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An ensemble deep learning model for automatic classification of cotton leaves diseases
Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance
Post-operative brain magnetic resonance imaging (MRI) segmentation is inherently challenging due to the diverse patterns in brain tissue, which makes it difficult to accurately identify resected areas. Therefore, there is a crucial need for a precise segmentation model. Due to the scarcity of post-operative brain MRI scans, it is not feasible to use complex models that require a large amount of training data. This paper introduces an innovative approach for accurately segmenting and quantifying post-operative brain resection cavities in MRI scans. The proposed model, named Attention-Enhanced VGG-U-Net, integrates VGG16 initial weights in the encoder section and incorporates a self-attention module in the decoder, offering improved accuracy for postoperative brain MRI segmentation. The attention mechanism enhances its accuracy by concentrating on a specific area of interest. The VGG16 model is comparatively lightweight, has pre-trained weights, and allows the model to extract incredibly detailed information from the input. The model is trained on publicly available post-operative brain MRI data and achieved a Dice coefficient value of 0.893. The model is then assessed using a clinical dataset of postoperative brain MRIs. The model facilitates the quantification of the resected regions and enables comparisons with each brain region based on pre-operative images. The capabilities of the model assist radiologists in evaluating surgical success and directing follow-up procedures. 2024 by the authors of this article. -
Platinum decorated phosphorous doped graphitic carbon nitride supported molecularly imprinted carbon fibre electrode as a nano-interface for the detection of butylated hydroxy anisole
This research generated an electrochemical sensor using a carbon fibre (CFP) paper electrode coated using platinum-decorated phosphorous doped graphitic carbon nitride (Pt/PgCN). This sensor was designed to detect butylated hydroxy anisole (BHA) selectively and sensitively. The molecularly imprinted polymers (MIPs) were synthesized onto the Pt/PgCN coated CFP surface through electropolymerization using BHA as a template and 3-thiophene acetic acid as monomer. Numerous analytical methods were used to characterise the sensor electrode, including cyclic voltammetry, impedance spectroscopy, and electron microscopy. The results showed that the synergetic effect of PgCN, Pt nanoparticles, and PTAA, PgCN and Pt had a positive impact on the electrochemical detection, the sensor's linear range was determined to be between 5 10?10 M and 2.1 10?7 M. The sensor demonstrated excellent stability, good reproducibility, and high selectivity for detecting BHA. Moreover, the proposed sensor successfully detected BHA in real samples. 2024 Elsevier Ltd -
Commercialization potential of PET (polyethylene terephthalate) recycled nanomaterials: A review on validation parameters
Polyethylene Terephthalate (PET) is a polymer which is considered as one of the major contaminants to the environment. The PET waste materials can be recycled to produce value-added products. PET can be converted to nanoparticles, nanofibers, nanocomposites, and nano coatings. To extend the applications of PET nanomaterials, understanding its commercialization potential is important. In addition, knowledge about the factors affecting recycling of PET based nanomaterials is essential. The presented review is focused on understanding the PET commercialization aspects, keeping in mind market analysis, growth drivers, regulatory affairs, safety considerations, issues associated with scale-up, manufacturing challenges, economic viability, and cost-effectiveness. In addition, the paper elaborates the challenges associated with the use of PET based nanomaterials. These challenges include PET contamination to water, soil, sediments, and human exposure to PET nanomaterials. Moreover, the paper discusses in detail about the factors affecting PET recycling, commercialization, and circular economy with specific emphasis on life cycle assessment (LCA) of PET recycled nanomaterials. 2024 Elsevier Ltd -
Examining the Impact of Argument Quality and Source Credibility on Consumers Behavioral Intention Toward Green Cosmetics: The Moderating Role of Perceived Innovativeness
Purpose: This study integrated the knowledge-attitude-behavior (KAB) model and the theory of planned behavior (TPB) to analyze how elements of electronic word of mouth (eWOM)argument quality (AQ) and source credibility (CR)influenced customers green cosmetics behavioral intention (BI). Methodology: Data were collected from a sample of 350 customers through an online survey, and a two-stage process was used to evaluate the research model. In the first stage, linear associations between the various elements of the theoretical model were determined using structural equation modeling (SEM). The second stage involved evaluating the predicting efficacy of the constructs, using an artificial neural network (ANN) framework. Findings: The findings of the multi-analytical study revealed that attitude (Atd), perceived behavioral control (Pbcon), and source credibility (CR) influenced consumers intentions to buy green cosmetics. Moreover, the sources credibility (CR) and the arguments quality (AQ) also positively influenced consumer attitude (Atd). The models appeared to have acceptable prediction accuracy based on the ANN studys root mean square of error (RMSE) values. Originality: The study contributed to the body of green cosmetics literature by integrating knowledge-attitude-behavior (KAB) and planned behavior (TPB) theory. The novelty of this research also lies in examining the moderating effect of perceived innovativeness (PI) for developing a robust predictive framework for green cosmetics purchase intention using artificial neural networks (ANN). 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Determinants of renewable stock returns: The role of global supply chain pressure
This study investigates the determinants of the global renewable stocks index returns from November 2003 to August 2022. The explanatory variables include global supply chain pressure measures, climate policy uncertainty, global economic activity, and crude oil prices. The long-run panel dynamic Autoregressive Distributed Lag estimations show that the global supply chain pressure, climate policy uncertainty, and global economic activity redound renewable stock returns. These results are robust enough to utilise different long-run estimation techniques. Potential policy implications are also discussed. 2023 The Authors -
Nutritional, biochemical and antioxidant activities of edible and non-edible parts of Punica granatum L.
In the present study, the nutritional profiling and antioxidant analysis of the different parts of Punica granatum was done. In the nutritional profiling, different percentages of moisture content was found in the flower (9.63%), leaf (9.17%), peel (5.34%), root (8.08%), stem (4.09%) and fruit (3.54%). Ash content was recorded higher in the stem (30%/g) followed by root, leaf, flower, fruit and peel. Also, the major and minor elements like nitrogen, potassium, calcium, phosphorus, magnesium, sulphur, zinc, copper, manganese and iron were analysed in different parts of P. granatum. The fruit recorded the highest amount of nitrogen (5.710.01%) and phosphorus (5.710.01%) whereas peel was recorded with more potassium (0.990.01%). The phytochemical quantification showed the major content of carbohydrates in the flower (317.96 mg/g) and leaf (315.62 mg/g). The protein was recorded higher in fruit (69 mg/g) and proline in root (19.54 mg/g). The TPC was recorded more in the peel (240.72 g/g) followed by the flower (223.05 g/g). P. granatum peel was recorded with maximum flavonoid content (873.13 g/g) and had a higher antioxidative response in comparison to other plant parts of P. granatum. 2024, Indian journals. All rights reserved. -
CABiT Cabs Radhikas Dilemma
The focus of this case is the human resources management challenges faced by the Chief Human Resources Officer (CHRO) at CABiT Cabs. This case discusses the CHROs dilemma in resolving the challenges. CABiT Cabs operations and expansion are discussed to provide students an overview of the dynamics and challenges of the radio taxi business. In addition, this case explores the weakness in the CABiT business model and the challenges on the human resources front. The case extensively discusses the dynamics of a start-up and the pressures faced by the top management. Finally, the options available to the protagonist to address the challenges are discussed. The dilemma faced by the protagonist in her career front is also discussed in this case. 2024 by World Scientific Publishing Co. and Asia Academy of Management. -
On equitable near proper coloring of graphs
A defective vertex coloring of a graph is a coloring in which some adjacent vertices may have the same color. An edge whose adjacent vertices have the same color is called a bad edge. A defective coloring of a graph G with minimum possible number of bad edges in G is known as a near proper coloring of G. In this paper, we introduce the notion of equitable near proper coloring of graphs and determine the minimum number of bad edges obtained from an equitable near proper coloring of some graph classes. 2024 Azarbaijan Shahid Madani University. -
Mathematical modeling to investigate the influence of vaccination and booster doses on the spread of Omicron
The emergence of new variants, such as Omicron, has raised concerns regarding the transmission dynamics of COVID-19 and the effectiveness of vaccination strategies. This paper proposes a mathematical model to investigate the impact of vaccination and booster doses on Omicron transmission dynamics, considering various infection compartments. The model incorporates multiple compartments representing different stages of infection, including susceptible individuals, vaccinated individuals, boosted individuals, and those infected with Omicron. The infection dynamics are captured by parameters such as vaccine efficacy, vaccination with booster received efficacy, and infection rate. Using mathematical analysis and numerical simulations, we explore how different vaccination and booster strategies affect the spread of Omicron. The normalized sensitivity analysis method of R0 is investigated to understand the importance of parameters in disease transmission. Furthermore, we assess the influence of infection compartments, such as asymptomatic and symptomatic cases, on overall transmission dynamics. 2023 Elsevier B.V. -
Bounds on Sombor Index for Corona Products on R-Graphs
Operations in the theory of graphs has a substantial influence in the analytical and factual dimensions of the domain. In the realm of chemical graph theory, topological descriptor serves as a comprehensive graph invariant linked with a specific molecular structure. The study on the Sombor index is initiated recently by Ivan Gutman. The triangle parallel graph comprises of the edges of subdivision graph along with the edges of the original graph. In this paper, we make use of combinatorial inequalities related with the vertices, edges and the neighborhood concepts as well as the other topological descriptors in the computations for the determination of bounds of Sombor index for certain corona products involving the triangle parallel graph. 2024 Azarbaijan Shahid Madani University. -
Biogenic ZnO Nanoparticles Derived from Garcinia gummi-gutta Leaves: Synthesis, Characterization and its Multifaceted Applications
The current study focused on the bioreduction synthesis of ZnO nanoparticles using Garcinia gummi-gutta leaf extracts. The UV-vis analysis of the nanoparticles has reported the formation of an SPR peak at 379 nm. The functional groups taking part in the reduction reaction were analyzed using the FTIR technique and the average crystalline size of ZnO nanoparticles were found to be 22.27 nm from XRD measurements. The SEM and TEM images revealed the hexagonal shape of the nanoparticles with an average size 72.78 nm and 71.91 nm, respectively. Further, the synthesized nanoparticles were reported to be efficient degradation reactive textile dyes. The photodegradation results reported 92-100% degradation of the reactive dyes within 80-320 min. The antibacterial efficacy of the nanoparticles was investigated and the MIC of the nanoparticles was found to be 100 g/mL. The synthesized ZnO nanoparticles have exhibited significant cytotoxic effects on the MCF and HEP-G2 cell lines. 2024 Asian Publication Corporation. All rights reserved. -
Time resolved spectroscopy of a GRS 1915 + 105 flare during its unusual low state using AstroSat
Since its disco v ery in 1992, GRS 1915 + 105 has been among the brightest sources in the X-ray sky. Ho we ver, in early 2018, it dimmed significantly and has stayed in this faint state ever since. We report on AstroSat and NuSTAR observation of GRS 1915 + 105 in its unusual low/hard state during 2019 May. We performed time-resolved spectroscopy of the X-ray flares observed in this state and found that the spectra can be fitted well using highly ionized absorption models. We further show that the spectra can also be fitted using a highly relativistic reflection dominated model, where for the lamp post geometry, the X-ray emitting source is al w ays very close to the central black hole. For both interpretations, the flare can be attributed to a change in the intrinsic flux, rather than dramatic variation in the absorption or geometry. These reflection dominated spectra are very similar to the reflection dominated spectra reported for active galactic nuclei in their low flux states. 2024 The Author(s). -
Integrating rod-shaped nickel molybdate@polypyrrole matrix for sustainable adsorptive removal of organic dye: Kinetics, isotherm, and thermodynamics study
Water pollution presents a significant global challenge that impacts the environment. The release of industrial effluents significantly contributes to this. Adsorption studies offer a sustainable and cost-effective solution to efficiently remove organic pollutants from water. The current study comprises a polypyrrole/nickel molybdate composite for the effective adsorption of organic dyes, such as methylene blue, from aqueous solutions. The catalyst has been comprehensively characterized using various techniques, including XRD, FE-SEM, FT-IR, HR-TEM, XPS, BET, TGA, zeta potential, and DLS analysis. Adsorption studies demonstrate up to 97% removal efficiency in 60 min. This study also evaluates the impact of various parameters, such as temperature, pH, dye concentration, and quantity of the catalyst, on the adsorption efficiency. The R2 value of 0.99 that is obtained in the kinetics study suggests the suitability of the adsorption process toward pseudo-second-order kinetics. The adsorption isotherm study reveals that the adsorption follows Freundlich's adsorption isotherm. The maximum adsorption capacity of the study is found to be 17.76 mg/g. Investigations into thermodynamic study give a ?H value of ?19.21 J/mol K, indicating the exothermic behavior, and ?G of ?6.95 KJ/mol, suggesting the spontaneity of the composite during the adsorption process. These results demonstrate the potential of the developed material as an effective adsorbent for removing organic dyes from water sources. 2023 Wiley Periodicals LLC. -
Artificial Butterfly Optimizer Based Two-Layer Convolutional Neural Network with Polarized Attention Mechanism for Human Activity Recognition
Human activity recognition (HAR) is a focal point of study in the realms of human perception and computer vision due to its widespread applicability in various contexts, such as intelligent video surveillance, ambient assisted living, HCI, HRI, IR, entertainment, and intelligent driving. With the prevalence of deep learning techniques for image classification, researchers have shifted away from the labor-intensive practice of hand-crafting in favor of these methods in HAR. However, Convolutional Neural Networks (CNNs) face challenges such as the receptive field problem and limited sample issues that remain unsolved. This paper introduces a two-branch convolutional neural network for HAR classification, incorporating a polarized full attention method to address the aforementioned issues. The Artificial Butterfly Optimization (ABO) is employed for optimal hyper-parameter tuning. The proposed network utilizes twobranch CNNs to efficiently extract data, simplifying convolutional layers' kernel sizes to enhance network training and suitability for low-data settings. Feature extraction effectiveness is improved by implementing the one-shot assembly method. To amalgamate feature maps and provide global context, an enhanced full attention block called polarized full attention is utilized. Experimental results demonstrate the superiority of the proposed model in detecting human behaviors on the LoDVP Abnormal Behaviors dataset and the UCF50 dataset. Furthermore, the suggested model is adaptable to incorporate new sensor data, making it particularly valuable for real-time human activity identification applications. The Recall is 100 for the 1st dataset, 94 for the 2nd dataset, and 100 for the 3rd dataset, respectively. The F1-Score is 96.61836 for the 1st dataset, 96.90722 for the 2nd dataset, and 98.03922 for the 3rd dataset, respectively. 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). All Rights Reserved. -
Nexus of Monetary Policy and Productivity in an Emerging Economy: Supply-Side Transmission Evidence from India
Monetary policy and its transmissions have been debated by various schools of thought. The purpose of this paper is to empirically tests whether monetary policy has supply side effect influencing Indian economys total factor productivity. This study uses ARDL model to ascertain the long run relationship between monetary policy proxies and total factor productivity (TFP). Cointegration tests reveal that total factor productivity has a relationship with all of the monetary policy proxies. The ARDL results reveal a negative relationship between TFP and some monetary policy proxies in the short run, but a positive effect in the long run. These results showcase the possible supply side transmission of monetary policy in India, which can help in determining an optimal policy so as to augment TFP, an important driver of economic growth. The study only focusses on the Indian economy and spillover effects of other Asian economies on Indias TFP can also be examined. The Author(s), under exclusive licence to The Indian Econometric Society 2024. -
A Mixed-Methods Study of Training in Evidence-Based Practice in Psychology Among Students, Faculty, and Practitioners in India and the United States
The current mixed-method study in India and the United States assessed understanding of what evidencebased practice in psychology (EBPP) is, how EBPP training and implementation occurs, and perceived barriers and needs related to EBPP training. Graduate students (India, n = 282; United States, n = 214), faculty (India, n = 24; United States, n = 67), and practitioners (India, n = 24; United States, n = 49) were surveyed, and focus groups with students (India, n = 31; United States, n = 12), faculty (India, n = 10, United States, n = 9), and practitioners (India, n = 28; United States, n = 17) were held. Individuals across countries and across the professional continuum were only somewhat aware of EBPP, largely equating it to just using empirically supported treatments. In both the United States and India, EBPP training was largely infused across the curriculum, though a sizable percentage of participants did report only limited exposure to EBPP training. Participants perceived themselves as engaging in EBPP. The biggest barriers to EBPP training (largely shared across countries) were hesitancy about EBPP, investing the time in training, and being wedded to a single school of thought. Indian participants also noted a limitation in primarily relying on data from Western countries. EBPP training needs identified included desire for greater flexibility within EBPP, receiving more theoretical foundation in EBPP, and more applied EBPP training. Results demonstrated advances in EBPP training in the past 15 years since the release of American Psychological Associations task force report but also provide areas for growth in training, specifically surrounding balancing research evidence with clients cultural context as well as ways to promote lifelong EBPP learning. 2024 American Psychological Association -
Performance of DSSC with green synthesized and thermodynamically sintered Bi-phase TiO2 with various sensitizers
The production of green and clean energy in the current era is heavily reliant on light harvesting through the use of solar cells. A successful fabrication of any of the components of Dye-sensitized solar cells (DSSC) through an easy, environmental, and economic-friendly method would be an added advantage in promoting the production of green and clean energy. With this in mind, this paper highlights the green synthesis of materials for the preparation of photo-anodes as well as sensitizers. Apart from the routine synthesis method, this paper presents a new perspective that enhances inter-particle connections by providing an optimum calcination temperature (thermodynamic sintering) during the preparation procedure. The best calcination temperature for the preparation of photo-anode material is initially optimized by comparing the device output performance between synthetic and natural dyes. Further improvement in device performance is achieved through TiCl4 (Titanium tetrachloride) post-annealing treatment on the optimized photo-anodes. The improvement in performance of these optimized photo-anodes is checked and confirmed with different natural, synthetic, and cocktail sensitizers. The best natural dye-sensitized solar cell (NDSSC) device showed an efficiency of 4.65 % and the dye-sensitized solar cell (DSSC) device showed an efficiency of 5.78 %. This confirms the suitability of these green-synthesized TiO2 nanopowders as a promising material for photo-anode preparation that could work well for both NDSSC and DSSC. 2024 Elsevier B.V. -
Synthesis and characterization of 4-nitro benzaldehyde with ZnO-based nanoparticles for biomedical applications
Globally, cancer is the leading cause of death and morbidity, and skin cancer is the most common cancer diagnosis. Skin problems can be treated with nanoparticles (NPs), particularly with zinc oxide (ZnO) NPs, which have antioxidant, antibacterial, anti-inflammatory, and anticancer properties. An antibacterial activity of zinc oxide nanoparticles prepared in the presence of 4-nitrobenzaldehyde (4NB) was also tested in the present study. In addition, the influence of synthesized NPs on cell apoptosis, cell viability, mitochondrial membrane potential (MMP), endogenous reactive oxygen species (ROS) production, apoptosis, and cell adhesion was also examined. The synthesized 4-nitro benzaldehyde with ZnO (4NBZnO) NPs were confirmed via characterization techniques. 4NBZnO NPs showed superior antibacterial properties against the pathogens tested in antibacterial investigations. As a result of dose-based treatment with 4NBZnO NPs, cell viability, and MMP activity of melanoma cells (SK-MEL-3) cells were suppressed. A dose-dependent accumulation of ROS was observed in cells exposed to 4NBZnO NPs. As a result of exposure to 4NBZnO NPs in a dose-dependent manner, viable cells declined and apoptotic cells increased. This indicates that apoptotic cell death was higher. The cell adhesion test revealed that 4NBZnO NPs reduced cell adhesion and may promote apoptosis of cancer cells because of enhanced ROS levels. 2023 Wiley-VCH GmbH. -
The influence of social media on investment decision-making: examining behavioral biases, risk perception, and mediation effects
The increasing use of social media platforms for investment-related information and advice has raised concerns about the impact of social media on investment choices. In this paper, we investigated the role of behavioral biases and risk perception in investment decisions. Specifically, this paper aims to explore the impact of social media on these factors and their influence on investment decisions. To achieve this aim, we investigated the existing works on the impact of social media on investment decisions, including its influence on behavioral biases and risk perception. We also collected data through an online survey from individual investors who use social media for investment-related information and advice. The survey measured their investment decisions, behavioral biases, risk perception, and the impact of social media on these factors. The valuable insights offered by this paper shed light on how social media affects the decisions made regarding investments and extend our understanding of the role of behavioral biases and risk perception in this context. Our results indicate that social media has a significant impact on the investment-related behaviors and perceptions of individual investors. Specifically, social media can exacerbate the effects of behavioral biases, such as herding and overconfidence bias, and influence risk perception. Moreover, the paper highlights the significance of managing social media use to make rational investment decisions. The paper's results can help individual investors make more informed investment decisions by understanding the impact of social media on their investment-related behaviors and perceptions. Moreover, the paper provides useful information to policymakers and financial regulators to develop guidelines for the responsible use of social media in the investment industry. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023.