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Fabrication of disposable sensor strips for point-of-care testing of environmental pollutants
Biosensors are potentially used in detection of trace amount of environmental pollutants. Nanostructured materials are being widely explored for its application in the field of biosensors for monitoring environmental pollutants. Advances in biosensor technology with the use of micro-/nanomaterials can detect and analyze living and chemical matter with high specificity, which is relatively fast, sensitive, accurate, and inexpensive for the determination of chemical and biological contaminants. Recent finding shows that carbon nanotubes (CNTs) and nanomaterial-based biosensors utilizing the electrochemical and optical properties are being used for the analysis of contaminants at an incredible sensitivity and accuracy. In this chapter, the application of CNT-based biosensors and the fabrication of paper-based sensors in monitoring hazardous environmental pollutants are discussed. 2022 Elsevier Inc. All rights reserved. -
AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach
Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and nae Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work. The Author(s) 2024. -
Role of Constructivism Developing Metacognitive Abilities Among Secondary School Students
Research Tracks, Vol-1 (1), pp. 125-128. ISSN-2347-4637 -
Relationship Between Muilti-Media Exposure and Socio-Cultural Awareness of Secondary School Students
Learning Community, Vol-3 (2&3), pp. 233-241. ISSN-0976-3201 -
Is the Electronic Market the Way Forward to Overcome Market Failures in Agriculture?
This paper examines the performance of agricultural markets through analysing the primary data from 856 farm households in six states along with secondary data. It argues that adequate physical and storage infrastructure is crucial even for the functioning of the electronic market, and other related policy measures are needed to have a significant improvement in agricultural marketing. The results indicate that farmers obtained 3.75% higher prices in these markets vis-vis the prices received before selling to these markets. This is significant as the prices plummeted by 8.34% in the manual transactions. 2022 Economic and Political Weekly. All rights reserved. -
A fast survey on recent developments in designing colorimetric and fluorescent sensors for the selective detection of essential amino acids
Owing to the biological significance of various amino acids, developing accurate and cost-effective sensing techniques for the selective detection of amino acids has recently attracted growing interest. This review discusses the recent advancements of chemosensors in the selective detection of only essential amino acids out of a total of twenty amino acids, which have been applied in chemosensing research, and the mechanism of their action. The focus is directed towards the detection of the most important essential amino acids, like leucine, threonine, lysine, histidine, tryptophan and methionine, since isoleucine and valine are yet to be explored in regard to chemosensing. According to their chemical and fluorescence properties, different sensing techniques, such as the reaction-based approach, DNA-based sensors, nanoparticle formation, coordination ligand binding, host-guest chemistry, the fluorescence indicator displacement (FID) approach, electrochemical sensors, carbon dot-based sensors, MOF-based sensors and metal-based techniques, have been described. 2023 The Royal Society of Chemistry. -
Prospective memory in early and established psychosis: An Indian perspective
Individuals affected by psychosis often have deficits in several neurocognitive functions. Prospective memory (PM), the ability to remember to do things, is crucial for activities of daily living, social and occupational functioning, but very few studies have attempted to examine this domain of functioning in people with psychosis, particularly in India. A total of 71 patients with psychosis, (both early and established psychosis), and 140 age, gender and education-matched healthy controls were assessed using the Positive and Negative Symptom Scale, Hospital Anxiety and Depression scale, and Addenbrooke's Cognitive Examination. PM was assessed using the Cambridge Prospective Memory Test and the Prospective and Retrospective Memory Questionnaire (PRMQ). Group differences were evaluated using MannWhitney U-tests. Significantly greater cognitive deficits, higher anxiety and depression were evident in the psychosis group compared with controls. The psychosis group performed significantly poorer on both time- and event-based tests in CAMPROMPT than controls. These differences remained when controlling for age, education, general cognitive functioning and mood. The subjective measure of PM (PRMQ) did not differentiate the two groups. The PM performance of early and established psychosis patients was similar. Comparisons with cross-cultural data (PRMQ UK norms and CAMPROMPT and PRMQ Chinese data) revealed important differences in PM performance. Individuals with psychosis have significant deficits in both time- and event-based PM. CAMPROMPT emerged as a more sensitive PM measure compared with PRMQ. Results from cross-cultural comparisons underscore the need for cultural contextualization of assessments. 2023 The British Psychological Society. -
Estimation of secured wireless sensor networks and its significant observation for improving energy efficiency using cross- learning algorithms
Wireless sensor networks (WSNs) have as of late been created as a stage for various significant observation and control applications. WSNs are continuously utilized in different applications, for example, therapeutic, military, and mechanical segments. Since the WSN is helpless against assaults, refined security administrations are required for verifying the information correspondence between hubs. Because of the asset limitations, the symmetric key foundation is considered as the ideal worldview for verifying the key trade in WSN. The sensor hubs in the WSN course gathered data to the base station. Despite the fact that the specially appointed system is adaptable with the variable foundation, they are exposed to different security dangers. Grouping is a successful way to deal with vitality productivity in the system. In bunching, information accumulation is utilized to diminish the measure of information that streams in the system. 2021 by IGI Global. -
The role of meditation and mindfulness in the management of polycystic ovary syndrome: a scoping review
Polycystic Ovary Syndrome (PCOS) presents multifaceted challenges affecting womens reproductive, metabolic, and psychological systems, consequently impacting their psychological and emotional well-being. The utilization of meditation and mindfulness interventions (MMIs) is found to be increasing for the management of PCOS. This scoping review systematically explored the current literature to identify the type and application of MMIs for PCOS management. A systematic search of literature was conducted using CINAHL, PsycINFO, Scopus, MEDLINE, and PubMed databases for identifying studies conducted on the usage of MMIs in women diagnosed with PCOS, irrespective of age. The comprehensive search identified 14 trials (comprising 17 citations) meeting inclusion criteria, involving 723 participants across various age groups. Among these, nine were randomized controlled trials (RCTs), while the remaining comprised non-RCTs. Several types of MMIs, including Rajayoga of Brahmakumaris, Yoga Nidra, OM cyclic meditation, unspecified forms of meditation, mindfulness-based stress reduction programs, mindful yoga, and mindfulness-based activities, were used. Outcomes were predominantly assessed in psychological domains (n=11), followed by anthropometric (n=9), quality of life (n=7), and metabolic metrics (n=7). The review findings suggest the integration of meditation with conventional treatment modalities. Preliminary data indicate that MMIs have the potential to improve psychosocial well-being and quality of life among PCOS-affected women. However, adequately powered studies with extended follow-up periods are required to investigate the mechanisms and therapeutic efficacy of MMIs, particularly concerning reproductive outcomes and weight management. Furthermore, diligent monitoring and reporting of adverse events and adherence are essential for a comprehensive understanding of MMI utilization in PCOS management. Copyright 2024 Rao, Pena, James, Phadke, Grover, Blendis, Choudhary and Kampegowda. -
A novel chemical route for low-temperature curing of natural rubber using 2,4 dihydroxybenzaldehyde: improved thermal and tensile properties
A novel method for chemically curing natural rubber (NR) using 2,4-dihydroxybenzaldehyde (DHB) at low temperatures has been discovered. Adding varying amounts of DHB to NR increases the crosslinking between the NR molecular chains. The chemical reaction between NR molecular chains and DHB was confirmed through Fourier transform infrared (FTIR) and proton nuclear magnetic resonance (NMR) spectra. From the thermogravimetric analysis (TGA), the thermal stability and activation energy of degradation were determined. The variation in glass transition temperature (Tg), as an indication of increased crosslink density, reducing the mobility of rubber chains, has been confirmed through differential scanning calorimetry (DSC). The addition of DHB to latex significantly enhanced the thermal stability of the rubber. An increase in the activation energy of 5.52% was observed upon the addition of 80mL DHB into NRL when compared to the uncured one. Furthermore, the tensile properties, in terms of tensile strength and modulus of elasticity of rubber, were drastically increased through DHB crosslinking. Tensile strength values of rubber were found to increase by reducing its elongation at break due to the formation of crosslinks between the macromolecular chains. NR cured with 80mL DHB exhibited superior tensile and thermal properties among the series of cured samples. By adding 80mL of DHB, the tensile strength increased by 390% and the elongation at break decreased by 10%. The advantage of this curing method is that, it is an effective technique for crosslinking NR directly from NR latex at comparatively low temperature. Graphical abstract: (Figure presented.) Iran Polymer and Petrochemical Institute 2024. -
Investigation of photoluminescence emission from ?-Ga2O3: Ce thin films deposited by spray pyrolysis technique
Ce doped Ga2O3 thin films for different doping concentrations (3 at%, 4 at%, 5 at%, 6 at%, 7 at%, and 8 at%) were deposited by spray pyrolysis method. X-ray diffraction analysis confirmed the crystalline structure as that of monoclinic ?-Ga2O3. The effect of doping on the band gap of the material was studied by UV-Visible spectroscopic method and the thickness of the film and refractive index were measured by ellipsometric technique. The photoluminescence excitation and emission spectra were recorded for pure and doped samples and the energy band scheme with possible radiative and nonradiative transitions were elucidated. Concentration quenching effect was observed, and the underlying mechanism responsible for quenching effect was studied based on Dexter theory. 2021 Elsevier B.V. -
In vitro cytotoxicity studies of Ga2O3 microstructures on L929 and MCF-7 cell lines using MTT assay
Considering the therapeutic promise of gallium, its compounds are currently undergoing preclinical and clinical development in different phases. In this work, Ga2O3 microstructures were synthesized using hydrothermal methods followed by calcination at (Formula presented.). For structural and morphological analysis, x-ray diffraction spectrum and field emission scanning electron microscopy images were used. In vitro cytotoxicity and in vitro anticancer effects of the sample were determined by cell culture imaging and MTT assay method. The studies were carried out on L929 and MCF-7 cell lines. The present study reveals the possibility of extending Ga2O3 for anticancer drug applications. The Author(s), under exclusive licence to The Materials Research Society 2024. -
Characterisation of Sn-Cl co-doped ?-Ga2O3 thin films deposited via spray pyrolysis and their application in UV detector devices
Ga2O3, an ultrawide bandgap semiconducting oxide, is currently emerging as a promising candidate for various applications, such as power devices, solar-blind UV detectors, high temperature oxygen sensors and biomedical imaging. One significant limitation hindering the application of Ga2O3 as a wide-bandgap semiconductor is its poor conductivity. In this work, we investigate whether doping with tin and chlorine can mitigate this condition. Sn-Cl co-doped ?-Ga2O3 thin films are deposited on glass substrates using spray pyrolysis technique. The deposited films are subjected to comprehensive analysis, including structural, optical and morphological measurements using techniques like X-ray diffraction, UV-Vis-NIR spectroscopy, X-ray photoelectron spectroscopy and EDX studies. Electrical properties are assessed using the four-probe method and Hall measurements. The best conductivity of 8.86 ??1m?1 is observed when 8.68 at% of Sn and 3.37 at% of Cl were co-doped into Ga2O3 (S(3)) and its optical band gap is calculated to be 4.65 eV. This is about five orders of improvement in conductivity as compared to that of pure Ga2O3 thin film deposited by the same method. Furthermore, we have constructed a deep UV detector utilizing doped ?-Ga2O3 thin films as the semiconducting absorbing layer. The detector demonstrated the highest responsivity of 2.54 10?4 A/W at 260 nm and the corresponding specific detectivity is 1.4 109 Jones. The current research validates the potential of Sn-Cl co-doped ?-Ga2O3 thin film as an excellent choice for UV detector application. 2024 Elsevier B.V. -
Effect of substrate temperature on the properties of spray deposited Ga2O3 thin films, for solar blind UV detector applications
In this work, Ga2O3 thin films were deposited on glass substrates by chemical spray pyrolysis technique at three different substrate temperatures 350 C, 400 C, and 450 C. The structural, optical, morphological and electrical characteristics of the deposited sample thin films were investigated. From the studies, it is understood that by tuning substrate temperature, we can extensively change the properties of the film. Optimum temperature for coating Ga2O3 thin films was understood and the work was extended to demonstrate a simple deep UV detector, working in photoconductive mode. The fabricated device exhibit medium response to UV light at 254 nm. The present work report the fabrication of solar blind UV detector based on Ga2O3 thin film, grown using low cost, easily scalable spray deposition technique. 2022 Elsevier B.V. -
Empirical study on The Role of Machine Learning in Stress Assessment among Adolescents
Stress is a psychological condition that people who are experiencing difficulties in their social and environmental well-being face, and it can cause several health problems. Young individuals experience major changes during this crucial time, and they are expected to succeed in society. It's critical for people to master appropriate stress management techniques to ensure a smooth transition into adulthood. The transition to new settings, lifestyles, and interactions with a variety of people, things, and events occurs during adolescence. In this study, a dataset was utilized to classify 520 Indian individuals' stress levels into three categories: normal, moderate, and severe. Support Vector Machines, KNN, Decision Trees, Naive Bayes and CNN were among the different classification techniques that were taken into consideration. The CNN Algorithm was found to be the most reliable method for categorizing diseases linked to mental stress. The study's main goal is to create a classification model that can correctly classify a variety of samples into distinct levels of psychological discomfort. 2023 IEEE. -
Navigating the emotional maze: Understanding Adolescent suicidal ideation using CNN-LSTM model
Teenage suicidal ideation is on the rise, which emphasizes how crucial it is to recognize and comprehend the variables that contribute to this problem. Convolutional neural networks (CNNs), which are complex machine learning models capable of analysing intricate relationships within a network, are one possible strategy for addressing this issue. In our study, we employed a CNN-LSTM hybrid model to explore the complex relationships between teen suicide ideation and various risk variables, including depression, anxiety, and social support by analysing a substantial dataset of mental health surveys, seeking patterns and risk factors associated with suicidal thoughts. Our objective was clear: identify adolescents prone to suicidal ideation. With 24 parameters and a sample size of 3075 subjects, our model achieved an impressive F1-score of 97.8%. These findings provide valuable insights which helps in developing effective preventive interventions to address adolescent suicidal ideation, finding out the important patterns and risk variables related to suicidal thoughts. The study results offer important direction for developing preventive interventions that successfully address adolescent suicidal ideation. 2024 - IOS Press. All rights reserved. -
Computational Methods to Predict Suicide Ideation among Adolescents
Suicide has been a prominent cause of death worldwide, regardless of age, sex, geography, and so on, and predominantly suicide among teens, increased as the years have passed. Suicide ideation, suicide risk, suicide attempts have been studied extensively, and the most common cause has been identified as depression, followed by familial concerns, hereditary factors, stress, avoidance fear, and a variety of other variables. When visited by a doctor, most adolescents are unaware of their mental state and hence do not take action on their own or are not assisted by family or peer members to overcome their fear of social stigma or the treatment they must undergo. According to popular belief, early treatment and detection are the most effective ways to reduce the risk of suicide. As a result, the focus of this study is to illustrate some of the computational strategies utilized in deep learning and machine learning fields to detect kids at risk of suicide 2022 IEEE. -
Artists' moving image: South Asian trajectories /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.191-201, ISSN No: 2045-6298. -
Enhanced Data Security Architecture in Enterprise Networks
Encryption and storing important information is one of the risky and most challenging tasks. It is the need of the hour in todays fast growing technological transformations that the world is undergoing. A simple Enterprise network is the communication backbone of any organization. It mostly provides better information storage and efficient retrieval, which helps the organization to function smoothly, without having to think twice about their crucial datas security aspects. The information technology paradigm, cloud computing is used to help the organization to focus on its core business. In cloud computing is dealing with many services. That service is used for provide Platform service with infrastructure and software service. This paper, promotes the idea of combining various security and encryption algorithms to connect different enterprise networks using cloud computing, security layer concepts and giving no room for hackers to intrude into the confidential system of data. Springer Nature Switzerland AG 2020. -
Survey study on the methods of bird vocalization classification
The technologies holds the ability to change the world. Current digital era is a product of the evolutionary technologies. It created the necessity to increase the Human Computer Interaction (HCI) and it became one of the most emerging research areas of the decade. HCI is an interface between the users and the system to improve the interaction. HCI concept came into existence in early 1980's. One of the emerging new research area in HCI is Context Aware System (CAS). The technological advancements in HCI created a new outlook in the research of CAS. CAS is a system which understand the user, their surroundings, and location. CAS make this possible by processing the environmental and bio-acoustic. Sound is one of the important media for both humans and animals to communicate and understand information. Bird sound, vehicle sound, wind sound etc. are some of the environmental and bio acoustics. Processing these sounds or signals will help us to create a better performing CAS. This paper profiles a survey study on bird sound classification and identification. Automatic identification of bird sound is one among the difficult task in signal processing. Also, the paper will profile the previous research works on various phases in bird vocalization processing; such as preprocessing, feature selection and classification. 2016 IEEE.