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Changing look AGN Mrk 590: Broad-line region and black hole mass from photometric reverberation mapping
We present the results of photometric reverberation mapping observations on the changing look active galactic nucleus Mrk 590 at z = 0.026. The observations were carried out from 2018 July to December using broad-bandB-,R-, and narrow-band H ? and S ii filters. The B band traces the continuum emission from the accretion disc, the R band encompasses both the continuum emission from the accretion disc and the redshifted H ? line from the broad-line region (BLR), the S ii band contains the redshifted H ? emission and the H ? band traces the continuum emission underneath the S ii band. All the light curves showed strong variation with a fractional root-mean-square variation of 0.132 0.001 in the B band and 0.321 0.001 in H ? line. From cross-correlation function analysis, we obtained a delayed response of H ? line emission to the opticalB-band continuum emission of $21.44^{+1.49}_{-2.11}$ d in the rest frame of the source, corresponding to a linear size of the BLR of 0.018 pc. This is consistent with previous estimates using H ?. By combining the BLR size with the H ? line full width at half-maximum of 6478 240 km s-1 measured from a single-epoch spectrum obtained with the Subaru telescope, we derived a black hole mass of $1.96^{+0.15}_{-0.21}\times 10^8 {\rm M}_{\odot }$. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Optical Resonator-Enhanced Random Lasing using Atomically Thin Aluminium-based Multicomponent Quasicrystals
Photon trapping inside a gain medium using a dispersed two-dimensional (2D) passive scatterer is an impetus to obtain incoherent random lasing (ic-RL) emission due to non-resonant feedback. An optical resonator (OR) can be used to influence such lasing thresholds. Non-noble nanomaterials-based quasicrystals (QCs) are an intriguing research prospect due to their potential surface plasmon resonance (SPR) property and ability to be exfoliated into 2D. In this work, an aluminium-based multicomponent alloy (Al70Co10Fe5Ni10Cu5) has been synthesized via the arc melting method. Thereafter, ultrasonication-based liquid phase exfoliation was used to obtain 2D quasicrystals (2D-QCs). The SPR-induced light scattering properties of synthesized 2D-QCs were exploited to obtain ic-RL from DCM dye gain medium under 532 nm, 10 ns, 10 Hz pulsed laser pumping. The plasmonic field enhancement property of 2D-QCs which enables the gain medium to absorb photons outside its peak absorption band has been demonstrated. The transition from ic-RL to OR-enhanced ic-RL and vice versa in the presence of resonator walls has been achieved by tweaking the device architecture. In this way, the ability of 2D-QCs to be potential passive scatterers and the controllability of lasing thresholds in the presence of an OR has been demonstrated. 2024 Elsevier Ltd -
Electronic structure and intrinsic dielectric polarization of defect-engineered rutile TiO2
Experimental realization of colossal permittivity associated with intrinsic dielectric polarization of defect-engineered (Nb, In) co-doped rutile TiO2 appears to be most suitable for microelectronics and solid-state device applications. Combining resonant photoemission spectroscopy, X-ray absorption spectroscopy, and density functional theory calculations, we here present a coherent understanding of electronic structure, in-gap defect states, doped electron localization, and their connection with macroscopic polarization for various doping configurations. Most often, conventional sample preparation conditions introduce in-gap states of Ti3+? character, limiting the maximum achievable intrinsic polarization value. Our understanding provides a pathway to enhance intrinsic polarization and minimize dielectric loss through suitable defect-engineering. The Royal Society of Chemistry. -
Computational Relevance of Model Pruning and Quantization for Low-Powered AI
This study investigates how to make machine learning models more efficient for low-power devices by simplifying their structure and lowering size. Six models were evaluated: feed-forward neural networks (FNN), convolutional neural networks (CNN, notably VGG), decision trees, random forests, support vector machines (SVM), and auto encoders. Each was evaluated in its original form, after pruning and quantization, with a focus on model size, accuracy, and training time (as a measure of energy use). The results indicated that pruning for parameter like model size is greatly minimized with Decision Trees reducing by 95%, while it is observed quantization increases efficiency even more. In case of parameter, accuracy, it declined by about 7%. Results show that VGG model retain more accuracy than others after quantization. Pruning also increased training time, particularly for VGG and SVM models. This research thus provides insights into the trade-offs between model complexity, accuracy, and efficiency, guiding the selection for suitable models in resource-bounded environments. 2025 IEEE. -
An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities
In olden days it is difficult to identify the unsusceptible forces happening in the society but with the advancement of smart devices, government has started constructing smart cities with the help of IoT devices, to capture the susceptible events happening in and around the surroundings to reduce the crime rate. But, unfortunately hackers or criminals are accessing these devices to protect themselves by remotely stopping these devices. So, the society need strong security environment, this can be achieved with the usage of reinforcement algorithms, which can detect the anomaly activities. The main reason for choosing the reinforcement algorithms is it efficiently handles a sequence of decisions based on the input captured from the videos. In the proposed system, the major objective is defined as minimum identification time from each frame by defining if then decision rules. It is a sort of autonomous system, where the system tries to learn from the penalties posed on it during the training phase. The proposed system has obtained an accuracy of 98.34% and the time to encrypt the attributes is also less. 2021. All Rights Reserved. -
A Critical Review of Applications of Artificial Intelligence (AI) and its Powered Technologies in the Financial Industry
The present research shed light on the applications of AI technologies for the financial industry of the UK. The research has also investigated the different types of powered technologies of AI and their impact on finance operations and activities. This research possesses the tools and techniques used by the researcher in gathering the research evidence for the proper completion of the research work. 2022 IEEE. -
Aggression Behaviour and Physical Fitness of National Handball Girls Players
Aggression is one of the significant types of feeling and emotion, which is exceptionally fundamental for sports execution. It is ordinarily propelled conduct at any rate for that specific purpose of time in the genuine play, which drives a player brimming with his energies towards his point. 150 School National Handball female players aged 14-17 years who were concentrated in higher optional schools of Andhra Pradesh Rural and Urban were haphazardly chosen as subjects. An aggression scale is used to contemplate the degree of aggression in any age gathering (over 14 years). The scale comprises 55 articulations. It is a Likert type 5-guide scale toward locating the aggressive conduct among Handball players. The premise of the discoveries is that the shooters have phenomenal aggression conduct than the all-rounders and defenders and shooters have more physical fitness than the all-rounders and defenders. In the examination, the Shooter would have a more aggressive inclination and physical fitness when contrasted with all-rounders and defenders. It is very different on the grounds that the Shooter alone for example independently will confront the adversary gathering of players because of body contact and the battle for greatness will lead the shooter to more aggressive than others. 2022 by authors, all rights reserved. -
Patients' Perception about the Influence of CRM Factors in Selected Health Care Units
The primary motivation behind this exploration study targets introducing a portion of the CRM ideas and components, CRM procedure to take proactive measures towards the customer to Health supplier to improve patients' satisfaction, loyalty fabricates a decent connection with patients and increment income. Patients' consideration, needs, and making associations with patients is an everyday schedule action in a well-being supplier. CRM is fundamental in this foundation customer satisfaction, the customer saw worth and customer relationship the board upgrade the relationship of the customer with the support up the general execution of the Hospital. The exploration configuration depends on quantitative examination, hence the information was gathered through an organized poll, five Likert-scales, SPSS, relapse, and SEM Model were utilized to figure out the results. This audits and distinguishes fundamental service quality, framework, the executives, and correspondence is identified with patients' satisfaction and loyalty in the private clinics in Andhra Pradesh. This investigation features the degree of service quality of the clinic services chosen by the test respondents.. This paper is an endeavour to discover connections between patients' views of customers' satisfaction and customers' loyalty and to propose ideas to have better CRM rehearses. 2021 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License -
Risk Assessment Model for Quality Management System
The ecological and economic risk assessment system and its cost were also factored into the document. The distribution of workplace challenges and hazards, represented by quantitative or subjective occupational risk metrics, was typical in the areas of building safety and environmentally responsible workers. Environmental risk assessment refers to the identification & evaluation of risks, the formulation & application of managerial decisions to lessen the chance of unfortunate conditions, and also the substantial decrease of materials or other damages. Risk assessment facilitates the transition from an area of uncertainty to one where outcomes are more or less expected. The Deming-Shewhart cycle, which would be fully linked to the policy process and performance measurement system, appears to be the implementation technique of the ecological and economic structure under consideration. It would be a cyclical sequence of the associated effective measures. A high degree of adaptability to any internally or externally stressful conditions would be ensured by the synthesis of the fundamentals of the management system & mechanisms for controlling environmental potential costs. This also guarantees the rapid identification of expert hazards, optimization and efficiency gains. 2022 IEEE. -
From Waste to Strength: Unveiling the Mechanical Properties of Peanut-Shell-Based Polymer Composites
Peanut-shell-based polymer composites have gained significant attention as sustainable and cost-effective materials with potential applications as food packaging films, ceiling tiles, insulation panels, supercapacitors, and electrodes in various industries like the packaging industry, construction, furniture, and electronics. This review article presents a systematic roadmap of the mechanical properties of peanut-shell-based polymer composites, analyzing the influence of factors such as filler content, surface modification techniques, interfacial adhesion, and processing methods. Through an extensive literature review, we highlight the mechanical properties of peanut-shell-based polymer composites. Furthermore, challenges and ongoing research efforts in this field are discussed. This comprehensive review provides valuable insights for researchers, industry professionals, and policymakers, promoting the development and utilization of peanut-shell-based polymer composites for various applications. 2023 by the authors. -
An optimized back propagation neural network for automated evaluation of health condition using sensor data
Ships and other large equipment must meet strict standards for equipment integrity and operational dependability in order to perform missions. To meet this demand, one of the essential linkages is to guarantee the long-term safe and healthy functioning of their power transmission equipment. The Optimized Back Propagation Neural Network (OBPNN) technique used in this study introduces a unique method for monitoring sensor data and evaluating the health state, with the SVM being optimized using the fish swarm algorithm (FSA). A major problem that maintenance is facing nowadays is reliable fault prediction. One of the trickiest difficulties is arguably automatically modelling typical behaviour from condition monitoring data, particularly when there is little information about actual failures. A data-driven learning framework with the best bandwidth selection is suggested to address this challenge. It is based on nonparametric density estimation for outlier identification and OBPNN for normality modelling. The distance to the separating hyper plane's log-normalization is used to provide a health score that is also available. The algorithm's viability is shown by experimental findings while evaluating the progression of a major defect over time in a marine diesel engine. Improved prediction capabilities and low false positive rates on healthy data are realized. 2023 The Authors -
The Human Side of Sustainability: Environmental Leadership and Its Impact on Employee Growth
This paper explores the relationship between leadership and environmental sustainability, emphasizing the evolving role of leaders in fostering sustainable development. It examines how leadership practices drive environmental change by integrating sustainability into decision-making, organizational strategy, and stakeholder engagement. As organizations face increasing environmental challenges, regulatory demands, and societal expectations, leaders must balance financial performance with long-term sustainability goals. The study highlights the importance of stakeholder collaboration, corporate social responsibility, and strategic leadership in advancing sustainability initiatives. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Zeolite Framework-Anchored Carbon-Doped White Graphene as Antipoisoning Cathode Materials for Proton-Exchange Membrane Fuel Cells
Efficient, robust, and highly sustainable platinum (Pt)-free electrocatalysts are pivotal for advancing the fuel cell (FC) performance. This study introduces a facile and green approach for synthesizing a rationally designed Co-based zeolite imidazole framework (ZIF) anchored onto carbon (C)-doped white graphene (C-WG) as an electrocatalyst (Z@-C-WG) for the oxygen reduction reaction (ORR). The synergistic effects between the ZIF and C-WG yield an electrocatalyst with enriched active sites. The intrinsic dual active sites coupled with favorable physicochemical properties promote oxygen adsorption and enhance the mass transfer rate. The hybrid catalyst demonstrates significantly improved activity, stability, and poisoning resistivity compared to Pt/C. The synthesized electrocatalyst exhibits superior ORR activity with an onset potential of Eon ?0.967 V (EonPt/C ?0.94 V) in acidic medium and Eon ?0.931 V (Eon(Pt/C) ?0.919) in alkaline medium. Validation through intrinsic parameters including electrochemical active area (ECSA), active site density (ASD), mass activity (MA), and turnover frequency (TOF) corroborates the catalysts enhanced performance. The stability tested for over 35 h coupled with high methanol tolerance affirms the catalysts robust activity. The Z@-C-WG electrocatalyst surpasses Pt/C in resisting poisoning species (CO and KSCN); also, poststripping analysis strongly confirms the presence of abundant active centers. Overall, this study offers a unique perspective toward the engineering of ORR catalyst architecture for fuel cell cathode applications. 2024 American Chemical Society. -
Diametral paths in total graphs of complete graphs, complete bipartite graphs and wheels
The diametral path of a graph is the shortest path between two vertices which has length equal to diameter of that graph. In raising of structures with columns and beams in Civil Engineering, determining of diametral paths is of great significance. In this paper, the number of diametral paths is determined in complete graphs, complete bipartite graphs, wheels and their total graphs. IAEME Publication. -
Chest Diseases Prediction from X-ray Images using CNN Models: A Study
Chest Disease creates serious health issues for human beings all over the world. Identifying these diseases in earlier stages helps people to treat them early and save their life. Conventional Neural Networks play an important role in the health sector especially in predicting diseases in the earlier stages. X-rays are one of the major parameters which help to identify Chest diseases accurately. In this paper, we study the prediction of chest diseases such as Pneumonia, COVID-19, and Tuberculosis (TB) from the X-ray images. The prediction of these diseases is analyzed with the support of three CNN Models such as VGG19, Resnet50V2, and Densenet201, and results are elaborated in the terms of Accuracy and Loss. Though all three models are highly accurate and consistent, considering the factors like architectural size, training speed, etc. Resnet50V2 is the best model for all three diseases. It trained with F1 score accuracies of 0.98,0.92,0.97 for pneumonia, tuberculosis, covid respectively. 2021 -
Internalized stigma among patients with common mental disorders in South India
Introduction: Common mental disorders (CMDs), include depressive disorders and anxiety disorders, which are highly prevalent. There exists a huge stigma around mental health, and this challenge becomes further magnified in CMDs, especially in LMICs like India. Despite this burden, there is limited scientific evidence on the internalized stigma in CMDs. To address this evidence gap, this study aims to describe internalized stigma and its correlates among patients with CMDs attending the Psychiatry Outpatient Department (OPD) of an academic teaching hospital in South India. Materials and methods: A structured socio-demographic and morbidity questionnaire, along with the Internalized Stigma of Mental Illness (ISMI) Scale, was administered to 119 patients aged 18 years or older who were diagnosed with CMDs according to ICD-10 criteria. Patients with severe mental disorders and psychosis, epilepsy, intellectual disability, organic mental disorders, and those requiring hospital admissions, were excluded from the study. Results: A mild to moderate level of internalized stigma was reported among patients with common mental disorders. Age and history of suicidal thought were significant predictors of internalized stigma. Conclusion: Youth and those who have a history of suicidal thoughts tend to experience greater internalized stigma. A multi-pronged approach is needed to address internalized stigma, which includes a combination of education and awareness programs, peer support programs, psychotherapy, and medication adherence. Addressing stigma can positively influence help-seeking behavior, treatment compliance, and outcomes, thereby improving quality of life. The Author(s) 2025. -
Two-phase Sakiadis flow of a nanoliquid with nonlinear Boussinesq approximation and Brownian motion past a vertical plate: Koo-Kleinstreuer-Li model
This paper investigates the Sakiadis flow of a Al2O3-H2O nanoliquid with consistently scattered dust particles over a vertical plate. To account for the effect of the Brownian movement, the Koo-Kleinstreuer-Li model is considered. In some thermal systems such as reactor safety areas, and solar collectors, combustion works from moderate to high temperature, making the relationship between the temperature and density nonlinear. To consider this temperature-dependent density, the nonlinear Boussinesq estimation is utilized. The present physical structure, which includes energy and momentum equations, is converted into a system of ordinary, coupled, and nonlinear differential conditions through the help of similarity transformations. By using the finite difference code, the subsequent equations have been numerically solved. The impact on the velocity and the thermal profiles of the nondimensional parameters is visualized through graphs. Both the Nusselt number and friction factor strengthen with ahigher nonlinear thermal parameter in the case of nonlinear Boussinesq approximation compared to the linear Boussinesq case. Growing estimations of nonlinear thermal parameter deteriorate the thermal profile but it boosts the velocity profile of both liquid and dust phases. 2020 Wiley Periodicals LLC -
Marangoni convection flow of cntsal2o3water hybrid nanofluids with variable fluid properties
The current investigation reports the impact of amalgamations of carbon nanotubes and aluminum oxide in boosting the thermochemical properties of water as the base fluid. It is assumed that the viscosity of the base fluid varies with temperature exponentially and temperature varies linearly. The governing equations prevalent in the current problem accompanied by boundary conditions are transformed into a highly nonlinear system of ODEs by means of similarity trans-formations. The numerical result for the obtained mathematical model was acquired by using the bvp4c inbuilt function in MATLAB. The domain effects were analyzed through sketched graphs. It is further observed that the Nusselt number decreases with the rising value of the variable viscosity and the thermal conductivity parameters. 2021 Begell House, Inc. www.begellhouse.com. -
Evolving corporate sustainable development: a case study of Mysore Paper Mills Limited
In 1987, the World Commission on Economic Development (WCED) popularized the term sustainable development in its well-cited report, Our Common Future. According to this report, sustainable development is defined as the development that meets the needs of the present without compromising the ability of future generations to meet their own needs. The WCED asserted that sustainable development required simultaneous adoption of environmental, economical, and equity principles. Bansal (Strategic Management Journal, 26(3), 197218, 2005) has conducted a study of Canadian firms in the oil and gas, mining, and forestry industries from 1986 to 1995. The study found that both resources based and institutional factors influence corporate sustainable development. This paper studied the corporate sustainable development of Mysore Paper Mills Ltd. from 1995 to 2011 using the same model. The study found that independent variables with significant impact on environmental integrity and overall sustainability were fines, penalties, court cases (total) involved by the company, and log of total assets. On economic prosperity, the independent variable with significant impact is log of total assets. For social equity, the independent variable with significant impact is foreign sales as percentage of total sales, number of fines/penalties/court cases (total), number of fines/penalties/court cases (environmental), log(total assets), and return on equity. 2013, Springer Science+Business Media Dordrecht. -
Quantum optimization for machine learning
Machine learning is a branch of Artificial Intelligence that seeks to make machines learn from data. It is being applied for solving real world problems with huge amount of data. Though, Machine Learning is receiving wide acceptance, however, execution time is one of the major concerns in practical implementations of Machine Learning techniques. It largely comprises of a set of techniques that trains a model by reducing the error between the desired or actual outcome and an estimated or predicted outcome, which is often called as loss function. Thus, training in machine learning techniques often requires solving a difficult optimization problem, which is the most expensive step in the entire model-building process and its applications. One of the possible solutions in near future for reducing execution time of training process in Machine learning techniques is to implement them on quantum computers instead of classical computers. It is conjectured that quantum computers may be exponentially faster than classical computers for solving problems which involve matrix operations. Some of the machine learning techniques like support vector machines make extensive use of matrices, which can be made faster by implementing them on quantum computers. However, their efficient implementation is non-trivial and requires existence of quantum memories. Thus, another possible solution in near term is to use a hybrid of Classical Quantum approach, where a machine learning model is implemented in classical computer but the optimization of loss function during training is performed on quantum computer instead of classical computer. Several Quantum optimization algorithms have been proposed in recent years, which can be classified as gradient based and gradient free optimization techniques. Gradient based techniques require the nature of optimization problem being solved to be convex, continuous and differentiable otherwise if the problem is non-convex then they can find local optima only whereas gradient free optimization techniques work well even with non-continuous, non-linear and nonconvex optimization problems. This chapter discusses a global optimization technique based on Adiabatic Quantum Computation (AQC) to solve minimization of loss function without any restriction on its structure and the underlying model, which is being learned. Further, it is also shown that in the proposed framework, AQC based approach would be superior to circuit-based approach in solving global optimization problems. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.