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Floral waste as a potential feedstock for polyhydroxyalkanoate production using halotolerant Bacillus cereus TS1: optimization and characterization studies
The versatile properties and high degree of biodegradability of polyhydroxyalkanoates (PHA) have made them the ideal candidate for biomedical and other applications. Although extensive research on PHA-producing bacterial isolates from terrestrial environments is documented in the available literature, the potential of marine bacterial isolates in PHA production remains less explored and offers a great scope for future research. This research work primarily focuses on isolation and characterization of PHA-producing bacterial isolates from samples collected from coastal areas of Kerala, India. Furthermore, the possibility of PHA production from the most potential isolate Bacillus cereus TS1 using jasmine waste hydrolysate-based media was explored in this study. The utilization of floral waste hydrolysate (FWH) for PHA fermentation is not widely discussed in the available literature and is the major novelty factor of this research work. Under optimized conditions of glucose (1.2% w/v), yeast extract (0.15% w/v), NaCl (5.02% w/v), and incubation period (60h), a maximum PHA yield of 1.13g/L was achieved. The characterization of PHA polymer was done using Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA). Thus, this research work integrates floral waste valorisation with microbial biopolymer production and highlights an innovative approach for sustainable development. The scale of this method on an industrial scale in future may prove helpful in the cost-effective production of PHA using cheap raw materials. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Enhancement of Agriculture Feeder Performance by Optimal Sizing and Placing of Solar PV Tree through AEO-Based Optimization Technique
Electrical demand, which makes up a large share of the overall power market, agriculture at the top of the list of priorities. To provide end users with a dependable and high-quality supply via various feeders and renewable energy sources, distribution generations are now being developed. In recent years, solar PV systems have been used to meet the demands of numerous applications, including boosting the efficiency of distribution networks. This paper presents the system with effect ive optimization method like Artificial Eco-System based Optimization Technique for identification of the best location to install distribution generation and the optimum size to minimize feeder losses. To meet service expectations, the integration of a solar PV system is swapped out for a solar tree in this suggested work. A 28-bus Indian agriculture feeder is considered for better understanding the proposed algorithm. MATLAB software is used for implementing the proposed optimization technique and CREO-2.0 is used for designing the 3-dimensional solar PV tree. 2023 by the Kamal Kumar U and Varaprasad Janamala. -
Examining the facilitators of I4.0 practices to attain stakeholders collaboration: a circular perspective
The fourth industrial revolution (I4.0) has changed the traditional business model, bringing various benefits, including increased efficiency and productivity in organizations. However, to attain success in I4.0 practices requires collaboration from various stakeholders. This study objectives to identify the facilitators of I4.0 practices that can lead to successful collaboration among stakeholders from a circular perspective. An extensive literature review is performed to identify 14 potential facilitators. Further, the study adopts a mixed methodology of Best-Worst Method (BWM) and Interpretive Structural Modeling (ISM) to analyze the interconnectedness among the identified facilitators. BWM method was used to determine the relative importance of the identified facilitators, while ISM technique was used to determine the relationships between the facilitators of I4.0 practices. The findings from the study reveal that to strengthen stakeholder collaboration, organizations need to focus more on training and capacity-building programs and create more opportunities for technology exchange. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Total Global Dominator Coloring of Trees and Unicyclic Graphs
A total global dominator coloring of a graph G is a proper vertex coloring of G with respect to which every vertex v in V dominates a color class, not containing v and does not dominate another color class. The minimum number of colors required in such a coloring of G is called the total global dominator chromatic number, denoted by Xtgd (G). In this paper, the total global dominator chromatic number of trees and unicyclic graphs are explored. 2023 University of Baghdad. All rights reserved. -
Antecedents of brand love leading to purchase intention towards refurbished video game consoles
This paper examines different constructs that influence purchase intention of refurbished video game consoles to assess its multi-factorial association with brand love. The data was collected from video game console cafes in the cities of Bangalore and Pune, India. The findings demonstrate that adoption determinants and social influence have a positive influence on brand love, while notably, environmental involvement has a positive influence on an individuals purchase intention. Brand love would not singularly influence positive purchase intention in the context of refurbished video game consoles. The paper clarifies that brand love alone cannot influence the purchasing decision of an individual in the context of refurbished video game consoles, the companies selling or remanufacturing these products can benefit by advertising these products as being environmentally involved. This is the first paper that examines the effects of brand love and purchase intentions in the context of refurbished video game consoles. Copyright 2023 Inderscience Enterprises Ltd. -
SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT
Security and privacy are regarded as the greatest priority in any real-world smart ecosystem built on the Internet of Things (IoT) paradigm. In this study, a SqueezeNet model for IoT threat detection is built using Sine Cosine Sea Lion Optimization (SCSLnO). The Base Station (BS) carries out intrusion detection. The Hausdorff distance is used to determine which features are important. Using the SqueezeNet model, attack detection is carried out, and the network classifier is trained using SCSLnO, which is developed by combining the Sine Cosine Algorithm (SCA) with Sea Lion Optimization (SLnO). BoT-IoT and NSL-KDD datasets are used for the analysis. In comparison to existing approaches, PSO-KNN/SVM, Voting Ensemble Classifier, Deep NN, and Deep learning, the accuracy value produced by devised method for the BoT-IoT dataset is 10.75%, 8.45%, 6.36%, and 3.51% higher when the training percentage is 90. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Perception vs. reality: Analysing the nexus between financial literacy and fintech adoption
Fintech has revolutionized the financial services sector, fundamentally transforming how individuals and businesses manage their finances. However, effective and responsible utilization of these innovative services may require a certain degree of financial competence. To explore this possibility, this study investigates the nexus between financial literacy and fintech usage in the Indian context, considering two distinct measures of financial literacy. Primary data were collected conveniently from 391 respondents through a cross-sectional survey. Probit regression was applied to analyze the relationship between the two dimensions of financial literacy and the adoption of fintech services across three segments: mobile banking, mobile payments, and digital lending. The findings reveal a positive relationship between individuals subjectively perceived financial literacy and their propensity to use all three fintech services. Conversely, objectively measured financial literacy demonstrates a positive association only with the likelihood of using mobile banking. The study also identifies demographic characteristics as contributing factors to variations in fintech adoption. The studys findings hold value for policymakers and fintech service providers, as they underscore the importance of enhancing individuals subjective perceptions of their financial abilities to promote wider adoption of fintech services. Shamli Prabhakaran, Mynavathi L., 2023. -
Stacked LSTM and Kernel-PCA-based Ensemble Learning for Cardiac Arrhythmia Classification
Cardiovascular diseases (CVD) are the most prevalent causes of death and disability worldwide. Cardiac arrhythmia is one of the chronic cardiovascular diseases that create panic in human life. Early diagnosis aids physicians in securing life. ECG is a non-stationary physiological signal representing the heart's electrical activity. Automated tools to detect arrhythmia from ECG signals are possible with Machine Learning (ML). The ensemble learning technique combines the power of two or more classifiers to solve a computational intelligence problem. It enhances the performance of the models by fusing two or more models, which extremely increases its strength. The proposed ensemble Machine learning amalgamates the potency of Long Short-Term Memory (LSTM) and ensemble learning, opening up a new direction for research. In this research work, two novel ensemble methods of Extreme Gradient Boosting-LSTM (EXGB-LSTM) are developed, which use LSTM as a base learner and are transformed into an ensemble learner by coalescing with Extreme Gradient Boosting. Kernel Principal Component Analysis (K-PCA) is a significant non-linear dimensionality reduction technique. It can manage highdimensional datasets with various features by lowering the dimensionality of the data while retaining the most crucial details. It has been applied as a preprocessing step for feature reduction in the dataset, and the performance of EXGB-LSTM is tested with and without K-PCA. Experimental results showed that the first method, fusion of EXG-LSTM, has reached an accuracy of 92.1%, Precision of 90.6%, F1-score of 94%, and Recall of 92.7%. The second proposed method, KPCA with EXGB-LSTM, attained the highest accuracy of 94.3%, with a precision of 92%, F1-score of 98%, and Recall of 94.9% for multi-class cardiac arrhythmia classification. (2023), (Science and Information Organization). All Rights Reserved. -
A multi-model unified disease diagnosis framework for cyber healthcare using IoMT-cloud computing networks
The past several decades of research into machine learning have been of great assistance to humanity in the diagnosis of a variety of ailments using various forms of automated diagnostic procedures. Machine learning, combined with smart health devices, has improved health monitoring, timely diagnoses, and treatment. This paper introduces a unified disease diagnosis framework, integrating cloud computing, machine learning, and IoT. The framework has three layers: physical (collects patient data), fog (intermediate layer with a domain identification unit to determine input and diagnosis type), and transmission (cloud server with a disease detection unit). The performance evaluation shows the robustness and efficiency of the model as compared to state-of-art models. 2023, Taru Publications. All rights reserved. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
Reaction of Indian Stock Market to Outbreak of COVID-19: An Empirical Analysis of Extreme Inter-day Movements
The contagious COVID-19 pandemic has been considered a massive global crisis since World War II and has disturbed business and economic activities across the globe. The current study examined the reaction of the stock markets to the outbreak of COVID-19, considering the extreme inter-day movements in the Indian stock market. The extreme inter-day movements in S&P CNX Nifty-50 have been identified during the study period from January 2020 to December 2021 and further classified into decline and gain events based on positive and negative announcements related to COVID-19. The study utilized an event study approach and panel regression for empirical investigation. The results of the event study analysis illustrate that the significant abnormal loss ranges from 12.86% to 2.47% for the major decline events and significant abnormal return from 8.43% to 3.23% for the gain events. The regression analysis results showed that real return and Central Bank Policy rate have a considerable impact on the abnormal returns during COVID-19. The studys findings are helpful to policy implications that identified the need to focus on financial education and strengthen the health and finance-related policies to deal with such pandemics in the future. 2023 MDI. -
The role of guilt-shame proneness and locus of control in predicting moral injury among healthcare professionals
Despite the advances in studies conducted among healthcare professionals to explore the impact of the pandemic on their mental health, a large population still continues to display COVID-19 related psychological complaints. There has been recent awareness of moral injury related guilt and shame among doctors and nurses. However, the factors associated with moral injury have not received much attention, due to which the issue still persists. This study aims to explore the role of guilt-shame proneness, and locus of control in predicting moral injury among healthcare professionals. MISS-HP, PGI Locus of Control, and GASP scales were administered to a sample of 806 healthcare professionals. Pearson correlation coefficient indicated a significant positive relationship between moral injury and guilt-shame proneness, as well as the locus of control. Regression analysis indicated a significant role of guilt-shame proneness and locus of control in predicting moral injury. In conclusion, while studying moral injury, it becomes equally important to consider these factors to understand the concept better. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
The Illiberal turn in Indian democracy: shifting the trajectory of Indias foreign policy
Long-standing democracies such as India were not exempt from the global trend of democratic retreat. India has come under increasing international attention due to certain domestic policies such as the revocation of Article 370 of the Indian Constitution, the National Register of Citizens and Citizenship (Amendment) Act passed under the Bharatiya Janata Party government. In addition to Indias democratic decline being reflected in global democratic rankings, this has induced strains on Indias foreign relations. In its pursuit of becoming a leading power, Indias perceived democratic backsliding is likely to influence the direction of its foreign policy. To discern the impact of its perceived illiberal turn on its foreign engagement, the role of democracy in Indias foreign policy needs to be explored. While attempts have been made to understand democratic backsliding through a theoretical lens, the impact of a nations democratic status on its foreign relations and policy remains a largely unexplored area. 2023 Taylor & Francis. -
Text summarization using residual-based temporal attention convolutional neural network
To address the computational complexity and limited to large data Enhanced Residual based Temporal Attention Convolutional Neural Network (ERTACNN) with Improved Initialization strategy-based Aquila Optimization Algorithm (IIAOA) is proposed. Initially the document is pre-processed to get structured data and given to feature extraction. Then the features are selected with Aquila Optimization Algorithm to remove redundant or unrelated features from high-dimensional data, from which the entropy values are calculated and given to proposed classifier. In this classification, the temporal attention mechanism is combined with classifier to compute attention weight and accompanied with important time points for classifying the documents. Finally, the proposed method is implemented in python and evaluated against existing works which achieves 70.34, 55.6 and 72.4 Recall Oriented Understudy for Gisting Evaluation (ROUGE) score than existing approaches. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
Mathematical Modeling for Evaluating the Mechanical Properties of High Strength Concrete with Natural Zeolite and Additives
The cement manufacturing industry is a major contributor to atmospheric pollution, primarily due to carbon dioxide emissions. Consequently, there is a pressing need to develop eco-friendly concrete capable of mitigating air pollution by sequestering atmospheric carbon dioxide. In this context, the incorporation of Natural Zeolite in concrete has been investigated, as it can absorb environmental carbon dioxide. This study explored the effects of partial cement replacement with Natural Zeolite (5%) and varying percentages of Silica Fume, Metakaolin, and Fly Ash (5%, 10%, and 15%) on the mechanical properties and carbon sequestration potential of High Strength Concrete (HSC). Comprehensive testing was conducted to evaluate the split tensile, compressive, and flexural strengths of the modified HSC. Experimental results indicated that the addition of Natural Zeolite and Metakaolin enhanced the strength of HSC, with Mix 3 displaying a higher 90-day compressive strength compared to the reference mix. The findings suggest that incorporating Natural Zeolite and other supplementary cementitious materials in concrete has the potential to alleviate environmental pollution. The dataset, comprising 900 samples, exhibited no autocorrelation or multicollinearity issues, making it suitable for multiple regression analysis. The statistically significant regression models developed in this study can effectively predict concrete strength. (2023). All Rights Reserved. -
PROTECTING DATA AND PRIVACY: CLOUD-BASED SOLUTIONS FOR INTELLIGENT TRANSPORTATION APPLICATIONS
The interaction between transportation networks and intelligent transportation systems has been revolutionized by cloud computing. However, the reliance on cloud-based solutions raises security and privacy concerns. This article examines the challenges of safeguarding data and privacy in intelligent transportation applications and emphasizes the potential of cloud-based solutions to resolve these issues. Organizations can protect sensitive data and user privacy by employing encryption, access controls, threat detection mechanisms, and privacy protection measures. Adopting these cloud-based solutions will encourage the extensive adoption of intelligent transportation applications while infusing users and stakeholders with confidence. 2023 SCPE. -
Cooperation affects NGO staff performance patterns
In order to optimise employee productivity and overall profitability, non-profits must invest heavily in their human resources. Contrarily, the focus of this study will be on the value of cooperation and the strategies the non-governmental organisation (NGO) should use to improve the performance of the bank as a whole. Once the data have been collected using quantitative and qualitative techniques, SPSS descriptive statistics will be utilised to maintain the findings and support the research hypothesis. According to the study, qualities like trust, camaraderie, job happiness, and benefits directly impact employees productivity at the bank. The degree of teamwork among co-workers directly affects how productive an employee is. Using the statistical program SPSS, managers and staff of NGOs were surveyed; the results revealed a favourable correlation between employee performance and NGO cooperation. When employees cooperate at work, their productivity increases, and the efficacy of the organisations they work for rises. Good news for charitable organisations. Because of this, the collaborative NGO outperforms the non-collaborative NGO in terms of productivity. It was found that better communication results in greater cooperation amongst NGOs. Copyright 2023 Inderscience Enterprises Ltd. -
Efficient Ultra Wideband Radar Based Non Invasive Early Breast Cancer Detection
Ultra Wideband radar systems have emerged as a good alternative for non-invasive and harmless breast cancer detection. In this paper, bistatic and monostatic radar systems are proposed, which detects the deep-rooted and smallest formation of the tumor in the breast. The source signal for transmission through the breast is a seventh derivative Gaussian Ultra Wideband pulse. This pulse is shaped using the proposed sharp transition bandpass Finite Impulse Response filter. The pulse shaper filter design has a sharp transition, hence efficient for shaping very short-duration pulses, achieving higher data rate and less interference issues. Also, the pulse tightly fits the Federal Communication Commission spectral mask, thus achieving higher spectral utilization efficiency and meeting the signal safety standards for transmission through the breast. The shaped pulse fed to the antenna of the radar system provides higher antenna radiation efficiency and radiating power due to the concentration of power in the main lobe, sidelobe suppression, and less channel loss. Tumor detection is based on the time and frequency domain analysis of the backscattered signals from the tumor. These signals have higher amplitude, higher electric field intensity variations, and an increase in the scattering parameter values due to the presence of tumor. Simulation results show significant changes in the electric field intensity for normal and malignant breast tissue for tumor sizes ranging from 4 mm to 0.5 mm. To accurately detect the location of tumor inside the breast, Specific Absorption Rate (SAR) analysis is carried out. It is observed that the energy absorption in the cancerous breast is higher than that of the normal breast, thereby aids to detect the location of the tumor accurately by identifying the coordinates of the maximum value of SAR. The results obtained with an experimental setup consisting of fabricated heterogeneous breast phantom with tumor and monostatic radar closely confirms with the simulation results. 2013 IEEE. -
RIEMANN SOLITONS ON (?,?)-ALMOST COSYMPLECTIC MANIFOLDS
In this paper, we study almost cosymplectic manifolds with nullity distributions admitting Riemann solitons and gradient almost Riemann solitons. First, we consider Riemann soliton on (?,?)-almost cosymplectic manifold M with ? < 0 and we show that the soliton is expanding with (Formula Presented) and M is locally isometric to the Lie group G?. Finally, we prove the non-existence of gradient almost Riemann soliton on a (?,?)-almost cosymplectic manifold of dimension greater than 3 with ? < 0. 2023 Korean Mathematical Society -
Improved Random Forest Algorithm for Cognitive Radio Networks' Distributed Channel and Resource Allocation Performance
Modified Random Forest (MRF) machine learning algorithm aimed at improving the distributed channel allocation and resource allocation performance in cognitive radio networks (CRNs). The purpose of this research is to enhance the efficiency and effectiveness of CRNs by optimizing the allocation of channels and resources. The proposed MRF algorithm is developed by adapting and modifying the random forest technique to address the specific challenges of CRN allocation. Experimental evaluations demonstrate that the MRF algorithm achieves higher accuracy and efficiency compared to existing routing techniques and channel allocation algorithms like ACO and PSO. It exhibits a high packet delivery ratio, increased throughput, and reduced delay in channel selection, thus improving the overall performance of CRNs.The implications of this research are twofold. On a theoretical level, this study contributes to the field by extending the capabilities of the random forest algorithm and adapting it to the domain of CRNs. The modified algorithm demonstrates the potential of machine learning techniques in addressing allocation challenges in wireless communication systems. The findings emphasize the importance of advanced algorithms in improving the efficiency and effectiveness of channel and resource allocation processes. 2023, Success Culture Press. All rights reserved.