Jain University

A simple, novel, and less cost yellow (Erythrosine) modified pencil graphite electrode (Po-ERY/MGPE) was successfully fabricated via electropolymerization method using cyclic voltammetric techniques. The fabricated Po-ERY/MGPE opted as a sensor for the detection of Adrenaline (ADR) in 0.2 M PBS (7.4 pH). This reported senor displayed excellent electrocatalytic activity, increased sensitivity, high stability, superior electron transfer kinetics in the oxidation of ADR once relative to BGPE. The significance of pH, scan rate, and impact of concentration was assessed at the sensor. As per the pH and scan rate study, redox routes carry the same number of electrons and protons, and electro-oxidation of ADR was adsorption controlled respectively. The LOD of ADR was found to be 0.499 µM. The DPV data indicate that there is a significant peak divergence among ADR and uric acid (UA) which could make it easier to determine them alone and simultaneously on the sensor. The described method has been employed for the determination of ADR in injection sample. Good recovery values indicate the efficacy and applicability of the sensor in detecting ADR.

Given that citizen participation is considered the main pillar of ‘Development’, the political economy behind its practice (behaviour and utilisation) remains a question. To disentangle the complex web of relationships that the governance shares with the citizens’ interface, it would be worthwhile to examine the whole phenomenon at the grass root level. A review of issues surrounding democracy has led different schools of thought to realise the need for adopting a holistic development approach for ensuring citizens’ participation in development processes. One such school believes that it is only through addressing governance bottlenecks and ensuring spaces for participation in policy design, programme formulation and implementation supplemented with proper monitoring, that ‘real’ development can be achieved. It is also universally accepted that governance is an enabler for socio-economic transformation and this can help in the improvement of lives through the eradication of structural inequality. Hence, strengthening the local governments is critical for ensuring citizen empowerment, civic participation and better service delivery. Furthermore, governance is measurable and can be monitored; thus can ensure a measurable implementation, accountability and monitoring framework (Global Thematic Consultation on Governance and the Post-2015 Development Framework, 2013). Against these backdrops, the current study endeavours to unearth plausible factors influencing the health behaviour of rural people examining a case of India’s National Sanitation Program—Swachh Bharat Mission in Odisha villages. Analysis of primary data collected from six districts across different regions of the Odisha state shows that various managerial, governance and social factors have a significant effect on the health behaviour of people and present more insightful results.

In today's society, time is considered more valuable than money, and researchers often have limited time to find relevant papers for their research. Identifying and accessing essential information can be a challenge in this situation. To address this, the personalized suggestion system has been developed, which uses a user's behavior data to suggest relevant items. The collaborative filtering strategy has been used to provide a user with the top research articles based on their queries and similarities with other users' questions, thus saving time by avoiding time-consuming searches. However, when rating data is abundant but sparse, the usual method of determining user similarity is relatively straightforward. Furthermore, it fails to account for changes in users' interests over time resulting in poor performance. This research proposes a new similarity measure approach that takes both user confidence and time context into account to increase user similarity computation. The experimental results show that the proposed technique works well with sparse data, and improves accuracy by 16.2% compared to existing models, especially during prediction. Furthermore, it enhances the quality of recommendations.

Due to improper postures, and unhealthy lifestyle of millennials, there has been an exponential increase in spinal cord related diseases. These include Slip Discs, Spine Injuries, Tumours, etc. each of which has multiple side-effects on the human body. To analyze these conditions, a wide variety of image processing models are developed by researchers. But most of these models do not analyze side-effects of spinal cord tumours on other body parts, due to which their applicability is limited when used for clinical trials. The main novelty of this work is to analyze side effects resulting due to spinal cord tumours, and to perform this task a novel Bioinspired Reinforcement Learning Model for Side-Effect Analysis of Spinal Cord Tumours is discussed in this text. The proposed model initially uses a Recurrent Neural Network (RNN) based on combination of Long-Short-Term Memory (LSTM) & Gated Recurrent Unit (GRU) for extraction of highly dense image features. These features allow the model to estimate tumour positions in Computer Tomography (CT) scans. The extracted features are classified via the RNN Model, which assists in high accuracy classification & localization of spinal cord tumours. These classification & localization results are linked with blood reports to estimate side-effects on kidney, lungs, heart activity and vitamin levels. To perform this correlation, a Grey Wolf Optimization (GWO) Model is used, which assists in linking tumour type, and size with blood report parameters. The GWO Model evaluates a fitness function, that fuses tumour levels with its side-effects on individual body parts. This fusion is done via analysis of temporal blood reports, which evaluates effects of different tumour types-and-sizes on individual body parameters. Due to a combination of GWO with LSTM & GRU based RNN, the model is capable of showcasing high accuracy of tumour classification, with better precision of correlation with side effects when compared with state-of-the-art models. It was observed that the proposed model was able to achieve 98.5% accuracy for tumour classification, 96.4% correlation precision with kidney diseases, 95.8% correlation precision with lung diseases, 96.2% correlation precision with heart diseases, and 91.5% correlation precision with vitamin deficiencies. Due to such a high performance, the model is capable of deployment for real-time clinical applications.

A low-probability but serious self-extinction of smouldering incense sticks is an undesirable characteristic. This paper reports the investigations on its cause and remedy, conducted for an incense stick manufacturer. Measurement of smouldering rate and surface temperature of glowing incense stick tip of several known compositions were made by recording the time required for the propagation of smoldering front and using thermal camera respectively. Several possible explanations like presence of (a) phosphorous, potassium, and sodium related compounds, (b) inadvertent inclusion of inorganic compounds, and others ruled out the role of phosphorous and related compounds, and a simple role of inorganic compounds despite the fact that defective incense sticks invariably contained large fraction of silica (SiO2) as revealed by scanning electron microscopy/ energy dispersive X-ray diffraction (SEM/EDX). Ultimately the issue was traced to the presence of antigorite that must have got infused into the incense sticks through termite mud into the raw materials. X-ray diffraction (XRD) of incense stick samples confirmed silica is in the form of α-quartz associated with antigorite in all defective samples and also in termite mud infested sample. Thermal studies of the incense sticks using differential thermal analysis (DTA) show endothermic decomposition of defective samples at 550 to 580°C and is also confirmed through differential scanning calorimetry (DSC) which shows an endothermic peak around 576°C corresponding to the endothermic phase transformation temperature of antigorite. It is therefore inferred that the presence of materials like antigorite in combination with α-quartz in incense sticks produce significant endothermal decomposition leading to self-extinction. The primary practical cause has been traced to termite mud infusion into the raw materials used for making the incense sticks.

The world has been greatly affected by increased utilization of mobile methods as well as smart devices in field of health. Health professionals are increasingly utilizing these technologies' advantages, resulting in a significant improvement in clinical health care. For this purpose, machine learning (ML)as well as Internet of Things (IoT) can be utilized effectively. This study aims to propose a novel data analysis method for a health monitoring system based on machine learning. Goal of research is to create a ML based smart health monitoring method. It lets doctors keep an eye on patients from a distance as well as take periodic actions if they need to. Utilizing wearable sensors, a set of five parameters—the electrocardiogram (ECG), pulse rate, pressure, temperature, and position detection—have been identified. Kernelized component vector neural networks are used to choose the features in the input data. Then, a sparse attention-based convolutional neural network with a structural search algorithm was used to classify the selected features. For a variety of datasets, the proposed technique attained validation accuracy 95%, training accuracy 92%, RMSE 52%, F-measure 53%, sensitivity 77%.

Numerous aspects of healthcare have been altered by cloud-based computing. Scalability of required service as well as ability to upscale or downsize data storage, as well as the collaboration between AI and machine learning, are main benefits of cloud computing in healthcare. Current paper looked at a number of different research studies to find out how intelligent techniques can be used in health systems. The main focus was on security and privacy concerns with the current technologies. This study proposes a novel method for cloud service device-to-device communication using feature selection and classification for data analysis in an e-health system. Through a comprehensive requirement analysis as well as user study, the purpose of this research is to investigate viability of incorporating cloud as well as distributed computing into e-healthcare. After that, the smart healthcare system and conventional database-centric healthcare methods will be compared, and a prototype system will be created as well as put into use based on results. Convolutional adversarial neural networks with transfer perceptron are used to analyze the cloud-based e-health data that has been collected. Proposed technique attained training accuracy 98%, validation accuracy 93%, PSNR 66%, MSE 68%, precision 72%, QoS 63%, Latency 58%.

Nanotechnology gained a lot of attention nowadays because of its enormous applications. Due to the need for developing new synthetic methods, various physical, chemical, and biological methods are used in the synthesis of nanoparticles. From an environmental point of view, green synthetic methods provide advantages over conventional chemical and physical methods. Among green methods, biosynthesis is a widely emerging method for the synthesis of nanoparticles because it shows prodigious advantages such as low toxicity, simplicity, cheapness, use of less energy, and rapid. Biosynthesis is a highly efficient method for the fabrication of nanoparticles. Palladium nanoparticles are widely used in different areas of applications such as catalysis, medicine, biomedical applications, cosmetics, pharmaceutical products, and electronic components. This review includes recent catalytic advancements in organic transformations using biogenically synthesized palladium nanoparticles and their applications. This review highlights the potential of this field for researchers. Graphical Abstract Graphical presentation of biosynthesis, characterization, and applications of palladium nanoparticles

In this study, fiber laser beam welding was used to join 3 mm-thick nitronic-50 stainless steel. The effect of travel speed on the mechanical, microstructural, and corrosion properties of the weld joints was studied. The experimental findings reveal that travel speed significantly affected the size of bead geometry and the fusion boundary of the weld joints. The microstructure of columnar grains transformed into equiaxed dendritic grains at the center of the weld nugget due to fast cooling at higher travel speeds. The increase in ferrite content was due to the incomplete transformation of ferrite to austenite at higher travel speeds, which led to higher strength and microhardness in the weld joints. Similarly, the corrosion studies demonstrate that the weld joints made at higher travel speeds have superior corrosion resistance due to a higher fraction of equiaxed dendritic grains and smaller dendrites with less inter-dendritic arm spacing.

Among the most critical problems is minimising vehicle-animal accidents on highways, which cause environmental imbalances and huge public expenditures. This work covers the components of a detect and categorises the species of trapped picture with crop using bbox detect by automatically selecting the shared closest neighbour pixel to detect the large data set detected by MegaDetector. The model automatically selects the weighted average pixel using KNN regression to find the nearest neighbour of SNN density to group the minimal number of points. The ASNNP model crop the trapped image with high accuracy as well as, minimal loss identified in learning rate. The proposed and presented techniques are evaluated based on their ability to meet the mean average precision 8.02 MB (mAP) and detection speed with 94.2 in VGG16.

Cannabis and its related products have increasingly been consumed in India as they produce the desired euphoric effects in human body. Cannabis blends many of the properties of alcohol, opium, tranquilizers, and hallucinogens and develops severe effects when synthesized using other chemicals. In India, cannabis is listed as a narcotic drug, and the consumption of cannabis is increasing steadily across the nation. Long-term and regular use of cannabis can lead to addiction and drug-facilitated crimes in our society. In such predicaments, conducting a chemical analysis for these drugs is necessary to detect the type and the amount of drug consumed by an abuser. For this purpose, chromatography coupled with mass spectrometry setups has been highly employed by researchers. This study is a review conducted on articles that have been developed and validated chromatography and mass spectrometry techniques between years 2015 and 2022 for the detection of cannabinoids. The sample preparation and extraction procedures employed in each research paper have been discussed in detail. The analytical parameters of the instrument used in each article have been tabulated, and the range of the same has been calculated. The review is a cumulative report of chromatography and mass spectrometric techniques used for the detection of natural and synthetic cannabinoids. The study offers a wide range of novel and modified extraction and analytical techniques implemented on different sample matrices. The report will help the experts to review and select suitable analytical method and parameters according to the matrix available for analysis. The study shows that the researchers prefer liquid chromatography-mass spectrometry setups for the detection of cannabinoids.

Due to attractive application in the medical field, fiber Bragg grating sensor has become increasing attractive from past few decades for various strain sensing applications. FBG sensor has been used in many applications such as different surgical devices, vital sign detection devices, invasive surgery, heart rate, dental applications and biosensing application as wearable sensing devices. This paper reviews the 55 recent research articles published on fiber Bragg grating sensor for biomedical application used the qualitative, quantitative and experimental method to identify the recent advancement and challenges. In this study, particular focus is placed on applications for biomechanical devices, temperature monitors, respiratory monitors, and biosensing applications. Critical things, demands, and emerging trends for these sensing devices are also discussed in order to determine what will be needed for the next generation.

The Biginelli reaction has received significant consideration in recent years due to its easily accessible aldehyde, urea/thiourea, and active methylene compounds. When it comes to pharmacological applications, the Biginelli reaction end‐products, the 2‐oxo‐1,2,3,4‐tetrahydropyrimidines, are vital in pharmacological applications. Due to the ease of carrying out the Biginelli reaction, it offers a number of exciting prospects in various fields. Catalysts, however, play an essential role in Biginelli's reaction. In the absence of a catalyst, it is difficult to form products with a good yield. Many catalysts have been used in search of efficientmethodologies, including biocatalysts, Brønsted/Lewis acids, heterogeneous catalysts, organocatalysts, and so on. Nanocatalysts are currently being applied in the Biginelli reaction to improve the environmental profile as well as speed up the reaction process. This review describes the catalytic role in the Biginelli reaction and pharmacological application of 2‐oxo/thioxo‐1,2,3,4‐tetrahydropyrimidines. This study provides information that will facilitate the development of newer catalytic methods for the Biginelli reaction, by academics as well as industrialists. It also offers a broad scope for drug design strategies, which may enable the development of novel and highly effective bioactive molecules. This review describes the catalytic role of 2‐oxo/thioxo‐1,2,3,4‐tetrahydropyrimidines in the Biginelli reaction and in pharmacological applications. The provided information will facilitate the development of newer catalytic methods for the Biginelli reaction, by academics and industrialists. The broad scope of drug design strategies may enable the development of novel and highly effective bioactive molecules.

In recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.

Electronic Medical Records serve to be a crucial form of personal information. It's necessary to maintain the confidentiality and integrity of an electronic medical records. This problem is naturally solved by implementing a block-chain based health care system. The patient's electronic medical record stored and shared by beneficial mechanisms that exchange the health of medical data in companies related to healthcare. The securing decentralization network to ease and assist the distribution lender technology. Each transaction record serves to be an electronic medical records for each patient visit. The history of patient's medical record prescribe medication through block chain. The system implemented links doctors, patients and health bureaus for record sharing, review and remote care administration. This implementation is only used by the hospital nodes of the health-care system. This system uses consortium block-chain, making electronic medical records access role-specific. It also eases retrieval of specific records confined to the nodes requesting the electronic medical records.

The present paper has been written considering the robot interference in the field the mechanical forging. The robot interference in the forging is to related with the replacement of the processes which are earlier done by the human interaction. The main concepts behind the use of the robots in the forging operation is that an operator cannot be able to produce two products with the same proficiency and products have more or less differences. The robots are integrated with the automation techniques to pass the command to proceed the desired operation over the specimen. The advantages of the robotic and automation integration are to boost the production as well as the productivity in addition to enhanced the quality of the product. The forging operation is one of the best operation to improve the physical and mechanical of the material and all the product subjected to the heavy stress are preferred to manufacturing through this process only.

Fire-fighting robots are the human-established machines to keep people alive, as the accidents that occur during the procedure of stopping the fires are uncountable. This main function of the robot is to automatically sense fires and shift against the fires to finish it with water by keeping a safe distancing from it. A programmable raspberry pi can completely control the movement and actions of this device. This device must move right, east, front and back in the shape of a car to locate and quench the fires. An infra-red camera and thermal camera is used in this fire-fighter robot. The thermal camera attempts to monitor the flames, temperature and the infra-red camera, offering pictures of the view of the night, which record the whole extinction period directly. This live capture can be seen in a PC file which is also accompanied by a log from the computer.

In this study, a concrete cube was created by partially replacing Ordinary Portland Cement (OPC) with Nano Materials such as Multi-walled Carbon Nano Tubes (MWCNTs), Titanium Di Oxide (TiO2), and Copper Oxide (CuO) at various percentages. MWCNTs were replaced by OPC by 0.01, 0.025, 0.05, and 0.075%, TiO2 by 0.25, 0.5, 0.75, and 1 percent, and CuO by 0.5, 1, 1.2, 1.5, and 2%. Using Nano Materials gives more compressive strength than normal concrete cubes, and MWCNT outperforms TiO2 and CuO. Simply to reduce cement usage Fly ash was used while the compressive strength and amount of Nano Materials remained constant. As much as 39% Ordinary Portland Cement can be replaced with up to 35% MWCNTs and Fly Ash, 35% TiO2 and Fly Ash, and up to 34% CuO and Fly Ash. According to the cost analysis, TiO2 with Fly Ash costs 26.77Rs to prepare a single cube with a maximum replacement of 35% of OPC, while MWCNTs and CuO with Fly Ash cost 146.86 and 42.51Rs to prepare a single concrete cube with a maximum replacement of 39 and 34%, respectively, and normal OPC concrete cubes require 27.98 Rs. In OPC concrete, almost TiO2 cube preparation took a 10% reduction when compared to Normal Concrete cube. In this work, the carbon score was calculated, and the maximum carbon emission reduction up to By replacing cement with MWCNTs and Fly Ash, OPC concrete cube can be reduced by 35%. As a result, we concluded that TiO2 with Fly Ash Nano Material concrete is the most cost-effective when compared to MWCNTs and CuO with Fly Ash. When compared to TiO2 and CuO with Fly Ash, MWCNTs with Fly Ash provide the greatest replacement and carbon emission reduction.

Limited crude petroleum and growing awareness of fossil fuel depletion have enabled the development of alternative fuels and new energy sources. Biodiesel, also known as fatty acid methyl esters (FAME), has received a lot of attention due to its biodegradability, renewability, cost effective and nontoxicity. The purity of biodiesel production and uniform heating are the major hurdles for large scale biodiesel production. Recent microwave energy-based heating method has proved the potential for cleaner chemical production, short time duration, uniform heating, and purity over conventional heating method. The goal of this review is to discuss the biodiesel production using microwave-assisted heating. The different feedstocks used for biodiesel production, effects of microwave irradiation, factors affecting the rate of microwave-assisted transesterification to produce biodiesel were comprehensively discussed. Microwave irradiation has been compared to other technologies aiming to enhance the efficiency of overall process. The primary knowledge gaps in biodiesel production can be identified based on this research, ensuring the biodiesel industry's long-term sustainability.