Automated Continuous Crystallization Platform with Real-Time Particle Size Analysis via Laser Diffraction

The fourth industrial revolution is gaining momentum in the pharmaceutical industry. However, particulate processes and suspension handling remain big challenges for automation and the implementation of real-time particle size analysis. Moreover, the development of antisolvent crystallization processes is often limited by the associated time-intensive experimental screenings. This work demonstrates a fully automated modular crystallization platform that overcomes these bottlenecks. The system combines automated crystallization, sample preparation, and immediate crystal size analysis via online laser diffraction (LD) and provides a technology for rapidly screening crystallization process parameters and crystallizer design spaces with minimal experimental effort. During the LD measurements, to avoid multiple scattering events, crystal suspension samples are diluted automatically. Multiple software tools, i.e., LabVIEW, Python, and PharmaMV, and logic algorithms are integrated in the platform to facilitate automated control of all the sensors and equipment, enabling fully automated operation. A customized graphical user interface is provided to operate the crystallization platform automatically and to visualize the measured crystal size and the crystal size distribution of the suspension. Antisolvent crystallization of ibuprofen, with ethanol as solvent and water with Soluplus (an additive) as antisolvent, is used as a case study. The platform is demonstrated for antisolvent crystallization of small ibuprofen crystals in a confined impinging jet crystallizer, performing automated preplanned user-defined experiments with online LD analysis

A Study on the Transferability of Adversarial Attacks in Sound Event Classification

An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbations on one model and apply it the input to fool the output of a different model. Our work focuses on studying the transferability of adversarial attacks in sound event classification. We are able to demonstrate differences in transferability properties from those observed in computer vision. We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks

Audio tagging using a linear noise modelling layer

Label noise refers to the presence of inaccurate target labels in a dataset. It is an impediment to the performance of a deep neural network (DNN) as the network tends to overfit to the label noise, hence it becomes imperative to devise a generic methodology to counter the effects of label noise. FSDnoisy18k is an audio dataset collected with the aim of encouraging research on label noise for sound event classification. The dataset contains ~42.5 hours of audio recordings divided across 20 classes, with a small amount of manually verified labels and a large amount of noisy data. Using this dataset, our work intends to explore the potential of modelling the label noise distribution by adding a linear layer on top of a baseline network. The accuracy of the approach is compared to an alternative approach of adopting a noise robust loss function. Results show that modelling the noise distribution improves the accuracy of the baseline network in a similar capacity to the soft bootstrapping loss

Observed anomalous upwelling in the Lakshadweep Sea during the summer monsoon season of 2005

Repeat near-fortnightly expendable bathythermograph (XBT) transects made along Kochi-Kavaratti (KK) shipping lane in the Lakshadweep Sea (LS) during 2002–2006 are examined to describe the observed year-to-year variability of upwelling during summer monsoon season (SMS). Among all the years, the upwelling characterized by up-sloping of 25°C isotherm is relatively weaker and persisted until November during SMS of 2005 and is stronger during the SMS of 2002. As a result of prolonged upwelling, the sea surface temperature has shown cooling extending into the postmonsoon season. The estimated marine pelagic fish landings along the southwest coast of India (SWCI) have also shown increase until December. The governing mechanisms both in terms of local and remote forcings are examined to explain the observed anomalous upwelling during SMS of 2005. The equatorward alongshore wind stress (WS) along the KK XBT transect persisted in a transient manner beyond September only during SMS of 2005. The westerly wind bursts over the equator during the winter of 2004–2005 are both short-lived and relatively weaker triggering weaker upwelling Kelvin waves that propagated into LS in the following SMS of 2005. The observed distribution of negative sea surface height anomaly in the LS is relatively weaker during the SMS of 2005 and lasted longer. The correlation analysis suggests that the local alongshore WS off the SWCI and the remote forcing from the southern coast of Sri Lanka has greater influence on the observed interannual variability of upwelling in the LS when compared to the remote forcing from the equator