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Multivariate statistical optimization of phenolics and antioxidants from nutmeg seeds (Myristica fragrans Houtt)
The present study aimed to optimize the phenolic and antioxidant-rich extract from the nutmeg (Myristica fragrans Houtt) by using a two-factor 26-run central composite design-based response surface methodology tool. The selected parameters were extraction period (2 to 5days), solvent-to-water ratio (v/v) (50100%), and type of solvent (acetone or ethanol). The optimized extract at conditions of 3.14days incubation and 68% (v/v) acetone showed total phenolic content (TPC), total flavonoid content (TFC), and DPPH antioxidant assay as 376.38mg GAE/g DW, 34.40mg QUE/g DW and 842.46mg AAE/g DW, respectively. Among the nineteen (19) compounds identified by the LCMS, myristicin (37.74%) was found to be the highest. Nine (9) alkane-fatty acyl compounds were determined by the GCMS analysis, as well. Additionally, SEM and XRD revealed sheet-like anatomy with the presence of Carbon (C), Oxygen (O) and Potassium (K). The study presented a unique approach to optimizing phenolic-rich antioxidant extracts from nutmeg using response surface methodology, offering valuable insights for more efficient extraction of bioactive compounds with minimal resource waste and potentially enhancing the utilization of nutmeg's nutraceutical properties. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Exploring the effectiveness of mindfulness-based intervention among college students in India
This study investigated the effectiveness of an eight-week mindfulness-based intervention program on the trait mindfulness, psychological well-being and emotion regulation of college-going students. The experimental group participants were college-going students (N = 40) who enrolled for the intervention, and the participants in the control group (N = 40) were interested in the intervention and considered as a wait-list control group. The experimental group underwent mindfulness-based interventions, which included 1112 sessions, including brief exercises and meditations related to their trait mindfulness, emotion regulation, and psychological well-being. They received 23h of training per week for eight weeks. Repeated Measures of ANOVA together with an independent sample t-test were used to evaluate the effectiveness of this intervention programme. Further, Cohens d was used to calculate the effect size to explain the variance caused by the intervention program in trait mindfulness, emotion regulation, and psychological well-being. The results indicated that students significantly improved in their trait mindfulness, emotion regulation, and psychological well-being after receiving mindfulness training. In conclusion, the application of this eight-week mindfulness-based intervention sheds light on the common psychological issues confronted by college students in India, presenting itself as an advantageous tool for the professionals working in this field and offering positive effects on the overall well-being of college students. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Effect of functionalization on the energy storage performance of super capacitors derived from wood charcoal
The electrochemical performance of wood charcoal is investigated with respect to the disorders in the system after subjecting to oxidation and exfoliation conditions. The Cyclic voltammetry and galvanostatic charge discharge curves indicate an improvement in the electrochemical behavior, resulting in a marginal increase in the specific capacitance values at higher exfoliation temperatures. The improvement is predominantly due to the change in the structural disorder in the system accompanied by the incorporation of oxygen functional groups which act as electrochemical active species. The exfoliation of wood charcoal at 160 and 200C yield a specific capacitance of 6.23 and 12.24 F/g at a current density of 0.01 A/g. The ESR values representing the overall resistance of the system are observed to be 6.07 ? for 200C as opposed to 10.41 ? of the bare material, making the material more conducting. The drastic change in the structural morphology along with the optimal amount of oxygen functional groups can be the reason for this behavior. The acquired results offer useful information for investigating the possibilities of fabricating supercapacitors with wood charcoal by tuning the defects of the system. 2024 American Institute of Chemical Engineers. -
Improved reptile search algorithm with sequential assignment routing based false packet forwarding scheme for source location privacy protection on wireless sensor networks
Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models. 2024-IOS Press. All rights reserved. -
REVIEW OF CENSORING SCHEMES: CONCEPTS, DIFFERENT TYPES, MODEL DESCRIPTION, APPLICATIONS AND FUTURE SCOPE
Survival analysis is one of the key techniques utilized in the domains of reliability engineering, statistics, and medical domains. It focuses on the period between the initialization of an experiment and a subsequent incident. Censoring is one of the key aspects of survival analysis, and the techniques created in this domain are designed to manage various censoring schemes with ease, ensuring accurate and insightful time-to-event data analysis. The statistical efficiency of parameter estimates is improved by accurately incorporating censoring information by making use of the available data. This paper reviews the concepts, model descriptions, and applications of conventional and hybrid censoring schemes. The introduction of new censoring schemes from conventional censoring schemes has evolved by rectifying the drawbacks of the previous schemes, which are explained in detail in this study. The evolution of hybrid censoring schemes through the combination of various conventional censoring schemes, the data structures, concepts, methodology, and existing literature works of hybrid censoring schemes are reviewed in this work. 2024, Gnedenko Forum. All rights reserved. -
ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS
Social media news consumption is growing in popularity. Users find social media appealing because it's inexpensive, easy to use, and information spreads quickly. Social media does, however, also contribute to the spread of false information. The detection of fake news has gained more attention due to the negative effects it has on society. However, since fake news is created to seem like real news, the detection performance when relying solely on news contents is typically unsatisfactory. Therefore, a thorough understanding of the connection between fake news and social media user profiles is required. In order to detect fake news, this research paper investigates the use of machine learning techniques, covering important topics like feature integration, user profiles, and dataset analysis. To generate extensive feature sets, the study integrates User Profile Features (UPF), Linguistic Inquiry and Word Count (LIWC) features, and Rhetorical Structure Theory (RST) features. Principal Component Analysis (PCA) is used to reduce dimensionality and lessen the difficulties presented by high-dimensional datasets. The study entails a comprehensive assessment of multiple machine learning models using datasets from "Politifact" and "Gossipofact," which cover a range of data processing methods. The evaluation of the XGBoost classification model is further enhanced by the analysis of Receiver Operating Characteristic (ROC) curves. The results demonstrate the effectiveness of particular combinations of features and models, with XGBoost outperforming other models on the suggested unified feature set (ALL). 2023 Little Lion Scientific. -
Solid-State Organic Fluorophore for Latent Fingerprint Detection and Anti-Counterfeiting Applications
A highly fluorescent material exhibiting solid-state fluorescence is particularly important in detecting latent fingerprints (LFPs) and anti-counterfeiting applications. Herein, we have synthesized a coumarin-benzothiazole moiety 3-(benzo[d]thiazol-2-yl)-2H-chromen-2-one (3-BTC) to inspect its capability to visualize LFPs and work as an anti-counterfeiting ink. The compound showed yellow-greenish emission under UV excitation and good covertness under visible light conditions. With the help of the powder dusting method, the latent fingerprints were coated with 3-BTC powder and images of the LFPs developed over various substrates including plastic, steel, aluminium plate, rubber, etc. under UV 365 nm light displayed good resolution be able to discern the patterns of all the levels 13. Apart from fresh fingerprints (taken within 10 seconds), aged (over 60 days) and incomplete eccrine LFPs were successfully visualized using 3-BTC powder. Anti-counterfeiting ink prepared using 3-BTC also proved to be a promising candidate as an anti-counterfeiting ink. Various types of paper materials, including tissue paper, printing paper, newspaper, etc. were used for evaluating 3-BTC as a satisfactory anti-counterfeiting ink. 2024 Wiley-VCH GmbH. -
Implementing Innovative Weed Detection Techniques for Environmental Sustainability
Agriculture, supporting over half of India's population, grapples with the challenge of weed control. Current methods applied in plantation crops lack efficiency and pose environmental and health risks. This paper advocates a paradigm shift, emphasizing the critical need for effective weed detection using cluttered unmanned aerial vehicle (UAV) images. The research methodology integrates image processing, Mask R-Convolutional Neural Networks (R-CNN), and Internet of Things (IoT). A dataset of 200 UAV images was subjected to a thorough preprocessing. In the initial phase, weeds and crops were identified with precision employing an UAV-tailored Mask R-CNN with instance segmentation. This was found to surpass traditional methods in terms of communication between the model and the agricultural environment. For timely decision-making, real-time data were collected using IoT. Average Precision (AP) values reveal high accuracy, notably 89.1% for weeds, 88.9% for crops, and an overall precision of 89.4%. The Mask R-CNN network segments and classifies images, marking weed zones communicated to farmers via Raspberry Pi with a GSM module, enabling real-time alerts and informed decision-making for efficient weed control. This holistic approach, providing object classifications, detailed bounding boxes, and masks, addresses weed control challenges, highlighting the transformative potential of advanced technologies in agriculture. 2024, Institute for Environmental Nanotechnology. All rights reserved. -
Pseudocapacitive electrode performance of zinc oxide decorated reduced graphene oxide/poly(1,8-diaminonaphthalene) composite
Development of Reduced graphene oxide/Zinc oxide/poly(1,8-diaminonaphthalene) (rGO/ZnO/PDAN) composite and its supercapacitor performance has been evaluated. Functional, crystal and morphological structures along with the thermal stability of the polymer-impregnated composite were studied using analyses such as FTIR, powder XRD, Raman, SEM and TGA techniques. The SEM images showcased the random decoration of irregularly shaped ZnO in between the wrinkled rGO sheets and the PDAN. The defective structure of the hybrid composite contributes capacitive features through electron transfer kinetics. The specific capacitance value for the rGO/ZnO/PDAN composite modified NF electrode in 3 M KOH was found to be 239 F/g at the current density of 0.5 A/g. The capacitance retained is 92 % after 5000 cycles at 5 A/g current density. A solid-state symmetrical supercapacitor device based on a novel rGO/ZnO/PDAN composite was successfully developed, which exhibits the specific capacity (40 at 0.6 A/g), energy density (12.66 Wh/kg) and power density (993 W/kg). The synergistic combination of surface-active nitrogen heteroatoms, extended conjugation and ZnO particles decorated over rGO sheets of rGO/ZnO/PDAN ternary hybrid composite displayed significant structural stability and electrochemical pseudocapacitive performances. 2023 Elsevier Ltd -
Single-monomer dual templated MIP based electrochemical sensor for tartrazine and brilliant blue FCF
In this study, a dual-templated molecularly imprinted polymer-based electrochemical sensor was developed for the simultaneous analysis of two food additive dyes, brilliant blue FCF and tartrazine. Using a 3-aminophenyl boronic acid (3-APBA) monomer and the dual templates of brilliant blue FCF (BB) and tartrazine (TZ), the molecularly imprinted polymer (MIP) layer was electropolymerized on the carbon fibre paper (CFP) electrode. By using BB and TZ as template molecules along the electro-polymerization of 3-APBA, then removing both template molecules, the MIP film was generated on the surface of the CFP electrode. Due to the high surface area provided by modification, several complementary binding sites for template molecules are formed on the surface of the MIP sensor during this process of sensor fabrication. On the MIP/CFP electrode, the electrochemical behavior of BB and TZ was assessed. The monomer/template ratio, pH values, and influencing parameters like the electro-polymerization scanning cycles were all optimized. This sensor was applied to detect brilliant blue FCF and tartrazine in beverage and food samples using MIPAPBA/CFP electrode. 2023 -
Mathematical approach for impact of media awareness on measles disease
During the recent pandemic caused by COVID-19, media awareness played a crucial role in educating people about social distancing, wearing masks, quarantine, vaccination, and medication. Media awareness brought individual behavioral changes among the people, which in turn helped reduce the infection rate. Motivated by this, we have formulated a mathematical model introducing a media compartment to mitigate measles disease transmission. In this paper, the SEIR model is used to study measles disease in three cases: one with a delay in vaccination, the second with regular vaccination, and the third with the impact of media awareness on the spreading of measles disease. Further, the dynamical behavior of the models is studied in terms of positivity, boundedness, equilibrium, and basic reproduction number (BRN). The sensitivity analysis of the models is conducted, which verifies the importance of the BRN ((Formula presented.)) to be less than one for disease eradication. The numerical study confirms the impact of media awareness on exposed and infected populations. 2023 John Wiley & Sons Ltd. -
Dual ion specific electrochemical sensor using aminothiazole-engineered carbon quantum dots
A novel electrochemical sensor capable of concurrently detecting Pb2+ and Hg2+ ions has been innovatively engineered. This sensor utilizes the anodic stripping voltammetry technique (ASV) with a composite consisting of carbon quantum dots and aminothiazole (CQD-AT). In this composite, both the carbon quantum dots and aminothiazole contribute significantly to the electroactive surface area, boasting an abundance of functional groups that include oxygen and nitrogen atoms. These functional groups serve as active sites that enhance sensor sensitivity by facilitating the electrostatic interaction-based adsorption of heavy metal ions. Aminothiazole surface is evenly covered with CQDs, which are essential for metal gets reoxidized into metal ions for stripping analysis. Due to this unique modification, the Pb2+ and Hg2+ electrochemical sensor using the CQD-AT composite coated on carbon fiber paper electrode (CQD-AT/CFP) exhibits superior analysis performance such as wide linear range (0.6 1011160 106 M) for Pb2+ and Hg2+ with a limit of detection (LOD) of 3.0 pM and 6.2 pM for Pb2+ and Hg2+. CQD-AT/CFP modified electrode can be considered as a potential material for electrochemical simultaneous determination of Pb2+ and Hg2+ in different water samples. 2023 Elsevier B.V. -
Efficient Brain Tumor Identification Based on Optimal Support Scaling Vector Feature Selection (OSSCV) Using Stochastic Spin-Glass Model Classification
Brain tumor detection is a developing defect finding task in medical imaging, as premature and early identification is a critical once for recommending early treatment. The tumor are identified by the laboratory through MRI images by finding the tumor regions. The Artificial intelligence play a vital role for finding, analyzing, the image data to attain the target results in medical image using various learning methodologies. Most of the existing system failed to find the find the feature dimension leads poor accuracy for identifying tumor regions due to low precision, recall rate, lower intensity in image coverage region. To resolve this problem, to propose an Optimal Support Scaling Vector Based Feature Selection (OSSCV) brain tumor identification using Stochastic Spin-Glass Model Classification (SSGM). Initially the preprocessing is done by bilateral filter and segmentation is applied by suing Active Region Slice Window Segmentation (ARSWS). To separate the tumor entity feature projection using Histogram color quantization and the features process are carried by Optimal Support Scaling Vector Based Feature Selection (OSSCV). The selected features get trained using Stochastic Spin-Glass Model Classification (SSGM) to find the tumor region. The proposed system outperforms traditional machine learning methods in brain tumor detection. Finally proposed system of Stochastic Spin-Glass Model (SSGM) performance of recall is 95.5%, the performance of F1-score is 96.1% and the performance of the 96.5%. The proposed approach has the potential to assist radiologists in diagnosing brain tumors more accurately and efficiently, leading to improved patient outcomes. 2024, Ismail Saritas. All rights reserved. -
INVESTIGATING THE ROLE OF UTAUT2 IN THE USER SATISFACTION AND CONTINUED USAGE OF MOBILE FITNESS APPS: EVIDENCES FROM INDIA
This study examines mobile fitness app (MFA) adoption and use using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). CB-SEM analysis of 445 respondents through IBM-AMOS was conducted. UTAUT2 constructs were examined including, consumer satisfaction, e-loyalty, and mobile fitness app reuse. Also, the roles of socio-demographic factors as control variables were examined. The results indicated that UTAUT2 significantly influenced user perception of MFAs. UTAUT2 constructs increased user satisfaction. User satisfaction positively and significantly influenced e-loyalty and app reuse. The study also suggests future research on UTAUT2's role on MFAs. The study highlights UTAUT2 by revealing the constructs that influence MFA adoption, and selected consequences. (2024), (Amity University). All rights reserved. -
STABILITY IN CHAOS: IMPACT OF MONETARY, FISCAL, AND FIRM CHARACTERISTICS ON INVESTOR SENTIMENT IN ASIAN EMERGING MARKETS
This study investigates the impact of firm characteristics, monetary policies, and fiscal policies on investor sentiment, specifically focusing on market volatility and trading volume in six Asian emerging markets during the pre-pandemic and pandemic periods. Using panel data regression on a sample of 5,619 firms between 2015 and 2023, this study analyses the distinct roles of firm-specific factors and macroeconomic policies in shaping market behaviour during periods of economic instability. The findings reveal that firm characteristics such as capital structure and payout policies consistently drive both volatility and trading volume. Monetary policies, particularly interest rates and money supply, showed heightened significance during the pandemic, while fiscal policies, though largely insignificant pre-pandemic, became more relevant during the crisis. The study's results provide critical insights for policymakers and investors on the dynamic interplay between firm-level and macroeconomic factors during crisis periods, emphasising the need for coordinated policy responses. 2024, Universiti Malaysia Sarawak. All rights reserved. -
Co-sputtered V2O5TiN composite on Ag-network current collector for high-performance flexible transparent thin-film supercapacitors
Next-generation wearables require extremely capable electrochemical energy-storage devices that exhibit improved performance with high flexibility and transparency. Herein, we present a highly flexible and transparent electrochemical thin-film supercapacitor electrode fabricated by co-sputtering V2O5 and TiN on an Ag-network-based current collector. The electrodes' physical properties, optical properties, and structural morphologies are studied using X-ray diffraction, UVvisible spectroscopy, and scanning electron microscopy, respectively. A symmetric device is fabricated using V2O5 and TiN on an Ag network, and the TiN sputter power is varied to optimize the performance. The device performance of the co-sputtered electrodes at various composition ratios is studied. The optimized V2O5TiN (200?40)/Ag electrode device with pseudocapacitive behavior delivers an excellent areal specific capacitance of 98.66 mF cm2 at a current density of 4 mA cm2 with a charge retention of 90.12 % after 6000 cycles. The V2O5TiN (200?40)/Ag electrode device outperforms other reported electrodes, with an energy density and power density of 30.83 ?Wh cm2 and 2999.67 ?W cm2, respectively, and excellent mechanical stability. 2023 -
A Novel Deep Learning Approach for Retinopathy Prediction Using Multimodal Data Fusion
In contemporary research on mild cognitive disorders (MCI) and Alzheimer's disease (AD), the predominant approach involves the utilization of double data modalities for making predictions related to AD stages. However, there is a growing recognition of the potential benefits that could be derived from the fusion of multiple data modalities to obtain a more comprehensive perspective in the analysis of AD staging. To address this, we have employed deep learning techniques to holistically assess data from various sources, including, genetic (single nucleotide polymorphisms (SNPs)), imaging (magnetic resonance imaging (MRI)), and clinical tests, with the objective of categorizing patients into distinct groups: AD, MCI, and controls (CN). For the analysis of imaging data, convolutional neural networks have been employed. Moreover, we have introduced a novel approach for data interpretation, enabling the identification of the most influential features learned by these deep models. This interpretation process incorporates clustering and perturbation analysis, shedding light on the crucial aspects of the data contributing to our classification results. Our experimentation, conducted on the dataset (i.e., ADNI), has yielded compelling results. Furthermore, our findings have underscored the significant advantage of integrating multi-modality data over solely relying on double modality models, as it has led to improvements in terms of accuracy, precision, recall, and mean F1 scores. 2024, Ismail Saritas. All rights reserved. -
Coloring of n-inordinate invariant intersection graphs
In the literature of algebraic graph theory, an algebraic intersection graph called the invariant intersection graph of a graph has been constructed from the automorphism group of a graph. A specific class of these invariant intersection graphs was identified as the n-inordinate invariant intersection graphs, and its structural properties has been studied. In this article, we study the different types of proper vertex coloring schemes of these n-inordinate invariant intersection graphs and their complements, by obtaining the coloring pattern and the chromatic number associated. 2024 The Author(s) -
Internet of things-based virtual private social networks on a text messaging strategy on mobile platforms
A virtual private social network (VPSN) allows a device to communicate securely with a network via the internet. Confidential data may be sent more securely thanks to the encrypted connection; it allows the user to operate remotely and prevents unauthorised parties from listening in. A mobile messaging platform is a text-enabled mailbox on the web. They enable companies and organisations to communicate with clients via text message. Social networking organisations may now use compute resources as a utility instead of creating and managing their computer infrastructures thanks to cloud computing (CC). An example of how a distributed sensor-actor environment might be used in a sociology-technical network is shown in this paper. The results are obtained as regular media access actively is 85.7%, security services in IoT is 85.37%, text attackers is 83.6%, loss of information is 82.8%, and blocking text messages is 91.48%. Copyright 2024 Inderscience Enterprises Ltd. -
Exploration of the dual fuel combustion mode on a direct injection diesel engine powered with hydrogen as gaseous fuel in port injection and diesel-diethyl ether blend as liquid fuel
The present study explores the possibilities of the use of diesel-diethyl ether (DDEE) blends as pilot fuel, and hydrogen (H2) as inducted gaseous fuel in a diesel engine operated on dual fuel mode (DFM). DEE was added to diesel in ratios of 525% in increasing steps of 5%, to prepare the DDEE5, DDEE10, DDEE15, DDEE20, and DDEE25 blends that were used as pilot fuel. In this current study, for hydrogen gas generation, a hydrogen production kit was fabricated which was powered by solar energy. The hydrogen gas was produced from the electrolysis of water-KOH solution. During the experiment, hydrogen was inducted through the engine intake port employing an electronic gas injector. The quantity of hydrogen injection was set constant of 0.2 lpm for all the test cases. DDEE-hydrogen (DDEE+H2) blends accomplished overall good results compared to diesel. DDEE20+H2 furnished optimal results compared to diesel and other DDEE+H2 blends. Peak cylinder pressure for DDEE20+H2 was 66.91 bar at 5.2oCA aTDC, and the maximum HRR was 32.75 J/deg.CA. Compared to diesel, the BTE of engine for DDEE20+H2 was augmented by about 0.6% and the BSFC was diminished by about 3.7%, at maximum load conditions. A decline in CO and HC emissions of 29.6%, and 35% were observed for DDEE20+H2 at maximum load condition, but the NO and CO2 emanation was observed to be higher by around 29.4%, and 17.4% in comparison to diesel respectively. 2023 Hydrogen Energy Publications LLC