Effects of resolution of lighting control systems

Advances in lighting technologies have spurred sophisticated lighting control systems (LCSs). To conserve energy and improve occupants’ wellbeing, LCSs have been integrated into sustainable buildings. However, the complexity of LCSs may lead to negative experiences and reduce the frequency of their use. One fundamental issue, which has not been systematically investigated, is the impact of control resolution (the smallest change produced by an LCS). In an ideal LCS, the resolution would be sufficiently fine for users to specify their desired lighting conditions, but the smallest change would be detectable. Thus, the design of optimal control systems requires a thorough understanding of the detectability and acceptability of differences in illuminance, luminance and colour. The control of colour is complicated by the range of interfaces that can be used to facilitate colour mixing. Four psychophysical experiments investigated the effect of LCS resolution. The first two experiments explored the effect of resolution in white light LCSs on usability and energy conservation. The results suggest that, in different applications, LCSs with resolutions between 14.8 % and 17.7 % (of illuminance) or 26.0 % and 32.5 % (of luminance) have the highest usability. The third experiment evaluated the usability of three colour channel control interfaces based on red, green, blue (RGB), hue, saturation, brightness (HSB) and opponent colour mixing systems. Although commonly used, the RGB interface was found to have the lowest usability. The fourth experiment explored the effect of hue resolution, saturation resolution and luminance resolution on the usability. Generally, middle range resolutions, which are approximately between three and five times the magnitude of the just noticeable difference (JND), for both hue and saturation were found to yield the greatest usability. The interaction between these three variables was characterised. Findings from this research provide a deeper understanding of the fundamental attribute of control resolution and can guide the development of useful and efficient lighting control systems

Enhancement in Interfacial Adhesion of Ti/Polyetheretherketone by Electrophoretic Deposition of Graphene Oxide

This is the peer reviewed version of the following article: Pan, L., Lv, Y., Nipon, R., Wang, Y., Duan, L., Hu, J., ... & Shi, Y. (2019). Enhancement in Interfacial Adhesion of Ti/Polyetheretherketone by Electrophoretic Deposition of Graphene Oxide. Polymer Composites, 40(S2), E1243-E1251, which has been published in final form at https://doi.org/10.1002/pc.24955. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.This article discusses about the significance of graphene oxide (GO) deposition on the surface of a titanium plate by electrophoretic deposition (EPD) method to improve the adhesive strength of Ti/polyetheretherketone (PEEK) interfacial adhesive. Firstly, the anodic EPD method was applied to a water dispersion solution of GO, and then the morphology and the properties of titanium plate surface were characterized by scanning electron microscopy and contact angle measurements before and after GO deposition. Furthermore, the changes in the properties of GO after heating at 390°C were characterized by Raman and Fourier transform infrared spectroscopies. According to the results of single lap tensile shear test, the adhesion strength of Ti/PEEK interface after the anodization and deposition of GO was 34.94 MPa, an increase of 29.2% compared with 27.04 MPa of sample with only anodization. Also, the adhesion strengths were 58.1 and 76.5% higher compared with the samples of only GO deposited (22.1 MPa) and pure titanium (19.8 MPa), respectively

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

Toward a Connected System—Understanding the Contribution of Light from Different Sources on Occupants’ Circadian Rhythms

Light that enters humans’ eyes and impacts circadian rhythms may come from various sources, including the sun, electric lighting systems, and self-luminous displays. Occupants’ activities strongly impact the light entering their eyes, which is difficult to predict and not yet well understood. This study investigated the circadian contributions of light from different sources in real building environments to better understand the variables that influence the circadian health of occupants. Spectral irradiance distributions at a position equivalent to the front of an eye of a seated occupant in various interior office spaces were collected. Daylight and electric light were measured separately, and light emitted from displays was measured when a variety of different computer tasks was performed. Circadian stimulus (CS) and α-opic irradiance, defined by CIE DIS026/E:2018, were further calculated, and the circadian effects of light from different sources were compared. The results show that daylight has the greatest circadian effect, while electric light in spaces that were predominantly designed with conventional downward lighting has a very limited impact. The circadian effect of light from screens was considerably high. The outcomes suggest that, to optimise the circadian effects of light, connected lighting systems are needed to control light from different sources

The Calculated Circadian Effects of Light Exposure from Commuting

Light entrains human circadian rhythms, but increased time spent indoors and decreased daylight exposure may disrupt human circadian regulation and cause health problems. Much research is focused on improving indoor lighting conditions to minimize the adverse circadian impact of electric lights, and few studies investigate the circadian impact of daylight during the incidental time that people spend outdoors. For instance, when people commute from home to work, they are exposed to daylight. The purpose of this study is to investigate daylight’s impact on commuters’ circadian rhythms. Measurements of the illuminance and the spectral irradiance distribution (SID) of daylight were taken for three modes of commuting: driving, riding on trains, and walking; and under different weather conditions, on different days, and at different locations throughout the summer and autumn in the Sydney metropolitan region in Australia. With the SID data, three metrics were calculated to estimate the circadian impacts: α-opic irradiance, circadian stimulus (CS), and equivalent melanopic lux (EML). The results suggest that driving or walking on sunny or cloudy days and riding trains on sunny days are beneficial for the commuters’ circadian synchronization

A practical man-in-the-middle attack on deep learning edge device by sparse light strip injection into camera data lane

The vulnerability of deep neural networks (DNNs) has been exposed by adversarial examples. Although the adversarial perturbations can be made visually imperceptible or photorealistic on any image, they have to be added offline on pre-captured static input in order to accomplish the malicious goal. As opposed to subtle distortion, real-time misclassification on streaming images can be realized by manipulating the objects in physical world. Recently, object-contactless physical attacks, as exemplified by a translucent sticker affixed to the lens of a camera, show that a sensor-enabled edge computing platform can be an alluring target of adversarial attack. Nevertheless, success rates of reported camera-based patch attacks are not high enough to overshadow other forms of evasion attacks even when they are performed under the white-box scenario. In this paper, we present a practical and robust fault injection approach cooperated with a hardware-friendly sparse strip pattern to deceive the deployed DNN device on real-time streaming images. The strip perturbation is generated in a line-offset form by an optimization algorithm. It can be injected into camera data lane between the image sensor and the endpoint node stealthily without disturbing the data traffic through an interface bridge implemented by a tiny off-the-shelf FPGA device. We demonstrate our attack on the Raspberry Pi 4 platform with the Pi camera v2 and the Intel NCS2 inference stick. By evaluating 280 physically captured images from ten objects in 28 viewing angles, we show that the proposed attack on four ImageNet models including ResNet50, MobileNet-v2, Inception-v3 and EfficientNet-B0 can achieve 89.2% ∼ 96.1% success rates.National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme/Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: CHFA-GC1-AW01)

Identification of key modules and hub genes for eosinophilic asthma by weighted gene co-expression network analysis

Eosinophilic asthma (EA) is one of the most important asthma phenotypes with distinct features. However, its genetic characteristics are not fully understood. This study aimed to investigate the transcriptome features and to identify hub genes of EA. Differentially expressed genes (DEGs) analysis, weighted gene coexpression network analysis (WGCNA) and protein–protein interaction (PPI) network analysis were performed to construct gene networks and to identify hub genes. Enrichment analyses were performed to investigate the biological processes, pathways and immune status of EA. The hub genes were validated in another dataset. The diagnostic value of the identified hub genes was assessed by receiver operator characteristic curve (ROC) analysis. Compared with NEA, EA had a different gene expression pattern, in which 81 genes were differentially expressed. WGCNA identified two gene modules significantly associated with EA. Intersections of the DEGs and the genes in the modules associated with EA were mainly enriched in chemotaxis and signal transduction by GO and KEGG enrichment analyses. Single-sample gene set enrichment analysis (ssGSEA) indicated that EA had different immune infiltration and functions compared with NEA. Seven hub genes of EA were identified and validated, including CCL17, CCL26, CD1C, CXCL11, CXCL10, CCL22, and CCR7, all of which have diagnostic values for distinguishing EA from NEA (All AUC > 0.7). This study demonstrated the distinct gene expression patterns, biological processes, and immune status of EA. Hub genes of EA were identified and validated. Our study could provide a framework of co-expression gene modules and potential therapeutic targets for EA.</p

A Cucumber Photosynthetic Rate Prediction Model in Whole Growth Period with Time Parameters

Photosynthetic rate prediction models can provide guidance for crop photosynthetic process optimization, which has been widely used in the precise regulation of the protected environment. The photosynthetic capacity of crops continuously changes during their whole growth process. Previous studies on photosynthetic models mainly consider the interaction between a crop’s photosynthetic rate and its outer environmental conditions and have been able to predict a crop’s photosynthetic rate in a certain growth period. However, photosynthetic rate prediction models for whole growth periods have not been proposed yet. To solve this question, this paper introduces growing time into a variable set and proposes a method for building a cucumber photosynthetic rate prediction model of whole growth periods. First, the photosynthetic rate of cucumber leaves under different environmental conditions (light, temperature, and CO2 concentration) during the whole growth period was obtained through a multi-gradient nested test. With the environmental data and the cultivation time as the inputs, a photosynthetic rate prediction model was built using the Support Vector Regression algorithm. In order to obtain better modeling results, multiple kernel functions were used for pretraining, and the parameters of the Support Vector Regression algorithm were optimized based on multiple population genetic algorithms. Compared with a Back Propagation neural network and Non-linear Regression method, the Support Vector Regression model optimized had the highest accuracy, with the coefficient of determination of the test set was 0.998, and the average absolute error was 0.280 μmol·m−2·s−1, which provides a theoretical solution for the prediction of the cucumber photosynthetic rate during the whole growth period