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Automated dynamic lighting control system to reduce energy consumption in daylight

Jagannathan et al. | Jun 17, 2024

Automated dynamic lighting control system to reduce energy consumption in daylight
Image credit: Jagannathan and Mehrotra 2024

Buildings, which are responsible for the majority of electricity consumption in cities like Dubai, are often exclusively reliant on electrical lighting even in the presence of daylight to meet the illumination requirements of the building. This inefficient use of lighting creates potential to further optimize the energy efficiency of buildings by complementing natural light with electrical lighting. Prior research has mostly used ballasts (variable resistors) to regulate the brightness of bulbs. There has been limited research pertaining to the use of pulse width modulation (PWM) and the use of ‘triodes for alternating current’ (TRIACs). PWM and TRIACs rapidly stop and restart the flow of current to the bulb thus saving energy whilst maintaining a constant illumination level of a space. We conducted experiments to investigate the feasibility of using TRIACs and PWM in regulating the brightness of bulbs. We also established the relationship between power and brightness within the experimental setups. Our results indicate that lighting systems can be regulated through these alternate methods and that there is potential to save up to 16% of energy used without affecting the overall lighting of a given space. Since most energy used in buildings is still produced through fossil fuels, energy savings from lighting systems could contribute towards a lower carbon footprint. Our study provides an innovative solution to conserve light energy in buildings during daytime.

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The Effects of Post-Consumer Waste Polystyrene on the Rate of Mealworm Consumption

Green et al. | Nov 29, 2018

The Effects of Post-Consumer Waste Polystyrene on the Rate of Mealworm Consumption

In a world where plastic waste accumulation is threatening both land and sea life, Green et al. investigate the ability of mealworms to breakdown polystyrene, a non-recyclable form of petrochemical-based polymer we use in our daily lives. They confirm that these organisms, can degrade various forms of polystyrene, even after it has been put to use in our daily lives. Although the efficiency of the degradation process still requires improvement, the good news is, the worms are tiny and themselves are biodegradable, so we can use plenty of them without worrying about space and how to get rid of them. This is very promising and certainly good news for the planet.

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Flight paths over greenspace in major United States airports

Lee et al. | Sep 26, 2023

Flight paths over greenspace in major United States airports
Image credit: Mostafijur Rahman Nasim

Greenspaces (urban and wetland areas that contain vegetation) are beneficial to reducing pollution, while airplanes are a highly-polluting method of transportation. The authors examine the intersection of these two environmental factors by processing satellite images to reveal what percentage of flight paths go over greenspaces at major US airports.

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The Effects of Antioxidants on the Climbing Abilities of Drosophila melanogaster Exposed to Dental Resin

Prashanth et al. | Jan 17, 2019

The Effects of Antioxidants on the Climbing Abilities of <em>Drosophila melanogaster</em> Exposed to Dental Resin

Dental resins can be a source of reactive oxygen species (ROS) which in unruly amounts can be toxic to cellular and overall health. In this report, the authors test whether the consumption of antioxidant rich foods like avocado and asparagus can protect against the effect of dental resin-derived ROS. However, rather than testing humans, they use fruit flies and their climbing abilities as an experimental readout.

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Automated classification of nebulae using deep learning & machine learning for enhanced discovery

Nair et al. | Feb 01, 2024

Automated classification of nebulae using deep learning & machine learning for enhanced discovery

There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.

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