Occupational health resource classification instrument (OHRCI) in the oil industry, Brazil

Background The study describes the development of the Occupational Health Resource Classification Instrument (OHRCI) aiming to classify the levels of occupational health care in the oil industry, Brazil, considering the aspects of prevention and health promotion. Methods OHRCI was developed by 6 recognized experts in occupational health management with at least 10 years of experience. For content validation, the Delphi Technique was used, with 20 specialists from the fields of medicine, dentistry, social work, nutrition and psychology participating in 40 meetings with an average duration of 2 hours each. All proposed adjustment recommendations were incorporated and accepted. Results OHCRI tool was structured. It considers the characteristics of the oil companies regarding the assessment of risk scenarios, location and exposed population, resulting in a score between 1 and 16. Nine levels of occupational health care were defined, which allows to objectively consider the dimension of the workforce and attendance modality. The results of OHCRI tool allow specific strategic actions in people management, related to the monitoring of work processes and adaptation of professional skills. Conclusions The proposed tool is considered validated and its application generates business value, ensuring the maintenance of resources for comprehensive health care and attendance level adequate to the needs of the oil industry and workers and associated compliance aspects.

Environmental Sensitivity Index Analysis for coastal protection of oil spill in Fakfak, Papua, Indonesia

<p>This study was conducted to analysis of the Environmental Sensitivity Index (ESI) for anticipating the effect of oil spills on the environment in coastal areas. The study location is the coastal area and waters of the Karas District, Fakfak Regency, West Papua Province, Indonesia. The purpose of the study is to determine the priority of areas that are sensitive to oil spills. This method was carried out by scoring each unit of land based on vulnerability, conservation, and social values. ESI Analysis was carried out through geographic information systems and classified into 5 classes of sensitivity levels. The land use classification was carried out through satellite imagery and field surveys conducted in December 2018. The results of resource classification can be divided into 16 classes. The ESI analysis showed that most (51%) were categorized as insensitive, sensitive low 24%, very sensitive 15%, moderately sensitive 6%, and sensitive 4%. Although most are not sensitive, it should be followed by effective environmental protection to maintain sustainable development.</p>

Rural Tourism Network Evaluation Based on Resource Control Ability Analysis: A Case Study of Ning’an, China

Organization of rural tourism resources is important for optimizing rural land use based on rational resource classification. Quantitative analysis was performed to evaluate the resource control ability of rural tourism networks. This was achieved by determining the resource control relationship and assessing the structure of the rural tourism network. The ability of resource control was analyzed via resource abstraction, which included the extraction of resource nodes and corridors, control scope analysis, and network structure level evaluation. The proposed approach was applied to the Ning’an in Heilongjiang Province, China, and proved to be effective for exploring the network degree and development trends in rural tourism resources. By examining the resource control ability, the spatial characteristics and development trend in rural tourism networks were quantitatively analyzed, especially the connection mode of key tourism resources, network structure analysis, and resource linking ability. The core resources showed a lack of outward ability in the network, and the secondary resource expansion ability was limited. Via resource control ability analysis, this study focused on areas with rich tourism but an unbalanced spatial structure, combining the directional characteristics of the network to provide suggestions for the optimization rural tourism resources network in other regions of the world.

A Classification Method for Academic Resources Based on a Graph Attention Network

Classification of resource can help us effectively reduce the work of filtering massive academic resources, such as selecting relevant papers and focusing on the latest research by scholars in the same field. However, existing graph neural networks do not take into account the associations between academic resources, leading to unsatisfactory classification results. In this paper, we propose an Association Content Graph Attention Network (ACGAT), which is based on the association features and content attributes of academic resources. The semantic relevance and academic relevance are introduced into the model. The ACGAT makes full use of the association commonality and the influence information of resources and introduces an attention mechanism to improve the accuracy of academic resource classification. We conducted experiments on a self-built scholar network and two public citation networks. Experimental results show that the ACGAT has better effectiveness than existing classification methods.