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Fossil fuel derived GQD as a photosensitizer in dye-sensitized solar cells
Solar energy is an abundantly available renewable source, and several generations of photovoltaic cells have been developed for harnessing it. Dye-sensitized solar cells (DSSC) are viable and potent solar energy harvesters. Sensitizer, a vital part of DSSC, has been researched for years. Alternatively, fossil fuel-Lignite is one of the world's least explored energy sources. Unfortunately, it has been used as a fuel for power generation and tagged as a pollutant. Therefore, in this study, we use lignite-derived graphene quantum dots (GQD) as a DSSC sensitizer and attempt to add value. GQDs with varied bandgaps were obtained and used as sensitizers, and a maximum PCE of 2.87 % was obtained. Additionally, GQD sensitizers were exposed to UV light for 48 h, and the fabricated device exhibited 2.90 % efficiency, showing the photostability of GQDs. Furthermore, the device showed a higher Rrec of 166.57 ?, substantiating the better performance of DSSC. Thus, sensitizers derived from lignite showed a novel use for feedstock previously used for combustion. 2023 Elsevier B.V. -
Extraction and characterization of preformed mixed phase graphene sheets from graphitized sub-bituminous coal
In present paper, a facile method is reported to extract mixed phase nanometre-sized carbon sheets from sub-bituminous coal. The lattice constants (La and Lc) of sub-bituminous coal were calculated to be 4.82 and 1.41 nm, respectively. The aromatic layers and average number of carbon atoms in the aromatic lamellae were estimated as 5 and 8, respectively. The obtained graphene sheets exhibits broadened D and G band in addition to a very broad 2D bump. Defect to graphitic ratio is found to be 0.54 indicating less disorder in graphene nanomaterial formed. This is further corroborated by (ID/ID') ratio which was observed to be 3.40, confirming the defect has originated from boundary. The SEM analysis reveals the formation of large number of carbon layers with different shape in the nanometer scale range. Formation of graphene dots in the shape of hexagonal, spherical, graphene layers and corn shaped carbon nanotubes are noticed in the TEM image. -
Lignite-derived nanocarbon as surface passivator and cosensitizer in dye-sensitized solar cell
Interfacial exciton recombination and narrow absorption region are two bottlenecks that limit the performance of a dye-sensitized solar cell (DSSC). The present study focuses on improving the solar cell's efficiency by utilizing a lignite-derived nanocarbon that behaves as a surface passivator and cosensitizer. Incorporating nanocarbon enhanced the spectral absorption region of the N719 dye with a bathochromic shift and played the role of a cosensitizer. In addition, the quenched photoluminescence spectra revealed that nanocarbon also aids in the swift transfer of electrons to the conduction band of TiO2 by reducing the exciton recombination and acting as a surface passivator. On measuring the fabricated DSSC under AM 1.5G irradiation with the intensity of 100 mW/cm2, the nanocarbon-based device exhibited an efficiency (?) of 9.02% with a photocurrent density of 20.45 mA/cm2, outperforming the pristine device (? = 6.21%). An enhancement of 45% in the power conversion efficiency was achieved. Thus, the results unveiled that nanocarbons derived from pollution-causing fuel synergistically aided in enhancing the performance of DSSC. 2024 Elsevier Ltd -
Development and Validation of the Social Media Self-Esteem Scale for Adolescents
Development of the self is a vital aspect during the period of adolescence. Interaction with peers contributes to the development of various aspects of self. Due to the technological advances in todays times, adolescents interact with their peers through social media sites and portals. It is essential to study this development in light of the increasing use of social media by adolescence. Thus, the study aimed at developing an item pool to tap the construct of social media influencing self-esteem of adolescents following the procedure of tool construction. Participants included adolescents ranging between 16 to 18 years of age, who have at least one social media account for personal use. There were 110 participants for the first phase and 397 participants for the second phase of the study. The scale has eight items with the overall reliability of .7. It indicates a fitting measure of self-esteem influenced by social media, with looking-glass self theory, according to which individuals develop their self, based on their perceptions of others responses to their behaviour. Copyright 2020, IGI Global. -
Efficiency Analysis of Modified Sepic Converter for Renewable Energy Applications
A boosting module and a traditional SEPIC (single ended primary inductance converter) are combined to create the suggested circuit. As a result, the converter gains from the SEPIC convertera's many benefits. Also, the converter that is being presented is appropriate for renewable energy sources due to its high voltage gain and continuous input current. In comparison to a traditional SEPIC with a single-controlled switch, it offers a higher voltage gain. The voltage gains of the converter that has been suggested is closely related to that of the converter that was recently developed. This converter was constructed on the foundation of the conventional converter, as well as the conventional DC-to-DC converter. One of the most important characteristics of a projected converter is that it is equipped with a single controlled device and has the capability to increase voltage gain without the utilisation of a coupled inductor structure or transformer. The non-idealities of the semiconductor devices and passive components have been taken into consideration in the analysis of voltage gain in continuous current mode (CCM). The conventional SEPIC converter can be modified by incorporating capacitors and diodes. The experimental results indicate that this converter can amplify the output voltage by approximately 10 times and has an efficiency of around 97%. The Authors, published by EDP Sciences, 2024. -
Change in Outlook of Indian Industrial OEMs Towards IIoT Adoption During COVID-19
Industrial Internet of Things (IIoT) is witnessing a steady increase in adoption by infrastructure and process industries. Industrial equipment manufacturers are one of the key stakeholders in this digitalization journey. The adoption of IIoT by the equipment manufacturers has been slower due to various valid reasons. The present pandemic COVID-19 created disruption in the factory operations in many parts of the world. This consequence has been hard on the manufacturing industry including the equipment manufacturers, and many of their strategic projects are slowing down or derailed. In India, a strict lockdown of three weeks which was later extended for another seven weeks was by far the longest lockdown effecting the industry and the equipment manufacturers. This study probes the impact of COVID-19 on the mindset of original equipment manufacturers (OEMs) towards adoption of IIoT. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
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. -
Sustainable removal of reactive blue 222 dye from aqueous solution using wheat bran
Water pollution is one of the major concerns, and establishing a potential sustainable approach can ensure effective water resource management. Textile dye pollution poses a major environmental challenge due to its toxicity and persistence in water bodies. Conventional dye removal methods are often costly and inefficient, necessitating the exploration of low-cost, sustainable adsorbent materials. This research explores the potential of natural wheat bran (WB), an agro-industrial by-product, as an economical and environmentally beneficial bio-adsorbent for the removal of RB222 dye. Adsorbent concentration (0.56?g/100?mL), Initial dye concentration (501000?mg/L), pH (3?11), and contact time were among the major influencing variables assessed to optimise the adsorption process. The structural and morphological changes in wheat bran before and after adsorption were examined to confirm their active role in the adsorption process. Some of the characterisation techniques, like Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), X-ray Diffraction (XRD) and Zeta Potential Analysis, were performed to highlight the changes in wheat bran due to Reactive Blue 222 (RB222) adsorption. This study aims to evaluate the feasibility of using wheat bran as a low-cost bio-adsorbent for the efficient removal of Reactive Blue 222 dye from wastewater. The findings showed that wheat bran had a high adsorption capacity of 9.65?mg/g with the best dye removal efficiency of 96% under optimal conditions. Adsorption kinetics studies confirmed that the process follows the Freundlich isotherm and the pseudo-second-order model. The desorption study was also performed to examine its regenerative and reusability potential in the removal of dye. The study proves that wheat bran is a cost-effective and environmentally friendly substitute for dye removal from wastewater and emphasises the extension of value addition to agricultural waste in environmental rehabilitation. These findings highlight the potential of agro-waste valorisation in advancing sustainable wastewater treatment technologies. 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license. http://creativecommons.org/licenses/by-nc/4.0/ -
"COMMUNITY RESILIENCE AND URBAN PLANNING: INTEGRATING SOCIAL CAPITAL AND INFRASTRUCTURE STRATEGIES"
Community resilience in urban settings is increasingly recognized as critical for mitigating and recovering from various disruptions. This research investigates the integration of social capital and infrastructure strategies in urban planning to enhance community resilience. A mixed-methods approach was employed, including qualitative interviews with stakeholders and case studies, alongside quantitative surveys. Findings underscore the pivotal roles of strong social networks and resilient infrastructure in fostering community resilience. Communities with robust social capital demonstrate enhanced capacity for collective action and resource mobilization during crises. Effective infrastructure systems, including resilient buildings and reliable utilities, support continuity of services and expedite recovery. The study advocates for integrated approaches in urban planning that prioritize community engagement, equitable resource distribution, and adaptive infrastructure design. Practical implications highlight the need for policymakers and planners to invest in both social capital initiatives and resilient infrastructure projects to build sustainable and resilient urban communities. 2025 Green Publication. All rights reserved. -
Enhanced Cloud Security: Certificateless Public Auditing Using LBMHT for Malicious TPA Detection
Leveraging the Lattice-Based Merkle Hash Tree (LBMHT), the paper presents a certificate-less publicly auditing technique that targets hostile third-party auditors (TPAs) in cloud settings. Without the usual burden of certificate-heavy and certificate-management-prone identity-based or encrypted with public keys structures, this method seeks to improve information safety and integrity. To enhance the effectiveness of the Key Generation Centre (KGC), decrease complexity of space, and optimise storage in the cloud, the suggested solution utilises multi-ciphertext searching using lattice-based, certificate-less verification. The concept guarantees collision-free hashes by using a Merkle Tree structure, which makes it effective for information confirmation. Based on simulation findings, LBMHT is superior to current AES and RSA methods in terms of performance, decreasing executions, encryption, and decryption durations while simultaneously decreasing communication expenses, responding duration, and memory utilisation. The suggested approach is more economical with resources and works well in scaled cloud settings because of its increased accuracy, less effect from malevolent attackers, and improved throughput. At the end of the section, we go over the benefits of the framework, which include reduced utilisation of resources and validated indicators of performance. Compared to Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES), the suggested LBMHT method has a higher overall accuracy of 99.4 percent. The efficiency of LBMHT in securely organising and analysing data is shown by its great accuracy. 2026 American Institute of Physics Inc.. All rights reserved. -
Proposing an AI-Enabled Waste Segregation System for Domestic Settings
This paper proposes an innovative AI-based system for automated domestic waste segregation. Utilizing Teachable Machine and MobileNet, the system accurately categorizes waste into dry and wet components, laying the foundation for sustainable waste management practices. Embedded in a Raspberry Pi 4, the system integrates real-time image processing with various sensors to streamline the sorting process. While the model has been simulated due to budgetary constraints, future implementation envisions real-world application. Potential advancements include expanding the dataset, enabling multi-category waste classification, and exploring low-power alternatives. This research contributes to the evolving landscape of smart waste management, addressing environmental sustainability and the pressing need for automated, efficient waste segregation at the domestic level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Quantum-Assisted Metaheuristics for Adaptive Resource Allocation in 6G Networks
With 6G wireless communication systems, the level of demands is now ultra-low-latency, connectivity of devices in large numbers, and flexible spectrum utilization. To resolve these issues, in the current paper, the Quantum-Assisted Metaheuristic (QAM) framework is proposed that combines quantum-inspired operators and traditional metaheuristic methods of adaptive resource allocation in 6G networks. This framework uses quantum-enhanced exploration, dynamically tuned parameters and hybrid quantum-classical computing to trade-off scalability against efficiency, depending on the traffic and channel conditions. Oh! SIM Evaluating a simulated 6G environment, it proves that QAM can be used to improve spectral efficiency by up to 28 percent and the allocation latency by 31 percent over the state-of-the-art metaheuristics, and still treats users of varying densities fairly with a fairness index of 0.94. The results demonstrate the strength and extensiveness of the QAM, and makes it a viable solution to the efficient and intelligent management of resources in next generation wireless networks. 2025 IEEE.