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A weighted-Weibull distribution: Properties and applications
The paper describes a two parameter model and its relationship to the widely used Weibull model. Mathematical properties of the distribution like survival and hazard functions, moments, harmonic and geometric means, Shannon entropy and mean residual life are derived. Different methods of estimation are discussed and a simulation study is performed to verify the efficiency of estimation methods. Applications of our distribution in different scenarios observed in real life areillustrated. 2023 John Wiley & Sons Ltd. -
Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem
Blackjack is a classic casino game in which the player attempts to outsmart the dealer by drawing a combination of cards with face values that add up to just under or equal to 21 but are more incredible than the hand of the dealer he manages to come up with. This study considers a simplified variation of blackjack, which has a dealer and plays no active role after the first two draws. A different game regime will be modeled for everyone to ten multiples of the conventional 52-card deck. Irrespective of the number of standard decks utilized, the game is played as a randomized discrete-time process. For determining the optimum course of action in terms of policy, we teach an agent-a decision maker-to optimize across the decision space of the game, considering the procedure as a finite Markov decision chain. To choose the most effective course of action, we mainly research Monte Carlo-based reinforcement learning approaches and compare them with q-learning, dynamic programming, and temporal difference. The performance of the distinct model-free policy iteration techniques is presented in this study, framing the game as a reinforcement learning problem. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Implementation of survivability aware protocols in WSN for IoT applications using Contiki-OS and hardware testbed evaluation
The Internet of Things is a network of devices capable of operating and communicating individually and working for a specific goal collectively. Technologically, many networking and computing mechanisms have to work together with a common objective for the IoT applications to function, and many sensing and actuating devices have to get connected to the Internet backbone. The networks of resource-constrained sensor devices constitute an integral part of IoT application networks. Network survivability is a critical aspect to consider in the case of a network of low-power, resource-constrained devices. Algorithms at different layers of the protocol stack have to work collectively to enhance the survivability of the application network. In this article, the survivability-aware protocols for wireless sensor networks for IoT applications are implemented in real network scenarios. The routing strategy, Survivable Path Routing protocol, and the channel allocation technique, Survivability Aware Channel Allocation, are implemented in Contiki-OS, the open-source operating system for IoT. Furthermore, the implementation scenarios are tested with the FIT IoT Lab hardware testbed. Simulated results are compared with the results obtained from the testbed evaluation. 2023 Elsevier B.V. -
Magnetically retractable tea extract stabilized palladium nanoparticles for denitrogenative cross-coupling of aryl bromides with arylhydrazines under green conditions: An alternate route for the biaryls synthesis
Novel palladium based magnetic nanocatalyst was synthesized by the co-precipitation method and coated with silica and tea extract as stabilizing agent. Palladation onto the prepared nanocomposite was done to get ION-SiO2/TE-Pd(0) catalyst. Our study is one of the limited number of studies reported for the catalytic denitrogenative coupling of arylbromide and arylhydrazine. This led to the construction of important substituted biaryls bearing various substituents with 8292% yields. The synthesized nanocatalyst was characterized using structural and morphological characterization techniques. It was also observed that only 2 mol% of ION-SiO2/TE-Pd(0) catalyst was sufficient for the catalysis and reusable upto six cycles. 2024 The Authors -
Synergistic effects of CuO/TiO2-chitosan-farnesol nanocomposites: Synthesis, characterization, antimicrobial, and anticancer activities on melanoma cells SK-MEL-3
The current investigation focuses on synthesizing copper oxide (CuO)-titanium oxide (TiO2)-chitosan-farnesol nanocomposites with potential antibacterial, antifungal, and anticancer properties against Melanoma cells (melanoma cells [SK-MEL-3]). The nanocomposites were synthesized using the standard acetic acid method and subsequently characterized using an X-ray diffractometer, scanning electron microscope, transmission electron microscopy, and Fourier transform infrared spectroscopy. The results from the antibacterial tests against Streptococcus pneumoniae and Stapylococcus aureus demonstrated significant antibacterial efficacy. Additionally, the antifungal studies using Candida albicans through the agar diffusion method displayed a considerable antifungal effect. For evaluating the anticancer activity, various assays such as MTT assay, acridine orange/ethidium bromide dual staining assay, reactive oxygen species (ROS) generation assay, and mitochondrial membrane potential (MMP) analysis were conducted on SK-MEL-3 cells. The nanocomposites exhibited the ability to induce ROS generation, decrease MMP levels, and trigger apoptosis in SK-MEL-3 cells. Collectively, the findings demonstrated a distinct pattern for the synthesized bimetallic nanocomposites. Furthermore, these nanocomposites also displayed significant (p < 0.05) antibacterial, antifungal, and anticancer effects when tested on the SK-MEL-3 cell line. 2023 Wiley-VCH GmbH. -
Optimized deep maxout for breast cancer detection: consideration of pre-treatment and in-treatment aspect
Breast cancer is one of the deadliest diseases, accounting for the second-highest rate of cancer mortality among females. Breast tissue begins to develop cancerous, malignant lumps as the disease progresses. Self-examinations and routine clinical checks aid in early diagnosis, which considerably increases the likelihood of survival. Because of this, we have created a revolutionary method for finding breast cancer that has the following four steps. Fuzzy filters are used in the initial pre-processing stage to reduce noise and improve outcomes from the incoming data. In the second stage, we have presented an Improved Hierarchical DBSCAN (Density-based clustering algorithm) for the segmentation of anomalous areas. Feature extraction will be carried out following segmentation. We have also developed a better kurtosis-based feature to complement traditional statistical and shape-based features and deliver better results. The Optimized Deep Maxout Neural Network is used for classification in the final step, with the suggested Shark Smell Indulged Shuffled Shepherd Optimization used to optimize the weight parameter (SSISSO). At 90% the learning percentage of the proposed model SSISSO model has achieved 0.984391 accuracy, which is superior to 22.54%, 28.46%, 17.44%, 17%, 15.04%, 13.28%, 29.45%, 28.59%, 21.58%, and 30.72% as compared to other methods like SVM-BS1, CNN-BS7, LSTM, NN, Bi-GRU, RNN, ARCHO, AOA, HGS, CMBO, SSOA, and SSO. Finally, the results of the proposed breast cancer detection technique are compared with conventional techniques. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Photocatalytic driven self-cleaning IPN membranes infused with a host-guest pair consisting of metal-organic framework encapsulated anionic nano-clusters for water remediation
Traditional water treatment membranes frequently encounter challenges in attaining an ideal equilibrium between permeability and selectivity. The performance of membranes is further hampered by hydrophobicity, scalability, and fouling problems, as well as excessive energy consumption. Hence, the current research is dedicated to the development of highly effective antifouling membranes, aiming for a significant balance between water permeance and separation efficiency, and featuring exceptional photocatalytic self-cleaning properties to ensure the sustainable reuse of membranes. In this study, a unique nanocomposite-based membrane is designed containing metal-organic frameworks (MOFs) MIL-101 (Fe) encapsulated copper-containing polyoxometalate (Cu-POM) incorporated into an interpenetrating polymer networks (IPNs) membrane. POMs are highly electronegative, oxo-enriched nanosized metal-oxygen cluster species and when composited with MOF yields POMOF which can help in the removal of pollutants from water through electrostatic site-specific binding. The IPN membrane designed by polymerizing aniline in the presence of polyvinylidene fluoride (PVDF) offers tunable pores of the membrane. The infusion of POMOF imparts a strong negative charge to the membrane surface, improving membrane hydrophilicity. This enhances pollutant removal through the Donnan exclusion principle and adds anti-fouling properties. Furthermore, the reduced pore size achieved by the IPN architecture in the POMOF@IPNs membrane effectively sieves out both cationic and anionic dyes, as well as pharmaceutical pollutants. Additionally, POMOF enhances the photocatalytic degradation of CR and MB dyes, coupled with essential self-cleaning attributes vital for separation processes. The IPNs structure, apart from housing POMOF, fortifies the membrane's mechanical strength with its distinctive network-like configuration. Furthermore, these advanced membranes showcase robust antibacterial and antiviral characteristics, while remaining non-cytotoxic to mammalian cells. Our findings indicate that the state-of-the-art POMOF@IPNs membrane is scalable and holds substantial promise for industrial wastewater treatment. 2024 Elsevier B.V. -
Exploring Mortality Salience and Pandemic Impact in the Context of COVID-19
Mortality salience refers to a state of conscious awareness of death and the inevitable conclusion of life, associated with psychological terror. The COVID-19 pandemic generated increased awareness of illness and death, and effectuated changes in death cognitions and peoples experiences around psychological or sociocognitive domains of media and life goals. To understand these changes, this study administered the Multidimensional Mortality Awareness Measure (Levasseur et al., 2015) to 103 emerging adults in India, post which 6 participants proceeded for a semi-structured interview exploring pandemic experiences, news consumption and goal prioritization, to examine specific areas in relation to death cognition. The thematic analysis demonstrates psychological effects, and discusses developments in health and death-related psychological processes. Focus on career goals and health maintenance, cautious news consumption and disadvantageous impacts on mental health are seen, significant in navigating healthcare measures for emerging adults, as we move forward into this new normal. The Author(s) 2021. -
On families of graphs which are both adjacency equienergetic and distance equienergetic
Let A(G) and D(G) be the adjacency and distance matrices of a graph G respectively. The adjacency energy or A-energy EA(G) of a graph G is defined as the sum of the absolute values of the eigenvalues of A(G). Analogously, the D-energy ED(G) is defined to be the sum of the absolute values of the eigenvalues of D(G). One of the interesting problems on graph energy is to characterize those graphs which are equienergetic with respect to both the adjacency and distance matrices. A weaker problem is to construct the families of graphs which are equienergetic with respect to both the adjacency and distance matrices. In this paper, we find the explicit relations between A-energy and D-energy of certain families of graphs. As a consequence, we provide an answer to the above open problem (Indulal in https://icgc2020.wordpress.com/invitedlectures, 2020; http://www.facweb.iitkgp.ac.in/rkannan/gma.html, 2020) The Indian National Science Academy 2022. -
A hybrid crypto-compression model for secure brain mri image transmission
Medical image encryption is a major issue in healthcare applications where memory, energy, and computational resources are constrained. The modern technological architecture of digital healthcare systems is, in fact, insufficient to handle both the current and future requirements for data. Security has been raised to the highest priority. By meeting these conditions, the hybrid crypto-compression technique introduced in this study can be used for securing the transfer of healthcare images. The approach consists of two components. In order to construct a cutting-edge generative lossy compression system, we first combine generative adversarial networks (GANs) with oearned compression. As a result, the second phase might address this problem by using highly effective picture cryptography techniques. A randomly generated public key is subjected to the DNA technique. In this application, pseudo-random bits are produced by using a logistic chaotic map algorithm. During the substitution process, an additional layer of security is provided to boost the techniques fault resilience. Our proposed system and security investigations show that the method provides trustworthy and long-lasting encryption and several multidimensional aspects that have been discovered in various public health and healthcare issues. As a result, the recommended hybrid crypto-compression technique may significantly reduce a photos size and remain safe enough to be used for medical image encryption. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
A novel approach to study generalized coupled cubic SchringerKorteweg-de Vries equations
The Kortewegde Vries (KdV) equation represents the propagation of long waves in dispersive media, whereas the cubic nonlinear Schringer (CNLS) equation depicts the dynamics of narrow-bandwidth wave packets consisting of short dispersive waves. A model that couples these two equations seems intriguing for simulating the interaction of long and short waves, which is important in many domains of applied sciences and engineering, and such a system has been investigated in recent decades. This work uses a modified Sardar sub-equation procedure to secure the soliton-type solutions of the generalized cubic nonlinear SchringerKorteweg-de Vries system of equations. For various selections of arbitrary parameters in these solutions, the dynamic properties of some acquired solutions are represented graphically and analyzed. In particular, the dynamics of the bright solitons, dark solitons, mixed bright-dark solitons, W-shaped solitons, M-shaped solitons, periodic waves, and other soliton-type solutions. Our results demonstrated that the proposed technique is highly efficient and effective for the aforementioned problems, as well as other nonlinear problems that may arise in the fields of mathematical physics and engineering. 2022 -
Comparing the Accuracy of CNN Model with Inception V3 for Music Instrument Recognition
Identification of music instruments from an audio signal is a complex but useful task in music information retrieval. Deep Learning and traditional machine learning models are extremely very useful in many music related tasks such as music genre classification, recognizing music similarity, identifying the singer etc. Music Instrument recognition and classification would be helpful in categorizing different categories of music. Many researchers have proposed models for classifying western music instruments. But very little research has been done in identifying instruments accompanied with South Indian music. This research aims at identifying string instrument such as violin and woodwind instrument such as flute accompanied in a Carnatic music concert and also in other categories of music. In order to identify the instruments accompanied, Convolutional Neural Network model and Inception V3 models were used. The Mel Frequency Cepstral Coefficients images were extracted from the audio input and fed in to the neural network model. The model has been trained for the above mentioned instruments, tested and validated on different types of audio input. This research also evaluates the performance of Inception V3 transfer learning model with CNN model in recognizing the instruments used in different categories of music. 2024, Ismail Saritas. All rights reserved. -
Ricci solitons on Riemannian manifolds admitting certain vector field
In this paper, we initiate the study of impact of the existence of a unit vector ?, called a concurrent-recurrent vector field, on the geometry of a Riemannian manifold. Some examples of these vector fields are provided on Riemannian manifolds, and basic geometric properties of these vector fields are derived. Next, we characterize Ricci solitons on 3-dimensional Riemannian manifolds and gradient Ricci almost solitons on a Riemannian manifold (of dimension n) admitting a concurrent-recurrent vector field. In particular, it is proved that the Riemannian 3-manifold equipped with a concurrent-recurrent vector field is of constant negative curvature -?2 when its metric is a Ricci soliton. Further, it has been shown that a Riemannian manifold admitting a concurrent-recurrent vector field, whose metric is a gradient Ricci almost soliton, is Einstein. Universitdegli Studi di Napoli "Federico II" 2021. -
AI-based online proctoring: a review of the state-of-the-art techniques and open challenges
So far, this pandemic has severely affected the education sector. As education undergoes a brilliant transformation with advancing technology, the digital acquisition of knowledge has yet to find widespread use - virtual exams. Faraway Proctoring offers several advantages of using manual and primarily based technology. Although this allows students to take an exam in any field with specific technical requirements, it eliminates the need for physical research centers. It is cost-effective and easy to plan, which can be challenging to manage, especially during aggressive trials. Finally, the paper discusses the performance characteristics of different styles of web-based inspection systems, along with their limitations and challenges. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Deposition and characterization of ZnO/CdSe/SnSe ternary thin film based photocatalyst for an enhanced visible light-driven photodegradation of model pollutants
A heterogeneous photocatalytic pathway is a possible approach to global energy and environmental issues. Sol-gel spin coating and physical vapour deposition were used to create a new ternary ZnO/CdSe/SnSe nanocomposite thin film photocatalyst. X-ray diffractometry, energy-dispersive X-ray spectroscopy (EDS), field emission-scanning electron microscopy, UV-Vis, and photoluminescence (PL) spectrophotometers were used to characterize the deposited films. When exposed to solar light, the ternary photocatalyst exhibits high photocatalytic activity in photocatalytic dye degradation processes. it demonstrates excellent visible light absorption, enhanced charge carrier separation, and solar light simulation. It was proposed that the charge in the ternary ZnO/CdSe/SnSe photocatalyst moves in a double type-II and cascade manner between the various components. In this study, ternary thin film heterostructures are synthesized, exhibiting outstanding stability and solar light-induced photocatalytic activity.The thin film composed of ZnO/CdSe/SnSe exhibits a degradation efficiency of 96% when exposed to visible light, and a degradation efficiency of 90% for methylene blue under sunlight within a time period of 150 min. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Stability Analyses of BrinkmanBard Convection in Hybrid-Nanoliquid Saturated-Porous Medium Using Local Thermal Non-equilibrium Model
This paper carries out linear and weakly non-linear stability analyses of natural convection in a Newtonian hybrid-nanoliquid saturated porous medium. The Boussinesq approximation is assumed to be valid in the study, and a two-phase energy model is used. The weighted residual Galerkin technique is employed to obtain the expression for the Rayleigh number and Lorenz model by using a truncated double Fourier series solution. The quadratic non-linear Lorenz model is solved numerically by using the RungeKuttaFehlberg method. Water is considered as a carrier liquid, and copper and alumina nanoparticles are considered with dilute concentration. Linear stability analysis reveals the onset of convection prepones in a hybrid nanoliquid-saturated porous medium. The amount of heat transport is maximum in a hybrid nanoliquid saturated porous medium and minimum in a liquid-saturated porous medium. Local thermal non-equilibrium situation ceases at higher rates of interphase heat transfer coefficient. The assumption of local thermal non-equilibrium is prominent in hybrid nanoliquid saturated porous medium. The results of the hybrid-nanoliquid channel, a hybrid nanoliquid saturated porous medium with the local thermal assumption, are presented as a limiting case of the study. 2024, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks
Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model CNN8GN. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy. The Author(s) 2024. -
Diverse Morphologies of Nb2O5 Nanomaterials: A Comparative Study for the Growth Optimization of Elongated Spiky Nb2O5 and Carbon Nanosphere Composite
Controlled synthesis and design of nanomaterials with intricate morphologies and active phases offer new prospects in harnessing their unique chemical and physical properties for various applications. Herein, a facile and efficient hydrothermal approach is reported for obtaining various complex Nb2O5 nanostructures, including thin sheets, thick flakes, spiky and elongated spiky sea urchin morphologies using urotropin as a growth-directing and hydrolyzing agent in various mixed and pure solvents. The detailed structural and chemical composition, surface morphology and crystallinity of as-synthesized Nb2O5 nanostructures are presented. The urotropin concentration, reaction time, and water-ethanol solvent mixture play a critical role for obtaining the elongated spiky sea urchin morphologies. The spiky Nb2O5 structures show a pseudohexagonal phase with less urotropin content, while thin sheets are obtained with a higher urotropin concentration and are primarily amorphous. These structures undergo transformation in their crystal phase and morphologies during calcination at higher temperatures revealing the active role of urotropin in stabilizing them. A composite of spiky sea urchin Nb2O5-carbon nanospheres (suNb2O5-CNS) is achieved by in-situ growth of Nb2O5 in the presence of CNS without compromising on morphology, phase, and crystallinity. suNb2O5-CNS composite is shown to possess higher charge storage capacity compared to its constituents for supercapacitor applications. 2023 Wiley-VCH GmbH. -
Transformational leadership and organizational citizenship behavior: new mediating roles for trustworthiness and trust in team leaders
This study investigates the pivotal role of trust in bridging the effects of transformational leadership on organizational citizenship behavior (OCB). The study was conducted using a multilevel longitudinal approach with 276 employees in 71 teams from private medium-sized organizations in Kuala Lumpur, Malaysia. Transformational leadership was found to be positively related to: (1) three facets of trustworthiness (ability, benevolence, and integrity); (2) trust in the leader; and (3) OCB. All three facets of trustworthiness mediated the relationship between transformational leadership and trust in leaders. In addition, trust in the leader mediated only the relationship between the benevolence facet of trustworthiness and OCB. As OCB is inherently benevolent, these findings not only are consistent with the principle of compatibility, but they also contribute to theorizing about how trust plays an important role in the influence of transformational leadership on employees. The Author(s) 2023. -
A Metal-Free KOtBu-Mediated Protocol towards the Synthesis of Quinolines, Indenoquinolines and Acridines
An expeditious strategy has been developed for the synthesis of diverse quinolines, indenoquinolines and acridines using KOtBu-mediated reaction conditions. The designed process utilizes 2-aminoaryl carbaldehydes/2-aminoaryl ketones and methyl/methylene group containing ketones as readily available feedstock. The chemical transformation was affected at room temperature within a short duration of time to obtain diverse N-heterocycles yields up to 92 %. The established process also exhibits considerable functional group tolerance with an operational simplicity. 2024 Wiley-VCH GmbH.