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White Light Emission from Dy3+-Activated CaY2O4 Phosphor
Synthesis and characterization of a Dy3+-activated calcium yttrium oxide (CaY2O4) phosphor are reported. The CaY2O4:Dy3+ (1.5 mol%) phosphor is synthesized using a modified solid-state reaction technique for calcination and sintering. The cubic structure is revealed by the X-ray diffraction technique. The morphology and particle size distribution of the prepared phosphor are investigated by the FEGSEM technique. The chemical bonds and functional group analysis are confirmed by the FTIR. A photoluminescence analysis of the CaY2O4:Dy3+ phosphor shows dual excitation wavelengths at 285 and 348 nm, especially in the ultraviolet region. At 383 nm, three distinct emission peaks are found at the wavelengths 238, 485, and 571 nm. The spectroscopic parameters are calculated using the CIE chromaticity coordinates. The CIE coordinates of the Dysprosium ion-activated CaY2O4 phosphor (1.5 mol%) show an emission near the white light region of the chromaticity diagram, suggesting that it is suitable for W-LED applications. The Author(s), under exclusive licence to Springer Nature Switzlerland AG 2024. -
Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model
Over two centuries, concrete has been crucial to building. Thus, eco-friendly concrete is being developed. Emulating these tangible traits has recently gained popularity. Ceramic waste concretes mechanical properties were modeled in this study. Ceramic waste percentages ranged from 5 to 20%. Compressive and tensile concrete strengths were modeled. To predict concrete hardness, regression modeling and artificial neural network (ANN) were used. Model performance was evaluated using prediction coefficients and root-mean-square error (RMSE). ANN models outperformed linear prediction with a coefficient for determination (R2) of 0.97. ANN models achieved root-mean-square errors (RMSEs) of 1.22MPa, 1.21MPa, and 1.022MPa after 7, 14, and 28days of retraining, respectively. Linear regression model showed RMSE values of 1.21, 1.32, and 1.27MPa at 7, 14, and 28days, respectively. In determining the compressive and tensile strength, the R2 was 0.70, meanwhile the ANN model achieved 0.87. Given its accuracy in predicting the strength qualities of ceramics cement and structural stiffness, the ANN model presents a promising tool for representing various types of concrete. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Climate Change inflicted Environmental Degradation leading to the Crumbling of Arctic Ecosystem
The Arctic and Antarctic regions serve as the air conditioners of planet Earth. The polar regions located thousands of miles away from us determine the climatic patterns of our geographical area. They maintain our planet at bearable temperatures which are ideal for the existence of diverse flora and fauna and to support different types of ecosystems all around the world. Apart from controlling the temperatures, they also regulate ocean currents which in turn have an effect on the monsoons, winds, hurricanes etc. The poles were pristine till a few decades back. Due to mans greed, the poles started deteriorating at an alarming scale. Climate change, biodiversity changes, oil drilling, seismic testing, toxin accumulation are a few of the challenges faced by the Arctic ecosystem having serious effects on its topography, terrestrial and marine life-forms and the whole ecosystem. Due to the alarming scale of global warming, there is also the danger of permafrost meltdown which can unleash a plethora of dangerous pathogens buried underneath and also let out the huge amounts of locked down carbon. The crumbling of the polar ecosystem is leading to rampant consequences not only in the poles but also elsewhere in the world thousands of miles away. Here, we attempt to discuss the repercussions of the crumbling Arctic ecosystem due to the physical, chemical and geological changes caused by such anthropogenic activities and look at the efforts being carried out to save the Arctic ecosystem in a frantic effort to save our planet. 2024, World Researchers Associations. All rights reserved. -
Coloring of Non-zero Component Graphs
The non-zero component graph of a finite dimensional vector space V over a finite field F is the graph G(V?) = (V, E), where vertices of G(V?) are the non-zero vectors in V, two of which are adjacent if they share at least one basis vector with non-zero coefficient in their basic representation. In this paper, we study the various types of colorings of non-zero component graph. (2024), (Universidad Catolica del Norte). All rights reserved. -
Concentration-dependent luminescence characterization of terbium-doped strontium aluminate nanophosphors
The present investigation describes the synthesis of luminescent terbium-doped strontium aluminate nanoparticles emitting bright green light, which were synthesized through a solid-state reaction method assisted by microwave radiation. Various samples containing different concentrations of Tb were synthesized, and an analysis of their structural and morphological features was conducted using powder x-ray diffraction, Fourier transform infrared spectroscopy and field emission scanning electron microscopy. The band gaps of the samples were determined utilizing the KubelkaMunk method. The quenching mechanism observed was identified to be due to dipoledipole interaction using the Dexter theory. The optimized sample with a terbium concentration of 4at.% has a luminescence lifetime of 1.05 ms with 20.62% quantum efficiency. The results of this study indicate that the terbium-doped strontium aluminate fluorescent nanoparticles exhibit promising potential for a wide range of applications, including bioimaging, sensing and solid-state lighting. 2024 John Wiley & Sons Ltd. -
Development of a fluorescent scaffold by utilizing quercetin template for selective detection of Hg2+: Experimental and theoretical studies along with live cell imaging
Quercetin is an important antioxidant with high bioactivity and it has been used as SARS-CoV-2 inhibitor significantly. Quercetin, one of the most abundant flavonoids in nature, has been in the spot of numerous experimental and theoretical studies in the past decade due to its great biological and medicinal importance. But there have been limited instances of employing quercetin and its derivatives as a fluorescent framework for specific detection of various cations and anions in the chemosensing field. Therefore, we have developed a novel chemosensor based on quercetin coupled benzyl ethers (QBE) for selective detection of Hg2+ with naked-eye colorimetric and turn-on fluorometric response. Initially QBE itself exhibited very weak fluorescence with low quantum yield (? = 0.009) due to operating photoinduced electron transfer (PET) and inhibition of excited state intramolecular proton transfer (ESIPT) as well as intramolecular charge transfer (ICT) within the molecule. But in presence of Hg2+, QBE showed a sharp increase in fluorescence intensity by 18-fold at wavelength 444 nm with high quantum yield (? = 0.159) for the chelation-enhanced fluorescence (CHEF) with coordination of Hg2+, which hampers PET within the molecule. The strong binding affinity of QBE towards Hg2+ has been proved by lower detection limit at 8.47 M and high binding constant value as 2 104 M?1. The binding mechanism has been verified by DFT study, Cyclic voltammograms and Jobs plot analysis. For the practical application, the binding selectivity of QBE with Hg2+ has been capitalized in physiological medium to detect intracellular Hg2+ levels in living plant tissue by using green gram seeds. Thus, employing QBE as a fluorescent chemosensor for the specific identification of Hg2+ will pave the way for a novel approach to simplifying the creation of various chemosensors based on quercetin backbone for the precise detection of various biologically significant analytes. 2024 Elsevier B.V. -
Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments
Air pollution poses a significant environmental and health challenge in Delhi, India. This research focuses on predicting the Air Quality Index (AQI) for Delhi utilizing machine learning techniques. The research methodology encompasses comprehensive steps such as data collection, preprocessing, analysis, and modeling. Data comprising various pollutants and meteorological parameters were gathered from the Central Pollution Control Board (CPCB) spanning from January 1, 2016, to December 30, 2022. Missing values were imputed using the IterativeImputer method with RandomForestRegressor as the estimator. Data normalization and variance reduction were achieved through Box-Cox transformation. Spearman Rank Correlation analysis was employed to explore relationships between features and AQI. Initial evaluation of nine machine learning algorithms identified Random Forest and XGBoost as the top performers based on accuracy. These algorithms were further optimized using 5-fold cross-validation with RandomizedSearchCV. The results demonstrated the efficacy of both algorithms in AQI prediction. Notably, PM2.5 and CO concentrations emerged are most influential features, highlighting the potential for AQI improvement in Delhi through the reduction of these pollutants. This research distinguishes itself through a meticulous examination of the complex interconnections between pollutants and AQI, providing invaluable insights to inform targeted interventions and enduring policies geared towards improving air quality in Delhi. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Enhancement of the Electrochemical behaviour of Carbon Black via a defect induced approach
In order to address the rising global concern of energy storage, carbon-based materials have established themselves due to their distinct features. Despite the demand for the fabrication of supercapacitors from natural, inexpensive carbonaceous materials is on the rise, the intrinsic disorders present in such materials hinder their performance, and hence, tuning these defects can aid in the improvement of their electrochemical performance. In this study, carbon black is introduced with defects in the form of oxygen functional groups via oxidation and thermal exfoliation and the impact on its electrochemical performance is studied. Careful tuning of the type of oxygen functional moieties at the basal plane of the carbon lattice is observed to be the contributing factor for the electrochemical behaviour. The distortion in the graphitic lattice caused by the epoxy and hydroxyl groups alters the specific surface area, porosity, and thermal stability, facilitating easier ion diffusion rates and enhanced faradaic reactions. The obtained specific capacitance of the thermally exfoliated carbon black is as high as 246.49 Fg?1 in a three-electrode system and 82.85 F/g in a two-electrode setup, owing to an energy density of 5.63 Whkg?1 and a power density of 189.75 Wkg?1. It has also exhibited excellent cyclic stability and capacitance retention up to 4000 cycles. The equivalent series resistance is found to decrease from 5.67 to 4.96 ? making the material conductive. As a result, the electrochemical properties of carbon black can be enhanced by tuning the oxygen functional groups, making it a promising supercapacitive material. Graphical Abstract: (Figure presented.). Qatar University and Springer Nature Switzerland AG 2024. -
Quantum computational, solvation and in-silico biological studies of a potential anti-cancer thiophene derivative
Heterocyclic molecules display a wide spectrum of properties that span both material and biological domains. Material properties stem from their interactions in the bulk, where a large number of molecules of the same type get together resulting in an enhancement of properties. However, biological properties emanate from the interaction of a single or a few molecules with a biologically functional macromolecule. Computational tools offer a particularly useful way of theoretically studying molecules to arrive at a conclusion regarding such properties, even though they may vary when experimentally evaluated. This study concerns itself with the theoretical investigation comprising density functional theory calculations, topological analyses and in-silico biological evaluation of a thiophene compound, i.e. the title compound. Density functional theory was used to compute properties of the title molecule and their variations in unsolvated and solvated phases using Gaussian 09. The molecule in solvent phases encompassing organic polar protic, organic polar aprotic and inorganic polar protic nature have been subjected to theoretical investigations. The suitability of the molecule for deployment as a modern optical material is examined with positive results. Topological characteristics of the molecule were evaluated using Multiwfn 3.8 to examine electron density distribution and the possible resulting covalent, non-covalent and weak interactions because of such distribution. The potency of the molecule towards brain cancer was evaluated by molecular docking with Auto Dock Tools against two brain cancer protein targets 6ETJ and 6YPE with a good docking score of ?6.63 and ?6.21 kcal mol?1 respectively and the resulting interactions visualized and its pharmacokinetic properties obtained using online tools. 2024 Elsevier B.V. -
Neuropalliative Care Needs Checklist for Motor Neuron Disease and Parkinson's Disease: A Biopsychosocial Approach
Objectives: Neurodegenerative disorders necessitate comprehensive palliative care due to their progressive and irreversible nature. Limited studies have explored the comprehensive assessment needs of this population. This present study is designed to develop a checklist for evaluating the palliative care needs of individuals with motor neuron disease (MND) and Parkinson's disease (PD). Materials and Methods: The checklist was created through an extensive literature review and discussions with stakeholders in neuropalliative. Feedback from six field experts led to the finalisation of the checklist, which comprised 53 items addressing the unique biopsychosocial needs of MND and PD. Sixty patient-caregiver dyads receiving treatment in a tertiary referral care centre for neurology in south India completed the checklist. Results: People with MND had more identified needs with speech, swallowing, and communication, while people with PD reported needs in managing tremors, reduced movements, and subjective feelings of stiffness. People denying the severity of the illness was found to be a major psychosocial issue. The checklist addresses the dearth of specific tools for assessing palliative care needs in neurodegenerative disorders, particularly MND and PD. By incorporating disease-specific and generic items, the checklist offers a broad assessment of patients' multidimensional needs. Conclusion: This study contributes to the area of neuropalliative care by developing the neuropalliative care needs checklist (NPCNC) as a valuable tool for assessing the needs of individuals with neurodegenerative diseases. Future research should focus on refining and validating the NPCNC with larger and more diverse groups, applicability in different contexts, and investigating its sensitivity to changes over time. 2024 Published by Scientific Scholar on behalf of Indian Journal of Palliative Care. -
Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms
The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the SensorNetGuard dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The SensorNetGuard dataset is available on the IEEE data port and Kaggle platform. 2024, Prof.Dr. ?skender AKKURT. All rights reserved. -
Hazard identification of endocrine-disrupting carcinogens (EDCs) in relation to cancers in humans
Endocrine disrupting chemicals or carcinogens have been known for decades for their endocrine signal disruption. Endocrine disrupting chemicals are a serious concern and they have been included in the top priority toxicants and persistent organic pollutants. Therefore, researchers have been working for a long time to understand their mechanisms of interaction in different human organs. Several reports are available about the carcinogen potential of these chemicals. The presented review is an endeavor to understand the hazard identification associated with endocrine disrupting carcinogens in relation to the human body. The paper discusses the major endocrine disrupting carcinogens and their potency for carcinogenesis. It discusses human exposure, route of entry, carcinogenicity and mechanisms. In addition, the paper discusses the research gaps and bottlenecks associated with the research. Moreover, it discusses the limitations associated with the analytical techniques for detection of endocrine disrupting carcinogens. 2024 Elsevier B.V. -
Dirty Tracks Across the Border: Global Operations of Extraction, Labour and Migration at a Railway Station on the BiharNepal Border
This article is based on an ethnography of the railway siding at Raxaul Junction railway station, a town on the BiharNepal border, which finds itself at the intersection of a massive logistical exercise by China in the form of the Belt Road Initiative, counter-logistical apparatus building by India and incremental hardening of an otherwise open border by Nepal. The article will analyse in detail the intricate network of the labour market that operates at and through the railway siding. It will also trace the origins of commodities used in the cement factories in the industrial corridor of Nepal that are extracted from some of the most deprived regions of India at great human and social costs. Finally, I will describe some of the latest exercises in logistical operations such as containerisation, opening of a new land port, the Integrated Check Post in Raxaul and operationalisation of a new dedicated freight corridor from Vishakhapatnam port to Raxaul, which is reconfiguring the logistical arrangements away from Kolkata and Haldia port and their implications on labour and labour practices. The Raxaul railway siding will be, hence, studied on multiple scales: global, national and local. The article will also try to understand the transformation of this very peculiar border town located on a unique border. This transformation is creating new labour processes, migratory processes and networks, and new modes of production of workers subjectivities and resistance along the global logistical apparatus and supply chains. It will also open up the possibilities of thinking conceptually about South Asian Border Systems. 2024 South Asian University. -
A versatile sensor capable of ratiometric fluorescence detection of trace water and turn-on detection of Cu2+ modulating the binding interaction of a Cu(ii) complex with BSA and DNA complemented by docking studies
A fluorescent molecule, pyridine-coupled bis-anthracene (PBA), has been developed for the selective fluorescence turn-on detection of Cu2+. Interestingly, the ligand PBA also exhibited a red-shifted ratiometric fluorescence response in the presence of water. Thus, a ratiometric water sensor has been utilized as a selective fluorescence turn-on sensor for Cu2+, achieving a 10-fold enhancement in the fluorescence and quantum yield at 446 nm, with a lower detection limit of 0.358 ?M and a binding constant of 1.3 106 M?1. For practical applications, sensor PBA can be used to detect Cu2+ in various types of soils like clay soil, field soil and sand. The interaction of the PBA-Cu(ii) complex with transport proteins like bovine serum albumin (BSA) and ct-DNA has been investigated through fluorescence titration experiments. Additionally, the structural optimization of PBA and the PBA-Cu(ii) complex has been demonstrated by DFT, and the interaction of the PBA-Cu(ii) complex with BSA and ct-DNA has been analyzed using theoretical docking studies. 2024 The Royal Society of Chemistry. -
Online cooperative learning: exploring perspectives of pre-service teachers after the pandemic
Mainly, research has explored pre-service teachers perspectives toward cooperative learning within face-to-face teaching. However, in a post-pandemic scenario, previous research has yet to effectively explore pre-service teachers (PSTs) perspectives toward online cooperative learning (OCL) in teacher education programs. So, recognizing the gap in the literature, this paper aims to explore the perspectives of PSTs towards OCL. The researchers employed a qualitative research design for the present study. The researchers conducted semi-structured interviews with 10 PSTs who underwent OCL during the pandemic. These PSTs may possess digital proficiency, virtual collaboration abilities, flexibility in evolving educational environments, and an enhanced understanding of online cooperative learning methodologies within modern education. Researchers employed a thematic analysis to analyze the qualitative data obtained. The various themes that emerged from the study are perceived benefits of OCL, challenges to OCL, technological proficiency, learning strategies and support, and building a supportive online learning community. Future researchers may contribute to advancing effective online learning practices by gaining a deeper understanding of pre-service teachers perspectives towards OCL through research on a larger scale, including various teacher education programs in various countries. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Experimental Investigation of Uniaxial Compressive Behavior of Composite Columns without and with Full and Partial CFRP Wraps
Concrete columns are the backbone of any major structure, and their strengthening, repair, and retrofit have always drawn special research attention. One of the techniques for strengthening and improving the ductility of concrete columns has been the application of carbon fiber-reinforced polymer (CFRP) materials. A total of 43 columns of different configurations were experimentally investigated to evaluate the uniaxial compressive behavior of composite columns. Experimental and international code-recommended load-carrying capacities, stress-strain relations, axial stiffness, ductility factor, and failure modes were examined in the study. When fully wrapped, the strength of both plain cement concrete and reinforced cement concrete columns improved by 21% each with reference to the unwrapped columns. In addition to providing the advantages of external confinement to the columns, full wrapping contributed to a strength increment of 21%, which compared well with the steel reinforcement contribution to a strength increment of 28% to 39%. The partial wrapping technique was found to be an economical alternative to the full wrapping technique, with strength enhancements of 6% to 12% in the case of both plain cement concrete and reinforced cement concrete partially wrapped columns. Two regression models for the load-carrying capacity for columns with and without wraps were developed with four key performance parameters: percentage steel reinforcement, percentage concrete, percentage carbon fiber-reinforced polymer wrap, and the weight of the specimen. The formulated models were validated and found to be robust and consistent with the results. 2024 American Society of Civil Engineers. -
Magnetohydro-convective instability in a saturated DarcyBrinkman medium with viscous dissipation
The influence of dissipation with viscosity on magnetohydro-convective instability in a saturated DarcyBrinkman medium is examined. The bottom boundary is designated as adiabatic, whereas the top boundary is isothermal. Numerical linear stability analysis investigates normal modes that disturb the horizontal base flow at different inclinations. The case study shows that the most unstable disturbances are horizontal rolls, normal modes characterized by a wave vector perpendicular to the main flow direction. The horizontal rolls are the favored instability mode. Barletta et al. also showed that horizontal rolls are more unstable than any other oblique roll mode in the hydromagnetic scenario. This finding provides insights into the behavior of MHD fluid flow and heat transfer in porous media, with implications for applications in geoscience, engineering, and environmental science. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Machine Learning in Financial Distress: A Scoping Review
Predicting financial distress is crucial for stakeholders, policymakers, governments, and management in decision-making processes. Researchers have developed various prediction models encompassing both traditional and machine-learning approaches. Notably, recent attention has shifted towards employing machine learning models to address the limitations of traditional methods. This study seeks to offer insights into current trends, identify gaps, and suggest future research directions using machine learning models for financial distress prediction, employing the PRISMA Extension for Scoping Reviews methodology. To achieve this, a comprehensive search was conducted across three databasesScience Direct, EBSCO, and ProQuestspanning from 2020 to 2023, identifying 34 relevant articles for analysis. The findings underscore the prevalent use of Support Vector Machine in financial distress prediction, followed by the Random Forest Classifier and Artificial Neural Network, with little attention paid to other models. Furthermore, the study underscores the necessity for more research in developing countries, noting the predominance of studies from developed nations. While machine learning models hold promise for enhancing the accuracy and efficiency of financial distress prediction, additional research is imperative to evaluate their effectiveness and applicability across diverse contexts. This scoping review aims to furnish researchers, policymakers, and institutions with valuable insights and policy recommendations, shedding light on underexplored machine-learning techniques. 2024, Iquz Galaxy Publisher. All rights reserved. -
Forecasting the Volatility of Indian Forex Market: An Evidence from GARCH Model
Forecasting the volatility of forex market will create more trading opportunities to investors, despite of ups and downs in the forex market. The present study attempted to examine how the volatility in the exchange rate between Indian rupee and selected four foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the market return. The data, used in the present study, covered the daily price observation of four foreign currencies, for a period of 5 years, from 2019-2023. The GARCH (1, 1) (generalized autoregressive conditional hetero skedasticity) was used for develop the model for foreign exchange (FX) rates volatility. Mean equation model confirmed that the series had attained stationary and previous price did influence the current price. It was also supported by co-efficient values in the variance equation. The co-efficient value, in the variance equation, was around one, which showed that the forex market was efficient. Further, it was validated that the volatility shocks in forex market were quite persistent. The active investors in the market may use this opportunity immediately. The policy maker may correct this deviation through timely intervention in the currency market. 2024, Iquz Galaxy Publisher. All rights reserved. -
Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem
Portfolio optimization has long been a challenging proposition and a widely studied topic in finance and management. It involves selecting and allocating the right assets according to the desired objectives. It has been found that this nonlinear constraint problem cannot be effectively solved using a traditional approach. This paper covers and compares quantum-inspired versions of four popular evolutionary techniques with three benchmark datasets. Genetic algorithm, differential evolution, particle swarm optimization, ant colony optimization, and their quantum-inspired incarnations are implemented, and the results are compared. Experiments have been carried out with more than 10 years of stock price data from NASDAQ, BSE, and Dow Jones. This work proposes several enhancements to allocate funds efficiently, such as improved crossover techniques and dynamic and adaptive selection of parameters. Furthermore, it is observed that the quantum-inspired techniques outperform the classical counterparts. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.