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Tuning of the surface structure of silver nanoparticles using Gum arabic for enhanced electrocatalytic oxidation of morin
Gum arabic stabilized silver nanoparticles have been used to modify carbon paste electrodes (AgNPs-GA/CPE) for the electrochemical sensing of morin, a flavanoid. The synthesized nanoparticles, before and after modification of electrodes were characterized by UVvisible spectroscopy, X-ray diffraction (XRD), fourier transformation infrared (FTIR) spectroscopy, transmission electron microscopy (TEM), dynamic light scattering (DLS), zeta potential measurements, and thermogravimetric analysis (TGA). The uniform-sized spherical silver nanoparticles have particle sizes less than 10 nm. AgNPs-GA/CPE electrode has shown excellent electrocatalytic activity towards the oxidation of morin at a potential of 0.14 V. The factors influencing the electrochemical determination of morin such as the effect of pH, the effect of scan rate, and the effect of concentration were studied in detail. The linear dynamic range was found to be 0.65 nM to 7.0 nM with a detection limit of 0.216 nM. The developed sensor has been successfully applied for the determination of morin in mulberry leaves and almonds. 2021 The Author(s) -
Tunable graphene nanopatch antenna design for on-chip integrated terahertz detector arrays with potential application in cancer imaging
Aim: Further to our reports on chip-integrable uncooled terahertz microbolometer arrays, compatible with medium-scale semiconductor device fabrication processes, the possibility of the development of chip-integrable medical device is proposed here. Methods: The concept of graphene-based nanopatch antennas with design optimization by the finite element method (FEM) is explored. The high-frequency structure simulator (HFSS) utilized fine FEM solver for analyzing empirical mode decomposition preprocessing and for modeling and simulating graphene antennas. Results: Graphene nanopatch antennas exhibited tunable features with varying patch dimensions and dependence on substrate material permittivity. Conclusion: This work implements reconfigurable graphene nanopatch antenna compatible with terahertz microbolometer arrays. This design concept further develops on-chip medical devices for possible screening of cancer cell with terahertz image processing. 2021 Future Medicine Ltd. -
Tunable direct band gap photoluminescent organic semiconducting nanoparticles from lignite
Fluorescent organic semiconducting dots (OSDs) with tunable particle size and surface functionality are synthesized from lignite by chemical oxidation method followed by ultra-sonication techniques and dialysis. The defects and oxygen functionalities play a vital role in the photoluminescent property of the synthesized nanoparticles along with quantum confinement effect. These nanomaterials are suitable for imaging and chemical sensing applications as there is no photobleaching and quenching even after a continuous UV exposure of 24 hours and storage of 2 years. The excellent excitation dependent luminescence of the synthesized carbon dots can be utilized for making a low-cost carbon-based sensor for Cu2+ metal ions sensing. The OSDs show good ratiometric fluorescent sensing and can be used as a reliable probe for the detection of Cu2+ ions. They exhibit excellent detection limit of copper ion in acidic solution to a very low concentration of 0.0089 nM. The fluorescent nanodots synthesized from such an abundant and cost-effective precursor exhibiting high copper ion sensitivity is being reported for the first time. 2017 The Author(s). -
Tunable Capacitive Behavior in Metallopolymer-based Electrochromic Thin Film Supercapacitors
Volumetric capacitance is a more critical performance parameter for rechargeable power supply in lightweight and microelectronic devices as compared to gravimetric capacitance in larger devices. To this end, we report three electrochromic metallopolymer-based electrode materials containing Fe2+as the coordinating metal ion with high volumetric capacitance and energy densities in a symmetric two-electrode supercapacitor setup. These metallopolymers exhibited volumetric capacitance up to 866.2 F cm-3at a constant current density of 0.25 A g-1. The volumetric capacitance (poly-Fe-L2: 544.6 F cm-3> poly-Fe-L1: 313.8 F cm-3> poly-Fe-L3: 230.8 F cm-3at 1 A g-1) and energy densities (poly-Fe-L2: 75.5 mWh cm-3> poly-Fe-L1: 43.6 mWh cm-3> poly-Fe-L3: 31.2 mWh cm-3) followed the order of the electrical conductivity of the metallopolymers and are among the best values reported for metal-organic systems. The variation in the ligand structure was key toward achieving different electrical conductivities in these metallopolymers with excellent operational stability under continuous cycling. High volumetric capacitances and energy densities combined with tunable electro-optical properties and electrochromic behavior of these metallopolymers are expected to contribute to high performance and compact microenergy storage systems. We envision that the integration of smart functionalities with thin film supercapacitors would warrant the surge of miniaturized on-chip microsupercapacitors integrated in-plane with other microelectronic devices for wearable applications. 2022 American Chemical Society. All rights reserved. -
TumorInsight: GAN-Augmented Deep Learning for Precise Brain Tumor Detection
In addition to the shortage in data as well as the low quality of MRI images, one of the most difficult tasks in contemporary medical imaging is the diagnosis of tumors in brain. This work presents a new approach to enhance diagnostic accuracy using sophisticated preprocessing techniques. Combining BRATS 2023 and Cheng et al. datasets to apply cutting-edge deep learning preprocessing methods with Generative Adversarial Networks (GANs), specifically DCGAN, Contrast Limited Adaptive Histogram Equalization (CLAHE), and gamma correction, it aims to significantly improve the quality of MRI images. As a result, updated data should be generated with greater precision and detail, making it possible to identify tumor-affected areas with greater accuracy. Thorough assessment, demonstrated by metrics such as Accuracy (0.98), Specificity (0.99), Sensitivity (0.99), AUC (0.65), Dice Coefficient (0.67), and Precision (0.71), highlights possible advancements in brain tumor identification and treatment, thereby highlighting the effectiveness of the suggested approach. 2024 IEEE. -
Tumor Infiltration of Microrobot using Magnetic torque and AI Technique
Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. 2023 IEEE. -
TSM: A Cloud Computing Task Scheduling Model
Cloud offers online-based runtime computing services through virtualized resources, ensuring scalability and efficient resource utilization on demand. Resource allocation in the dynamic cloud environment poses challenges for providers due to fluctuating user demand and resource availability. Cloud service providers must dynamically and economically allocate substantial resources among dispersed users worldwide. Users, in turn, expect reliable and cost-effective computing services, requiring the establishment of Service Level Agreements (SLAs). Resource distribution uncertainty arises in view of the dynamicity of the cloud, where VMs, memory capacity requirement, processing power, and networking are allocated to user applications using virtualization technology. Resource allocation strategies must address issues such as insufficient provisioning, scarcity, competition, resources fragmentation. CPU scheduler plays a crucial role in task completion, by selecting job from queue considering specific requirements. The Task Scheduling Model (TSM) algorithm improves scheduling by considering expected execution time, standard deviation, and resource completion time, aiming to address resource imbalances and task waiting times. The research discusses previous work, presents experimental findings, describes the experimental setup and results, and concludes with future research directions. 2023 IEEE. -
Truth Twisters: Large Language Models Beating Humans at Fake News
Misinformation has become a serious global problem, affecting the process of referendum and decision-making in areas such as politics, healthcare, and social movements. With the rise of advanced artificial intelligence, especially large language models (LLM), the scenario of misinformation building has changed dramatically. These models, which are known to generate coherent and human reactions, can also be used to generate reliable but false or harmful materials. This study examines the dual nature of LLM and highlights its possible misuse to create misinformation that can evade the identity mechanism. The objective of this article is to explain how LLMs can be manipulated through prompt engineering and vocabulary attacks, where adversaries use obfuscated or subtly altered language to bypass content filters and safety guidelines. Despite being fine-tuned for ethical alignment, many LLMs can still be 'jailbroken' - a process by which users modify prompts to elicit inappropriate or restricted outputs. Through a series of controlled experiments, we demonstrate sensitivity to such adverse information of state -of -the -art LLM. These findings create serious concerns about the deployment of LLM in an open-wheel environment. Although these models offer immense possibilities of innovation and productivity, their sensitivity to manipulation outlines the immediate need for strong safety measures. We conclude by discussing moral implications and proposing strategies to reduce abuse, such as better adverse training, strict deployment protocols, and continuous monitoring to balance between safety and innovation in AI. 2025 IEEE. -
Trusted explainable AI based implementation for detection of neurodegenerative disorders (ND)
The potential of explainable artificial intelligence (XAI) in detection of neurodegenerative disorders (ND) holds great promise in the field of healthcare. These diseases interfere with the daily functioning and independence of a person. The current studies lack in highlighting the aspect of explainability in their predictions and the various algorithms cannot provide any plausible explanations for their predictions making it difficult for medical professionals to place trust in their findings. Thus, the proposed framework aims to bridge this gap by exploring the development of a trustworthy framework for XAI-based ND detection, focusing on key aspects that can significantly impact its effectiveness and acceptance. The framework makes use of Trust-based SHAP (SHapley Additive exPlanations) values in classification. By computing trust values, the framework ensures more reliable predictions and increases interpretability, instilling confidence in clinicians and patients. The results show that with the inclusion of the trust-driven framework, the accuracy of the algorithm increased from 93.33% in the normal circumstances to 98.21%, highlighting the efficacy of the framework as compared to the other works. This shows that a trustworthy framework for XAI-driven ND detection can reshape care by enabling early detection, personalized treatment plans and enhancing decision-making process. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Trusted explainable AI based implementation for detection of neurodegenerative disorders (ND)
The potential of explainable artificial intelligence (XAI) in detection of neurodegenerative disorders (ND) holds great promise in the field of healthcare. These diseases interfere with the daily functioning and independence of a person. The current studies lack in highlighting the aspect of explainability in their predictions and the various algorithms cannot provide any plausible explanations for their predictions making it difficult for medical professionals to place trust in their findings. Thus, the proposed framework aims to bridge this gap by exploring the development of a trustworthy framework for XAI-based ND detection, focusing on key aspects that can significantly impact its effectiveness and acceptance. The framework makes use of Trust-based SHAP (SHapley Additive exPlanations) values in classification. By computing trust values, the framework ensures more reliable predictions and increases interpretability, instilling confidence in clinicians and patients. The results show that with the inclusion of the trust-driven framework, the accuracy of the algorithm increased from 93.33% in the normal circumstances to 98.21%, highlighting the efficacy of the framework as compared to the other works. This shows that a trustworthy framework for XAI-driven ND detection can reshape care by enabling early detection, personalized treatment plans and enhancing decision-making process. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Trust Model for Cloud Using Weighted KNN Classification for Better User Access Control
The majority of the time, cloud computing is a service-based technology that provides Internet-based technological services. Cloud computing has had explosive growth since its debut, and it is now integrated into a wide variety of online services. These have the primary benefit of allowing thin clients to access the resources and services. Even while it could appear favorable, there are a lot of potential weak points for various types of assaults and cyber threats. Access control is one of the several protection layers that are available as part of cloud security solutions. In order to improve cloud security, this research introduces a unique access control mechanism. For granting users access to various resources, the suggested approach applies the trust concept. For the purpose of predicting trust, the KNN model was recently proposed, however the current approach for categorizing options is sensitive and unstable, particularly when an unbalanced data scenario occurs. Furthermore, it has been discovered that using the exponent distance as a weighting system improves classification performance and lowers variance. The prediction of the users trust levels using weighted K-means closest neighbors is presented in this research. According to the findings, the suggested approach is more effective in terms of throughput, cost, and delay. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Trust green, pay more: Decoding green brand loyalty and willingness to pay more for electric vehicles through green transparency and green perceived value
The StimulusOrganismResponse framework is applied in this study to explore the impact of Green Transparency (stimuli) and Green Perceived Value (stimuli) on Green Brand Trust (organism) and, subsequently, on Green Brand Loyalty (response) and Willingness to Pay More (response). Self-Brand Connection is examined as a moderator. An online survey was distributed to 557 EV consumers. We employed both PLS-SEM (SmartPLS 4) and CB-SEM (AMOS 29) to test the direct, mediating, and moderating effects, with CB-SEM used as a robustness check for model stability. The results show that both Green Transparency and Green Perceived Value are positive antecedents of Green Brand Trust. Green Brand Trust, in turn, positively influences Green Brand Loyalty and Willingness to Pay More and mediates the effects of the two stimuli. The results also confirm that Self-Brand Connection significantly and positively strengthens the Green Brand Trust?Green Brand Loyalty and Green Brand Trust?Willingness to Pay More relationships. This study establishes Green Brand Trust as a core green consumer behavior mechanism and identity alignment as a catalyst for Green Brand Loyalty and Willingness to Pay More, offering actionable guidance to EV brands for credibility building, customer retention, and sustainable consumption. 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
True and fair financial reporting: A tool for better corporate governance
Purpose - Transparency of financial information promotes corporate growth. The purpose of this paper is to concentrate on the need for strengthening the law governing true and fair corporate accounting. The first part of the paper concentrates on nexus between the importance of transparency in accounting embodied under the provisions of the Companies Act in India and in the UK. Second, the paper focuses on the board of director's duty to prevent corporate fraud through proper financial reporting. Design/methodology/approach - The methodology for this study is analytical. Comparative study of the law governing accounting provisions in India and UK is also looked into. Findings - The law governing financial transparancy envisaged under the Companies Act in India makes it obligatory on the part of the companies to disclose the material information relevant to the investors. However, the directors of the company often show an unreal picture of the financial position of the company, so as to retain the existing shareholders and to attract more investors. This can be avoided if the composition of audit committees in the companies includes a few representatives of shareholders who are competent to asses the true and fair view of the company accounts prepared by the auditors. Research limitations/implications - The focus of this research paper is mainly on the legal regimes and the accounting and auditing provisions of India and the UK. Originality/value - The paper shows that the Companies Act in India should strengthen the accounting provisions and it should mandate the compulsory observance of accounting standards. Emerald Group Publishing Limited. -
True and Fair Financial Reporting: A Tool for Better Corporate Governance
Journal of Financial Crime, Vol-19 (4), pp. 332-342. ISSN-1359-0790 -
Trivikram Ramchandra Kulkarni (19121983)
Trivikram Ramchandra Kulkarni was an influential educationist and licensed medical practitioner who passionately advocated for the study of Indian philosophy and psychology, particularly in an academic context. His pivotal contributions included establishing psychology as a formal discipline at universities in Mumbai. Kulkarnis groundbreaking research is encapsulated in his seminal works, notably Pranayama and Perception, which empirically examined the intricate link between pranayama and sensory perception as explicated by Patanjali. In Empirical Basis of Yoga, he explored the methodological applicability of Yoga within standard empirical frameworks, introducing the concept of samapattia state of focused mental function. In 1969, his work Psychosynthesis and Psycholysis compared the esoteric elements of the Rigveda with their empirical ramifications, depicting the human psyche as a dynamic interplay of goings (rta) governed by the transformative energy of agni, representing the cyclical nature of human experience and perception. 2025 selection and editorial matter, Braj Bhushan; individual chapters, the contributors. -
Triple diffusive convection in a vertically oscillating oldroyd-b liquid /
International Journal of Mechanical and Mechatronics Engineering, Vol.12, Issue 9, pp.863-869, ISSN No: 1307-6892. -
Trigonometric Cosine, Square, Sawtooth and Triangular Waveforms of Internal Heating Modulations for Three-Component Convection in a Couple Stress Liquid: A Detailed Analysis
In this paper, the main focus is to study the effect of internal heating modulations of sinusoidal and non-sinusoidal waveforms on a three-component convection in a couple stress liquid. This three-component layer is heated from below and salted with two solutes at the bottom. In order to facilitate a solution to the problem, linear case is formulated using the Venezian approach for modulations while the non-linear case is modeled using 7-mode generalized Lorenz equations. With the aim of quantifying the heat and mass transfer, average Nusselt and average Sherwood numbers are computed. The investigation reveals that, internal heating modulations show a stabilizing or destabilizing trend that precisely depends on the modulated frequency and appropriate waveforms. The effect of heat source and sink is recorded on different convection processes. The effect of the pertinent parameters and waveforms on the stability of the system and on heat and mass transfer have been captured via graphs. The results confirm that the heat and mass transfer escalates to its maximum due to the square waveform. In this research paper, six problems involving three types of convection in two different liquids are solved as limiting cases of the problem. 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Triggers of Changes in Business Processes and Applications: A Systematic Review
Organizations must constantly adapt due to the rapid rate of technological development, market conditions, and customer expectations. The multidimensional world of catalysts that drive changes in corporate processes and applications is explored in this systematic review. Every business must adopt the changes if it wants to compete in the market and outlast its rivals. A wide range of factors, including internal and external forces, can cause applications and business processes to change. These changes are frequently necessary to stay current with the shifting demands of the market, technology advancements, organizational requirements, competitive pressures, legal compliance, environmental and sustainability programs, market trends, and consumer insights. Taking this into account, this chapter attempts to concentrate on the causes of changes in business processes and applications by analyzing the perspective. 2024, Iquz Galaxy Publisher. All rights reserved. -
Trident Shaped Compact Planar Antenna for Microwave Applications
A compact planar antenna for X/Ku-band microwave communication is suggested in this paper. The presented geometry is capable of radiating the large frequency band from 6.8 to 20GHz, which covers the X-Band/Ku-Band Communication with high efficiency. The impedance bandwidth of the radiator is 98.5%, with an electrical size of. 34?x.34?x0.034A in lambda. The suggested design includes a modified patch in the trident shape fed by a microstrip line. Rectangular elements have been designed for better resonances at lower modes. The antenna is simulated with an FR4 substrate using CST Simulator. The exact dimensions of the antenna are 15x15x1.5 cubic millimeter. Five stages evolution process is also investigated by simulations, and corresponding S-parameter results are presented. The proposed structure also demonstrates stable radiation patterns across the operating bandwidth. The proposed radiator has a high gain of 3.1 dBi, and an efficiency of 87%. Therefore, it is useful for X-band, and Ku-band, including Radar, Space, Terrestrial, and Satellite microwave communication. 2022 IEEE. -
Tribological aspects of Al and Mg composites
It is well known that the technical function of a large number of engineering components/parts depends on motion. However, the term motion here is not as simple as it sounds, because it comes with consequences in the form of friction and wear. Along with lubrication, the science that deals with friction and wear is known as tribology. Therefore, it is necessary to pay more attention to tribology and acquire knowledge on the tribological behavior of materials, as the tribological characteristics such as friction and wear have been causing poor efficiency in engineering structures, huge economic losses, and environmental impacts. One way of addressing these issues lies in the development of lightweight materials based on metals such as aluminum and magnesium. Although one cannot employ these metals in their pure form, but modification in their microstructure and properties can certainly address the needs required for tribological applications. Keeping this in mind, this chapter covers the properties of aluminum and magnesium metals, basic aspects of tribology and most importantly, the work carried out on the friction and wear behavior of aluminum- and magnesium-based composites. The importance of this chapter lies in promoting better knowledge of the tribological behavior of aluminum and magnesium composites, especially from a various wear parameters point of view. The influence of material composition and wear parameters on tribological behavior is covered with a follow-up section on numerical and optimization methods employed for predicting tribological characteristics. 2026 Elsevier Inc. All rights reserved..