Reply to Taylor et al. Comment on "Manna et al. SARS-CoV-2 Inactivation in Aerosol by Means of Radiated Microwaves. Viruses 2023, 15, 1443".

SARS-CoV-2 is inactivated in aerosol (its primary mode of transmission) by means of radiated microwaves at frequencies that have been experimentally determined. Such frequencies are best predicted by the mathematical model suggested by Taylor, Margueritat and Saviot. The alignment between such mathematical prediction and the outcomes of our experiments serves to reinforce the efficacy of the radiated microwave technology and its promise in mitigating the transmission of SARS-CoV-2 in its naturally airborne state.

SARS-CoV-2 Inactivation in Aerosol by Means of Radiated Microwaves.

Coronaviruses are a family of viruses that cause disease in mammals and birds. In humans, coronaviruses cause infections on the respiratory tract that can be fatal. These viruses can cause both mild illnesses such as the common cold and lethal illnesses such as SARS, MERS, and COVID-19. Air transmission represents the principal mode by which people become infected by SARS-CoV-2. To reduce the risks of air transmission of this powerful pathogen, we devised a method of inactivation based on the propagation of electromagnetic waves in the area to be sanitized. We optimized the conditions in a controlled laboratory environment mimicking a natural airborne virus transmission and consistently achieved a 90% (tenfold) reduction of infectivity after a short treatment using a Radio Frequency (RF) wave emission with a power level that is safe for people according to most regulatory agencies, including those in Europe, USA, and Japan. To the best of our knowledge, this is the first time that SARS-CoV-2 has been shown to be inactivated through RF wave emission under conditions compatible with the presence of human beings and animals. Additional in-depth studies are warranted to extend the results to other viruses and to explore the potential implementation of this technology in different environmental conditions.

Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation.

Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels. Unsupervised domain adaptation (UDA) studies the problem of transferring models trained on one labeled source domain to another unlabeled target domain. In this article, we focus on UDA in visual emotion analysis for both emotion distribution learning and dominant emotion classification. Specifically, we design a novel end-to-end cycle-consistent adversarial model, called CycleEmotionGAN++. First, we generate an adapted domain to align the source and target domains on the pixel level by improving CycleGAN with a multiscale structured cycle-consistency loss. During the image translation, we propose a dynamic emotional semantic consistency loss to preserve the emotion labels of the source images. Second, we train a transferable task classifier on the adapted domain with feature-level alignment between the adapted and target domains. We conduct extensive UDA experiments on the Flickr-LDL and Twitter-LDL datasets for distribution learning and ArtPhoto and Flickr and Instagram datasets for emotion classification. The results demonstrate the significant improvements yielded by the proposed CycleEmotionGAN++ compared to state-of-the-art UDA approaches.

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation.

Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain. Unfortunately, direct transfer across domains often performs poorly due to the presence of domain shift or dataset bias. Domain adaptation (DA) is a machine learning paradigm that aims to learn a model from a source domain that can perform well on a different (but related) target domain. In this article, we review the latest single-source deep unsupervised DA methods focused on visual tasks and discuss new perspectives for future research. We begin with the definitions of different DA strategies and the descriptions of existing benchmark datasets. We then summarize and compare different categories of single-source unsupervised DA methods, including discrepancy-based methods, adversarial discriminative methods, adversarial generative methods, and self-supervision-based methods. Finally, we discuss future research directions with challenges and possible solutions.

Adaptive Body Area Networks Using Kinematics and Biosignals.

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.

A model-based approach for bridging virtual and physical sensor nodes in a hybrid simulation framework.

The Model Based Design (MBD) approach is a popular trend to speed up application development of embedded systems, which uses high-level abstractions to capture functional requirements in an executable manner, and which automates implementation code generation. Wireless Sensor Networks (WSNs) are an emerging very promising application area for embedded systems. However, there is a lack of tools in this area, which would allow an application developer to model a WSN application by using high level abstractions, simulate it mapped to a multi-node scenario for functional analysis, and finally use the refined model to automatically generate code for different WSN platforms. Motivated by this idea, in this paper we present a hybrid simulation framework that not only follows the MBD approach for WSN application development, but also interconnects a simulated sub-network with a physical sub-network and then allows one to co-simulate them, which is also known as Hardware-In-the-Loop (HIL) simulation.