The sacred and the profane in life science work: The case of assisted reproduction laboratories

Reproductive medicine and assisted reproduction therapies have been developed over the last decades resulting in over five million babies. The handling of human reproductive materials and patients is based on the ability to combine health care work and techno-scientific expertise in both the clinic and the laboratory setting. This study of Swedish assisted reproductive technology clinics demonstrates that the active day-to-day manipulation of human reproductive materials enact both the ‘profane’, through treating the embryos as raw materials in standardized procedures enabling economies of scale, and the ‘sacred’ through enacting a separation, the potential to human life, the patients ordeals, and seriousness. The enactment of the profane and the sacred is mostly balanced but at certain points in the work procedures, their intersecting becomes particularly salient. Such points provide opportunities for the study of the sense making of professionals in organizational grey zones, during techno-scientific activities.     

The maximum cardinality cut problem in co-bipartite chain graphs

A co-bipartite chain graph is a co-bipartite graph in which the neighborhoods of the vertices in each clique can be linearly ordered with respect to inclusion. It is known that the maximum cardinality cut problem (\({\textsc {MaxCut}}\)) is \({\textsc {NP}}{\text {-hard}}\) in co-bipartite graphs (Bodlaender and Jansen, Nordic J Comput 7(2000):14–31, 2000). We consider \({\textsc {MaxCut}}\) in co-bipartite chain graphs. We first consider the twin-free case and present an explicit solution. We then show that \({\textsc {MaxCut}}\) is polynomial time solvable in this graph class.     

PRSP Participatory Process in Armenian: Lessoned Learned

Once the Poverty Reduction Strategy Paper (PRSP) was adopted by the Armenian Government, preconditions for institutionalization of the participatory process in Armenia were initiated. The clear mechanism of participatory process was created. Reluctance of the Government towards participatory mechanisms and insufficient public participation put in doubt effectiveness and transparency of public participation process in PRSP implementation. This article discusses the Armenian experience of the institutionalization of participatory process within PRSP framework presenting achievements, failures and obstacles of this process as well as attempts towards creating PRSP new Social Partnership Institute as an advanced step of institutionalization of the participatory process.

Remittances: The Impact on Families in Armenia

Armenian families have a long history of receiving remittances. Currently remittances are a major component of the Armenian economy and have a significant impact on families. Utilizing the results of in-depth semi-structured interviews conducted in the capital city of Yerevan, this article examined how remittances affected the lives, well-being, and family relationships of the people who received them. As families managed without their migrant family members, they developed systems and routines that helped them cope with the absence. In addition, they utilized networks of social capital that allowed them to persist despite significant interpersonal challenges. Insights from this study will inform specialists who serve migrant families, helping them better understand what their clients are experiencing and enabling them to provide strength-based approaches.     

A Pitman measure of similarity in k-means for clustering heavy-tailed data

One of the most popular methods and algorithms to partition data to k clusters is k-means clustering algorithm. Since this method relies on some basic conditions such as, the existence of mean and finite variance, it is unsuitable for data that their variances are infinite such as data with heavy tailed distribution. Pitman Measure of Closeness (PMC) is a criterion to show how much an estimator is close to its parameter with respect to another estimator. In this article using PMC, based on k-means clustering, a new distance and clustering algorithm is developed for heavy tailed data.