On optimal cloaking-by-mapping transformations

A central ingredient of cloaking-by-mapping is the diffeomorphisn which transforms an annulus with a small hole into an annulus with a finite size hole, while being the identity on the outer boundary of the annulus. The resulting meta-material is anisotropic, which makes it difficult to manufacture. The problem of minimizing anisotropy among radial transformations has been studied in [4]. In this work, as in [4], we formulate the problem of minimizing anisotropy as an energy minimization problem. Our main goal is to provide strong evidence for the conjecture that for cloaks with circular boundaries, non-radial transformations do not lead to lower degree of anisotropy. In the final section, we consider cloaks with non-circular boundaries and show that in this case, non-radial cloaks may be advantageous, when it comes to minimizing anisotropy.

In silico assessment of chronic toxicity of a combination drug namely ‘Olmesartan medoxomil and Hydrochlorothiazide’, marketed in Bangladesh

Background Nowadays combination therapy has become more popular due to their additional effect, synergistic effect and antagonistic effect. Any of these can influence the treatment profile. Combination therapy is used to treat some chronic diseases like diabetes, hypertension, cancer etc. But recently India has banned some fixed dose drug combinations due to their increased chances of adverse drug effects and drug interactions. So it is the time to take a look on the present drug combinations available in Bangladesh. An in silico study may provide important information about their probable toxicities. Drugs available in the combination may deposit slowly in the body and may lead to toxicities. Here an antihypertensive drug combination ‘Olmesartan medoxomil and Hydrochlorothiazide’ had been studied. Results Olmesartan medoxomil and Hydrochlorothiazide have not been found to comply any similar protein to interact with each other, thus no possible chance of additional toxicity of the combination in case of long term use. Conclusions At first, using PubChem the ligand was searched for a canonical SMILE. By inputting the canonical SMILE in Protox, a basic information about toxicities was predicted. From Swiss Target Prediction, target proteins responsible for both efficacy and toxicity were identified. These protein structures were downloaded from Protein Data Bank and edited with Flare. Undesired amino acid, ligand–ligand complex, fatty acid, and water molecules were removed by PyMOL. Structurally modified proteins and ligands were inputted in Swiss PDB viewer for energy minimization. Energy minimization is a very important step because unfavorable bond length, bond strength and torsion angle between protein and ligand may interfere with docking procedure. Then docking between Olmesartan medoxomil (ligand) and the proteins responsible for efficacy and toxicity was performed by PyRx. Vina binding affinity provided the value of binding strength between the ligand and the proteins, which determines how strong the bond is. The more negative the vina binding affinity, the stronger the bond. Discovery studio software was used to visualize the docking complexes. Same steps were followed for Hydrochlorothiazide to identify proteins responsible for desired and undesired effects, but no toxic effect was found from protox.

Active Inference Through Energy Minimization in Multimodal Affective Human–Robot Interaction

During communication, humans express their emotional states using various modalities (e.g., facial expressions and gestures), and they estimate the emotional states of others by paying attention to multimodal signals. To ensure that a communication robot with limited resources can pay attention to such multimodal signals, the main challenge involves selecting the most effective modalities among those expressed. In this study, we propose an active perception method that involves selecting the most informative modalities using a criterion based on energy minimization. This energy-based model can learn the probability of the network state using energy values, whereby a lower energy value represents a higher probability of the state. A multimodal deep belief network, which is an energy-based model, was employed to represent the relationships between the emotional states and multimodal sensory signals. Compared to other active perception methods, the proposed approach demonstrated improved accuracy using limited information in several contexts associated with affective human–robot interaction. We present the differences and advantages of our method compared to other methods through mathematical formulations using, for example, information gain as a criterion. Further, we evaluate performance of our method, as pertains to active inference, which is based on the free energy principle. Consequently, we establish that our method demonstrated superior performance in tasks associated with mutually correlated multimodal information.

Thermochemical Equilibrium Modeling Indicates That Hg Minerals Are Unlikely to Be the Source of the Emissivity Signal on the Highlands of Venus

Several of the highlands of Venus exhibit unexpectedly low radar emissivity compared to that of the lowlands. The source has been hypothesized to be a mineral with a high dielectric constant. Recently HgTe (coloradoite) has been suggested to explain the low emissivity signal; however, little research has been completed to verify its stability on Venus. In this project, we used a Gibbs free energy minimization software to investigate whether HgTe, as well as HgS and HgSe, can form at simulated highland conditions. According to our calculations, approximately 1.3 wt% of mercury in the crust needs to be outgassed in order for HgS to be stable at 4 km in altitude. In addition, approximately 250 ppb of tellurium in the crust needs to be outgassed for HgTe to precipitate at the same altitude. The required mercury abundance for HgSe to be stable at this altitude is less, approximately 0.6 wt%; however, this is significantly larger than the 10–90 ppb generally present in basaltic rocks on Earth. Therefore, Hg-bearing minerals are likely not the source of the low radar emissivity signal.