Quantum Entanglement and Cryptography for Automation and Control of Dynamic Systems

This paper addresses the application of quantum entanglement and cryptography for automation and control of dynamic systems.A dynamic system is a system where t...     

Prolonged drug release using PCL–TMZ nanofibers induce the apoptotic behavior of U87 glioma cells

In situ prolonged delivery of drugs at the site of tumor can be satisfactorily accelerated patient recovery. We compared the effect of temozolomide while incorporated by polycaprolactone nanofibers on the apoptotic behavior of U87 glioma cells. After biocompatibility evaluation of nanofibers by scanning electron microscope and 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide analysis, the apoptosis of U87 cells was evaluated using p53, Bcl2 and Bax genes expression. It was found that nanofiber-temozolomide group showed a greater ability to induce apoptosis as well as have a significantly diminished initial burst release of drug compared with other groups and have promising potential in treating cancer.     

A systematic study on the effects of synthesis conditions of polyamide selective layer on the CO2/N2 separation of thin film composite polyamide membranes

Different top layer fabrication methods (amine-first, acid-first, spin coating), organic phase solvents (hexane, heptane, mixed hexane/heptane), acid acceptors (triethylamine, sodium carbonate, sodium hydroxide), and surfactant sodium dodecyl sulfate concentrations (0, 0.05, and 0.1 wt%) were utilized to fabricate thin film composite polyamide membranes for CO₂/N₂ separation. The results, according to an L9 orthogonal array of Taguchi approach, showed that employing acid-first method increases both CO₂ permeance and CO₂/N₂ selectivity of the membranes at a feed gas pressure of 3 bars. On the other hand, sodium hydroxide, and triethylamine should be used, as acid acceptors, to maximize CO₂ permeance and CO₂/N₂ selectivity, respectively. Moreover, the use of hexane solvent and 0 wt% surfactant led to maximum permeance, while, hexane solvent and 0.1 wt% surfactant were needed to reach the highest selectivity. The above level setting of synthesis parameters also resulted in the minimum sensitivity of the fabrication process to the noise factors effects. As shown by the analysis of variance, acid acceptor, and organic solvent types were the most influential parameters on CO₂ permeance and CO₂/N₂ selectivity, respectively. The effects of fabrication method and surfactant concentration, as single factors, on permeation/selectivity responses were also investigated.     

Space Efficient Quantization for Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have shown outstanding performance in the fields of computer vision, natural language processing, and complex system analysis. With the improvement of performance with deeper layers, DCNNs incur higher computational complexity and larger storage requirement, making it extremely difficult to deploy DCNNs on resource-limited embedded systems (such as mobile devices or Internet of Things devices). Network quantization efficiently reduces storage space required by DCNNs. However, the performance of DCNNs often drops rapidly as the quantization bit reduces. In this article, we propose a space efficient quantization scheme which uses eight or less bits to represent the original 32-bit weights. We adopt singular value decomposition (SVD) method to decrease the parameter size of fully-connected layers for further compression. Additionally, we propose a weight clipping method based on dynamic boundary to improve the performance when using lower precision. Experimental results demonstrate that our approach can achieve up to approximately 14x compression while preserving almost the same accuracy compared with the full-precision models. The proposed weight clipping method can also significantly improve the performance of DCNNs when lower precision is required.

     

Design of a lightweight and anonymous authenticated key agreement protocol for wireless body area networks

Wireless body area networks (WBANs) are a network designed to gather critical information about the physical conditions of patients and to exchange this information. WBANs are prone to attacks, more than other networks, because of their mobility and the public channel they use. Therefore, mutual authentication and privacy protection are critical for WBANs to prevent attackers from accessing confidential information of patients and executing undetectable physical attacks. In addition, in the authentication and key agreement process, messages should be transferred anonymously such that they are not linkable. In this paper, we first indicate that one of the most recently introduced authentication protocol is vulnerable to the wrong session key agreement attack and desynchronization attack. Second, we propose a lightweight authentication and key agreement protocol, which can withstand the well‐known attacks and provide the anonymity feature. Eventually, we analyze the security of our proposed protocol using both Automated Validation of Internet Security Protocols and Applications (AVISPA) and random oracle model and compare its performance with the related works. The results demonstrate the superiority of our proposed protocol in comparison with the other protocols.     

MXene Composite and Coaxial Fibers with High Stretchability and Conductivity for Wearable Strain Sensing Textiles

The integration of nanomaterials with high conductivity into stretchable polymer fibers can achieve novel functionalities such as sensing physical deformations. With a metallic conductivity that exceeds other solution‐processed nanomaterials, 2D titanium carbide MXene is an attractive material to produce conducting and stretchable fibers. Here, a scalable wet‐spinning technique is used to produce Ti₃C₂T_x MXene/polyurethane (PU) composite fibers that show both conductivity and high stretchability. The conductivity at a very low percolation threshold of ≈1 wt% is demonstrated, which is lower than the previously reported values for MXene‐based polymer composites. When used as a strain sensor, the MXene/PU composite fibers show a high gauge factor of ≈12900 (≈238 at 50% strain) and a large sensing strain of ≈152%. The cyclic strain sensing performance is further improved by producing fibers with MXene/PU sheath and pure PU core using a coaxial wet‐spinning process. Using a commercial‐scale knitting machine, MXene/PU fibers are knitted into a one‐piece elbow sleeve, which can track various movements of the wearer's elbow. This study establishes fundamental insights into the behavior of MXene in elastomeric composites and presents strategies to achieve MXene‐based fibers and textiles with strain sensing properties suitable for applications in health, sports, and entertainment.     

Highly Conductive Ti3C2Tx MXene Hybrid Fibers for Flexible and Elastic Fiber‐Shaped Supercapacitors

Fiber‐shaped supercapacitors (FSCs) are promising energy storage solutions for powering miniaturized or wearable electronics. However, the scalable fabrication of fiber electrodes with high electrical conductivity and excellent energy storage performance for use in FSCs remains a challenge. Here, an easily scalable one‐step wet‐spinning approach is reported to fabricate highly conductive fibers using hybrid formulations of Ti₃C₂T_x MXene nanosheets and poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate. This approach produces fibers with a record conductivity of ≈1489 S cm^?1, which is about five times higher than other reported Ti₃C₂T_x MXene‐based fibers (up to ≈290 S cm^?1). The hybrid fiber at ≈70 wt% MXene shows a high volumetric capacitance (≈614.5 F cm^?3 at 5 mV s^?1) and an excellent rate performance (≈375.2 F cm^?3 at 1000 mV s^?1). When assembled into a free‐standing FSC, the energy and power densities of the device reach ≈7.13 Wh cm^?3 and ≈8249 mW cm^?3, respectively. The excellent strength and flexibility of the hybrid fibers allow them to be wrapped on a silicone elastomer fiber to achieve an elastic FSC with 96% capacitance retention when cyclically stretched to 100% strain. This work demonstrates the potential of MXene‐based fiber electrodes and their scalable production for fiber‐based energy storage applications.     

3D Semantic Deep Learning Networks for Leukemia Detection

White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body’s immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model. The segmented images are used in Phase III, in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs. The proposed methodology is validated on three publically available benchmark datasets, namely ALL-IDB1, ALL-IDB2, and LISC, in terms of different metrics, such as precision, accuracy, sensitivity, and dice scores. The results of the proposed method are comparable to those of recent existing methodologies, thus proving its effectiveness.     

Viscoplastic fluid flow in irregular eccentric annuli due to axial motion of the inner pipe

Fully developed axial laminar flow of viscoplastic Herschel-Bulkley fluids in eccentric annuli between two pipes has been investigated numerically. The pipes are closed at one end and flow is due to the axial motion of the inner pipe. The annuli may be filly open or partially blocked. General non-orthogonal, boundary-fitted curvilinear coordinates have been used to accurately model the irregular annular geometry due to the presence of a flow blockage. A computer code has been developed using a second-order finite-difference scheme. An exponential model for the shear stress, valid for both yielded and unyielded regions of the flow, is used in the computation. The effects of generalized Bingham number, flow behavior index, eccentricity, and blockage height on the pressure gradient or the surge pressure have been studied and the results are presented in dimensionless form. The pressure gradient is found to decrease with increasing eccentricity. For a partially blocked eccentric annulus the pressure gradient is found to decrease with an increase in the blockage height.