Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors.

Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network.

The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques.

A New Hydrometallurgical Process for Metal Extraction from Electric Arc Furnace Dust Using Ionic Liquids.

This research proposes a new hydrometallurgical method for Zn, In, and Ga extraction, along with Fe as a common impurity, from electric arc furnace dust (EAFD), using ionic liquids. EAFD is a metal-containing waste fraction generated in significant amounts during the process of steelmaking from scrap material in an electric arc furnace. With valuable metal recovery as the main goal, two ionic liquids, [BmimHSO] and [BmimCl], were studied in conjunction with three oxidants: Fe(SO), KMnO, and HO.

A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables.

Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets.

Prophylaxis is the new standard of care in patients with haemophilia.

Randomised controlled clinical trial evidence on prophylaxis as optimal care for patients with haemophilia was generated more than a decade ago. However, this knowledge has not translated into clinical practice in South Africa (SA) owing to many barriers to prophylaxis. These include the high treatment burden imposed by prophylaxis (frequent injections two to four times a week), the need for intravenous access to administer replacement clotting factor therapies, and the higher volume of clotting factor required compared with episodic treatment.