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A Study on the Coagulating Properties of the M. oleifera Seed

Lakshmanan et al. | Feb 14, 2020

A Study on the Coagulating Properties of the <em>M. oleifera</em> Seed

In this study, the authors investigate whether Moringa Oleifera seeds can serve as material to aid in purifying water. M. oleifera seeds have coagulating properties and the authors hypothesized that including it in a water filtration system would reduce particles, specifically bacteria, in water. Their results show that this system removed the largest percent of bacteria. When used in combination with cilantro, it was actually more efficient than the other techniques! These findings have important implications for creating better and more economical water purification systems.

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Artificial Intelligence Networks Towards Learning Without Forgetting

Kreiman et al. | Oct 26, 2018

Artificial Intelligence Networks Towards Learning Without Forgetting

In their paper, Kreiman et al. examined what it takes for an artificial neural network to be able to perform well on a new task without forgetting its previous knowledge. By comparing methods that stop task forgetting, they found that longer training times and maintenance of the most important connections in a particular task while training on a new one helped the neural network maintain its performance on both tasks. The authors hope that this proof-of-principle research will someday contribute to artificial intelligence that better mimics natural human intelligence.

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Mathematical modeling of plant community composition for urban greenery plans

Fang et al. | Jul 05, 2023

Mathematical modeling of plant community composition for urban greenery plans
Image credit: CHUTTERSNAP

Here recognizing the importance of urban green space for the health of humans and other organisms, the authors investigated if mathematical modeling can be used to develop an urban greenery management plan with high eco-sustainability by calculating the composition of a plant community. They optimized and tested their model against green fields in a Beijing city park. Although the compositions predicted by their models differed somewhat from the composition of testing fields, they conclude that by using a mathematical model such as this urban green space can be finely designed to be ecologically and economically sustainable.

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Pressure and temperature influence the efficacy of metal-organic frameworks for carbon capture and conversion

Lin et al. | May 07, 2023

Pressure and temperature influence the efficacy of metal-organic frameworks for carbon capture and conversion

Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO2 to other useful carbon products. This research used computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO2. In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO2 emissions.

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Optimizing airfoil shape for small, low speed, unmanned gliders: A homemade investigation

Lara et al. | Mar 30, 2023

Optimizing airfoil shape for small, low speed, unmanned gliders: A homemade investigation
Image credit: Konrad Wojciechowski

Here, the authors sought to identify a method to optimize the lift generated by an airfoil based solely on its shape. By beginning with a Bernoullian model to predict an optimized wing shape, the authors then tested their model against other possible shapes by constructing them from Styrofoam and testing them in a small wind tunnel. Contrary to their hypothesis, they found their expected optimal airfoil shape did not result in the greatest lift generation. They attributed this to a variety of confounding variables and concluded that their results pointed to a correlation between airfoil shape and lift generation.

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Comparing the effects of electronic cigarette smoke and conventional cigarette smoke on lung cancer viability

Choe et al. | Sep 18, 2022

Comparing the effects of electronic cigarette smoke and conventional cigarette smoke on lung cancer viability

Here, recognizing the significant growth of electronic cigarettes in recent years, the authors sought to test a hypothesis that three main components of the liquid solutions used in e-cigarettes might affect lung cancer cell viability. In a study performed by exposing A549 cells, human lung cancer cells, to different types of smoke extracts, the authors found that increasing levels of nicotine resulted in improve lung cancer cell viability up until the toxicity of nicotine resulted in cell death. They conclude that these results suggest that contrary to conventional thought e-cigarettes may be more dangerous than tobacco cigarettes in certain contexts.

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Indoor near-field target detection characteristics under radio and radar joint operation at 2.4 GHz ISM band

Koh et al. | Apr 29, 2022

Indoor near-field target detection characteristics under radio and radar joint operation at 2.4 GHz ISM band

In our modern age, the burgeoning use of radios and radars has resulted in competition for electromagnetic spectrum resources. With recent research highlighting solutions to radio and radar mutual interference, there is a desperate need for a cost-effective configuration that permits a radar-radio joint system. In this study, the authors have set out to determine the feasibility of using single-tone continuous-wave radars in a radar-joint system. With this system, they aim to facilitate cost-effective near-field target detection by way of the popularized 2.4-GHz industrial, scientific, and medical (ISM) band.

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Modeling the effects of acid rain on bacterial growth

Shah et al. | Nov 17, 2020

Modeling the effects of acid rain on bacterial growth

Acid rain has caused devastating decreases in ecosystems across the globe. To mimic the effect of acid rain on the environment, the authors analyzed the growth of gram-negative (Escherichia coli) and gram-positive (Staphylococcus epidermidis) bacteria in agar solutions with different pH levels. Results show that in a given acidic environment there was a significant decrease in bacterial growth with an increase in vinegar concentration in the agar, suggesting that bacterial growth is impacted by the pH of the environment. Therefore, increased levels of acid rain could potentially harm the ecosystem by altering bacterial growth.

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Fire and dry grass: Effects of Pennisetum villosum on a California native, Nassella pulchra, in drought times

Chang et al. | Jan 23, 2022

Fire and dry grass: Effects of <i>Pennisetum villosum</i> on a California native, <i>Nassella pulchra</i>, in drought times

Invasive species pose a significant threat to many ecosystems, whether by outcompeting native species and disturbing food webs, or through increasing risks of natural disasters like flooding and wildfires. The ornamental grass species Pennisetum villosum R. Br. was previously identified by the California Invasive Plant Council as being potentially invasive; this experiment was conducted to determine if P. villosum displays characteristics of an invasive species when grown in a California chaparral environment. Reults found that in both conditions, the two species had similar germination rates, and that P. villosum grew significantly larger than N. pulchra for around 95 days.

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Propagation of representation bias in machine learning

Dass-Vattam et al. | Jun 10, 2021

Propagation of representation bias in machine learning

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

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