Real-time information processing of environmental sensor network data using Bayesian Gaussian processes

In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored, and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently reuses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered

Self-esteem and its associated factors among secondary school students in Klang District, Selangor.

Self-esteem is an important determinant of psychological well-being that is particularly problematic during adolescent life stage. There is a correlation between low self-esteem and other social problems among today's adolescents. This study was conducted to determine the mean self-esteem score, and to determine the association between self-esteem and age, sex, race, religion, number of siblings, ranking among siblings, family function, parental marital status and smoking among adolescents aged 12 to 20-years-old. A cross sectional study design using random cluster sampling method was done. Four out of a total of 35 secondary schools in Klang District, Selangor were selected. Respondents consisted of individual students in selected classes from the four selected schools. Data was collected using a self-administered, structured, pre-tested questionnaire and was analyzed using the SPSS version 12.0. Out of 1089 respondents, 793 completed the questionnaire (response rate 73.82%). The overall mean self-esteem score was 27.65. The mean self-esteem score for males (27.99) was slightly higher than females (27.31). The differences in the mean scores by race were statistically significant. There was a statistically significant relationship between mean self-esteem scores and sex, age, race, religion, number of siblings, smoking and family function. There was no statistically significant difference between mean self-esteem score with parental marital status and with ranking among siblings. The overall mean self-esteem score was 27.65. Self-esteem was associated with sex, age, race, religion, number of siblings, smoking and family function

The risk of miscarriage following COVID-19 vaccination: a systematic review and meta-analysis

STUDY QUESTION: What is the risk of miscarriage among pregnant women who received any of the COVID-19 vaccines? SUMMARY ANSWER: There is no evidence that COVID-19 vaccines are associated with an increased risk of miscarriage. WHAT IS KNOWN ALREADY: In response to the COVID-19 pandemic, the mass roll-out of vaccines helped to boost herd immunity and reduced hospital admissions, morbidity and mortality. Still, many were concerned about the safety of vaccinesfor pregnancy, which may have limited their uptake among pregnant women and those planning a pregnancy. STUDY DESIGN, SIZE, DURATION: For this systematic review and meta-analysis, we searched MEDLINE, EMBASE and Cochrane CENTRAL from inception until June 2022 using a combination of keywords and MeSH terms. PARTICIPANTS/MATERIALS, SETTING, METHODS: We included observational and interventional studies that enrolled pregnant women and evaluated any of the available COVID-19 vaccines compared to placebo or no vaccination. We primarily reported on miscarriage in addition to ongoing pregnancy and/or live birth. MAIN RESULTS AND THE ROLE OF CHANCE: We included data from 21 studies (5 randomised trials and 16 observational studies) reporting on 149,685 women. The pooled rate of miscarriage among women who received a COVID-19 vaccine was 9% (n = 147,49/123,185, 95%CI 0.05-0.14). Compared to those who received a placebo or no vaccination, women who received a COVID-19 vaccine did not have a higher risk of miscarriage (RR 1.07, 95%CI 0.89-1.28, I2 35.8%) and had comparable rates for ongoing pregnancy or live birth (RR 1.00, 95%CI 0.97-1.03, I2 10.72%). LIMITATIONS, REASONS FOR CAUTION: Our analysis was limited to observational evidence with varied reporting, high heterogeneity and risk of bias across included studies, which may limit the generalisability and confidence in our findings. WIDER IMPLICATIONS OF THE FINDINGS: COVID-19 vaccines are not associated with an increase in the risk of miscarriage or reduced rates of ongoing pregnancy or live birth among women of reproductive age. The current evidence remains limited and larger population studies are needed to further evaluate the effectiveness and safety of COVID-19 in pregnancy. STUDY FUNDING/COMPETING INTEREST: No direct funding was provided to support this work. MPR is funded by the Medical Research Council Centre for Reproductive Heath Grant No: MR/N022556/1. BHA hold a personal development award from the National Institute of Health Research in the UK. All authors declare no conflict of interest. REGISTRATION NUMBER: CRD42021289098

Modelling ambitious climate mitigation pathways for Australia's built environment

Achieving net zero operational and embodied greenhouse gas (GHG) emissions in the built environment is recognised in Australia and globally as a key strategy to address climate change and achieve the United Nations Sustainable Development Goals (SDGs). However, gaps in knowledge remain regarding potential national pathways to achieve this outcome in Australia. This study further extends and applies a national-scale integrated macroeconomic simulation model to explore coherent pathways to net zero emissions in the built environment sector by 2050. The scope of the study includes residential and commercial buildings and both operational and embodied emissions. It applies scenario analysis incorporating different levels of climate ambition, including a shift to renewable energy, electrifying buildings, improving energy efficiency and replacing carbon-intensive materials. We find that a high ambition scenario (Scenario 2) delivers a 94% reduction in GHG emissions by 2050 when compared against business-as-usual, placing a net-zero target within reach. Improvements on Australia's SDGs performance are also attained. Through subsequent pathways analysis we find that achieving net zero or even net negative operational and embodied emissions is feasible with more ambitious action in key areas, including increasing the share of mass-timber buildings and reducing end-of-life losses in sequestered carbon

Absence of Street Lighting May Prevent Vehicle Crime, but Spatial and Temporal Displacement Remains a Concern

OBJECTIVES: This paper estimates the effect of changes in street lighting at night on levels of crime at street-level. Analyses investigate spatial and temporal displacement of crime into adjacent streets. METHODS: Offense data (burglaries, robberies, theft of and theft from vehicles, and violent crime) were obtained from Thames Valley Police, UK. Street lighting data (switching lights off at midnight, dimming, and white light) were obtained from local authorities. Monthly counts of crime at street-level were analyzed using a conditional fixed-effects Poisson regression model, adjusting for seasonal and temporal variation. Two sets of models analyzed: (1) changes in night-time crimes adjusting for changes in day-time crimes and (2) changes in crimes at all times of the day. RESULTS: Switching lights off at midnight was strongly associated with a reduction in night-time theft from vehicles relative to daytime (rate ratio RR 0.56; 0.41–0.78). Adjusted for changes in daytime, night-time theft from vehicles increased (RR 1.55; 1.14–2.11) in adjacent roads where street lighting remained unchanged. CONCLUSION: Theft from vehicle offenses reduced in streets where street lighting was switched off at midnight but may have been displaced to better-lit adjacent streets. Relative to daytime, night-time theft from vehicle offenses reduced in streets with dimming while theft from vehicles at all times of the day increased, thus suggesting temporal displacement. These findings suggest that the absence of street lighting may prevent theft from vehicles, but there is a danger of offenses being temporally or spatially displaced

Decay and coherence of two-photon excited yellow ortho-excitons in Cu2O

Photoluminescence excitation spectroscopy has revealed a novel, highly efficient two-photon excitation method to produce a cold, uniformly distributed high density excitonic gas in bulk cuprous oxide. A study of the time evolution of the density, temperature and chemical potential of the exciton gas shows that the so called quantum saturation effect that prevents Bose-Einstein condensation of the ortho-exciton gas originates from an unfavorable ratio between the cooling and recombination rates. Oscillations observed in the temporal decay of the ortho-excitonic luminescence intensity are discussed in terms of polaritonic beating. We present the semiclassical description of polaritonic oscillations in linear and non-linear optical processes.Comment: 14 pages, 12 figure

Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework to model such dependencies. We develop the LDDP in the context of mixture modeling, and develop a mean field variational inference algorithm for this mixture model. The effectiveness of the proposed modeling framework is shown on an image segmentation task