Ontological Stakeholder View: An Innovative Proposition

This article describes a theoretical way of understanding business enterprise, for what it is used the stakeholder theory as a theory of the firm. Thus, the purpose of this article is to show an innovative perspective called ontological perspective of stakeholders that relies on a phenomenological model where the subjective perspective of agents is the key, from a purely monetarist model to an economic, social and emotional value creation model, and from a deductive model of stakeholder interests to an inductive model. The main contributions are: add a new perspective to the different classifications made of stakeholder theory, avoid monetarist reductionism under the concept of value in a way that the manager takes into account all interconnected interests of stakeholders, and finally prioritize interests map instead of roles map without accepting the assumption that the role involves joint and no conflicting interests

Organic farms in the Czech Republic – Map Portal presentation opportunities

The paper is aimed at presenting the map portal of organic farms in the Czech Republic. The pilot project is concerned with the South Bohemia Region. Extensive map data and resources are displayed by means of a purpose-developed universal software solution called Regional Development Map Portal (RDMP) version 1.0. The database was generated and updated on the basis of detailed content validation and strives for maximum accuracy of map object location. The software solution – apart from supporting all standard functions – represents qualitatively a brand new perspective of map data creation and entails many original elements and functionalities

Proficient brain for optimal performance: the MAP model perspective

Background. The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS) activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP) model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1) and optimal-controlled (Type 2) performances. Methods. Ten elite shooters (6 male and 4 female) with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time) repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged. Results. We found ERS to be mainly associated with optimal-automatic performance, in agreement with the “neural efficiency hypothesis.” We also observed more ERD as related to optimal-controlled performance in conditions of “neural adaptability” and proficient use of cortical resources. Discussion. These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback technique

A review of electric vehicle charge point map websites in the NSR: Interim report

This interim report is a review of the EV charge point (station) map websites in the North Sea Region (NSR) with the aim to identify if there are any patterns, or any noticeable gaps on the information presented by the interactive EV charge point tools. For each example of the charge point (station) map website, a review has been undertaken by visiting the charge point (station) map website and recording if the site contains the information, which is of key importance from an EV user perspective, for example an interactive map; any information on the charger power of the charge points (stations); the type of connection of the charge points (stations); the addresses of the charge points (stations) and further helpful details

V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly non-linear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.Comment: HANDS 2017 Challenge Frame-based 3D Hand Pose Estimation Winner (ICCV 2017), Published at CVPR 201