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Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Fait ◽  
Marián Varga ◽  
Karel Hruška ◽  
Alexander Kromka ◽  
Bohuslav Rezek ◽  
...  

Abstract The controlled extraction of light from diamond optical color centers is essential for their practical prospective applications as single photon sources in quantum communications and as biomedical sensors in biosensing. Photonic crystal (PhC) structures can be employed to enhance the collection efficiency from these centers by directing the extracted light towards the detector. However, PhCs must be fabricated with nanoscale precision, which is extremely challenging to achieve for current materials and nanostructuring technologies. Imperfections inherently lead to spectral mismatch of the extraction (leaky) modes with color center emission lines. Here, we demonstrate a new and simple two-step method for fabricating diamond PhC slabs with leaky modes overlapping the emission line of the silicon vacancy (SiV) centers. In the first step, the PhC structure with leaky modes blue shifted from the SiV emission line is fabricated in a nanocrystalline diamond without SiV centers. A thin layer of SiV-rich diamond is then deposited over the PhC slab so that the spectral position of the PhC leaky modes is adjusted to the emission line of the SiV centers, thereby avoiding the need for nanoscale precision of the structuring method. An intensity enhancement of the zero-phonon line of the SiV centers by a factor of nine is achieved. The color centers in the thin surface layer are beneficial for sensing applications and their properties can also be further controlled by the diamond surface chemistry. The demonstrated PhC tuning method can also be easily adapted to other optical centers and photonic structures of different types in diamond and other materials.


2021 ◽  
Vol 2 (2) ◽  
pp. 41-54
Author(s):  
Andrijana Ristovska ◽  
◽  
Ljupco Eftimov ◽  

The process of globalization and intensive technological development imposes the need to constantly introduce different types of organizational changes. Human resource managers in organizations are becoming increasingly aware that hiring and retaining talents are the most important determinants of success in the complex global world and that they must work more intensively on modernizing the process of change management to help employees, not only for acceptance, but also for their involvement in the change implementation process. This paper analyzes the impact of four different types of organizational change on employee turnover intention, according to the Cummings and Worley (2014) organizational change classification. The statistical method of simple linear regression was applied to predict and evaluate the turnover intention of the employees in the Republic of North Macedonia (as a dependent variable “Y”) based on the value of each of the types of organizational changes (as independent variables “X”). A multiple regression method was also applied in order to analyze the associations between the independent variables and the dependent variable and identify the type of organizational changes that most significantly affects the employee turnover intention. The analysis was conducted based on the findings obtained from the respondents who completely answered the survey questionnaire (282 employees in the Republic of North Macedonia, different according to their demographic characteristics). The correlation analysis shows there are positive correlation as well as causal relationship between all four types of organizational changes and the employee turnover intention, where techno-structural interventions have the most significant impact.


Author(s):  
Beibei Yuan ◽  
Willem Heiser ◽  
Mark de Rooij

AbstractThe $$\delta $$ δ -machine is a statistical learning tool for classification based on dissimilarities or distances between profiles of the observations to profiles of a representation set, which was proposed by Yuan et al. (J Claasif 36(3): 442–470, 2019). So far, the $$\delta $$ δ -machine was restricted to continuous predictor variables only. In this article, we extend the $$\delta $$ δ -machine to handle continuous, ordinal, nominal, and binary predictor variables. We utilized a tailored dissimilarity function for mixed type variables which was defined by Gower. This measure has properties of a Manhattan distance. We develop, in a similar vein, a Euclidean dissimilarity function for mixed type variables. In simulation studies we compare the performance of the two dissimilarity functions and we compare the predictive performance of the $$\delta $$ δ -machine to logistic regression models. We generated data according to two population distributions where the type of predictor variables, the distribution of categorical variables, and the number of predictor variables was varied. The performance of the $$\delta $$ δ -machine using the two dissimilarity functions and different types of representation set was investigated. The simulation studies showed that the adjusted Euclidean dissimilarity function performed better than the adjusted Gower dissimilarity function; that the $$\delta $$ δ -machine outperformed logistic regression; and that for constructing the representation set, K-medoids clustering achieved fewer active exemplars than the one using K-means clustering while maintaining the accuracy. We also applied the $$\delta $$ δ -machine to an empirical example, discussed its interpretation in detail, and compared the classification performance with five other classification methods. The results showed that the $$\delta $$ δ -machine has a good balance between accuracy and interpretability.


2021 ◽  
Vol 12 ◽  
Author(s):  
Erica Löfström ◽  
Isabel Richter ◽  
Ine H. Nesvold

Environmental degradation and how we care for our planet are some of the greatest challenges the world is up against at this moment. These challenges has received increased focus in both, research and the public sphere. So far, most of this attention revolved around adult’s attitudes and behavior. However, environmental engagement amongst the younger generation gains in popularity. Using plastic pollution as a case, this qualitative study aims to acquire insights into the mental models of children. We collected qualitative data during an innovative, structured workshop according to the “Nature In Your Face” (NIYF) framework. The approach challenges the assumption that the societal change can be achieved gradually, with non-invasive techniques. Instead, we explore the potential of disruption to push citizens out of their comfort zone, thereby making room for co-creation. The disruption was performed by confronting 36 fifth graders from a Norwegian primary school, with disturbing images of plastic contaminating their local shorelines. The data was obtained by using the workshop framework, combined with semi-structured group interviews. The interview data was analyzed by means of thematic analysis. We found that the disruptions triggered emotional reactions like anger and fear. With these emotions as a driving force, the first workshop step was introduced, the Framing of the problem. The next step, Twisting the problem, was reflected in the children developing their own, creative solutions and creatively engaged with them in groups. The last step, Using, was only touched upon in the workshop and is therefore beyond the scope of this paper. Our results indicate that there are three prominent themes reflecting how children discuss plastic pollution. The children talked about their (1) Emotions related to plastic pollution, (2) Attitudes related to plastic, and (3) Perceptions of plastic pollution. These themes were further subdivided into different types of emotions, characteristics of plastic as a material as well as perceptions on different locations of unnecessary plastic. Psychologically, the mechanisms underlying the identified themes were linked to eco-anxiety, denial, self-efficacy, and cognitive dissonance. We conclude that disruptive eco-visualization can create an emotional response amongst children, which can be transformed into co-creation of ideas.


2021 ◽  
Author(s):  
Satu Helske ◽  
Guilherme Kenji Chihaya ◽  
Jouni Helske

Sequence analysis (SA) has gained increasing interest in social sciences for theholistic analysis of life course and other longitudinal data. The usual approach isto construct sequences, calculate dissimilarities, group similar sequences with clusteranalysis, and use cluster membership as a dependent or independent variable in a linear or nonlinear regression model.This approach may be problematic as the cluster memberships are assumed to befixed known characteristics of the subjects in subsequent analysis. Furthermore, often it is more reasonable to assume that individual sequences are mixtures of multiple ideal types rather than equal members of some group. Failing to account for these issues may lead to wrong conclusions about the nature of the studied relationships.In this paper, we bring forward and discuss the problems of the "traditional" useof SA clusters and compare four approaches for different types of data. We conduct a simulation study and an empirical study, demonstrating the importance of considering how sequences and outcomes are related and the need to adjust the analysis accordingly. In many typical social science applications, the traditional approach is prone to result in wrong conclusions and so-called position-dependent approaches such as representativeness should be preferred.


Author(s):  
Ehab Soliman ◽  
◽  
Khaled Alrasheed ◽  

Project cash flow and contractor S-curve are tools that can be used to measure, control and anticipate project progress. Few studies dedicated to evaluate and judge the behaviour of the original S-curve. This study aims to evaluate the similarities and changes of construction projects S curves between different project types. More than 40 S-curves were collected from the state of Kuwait for different types of construction projects. The list of collected curves divided into six groups based on the type of client, no of buildings and number of floors. Statistical analysis used to compare the curves inside each group of projects. Statistical analysis using test of normality, T-paired test then Standard Euclidean Distance were applied to evaluate the similarity and changes between groups. This study revealed that there is a level of similarity of S-curves for high rise buildings and there is no similarity of S-curves for one or multi-building projects. The maximum gap between S-curves for one and high rise building laying in the middle part of project duration, while the maximum gap between S-curves for multi-building project laying in 70% to 80% of project duration. This study revealed that the variance of S-curve behaviour indicating there is no common attitude for all types of construction project types. This study can help construction stakeholders to anticipate their expected expenses and help in project cash flow management.


2021 ◽  
Author(s):  
Caro Castro

This MRP works to examine the intersectional experiences of Trans Latinx Refugees around gender identity, language, citizenship, sexual orientation, race and class during the settlement process by answering the research question: What are the experiences of Trans folks during their Refugee and Settlement process? The study used Qualitative Phenomenological design to focus on two different types of phenomena: the participants' trans identity and their settlement process as refugees. The following themes were found and analyzed: 1) Intersections of Transphobia and Racism; 2) Systemic Barriers & Access to Services; and 3) Moving Forward: Empowerment, Community Building & Allies on the Inside. In conclusion, the implications for both social work and research with these communities have been identified.


2021 ◽  
Author(s):  
Caro Castro

This MRP works to examine the intersectional experiences of Trans Latinx Refugees around gender identity, language, citizenship, sexual orientation, race and class during the settlement process by answering the research question: What are the experiences of Trans folks during their Refugee and Settlement process? The study used Qualitative Phenomenological design to focus on two different types of phenomena: the participants' trans identity and their settlement process as refugees. The following themes were found and analyzed: 1) Intersections of Transphobia and Racism; 2) Systemic Barriers & Access to Services; and 3) Moving Forward: Empowerment, Community Building & Allies on the Inside. In conclusion, the implications for both social work and research with these communities have been identified.


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