Prediction of stress levels in the workplace using surrounding stress

Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary aim is to analyse physiological and behavioural data. However, evidence suggests that solutions based on physiological and behavioural data alone might be challenging when implemented in real-world settings. These solutions are sensitive to data problems arising from losses in signal quality or alterations in body responses, which are common in everyday activities. The contagious nature of stress and its sensitivity to the surroundings can be used to improve these methods. In this study, we sought to investigate automatic stress prediction using both surrounding stress data, which we define as close colleagues’ stress levels and the stress level history of the individuals. We introduce a real-life, unconstrained study conducted with 30 workers monitored over 8 weeks. Furthermore, we propose a method to investigate the effect of stress levels of close colleagues on the prediction of an individual’s stress levels. Our method is also validated on an external, independent dataset. Our results show that surrounding stress can be used to improve stress prediction in the workplace, where we achieve 80% of F-score in predicting individuals’ stress levels from the surrounding stress data in a multiclass stress classification

Latent ion tracks in amorphous silicon

We present experimental evidence for the formation of ion tracks in amorphous Si induced by swift heavy-ion irradiation. An underlying core-shell structure consistent with remnants of a high-density liquid structure was revealed by small-angle x-ray scattering and molecular dynamics simulations. Ion track dimensions differ for as-implanted and relaxed Si as attributed to differentmicrostructures andmelting temperatures. The identification and characterization of ion tracks in amorphous Si yields new insight into mechanisms of damage formation due to swift heavy-ion irradiation in amorphous semiconductors

A scalable infrastructure for CMS data analysis based on OpenStack Cloud and Gluster file system

Volume: 513Peer reviewe

Tracks and voids in amorphous Ge induced by swift heavy-ion irradiation

Ion tracks formed in amorphous Ge by swift heavy-ion irradiation have been identified with experiment and modeling to yield unambiguous evidence of tracks in an amorphous semiconductor. Their underdense core and overdense shell result from quenched-in ra

An overview of the DII-HEP Open Stack based CMS data analysis

An OpenStack based private cloud with the Gluster File System has been built and used with both CMS analysis and Monte Carlo simulation jobs in the Datacenter Indirection Infrastructure for Secure High Energy Physics (DII-HEP) project. On the cloud we run the ARC middleware that allows running CMS applications without changes on the job submission side. Our test results indicate that the adopted approach provides a scalable and resilient solution for managing resources without compromising on performance and high availability. To manage the virtual machines (VM) dynamically in an elastic fasion, we are testing the EMI authorization service (Argus) and the Execution Environment Service (Argus-EES). An OpenStack plugin has been developed for Argus-EES. The Host Identity Protocol (HIP) has been designed for mobile networks and it provides a secure method for IP multihoming. HIP separates the end-point identifier and locator role for IP address which increases the network availability for the applications. Our solution leverages HIP for traffic management. This presentation gives an update on the status of the work and our lessons learned in creating an OpenStack based cloud for HEP.Peer reviewe

Freedom of choice to migrate: adaptation to climate change in Bangladesh

Adaptation is an essential part of climate change policy. In areas where impacts are likely to be severe, migration is considered to be an adaptation option. In Bangladesh coastal areas migration due to climate change is contingent on people’s freedom of choice at individual and household level. Following Amartya Sen’s capability approach, we argue that there should be a line drawn between migrations by free choice versus forced migration. Sen’s capability approach focuses on the importance of people’s freedom of choice to act, and the ability to achieve what they consider valuable in their life. In this paper, we use an extensive empirical work engaging 22 focus groups discussions (8–12 individuals in each group) and 14 Key Informants Interviews in South-West Bangladesh to elicit how freedom of choice changes with the economic class and social status of an individual. Using these data we apply Sen’s capability approach to understand the role of the freedom of choice when considering migration as an adaptation option. We argue that the capability approach is essential in revealing a thin border between migration as a (planned) adaptation option and forced migration

Rethinking resilience: home gardening, food sharing and everyday resistance

Resilience and food self-provisioning (FSP), terms that until recently were deployed primarily in the study of livelihoods in the Global South, are now attracting attention from alternative food scholarship in the Global North. Drawing on a large-scale survey conducted in the Czech Republic, this article investigates FSP as a social resilience-enhancing set of practices. In addition to the traditional reading of FSP as a passive, defensive and crisis-deflecting form of resilience, this article puts forward an alternative conceptualisation of resilience as a proactive, preventative, future-oriented and transformation-enabling capacity that runs counter to the tenets of neoliberalism