Protection in Low Voltage DC Microgrids

Protection is an important aspect when designing a microgrid system, as it ensures the network is able to run safely. As the debate between AC vs. DC protection schemes continue, there appear to be distinct advantages and disadvantages on each side with respect to reliability, efficiency, security, environmental and economic concerns. In this thesis, a low voltage DC microgrid protection scheme used in a data center is proposed. The final goal of this project is to develop a network and perform a fault analysis study while investigating different aspects of power protection schemes. Research is done on different protection devices which will be used to protect their respective components. Three types of faults will be tested on the system for fault current observation purposes. In order to calculate the theoretical fault current of the battery and converter, Microsoft Excel will be used. ICAPS by Intusoft will be used to simulate three different faults in the network. Fault 1 will be on the positive and negative pole of the converter/battery and the load. Fault 2 is a double line to ground fault located on one of the feeders near the load. Fault 3 is a single line to ground impedance located on one of the positive pole of the feeder with a high impedance. Results show that there are commercial devices available to protect components in such a system. Ultra hybrid DC circuit breakers are used to protect the converter, Molded Case Circuit Breakers are used for feeder protection, and lastly fuses or circuit breakers can be used for battery protection

Defective hierarchical porous copper-based metal-organic frameworks synthesised via facile acid etching strategy

Introducing hierarchical pore structure to microporous materials such as metal-organic frameworks (MOFs) can be beneficial for reactions where the rate of reaction is limited by low rates of diffusion or high pressure drop. This advantageous pore structure can be obtained by defect formation, mostly via post-synthetic acid etching, which has been studied extensively on water-stable MOFs. Here we show that a water-unstable HKUST-1 MOF can also be modified in a corresponding manner by using phosphoric acid as a size-selective etching agent and a mixture of dimethyl sulfoxide and methanol as a dilute solvent. Interestingly, we demonstrate that the etching process which is time- and acidity- dependent, can result in formation of defective HKUST-1 with extra interconnected hexagonal macropores without compromising on the bulk crystallinity. These findings suggest an intelligent scalable synthetic method for formation of hierarchical porosity in MOFs that are prone to hydrolysis, for improved molecular accessibility and diffusion for catalysis.Comment: 14 pages, 8 figure

Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.Comment: Technical repor

Goal-Focused Emotion-Regulation Therapy (GET) for young adult survivors of testicular cancer: a pilot randomized controlled trial of a biobehavioral intervention protocol.

BackgroundTesticular cancer diagnosis and treatment, especially given its threat to sexuality and reproductive health, can be distressing in the formative period of young adulthood and the majority of young survivors experience impairing, distressing, and modifiable adverse outcomes that can persist long after medical treatment. These include psychological distress, impairment in pursuit of life goals, persistent physical side effects, elevated risk of secondary malignancies and chronic illness, and biobehavioral burden (e.g., enhanced inflammation, dysregulated diurnal stress hormones). However, few targeted interventions exist to assist young survivors in renegotiating life goals and regulating cancer-related emotions, and none focus on reducing the burden of morbidity via biobehavioral mechanisms. This paper describes the methodology of a randomized controlled biobehavioral trial designed to investigate the feasibility and preliminary impact of a novel intervention, Goal-focused Emotion-Regulation Therapy (GET), aimed at improving distress symptoms, emotion regulation, goal navigation skills, and stress-sensitive biomarkers in young adult testicular cancer patients.MethodsParticipants will be randomized to receive six sessions of GET or Individual Supportive Therapy (ISP) delivered over 8 weeks. In addition to indicators of intervention feasibility, we will measure primary (depressive and anxiety symptoms) and secondary (emotion regulation and goal navigation skills, career confusion) psychological outcomes prior to (T0), immediately after (T1), and 12 weeks after (T2) intervention. Additionally, identified biomarkers will be measured at baseline and at T2.DiscussionGET may have the potential to improve self-regulation across biobehavioral domains, improve overall cancer adjustment, and address the need for targeted supportive care interventions for young adult cancer survivors.Trial registrationClinicaltrials.gov, NCT04150848. Registered on 28 October 2019

CFHT Legacy Ultraviolet Extension (CLUE): witnessing galaxy transformations up to 7 Mpc from rich cluster cores

Using the optical data from the Wide component of the Canada-France-Hawaii Telescope (CFHT) Legacy Survey, and new ultraviolet (UV) data from GALEX, we study the colours and specific star formation rates (SSFRs) of ∼ 100 galaxy clusters at 0.16 < z < 0.36, over areas extending out to radii of r∼ 7 Mpc. We use a multicolour, statistical background subtraction method to study the galaxy population at this radius; thus our results pertain to those galaxies which constitute an excess over the average field density. We find that the average SSFR and its distribution of the star-forming galaxies (with at z∼ 0.2 and at z∼ 0.3) have no measurable dependence on the clustercentric radius and are consistent with the field values. However, the fraction of galaxies with SFR above these thresholds, and the fraction of optically blue galaxies, are lower for the overdense galaxy population in the cluster outskirts compared with the average field value, at all stellar masses and at all radii out to at least 7 Mpc. Most interestingly, the fraction of blue galaxies that are forming stars at a rate below our UV detection limit is much higher in all radial bins around our cluster sample compared with the general field value. This is most noticeable for massive galaxies ; while almost all blue field galaxies of this mass have detectable star formation, this is true for less than 20 per cent of the blue cluster galaxies, even at 7 Mpc from the cluster centre. Our results support a scenario where galaxies are pre-processed in locally overdense regions in a way that reduces their SFR below our UV detection limit, but not to zer

Inhibitors of SARS-CoV entry--identification using an internally-controlled dual envelope pseudovirion assay.

Severe acute respiratory syndrome-associated coronavirus (SARS-CoV) emerged as the causal agent of an endemic atypical pneumonia, infecting thousands of people worldwide. Although a number of promising potential vaccines and therapeutic agents for SARS-CoV have been described, no effective antiviral drug against SARS-CoV is currently available. The intricate, sequential nature of the viral entry process provides multiple valid targets for drug development. Here, we describe a rapid and safe cell-based high-throughput screening system, dual envelope pseudovirion (DEP) assay, for specifically screening inhibitors of viral entry. The assay system employs a novel dual envelope strategy, using lentiviral pseudovirions as targets whose entry is driven by the SARS-CoV Spike glycoprotein. A second, unrelated viral envelope is used as an internal control to reduce the number of false positives. As an example of the power of this assay a class of inhibitors is reported with the potential to inhibit SARS-CoV at two steps of the replication cycle, viral entry and particle assembly. This assay system can be easily adapted to screen entry inhibitors against other viruses with the careful selection of matching partner virus envelopes

IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry

While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers’ needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to single-modal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality