The system of tracking the position of the bucket excavator's wheel for prevention of risk situations

For companies doing business in mining mineral deposits, ensuring safe work is one of the key tasks (Safety First!). One of the important trends in this area is prevention and endeavour to forestall risk situations. Risks need to be searched, technically described, spatially defined, evaluated and categorized by degree of risk. Complex geological and stability conditions can be one of the sources of persistent and significant risks, which are mainly landslides and rockslides threatening both mining equipment and employees. The problem described in this article and its solution concerns the Most Basin (formerly the North Bohemian Lignite Basin). This is a tertiary basin that was founded in the Oligocene. The main mineral is lignite and mining takes place on the surface. The main excavating machinery in the surface lignite quarries in Europe (Czech Republic, Germany, Poland) is the bucket wheel excavator.Web of Science15328727

Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation

We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise which is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net

Millennial Employees in Contact Centers: Leadership Style Preferences Contribution to Job Satisfaction

Millennials account for the largest generational cohort in the workforce. Their propensity for turnover is costly to organizations, not just monetary impact, but skill development and sustainability as well. This study attempted to understand if leadership and the preferred leadership style of Millennial employees contribute to job satisfaction. This mixed-methods sequential explanatory study examined how leadership and the preferred leadership style of Millennial employees, from the perspectives of Millennial employees and those who manage them, contribute to job satisfaction. The Job Satisfaction Survey (JSS) was used as the survey tool for the qualitative study (see Appendix A). A questionnaire was sent via email to collect interview responses from Millennial employees and supervisors of Millennials. The sample used for the study consisted of Millennial employees and supervisors of Millennials within Texas and SatInc, a satellite internet company. The JSS tool and its results were used, as well as the raw data to further analyze trends and correlations between job characteristics and job satisfaction. Inductive and deductive coding was used in the qualitative portion of the study. The supervisory factor was the most impactful characteristic of job satisfaction. It is imperative that organizations understand the high level of impact that direct management and their leadership style can have on Millennial employees and their job satisfaction

Human Pose Estimation Using Per-Point Body Region Assignment

In recent years, the task of human pose estimation has become increasingly important, due to the large scale of usage, including VR applications, as well as higher-level tasks, such as human behavior understanding. In this paper, we introduce a novel two-stage deep learning approach named Segmentation-Guided Pose Estimation (SGPE). The pipeline is based on two neural networks working in a sequential fashion, while both models effectively process unorganized point clouds on the input. First, the segmentation network performs a pointwise classification into the corresponding body regions. In the next step, the point cloud with the per-point region assignment, forming the fourth input channel, is passed to the regression network. This way, both local and global features of the point cloud are preserved, helping the model fully maintain the body pose structure. Our strategy achieves competitive results on all of the examined benchmark datasets, and outperforms state-of-the-art methods

Analysis of determinants influencing the level of intellectual capital disclosure: The case of FTSE 100 entities

Purpose: The paper deals with the issue of intellectual capital (IC) and its disclosure in the financialstatements and other parts of annual reports of FTSE 100 entities. The paper aims to identify thedeterminants that influence entities to reveal IC related information and to highlight the theoreticalaspects behind such determinants, resulting in comprehensive findings. The results of the analysis can beused to understand what leads entities to make decisions in the field of non-financial disclosure and helpin the development of the IC reporting framework.Design/methodology: The research is devoted to the analysis of the relationship between the level ofIC disclosures by companies and the analysed determinants – size, asset structure, profitability, industryand the factor of time. The dataset can be characterised as a panel data set containing 100 firms fromthe FTSE100 Index for the four most recent financial years (2018-2021). To produce a comprehensiveset of results, descriptive statistics are used, followed by regression and correlation analysis. The randomeffect method is used as it has a higher predictive power than pooled OLS and fixed effect methods inanalysing panel data.Findings: Based on the results of the analysis, it was concluded that the profitability measured as ROAis not a key factor of intellectual capital disclosure in the annual reports of FTSE 100 companies. Fromthe point of view of size, there exists a statistically significant relationship between total assets and allcomponents of IC, respectively overall IC. The analysis also showed a statistically significant impact ofthe sector in which companies operate. Particularly, companies in the service sector report moreinformation on human capital and companies in the high-tech sector report more information onstructural capital. A significant effect of asset structure was found for structural capital but only takinginto account the effect of goodwill, not through the effect of other intangible assets. Finally, the paperdemonstrated a positive and significant effect of the time factor on the level of reporting of all ICcomponents.Originality/value: This paper focuses on the determinants influencing the level of IC reporting in arepresentative sample of entities from the highly active FTSE100 Index, which provides a very recentand specific data sample from a research perspective. The paper is based on determinants that arefrequently reported in existing research, and it extends the scope by incorporating the effect ofintangible assets and goodwill as variables representing the asset structure in addition to the effect oftime. This paper presents statistically based results on the relationships between the determinants and ICbut also between the different elements of IC (human capital, structural capital and relational capital),which provide insights into the structure of reported information on intellectual capital. This insight is very substantial given that many studies ignore the characteristics of the different components of the ICas they may be affected by different determinantsPeer Reviewe

End-to-end detection-segmentation network with ROI convolution

We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation. Code is publicly available at https://github.com/vincentzhang/roi-fcn.Comment: ISBI 201

Systemwide Reviews in the CGIAR: Concepts, Options, and Recommendations

Study commissioned by the Oversight Committee of system-wide reviews in the CGIAR, conducted by a team headed by Martin Piñeiro and including Elliott Stern, and Dana Dalrymple. The study was considered by the Oversight Committee at CGIAR International Centers Week 2000, and circulated to CGIAR members. The Committee said it would implement the 15 recommendations of the study, and invited comments.Originally conceived as a retrospective review of the third system review of the CGIAR, the study was expanded to cover the first two system reviews, and system-level review processes in general. The study found that in contrast to the first two, the third review of the System suffered from the combination of a largely external review panel whose members were unfamiliar with the CGIAR, and the lack of preparatory work to define the issues. It considered various options for future reviews from the point of view of objectives, structure, and procedure, and made recommendations for each.There are two annexes with detailed information on the CGIAR review system, and the third System Review

On the uses of intermediate infrared and microwave infrared in meteorological satellites Semiannual report

Intermediate infrared and microwave infrared applications in meteorological satellite