Graphene-based bipolar plates for polymer electrolyte membrane fuel cells

Bipolar plates (BPs) are a major component of polymer electrolyte membrane fuel cells (PEMFCs). BPs play a multifunctional character within a PEMFC stack. It is one of the most costly and critical part of the fuel cell, and hence the development of efficient and cost-effective BPs is of much interest for the fabrication of next-generation PEMFCs in future. Owing to high electrical conductivity and chemical inertness, graphene is an ideal candidate to be utilized in BPs. This paper reviews recent advances in the area of graphene-based BPs for PEMFC applications. Various aspects including the momentous functions of BPs in the PEMFC, favorable features of graphene-based BPs, performance evaluation of various reported BPs with their advantages and disadvantages, challenges at commercial level products and future prospects of frontier research in this direction are extensively documented.     

Fair end‐to‐end window‐based congestion control in time‐varying data communication networks

Communication networks are time varying and, hence, fair sharing of network resources among the users in such a dynamic environment is a challenging task. In this context, a time‐varying network model is designed, and the shortest user's route is found. In the designed network model, an end‐to‐end window‐based congestion control scheme is developed with the help of internal nodes or router, and the end user can get implicit feedback (throughput). This scheme is considered as fair if the allocation of resources among users minimizes overall congestion or backlog in the networks. Window update approach is based on a multi‐class fluid model and is updated dynamically by considering delays (communication, propagation, and queuing) and the backlog of packets in the user's routes. Convergence and stability of the window size are obtained using a Lyapunov function. A comparative study with other window‐based methods is also provided.     

Allosteric Binding Sites of Aβ Peptides on the Acetylcholine Synthesizing Enzyme ChAT as Deduced by In Silico Molecular Modeling

The native function of amyloid-β (Aβ) peptides is still unexplored. However, several recent reports suggest a prominent role of Aβ peptides in acetylcholine homeostasis. To clarify this role of Aβ, we have reported that Aβ peptides at physiological concentrations can directly enhance the catalytic efficiency of the key cholinergic enzyme, choline acetyltransferase (ChAT), via an allosteric interaction. In the current study, we further aimed to elucidate the underlying ChAT-Aβ interaction mechanism using in silico molecular docking and dynamics analysis. Docking analysis suggested two most probable binding clusters on ChAT for Aβ₄₀ and three for Aβ₄₂. Most importantly, the docking results were challenged with molecular dynamic studies of 100 ns long simulation in triplicates (100 ns × 3 = 300 ns) and were analyzed for RMSD, RMSF, RoG, H-bond number and distance, SASA, and secondary structure assessment performed together with principal component analysis and the free-energy landscape diagram, which indicated that the ChAT-Aβ complex system was stable throughout the simulation time period with no abrupt motion during the evolution of the simulation across the triplicates, which also validated the robustness of the simulation study. Finally, the free-energy landscape analysis confirmed the docking results and demonstrated that the ChAT-Aβ complexes were energetically stable despite the unstructured nature of C- and N-terminals in Aβ peptides. Overall, this study supports the reported in vitro findings that Aβ peptides, particularly Aβ₄₂, act as endogenous ChAT-Potentiating-Ligand (CPL), and thereby supports the hypothesis that one of the native biological functions of Aβ peptides is the regulation of acetylcholine homeostasis.     

Gray-scale image watermarking using GA-BPN hybrid network

In this paper, a novel watermarking scheme is proposed by embedding a binary watermark into gray-scale images using a hybrid GA-BPN intelligent network. HVS characteristics of the images in DCT domain are used to obtain a sequence of weighting factor from a GA-BPN. This weighting factor is used to embed and extract the watermark from the image in DWT domain. The GA-BPN is trained by 27 inference rules that includes three input HVS parameters namely luminance sensitivity, edge sensitivity computed using threshold and contrast sensitivity computed using variance. The robustness of the embedding scheme is examined by executing seven different image processing attacks. Visual quality of signed images before and after the attacks is examined by PSNR. The extracted watermarks from signed and attacked images show a high degree of similarity with the embedded content. Overall, the algorithm is robust against selected attacks and is well optimized.     

Design,preparation, and evaluation of liposomal gel formulations for treatment of acne: in vitro and in vivo studies

The study highlights the significance of co-application of bioactive components into liposomal gel formulations and their comparison to azithromycin for treatment of Acne. A Design of Experiments (DoE) approach was utilized to obtain optimized liposomal formulation encapsulating curcumin, with size and zeta potential of ～100?nm and ～14?mV, respectively, characterized by DLS, HR-TEM, FESEM, and AFM. The curcumin liposomal dispersion depicted excellent stability over the period of 60?days, which was further converted in gel form using Carbopol. Pharmacokinetics of curcumin-loaded liposomal gel showed that T_max for curcumin was achieved within 1?h of post application in both stratum corneum and skin, indicating quick penetration of nano-sized liposomes. Stratum corneum depicted C_max of 688.3?ng/mL and AUC_0-t of 5857.5?h?×?ng/mL, while the skin samples displayed C_max of 203.3?ng/gm and AUC_0-t of 2938.1?h?×?ng/gm. Lauric acid and azithromycin liposomal gel formulations were prepared as per the optimum parameters obtained by DoE. In antibacterial activity using agar diffusion assay, lauric acid gel formulation revealed ～1.5 fold improved antibacterial effect than curcumin gel formulation. Interestingly, their co-application (1:1) exhibited significantly enhanced antibacterial effect against both macrolide-sensitive (1.81 versus 1.25 folds) and resistant strains of P. acnes (2.93 versus 1.22 folds) than their individual counterparts. The in vivo studies in rat ear model displayed a ～2 fold reduction in comedones count and cytokines (TNF-α and IL-1β) on co-application with curcumin and lauric acid liposomal gel compared to placebo treated group.     

Markov logic networks for adverse drug event extraction from text

Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work, we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.     

Performance of two different quantum annealing correction codes

Quantum annealing is a promising approach for solving optimization problems, but like all other quantum information processing methods, it requires error correction to ensure scalability. In this work, we experimentally compare two quantum annealing correction (QAC) codes in the setting of antiferromagnetic chains, using two different quantum annealing processors. The lower-temperature processor gives rise to higher success probabilities. The two codes differ in a number of interesting and important ways, but both require four physical qubits per encoded qubit. We find significant performance differences, which we explain in terms of the effective energy boost provided by the respective redundantly encoded logical operators of the two codes. The code with the higher energy boost results in improved performance, at the expense of a lower-degree encoded graph. Therefore, we find that there exists an important trade-off between encoded connectivity and performance for quantum annealing correction codes.