Mutual Mobile Membranes with Timers

A feature of current membrane systems is the fact that objects and membranes are persistent. However, this is not true in the real world. In fact, cells and intracellular proteins have a well-defined lifetime. Inspired from these biological facts, we define a model of systems of mobile membranes in which each membrane and each object has a timer representing their lifetime. We show that systems of mutual mobile membranes with and without timers have the same computational power. An encoding of timed safe mobile ambients into systems of mutual mobile membranes with timers offers a relationship between two formalisms used in describing biological systems

Parenting behavior and the risk of becoming a victim and a bully/victim : a meta-analysis study

Objective: Being bullied has adverse effects on children's health. Children's family experiences and parenting behavior before entering school help shape their capacity to adapt and cope at school and have an impact on children's peer relationship, hence it is important to identify how parenting styles and parent–child relationship are related to victimization in order to develop intervention programs to prevent or mitigate victimization in childhood and adolescence. Methods: We conducted a systematic review of the published literature on parenting behavior and peer victimization using MEDLINE, PsychINFO, Eric and EMBASE from 1970 through the end of December 2012. We included prospective cohort studies and cross-sectional studies that investigated the association between parenting behavior and peer victimization. Results: Both victims and those who both bully and are victims (bully/victims) were more likely to be exposed to negative parenting behavior including abuse and neglect and maladaptive parenting. The effects were generally small to moderate for victims (Hedge's g range: 0.10–0.31) but moderate for bully/victims (0.13–0.68). Positive parenting behavior including good communication of parents with the child, warm and affectionate relationship, parental involvement and support, and parental supervision were protective against peer victimization. The protective effects were generally small to moderate for both victims (Hedge's g: range: −0.12 to −0.22) and bully/victims (−0.17 to −0.42). Conclusions: Negative parenting behavior is related to a moderate increase of risk for becoming a bully/victim and small to moderate effects on victim status at school. Intervention programs against bullying should extend their focus beyond schools to include families and start before children enter school

Spatial and temporal evaluations of the liquid argon purity in ProtoDUNE-SP

Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-Phase (ProtoDUNE-SP) was an approximately 700-ton liquid argon detector intended to prototype the Deep Underground Neutrino Experiment (DUNE) Far Detector Horizontal Drift module. It contains two drift volumes bisected by the cathode plane assembly, which is biased to create an almost uniform electric field in both volumes. The DUNE Far Detector modules must have robust cryogenic systems capable of filtering argon and supplying the TPC with clean liquid. This paper will explore comparisons of the argon purity measured by the purity monitors with those measured using muons in the TPC from October 2018 to November 2018. A new method is introduced to measure the liquid argon purity in the TPC using muons crossing both drift volumes of ProtoDUNE-SP. For extended periods on the timescale of weeks, the drift electron lifetime was measured to be above 30 ms using both systems. A particular focus will be placed on the measured purity of argon as a function of position in the detector

Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours

Silver travel in golden age : how virtual travel guided by GenAI-enabled avatars boost well-being for older adults

202502 bcchNot applicableRGCOthersHong Kong Polytechnic University (UGC); University Grants Committee (Hong Kong); Innovation and Technology CommissionPublished18 monthsGreen (AAM

The European Physical Journal C

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.Published versio