Siemens (Hungary)

The contemporary system of education is evolving at a very fast rate due to emerging technologies, automation, and various requirements of the job market. In a bid to accommodate such change vocational and technical schools are bringing into their premises more industry players such as graphic designers, digital marketing agencies, and content workers to impart to their students. These people are conversant with their respective technical areas yet the majority of them have never even been trained to be instructors. They do not know how students learn, how well to design good lessons, and how to achieve success with various kinds of learners.This is the gap we refer to as the expert-educator gap. The fact that a person is so great at doing a certain job does not mean that he/she necessarily knows how to teach people to do it. Going into 2025 and beyond schools should reconsider the way they train teachers. It is not enough to recruit talented workers and hope that they will make good teachers unless they are trained on the issue, and this is what this research paper scrutinizes keenly before presenting a workable solution. The three primary strategies that we have proposed include first, providing short training programs (micro-credentials) to impart basic teaching skills; second, encouraging teachers to view themselves as guides and mentors, not merely experts in their content, and; thirdly, the adoption of team-teaching, in which experts in an industry operate in collaboration with educated teachers. Such methods can assist to form teachers who are not only going to demonstrate to the students how to utilize the current tools, but also assist the students on how to think, adapt, and continue to learn in the course of their careers.

The variational quantum eigensolver (VQE) is an algorithm for finding the ground states of a given Hamiltonian. Its application to binary-formulated combinatorial optimization (CO) has been widely studied in recent years. However, typical VQE approaches for CO problems often suffer from local minima or barren plateaus, limiting their ability to achieve optimal solutions. The quantum imaginary time evolution (QITE) offers an alternative approach for effective ground-state preparation but requires large circuits to approximate non-unitary operations. Although compressed QITE (cQITE) reduces circuit depth, accumulated errors eventually cause energy increases. To address these challenges, we propose an Adaptive Weighted QITE-VQE (AWQV) algorithm that integrates the VQE gradients with the cQITE updates through an adaptive weighting scheme during optimization. In numerical simulations for MaxCut on unweighted regular graphs, AWQV achieves near-optimal approximation ratios, while for weighted Erdős-Rényi instances, it outperforms the classical Goemans-Williamson algorithm.

High-dimensional data is a key study object for both machine learning (ML) and information visualization. In the field of visualization, dimensionality reduction (DR) methods, also known as projections, are one of the most frequently used classes of techniques for visually exploring large and high-dimensional datasets. In ML, high-dimensional data is generated and processed by classifiers and regressors, which increasingly require visualization for explanation and exploration. This thesis focuses on a recent visualization technique called decision maps. A decision map is a 2D image that visualizes the decision boundaries of a classifier in the data space, and can be used to explain and improve the behavior of ML classifiers. Constructing decision maps essentially involves a DR method and its inverse process (inverse projection). As such, ML techniques can help to create decision maps by providing improved (inverse) projections. We begin with a case study applying decision maps to explain the classification of mineral deposit genesis. Our findings show that decision maps provide extra insights into the mineral classification model, aiding geologists in interpreting the model. However, we also identified gaps in current decision map techniques that present opportunities for improvement. Following this case study, we conducted a comprehensive evaluation of three notable decision map techniques. Our evaluation shows that each technique has unique advantages and disadvantages. Our results can guide users in selecting the most suitable technique for specific tasks. A particularly salient finding of this evaluation was that all tested decision maps exhibit a surface-like behavior when applied to a 3D dataset. We explored the aforementioned surface-like behavior of decision maps across more scenarios. By estimating the intrinsic dimensionality of the maps, we found that existing decision map methods cover only a small portion of the intrinsic dimensionality of high-dimensional data spaces. This finding highlights fundamental limitations in all current approaches to constructing decision maps. To address these limitations, we propose a novel approach for computing inverse projections with the support of ML. Our method allows users to interactively control the location of the inversely projected points, thus also of visualizations like decision maps, within the high-dimensional space. In this way, users can explore larger parts of the data space, bypassing the practical limitations imposed by the aforementioned surface-like behavior of decision maps. We demonstrate the effectiveness of our approach through its application to a style transfer task. Finally, we introduce an accelerated computation method for decision maps. Our method significantly reduces the computation time for both basic decision maps and enhanced variations thereof such as gradient maps. The acceleration facilitates the further deployment of such visualizations in interactive visual analytics workflows for classifier engineering.

This article examines discussions about home nursing professionalism in the emergence of the municipal healthcare system. Based on a registrant analysis based on Ejersbo and Greve's projection of the modernization of the public sector and a professional theoretical understanding of professionalism, the article examines how home nursing professionalism is articulated in internal and external texts on home nursing professionalism in the municipal sector in the period 1983-2005. It then examines how home nursing professionalism is shaped through internal and external struggles over time. This is seen in relation to Julia Evett's concepts of professional theory and contemporary expectations of home nursing.

The paper reports the development of a case study for creating a unified development environment for the process of code generation from Matlab/Simulink into both HDL and C languages of FPGA and DSP targets. The FPGA is used for the Hardware-In-the-Loop (HIL) simulation of the high power, main circuit of a simple solar-based battery charger while the control functions belonging to this system are implemented in a DSP. The case study is planned to be a building block of the HIL simula tion of either such a sophisticated, cyber-physical system like a microgrid which connects various forms of energy produced by renewable sources.

Introdução: O câncer de mama é a neoplasia que mais mata mulheres no mundo, gerando inúmeros danos psicossociais e econômicos. Seu principal e mais comum tratamento definitivo é a mastectomia, que pode ser simples (nas fases iniciais da doença, com retirada de toda a mama) ou radical com linfadenectomia (em fases mais avançadas da doença, com a retirada da mama, músculos e linfonodos axilares). Cada procedimento tem diferentes benefícios e malefícios, como o resultado estético, o risco de recidiva, a necessidade de radioterapia, a qualidade de vida e a sobrevida das pacientes. Objetivo: avaliar a eficácia e a segurança da cirurgia conservadora e da mastectomia radical no tratamento do câncer de mama inicial. Metodologia: Esta revisão foi realizada com base no checklist PRISMA, seguindo as etapas de identificação, seleção, elegibilidade e inclusão dos estudos. As bases de dados utilizadas foram PubMed, Scielo e Web of Science, com os seguintes descritores: "câncer de mama", "mastectomia", "cirurgia conservadora", "radioterapia" e "sobrevida". Foram incluídos apenas artigos publicados nos últimos 10 anos, em português ou inglês, que comparassem as duas modalidades de cirurgia em mulheres com câncer de mama estágio inicial. Foram excluídos artigos que não apresentassem dados suficientes, que tivessem viés de seleção, que envolvessem outras técnicas cirúrgicas ou que não fossem relevantes para o tema. Resultados: Foram selecionados 10 estudos. A cirurgia conservadora seguida de radioterapia demonstrou melhora na sobrevida em relação à mastectomia, independentemente do tratamento com radioterapia. A cirurgia conservadora apresentou melhor resultado estético e menor impacto psicológico do que a mastectomia. A cirurgia conservadora não aumentou o risco de recidiva local ou à distância do que a mastectomia. A cirurgia conservadora não reduziu a qualidade de vida ou a funcionalidade do que a mastectomia. A cirurgia conservadora não foi possível em todos os casos, dependendo do tamanho e da localização do tumor. Conclusão: A cirurgia conservadora e a mastectomia radical são modalidades de tratamento eficazes e seguras para o câncer de mama inicial, mas que a cirurgia conservadora apresenta algumas vantagens em relação à mastectomia, como melhor sobrevida, melhor resultado estético, menor impacto psicológico e menor risco de complicações.

Introduction: Climate change is the prevailing issue on hand. It has both direct and indirect impact on public health that ranges from increasing respiratory, infectious and vector-borne diseases to its impact on mental health, agricultural sector and food security. This increases disease burden and raises challenge for health sector in many ways. Nurses and other healthcare professionals are at key position to address this issue. Purpose: The purpose of this study is to investigate level of knowledge and awareness regarding climate change and its impact on health among general population of public sector and Bari Imam Community. Methods: Descriptive cross-sectional study was conducted among general population of Bari Imam Community and public sector. Data was collected from 150 participants. A structured questionnaire was used for data collection. The data were analyzed using SPSS version 26. The results were expressed in descriptive statistics. Results: Study findings show that 62% participants have knowledge about climate change. Out of 150 participants 80% thinks that climate change has impact on health. Only 32% participants are familiar with the fact that climate change can also impact mental health. 68% of participants are not aware policies and initiatives for mitigating impact of climate change. Only 42% of participants think that they can individually play a role in addressing climate change. Conclusion: Climate change needs to be viewed as a public health issue and it is important to address the issue of climate change to decrease morbidity and mortality rate.

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