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Commercialization potential of agro-based polyhydroxyalkanoates biorefinery: A technical perspective on advances and critical barriers
The exponential increase in the use and careless discard of synthetic plastics has created an alarming concern over the environmental health due to the detrimental effects of petroleum based synthetic polymeric compounds. Piling up of these plastic commodities on various ecological niches and entry of their fragmented parts into soil and water has clearly affected the quality of these ecosystems in the past few decades. Among the many constructive strategies developed to tackle this global issue, use of biopolymers like polyhydroxyalkanoates as sustainable alternatives for synthetic plastics has gained momentum. Despite their excellent material properties and significant biodegradability, polyhydroxyalkanoates still fails to compete with their synthetic counterparts majorly due to the high cost associated with their production and purification thereby limiting their commercialization. Usage of renewable feedstocks as substrates for polyhydroxyalkanoates production has been the thrust area of research to attain the sustainability tag. This review work attempts to provide insights about the recent developments in the production of polyhydroxyalkanoates using renewable feedstock along with various pretreatment methods used for substrate preparation for polyhydroxyalkanoates production. Further, the application of blends based on polyhydroxyalkanoates, and the challenges associated with the waste valorization based polyhydroxyalkanoates production strategy is elaborated in this review work. 2023 Elsevier B.V. -
Commercialization potential of PET (polyethylene terephthalate) recycled nanomaterials: A review on validation parameters
Polyethylene Terephthalate (PET) is a polymer which is considered as one of the major contaminants to the environment. The PET waste materials can be recycled to produce value-added products. PET can be converted to nanoparticles, nanofibers, nanocomposites, and nano coatings. To extend the applications of PET nanomaterials, understanding its commercialization potential is important. In addition, knowledge about the factors affecting recycling of PET based nanomaterials is essential. The presented review is focused on understanding the PET commercialization aspects, keeping in mind market analysis, growth drivers, regulatory affairs, safety considerations, issues associated with scale-up, manufacturing challenges, economic viability, and cost-effectiveness. In addition, the paper elaborates the challenges associated with the use of PET based nanomaterials. These challenges include PET contamination to water, soil, sediments, and human exposure to PET nanomaterials. Moreover, the paper discusses in detail about the factors affecting PET recycling, commercialization, and circular economy with specific emphasis on life cycle assessment (LCA) of PET recycled nanomaterials. 2024 Elsevier Ltd -
Commitment of Information Technology Employees in Relation to Perceived Organizational Justice
The IUP Journal of Organizational Behaviour Vol. XI, No. 3. pp 23-40, ISSN No. 0972-687X -
Community based open source geographical classical data analysis
The traditional Geographical Information Systems (GIS) have to be migrated to the internet eventually much like every other software today. The article has explored ways of utilizing the Open Geospatial Consortium (OGC) standards to come up with ways of achieving a workflow for the development of a service-based implementation of a customized Web Processing Service (WPS). The proposed concept has explored multiple workflows using various combinations of the publishing and development options and the simplest and the least resource intensive one has been identified as the outcome of this project. The workflow identified was then split into two section to make it even more simplify and adaptable, aiding development from the WPS that has to publish. The development process used for the final workflow is done without the use of a resource intensive IDE keeping in mind the major aim of the proposed model is to reduce the dependency on resource intensive software and services. The proposed model is built solely on open source platforms which are in tandem with second stipulation of proposed model is promoting community-based development. The proposed system provides the better execution time and retrieval time. The execution time is compared with similar system, open source Geographical system provide less execution time. The retrieval time is also reduced this indicated Quality of Service is increased. BEIESP. -
Community Resilience and Crisis Management: Stakeholders Perspective of the Tourism Industry
The tourism industry is very vulnerable and has been extensively impacted by varied types of crisis. An attempt is made to precipitate and reflect on the nature of tourism disasters indicating an imperative need for an integrated approach to deal with crisis with disaster planning and a response system. Destinations at crisis impacts humankind causing environmental impacts and economic downfall and largely impacts the local community to recover from the disaster. This chapter examines varied impacts affecting the tourism industry and addressed negative impacts like the Economic crisis and loss of brand image in the post-crisis situation. The conceptual framework indicates Community Resilience model towards destination development and Resilience Building. The role of key stakeholders supporting e-governance and financial resilience pertaining to tourism business is further examined. The chapter explores mechanisms to re-establish the brand image during the restoration phase and have indicated possible strategies and suggestions in the recovery phase of an affected region. Disaster risk reduction is a significant and major phenomenon in handling all kinds of crisis management, therefore this chapter will be an essential reading for tourism education and destination managers who are engaged in destination crisis planning and disaster management. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Community-based educational intervention on emotion regulation, self-esteem, and behavioural problems among school children
Recently, there has been a trend where higher education institutions are designing and implementing community-based educational interventions for underprivileged children in the community. It is important to understand whether these interventions are useful to the children in improving their psychosocial development. In this chapter, the author discusses the learnings from an explanatory sequential mixed methods study which aimed at assessing the impact of community educational intervention provided by a higher educational institution on self-esteem, emotional regulation and bbehavioralproblems among adolescents in rural Karnataka. The study included 250 adolescents who were beneficiaries of community educational intervention and another 250 who were non-beneficiaries. Besides this, the chapter also highlights the qualitative results grounded in the focus group discussions to understand the stakeholder's perspective on community educational interventions. Finally, the author demonstrates the processes and mechanisms of change and presents a critical discussion from the quantitative and qualitative data analytic lens. The author anticipates that community educational interventions provided by higher educational institutions are extremely impactful. Several critical factors of stakeholders, institutional, and rural communities might bring change and sustainability in benefits among rural adolescents. 2024 Nova Science Publishers, Inc. -
Compact Dual-Band Millimeter Wave MIMO Antenna for Wireless Communication Systems
The article presents the compact dual-band MIMO antenna resonating at 27.5 and 32 GHz. The radiating structure is a rose-shape with elliptical slots and a horizontal slit to achieve the above resonances. The MIMO antenna dimension is 6.2 0 mm2, where an edge-to-edge distance of 1.82 mm separates radiating elements. The ground plane has simple slits to suppress the mutual coupling. The simulation results of the MIMO antenna is validated through measured and diversity parameter results. 2024 IEEE. -
Compact out-of-phase wideband substrate integrated waveguide based power divider loaded by slots for Ku and K band applications
In this paper a novel Substrate Integrated Waveguide (SIW) based single layer ground-loaded compact wideband out-of phase equal power divider is proposed . The wide-band and out-of-phase operation of the proposed power divider is obtained by creating defects in the ground plane with rectangular slots. The Defected Ground Structure (DGS) allows the power divider to exhibit a wide passband. The obtained passband is 11.5 GHz wide considering the return loss better than -10dB. Compactness in the proposed design is attributed to the dispersion characteristic of the slow-wave. The proposed design working in the passband from 14.5 GHz to 26 GHz is fabricated and tested. The size of the proposed design is 0.57 ?2g excluding feed lines. Here ?g is the guided wavelength at free space. The measured amplitude imbalance of (01) dB is obtained within the passband. The measured and simulated results are compared and found with in good agreement. 2019 IEEE. -
Compact substrate integrated waveguide power divider with slot-loaded ground plane for dual-band applications
In this paper, a novel design of compact substrate integrated waveguide (SIW) dual-band power divider is proposed. The dual-band operation of the power divider is obtained by exploiting the loading of slots on the ground plane. The electric-dipole nature of these slots allows the power divider to exhibit a passband below the cutoff frequency of the SIW. An in-depth description of the proposed power divider, supported by detailed parametric analysis over the operating frequency bands is reported. Design examples are illustrated to achieve different operating frequency bands. To validate the design studies, a prototype of the dual-band power divider operating at 4.7 GHz and 11.7 GHz is designed, fabricated and tested. The measurement results are found to be in good agreement with the simulation results. 2018 IEEE. -
Comparative Analysis and Development of Recommendations for the Use of Machine Learning Methods to Identify Network Traffic Anomalies in the Development of a Subsystem for User Behavioral Analysis
This article discusses various machine learning methods in order to conduct a more effective analysis of user network traffic using a subsystem for analyzing user behavior and detecting network anomalies, since there is a need to evaluate big data. The methods and techniques used to detect network anomalies are analyzed. In analyzing the methods and technologies used to detect network anomalies, a classification of anomaly detection methods is proposed. To solve these problems, different algorithms can be used, differing in specificity and, as a result, efficiency. The classification of machine learning methods for detecting network anomalies is considered separately, since machine learning algorithms will be the most effective for the task. Various criteria for evaluating the effectiveness of machine learning models in solving the problem of network traffic profiling are considered. In accordance with the specifics of the tasks of user recognition and network anomaly detection, the most appropriate criteria for evaluating the effectiveness of machine learning models have been selected: AUC ROC the area under the error curve. Four stages of the subsystem for analyzing user behavior and detecting network anomalies are highlighted. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Comparative analysis and suggestion of architectures for reduction of road accidents
As Road Accidents are increasing all over the world, it is very important to save peoples lives. With the advancement in technology we can make use of various real time sensors and technology to save peoples lives. This paper focuses on comparing various architectures which consists of various real time sensors like Eye blink sensor, Alcohol sensor, Speed sensor, load sensor, tilt and turning sensor and various technologies like GPS, GSM. After comparison paper suggests which architecture should be used in the vehicle based on certain attributes. For E.g. If the car always travels outside the city then this paper suggests the architecture which has Eye blink sensor, Speed Sensor GPS and GSM. IAEME Publication. -
Comparative analysis between 36 nm and 47 nm aluminawater nanofluid flows in the presence of Hall effect
White crystalline powder (aluminum oxide- Al 2 O 3 ) and water are the products often formed after the heating of aluminum hydroxide. In this report, boundary layer flow of two different nanofluids (i.e., 36nm Al 2 O 3 -water and 47nm Al 2 O 3 -water) over an upper horizontal surface of a paraboloid of revolution under the influence of magnetic field is presented. The combined influence of magnetic field strength, electric current density, electric charge, electron collision time, and the mass of an electron in the flows are considered in the governing equations. Three-dimensional transport phenomenon was considered due to the influence of the Lorentz force (F?) along the z-direction as in the case of Hall currents. In this study, the dynamic viscosity and density of the nanofluids are assumed to vary with the volume fraction ?. The dimensional governing equations were non-dimensionalization and parametrization using similarity variables. The corresponding boundary value problem was transformed into initial value problem using the method of superposition and solved numerically using fourth-order RungeKutta method with shooting technique (RK4SM). Magnetic field parameter is seen to have dual effects on the cross-flow velocity profiles of both nanofluids. The maximum cross-flow velocity is attained within the fluid domain when 36nm nanoparticles alumina is used. The cross-flow velocity gradient at the wall increases with magnetic field parameter (M) and also increases significantly with Hall parameter at larger values of M. 2018, Akadiai Kiad Budapest, Hungary. -
Comparative Analysis of CPI prediction for India using Statistical methods and Neural Networks
Inflation is one of the main issues affecting the world economy right now, necessitating the accurate inflation prediction for the development of tools and policies by the monetary authorities to prevent extreme price volatility. Expectations of inflation influence many financial and economic actions, and this dependence motivates economists to develop techniques for precise inflation forecasting. Nearly everyone in the economy is impacted by inflation, including lending institutions, stock brokers, and corporate financial officials. In many cases, inflation determines whether a firm will accept a particular project or if banks will make a particular loan. These different economic actors can modify their financial portfolios, strategic goals, and upcoming investments if they are able to forecast changes in inflation rates. The multiple interaction economic components that depend on inflation will be better understood by economic agents operating in a business context if inflation forecasting accuracy is improved. There are numerous techniques to forecast inflation ranging from basic statistical methods to complex neural network methods. Therefore, this paper employs LSTM model to train and analyze the Consumer Price Index (CPI) indicators to obtain inflation-related prediction results. The experimental results on historical data show that the statistical model has good performance in predicting India's inflation rate compared to deep learning methods in case of smaller dataset. 2023 IEEE. -
Comparative Analysis of Different Machine Learning Prediction Models for Seasonal Rainfall and Crop Production in Cultivation
Agriculture is one of the strengths of India, from the last few years, gradually the agriculture growth is going downwards in other side the population growth is upwards. Reason for agricultural downward growth depends on so many parameters. The rainfall is one of the main parameters which affects the crop yield. Because of this, the farmers are also facing the loss. If they know this information in prior, the farmers can plan accordingly the type of crop suited for the particular season and it helps the farmer to get good profit out of it. Machine learning scientific and statistical methods are used for predicting the rain fall and crop yield. Kharif and Rabi are two seasons taken for analysis. The regressor predicting models are constructed to predict the seasonal rainfall and crop yield. This study primarily focuses on seasonal crop production prediction, which is dependent on rainfall. The different types of machine learning regression method are used to achieve better results. The performance of comparison models is evaluated using different metrics. Finally, the linear regression and Bayesian linear regression models comparatively produce the best result in terms of accuracy for rainfall prediction. The boosted decision tree regression model is achieving the better result for crop prediction. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Comparative Analysis of Digital Business Models
This paper discusses the comparative analysis of different attributes of Google and Facebook business model and their novel features for handling innovative business framework. We have compared Google and Facebook business model on different key attributes and also discussed the statistical analysis of business models using Google business analytics platform. We have argued performance analysis of these models. One important point which we discuss and analyze in this paper is that a business model is not about just building revenue generating machine, but it is indeed more than that. It explores the strategy and business approaches of both the models of revenue generating line of attacks. Our research contributes a considerate understanding of Google and Facebook architectural model and its influence on business framework. Statistical enactment and results are analyzed, precisely when big data and media are applied. This paper also provides better understanding of the digital marketplace for both of the platforms and its earning methodology. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Comparative analysis of Histogram Equalization techniques
Histogram Equalization (HE) is one of the techniques which is used for Image enhancement. This paper shows the comparative studies of Global Histogram Equalization, Local Histogram Equalization and Fast Quadratic Dynamic Histogram Equalization based on the execution time, mean squared error and Peak Signal to Noise Ratio (PSNR). This paper shows the experimental results for these three methods with graphical representation. 2014 IEEE. -
Comparative Analysis of Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics
In the evolving landscape of educational research, the predictive analysis of student performance using data science has garnered significant interest. This study investigates the influence of diverse factors on academic outcomes, ranging from personal demographics to socioeconomic conditions, to enhance educational strategies and support mechanisms. We employed a diverse ml models to analyze a information containing academic records and socioeconomic information. The models tested include Logistic Regression, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Decision Trees. The process involved comprehensive data preprocessing, exploratory analysis, model training, and evaluation based on metrics such as precision, recall, accuracy, and F1 score. The results indicate that ensemble methods, specifically RF and GB, demonstrate superior efficacy in accurately predicting categories of student performance such as 'Enrolled,' 'Graduated,' and 'Dropped Out.' These models excelled in handling the complex interplay of varied predictors affecting student success. The results further underline the potential of advanced ensemble ML techniques in significantly outperforming the prediction accuracy in the academic domain, hence facilitating the tailoring of educational interventions to foster improved engagement and better outcomes for students. This has provided a comparative analysis of the methods that guide the future application of predictive analytics in education. 2024 IEEE. -
Comparative Analysis of Maize Leaf Disease Detection using Convolutional Neural Networks
Worldwide, maize is a significant cereal crop for crop productivity, identifying diseases in the plant's leaves is essential to raise a good crop. Deep learning methods that have been used in recent years to precisely identify and categorize these serious diseases, offering a non-destructive and effective way to find maize leaf ailments. In order to detect maize leaf disease, this paper suggests using three well-liked deep learning models: VGG16, Inception V3, and EfficientNet. The models were trained and assessed using a datasets of 4000 images of three distinct maize leaf diseases and a healthy class. All three models had high accuracy rates, according to the results, though EfficientNet outperformed the other two models. The suggested method can detect and track diseases in maize crops with high accuracy and can be applied practically. It can accurately classify various diseases. The study also demonstrates that deep learning models can offer a trustworthy and effective solution for detecting crop diseases, which can aid in lowering crop losses, raising crop yields, and enhancing food security. 2023 IEEE. -
Comparative Analysis of Non-Destructive Silkworm Cocoon Sex Classification using Machine Learning Models Based on X-Ray and Camera Images
Silk production plays a vital role in global economies, with sericulture heavily dependent on efficient seed production processes. Traditional methods involve manually cutting cocoons to classify silkworm sex, which leads to silk damage, labor intensiveness, and potential inaccuracies. In response, non-destructive technologies like X-ray and camera imaging have emerged, enabling sex classification without cocoon damage, thereby enhancing efficiency and reducing manual errors. This study undertakes a comparative analysis of X-ray and camera imaging methods for silkworm sex classification. X-ray imaging demonstrates superior efficiency in extracting detailed features from silkworm pupae, crucial for accurate classification. In contrast, camera imaging excels in the rapid and cost-effective classification of silkworms based on extracted features. The results reveal significant findings: using X-ray imaging model achieves 97.1% accuracy for FC1 and 96.3% accuracy for FC2, employing ensemble learning technique like AdaBoost. Meanwhile, camera imaging achieves an accuracy above 98% for both FC1 and FC2 using XGBoost, showcasing its effectiveness in real-time classification scenarios. Computational time analysis indicates that X-ray imaging is faster in feature extraction, while camera imaging consumes less memory during classification. These findings underscore the practical advantages of non-destructive imaging technologies and machine learning in revolutionizing sericulture practices. By enhancing productivity and sustainability through accurate sex classification of silkworms, these methods contribute significantly to the growth and efficiency of the silk industry. 2024 IEEE. -
Comparative Analysis of Phytochemicals and Antioxidant Potential of Ethanol Leaf Extracts of Psidium guajava and Syzygium jambos
Background: Plant-based drugs for various human ailments are becoming very important in the current domain of therapeutics. Aim: Psidium guajava and Syzygium jambos are two such plant species known for their medicinal properties in traditional systems of medicine like Ayurveda. Methods: Phytochemical analysis including GCMS, and antioxidant studies (DPPH) was carried out for both plant extracts. Results: Comparative phytochemical analyses of ethanol extracts of both these plants have shown the existence of bioactive components like tannins, polyphenols, alkaloids, flavonoids and terpenoids. These phytochemicals were quantified and the ethanol extracts were subjected to GCMS analysis which showed the presence of cis-?-farnesene, cis-calamenene, copaene, humulene, caryophyllene, phytol, neophytadiene, n-hexadecanoic acid etc, many of which possess diverse properties like antimicrobial, antibiofilm, antioxidant and anti-inflammatory. DPPH and reducing power assays revealed the excellent radical scavenging activity of the extracts. Conclusion: Among the two plants under the current study, S. jambos extract showed better results when compared to P. guajava concerning the antioxidant potential and the quantity of flavonoids, alkaloids, polyphenols and tannins present in the plant samples. 2024, Informatics Publishing Limited. All rights reserved.