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This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.

This study aims to investigate how Germans' misperceptions of the nature of entrepreneurship influence their attitudes towards entrepreneurial failure.

Adopting a multivariate regression analysis, the study used data collected from a commercial online market research panel (N = 2,027) reflecting the overall German working population. Attitudinal items on business failure were used to measure the study variables. The study controlled for age, education, employment status, gender, income, whether the respondent knows a failed entrepreneur and the German federal state in which the respondent resides.

The findings suggest that reservations about failed entrepreneurs become stronger as misperceptions of the nature of entrepreneurship worsen. The results also show that failure reservations vary regionally over the 16 German federal states.

Nationwide efforts regarding the stimulation of entrepreneurship and the acceptance of entrepreneurial failure are insufficient for removing failure reservations, as they neglect regional cultural differences. The results suggest that it is not enough just to invest in efforts to create a failure-friendly culture, and that a better general education about the realities of entrepreneurship is a prerequisite.

The study generates insights into how the overall population in an innovation-driven economy perceives entrepreneurship and entrepreneurial failure. Moreover, the work delves into the reasons why parts of German society reject failed entrepreneurs. Hence, this study can aid the drafting of effective policy initiatives at the regional and national levels.

The purpose of this study is to present a quality improvement approach titled “Picture-Understanding-Action” used in Ireland to enhance the role of healthcare boards in the oversight of healthcare quality and its improvement.

The novel and practical “Picture-Understanding-Action” approach was implemented using the Model for Improvement to iteratively introduce changes across three quality improvement projects. This approach outlines the concepts and activities used at each step to support planning and implementation of processes that allow a board to effectively achieve its role in overseeing and improving quality. This approach matured over three quality improvement projects.

The “Picture” included quantitative and qualitative aspects. The quantitative “Picture” consisted of a quality dashboard/profile of board selected outcome indicators representative of the health system using statistical process control (SPC) charts to focus discussion on real signals of change. The qualitative picture was based on the experience of people who use and work in health services which “people-ised” the numbers. Probing this “Picture” with collective grounding, curiosity and expert training/facilitation developed a shared “Understanding”. This led to “Action(s)” from board members to improve the “Picture” and “Understanding” (feedback action), to ask better questions and make better decisions and recommendations to the executive (feed-forward action). The Model for Improvement, Plan-Do-Study-Act cycles and a co-design approach in design and implementation were key to success.

To the authors’ knowledge, this is the first time a board has undertaken a quality improvement (QI) project to enhance its own processes. It addresses a gap in research by outlining actions that boards can take to improve their oversight of quality of care.

Doctoral students’ ill-being in terms of stress, exhaustion and high levels of mental health problems has been well documented. Yet, the well-being of doctoral students is more than the absence of these negative symptoms. The number of studies exploring the combination of positive and negative attributes of doctoral students’ well-being is limited. Therefore, this study aims to focus on exploring individual variation in doctoral students’ experienced engagement and burnout across two distinct socio-cultural contexts in Finland and in South Africa.

A total of 884 doctoral students from Finland (n = 391) and South Africa (n = 493) responded to the cross-cultural Doctoral Experience Survey. The data were quantitatively analyzed.

Altogether four distinctive engagement–burnout profiles were detected, including engaged, engaged–exhausted, moderately engaged–burnout and burnout profiles. Differences between the Finnish and South African students were identified in profile emphasis. The profiles were also related to several study progress attributes such as drop-out intentions, time-to-candidacy and satisfaction with study.

This study provides new understanding on doctoral students’ well-being by focusing on both positive and negative attributes and exploring doctoral students’ discrepant profiles with a cross-country design.

The purpose of this paper is to analyze the individual steps during the printing of capacitor structures. The method of substrate preparation, the obtained roughness of conductive and dielectric layers are examined. Moreover, the capacitances of the obtained capacitors were examined.

Surface roughness and microscopic analysis were used to assess the quality of printed conductive structures. Two criteria were used to assess the quality of printed dielectric structures: the necessary lack of discontinuity of layers and minimal roughness. To determine the importance of printing parameters, a draft experimental method was proposed.

The optimal way to clean the substrate has been determined. The most important parameters for the dielectric layer (i.e. drop-space, table temperature, curing time and temperature) were found.

If dielectric layers are printed correctly, most problems with printing complex electronic structures (transistors, capacitors) will be eliminated.

The tests performed identified the most important factors for dielectric layers. Using them, capacitors of repeatable capacity were printed.

In the literature on this subject, no factors were found which were responsible for obtaining homogeneous dielectric layers.