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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. -
Optimized production of keratinolytic proteases from Bacillus tropicus LS27 and its application as a sustainable alternative for dehairing, destaining and metal recovery
The present study describes the isolation and characterization of Bacillus tropicus LS27 capable of keratinolytic protease production from Russell Market, Shivajinagar, Bangalore, Karnataka, with its diverse application. The ability of this strain to hydrolyze chicken feathers and skim milk was used to assess its keratinolytic and proteolytic properties. The strain identification was done using biochemical and molecular characterization using the 16S rRNA sequencing method. Further a sequential and systematic optimization of the factors affecting the keratinase production was done by initially sorting out the most influential factors (NaCl concentration, pH, inoculum level and incubation period in this study) through one factor at a time approach followed by central composite design based response surface methodology to enhance the keratinase production. Under optimized levels of NaCl (0.55 g/L), pH (7.35), inoculum level (5%) and incubation period (84 h), the keratinase production was enhanced from 41.62 U/mL to 401.67 9.23 U/mL (9.65 fold increase) that corresponds to a feather degradation of 32.67 1.36% was achieved. With regard to the cost effectiveness of application studies, the crude enzyme extracted from the optimized medium was tested for its potential dehairing, destaining and metal recovery properties. Complete dehairing was achieved within 48 h of treatment with crude enzyme without any visible damage to the collagen layer of goat skin. In destaining studies, combination of crude enzyme and detergent solution [1 mL detergent solution (5 mg/mL) and 1 mL crude enzyme] was found to be most effective in removing blood stains from cotton cloth. Silver recovery from used X-ray films was achieved within 6 min of treatment with crude enzyme maintained at 40 C. 2023 Elsevier Inc. -
Bioconversion of chicken feather waste into feather hydrolysate by multifaceted keratinolytic Bacillus tropicus LS27 and new insights into its antioxidant and plant growth-promoting properties
Abstract: Keratin, the main structural constituent of feathers, contains a lot of valuable amino acids which are potential bioactive compounds as well. Since conventional methods are not efficient enough to achieve complete removal of chicken feather waste, biological mode of feather degradation is one of the most appropriate ways to utilize feathers, thereby reducing wastes as well as generating value-added products from feathers. This study was focussed on valorizing chicken feather into feather hydrolysate (FH) containing bioactive compounds for plant growth promotion. Keratinolytic bacteria capable of degrading chicken feathers were isolated from the poultry waste dumping site of Russell Market, Shivajinagar, Bangalore, Karnataka, India. The isolated bacteria was identified as Bacillus tropicus LS 27. A minimal media with chicken feather as the sole source of carbon and nitrogen was prepared and inoculated with Bacillus tropicus LS 27 [5% (v/v)]. Degradation of keratin protein by bacteria caused the solubilization of amino acids which was confirmed by high-performance liquid chromatography (HPLC) analysis where an appreciable amount of amino acids like cysteine, valine, isoleucine, proline, lysine, methionine, and phenylalanine was detected. The Fourier transform infrared spectroscopy (FTIR) analysis of hydrolysed chicken feathers showed C=0 stretching, S-H bond stretching, and formation of carboxylic acid groups indicating effective degradation of chicken feathers. Scanning electron microscope (SEM) images revealed the degradation pattern of feathers showing complete degradation of barbs and barbules with a portion of rachis remaining. Feather hydrolysate was further explored for its antioxidant activity using DPPH scavenging assay, and the value was found to be 1.5 mg/mL. The bacterial cells when screened for heavy metal tolerance showed significant metal tolerance to lead (Pb) and chromium (Cr). Since Bacillus tropicus LS27 showed indole-3-acetic acid (IAA), siderophore, and ammonia production, the prepared feather hydrolysate along with the bacterial cells were used as soil amendment for plant growth studies over Spinacia oleracea L. The study revealed that plants supplemented with 20% (v/v) FH showed elevated plant growth, therefore proving to be optimum for the support of plant growth. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
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Machine Learning Based Spam E-Mail Detection Using Logistic Regression Algorithm
The rise of spam mail, or junk mail, has emerged as a significant nuisance in the modern digital landscape. This surge not only inundates user's email inboxes but also exposes them to security threats, including malicious content and phishing attempts. To tackle this escalating problem, the proposed machine learning-based strategy that employs Logistic Regression for accurate spam mail prediction. This research is creating an effective and precise spam classification model that effectively discerns between legitimate and spam emails. To achieve this, we harness a meticulously labeled dataset of emails, each classified as either spam or non-spam. This model is to apply preprocessing techniques to extract pertinent features from the email content, encompassing word frequencies, email header data, and other pertinent textual attributes. The choice of Logistic Regression as the foundational classification algorithm is rooted in its simplicity, ease of interpretation, and appropriateness for binary classification tasks. To process train the model using the annotated dataset, refining its hyper parameters to optimize its performance. By incorporating feature engineering and dimensionality reduction methodologies, bolster the model's capacity to generalize effectively to unseen data. Our evaluation methodology encompasses rigorous experiments and comprehensive performance contrasts with other well-regarded machine learning algorithms tailored for spam classification. The assessment criteria encompass accuracy, precision, recall, and the F1 score, offering a holistic appraisal of the model's efficacy. Furthermore, we scrutinize the model's resilience against diverse forms of spam emails, in addition to its capacity to generalize to new data instances. This model is to findings conclusively demonstrated that our Logistic Regression-driven spam mail prediction model achieves a competitive performance standing when juxtaposed with cutting-edge methodologies. The model adeptly identifies and sieves out spam emails, thereby cultivating a more trustworthy and secure email environment for users. The interpretability of the model lends valuable insights into the pivotal features contributing to spam detection, thereby aiding in the identification of emerging spam patterns. 2023 IEEE. -
Ochratoxin A as an alarming health threat for livestock and human: A review on molecular interactions, mechanism of toxicity, detection, detoxification, and dietary prophylaxis
Ochratoxin A (OTA) is a toxic metabolite produced by Aspergillus and Penicillium fungi commonly found in raw plant sources and other feeds. This review comprises an extensive evaluation of the origin and proprieties of OTA, toxicokinetics, biotransformation, and toxicodynamics of ochratoxins. In in vitro and in vivo studies, the compatibility of OTA with oxidative stress is observed through the production of free radicals, resulting in genotoxicity and carcinogenicity. The OTA leads to nephrotoxicity as the chief target organ is the kidney. Other OTA excretion and absorption rates are observed, and the routes of elimination include faeces, urine, and breast milk. The alternations in the Phe moiety of OTA are the precursor for the amino acid alternation, bringing about Phe-hydroxylase and Phe-tRNA synthase, resulting in the complete dysfunction of cellular metabolism. Biodetoxification using specific microorganisms decreased the DNA damage, lipid peroxidation, and cytotoxicity. This review addressed the ability of antioxidants and the dietary components as prophylactic measures to encounter toxicity and demonstrated their capability to counteract the chronic exposure through supplementation as feed additives. 2022 Elsevier Ltd -
Further Discussion on the Significance of Quartic Autocatalysis on the Dynamics of Water Conveying 47nm Alumina and 29nm Cupric Nanoparticles
Improvement of product performance, efficiency, and reliability is a major concern of experts, scientists, and technologists dealing with the dynamics of water conveying nanoparticles on objects with nonuniform thickness either coated or sprayed with the catalyst. However, little is known on the significance of quartic autocatalysis as it affects the dynamics of water conveying alumina and cupric nanoparticles. In this study, comparative analysis between the dynamics of water conveying 29nm CuO and 47nm Al 2O 3 on an upper horizontal surface of a paraboloid of revolution is modeled and presented. In the transport phenomena, migration of nanoparticles due to temperature gradient, the haphazard motion of nanoparticles, and diffusion of motile microorganisms were incorporated into the mathematical models. Due to the inherent nature of the thermophysical properties of the two nanofluids, viscosity, density, thermal radiation, and heat capacity of the two nanofluids were incorporated in the mathematical model. The nonlinear partial differential equations that model the transport phenomenon were transformed, non-dimensionalized and parameterized. The corresponding boundary value problems were converted to an initial value problem using the method of superposition and solved numerically. The concentration of the catalyst increases significantly with buoyancy at a larger magnitude of space-dependent internal heat source in the flow of 29nm CuOwater nanofluid. Negligible migration of nanoparticles due to temperature gradient decreases the concentration of the fluid throughout the domain. 2020, King Fahd University of Petroleum & Minerals. -
Amine functionalized carbon quantum dots from paper precursors for selective binding and fluorescent labelling applications
We report a novel synthesis route for preparing carbon quantum dots (CQDs) of customized surface functionality from readily available precursors. The synthetic strategy is based on the chemical modification of paper precursors prior to preparing CQDs from them. The pre-synthesis modification of paper precursors with (3-Aminopropyl) triethoxy silane (APTES) enabled us to synthesize CQDs with amine functional groups on the surface. The silane coupling via condensation between the ethoxy group of APTES and the cellulose hydroxyl group on the paper resulted in the tethering of amine groups on the paper substrates, which are retained as surface-bound species during the synthesis of CQDs from the modified paper. Amine functionalization on the surface of CQDs helped us use them in applications such as DNA binding. We analyzed the interaction of CQDs with calf thymus DNA (CT-DNA), and the results imply their propensity as an efficient biological probe. The synthetic strategy presented here can also be extended to other functional groups. 2022 Elsevier Inc. -
Carbon dots derived from frankincense soot for ratiometric and colorimetric detection of lead (II)
We report a simple one-pot hydrothermal synthesis of carbon dots from frankincense soot. Carbon dots prepared from frankincense (FI-CDs) have narrow size distribution with an average size of 1.80 nm. FI-CDs emit intense blue fluorescence without additional surface functionalization or modification. A negative surface charge was observed for FI-CDs, indicating the abundance of epoxy, carboxylic acid, and hydroxyl functionalities that accounts for their stability. A theoretical investigation of the FI-CDs attached to oxygen-rich functional groups is incorporated in this study. The characteristics of FI-CDs signify arm-chair orientation, which is confirmed by comparing the indirect bandgap of FI-CDs with the bandgap obtained from Tauc plots. Also, we demonstrate that the FI-CDs are promising fluoroprobes for the ratiometric detection of Pb2+ ions (detection limit of 0.12 ?M). The addition of Pb2+ to FI-CD solution quenched the fluorescence intensity, which is observable under illumination by UV light LED chips. We demonstrate a smartphone-assisted quantification of the fluorescence intensity change providing an efficient strategy for the colorimetric sensing of Pb2+ in real-life samples. 2022 IOP Publishing Ltd. -
Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
A Potential Review on Self-healing Material Bacterial Concrete Methods and Its Benefits
Building plays an important role for survival of human being in a safe place to live and store basic requirements. The building can be constructed for any purpose and the architecture of each building (official, residential) differs according to the plan. Beyond the plan for a building, it is also significant in designing plans for the construction of bridges, dams, canals, etc. In all the construction, the key goal is the strength of a building which completely depends on the materials that are chosen for each work. Hence, it is essential to prefer high quality materials for the construction of a building and the major materials are such as cement, concrete, steel, bricks, and sand. Among these materials, the concrete is often used for construction which enables to harden the building by combining cement, sand, and water. The concrete looks like a paste that reinforce to prolong life of the building. In this paper, we discuss a review on the use of bacteria in concrete that has the ability of self-healing cracks in the building. The procedural process behind the activation and reaction of bacteria into concrete is studied with the benefits of this process. This bacterial concrete usage assures to enhance the property of durability and but still it is yet to be introduced in the industries. Hereby, we review the recent research works undergone in concrete using bacteria. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images
One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%. 2024 IEEE. -
Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model
Mitosis count serves as a critical biomarker in breast cancer research, aiding in the prediction of aggressiveness, prognosis, and grade of the disease. However, accurately identifying mitotic cells amidst shape and stain variations, while distinguishing them from similar objects like lymphocytes and cells with dense nuclei, presents a significant challenge. Traditional machine learning methods have struggled with this task, particularly in detecting small mitotic cells, leading to high inter-rater variability among pathologists. In recent years, the rise in deep learning has reduced the subjectivity of mitosis detection. However, Deep Learning models face challenges with segmenting and classifying mitosis due to its intricate morphological variations, cellular heterogeneity, and overlapping structures. In response to these challenges, this study presents an Intelligent Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using Deep Learning (IMSD-BCHIDL) Model. The purpose of the IMSD-BCHIDL technique is to segment and classify mitosis in the histopathological images. To accomplish this, the IMSD-BCHIDL technique mainly employs YOLO-v5 model, which proficiently segments and classifies the mitosis cells. In addition, InceptionV3 is applied as a backbone network for the YOLO-v5 model, which helps in capturing extensive contextual details from the input image and results in improved detection tasks. For demonstrating the greater solution of the IMSD-BCHIDL method of the IMSD-BCHIDL technique, a wide range of experimental analyses is made. The simulation values portrayed the improved solution of the IMSD-BCHIDL system with other recent DL models. 2024 by the authors. -
LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
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Optimized Handoff Strategy for Vehicular Ad-hoc Network based Communication
The dissertation titled ???Optimized Handoff Strategy for Vehicular Ad-hoc Network based Communication??? is the compilation of all efforts taken and tasks completed in order to implement an optimal handoff method in Vehicular Ad-hoc Network communication.Wireless communication technologies have been improving exponentially. Ad-hoc networks can form a network of wireless nodes anywhere and they are not bound by the limitations of a static infrastructure. This enhances the ability of mobile nodes to communicate with each other even in situations where a defined architecture is absent. Vehicular Ad-hoc Networks (VANETs) has its applications in dynamic environments that involve nodes with high mobility. The nodes frequently move between the coverage areas of different access points. This increases the chance of link breakage and new link formation in communication network. Handoff is a process that helps in transferring the session details between one access point to another whenever the node is about to move away from a currently serving access point. Many handoff methods have been proposed but a majority of them utilize just a particular attribute of a network to employ the channel selection process. This process of network selection would be skewed as other attributes of a network play important roles in improving its overall efficiency. Multiple Attributes Decision Making (MADM) methods make use of different attributes in order to perform the network selection process. Use of MADM methods help in selecting optimal access points that can provide services to the nodes for a longer duration. In the proposed system, MADM methods have been utilized to modify existing protocols in order to optimize their approach for handoff operations. Various scenarios involving vehicular nodes and different access points have been considered in order to improve the efficiency of the proposed system across applications. The proposed handoff mechanism follows a proactive approach where the target access points are selected before the mobile node reaches the edge of its coverage area. This leads to a seamless transition of the communication channels. Based on the client/access point information stored in the data log, optimal access points which are situated along the direction of the node???s movement can be selected. NS2 and SUMO have been implemented to simulate mobile environments that accommodate handoff operations. -
Effects of activation energy and chemical reaction on unsteady MHD dissipative DarcyForchheimer squeezed flow of Casson fluid over horizontal channel
The impact of chemical reaction and activation energy plays a vital role in the analysis of fluid dynamics and its thermal properties. The application of the flow of fluid is significantly considered in nuclear reactors, automobiles, manufacturing setups, electronic appliances etc. This study explores the impacts of activation energy and chemical reaction on the magnetohydrodynamic DarcyForchheimer squeezed Casson fluid flow through a porous material across the horizontal channel where the two parallel plates are assumed to be in motion. By using similarity variables, partial differential equations are converted to ordinary differential equations. Numerical method is applied using MATLAB to solve the problems and acquire the data for velocity field, thermal distribution, and concentration distribution. The graphs indicate that fluid velocity and temperature increases as the plates are brought closer. In addition, there was a correlation between a rise in the Hartmann number and a decrease in the fluid's velocity because of the existence of strong Lorentz forces. The temperature and the concentration of the liquid will increase due to the Brownian motion. When the DarcyForchheimer and activation energy parameters are both increased, the velocity and concentration decreases. 2023, The Author(s). -
Analysis of the Thomson and Troian velocity slip for the flow of ternary nanofluid past a stretching sheet
In this article, the flow of ternary nanofluid is analysed past a stretching sheet subjected to Thomson and Troian slip condition along with the temperature jump. The ternary nanofluid is formed by suspending three different types of nanoparticles namely TiO 2, Cu and Ag into water which acts as a base fluid and leads to the motion of nanoparticles. The high thermal conductivity and chemical stability of silver was the main cause for its suspension as the third nanoparticle into the hybrid nanofluid Cu-TiO 2/ H 2O. Thus, forming the ternary nanofluid Ag-Cu-TiO 2/ H 2O. The sheet is assumed to be vertically stretching where the gravitational force will have its impact in the form of free convection. Furthermore, the presence of radiation and heat source/sink is assumed so that the energy equation thus formed will be similar to most of the real life applications. The assumption mentioned here leads to the mathematical model framed using partial differential equations (PDE) which are further transformed to ordinary differential equations (ODE) using suitable similarity transformations. Thus, obtained system of equations is solved by incorporating the RKF-45 numerical technique. The results indicated that the increase in the suspension of silver nanoparticles enhanced the temperature and due to density, the velocity of the flow is reduced. The slip in the velocity decreased the flow speed while the temperature of the nanofluid was observed to be increasing. 2023, The Author(s). -
Thermal and solutal stratified Heimanz flow of AA7072-deionized water over a wedge in the presence of bioconvection
The bioconvective Heimanz flow of nanofluid across a wedge with thermal stratification is analyzed in this article. The wedges are often seen in glider aircraft, rocket climbing frames, etc. The nanofluid considered in this study is composed of aluminum alloys of AA7072 and deionized water. The AA7072 alloys are specially manufactured materials composed of Aluminum and Zinc in the ratio of (Formula presented.) along with metals like silicon, ferrous, and copper so that they possess enhanced heat transfer features. The mathematical model is formed using the modified Buongiornos model that includes the discussions related to slip mechanisms and volumetric analysis in terms of the weight of the nanoparticle. The model is in the form of partial differential equations and is later converted to ordinary differential equations with the assistance of similarity transformation. This set of equations is solved by the Differential Transformation Method (DTM) and the outcomes are discussed through graphs.,. 2024 Taylor & Francis Group, LLC.