Indoor environment propagation review

A survey of indoor propagation characteristics is presented, including different models for path loss, shadowing and fast fading mechanisms, different channel parameters including signal strength, power delay, coherence bandwidth, Doppler spread and angle of arrival. The concepts of MIMO channels are also covered. The study also explores many types of deterministic channel modelling, such as Finite Difference Time Domain, Finite Integration Method, Ray tracing and the Dominant path model. Electromagnetic properties of building materials, including frequency dependence, are also investigated and several models for propagation through buildings are reviewed

Expatriate-deployment Levels and Subsidiary Growth: A Temporal Analysis

We investigated the relationship between expatriate-deployment levels and the growth of international subsidiaries over time. Latent-curve analysis revealed that higher subsidiary growth over the long term was achieved through both (a) a higher proportion of expatriates at subsidiary founding and (b) a slower reduction in the proportion of expatriates over time. These results suggest that the decision to reduce the proportion of expatriates due to cost considerations should be tempered with the potential long-term benefits of expatriates for improving subsidiary growth. Our results point to the importance of two factors that impact subsidiary changes over time: path dependence and dynamic adjustment costs

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be missing significant periods of measurements. Models built using these datasets can therefore be biased. Although various methods have been proposed to handle missing data in many application areas, more air quality missing data prediction requires additional investigation. This study proposes an autoencoder model with spatiotemporal considerations to estimate missing values in air quality data. The model consists of one-dimensional convolution layers, making it flexible to cover spatial and temporal behaviours of air contaminants. This model exploits data from nearby stations to enhance predictions at the target station with missing data. This method does not require additional external features, such as weather and climate data. The results show that the proposed method effectively imputes missing data for discontinuous and long-interval interrupted datasets. Compared to univariate imputation techniques (most frequent, median and mean imputations), our model achieves up to 65% RMSE improvement and 20–40% against multivariate imputation techniques (decision tree, extra-trees, k-nearest neighbours and Bayesian ridge regressors). Imputation performance degrades when neighbouring stations are negatively correlated or weakly correlated

Optimising deep learning at the edge for accurate hourly air quality prediction

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively. View Full-Text 10 selecting the best model, we optimise the model for edge devices, using Raspberry Pi 3 Model B+ 11 (RPi3B+) and Raspberry Pi 4 Model B boards (RPi4B). The lite version produced 4 times smaller 12 file size compared to the original version. From the lite version, further size reduction can be 13 achieved by implementing different post-training quantisations. About a 47% reduction can be 14 achieved by dynamic range quantisation, about 45% by full integer quantisation, and about 35% 15 by float16 quantisation. A total of 8272 hourly samples were continuously executed directly at the 16 edge. The RPi4B executed these data two times faster compared to the RPi3B+ in all quantisation 17 modes. Full-integer quantisation produced the most effective time execution, with latencies of 18 2.19 seconds and 4.73 seconds for RPi4B and RPi3B+, respectively

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Adaptive mechanisms of plants against salt stress and salt shock

Salinization process occurs when soil is contaminated with salt, which consequently influences plant growth and development leading to reduction in yield of many food crops. Responding to a higher salt concentration than the normal range can result in plant developing complex physiological traits and activation of stress-related genes and metabolic pathways. Many studies have been carried out by different research groups to understand adaptive mechanism in many plant species towards salinity stress. However, different methods of sodium chloride (NaCl) applications definitely give different responses and adaptive mechanisms towards the increase in salinity. Gradual increase in NaCl application causes the plant to have salt stress or osmotic stress, while single step and high concentration of NaCl may result in salt shock or osmotic shock. Osmotic shock can cause cell plasmolysis and leakage of osmolytes in plant. Also, the gene expression pattern is influenced by the type of methods used in increasing the salinity. Therefore, this chapter discusses the adaptive mechanism in plant responding to both types of salinity increment, which include the morphological changes of plant roots and aerial parts, involvement of signalling molecules in stress perception and regulatory networks and production of osmolyte and osmoprotective proteins