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Maximizing anaerobic biogas production using temperature variance

Verma et al. | Aug 03, 2023

Maximizing anaerobic biogas production using temperature variance

We conducted this research as our start-up's research that addresses the problem of biogas production in cow-dense regions like India. We hypothesized that the thermophilic temperature (45-60oC) would increase biogas production. The production process is much faster and more abundant at temperatures around 55-60oC.

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A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting

Ahmed et al. | Jun 09, 2023

A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

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People’s Preference to Bet on Home Teams Even When Losing is Likely

Weng et al. | Mar 10, 2020

People’s Preference to Bet on Home Teams Even When Losing is Likely

In this study, the authors investigate situations in which people make sports bets that seem to go against their better judgement. Using surveys, individuals were asked to bet on which team would win in scenarios when their home team was involved and others when they were not to determine whether fandom for a team can overshadow fans’ judgment. They found that fans bet much more on their home teams than neutral teams when their team was facing a large deficit.

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Collaboration beats heterogeneity: Improving federated learning-based waste classification

Chong et al. | Jul 18, 2023

Collaboration beats heterogeneity: Improving federated learning-based waste classification

Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.

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Comparing the Effectiveness of Popular Treatments for Swelling and Scarring

Benavides et al. | Jul 08, 2020

Comparing the Effectiveness of Popular Treatments for Swelling and Scarring

Numerous specialty treatments claim to reduce swelling and scarring; however, it is unknown if these treatments are more effective than less expensive treatments. In an attempt to determine if one outperforms the other, treatments were applied to the same subject following bilateral orthopedic foot surgery. No difference was found the specialty treatments compared to more cost-effective treatments.

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Exploring a Possible Link Between ADHD and Inattentional Blindness

Younger et al. | Dec 21, 2020

Exploring a Possible Link Between ADHD and Inattentional Blindness

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by impulsivity, hyperactivity, and inattention. The authors hypothesized that people with ADHD would display more inattentional blindness in perceptually simple tasks and less inattentional blindness in perceptually complex tasks. The results indicate that there is no significant correlation between ADHD and inattentional blindness in either type of task.

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