Design and characterization of PNVCL-based nanofibers and evaluation of their potential applications as scaffolds for surface drug delivery of hydrophobic drugs

In this work, nanofiber scaffolds for surface drug delivery applications were obtained by electrospinning poly(N-vinylcaprolactam) (PNVCL) and its blends with poly(ε-caprolactone) and poly(N-vinylcaprolactam)-b-poly(ε-caprolactone). The process parameters to obtain smooth and beadless PNVCL fibers were optimized. The average fibers diameter was less than 1 μm, and it was determined by scanning electron microscopy analyses. Their affinity toward water was evaluated by measuring the contact angle with water. The ketoprofen release behavior from the fibers was analyzed using independent and model-dependent approaches. The low values of the release exponent (n < 0.5) obtained for 20 and 42 °C, indicating a Fickian diffusion mechanism for all formulations. Dissolution efficiencies (DEs) revealed the effect of polymer composition, methodology used in the electrospinning process, and temperature on the release rate of ketoprofen. PNVCL/poly(N-vinylcaprolactam)-b-poly(ε-caprolactone)-based nanofibers showed greater ability to control the in vitro release of ketoprofen, in view of reduced kinetic constant and DE, making this material promising system for controlling release of hydrophobic drugs. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48472.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Analysis of e-business models in real estate

We use Canvas Application Framework to analyze e-business models of Realestate.yahoo.com, Yahoo.com, Redfin.com, Realtor.com, Trulia.com, Zillow.com, and Craigslist.com. We investigate the similarities and differences among e-business models and how e-business model differentiation affects company success. We looked at which business model components, such as distribution channels, business partnerships, attracting customers, revenue streams, etc., are important. We further investigate whether the web-advertising, brokerage, virtual value-chain, or the diversified internet business model is used by each company, and which ones are more successful. We discover similarities and differences among e-business models. Differences include the use of revenue streams for generating revenue, the extent of partnerships, and the role they play in a company’s success.     

Polyvinylidene fluoride nanofibers obtained by electrospinning and blowspinning: Electrospinning enhances the piezoelectric β-phase – myth or reality?

Considerable effort has been devoted to improving the properties of PVDF (polyvinylidene fluoride), arguably the most technologically important piezoelectric polymer. Electrospinning has been found to be a particularly effective method of producing PVDF nanofibers with superior piezoelectric properties due to the resulting exceptionally high fraction of the piezoelectrically active crystalline β-phase. It is typically assumed that the high external electric fields applied during electrospinning enhance the formation of this β-phase, with the confused literature offering various unsatisfactory mechanistic explanations. However, by comparing PVDF nanofibers produced by two different processes (electrospinning and blowspinning), we show that the electric field is entirely unnecessary; indeed, the crystallization dynamics are principally driven by the applied mechanical stress, as evidenced by structurally identical 200 nm diameter PVDF fibers produced with and without external electric fields.     

Optimization of Quantum Cost for Low Energy Reversible Signed/Unsigned Multiplier Using Urdhva-Tiryakbhyam Sutra

One of the elementary operations in computing systems is multiplication. Therefore, high-speed and low-power multipliers design is mandatory for efficient computing systems. In designing low-energy dissipation circuits, reversible logic is more efficient than irreversible logic circuits but at the cost of higher complexity. This paper introduces an efficient signed/unsigned 4 × 4 reversible Vedic multiplier with minimum quantum cost. The Vedic multiplier is considered fast as it generates all partial product and their sum in one step. This paper proposes two reversible Vedic multipliers with optimized quantum cost and garbage output. First, the unsigned Vedic multiplier is designed based on the Urdhava Tiryakbhyam (UT) Sutra. This multiplier consists of bitwise multiplication and adder compressors. Compared with Vedic multipliers in the literature, the proposed design has a quantum cost of 111 with a reduction of 94% compared to the previous design. It has a garbage output of 30 with optimization of the best-compared design. Second, the proposed unsigned multiplier is expanded to allow the multiplication of signed numbers as well as unsigned numbers. Two signed Vedic multipliers are presented with the aim of obtaining more optimization in performance parameters. DesignI has separate binary two’s complement (B2C) and MUX circuits, while DesignII combines binary two’s complement and MUX circuits in one circuit. DesignI shows the lowest quantum cost, 231, regarding state-of-the-art. DesignII has a quantum cost of 199, reducing to 86.14% of DesignI. The functionality of the proposed multiplier is simulated and verified using XILINX ISE 14.2.     

Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.     

Rapid real-time generation of super-resolution hyperspectral images through compressive sensing and GPU

Recently, compressive sensing (CS) has offered a new framework whereby a signal can be recovered from a small number of noisy non-adaptive samples. This is now an active area of research in many image-processing applications, especially super-resolution. CS algorithms are widely known to be computationally expensive. This paper studies a real time super-resolution reconstruction method based on the compressive sampling matching pursuit (CoSaMP) algorithm for hyperspectral images. CoSaMP is an iterative compressive sensing method based on the orthogonal matching pursuit (OMP). Multi-spectral images record enormous volumes of data that are required in practical modern remote-sensing applications. A proposed implementation based on the graphical processing unit (GPU) has been developed for CoSaMP using computed unified device architecture (CUDA) and the cuBLAS library. The CoSaMP algorithm is divided into interdependent parts with respect to complexity and potential for parallelization. The proposed implementation is evaluated in terms of reconstruction error for different state-of-the-art super-resolution methods. Various experiments were conducted using real hyperspectral images collected by Earth Observing-1 (EO-1), and experimental results demonstrate the speeding up of the proposed GPU implementation and compare it to the sequential CPU implementation and state-of-the-art techniques. The speeding up of the GPU-based implementation is up to approximately 70 times faster than the corresponding optimized CPU.     

Using genetic algorithms for early schedulability analysis and stress testing in real-time systems

Reactive real-time systems have to react to external events within time constraints: Triggered tasks must execute within deadlines. It is therefore important for the designers of such systems to analyze the schedulability of tasks during the design process, as well as to test the system's response time to events in an effective manner once it is implemented. This article explores the use of genetic algorithms to provide automated support for both tasks. Our main objective is then to automate, based on the system task architecture, the derivation of test cases that maximize the chances of critical deadline misses within the system; we refer to this testing activity as stress testing. A second objective is to enable an early but realistic analysis of tasks' schedulability at design time. We have developed a specific solution based on genetic algorithms and implemented it in a tool. Case studies were run and results show that the tool (1) is effective at identifying test cases that will likely stress the system to such an extent that some tasks may miss deadlines, (2) can identify situations that were deemed to be schedulable based on standard schedulability analysis but that, nevertheless, exhibit deadline misses.