Facile Deposition of Silver Nanoparticles on Photonic Cellulose Nanocrystals Films: A Study on Solvent Stability and Post Antibacterial Activity

Cellulose nanocrystals (CNCs) incorporated with silver nanoparticles (AgNPs) photonic films have drawn considerable attention due to their plasmonic chiroptical activity. However, the exploitation of some fundamental properties for practical use such as the affinity analysis of metal nanoparticles attached to the surface of photonic films according to the solvent compatibility and antibacterial activity under physical conditions has yet not been studied. Hence, a facile process of in situ deposition of AgNPs into the chiral structure of CNC films is proposed. CNC photonic films, cross-linked by glutaraldehyde are prepared. This interaction generated the solvents-stable photonic film with a considerable amount of unreacted aldehyde functional groups that facilitates the reduction of Ag salt to AgNPs. The formed AgNPs in the photonic films show excellent stability over immersion in various polar and non-polar solvents. The post-solvent treated photonic films display excellent contact-based antibacterial behavior against gram-negative Escherichia coli.     

Deep learning approaches for speech emotion recognition: state of the art and research challenges

Speech emotion recognition (SER) systems identify emotions from the human voice in the areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and human-machine interaction. In the classical SER process, discriminative acoustic feature extraction is the most important and challenging step because discriminative features influence the classifier performance and decrease the computational time. Nonetheless, current handcrafted acoustic features suffer from limited capability and accuracy in constructing a SER system for real-time implementation. Therefore, to overcome the limitations of handcrafted features, in recent years, variety of deep learning techniques have been proposed and employed for automatic feature extraction in the field of emotion prediction from speech signals. However, to the best of our knowledge, there is no in-depth review study is available that critically appraises and summarizes the existing deep learning techniques with their strengths and weaknesses for SER. Hence, this study aims to present a comprehensive review of deep learning techniques, uniqueness, benefits and their limitations for SER. Moreover, this review study also presents speech processing techniques, performance measures and publicly available emotional speech databases. Furthermore, this review also discusses the significance of the findings of the primary studies. Finally, it also presents open research issues and challenges that need significant research efforts and enhancements in the field of SER systems.

     

Numerical Solution of Boussinesq Equations to Simulate Dam-Break Flows

To investigate the effect of nonhydrostatic pressure distribution, dam-break flows are simulated by numerically solving the one-dimensional Boussinesq equations by using a fourth-order explicit finite-difference scheme. The computed water surface profiles for different depth ratios have undulations near the bore front for depth ratios greater than 0.4. The results obtained by using the Saint Venant equations and the Boussinesq equations are compared to determine the contribution of individual Boussinesq terms in the simulation of dam-break flow. It is found that, for typical engineering applications, the Saint Venant equations give sufficiently accurate results for the maximum flow depth and the time to reach this value at a location downstream of the dam.     

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence,Severity, and Treatment Response

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.     

Phase diagrams for oil/methanol/ether mixtures

One-phase transmethylations of vegetable oils with methanol to form methyl esters occur considerably faster than conventional two-phase reactions. Addition of simple ethers is an efficient method for producing a single phase. Ternary phase diagrams have been determined at 23°C for oil/methanol/ether mixtures; these are useful when applying the one-phase method across a wide range of conditions. Soybean, canola, palm, and coconut oils were used in combination with five ethers, namely, tetrahydrofuran (THF), 1,4-dioxane (DO), diethyl ether (DE), diisopropyl ether (DI), andtert-butyl methyl ether (TBM). All five ethers can produce miscibility for all methanol/oil compositions. The ether/methanol volumetric ratios required for miscibility at a methanol/soybean or canola oil volumetric ratio of 0.20 (5.4 molar ratio) at 23°C are: THF, 1.15; DO, 1.60; DE, 1.38 DI, 1.57; and TBM, 1.57. For THF, this results in one-phase mixtures that contain 65 vol% oil. Soybean and canola oil form identical diagrams. Palm oil requires slightly less ether at the lower methanol concentrations, but coconut oil requires considerably less across the whole concentration range. Acid-catalyzed reactions, when performed at the boiling point of the most volatile component, require less ether than predicted from the diagrams.     

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.     

Diphenylamine‐Substituted Cruciform Oligo(phenylene vinylene): Enhanced One‐ and Two‐Photon Excited Fluorescence in the Solid State

1,4‐di(4′‐N,N‐diphenylaminostyryl)benzene (DPA‐DSB) is a well known compound with a large two‐photon absorption (TPA) section and strong fluorescence in solution. However, the ease with which it crystallizes results in the formation of discontinuous crystalline phases during vacuum deposition processes, thereby greatly limiting its applicability in solid‐state devices. A cruciform dimer of DPA‐DSB, 2,5,2′,5′‐tetra(4′‐N,N‐diphenylaminostyryl)biphenyl (DPA‐TSB) is reported, wherein two DPA‐DSB molecules are linked through a central biphenyl bond. The DPA‐TSB molecules take on a cruciform configuration because of the steric crowding around the central biphenyl core, which has the effect of efficiently suppressing crystalline and intermolecular interactions. The neat DPA‐TSB solid shows strong green–blue fluorescence because of both steady‐state absorption as well as TPA. The DPA‐TSB solid exhibits a photoluminescence (PL) efficiency (η_solid) of 29 % and a solid‐state two‐photon action cross section (δη_solid) of 954 GM (1 GM = 1 × 10^–50 cm⁴ s photon^–1 molecule^–1), which is much greater than for the model compound DPA‐DSB (η_solid = 16 % and δη_solid = 150 GM, where δ is the TPA cross section and η is the fluorescence quantum yield). Based on its high PL efficiency, good film‐forming ability, and strong TPA, DPA‐TSB seems to be a good candidate for applications in solid‐state optical devices.     

A Skin‐Inspired Substrate with Spaghetti‐Like Multi‐Nanofiber Network of Stiff and Elastic Components for Stretchable Electronics

Mimicking the skin's non‐linear self‐limiting mechanical characteristics is of great interest. Skin is soft at low strain but becomes stiff at high strain and thereby can protect human tissues and organs from high mechanical loads. Herein, the design of a skin‐inspired substrate is reported based on a spaghetti‐like multi‐nanofiber network (SMNN) of elastic polyurethane (PU) nanofibers (NFs) sandwiched between stiff poly(vinyldenefluoride‐co‐trifluoroethylene) (P(VDF‐TrFE)) NFs layers embedded in polydimethylsiloxane elastomer. The elastic moduli of the stretchable skin‐inspired substrate can be tuned in a range that matches well with the mechanical properties of skins by adjusting the loading ratios of the two NFs. Confocal imaging under stretching indicates that PU NFs help maintain the stretchability while adding stiff P(VDF‐TrFE) NFs to control the self‐limiting characteristics. Interestingly, the Au layer on the substrate indicates a negligible change in the resistance under cyclic (up to 7000 cycles at 35% strain) and dynamic stretching (up to 35% strain), which indicates the effective absorption of stress by the SMNN. A stretchable chemoresistive gas sensor on the skin‐inspired substrate also demonstrates a reasonable stability in NO₂ sensing response under strain up to 30%. The skin‐inspired substrate with SMNN provides a step toward ultrathin stretchable electronics.     

Parallel query execution over encrypted data in database-as-a-service (DaaS)

The Journal of Supercomputing - The main challenge in database-as-a-service is the security and privacy of data because service providers are not usually considered as trustworthy. So, the data...