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A computational approach for shallow water forced KortewegDe Vries equation on critical flow over a hole with three fractional operators
The KortewegDe Vries (KdV) equation has always provided a venue to study and generalizes diverse physical phenomena. The pivotal aim of the study is to analyze the behaviors of forced KdV equation describing the free surface critical flow over a hole by finding the solution with the help of q-homotopy analysis transform technique (q-HATT). he projected method is elegant amalgamations of q-homotopy analysis scheme and Laplace transform. Three fractional operators are hired in the present study to show their essence in generalizing the models associated with power-law distribution, kernel singular, non-local and non-singular. The fixed-point theorem employed to present the existence and uniqueness for the hired arbitrary-order model and convergence for the solution is derived with Banach space. The projected scheme springs the series solution rapidly towards convergence and it can guarantee the convergence associated with the homotopy parameter. Moreover, for diverse fractional order the physical nature have been captured in plots. The achieved consequences illuminates, the hired solution procedure is reliable and highly methodical in investigating the behaviours of the nonlinear models of both integer and fractional order. 2021 Balikesir University. All rights reserved. -
Drivers of value creation in Indian corporate - An empirical evidence /
International Journal of Applied Business And Economic Research, Vol.15, Issue 22(2), pp. 563-574, ISSN No. 0972-7302. -
Prevalence of hypertension and determination of its risk factors in korangrapady, udupi district, coastal Karnataka, India
Objective: Hypertension is a global public health problem that estimates about 4.5% of overall disease burden. It is a general health challenge in economically developing and developed countries. High blood pressure prevalence is increased from 11.2% to 28% (p<0.001) and 2342.2% in rural and urban area according to the study done in Delhi for about 20 years. It is one of the important risk factors of cardiovascular disease, which is associated with morbidity and mortality. The aim was to identify the significant correlates of hypertension in a rural village in south India. Methods: Data were collected through a door-to-door survey among the residents of the village. Data collected was related to demographics and anthropometric measures. Blood pressure was measured with the help of the medical supervisor. Data were analyzed using Chi-square test for comparison between attributes. The potential hazard factor of hypertension was found by performing binary logistic regression model. Result: Of 299 participants considered for the study, 50 were hypertensive contributing to the overall prevalence of 16.72% with 95% confidence interval of 0.12920.2137, in which females have the prevalence rate of 17.8% and males with the prevalence rate of 15.5%. The study outcome identified education level, occupation, and family history of hypertension is the predicted risk factors. Conclusion: The high blood pressure prevalence is low and comparable with the studies conducted in other rural regions of India. More studies are, however, required to decide the appropriation and determinants of hypertension in different parts of this region. 2018 The Authors. -
Heterojunction engineered MWCNT/Ag3PO4 via organic acid and its natural light-assisted photocatalytic efficiency
Compositing photoactive, but unstable semiconductors with low dimensional carbon-based materials and modulating the hetero junction between them can assure more efficient and stable systems for the remediation of severe pollutants. The current study has given emphasis to understand the role of sulfonic acid in making a compact heterojunction between AP and MWCNTs, considering the effective delocalization of carriers and the direct relationship with the photoactivity. The significant reduction in the band gap of AP from 2.320 to 2.0516ev after the introduction of MWCNTs unmistakably confirmed the compatibility between the composite moieties. The intensity of the photoluminescence peak observed at an emission wavelength of 350nm for pure AP was found to be minimized in the composite, which confirms the effective charge delocalization from AP to the conductive MWCNTs. The closest bond distance was observed in the range of 2.3 to 2.5between an O atom of Ag3PO4 and a C atom of CNT, which explains the tight contact between the species. The photoactivity studies unambiguously confirmed the potential of the organic acid at the composite interface as it could accomplish 99% dye degradation within a span of 8min, whilst the system without the organic acid exhibited complete degradation within a span of 60min. The p-XRD analysis of the catalyst recovered from the reaction mixture revealed its high stability. 2023 Elsevier B.V. -
In-Vitro Investigation of the ?-Amylase Inhibition Activity of Bare Bis-Benzylidene-Cyclohexanone Synthesized by a Highly Selective Solvent-Free Route
Current work reports the highly selective, solvent-free synthesis of an endorsed bioactive compound, Bis benzylidine cyclohexanone (BBC) through solid acid catalysed cross aldol condensation route and checks its in-vitro bio activity. The catalytic support (Multiwalled carbon nanotube) employed was synthesized through the highly sophisticated catalytic chemical vapor deposition (CVD) method and simple mechanical grinding strategy was adopted to decorate sulfonic acid on the support. The C1s X-ray photoelectron peak of at 290.3 eV confirms the effective interaction of sulfonic acid with MWCNT. The sharp and intense desorption peaks observed at approximately 528.7 C and 655.15 C in the TPD analysis unmistakably substantiate the strong acidity of the synthesized system. The alpha amylase inhibition activity of the synthesized compound was calculated to be around 88.5 %, which is in par with the commercial drug as it could inhibit only 96 %. Further, the in-silico (Docking and Molecular Dynamic Simulation) investigations unveiled a new target site for the compound and this can further be studied in detail to advance the applications in drug design. Detailed scrutiny of various parameters was conducted to validate the bioactivity and pharmaceutical potential of the synthesized compound. 2023 Wiley-VCH GmbH. -
Synergistic Co-grafting of multiwalled carbon nanotubes using SO3H and choline chloride-urea in fabricating uniform thin films with enhanced visible light transparency and reduced sheet resistance
New materials and innovative modification methods are indispensable to advance the energy field. The present work reports the fabrication of transparent conducting electrodes using Multiwalled carbon nanotube (MWCNT) that have been modified with sulfonic acid (SO3H) and a combination of Choline Chloride-Urea/Sulfonic acid (DES/SO3H). A comprehensive investigation was conducted to ascertain the impact of employing DES and SO3H in achieving consistent and long-lasting dispersions of multiwalled carbon nanotubes in different solvents and the most favourable condition was achieved when employing n-heptanol. The films were fabricated on glass substrate by using the spray pyrolysis technique. The stability of the system following the modification was unequivocally confirmed by SEM analysis, while the electronic structure was assessed qualitatively by EDX analysis. Optical profilometry analysis revealed that the film thickness fell within the range of 350385 nm. The co-grafted film demonstrated an optical transparency of approximately 84.96%, modestly exceeding that of the singly grafted film, which was determined to be 70.14% in the visible region. The sheet resistance of DES/S-MWCNT and S-MWCNTs was calculated to be approximately 3.33k?/Sq and 5.02k?/Sq, respectively. The calculated charge transfer resistance (RCT) for the co-grafted and singly grafted MWCNT systems was 0.185? and 0.190?, respectively. These values align closely with the corresponding sheet resistance values obtained. The electrochemical investigations also showed an increased specific capacity for DES/S-MWCNT, approximately 896.2C/g, whereas the calculated value for the S-MWCNT system was 826.8C/g. 2024 Elsevier B.V. -
Influence of Economic Benefits and Social Interaction on Buyer Participation in a Rural Retail Institution: Study of an Indian Periodic Market
Retail institutions offer economic and social benefits to the participants in a market. It is expected that in a less developed economy the social factors influence economic behaviour much more than in developed economies. The rural markets offer increased opportunities for the influence of social factors on economic transactions. This study examined the case of a rural periodic market. To ensure reliability the case study protocol questions reflected propositions developed on the research questions. It was expected that the participants would exhibit the influence of social relations in their market transactions. The results indicated that the economic benefits than social considerations influenced participant behaviour in the rural periodic market. Contrary to expectations not all consumers in a less developed economy exhibit social embeddedness in economic behaviour. Implications are for policymakers involved in planning and regulating rural markets. They need to take into consideration the differing behaviour of consumer groups in designing or regulating retail markets. This study examining the social embeddedness of buyer behaviour in the rural retail context of a less developed economy is presumably the first. 2021 Institute of Rural Management, Anand, Gujarat, India. -
Evaluation of an interprofessional collaborative practice training module for the management of children with autism spectrum disorder
Background: Protocols instituted for behavioral treatment and skills training programs for the management of autism spectrum disorder (ASD) suffer from lack of collaborative approaches. The tenets of interprofessional collaborative practice (IPCP) focus on preparing a panel of health care professionals (HCPs) from different professions who can work together to enable the common goal of ensuring that children with ASD can participate in society. This study was designed to pilot this approach through an IPCP training module on ASD for care providers from multiple professions. Methods: An interventional study with pre-post analysis began with formation of the interprofessional (IP) team, who developed an IPCP module, addressing the knowledge and skills needed for the collaborative management of neurodevelopmental issues of children with ASD. This module was delivered through an online training workshop using various teaching learning methods to the participants from seven different health professions after obtaining informed consent. Perceptions of interprofessional collaboration and competencies of IPCP were assessed using standard IP tools and reflective summaries and analyzed through a mixed-methods approach. Results: A total of 42 HCPs from seven professions, including speech and hearing, occupational therapy, clinical psychology, physiotherapy, pediatrics, nursing, and pedodontics, participated in the study. Pre-post analysis of PINCOM-Q and Dow-IPEC data and thematic analysis revealed a significant difference in the perceptions of interprofessional collaboration and competencies levels of IPCP. Conclusion: This study suggests that use of IPCP principles in the training of professionals working with ASD is a promising and feasible option to develop more competent health professionals. The training enhanced the abilities of professionals to work in field of ASD as conveyed by the participants. They also expressed confidence in the knowledge of IP core competencies after the completion of the module. 2022 -
6-Bromo-2-[(E)-thiophen-2-ylmethylidene]-2,3,4,9-tetrahydro-1H-carbazol-1- one
In the title compound, C17H12BrNOS, the cyclohexene ring deviates only slightly from planarity (r.m.s. deviation for non-H atoms = 0.047 . In the crystal, the molecules are linked into centrosymmetric R2 2(10) dimers via pairs of N-H?O hydrogen bonds. The thio-phene ring is disordered over two positions rotated by 180and with a site-occupation factor of 0.843 (4) for the major occupied site. -
6-Bromo-2-(3-phenylallylidene)-2,3,4,9-tetrahydro-1H-carbazol-1-one
molecules of the title compound, C21H16BrNO, are linked through pairs of N-H?O intermolecular hydrogen bonds into centrosymmetric R2 2(10) dimers. One of the C atoms of the cyclohex-2-enone ring is disordered with refined occupancies of 0.61 (2) and 0.39 (2). -
Solvent free microwave assisted synthesis and evaluation of potent antimicrobial activity of 1,11H-pyrimido[4,5-a]carbazol-2-ones, 1,11H-pyrimido [4,5-a]carbazol-2-thiones and pyrazolo[3,4-a]carbazoles
Microwave assisted condensation of urea, thiourea and hydrazine hydrate with 1-chloro-2-formyl carbazoles in the presence of PTSA as catalyst yields 1,11H-pyrimido[4,5-a]carbazol-2-ones, 1,11H-pyrimido[4,5-a]carbazol-2-thiones and pyrazolo[3,4-a]carbazoles, respectively. The structures of the synthesized compounds have been confirmed on the basis of elemental analysis and spectral data. All the synthesized compounds have been evaluated for their antibacterial and antifungal activities. Some of the synthesized compounds 2a-g and 3a-g exhibit significant antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. The compounds 2a-g and 3a-g exhibit good antifungal activity against Candida albicans, Aspergillus flavus. Pyrazolo[3,4-a]carbazoles 4a-g register good antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. The compound 4e indicate maximum activity of 20 and 24 mm at 500 and 1000?g/disc, respectively, against Lipomyces lopofera fungi. -
Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks
Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network. Copyright 2023 KSII. -
A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN
Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested methods purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs. 2022, Tech Science Press. All rights reserved. -
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. -
Value Addition for Technology Start-Ups Through Physical Co-Location
Numerous economic theories, knowledge, social, and communication theories have extensively explored the phenomenon of physical co-location in various contexts. However, limited scholarly attention has been given to co-location in emerging contexts such as co-working spaces, predominantly used by start-ups. One of the critical questions examined is how co-location adds value to technology start-ups in the early and growth stages of their development. We chose a premium coworking space in Bangalore, Indias start-up capital, as the studys research setting during January March 2020. The qualitative research employed semi-structured interviews to explore the phenomenon. Our findings revealed that start-ups actively used co-located resources to explore, experiment, and validate new business ideas in the early stage. As they transitioned into the growth phase, they exploited co-located industry networks to expand into new markets. They also learned vicariously from other co-located resources and used them to solve complex problems and refined their processes and routines. As start-ups begin to grow and expand, co-location infrastructure-related costs are not justifiable, operations are less secure, and the meta culture of the co-located environment is in conflict with the firms operating culture. The results of this study have the potential to be significant for technology start-ups that are exploring new ways of working and addressing uncertainties during the early and growth stages of their development. 2021, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Physical Co-location: an intersection of problem-solving and vicarious learning
Scholars have examined Revans' problem-solving praxeology in many contexts but have not fully explored the concept in the case of physical co-location. Hence, we focussed on investigating Revans' conceptualisation in a co-located context by paying particular attention to the different forms of learning' that emerged from it. The research setting for this study involved two coworking spaces in Bangalore, India, whose constituents were co-located start-ups and established enterprises. Held from January to March 2020, the study involved conducting exploratory, semi-structured interviews with twelve firms. The findings suggested that in a co-located environment, a) firms learnt vicariously' from a rich, external knowledge base during the enquiry-led Alpha phase b) firms learnt experientially', through learning by doing and reflecting in the implementation-focussed Beta phase c) firms learnt through the process of emergence that resulted from personal reflection and team interaction, in the revelatory Gamma phase. This study lends a novel direction in acknowledging that vicarious learning, that is, learning through the experience of others, serves as a starting point for problem-solving in a co-located context. We demonstrate that firms gain familiarity with the problem through vicarious sources, that is, from those experienced co-located firms who had journeyed on a similar path. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT
The diabetes is a critical disease from the small children to old age people. Due to improper diet and physical activities of the living population, obesity becomes prevalent in young generation. If we analyze self care of individual life, no man or women ready to spend their time for health care. It leads to problem like diabetes, blood pressure etc. Today is a busy world were robots and artificial machines ready to take care of human personal needs. Automatic systems help humans to manage their busy schedule. It motivates us to develop a diabetes motoring system for patients using IoT device in their body which monitors their blood sugar level, blood pressure, sport activities, diet plan, oxygen level, ECG data. The data are processed using feature selection algorithm called as particle swarm optimization and transmitted to nearest edge node for processing in 5G networks. Secondly, data are processed using DBN Layer. Thirdly, we share the diagnosed data output through the wireless communication such as LTE/5G to the patients connected through the edge nodes for further medical assistance. The patient wearable devices are connected to the social network. The Result of our proposed system is evaluated with some existing system. Time and Performance outperform than other techniques. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Cross-layer hidden Markov analysis for intrusion detection
Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based on intrusion detection methods. First, the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks. Moreover, delay, energy consumption, network lifetime, and link accomplishment are highly addressed by the proposed algorithm. The hidden Markov model is used to maintain the data transition and distributions in the network. Every data communication network, like ad hoc mobile cloud computing, faces security and confidentiality issues. However, the main security issues in this article are addressed using the storage and search optimization approach. Hence, the new algorithm developed helps detect intruders through intelligent cross layer analysis with the Markov model. The proposed model was simulated in Network Simulator 3, and the outcomes were compared with those of prevailing methods for evaluating parameters, like accuracy, end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput. 2022 Tech Science Press. All rights reserved. -
A hybrid approach for COVID-19 detection using biogeography-based optimization and deep learning
The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron emission tomography, lung ultrasound, and magnetic resonance imaging, as inputs. In the comparative analysis, the proposed deep learning model CNN is compared with other existing models, namely, VGG16, InceptionV3, ResNet50, and MobileNet. In the fitness function formation, classification accuracy is considered to enhance the prediction capability of the proposed model. Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50. 2022 Tech Science Press. All rights reserved. -
Bioparametric Investigation of Mutant Bacillus subtilis MTCC 2414 Extracellular Laccase Production under Solid State Fermentation
This work has been undertaken to investigate the bio parameters such as various substrates, initial moisture level, inoculum size, pH, incubation temperature, incubation period, metal ions and nitrogen sources effect on the production of laccase in solid-state fermentation using mutant Bacillus subtilis MTCC 2414. The laccase production was observed with a sesame oil cake (183.32 0.29 U/g), initial moisture level 80% (189.28 0.52 U/ g), inoculum size 1.5% (196.12 0.26 U/g), initial pH 8 (215.20 0.48 U/g), incubation temperature 37C (225.80 0.52 U/g), incubation period 48h (258.80 0.29 U/g), CuSO4 (263.16 0.12 U/g) and yeast extract (268.14 0.16 U/g) in the production medium. 2018, Association of Biotechnology and Pharmacy. All rights reserved.