Novel Machine Learning for Big Data Analytics in Intelligent Support Information Management Systems

Two-dimensional 1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. Scientific information technology has been developed rapidly. Here, the purposes are to make people's lives more convenient and ensure information management and classification. The machine learning algorithm is improved to obtain the optimized Light Gradient Boosting Machine (LightGBM) algorithm. Then, an Android-based intelligent support information management system is designed based on LightGBM for the big data analysis and classification management of information in the intelligent support information management system. The system is designed with modules of employee registration and login, company announcement notice, attendance and attendance management, self-service, and daily tools with the company as the subject. Furthermore, the performance of the constructed information management system is analyzed through simulations. Results demonstrate that the training time of the optimized LightGBM algorithm can stabilize at about 100s, and the test time can stabilize at 0.68s. Besides, its accuracy rate can reach 89.24%, which is at least 3.6% higher than other machine learning algorithms. Moreover, the acceleration efficiency analysis of each algorithm suggests that the optimized LightGBM algorithm is suitable for processing large amounts of data; its acceleration effect is more apparent, and its acceleration ratio is higher than other algorithms. Hence, the constructed intelligent support information management system can reach a high accuracy while ensuring the error, with apparent acceleration effect. Therefore, this model can provide an experimental reference for information classification and management in various fields.

WELL-BEING AT WORK IN PROCESSES OF ORGANIZATIONAL CHANGE: THE ROLE OF INFORMAL SOCIAL NETWORKS

ABSTRACT Purpose: This study evaluates the role of social networks of support, information, and trust in well-being at work, regarding the positive and negative affects and professional fulfillment of workers immersed in processes of organizational change. Originality/value: Organizational change is characterized as a dynamic process, constituted through relationships, immersed in a context of uncertainties. The mapping of relationships can help in understanding the information flows and the assessment of resource availability. Design/methodology/approach: 151 professionals from a holding participated. This company undergoes changes in the scope of services offered and the organizational design. Links of support, information, and trust established according to the change processes were mapped. Associated with the network, the Well-Being at Work Scale was used. For data analysis, multiple regressions were used to construct explanatory models for well-being factors: fulfillment, positive and negative affects. Findings: Variables in support and information social network analysis (SNA) composed the predictive model of well-being in the three models. Among the researched ties, the support and information ties were part of the predictive model of well-being. The metrics that reveal how many times the employee is indicated and indicates others, proximity to highly cited neighbors, degree of participation in cohesive subgroups, the degree to which they assume a central position in the subgroups are indicators of actors’ positions capable of predicting well-being. The influence of the pattern of interaction between managers and employees should be considered in promoting well-being in organizations in the process of change.

IMPORTÂNCIA DA ASSISTÊNCIA DA TERAPIA OCUPACIONAL AO CUIDADOR DE CRIANÇAS COM CÂNCER EM CUIDADOS PALIATIVOS

Cancer has a great possibility of cure, however there is a group of patients who can reach the critical stage and no longer respond to conventional therapies, making it impossible to obtain a cure. In such cases, it will be necessary to provide other types of care in order to provide support, information and comfort to patients in the final stage of life and their families, which characterizes Palliative Care. In pediatrics, palliative care is centered on the child and the family. This study aimed to conduct a literature review on the intervention of Occupational Therapy with caregivers of children in Palliative Care, due to childhood and juvenile cancer. We searched for articles that corresponded to the descriptors that could support this study, however the search resulted in only two articles, a factor that made the study turn to a bibliographic search. Despite the reduced number of articles on occupational therapy in caring for children in palliative care, this study made it possible to present the importance of the attention and contribution of Occupational Therapy in caring for caregivers of children in the final stage of life.

Narrow Band Active Contour Attention Model for Medical Segmentation

Medical image segmentation is one of the most challenging tasks in medical image analysis and widely developed for many clinical applications. While deep learning-based approaches have achieved impressive performance in semantic segmentation, they are limited to pixel-wise settings with imbalanced-class data problems and weak boundary object segmentation in medical images. In this paper, we tackle those limitations by developing a new two-branch deep network architecture which takes both higher level features and lower level features into account. The first branch extracts higher level feature as region information by a common encoder-decoder network structure such as Unet and FCN, whereas the second branch focuses on lower level features as support information around the boundary and processes in parallel to the first branch. Our key contribution is the second branch named Narrow Band Active Contour (NB-AC) attention model which treats the object contour as a hyperplane and all data inside a narrow band as support information that influences the position and orientation of the hyperplane. Our proposed NB-AC attention model incorporates the contour length with the region energy involving a fixed-width band around the curve or surface. The proposed network loss contains two fitting terms: (i) a high level feature (i.e., region) fitting term from the first branch; (ii) a lower level feature (i.e., contour) fitting term from the second branch including the (ii1) length of the object contour and (ii2) regional energy functional formed by the homogeneity criterion of both the inner band and outer band neighboring the evolving curve or surface. The proposed NB-AC loss can be incorporated into both 2D and 3D deep network architectures. The proposed network has been evaluated on different challenging medical image datasets, including DRIVE, iSeg17, MRBrainS18 and Brats18. The experimental results have shown that the proposed NB-AC loss outperforms other mainstream loss functions: Cross Entropy, Dice, Focal on two common segmentation frameworks Unet and FCN. Our 3D network which is built upon the proposed NB-AC loss and 3DUnet framework achieved state-of-the-art results on multiple volumetric datasets.