brought new new risks to human life, among them rumors. In this way and given the large number of publications that could denote the level of misinformation about COVID-19 and the impact it could have on global public health, various scientific publications were analyzed and identified from a bibliometric point of view. Potential relationships between the descriptors obtained from the bibliometric search were identified. The results were conglomerated into 5 clusters: Cluster 1, related to studies on access to information provided on COVID-19; cluster 2 shows the list of studies that have been carried out on the information on the COVID-19 vaccine, cluster 3 analyzes the different responses given by conspiracy theories, rumors and misinformation about COVID-19, the Group 4 shows cross-sectional and longitudinal research on COVID-19 and the information it provides to the health sector, and cluster 5 represents studies on scientific production and communication that have contributed to global health during the pandemic.
El objetivo de la investigación fue determinar la valoración por los servicios ecosistémicos ofrecidos en el área de conservación regional en un área de 8,457.76 ha para la gestión de los recursos y conservación de los bienes y servicios, se utilizó la metodología la valoración contingente, procediéndose a encuestar a 201 visitantes a la reserva entre nacionales y extranjeros, validadas con el Alfa de Cronbach, encontrándose una asociación significativa y directas de las variables sexo, edad, población, grado de instrucción, ingresos y medidas ambientales las cuales intervienen en el modelo econométrico de regresión logística empleado. Los resultados obtenidos corresponden a una disposición a pagar (DPA) de 11.16 soles por turista que visita el área de conservación regional Moyán Palacio, monto que permitirá conservar y mejorar los servicios ofrecidos actualmente, en las diferentes zonas tales como: zona silvestre, de recuperación, turístico y recreativo, así como un área de uso especial, que son ofrecidos en la actualidad a los turistas.
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