A Smart Colorimetric Platform for Detection of Methanol, Ethanol and Formic Acid

Carbon dioxide (CO2) is a greenhouse gas in the atmosphere and scientists are working on converting it to useful products, thereby reducing its quantity in the atmosphere. For converting CO2, different approaches are used, and among them, electrochemistry is found to be the most common and more efficient technique. Current methods for detecting the products of electrochemical CO2 conversion are time-consuming and complex. To combat this, a simple, cost-effective colorimetric method has been developed to detect methanol, ethanol, and formic acid, which are formed electrochemically from CO2. In the present work, the highly efficient sensitive dyes were successfully established to detect these three compounds under optimized conditions. These dyes demonstrated excellent selectivity and showed no cross-reaction with other products generated in the CO2 conversion system. In the analysis using these three compounds, this strategy shows good specificity and limit of detection (LOD, ~0.03–0.06 ppm). A cost-effective and sensitive Internet of Things (IoT) colorimetric sensor prototype was developed to implement these dyes systems for practical and real-time application. Employing the dyes as sensing elements, the prototype exhibits unique red, green, and blue (RGB) values upon exposure to test solutions with a short response time of 2 s. Detection of these compounds via this new approach has been proven effective by comparing them with nuclear magnetic resonance (NMR). This novel approach can replace heavy-duty instruments such as high-pressure liquid chromatography (HPLC), gas chromatography (G.C.), and NMR due to its extraordinary selectivity and rapidity.

A review on modelling and dynamic analysis of viscoelastic rotor systems

Purpose The purpose of this study is to bring down collective information about various issues encountered in modelling of rotor systems. Design/methodology/approach The most important and basic part of “rotor dynamics” is the study related to its different modelling techniques which further involves the analysis of shaft for understanding the system potential, competence and reliability. The issues addressed in this study are classified mainly into two parts: the initial part gives out a vast overview of significant problems as well as different techniques applied to encounter modelling of rotor systems, while the latter part of the study describes the post-processing problem that occurs while performing the dynamic analysis. Findings The review incorporates the most important research works that have already placed a benchmark right from the beginning as well as the recent works that are still being carried out to further produce better outcomes. The review concludes with the modal analysis of rotor shaft to show the importance of mathematical model through its dynamic behaviour. Originality/value A critical literature review on the modelling techniques of rotor shaft systems is provided from earliest to latest along with its real-time application in different research and industrial fields.

Nanomedicine and Healthcare Systems

The chapter presents an outlook on the recent techniques of developing nanoscale medicines. With advancement in technology, nanoscale therapeutics is slowly becoming the future of medicine and smart diagnostics. The combined activity of therapeutic agents with assistance of nanomaterials have proved effective in troubleshooting the issues concerned with the conventional therapeutic techniques. Despite of these benefits, improvement in certain issues like side effects and toxicity needs to be studied extensively before real-time application in biological systems. Thus, in this chapter, emphasis has been made on understanding the concept of a nanomaterial-based therapeutic system with recent advances and exploration of the characteristics of nanomaterials which would allow us to further develop strategies that are supportive towards effective treatment and disease diagnosis.

Knowledge-driven feature component interpretable network for motor imagery classification

Objective. End-to-end convolution neural network (CNN) has achieved great success in motor imagery classification without manual feature design. However, all the existing deep network solutions are purely data-driven and lack interpretability, which makes it impossible to discover insightful knowledge from the learnt features, not to mention to design specific network structure. The heavy computational cost of CNN also makes it challenging for real time application along with high classification performance. Approach. To address these problems, a novel Knowledge-driven Feature Component Interpretable Network (KFCNet) was proposed, which combines spatial and temporal convolution in analogy to ICA and power spectrum pipeline. Prior frequency band knowledge of sensory motor rhythms (SMR) has been formulated as band-pass linear-phase digit FIR filters to initialize the temporal convolution kernels to enable knowledge driven mechanism. To avoid signal distortion and achieve linear phase and unimodality of filters, a symmetry loss is proposed, which is used in combination with the cross-entropy classification loss for training. Besides the general prior knowledge, subject specific time-frequency property of ERDS (event-related desynchronization and synchronization) has been employed to construct and initialize the network with significantly fewer parameters. Main results. Comparison experiments on two public datasets have been performed. Interpretable feature components could be observed in the trained model. The physically meaningful observation could efficiently assist the network structure design. Excellent classification performance on motor imagery has been obtained. Significance. The performance of KFCNet is comparative to the state-of-the-art methods but with much fewer parameters and makes real time application possible.

Highly Secured Dynamic Color QR Pattern Generation for Real Time Application

This work advances the state-of-art secured WBAN system and QR pattern enabled authentication for privacy measures. An attempt was made to integrate all the above process to build high performance WBAN system. In this work, a comprehensive statistical framework is developed with randomized key generation and secured cipher transformation for secured sensor node communication. We create primary colour channels based on three different QR codes that are widely used for colour printing and complementary channels for capturing colour images. Last but not least, we produced a colour QR pattern.

SMART WHEELCHAIR/VEHICLE FOR PHYSICALLY CHALLENGED PEOPLE WITH REVERSE GEAR OPERATION

This paper aims to design the vehicle for the physically challenged person with reverse gear system. This proposed vehicle helps them not to believe any third persons to require a reverse gear. Here we used \"tumbler gear” mechanism for our prototype where the gear is accustomed by changing the direction of gear. It contains two gears which place in parallel by changing their position with motor direction; that are often changed but in real time application we\'d wish to use ideal gear system with gear box. Also the bike contains ultrasonic sensor which supports echo signals to supply alert on taking reverse to avoid collision between other object. this technique also contains \"GPS\" which help their family to locate the position of the physically challenged person just easily. in case of any emergency, an ultrasonic sensor, GPS module and relay circuits are employed to drive the motor in our prototype.

Sustainable Smart Cities: Convergence of Artificial Intelligence and Blockchain

Recently, 6G-enabled Internet of Things (IoT) is gaining attention and addressing various challenges of real time application. The artificial intelligence plays a significant role for big data analytics and presents accurate data analysis in real time. However, designing big data analysis through artificial intelligence faces some issues in terms of security, privacy, training data, and centralized architecture. In this article, blockchain-based IoT framework with artificial intelligence is proposed which presents the integration of artificial intelligence and blockchain for IoT applications. The performance of the proposed architecture is evaluated in terms of qualitative and quantitative measurement. For qualitative measurement, how the integration of blockchain and artificial intelligence addresses various issues are described with the description of AI oriented BC and BC oriented AI. The performance evaluation of proposed AI-BC architecture is evaluated and compared with existing techniques in qualitative measurement. The experimental analysis shows that the proposed framework performs better in comparison with the existing state of art techniques.