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Pufferfish, globally recognized for its distinctive delicacy, carries high culinary value. However, it is also notorious for the lethal toxicity, and there is a great demand for traceability measures in the commercial trade of pufferfish to assure safety and accountability. This research introduces a novel deep learning approach, utilizing facial recognition techniques, to identify pufferfish individuals. This method specifically leverages distinctive back skin texture patterns as key biological traits. Our initial step involved assembling a collection of annotated and augmented images of Takifugu bimaculatus, a species of pufferfish native to East China Sea, which is accessible upon request. We then extensively investigated fundamental components of Deep Face Recognition (deep FR) systems, focusing on segmentation and extraction models, and assessed their effectiveness in identifying pufferfish. Following this, we developed FishIR (Fish Individual Recognition), a framework to identify pufferfish individuals that consists of four deep FR stages while incorporating enhanced segmentation and feature extraction techniques. Experimental results show that this framework successfully captures unique representations of individual pufferfish, as verified by the high accuracy achieved in recognition tasks.

An intelligent reflecting surface (IRS) enhances the performance of a wireless channel by controlling the phase of signals reflected by them. This paper investigates an IRS-aided single input single output wireless communication system. Owing to the closely spaced placement of the IRS elements, the channels between the IRS and the transmitter, as well as the channels between the IRS and the receiver, are considered to be spatially correlated. Assuming optimal phase configuration at the IRS, correlated Nakagami- $m$ random vectors are used to model the magnitude of the channel coefficient vectors between the IRS and the transmitter as well as between the IRS and the receiver. A statistical approximation of the distribution of composite channel is obtained as a Gamma distribution. The parameters of the approximated Gamma distribution are derived in terms of the scale parameter and spread parameter of Nakagami- $m$ random vectors. The approximated Gamma distribution is validated using Monte-Carlo simulations and found to be closely following the actual distribution of the composite channel coefficient. Using the approximation, closed form expressions of the performance metrics such as symbol error probability (SEP), outage probability and average channel capacity are derived and validated.

Rapidly building molecular structures with both elements of complexity and flexibility is a key goal of organic synthesis. Here the authors show a tandem copper-catalyzed β-borylation/Michael addition on prochiral enone-tethered 2,5-cyclohexadienones, to generate bicyclic borylated products in high yield and enantioselectivity.

COVID-19—a severe acute respiratory syndrome disease caused by coronavirus 2 (SARS-CoV-2)—has recently attracted global attention, due to its devastating impact, to the point of being declared a pandemic. The search for new natural therapeutic drugs is mandatory, as the screening of already-known antiviral drugs so far has led to poor results. Several species of marine algae have been reported as sources of bioactive metabolites with potential antiviral and immunomodulatory activities, among others. Some of these bioactive metabolites might be able to act as antimicrobial drugs and also against viral infections by inhibiting their replication. Moreover, they could also trigger immunity against viral infection in humans and could be used as protective agents against COVID-In this context, this article reviews the main antiviral activities of bioactive metabolites from marine algae and their potential exploitation as anti-SARS-CoV-2 drugs.