A Polyallylamine Anchored Amine‐Rich Laser‐Ablated Graphene Platform for Facile and Highly Selective Electrochemical IgG Biomarker Detection

Current immunosensors have an insufficient number of binding sites for the recognition of biomolecules, which leads to false positive or negative results. In this research, a facile, cost‐effective, disposable, and highly selective electrochemical immunosensing platform is developed based on cationic polyelectrolyte polyallylamine (PAAMI) anchored laser‐ablated graphene (LAG). Here, for the first time, PAAMI is introduced to stabilize LAG flakes, while retaining the intrinsic thermal and electronic properties of the substrate by noncovalent π–π interaction and electrostatic physical absorption. The sensing platform offers a suitable number of anchoring sites for the immobilized antibodies by providing ? NH₂ functional groups. The proper grafting of PAAMI is confirmed through X‐ray photoelectron spectroscopy and Raman spectroscopy. The immunosensing platform is applied to detect immunoglobulin (IgG) biomarkers as a proof of concept. Under optimized conditions, the sensing platform exhibits a linear range of 0.012–15 and 15–352 ng mL^?1 with a limit of detection of 6 pg mL^?1 for IgG detection with high selectivity. Based on the analysis, the developed immunosensing platform can be used for point‐of‐care detection of IgG in clinical diagnostic centers. Furthermore, the developed strategy is well suited for the detection of other cancer biomarkers after immobilizing the relevant antibodies.     

The effect of combustion parameters on the nitric oxide emission in direct injection type diesel engine

This paper presents the investigation of influence factors on the output performance and the reduction of exhaust emission in the direct injection type diesel engine. In this work, the analysis of combustion products and combustion characteristics are investigated by numerical method and experiment under the various engine operating conditions. The combusion performance and exhaust emissions are analyzed in terms of the heat release, cylinder pressure and major exhaust emissions of engine. The accuracy of the prediction versus experimental data and the capability of the heat release, cylinder pressure and all the major exhaust emissions are demonstrated. The results of this study show that the combustion parameters have influence on the combustion processes and the nitric oxide emission in the direct injection type diesel engine. The nitric oxide concentration decreases with the increase of engine speed and the advance of injection timing.     

Software‐defined networking approach for enhanced evolved packet core network

The evolved packet core (EPC) network is the mobile network standardized by the 3rd Generation Partnership Project and represents the recent evolution of mobile networks providing high‐speed data rates and on‐demand connectivity services. Software‐defined networking (SDN) is recently gaining momentum in network research as a new generation networking technique. An SDN‐based EPC is expected to introduce gains to the EPC control plane architecture in terms of simplified, and perhaps even software‐based, vendor independent infrastructure nodes. In this paper, we propose a novel SDN‐based EPC architecture along with the protocol‐level detailed implementation and provide a mechanism for identifying information fields exchanged between SDN‐EPC entities that maintains correct functionality with minimal impact on the conventional design. Furthermore, we present the first comprehensive network performance evaluation for the SDN‐based EPC versus the conventional EPC and provide a comparative analysis of 2 networks performances identifying potential bottlenecks and performance issues. The evaluation focuses on 2 network control operations, namely, the S1‐handover and registration operations, taking into account several factors, and assessing performance metrics such as end‐to‐end delay (E2ED) for completion of the respective control operation, and EPC nodes utilization figures.     

A new algorithm of modified binary particle swarm optimization based on the Gustafson-Kessel for credit risk assessment

Neural Computing and Applications - To increase the quality of loans provision and reduce the risk involved in this process, several credit scoring models have been developed and utilized to...     

Fast and Ultraclean Approach for Measuring the Transport Properties of Carbon Nanotubes

In this work, a fast approach for the fabrication of hundreds of ultraclean field‐effect transistors (FETs) is introduced, using single‐walled carbon nanotubes (SWCNTs). The synthesis of the nanomaterial is performed by floating‐catalyst chemical vapor deposition, which is employed to fabricate high‐performance thin‐film transistors. Combined with palladium metal bottom contacts, the transport properties of individual SWCNTs are directly unveiled. The resulting SWCNT‐based FETs exhibit a mean field‐effect mobility, which is 3.3 times higher than that of high‐quality solution‐processed CNTs. This demonstrates that the hereby used SWCNTs are superior to comparable materials in terms of their transport properties. In particular, the on–off current ratios reach over 30 million. Thus, this method enables a fast, detailed, and reliable characterization of intrinsic properties of nanomaterials. The obtained ultraclean SWCNT‐based FETs shed light on further study of contamination‐free SWCNTs on various metal contacts and substrates.     

The Growing Impact of Micro/Nanomaterial‐Based Systems in Precision Oncology: Translating “Multiomics” Technologies

The field of precision oncology is rapidly progressing toward integrated “multiomics” analysis of multiple molecular species (such as DNA, RNA, or proteins) to provide a more complete profile of tumor heterogeneity. Micro/nanomaterial‐based systems, which leverage the unique properties of miniature materials, are currently well positioned to expand beyond rudimentary biomarker detection toward multiomics signature analysis. To enable clinical translation, the rational design and implementation of miniaturized systems should be driven by the unique clinical challenges present at various crucial cancer stages. This review features micro/nanomaterial‐based systems that are robustly tested on real patient samples for molecular biomarker detection at i) initial cancer screening and/or diagnosis, ii) cancer prognosis and risk stratification, and iii) longitudinal treatment/recurrence monitoring. Furthermore, this review discusses the use of micro/nanomaterials to facilitate sample preparation for different molecular biomarker species. Finally, this review deliberates on the recent paradigm shift of micro/nanomaterial‐based system innovation toward integrated multiomics cancer signature analysis and puts forth insights and perspectives on existing challenges. It is anticipated that this review could stimulate the propagation of new concepts and approaches to kick‐start a new generation of clinically translational technologies that capitalize on multiomics cancer signatures.     

Numerical solutions of systems of first-order,two-point BVPs based on the reproducing kernel algorithm

The aim of the present analysis is to implement a relatively recent computational algorithm, reproducing kernel Hilbert space, for obtaining the solutions of systems of first-order, two-point boundary value problems for ordinary differential equations. The reproducing kernel Hilbert space is constructed in which the initial–final conditions of the systems are satisfied. Whilst, three smooth kernel functions are used throughout the evolution of the algorithm in order to obtain the required grid points. An efficient construction is given to obtain the numerical solutions for the systems together with an existence proof of the exact solutions based upon the reproducing kernel theory. In this approach, computational results of some numerical examples are presented to illustrate the viability, simplicity, and applicability of the algorithm developed. Finally, the utilized results show that the present algorithm and simulated annealing provide a good scheduling methodology to such systems compared with other numerical methods.     

Recent trends in graphene materials synthesized by CVD with various carbon precursors

Graphene is a single layer of carbon atoms arranged in an sp²-hybridized structure with properties far superior compared to other materials. Research and development in graphene synthesis have been rapidly growing the past few years, especially using chemical vapor deposition (CVD) over various types of carbon precursor. The nature and the type of carbon precursor is one important parameter of growth by CVD, especially for graphene production, since they can dramatically impact graphene growth yield and rate. However, effects of the used carbon precursor on graphene growth mechanisms are rarely discussed. In the course of large-scale and low-cost graphene preparation, this review on the recent trends regarding the utilization of diverse carbon precursors used to synthesize graphene via the CVD method is of great interest for development of improved or alternative synthesis methods. The details and the mechanisms involved in graphene synthesis using carbon precursors in the form of gaseous, liquids and solids are compared, analyzed and discussed thoroughly. In this review, we present a thorough overview on the impact and mechanisms of carbon precursors in achieving high-quality graphene with competitive edge in the near future.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.     

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.