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Monopsonistic exploitation in contract farming: Articulating a strategy for grower cooperation
Contract farming has been considered a new hope to instil dynamism in third world agriculture. However, there remains serious concern whether small peasants will be able to benefit from this system since buyers may often be a single large or at most, few large corporations, a typical case of monopsony. In this paper we question the basis of the fears that are often raised in the literature. A clear analytical approach to understanding the (economic) meaning of monopsony helps us articulate a strategy for grower cooperation that could effectively deal with monopsony power in contract farming systems. Copyright 2007 John Wiley & Sons, Ltd. -
MOOCs: A disruptive teaching-learning process in interdisciplinary boundaries /
International Journal of Language & linguistics, Vol.1, Issue 2, pp.54-61, ISSN No: 2374-8869. -
More insights into bar quenching: Multi-wavelength analysis of four barred galaxies
The underlying nature of the process of star formation quenching in the central regions of barred disc galaxies that is due to the action of stellar bar is not fully understood. We present a multi-wavelength study of four barred galaxies using the archival data from optical, ultraviolet, infrared, CO, and HI imaging data on star formation progression and stellar and gas distribution to better understand the process of bar quenching. We found that for three galaxies, the region between the nuclear or central sub-kiloparsec region and the end of the bar (bar region) is devoid of neutral and molecular hydrogen. While the detected neutral hydrogen is very negligible, we note that molecular hydrogen is present abundantly in the nuclear or central sub-kiloparsec regions of all four galaxies. The bar co-rotation radius is also devoid of recent star formation for three out of four galaxies. One galaxy shows significant molecular hydrogen along the bar, which might mean that the gas is still being funnelled to the centre by the action of the stellar bar. Significant star formation is also present along the bar co-rotation radius of this galaxy. The study presented here supports a scenario in which gas redistribution as a result of the action of stellar bar clears the bar region of fuel for further star formation and eventually leads to star formation quenching in the bar region. 2020 ESO. -
Morphological and Elemental Investigations on CoFeBO Thin Films Deposited by Pulsed Laser Deposition for Alkaline Water Oxidation: Charge Exchange Efficiency as the Prevailing Factor in Comparison with the Adsorption Process
Abstract: Mixed transition-metals oxide electrocatalysts have shown huge potential for electrochemical water oxidation due to their earth abundance, low cost and excellent electrocatalytic activity. Here we present CoFeBO coatings as oxygen evolution catalyst synthesized by Pulsed Laser Deposition (PLD) which provided flexibility to investigate the effect of morphology and structural transformation on the catalytic activity. As an unusual behaviour, nanomorphology of 3D-urchin-like particles assembled with crystallized CoFe2O4 nanowires, acquiring high surface area, displayed inferior performance as compared to coreshell particles with partially crystalline shell containing boron. The best electrochemical activity towards water oxidation in alkaline medium with an overpotential of 315 mV at 10 mA/cm2 along with a Tafel slope of 31.5 mV/dec was recorded with coreshell particle morphology. Systematic comparison with control samples highlighted the role of all the elements, with Co being the active element, boron prevents the complete oxidation of Co to form Co3+ active species (CoOOH), while Fe assists in reducing Co3+ to Co2+ so that these species are regenerated in the successive cycles. Thorough observation of results also indicates that the activity of the active sites play a dominating role in determining the performance of the electrocatalyst over the number of adsorption sites. The synthesized CoFeBO coatings displayed good stability and recyclability thereby showcasing potential for industrial applications. Graphic Abstract: [Figure not available: see fulltext.] 2021, The Author(s). -
Morphology-dependent supercapacitive properties of Co3O4 nanomaterials synthesized via coprecipitation and hydrothermal methods
The supercapacitive properties of Co3O4 nanocrystalline powders with two different morphologies synthesized by coprecipitation (referred to as Co3O4C) and hydrothermal (referred to as Co3O4-H) methods were compared and studied. The samples were analyzed for their phase purity, crystal structure, surface morphology, and surface area. Both samples were found to be single-phase nanostructures with a normal spinel-type cubic crystal structure (space group Fd3m), as indicated by Raman and XRD (X-ray diffraction) data analyses. TEM (Transmission electron microscopy) images clearly show that the Co3O4C sample exhibits spherical particles with a mean size of 10 nm. On the other hand, the Co3O4H sample shows a flower-like assembly of particles. The Co3O4C sample has a higher specific surface area than the Co3O4-H sample due to its smaller particle size. XPS (X-ray photoelectron spectroscopy) data were collected to analyze the chemical states and cation distribution of the samples, revealing a 2:1 ratio of Co3+ and Co2+ in both samples. Both samples displayed pseudocapacitive behaviour in CV (cyclic voltammetry) and GCD (galvanostatic chargedischarge) analyses. Despite having a smaller surface area, the Co3O4H electrode exhibited a higher CS (specific capacitance) compared to the Co3O4C electrode at all current densities when tested using 1 M KOH electrolyte. At a specific current density (0.5 A/g), the Cs values for Co3O4C and Co3O4H are found to be 366 F/g and 233 F/g, respectively. As the current density increases, the specific capacitance of both electrodes decreases, but this reduction is more prominent for Co3O4-C than Co3O4-H. The study indicates that besides surface area, the morphology of the sample also plays a crucial role in determining the capacitance of a material. 2023 Elsevier B.V. -
MoS2-TiO2 Nanocomposites for Enhanced Photo-electrocatalytic Hydrogen Evolution
The investigation on the designing and fabrication of highly efficient electrocatalysts for hydrogen evolution reaction (HER) is critical for future applications in renewable sustainable energy. The present work reports the hydrothermal synthesis of two-dimensional MoS2 and MoS2-TiO2 nanostructures. The as-prepared nanostructures were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), Raman analysis, UV-vis-NIR, and photoluminescence spectrophotometry and vibrating sample magnetometer (VSM). Systematic electrochemical measurements for HER were performed and MoS2-TiO2 nanocomposites demonstrated the lowest onset potential in comparison with MoS2. The results suggest that the nanofusion interface between MoS2 nanoflakes and TiO2 nanoparticles induced an efficient charge transfer from the conduction band of MoS2 to TiO2 and favored the reduction of H+ at active sites. We believe the present work can open up new possibilities that would provide deep insights for the rational design of 2D materials-based catalysts for energy storage and conversion applications. 2023 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. -
Motivational factors leading to the limited presence of women chefs in the hotel industry of Bengaluru /
International Journal of Innovative Studies In Sociology And Humanities, Vol.3, Issue 8, pp.108-121, ISSN No: 2456-4931. -
Mulberry Leaves (Morus Rubra)-Derived Blue-Emissive Carbon Dots Fed to Silkworms to Produce Augmented Silk Applicable for the Ratiometric Detection of Dopamine
Silk fibers (SF) reeled from silkworms are constituted by natural proteins, and their characteristic structural features render them applicable as materials for textiles and packaging. Modification of SF with functional materials can facilitate their applications in additional areas. In this work, the preparation of functional SF embedded with carbon dots (CD) is reported through the direct feeding of a CD-modified diet to silkworms. Fluorescent and mechanically robust SFare obtained from silkworms (Bombyx mori) that are fed on CDs synthesized from the Morus rubra variant of mulberry leaves (MB-CDs). MB-CDs are introduced to silkworms from the third instar by spraying them on the silkworm feed, the mulberry leaves. MB-CDs are synthesized hydrothermally without adding surface passivating agents and are observed to have a quantum yield of 22%. With sizes of ?4nm, MB-CDs exhibited blue fluorescence, and they can be used as efficient fluorophores to detect Dopamine (DA) up to the limit of 4.39nM. The nanostructures and physical characteristics of SF weren't altered when the SF are infused with MB-CDs. Also, a novel DA sensing application based on fluorescence with the MB-CD incorporated SF is demonstrated. 2023 Wiley-VCH GmbH. -
Multi-atlas Graph Convolutional Networks and Convolutional Recurrent Neural Networks-Based Ensemble Learning for Classification of Autism Spectrum Disorders
Autism spectrum disorder (ASD) has an influence on social conversation and interaction, as well as encouraging people to engage in repetitive behaviors. The complication begins in childhood and persists through adolescence and maturity. Autism spectrum disorder has become the most common kind of childhood development worldwide. ASD hinders the capacity to interact, socialize, and build connections with individuals of all ages, and thus its early intervention is critical. This paper discusses some of the most recent approaches to diagnostics using convolutional networks and multi-atlas graphs for autism spectrum disorders. Also, several pre-processing approaches are elaborated. Graph convolutional neural networks (GCNs) to diagnose autism spectrum disorder (ASD) because of their remarkable effectiveness in illness prediction using multi-site data. Convolutional neural network (CNN) and recurrent neural networks (RNN) infrastructure studies functional connection patterns between various brain regions to find particular patterns to diagnose ASD. In our research, we implemented the GCN + CRNN ensemble method and achieved 89.01% accuracy based on resting-state data from the fMRI (ABIDE-II), a novel framework for detecting early signs of autism spectrum disorders is presented and discussed. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Multi-class SVM based network intrusion detection with attribute selection using infinite feature selection technique
An intrusion detection mechanism is a software program or a device that monitors the network and provides information about any suspicious activity. This paper proposes a multi-class support vector machine (SVM) based network intrusion detection using an infinite feature selection technique for identifying suspicious activity. Single and multiple classifiers generally have high complexity. To overcome all the limitations of single and multiple classifiers, we used a multi-class classifier using an infinite feature selection technique, which performed well with multiple classes and gave better results than other classifiers in terms of accuracy, precision, recall, and f_score. Infinite feature selection is a graph-based filtering approach that analyses subsets of features as routes in a graph. We used a standard dataset, namely the UNSW_NB15 data set generated by the IXIA perfect-storm tool in the Australian Centre for Cyber Security. This dataset has a total of nine types of attacks and 49 features. The comparative analysis of the manuscript work is done against eight different techniques, namely, hybrid intrusion detection system (HIDS), C5, one-class support vector machine, and others. The proposed work gave better simulation results using the 2015a Matlab simulator. 2021 Taru Publications. -
Multi-Criteria Usability Evaluation of mHealth Applications on Type 2 Diabetes Mellitus Using Two Hybrid MCDM Models: CODAS-FAHP and MOORA-FAHP
People use mHealth applications to help manage and keep track of their health conditions more effectively. With the increase of mHealth applications, it has become more difficult to choose the best applications that are user-friendly and provide user satisfaction. The best techniques for any decision-making challenge are multi-criteria decision-making (MCDM) methodologies. However, traditional MCDM methods cannot provide accurate results in complex situations. Currently, researchers are focusing on the use of hybrid MCDM methods to provide accurate decisions for complex problems. Thus, the authors in this paper proposed two hybrid MCDM methods, CODAS-FAHP and MOORA-FAHP, to assess the usability of the five most familiar mHealth applications that focus on type 2 diabetes mellitus (T2DM), based on ten criteria. The fuzzy Analytic Hierarchy Process (FAHP) is applied for efficient weight estimation by removing the vagueness and ambiguity of expert judgment. The CODAS and MOORA MCDM methods are used to rank the mHealth applications, depending on the usability parameter, and to select the best application. The resulting analysis shows that the ranking from both hybrid models is sufficiently consistent. To assess the proposed frameworks stability and validity, a sensitivity analysis was performed. It showed that the result is consistent with the proposed hybrid model. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Multi-dynamics and emission tailored fluoroperovskite-based down-conversion phosphors for enhancing the current density and stability of the perovskite solar cells
State-of-the-art and innovative research is being intensively employed on perovskite solar cells (PSCs) to expand their frontiers further. This study is a successful attempt to drive the limit of photocurrent density (Jsc) beyond conventional PSCs (which typically utilize the visible spectrum alone) through a nonlinear optical phenomenon called down-conversion (DC). The use of DC luminescence to harness the UV region from the solar spectrum is explored by utilizing Eu3+ activated RbCaF3, a fluoroperovskite-based phosphor material. It is observed that PSCs, which used RbCaF3:Eu3+ incorporated TiO2 electron transport layer (ETL), enhanced their Jsc and UV stability compared to those with pristine TiO2-oriented ETL. Such improvement in the aforementioned devices is due to the result of converting high-energy UV photons to effectively absorbable low-energy visible photons for perovskite absorbers. Overall, the DC-aided PSC offered a substantial Jsc of 23.54 mA cm?2 (9.2% superior to the conventional PSC) and boosted its power conversion efficiency (PCE) from 11.2% to 13.3%. It is evident that DC-based PSCs show a much better shelf-life when compared to conventional PSCs. This unique approach for boosting the Jsc with enhanced stability can be utilized for the potential applications of PSCs. 2023 The Royal Society of Chemistry. -
Multi-frame twin-channel descriptor for person re-identification in real-time surveillance videos
Automatic re-identification of people entering the camera network is an important and challenging task. Multiple frames of the same person will be easily available in surveillance videos for re-identification. Dealing with pose variations of the person in the image and partial occlusion issues is major challenge in single-frame re-identification process. The use of more frames from the surveillance videos can generate robust descriptor to tackle issues of pose variations and occlusion. In this paper, we have emphasized on using multiple frames from the same video to generate a multi-frame twin-channel descriptor. The work deals with building a spatial-temporal descriptor which takes advantage of the twin paths to extract features of the person image. Mahalanobis distance metric learning algorithms is used for matching and evaluation. Our descriptor is evaluated on two benchmark datasets and found to surpass the performance of the existing methods. 2017, Springer-Verlag London Ltd. -
Multi-layer Stacking-based Emotion Recognition using Data Fusion Strategy
Electroencephalography (EEG), or brain waves, is a commonly utilized bio signal in emotion detection because it has been discovered that the data recorded from the brain seems to have a connection between motions and physiological effects. This paper is based on the feature selection strategy by using the data fusion technique from the same source of EEG Brainwave Dataset for Classification. The multi-layer Stacking Classifier with two different layers of machine learning techniques was introduced in this approach to concurrently learn the feature and distinguish the emotion of pure EEG signals states in positive, neutral and negative states. First layer of stacking includes the support vector classifier and Random Forest, and the second layer of stacking includes multilayer perceptron and Nu-support vector classifiers. Features are selected based on a Linear Regression based correlation coefficient (LR-CC) score with a different range like n1, n2,n3,n4 a, for d1 used n1 and n2 dataset,for d2 dataset, combined dataset of n3 and n4 are used and developed a new dataset d3 which is the combination of d1 and d2 by using the feature selection strategy which results in 997 features out of 2548 features of the EEG Brainwave dataset with a classification accuracy of emotion recognition 98.75%, which is comparable to many state-of-the-art techniques. It has been established some scientific groundwork for using data fusion strategy in emotion recognition. 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved. -
Multi-Model Traffic Forecasting in Smart Cities using Graph Neural Networks and Transformer-based Multi-Source Visual Fusion for Intelligent Transportation Management
In the intelligent transportation management of smart cities, traffic forecasting is crucial. The optimization of traffic flow, reduction of congestion, and improvement of theoverall transportation systemefficiency all depend on accurate traffic pattern projections. In order to overcome the difficulties causedby the complexity and diversity of urban traffic dynamics, this research suggests a unique method for multi-modal traffic forecasting combining Graph Neural Networks (GNNs) and Transformer-based multi-source visual fusion. GNNs are employed in this method to capture the spatial connections betweenvarious road segments and to properly reflect the basic structure of the road network. The model's ability to effectively analyse traffic dynamics and relationships between nearby locations is enhanced by graphsrepresenting the road layout, which also increases theoutcome of traffic predictions. Recursive Feature Elimination (RFE) is employed to improve the model's feature selection process and choose the most pertinent features for traffic prediction, producing forecasts that are more effective and precise. Utilizing real-time data, the performance of the suggested strategywasassessed, enabling it to adjust to shifting traffic patterns and deliver precise projections for intelligent transportation management. The empirical outcomes show exceptional results ofperformance metrics for the proposed approach, achieving anamazing accuracy of 99%. The resultsshow that the suggested techniques findings have the ability to anticipate traffic and exhibit a superior level of reliability whichsupports efficient transportation management in smart cities. The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2024. -
Multi-objective ANT lion optimization algorithm based mutant test case selection for regression testing
The regression testing is principally carried out on modified parts of the programs. The quality of programs is the only concern of regression testing in the case of produced software. Main challenges to select mutant test cases are related to the affected classes. In software regression testing, the identification of optimal mutant test case is another challenge. In this research work, an evolutionary approach multi objective ant-lion optimization (MOALO) is proposed to identify optimal mutant test cases. The selection of mutant test cases is processed as multi objective enhancement problem and these will solve through MOALO algorithm. Optimal identification of mutant test cases is carried out by using the above algorithm which also enhances the regression testing efficiency. The proposed MOALO methods are implemented and tested using the Mat Lab software platform. On considering the populace size of 100, at that point the fitness estimation of the proposed framework, NSGA, MPSO, and GA are 3, 2.4, 1, and 0.3 respectively. The benefits and efficiencies of proposed methods are compared with random testing and existing works utilizing NSGA-II, MPSO, genetic algorithms in considerations of test effort, mutation score, fitness value, and time of execution. It is found that the execution times of MOALO, NSGA, MPSO, and GA are 2.8, 5, 6.5, and 7.8 respectively. Finally, it is observed that MOALO has higher fitness estimation with least execution time which indicates that MOALO methods provide better results in regression testing. 2021 Scientific Publishers. All rights reserved. -
Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans
Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making. 2020 Elsevier B.V. -
Multidrug Resistant Bacteria: The Fatal Menace in Healthcare
Mapana Journal of Sciences, Vol-11 (1), pp. 31-47. ISSN-0975-3303 -
Multifaceted Destination Personality Traits: A Short Communication on Understanding from Tourists Perspective
This short communication is an extract from a major research work on destination branding, and this cull out of analysis focused on the multifaceted destination personality traits that the destinations possess and perhaps how such perceptions of tourists differ based on the selected personal factors. Though there are many studies in the destination branding literature, the evidence regarding the personality traits is still at the stage of progression, and approaches referring to multifaceted personality traits are unseen. After the pilot testing, a structured questionnaire was floated to 400 tourists who visited the selected destinations a district in Tamil Nadu, India, between June 2019 and February 2020, where 327 responses were finalized. The questionnaire had statements measuring the destinations personality traits and other questions on tourists characteristics. Combined mean calculation and multivariate results revealed that two personality traits, welcoming and friendly, were emphasized by the tourists and perceived in common. Also, personality traits such as spiritual and charming were found to be commonly perceived. The mean values also indicated the existence of multifaceted destination personality traits some inherent and some perceived. Marketers and others thereof have been recommended on the branding and advertising strategies based on the outcome of this communication. The limitations and scope of this research have been indicated. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Multifarious Potential of Biopolymer-Producing Bacillus subtilis NJ14 for Plant Growth Promotion and Stress Tolerance in Solanum lycopercicum L. and Cicer arietinum L: A Way Toward Sustainable Agriculture
Diverse practices implementing biopolymer-producing bacteria have been examined in various domains lately. PHAs are among the major biopolymers whose relevance of PHA-producing bacteria in the field of crop improvement is one of the radical unexplored aspects in the field of agriculture. Prolonging shelf life is one serious issue hindering the establishment of biofertilizers. Studies support that PHA can help bacteria survive stressed conditions by providing energy. Therefore, PHA-producing bacteria with Plant Growth-Promoting ability can alter the existing problem of short shelf life in biofertilizers. In the present study, Bacillus subtilis NJ14 was isolated from the soil. It was explored to understand the ability of the strain to produce PHA and augment growth in Solanum lycopersicum and Cicer arietinum. NJ14 strain improved the root and shoot length of both plants significantly. The root and shoot length of S. lycopersicum was increased by 3.49 and 0.41cm, respectively. Similarly, C. arietinum showed a 9.55 and 8.24cm increase in root and shoot length, respectively. The strain also exhibited halotolerant activity (up to 10%), metal tolerance to lead (up to 1000?g/mL) and mercury (up to 100?g/mL), indicating that the NJ14 strain can be an ideal candidate for a potent biofertilizer. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.