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Similarity Graph-Based Semi-supervised Methods for Multiclass Data Classification

Balaji et al. | Sep 11, 2021

Similarity Graph-Based Semi-supervised Methods for Multiclass Data Classification

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.

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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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Spectrophotometric comparison of 4-Nitrophenyl carbonates & carbamates as base-labile protecting groups

Kocalar et al. | Dec 12, 2022

Spectrophotometric comparison of 4-Nitrophenyl carbonates & carbamates as base-labile protecting groups

In organic synthesis, protecting groups are derivatives of reactive functionalities that play a key role in ensuring chemoselectivity of chemical transformations. To protect alcohols and amines, acid-labile tert-butyloxycarbonyl protecting groups are often employed but are avoided when the substrate is acid-sensitive. Thus, orthogonal base-labile protecting groups have been in demand to enable selective deprotection and to preserve the reactivity of acid-sensitive substrates. To meet this demand, we present 4-nitrophenyl carbonates and carbamates as orthogonal base-labile protecting group strategies.

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Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation

Gupta et al. | Jan 31, 2023

 Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation
Image credit: Markus Spiske

Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.

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Artificial Intelligence-Based Smart Solution to Reduce Respiratory Problems Caused by Air Pollution

Bhardwaj et al. | Dec 14, 2021

Artificial Intelligence-Based Smart Solution to Reduce Respiratory Problems Caused by Air Pollution

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.

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Comparing the Biodegradability of Petroleum-based Plastic with a Novel, Sustainable Bio-plastic Alternative

Van Note et al. | Dec 02, 2020

Comparing the Biodegradability of Petroleum-based Plastic with a Novel, Sustainable Bio-plastic Alternative

In this research, a novel bioplastic inclusive of bamboo tannins and chitosan is selected from more than 60 trial formula variations based on resulting strength, fatigue, and transparency attributes. The biodegradability of the finalized bioplastic is compared to that of conventional polyethylene, in addition to investigating its solubility and water absorbance. This research displays the potential of a legitimate, fully biodegradable plastic alternative to current marketplace bioplastics.

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Development and Implementation of Enzymatic and Volatile Compound-based Approaches for Instantaneous Detection of Pathogenic Staphylococcus aureus

Nori et al. | Feb 20, 2021

Development and Implementation of Enzymatic and Volatile Compound-based Approaches for Instantaneous Detection of Pathogenic <i>Staphylococcus aureus</i>

Staphylococcus aureus (S. aureus) has a mortality rate of up to 30% in developing countries. The purpose of this experiment was to determine if enzymatic and volatile compound-based approaches would perform more quickly in comparison to existing S. aureus diagnostic methods and to evaluate these novel methods on accuracy. Ultimately, this device provided results in less than 30 seconds, which is much quicker than existing methods that take anywhere from 10 minutes to 48 hours based on approach. Statistical analysis of accuracy provides preliminary confirmation that the device based on enzymatic and volatile compound-based approaches can be an accurate and time-efficient tool to detect pathogenic S. aureus.

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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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Developing novel plant waste-based hydrogels for skin regeneration and infection detection in diabetic wounds

Mathew et al. | Aug 10, 2023

Developing novel plant waste-based hydrogels for skin regeneration and infection detection in diabetic wounds

The purpose of this investigation is to develop a hydrogel to aid skin regeneration by creating an extracellular matrix for fibroblast growth with antibacterial and infection-detection properties. Authors developed two natural hydrogels based on pectin and potato peels and characterized the gels for fibroblast compatibility through rheology, scanning electron microscopy, swelling, degradation, and cell cytotoxicity assays. Overall, this experiment fabricated various hydrogels capable of acting as skin substitutes and counteracting infections to facilitate wound healing. Following further testing and validation, these hydrogels could help alleviate the 13-billion-dollar financial burden of foot ulcer treatment.

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