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Psychological impact of olfactory branding: The future of smell marks in India
Scents have been used by business organizations for commercializing their products since historic times. Because of the psychological connect that a consumer immediately makes as he smells something unique, olfactory branding is considered as a very productive and effective marketing tool. Trademark law attempts to protect a brand's identity with the ultimate motive of preventing consumers from deceptively similar goods. Scholars and businesses have been raising their voice in a demand call for smell mark protection under trademark law, arguing that smell is just as important for identifying the origin-point of a product for a consumer as is the brand's logo or name or product shape. While the US courts have been liberal in granting smell mark registrations, EU courts have interpreted the graphical representation requirement under trademark law very strictly. Indian law, though not entirely closed on the prospect of smell mark protection, is inclined toward the EU position. After analyzing the current legal scenario, this article explores the more fundamental question as to the feasibility of smell marks, questioning their justification under the philosophical foundations of trademark law, the subjective associations of consumers with respect to smells, the difficulty in evidence analysis by courts in infringement suits and the apprehension relating to the functionality doctrine. 2022 John Wiley & Sons Ltd. -
EFFECTS OF VIRTUAL PRIVATE SOCIAL NETWORKING IN ACADEMIC PERFORMANCE OF STUDENTS
A virtual private social network (VPSN) is generated automatically amongst peers using a social media app to build ties. One of the most significant repercussions of students' excessive usage of social networking sites is a decline in their academic performance. In a study of medical students, social media and the internet were shown to harm students' academic performance and classroom attentiveness. An increasing number of studies link the use of social media to poorer academic performance, such as fewer students doing their assignments and lower test scores. Students who receive specialised training in deep learning will have the superior cognitive abilities needed to succeed in today's more cognitively demanding workplaces. It teaches children to be critical thinkers, productive members of society, and active participants in a democratic society. As a perceptron used in image recognition and processing, a convolutional neural network (CNN) processes pixel data from social networks. A CNN uses multiplayer perception to lessen the processing needs of pupils. Humans and neurons make up the VPSN-CNN network, which the article explains. Neurons generate dendrites and axons to receive and transmit signals, while humans engage with long-reaching telecommunication equipment or biological communication systems. These will help remember, learn, unlearn, and relearn what has already been learned. In courses where social networking sites were utilised in addition to traditional teaching methods, most students reported feeling more socially engaged and more positive about their educational experiences. Students' and instructors' concerns regarding the educational usage of social media are addressed with recommendations for further study and practice in better performance and accuracy for student's data secure and comparison with existing methods. 2023 Little Lion Scientific. All rights reserved. -
Does the green finance initiatives transform the world into a green economy? A study of green bond issuing countries
Green finance initiatives have received global support in modern times, relatively in response to safeguard the environment and preserve natural resources through channelizing the investments to create a green economy. This paper attempts to evaluate and compare the green finance growth in green bond issuing nations across the world. This study also assesses the effect of green finance growth on the dependence of non-renewable energy resources especially fossil fuels that have been creating several environmental issues for the past years. This study develops a pressure-state-response framework to evaluate the comprehensive system of green finance growth that depicts the interaction of sub-aspects. We employ the entropy technique to calculate the weights at each level within the evaluation system. We also constructed empirical models to assess the relationship between green finance growth and dependence on fossil fuel consumption and found that there exists a negative relationship between the two. The results convey that proliferation of green finance instruments can reduce the dependence on fossil fuels and smoothen the transition towards a carbon negative world. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Public debt - economic growth nexus in emerging and developing economies: Exploring nonlinearity
This paper explores the nonlinear dynamics between public debt and economic growth by estimating the threshold level of debt for thirty-nine emerging and developing economies. The study found a considerable variation amongst the debt thresholds in these countries, ranging between 24 and 132 per cent. We observed the evidence for an inverted U-shape relationship either partially or fully only in six countries. On the contrary, our study found that expanding debt even beyond the threshold promotes economic growth in some countries, while debt hinders growth even at low debt levels in a few countries. 2022 Elsevier Inc. -
Zomato Instant - "10-Minute Delivery Plan" Controversy
[No abstract available] -
Design optimisation and fabrication of amino acid based molecularly imprinted sensor for the selective determination of food additive tartrazine
In this work, we developed a new molecularly imprinted polymer detector for tartrazine's rapid and selective detection. Electropolymerisation using L-Methionine resulted in the polymer immobilised on the carbon fibre paper electrode's surface. MIP film was formed by electropolymerisation in the presence of the template tartrazine. The polymer frame comprises cavities after template removal, which can specifically bind to the analyte molecule. Without pre-treatment, the developed sensor MIPMet/CFP detects tartrazine in beverage samples precisely and rapidly. The sensor has a linear response in the concentration range of 0.6 nM- 160 nM, high sensitivity (601964 AM-1cm?2), and a low detection limit of 27 pM under optimum conditions. MIPMet/CFP sensor displayed the ability to distinguish target analyte from interferants selectively. The performance of the MIPMet/CFP sensor in assessing tartrazine in different saffron powder and packed juice samples suggests that it could be used to detect tartrazine fast and effectively. 2022 Elsevier Ltd -
Spectrum of corona products based on splitting graphs
Let G be a simple undirected graph. Three new corona products of graphs based on splitting graph of G are defined. The adjacency spectra of the three new graphs based on splitting graph of G are determined. The number of spanning trees and the Kirchoff index of the new graphs are determined using their nonzero Laplacian eigenvalues. 2023 World Scientific Publishing Company. -
A survey on artificial intelligence for reducing the climate footprint in healthcare
The primary mission of the healthcare sector is to protect from various ailments with improved healthcare services and to use advanced diagnostic solutions to promote reliable treatments for complex diseases. However, healthcare is among the significant contributors to the current climate crisis. Therefore, research is underway to identify various measures to reduce the emissions from advanced healthcare systems. Modern healthcare facilities invest significantly in renewable energy, efficient energy solutions, and intelligent climate cooling and control technologies. Furthermore, innovative technologies like artificial intelligence (AI) are proposed to enable automation for patient health monitoring. With the advances in AI, there are green AI goals for potentially reducing emissions through data-driven and well-optimized models for healthcare. Furthermore, novel machine learning and deep learning techniques are continually proposed for improved efficiency to reduce emissions. Therefore, the scope of the research is to review the potential of AI in healthcare for lowering emission rates and its methodologies, current approaches, metrics, challenges, and future trends to attain a straightforward pathway. 2022 -
Co-Electrodeposited Pi-MnO2-rGO as an Efficient Electrode for the Selective Oxidation of Piperonyl Alcohol
Pi-MnO2-rGO-CFP electrode was developed through a concurrent deposition of Pi-MnO2 and reduced graphene oxide (rGO) on carbon fiber paper (CFP). Cyclic voltammetry (CV) and electrochemical impedance studies (EIS) were applied for the electrochemical characterization of the electrode. The electro catalytic activity of the modified electrode was improved by the increased synergistic characteristics of the CFP and electrochemically deposited rGO-Pi-MnO2 composite. The performance of the modified electrode was remarkable due to its lowest charge transfer resistance (R ct), and highest surface area offering more active sites and quicker electron transport kinetics. X-ray diffraction spectroscopy (XRD), Raman spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and optical profilometry (OP) were employed to study the physicochemical properties. Furthermore, the modified electrode was availed to oxidize piperonyl alcohol mediated by 4-acetamido-2,2,6,6-tetramethylpiperidine-1-oxyl (4-acetamido TEMPO or 4-ACT). The product obtained was purified and characterized by 1HNMR. The turnover frequency of 4-ACT was studied at different concentrations of the reactant, and the reaction parameters were also optimized using statistical tool design of experiment. This methodology is demonstrated to be economical, environmentally benign, and highly efficient in obtaining piperonal as it is carried out under milder reaction conditions. 2023 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. -
Switchable surface activity of Bi2Al4O9 nano particles: A contemporary approach in heterocyclic synthesis
Ferroelectric catalysis is emerging as an efficient chemical transformation strategy, especially in the field of clean energy production, wastewater treatment and degradation of pollutants. The core of ferroelectric catalysis is the dynamically switchable electrical polarization on their surface. It enables them to switch their surface activity, more precisely due to binding strength with the substrate. Even though a plethora of reports are available, the introduction of ferroelectric catalytic surfaces for the generation of heterocyclic compounds is a novel aspect. Here, we introduce ferroelectric Bismuthaluminate nanoparticles as catalysts for generating derivatives of azalactone, tetrahydro-benzopyran and pyranopyrazole with improved catalytic efficiency. This can be achieved by switching the direction of polarization of the catalyst which indeed alters the surface electronic states and stimulates the reaction followed by the excellent yield. Here the switchable property is due to the thermally induced polarization of water. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Lead-free inorganic metal perovskites beyond photovoltaics: Photon, charged particles and neutron shielding applications
Over the last few years, lead-free inorganic metal perovskites have gained impressive ground in empowering satellites in space exploration owing to their material stability and performance evolution under extreme space environments. The present work has examined the versatility of eight such perovskites as space radiation shielding materials by computing their photon, charged particles and neutron interaction parameters. Photon interaction parameters were calculated for a wide energy range using PAGEX software. The ranges of heavy charged particles (H, He, C, N, O, Ne, Mg, Si and Fe ions) in these perovskites were estimated using SRIM software in the energy range 1 keV10 GeV, and that of electrons was computed using ESTAR NIST software in the energy range 0.01 MeV1 GeV. Further, the macroscopic fast neutron removal cross-sections were also calculated to estimate the neutron shielding efficiencies. The examined shielding parameters of the perovskites varied depending on the radiation type and energy. Among the selected perovskites, Cs2TiI6 and Ba2AgIO6 displayed superior photon attenuation properties. A 3.5 cm thick Ba2AgIO6-based shield could reduce the incident radiation intensity to half its initial value, a thickness even lesser than that of Pb-glass. Besides, CsSnBr3 and La0.8Ca0.2Ni0.5Ti0.5O3 displayed the highest and lowest range values, respectively, for all heavy charged particles. Ba2AgIO6 showed electron stopping power (on par with Kovar) better than that of other examined materials. Interestingly, La0.8Ca0.2Ni0.5Ti0.5O3 demonstrated neutron removal cross-section values greater than that of standard neutron shielding materials - aluminium and polyethylene. On the whole, the present study not only demonstrates the employment prospects of eco-friendly perovskites for shielding space radiations but also suggests future prospects for research in this direction. 2022 Korean Nuclear Society -
Towards Automated and Optimized Security Orchestration in Cloud SLA
In cloud computing, providers pool their resources and make them available to customers. Next-generation computer scientists are flocking to the cutting-edge field of cloud computing for their research and exploration of uncharted territory. There are still several barriers that cloud service providers must overcome in order to provide cloud services in accordance with service level agreements. Each cloud service provider aspires to achieve maximum performance as per Service Level Agreements (SLAs), and this is especially true when it comes to the delivery of services. A cloud service level agreement (SLA) guarantees that cloud service providers will satisfy the needs of large businesses and offer their clients with a specified list of services. The authors offer a web service level agreementinspired approach for cloud service agreements. We adopt patterns and antipatterns to symbolize the best and worst practices of OCCI (Open Cloud Computing Interface Standard), REST (Representational State Transfer), and TOSCA (Topology and Orchestration Specification for Cloud Applications) with DevOps solutions, all of which API developers should bear in mind when designing APIs. When using this method, everything pertaining to the cloud service, from creation to deployment to measurement to evaluation to management to termination, may be handled mechanically. When distributing resources to cloud apps, our system takes into account the likelihood of SLA breaches and responds by providing more resources if necessary. We say that for optimal performance, our suggested solution should be used in a private cloud computing setting. As more and more people rely on cloud computing for their day-to-day workloads, there has been a corresponding rise in the need for efficient orchestration and management strategies that foster interoperability. 2023 International Journal on Recent and Innovation Trends in Computing and Communication. All rights reserved. -
On an extension of the two-parameter Lindley distribution
AIM: Lindley distribution has been widely studied in statistical literature because it accommodates several interesting properties. In lifetime data analysis contexts, Lindley distribution gives a good description over exponential distribution. It has been used for analysing copious real data sets, specifically in applications of modeling stress-strength reliability. This paper proposes a new generalized two-parameter Lindley distribution and provides a comprehensive description of its statistical properties such as order statistics, limiting distributions of order statistics, Information theory measures, etc. METHODS: We study shapes of the probability density and hazard rate functions, quantiles, moments, moment generating function, order statistic, limiting distributions of order statistics, information theory measures, and autoregressive models are among the key characteristics and properties discussed. The two-parameter Lindley distribution is then subjected to statistical analysis. The paper uses methods of maximum likelihood to estimate the parameters of the proposed distribution. The usefulness of the proposed distribution for modeling data is illustrated using a real data set by comparison with other generalizations of the exponential and Lindley distributions and is depicted graphically. RESULTS/FINDINGS: This paper presents relevant characteristics of the proposed distribution and applications. Based on this study, we found that the proposed model can be used quite effectively to analyzing lifetime data. CONCLUSIONS: In this article, we proffered a new customized Lindley distribution. The proposed distribution enfolds exponential and Lindley distributions as sub-models. Some properties of this distribution such as quantile function, moments, moment generating function, distributions of order statistics, limiting distributions of order statistics, entropy, and autoregressive time series models are studied. This distribution is found to be the most appropriate model to fit the carbon fibers data compared to other models. Consequently, we propose the MOTL distribution for sketching inscrutable lifetime data sets. 2023 DSR Publishers/The University of Jordan. -
Optical Spectroscopy of Classical Be Stars in Old Open Clusters
We performed the optical spectroscopy of 16 classical Be stars in 11 open clusters older than 100 Myr. Ours is the first spectroscopic study of classical Be stars in open clusters older than 100 Myr. We found that the H? emission strength of most of the stars is less than 40 in agreement with previous studies. Our analysis further suggests that one of the stars, [KW97] 35-12, might be a weak H? emitter in nature, showing H? equivalent width of ?0.5 Interestingly, we also found that the newly detected classical Be star LS III +47 37b might be a component of the possible visual binary system LS III +47 37, where the other companion is also a classical Be star. Hence, the present study indicates the possible detection of a binary Be system. Moreover, it is observed that all 16 stars exhibit a lesser number of emission lines compared to classical Be stars younger than 100 Myr. Furthermore, the spectral type distribution analysis of B-type and classical Be stars for the selected clusters points out that the existence of CBe stars can depend on the spectral type distribution of B-type stars present in these clusters. 2023. National Astronomical Observatories, CAS and IOP Publishing Ltd. -
PATENTABILITY OF BIOTECHNOLOGY INVENTIONS: Indian Ethical Guidelines
Advances in technology have made patenting of biotechnology inventions mandatory. The patent law system contains general clauses prohibiting the patenting of inventions contrary to public order or morality. Recent years have brought numerous debates on limiting the possibility of patent protection for biotechnology inventions for ethical reasons. The existing literature examines bioethical justification in terms of the principles of respect for autonomy, non-maleficence, beneficence and justice. The primary purpose of this study is to explore the uncertainty of ethics guidelines provided by the Indian Council of Medical Research (ICMR) and section 3(b) of the Indian Patents Act 1970 and the applicability of the bioethics principles. 2023 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore), ISSN: 0253-7222. -
Differential Laccase Production among Diverse Fungal Endophytes in Aquatic Plants of Hulimavu Lake in Bangalore, India
The ability of plants to acclimatise and thrive in stressed environments can be attributed, in part, to the reserve of endophytic fungi that they harbour, that help enhance physiological and immunological defence and tolerance to various biotic and abiotic stressors. The present work has focussed on screening laccase producing endophytic fungi residing in different aquatic plants isolated from Hulimavu Lake, Bengaluru. This lake is well known for its water pollution contributed by anthropogenic factors. Survival of plants in this lake can hence be associated with their rich repertoire of endophytic fungi that enhance host plant defence towards stressors. Upon isolation and culturing of endophytic fungi, qualitative laccase detection using laccase specific growth media and quantitative laccase estimation using ABTS (2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) substrate were performed. Differential production rates were observed for the laccase enzyme by different endophytic fungi; production rates also varied between fungi isolated from different parts like node, stem, root and leaf of the same plant species too. Phylogenetic analysis of fungal isolates with highest laccase production was performed and the species was found to be Cladosporium tenuissimum. Even the crude extract of this strain displayed laccase production of 42.16U/L, as revealed by ABTS assay. Hence this strain is a promising candidate for optimization studies for utilisation in the domain of bioremediation and industrial applications. The Author(s) 2023. -
Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization
Global warming, rising fuel prices, and limited conventional fuel supplies are driving the use of renewable energy, battery energy storage, and electric vehicles, transforming traditional electrical distribution networks into active distribution networks. Stochastic technologies can present operational and control challenges, especially for radially configured active distribution networks. In this scenario, strengthening the existing active distribution networks is necessary. This study optimally integrates soft open points for dynamic network reconfiguration to handle uncertainty in active distribution networks. The location, size, and reconfiguration of the soft open points were obtained for the hourly load profile, which included electric vehicle fleet load penetration and PV distributed generation. The proposed multi-objective function uses active power loss, voltage profile, and reliability indices. The proposed multivariable optimization problem was solved using artificial rabbits optimization. The simulations were performed on a modified IEEE 33-bus radial distribution system. The computational efficiency of artificial rabbits optimization is competitive with other prominent algorithms. The proposed approach of optimal soft open points and dynamic network reconfiguration is utilized to cope with uncertainty and run the present active distribution networks with better technical and reliability characteristics. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Decision-making using regression analysis: a case study on Top Tier Holidays LLP
Research methodology: This study aims to investigate the factors that contribute to the overall tour experience and services provided by Top Tier Holidays. The study is mixed in nature, and the researchers have used analytical tools to analyse the data factually. Multiple regression using MS Excel is used in the study. Case overview/synopsis: This case is based on the experiences of a real-life travel and tour company located in New Delhi, India. The case helps understand regression analysis to identify independent variables significantly impacting the tour experience. The CEO of the company is focused on improving the overall customer experience. The CEO has identified six principal determinants (variables) applicable to tour companies success. These variables are hotel experience, transportation, cab driver, on-tour support, itinerary planning and pricing. Multiple regression analysis using Microsoft Excel is conducted on the above determinants (the independent variables) and the overall tour experience (the dependent variable). This analysis would help identify the relationship between the independent and dependent variables and find the variables that significantly impact the dependent variable. This case also helps us appreciate the importance of various parameters that affect the overall customer tour experience and the challenges a tour operator company faces in the current competitive business environment. Complexity academic level: This case is designed for discussion with the undergraduate courses in business management, commerce and tourism management programmes. The case will build up readers understanding of linear regression with multiple variables. It shows how multiple linear regression can help companies identify the significant variables affecting business outcomes. 2023, Emerald Publishing Limited. -
Heat and Mass Transport in Casson Nanofluid Flow over a 3-D Riga Plate with Cattaneo-Christov Double Flux: A Computational Modeling through Analytical Method
This work examines the non-Newtonian Cassonnanofluids three-dimensional flow and heat and mass transmission properties over a Riga plate. The Buongiorno nanofluid model, which is included in the present model, includes thermo-migration and random movement of nanoparticles. It also took into account the CattaneoChristov double flux processes in the mass and heat equations. The non-Newtonian Casson fluid model and the boundary layer approximation are included in the modeling of nonlinear partial differential systems. The homotopy technique was used to analytically solve the systems governing equations. To examine the impact of dimensionless parameters on velocities, concentrations, temperatures, local Nusselt number, skin friction, and local Sherwood number, a parametric analysis was carried out. The velocity profile is augmented in this study as the size of the modified Hartmann number increases. The greater thermal radiative enhances the heat transport rate. When the mass relaxation parameter is used, the mass flux values start to decrease. 2023 by the authors. -
Evaluating the Pertinence of Pose Estimation model for Sign Language Translation
Sign Language is the natural language used by a community that is hearing impaired. It is necessary to convert this language to a commonly understandable form as it is used by a comparatively small part of society. The automatic Sign Language interpreters can convert the signs into text or audio by interpreting the hand movements and the corresponding facial expression. These two modalities work in tandem to give complete meaning to each word. In verbal communication, emotions can be conveyed by changing the tone and pitch of the voice, but in sign language, emotions are expressed using nonmanual movements that include body posture and facial muscle movements. Each such subtle moment should be considered as a feature and extracted using different models. This paper proposes three different models that can be used for varying levels of sign language. The first test was carried out using the Convex Hull-based Sign Language Recognition (SLR) finger spelling sign language, next using a Convolution Neural Network-based Sign Language Recognition (CNN-SLR) for fingerspelling sign language, and finally pose-based SLR for word-level sign language. The experiments show that the pose-based SLR model that captures features using landmark or key points has better SLR accuracy than Convex Hull and CNN-based SLR models. 2023 World Scientific Publishing Europe Ltd.