Polylactide‐perylene derivative for blue biodegradable organic light‐emitting diodes

In this work we demonstrate, for the first time, the use of polylactic acid (PLA) as a biodegradable host matrix for the construction of the active emissive layer of organic light‐emitting diode (OLED) devices for potential use in bioelectronics. In this preliminary study, we report a robust synthesis of two fluorescent PLA derivatives, pyrene‐PLA ( AH10 ) and perylene‐PLA ( AH11 ). These materials were prepared by the ring opening polymerisation of l ‐lactide with hydroxyalkyl‐pyrene and hydroxyalkyl‐perylene derivatives using 1,8‐diazabicyclo[5.4.0]undec‐7‐ene as catalyst. OLEDs were fabricated from these materials using a simple device architecture involving a solution‐processed single‐emitting layer in the configuration ITO/PEDOT:PSS/PVK:OXD‐7 (35%): AH10 or AH11 (20%)/TPBi/LiF/Al (ITO, indium tin oxide; PEDOT:PSS, poly(3,4‐ethylenedioxythiophene) doped with poly(styrenesulfonic acid); PVK, poly(vinylcarbazole); OXD‐7, (1,3‐phenylene)‐bis‐[5‐(4‐tert‐butylphenyl)‐1,3,4‐oxadiazole]; TPBi, 2,2′,2″‐(1,3,5‐benzenetriyl)tris(1‐phenyl‐1H‐benzimidazole)). The turn‐on voltage for the perylene OLED at 10 cd m^–2 was around 6 V with a maximum brightness of 1200 cd m^–2 at 13 V. The corresponding external quantum efficiency and device current efficiency were 1.5% and 2.8 cd A^–1 respectively. In summary, this study provides proof of principle that OLEDs can be constructed from PLA, a readily available and renewable bio‐source. © 2020 The Authors. Polymer International published by John Wiley & Sons Ltd on behalf of Society of Industrial Chemistry.     

Crystal Structure and Physicochemical Properties of Two Supramolecular Compounds: (C4H8NH2)4[Mo8O26] and (NH4)Na2[AsIIIMo6O21(O2CCH2NH3)3]·8H2O

Two inorganic–organic hybrid supramolecular compounds based on polyoxometalates formulated as (C₄H₈NH₂)₄[Mo₈O₂₆] (1) and (NH₄)Na₂[As^IIIMo₆O₂₁(O₂CCH₂NH₃)₃]·8H₂O (2) have been synthesized by conventional solution method and characterized by infrared, UV–Vis and single-crystal X-ray diffraction analyses. Thermal analysis was performed to study their thermal stability. The atomic arrangement in compound (1) can be described as inorganic layers built by [Mo₈O₂₆]^4?, pyrrolidinium cations are embedded into layers. The fascinating structural feature of compound (2) is that the glycine molecules are bounded to two edge-sharing Mo centers via their carboxylate functionality leading to functionalized heteropolymolybdate [As^IIIMo₆O₂₁(O₂CCH₂NH₃)₃]^3?, extensive net hydrogen bonds between cations and anions contribute to the crystal packing. The electrochemical behavior of compound (2) has been studied.     

Synthesis and characterisation of fluorescent pyrene‐end‐capped polylactide fibres

Fluorescent markers are critical for tracking the position and movement of molecules both in vivo and in vitro. Conventionally, synthetic dyes are non‐covalently added to polymers for fluorescent tracking, but often diffuse away. Here we demonstrate, for the first time, a facile method for the synthesis of fluorescent poly(lactic acid) nano‐/microfibres for biomedical applications using solution spin blowing. Pyrene‐end‐capped poly(l ‐lactide) (PLLA) derivatives were synthesised using the ring‐opening polymerisation of l ‐lactide and they were characterised using spectroscopic and thermal analyses. Submicrometre‐sized fluorescent fibres were produced from these PLLA derivatives using solution blow spinning techniques generating polymer blends and core–shell fibres. Such system could be further exploited to incorporate electrically conductive carbon allotropes via the pyrene aromaticity, producing fluorescent and electrically active fibres for in vitro monitoring and electrical stimulation. © 2018 Society of Chemical Industry     

Neuroadaptive output feedback control for nonlinear nonnegative dynamical systems with actuator amplitude and integral constraints

A neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the control amplitude as well as the integral of the control input over a given time interval are constrained, and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and an integral drug dosing constraint over a specified time period. Copyright © 2010 John Wiley & Sons, Ltd.     

Two-tier network anomaly detection model: a machine learning approach

Network anomaly detection is one of the most challenging fields in cyber security. Most of the proposed techniques have high computation complexity or based on heuristic approaches. This paper proposes a novel two-tier classification models based on machine learning approaches Naïve Bayes, certainty factor voting version of KNN classifiers and also Linear Discriminant Analysis for dimension reduction. Experimental results show a desirable and promising gain in detection rate and false alarm compared with other existing models. The model also trained by two generated balance training sets using SMOTE method to evaluate the chosen similarity measure for dealing with imbalanced network anomaly data sets. The two-tier model provides low computation time due to optimal dimension reduction and feature selection, as well as good detection rate against rare and complex attack types which are so dangerous because of their close similarity to normal behaviors like User to Root and Remote to Local. All evaluation processes experimented by NSL-KDD data set.     

A novel augmented reality visualization in jaw surgery: enhanced ICP based modified rotation invariant and modified correntropy

Image registration, accuracy, processing time and occlusions are the main limitations of augmented reality (AR) based jaw surgery. Therefore, the main aim of this paper is to reduce the registration error, which will help in improving the accuracy and reducing the processing time. Also, it aims to remove outliers and remove the registration outcomes trapped in local minima to improve the alignment problems and remove the occlusion caused by surgery instrument. The enhanced Iterative Closest Point (ICP) algorithm with rotation invariant and correntropy was used for the proposed system. Markerless image registration technique was used for AR-based jaw surgery. The problem of occlusion caused by surgical tools and blood is solved by using stereo based tracing with occlusion handling techniques. This research reduced alignment error 0.59 mm?~?0.62 mm against 0.69?~?0.72 mm of state-of-the-art solution. The processing time of video frames was enhanced to 11.9?~?12.8 fps against 8?~?9.15 fps in state-of-the-art solution. This paper is focused on providing fast and accurate AR-based system for jaw surgery. The proposed system helps in improving the AR visualization during jaw surgery. The combination of methods and technology helped in improving AR visualization for jaw surgery and to overcome the failure caused by a large rotation angle and provides an initial parameter for better image registration. It also enhances performance by removing outliers and noises. The pose refinement stage provides a better result in terms of processing time and accuracy.

     

A novel secure solution of using mixed reality in data transmission for bowel and jaw surgical telepresence: enhanced rivest cipher RC6 block cipher

Security threats are crucial challenges that deter Mixed reality (MR) communication in medical telepresence. This research aims to improve the security by reducing the chances of types of various attacks occurring during the real-time data transmission in surgical telepresence as well as reduce the time of the cryptographic algorithm and keep the quality of the media used. The proposed model consists of an enhanced RC6 algorithm in combination. Dynamic keys are generated from the RC6 algorithm mixed with RC4 to create dynamic S-box and permutation table, preventing various known attacks during the real-time data transmission. For every next session, a new key is created, avoiding possible reuse of the same key from the attacker. The results obtained from our proposed system are showing better performance compared to the state of art. The resistance to the tested attacks is measured throughout the entropy, Pick to Signal Noise Ratio (PSNR) is decreased for the encrypted image than the state of art, structural similarity index (SSIM) closer to zero. The execution time of the algorithm is decreased for an average of 20%. The proposed system is focusing on preventing the brute force attack occurred during the surgical telepresence data transmission. The paper proposes a framework that enhances the security related to data transmission during surgeries with acceptable performance.

     

Quasi-Monte Carlo methods for Markov chains with continuous multi-dimensional state space

We describe a quasi-Monte Carlo method for the simulation of discrete time Markov chains with continuous multi-dimensional state space. The method simulates copies of the chain in parallel. At each step the copies are reordered according to their successive coordinates. We prove the convergence of the method when the number of copies increases. We illustrate the method with numerical examples where the simulation accuracy is improved by large factors compared with Monte Carlo simulation.     

A statistical framework for few-shot action recognition

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition remains crucial. This work addresses the challenge of classifying short video clips, using a domain-specific feature design approach, capable of performing significantly well using as little as one training example per action. The method is based on Gunner Farneback’s dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate representations of the human movements/actions by clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each test video to the trained action with the highest similarity (a.k.a lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets using One-Shot and K-Shot learning approaches, and the results reveal the discriminative nature of our proposed methodology in comparison with state-of-the-art techniques.