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Investor's behaviour to COVID-19 vaccination campaign; An event study and panel data analysis in the southeast asian region
This study examines how the COVID-19 immunization campaign has influenced the stock market responses in the WHO Southeast Asian Region. The effects of the immunization campaign on the WHO Southeast Asian countries were different, and the study used event study techniques and panel-data regression models to investigate the impact of the WHO South-East Asian capital market. Some countries like India, Sri Lanka, and South Korea had positive markets that responded to the news, while others did not. The findings of this study suggest that investors make fair assessments and respond to events and announcements, but they tend to have a more visible reaction to negative incidents than to positive news/events. However, after 51 days, the WHO South East region as a whole had internalized the encouraging news. The study has a few limitations, such as a small dataset and period, only a few variables and models, and so on. Future studies could include a few additional countries and periods to produce more significant results. Originality/value- This study contributes to the existing knowledge about the impact of drugs and vaccinations on stock markets. It is the first study to investigate how the WHO Southeast Asian Region's COVID-19 immunization program affects the stock market reaction. The study used keywords such as Immunization campaign, abnormal returns, Cumulative average abnormal returns, Event Study, and WHO Southeast Asian Region. 2025 Universidad Nacional Autonoma de Mexico. All rights reserved. -
Iodine Mediated Oxidative Cross-Coupling of Benzo[d]Imidazo[2,1-b]Thiazoles with Ethylbenzene: An Unprecedented Approach of C3-Dicarbonylation
A versatile approach of iodine mediated C3-dicarbonylation of benzo[d]imidazo[2,1-b]thiazoles (IBTs) with ethylbenzene has been reported. The reaction conditions were optimized by screening in various solvents, catalysts, and oxidants. The reaction is compatible with various substrates and was successfully demonstrated to offer moderate to good yields. 2022 Taylor & Francis Group, LLC. -
Iodine promoted synthesis of pyrido[2?,1?:2,3]imidazo[4,5-c]quinoline derivatives via oxidative decarboxylation of phenylacetic acid
An unprecedented and efficient molecular iodine promoted domino protocol for the synthesis of N polycyclic pyrido[2?,1?:2,3]imidazo[4,5-c]quinolines were reported from phenylacetic acid and 2-(imidazoheteroaryl)anilines. This methodology was also extended for the preparation of benzo[4?,5?]thiazolo [2?,3?:2,3]imidazo[4,5-c]isoquinolines in good yields. However, this protocol proceeds via a sequential decarboxylation of phenylacetic acid with I2/DMSO system followed by Pictet-Spengler cyclization in good yields. 2022 Taylor & Francis Group, LLC. -
Ion-imprinted chitosan-stabilized biogenic silver nanoparticles for the electrochemical detection of arsenic (iii) in water samples
Arsenic is one of the most harmful heavy metals, and needs constant monitoring and control. A susceptible and selective sensor for arsenic (iii) detection in polluted water samples has been developed by using chitosan-stabilized silver nanoparticles (AgNPs). A glassy carbon electrode (GCE) is modified with chitosan-stabilized silver nanoparticles, which provides sufficient sites for interaction with the analyte. The synthesized silver nanoparticles are characterized using different characterization techniques, such as UV-Visible spectroscopy, transmission electron spectroscopy (TEM) and particle size distribution. The particle size and polydispersity index (PDI) value of the chitosan stabilized silver nanoparticles are found to be 6 nm and 0.3, respectively, which enhances the electroactive surface area and hence the sensor sensitivity. The ion imprinting technique is used to improve the selectivity of the sensor. The arsenic sensor is found to be very selective in the presence of other possible interferents like Pb2+, Zn2+, Cu2+, Ag+ and Fe2+. The detection of As(iii) was performed using differential pulse anodic stripping voltammetry (DPASV) and the detection limit was found to be 11.39 pM. The developed sensor is successfully tested for the picomolar level detection of arsenic (iii) in different water samples. 2023 The Royal Society of Chemistry. -
Ionic strength and phase systems influence nanotubular material functionality
We synthesized novel thiacyanine chromonic liquid crystals (CLCs) and structurally characterized using NMR and mass spectrometry. The impact of distinct substitution at the para position of aromatic counter anions, aliphatic counter ion chain length, and varied spacer parity of thiacyanine dyes on CLC formation is investigated. Liquid crystal properties of the synthesized dyes are characterized by polarizing optical microscopy (POM) and X-ray diffraction (XRD) studies. Dyes exhibit nematic (N), lamellar (L?), columnar rectangular (Colr), and columnar oblique (Colob) CLCs at different concentrations in the water. Electronic absorption spectra reveal Scheibe aggregation in all the dyes. Cylicvoltametry studies confirm redox behaviour in TC-1a and TC-5e dyes. Chromonic LCs hybrid nano-materials are synthesized using solgel method. Scanning electron microscopy employed to confirm nano tubular fiber structure of the hybrid nanomaterilals. 2024 Elsevier B.V. -
IOT based application for monitoring electricity power consumption in home appliances
Internet of Things is one of the emerging techniques that help in bridging the gap between the physical and cyber world. In the Internet of Things, the different smart objects connected, communicate with each other, data is gathered from the smart objects and based on the need of the users, and the data gathered are queried and sent back to the user. IoT helps in monitoring electrical and physical parameters. Electricity consumption from electronic devices is one among such parameters that need to be monitored. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper proposes a Wi-Fi enabled simple low cost electricity monitoring device that can monitor the electricity consumption on home appliances which helps to analyses the consumption of electricity on a daily and weekly basis. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
IOT based application to detect fall with a measured force
Fall of patients and aged individuals may end up deadly if unnoticed in time. A fall detection framework has been developed which sends caution notification to the concerned individuals or to the specialist, at the time of occurrence. To limit the consequences of associated wounds/damage caused by the fall, such a device has been developed. The model in this study, detects the fall and measures the force of the fall without using the force sensor and the direction of the fall. In this study, the body posture is obtained from change of increasing speed in three axes, which is measured with a triaxial accelerometer (ADXL335). The sensor is set on the lumbar area to interpret the tilt point. The value obtained from the sensor is compared with the threshold given to diminish the false cautions and furthermore provides the force by which the individual has fallen and the direction in which the person has fallen. The threshold value is computed by the execution of various trials on subjects in different directions of fall. The sensor data is collected on the fall is computed and analyzed in the Audrino microcontroller. The location of fall is detected by GPS beneficiary, which is customized to trace the subject persistently. On detecting the fall, the gadget sends an instant message through GSM module to the emergency contact. The developed model is tested on 7 volunteers who replicated falls in different direction with varying forces. Out of 28 trials, 80% of exactness is accomplished with zero false cautions for dayto-day activities like sitting, lying down on bed and grabbing objects. IAEME Publication. -
IoT based car accident detection and notification algorithm for general road accidents
With an increase in population, there is an increase in the number of accidents that happen every minute. These road accidents are unpredictable. There are situations where most of the accidents could not be reported properly to nearby ambulances on time. In most of the cases, there is the unavailability of emergency services which lack in providing the first aid and timely service which can lead to loss of life by some minutes. Hence, there is a need to develop a system that caters to all these problems and can effectively function to overcome the delay time caused by the medical vehicles. The purpose of this paper is to introduce a framework using IoT, which helps in detecting car accidents and notifying them immediately. This can be achieved by integrating smart sensors with a microcontroller within the car that can trigger at the time of an accident. The other modules like GPS and GSM are integrated with the system to obtain the location coordinates of the accidents and sending it to registered numbers and nearby ambulance to notify them about the accident to obtain immediate help at the location. 2019 Insitute of Advanced Engineeering and Science. All rights reserved. -
IOT based Green House monitoring system
With industrialization and continuously evolving climatic conditions, the urge to practice agriculture with the fusion of technology has become a necessity. In the era of Internet of Things where all eyes are witnessing the evolution of machine to machine interaction, there is also a lack of clarity in considering the type of protocol to be used in building a particular system like Green House. A green house is a regulated environment for agriculture where critical parameters like temperature, light, humidity, ph level of soil can be monitored with the help of sensor systems using Internet of Things protocols. Message Queue Telemetry Transfer protocol was chosen over Constrained Application Protocol and Extensible Messaging and Presence Protocol in the experiment conducted in terms of its light weight transmission, resource consumption and effectively providing the different quality of services to detect the temperature and humidity as well as the gas leaks encountered in a greenhouse environment. 2018 Tinu Anand Singh and J. Chandra. -
IoT based heart monitoring and alerting system with cloud computing and managing the traffic for an ambulance in India
Global Burden of Disease Report, released in Sept 2017, shows that Cardiovascular Diseases caused 1.7 million deaths (17.8%) in 2016 and it is the leading cause of deaths in India [1]. According to the Indian Heart Association, 25% of all heart attacks happen under the age of 40. In most cases, the initial heart attacks are often ignored. Even post-diagnosis, as per government data [2], 50% of heart attack cases reach the hospital in more than 400 minutes against the ideal window time of 180 minutes; post which damage is irreversible. The delay is often attributed to delay in reaching a hospital or receiving primary aid. In India, traffic conditions also add to the grimace of the situation. Although the government is taking various measures; a holistic solution is required to minimize the delay at each of the steps like accessing the patient situation, contacting the Medical aid or making available the nearest aid possible. In this paper, we aim at providing the holistic solution using the Internet of Things technology (IOT) along with data analytics. IoT enables real-time capturing and computation of medical data from smart sensors built-in wearable devices. The amalgamation of Internet-based services with Medical Things (Smart sensors) enhance the chances of survival of patients. The proposed system analyses the inputs collected from the sensors fit with the patients prone to cardiovascular diseases to ascertain the emergency situation. In addition, to these data, the system also considers age, maximum and minimum heart rate. Based on computational results received from the input parameters, the system triggers the alert to emergency contacts such as the close relatives of the patient, doctors, the hospitals and nearby ambulance. The proposed system combines with the optimized navigation platform to guide the medical assistance to find the fastest route. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model
This research proposes an IoT based technique for predicting rainfall forecast in coastal regions using a deep reinforcement learning model. The proposed technique utilizes Long Short-Term Memory (LSTM) networks to capture the temporal dependencies between the rainfall data collected from the coastal regions and the prediction model parameters. The proposed technique is evaluated on a dataset of rainfall data collected from the coastal regions of India and compared to traditional methods of rainfall forecasting. The accuracy and reliability of these models are evaluated by comparing them to prior models. Precipitation in coastal locations may be predicted with an average accuracy of 89% using the suggested model, as shown by the results. The suggested framework is computationally efficient and can be trained with little input. The results of this research give strong evidence that the proposed model is an effective tool for coastal precipitation forecasting. 2023 The Authors -
Iot based real time potholes detection system using image processing techniques
Accidents owing to potholes has become an alarming problem in todays life. The first step to solve this problem requires, designing a device embedded on the vehicle which can continuously scan the road surface for identifying potholes, alerting the driver in time and enable the driver to avoid the pothole. The second step is to introduce a technique to enable the device to locate the position of the pothole via GPS (Global Positioning System). The GPS data can be uploaded via a GPRS (General Packet Radio Service) module or Bluetooth module onto a data base which is stored locally. This database can then be transferred to the cloud using WiFi or 4G technology by connecting the system. The third aspect is to link the database to a network system incorporating mapping software such as Google Maps or Open-Street Map. The data in the system can be made available to the general public as well as municipalities and road maintenance agencies. Awareness of the location of potholes will help drivers to avoid those roads and being more careful while driving on the same roads. This paper focuses on the pothole detection task based on image processing algorithms and the data captured from ultrasonic sensor placed on the vehicle. The later steps were implemented through Bluetooth interface available in smartphones. IJSTR 2020. -
IoT enabled lung cancer detection and routing algorithm using CBSOA-based ShCNN
The Internet of Things (IoT) has tremendously spread worldwide, and it influenced the world through easy connectivity, interoperability, and interconnectivity using IoT devices. Numerous techniques have been developed using IoT-enabled health care systems for cancer detection, but some limitations exist in transmitting the health data to the cloud. The limitations can be accomplished using the proposed chronological-based social optimization algorithm (CBSOA) that effectively transmits the patient's health data using IoT network, thereby detecting lung cancer in an effective way. Initially, nodes in the IoT network are simulated such that patient's health data are collected, and for transmission of such data, routing is performed in order to transmit the health data from source to destination through a gateway based on cloud service using CBSOA. The fitness is newly modeled by assuming the factors like energy, distance, trust, delay, and link quality. Finally, lung cancer detection is carried out at the destination point. At the destination point, the acquired input data is fed to preprocessing phase to make the data acceptable for further mechanism using data normalization. Once the feature selection is done using Canberra distance, then the lung cancer detection is performed using shepard convolutional neural network (ShCNN). The process of routing as well as training of ShCNN is performed using the CBSOA algorithm, which is devised by the inclusion of the chronological concept into the social optimization algorithm. The proposed approach has achieved a maximum accuracy of 0.940, maximum sensitivity of 0.941, maximum specificity of 0.928, and minimum energy of 0.452. 2022 John Wiley & Sons Ltd. -
IOT-BASED cyber security identification model through machine learning technique
Manual vulnerability evaluation tools produce erroneous data and lead to difficult analytical thinking. Such security concerns are exacerbated by the variety, imperfection, and redundancies of modern security repositories. These problems were common traits of producers and public vulnerability disclosures, which make it more difficult to identify security flaws through direct analysis through the Internet of Things (IoT). Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting databases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators. This proposed methodology's full potential has yet to be fulfilled, both in terms of its execution and its significance for security evaluation in application software. The study shows that the recommended mental security methodology works better when addressing the above inadequacies and the constraints of variation among cybersecurity alert mechanisms. Intriguing trade-offs are presented by the experimental analysis of our program, in particular the ensemble method that detects tendencies of computational security defects on data sources. 2023 The Authors -
IoT-Based Response Time Analysis of Messages for Smart Autonomous Collision Avoidance System Using Controller Area Network
Many accidents and serious problems occur on the road due to the rapid increase in traffic congestion in all sections of the country. Autonomous vehicles provide a solution to successfully and cost-effectively avoid this problem while minimizing user disruption. Currently, more engaging electromechanical elements with an analog interface are used to develop affordable automobiles for efficient and cost-effective operation for a smart driving platform with a semiautonomous automobile, strengthening the vehicle involvement of the driver while increasing safety. As a result, it takes longer for various car elements to respond, which causes more problems during message transmission. This project aims to create a Controller Area Network (CAN) for analyzing message response times by incorporating a few application nodes on the IoT platform, such as an antilock braking system, flexible cruise control, and seat belt section, for some real-time control system applications. These application nodes are car analytical parts that are linked to IoT modules to prevent collisions. An autonomous device for collision avoidance and obstacle detection in a vehicle can impact road accidents if the CAN protocol is implemented. 2022 Anil Kumar Biswal et al. -
IoT-based smart alert system for drowsy driver detection
In current years, drowsy driver detection is the most necessary procedure to prevent any road accidents, probably worldwide. The aim of this study was to construct a smart alert technique for building intelligent vehicles that can automatically avoid drowsy driver impairment. But drowsiness is a natural phenomenon in the human body that happens due to different factors. Hence, it is required to design a robust alert system to avoid the cause of the mishap. In this proposed paper, we address a drowsy driver alert system that has been developed using such a technique in which the Video Stream Processing (VSP) is analyzed by eye blink concept through an Eye Aspect Ratio (EAR) and Euclidean distance of the eye. Face landmark algorithm is also used as a proper way to eye detection. When the drivers fatigue is detected, the IoT module issues a warning message along with impact of collision and location information, thereby alerting with the help of a voice speaking through the Raspberry Pi monitoring system. Copyright 2021 Anil Kumar Biswal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
IoT-based smart healthcare video surveillance system using edge computing
Managing distributed smart surveillance system is identified as a major challenging issue due to its comprehensive aggregation and analysis of video information on the cloud. In smart healthcare applications, remote patient and elderly people monitoring require a robust response and alarm alerts from surveillance systems within the available bandwidth. In order to make a robust video surveillance system, there is a need for fast response and fast data analytics among connected devices deployed in a real-time cloud environment. Therefore, the proposed research work introduces the Cloud-based Object Tracking and Behavior Identification System (COTBIS) that can incorporate the edge computing capability framework in the gateway level. It is an emerging research area of the Internet of Things (IoT) that can bring robustness and intelligence in distributed video surveillance systems by minimizing network bandwidth and response time between wireless cameras and cloud servers. Further improvements are made by incorporating background subtraction and deep convolution neural network algorithms on moving objects to detect and classify abnormal falling activity monitoring using rank polling. Therefore, the proposed IoT-based smart healthcare video surveillance system using edge computing reduces the network bandwidth and response time and maximizes the fall behavior prediction accuracy significantly comparing to existing cloud-based video surveillance systems. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
IoT-based traffic prediction and traffic signal control system for smart city
Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of 5 phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
IPWM Based IBMSC DC-AC Converter Using Solar Power for Wide Voltage Conversion System; [Convertisseur DC-AC IBMSC bassur l'IPWM et utilisant l'ergie solaire pour un syste de conversion large tension]
This article proposes isolated bidirectional micro dc-ac single phase controlled (IBMSC) converter based on in-phase-voltage pulsewidth modulation (IPWM). This resonant IPWM converter, ratio of voltage conversion can be controlled from 0 to ?. So, this converter is highly referred for huge range voltage conversion. However, voltage conversion ratio determines power transfer direction and duty ratio. Power flow direction and duty cycle value can be varying smoothly, so it is suitable for dc-ac bidirectional power conversion application. Inverter mode and also rectifier mode are possible from bidirectional operation, which is controlled by a unified current controller. The proposed solution can achieve smooth switching grid operation with high efficiency. Working principle, design procedure, control strategy, and characteristics of the proposed converter are implemented with a prototype model of power rating 500 W with a voltage range of 20-50 V to test the ability of withstanding. Performance, feasibility, and effectiveness of the proposed converter are tested with this hardware test-bench model. 2022 IEEE. -
Iron pulsing, a cost effective and affordable seed invigoration technique for iron bio-fortification and nutritional enrichment of rice grains
Rice being a major staple food for millions of people, it has been one of the major targets for bio-fortification and iron bio-fortification in rice has been in prime focus to address global micronutrient malnutrition. Commonly practiced methods for obtaining Fe biofortified rice includes soil amendments and foliar spray with iron salts, breeding and development of transgenic rice varieties with Fe-enriched grain are associated with impediments like high cost, labor intensiveness, sub-optimal outcome and approval for commercialization respectively. Iron pulsing technique has reportedly enhanced the carbon and nitrogen assimilation in rice seedlings, which has been translated in yield. Based on the previous findings, in the present study, we have undermined the efficacy of iron pulsing, in improving the iron content and nutritional status of rice kernel obtained from pulsed plants. The present study documents that kernel of seeds obtained from iron pulsed plants have a higher amounts of iron, carbohydrate, protein, lipid, vitamins, nutrient and anti-oxidants than that of non-treated ones. The iron localization studies revealed that iron was mostly present in the endosperm and embryo. Besides, the ferritin expression levels also validated the fact that, the treated grains have accumulated more iron. Thus, iron-pulsing can serve as a novel and propitious sustainable agricultural innovation for iron bio-fortification and improvisation of the overall nutritional value of the rice grains that is affordable, user and consumer friendly in years to come. 2023, The Author(s), under exclusive licence to Springer Nature B.V.