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Toy telephone /
Patent Number: 346943-001, Applicant: Ritwika Das Gupta. -
Toxicological Profiling of Onion-Peel-Derived Mesoporous Carbon Nanospheres Using In Vivo Drosophila melanogaster Model
Toxicological profiling of the novel carbon materials has become imperative, owing to their wide applicability and potential health risks on exposure. In the current study, the toxicity of mesoporous carbon nanospheres synthesized from waste onion peel was investigated using the genetic animal model Drosophila melanogaster. The survival assays at different doses of carbon nanoparticles suggested their non-toxic effect for exposure for 25 days. Developmental and behavioral defects were not observed. The biochemical and metabolic parameters, such as total antioxidant capacity (TAC), protein level, triglyceride level, and glucose, were not significantly altered. The neurological toxicity as analyzed using acetylcholinesterase activity was also not altered significantly. Survival, behavior, and biochemical assays suggested that oral feeding of mesoporous carbon nanoparticles for 25 days did not elicit any significant toxicity effect in Drosophila melanogaster. Thus, mesoporous carbon nanoparticles synthesized from waste onion peel can be used as beneficial drug carriers in different disease models. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Toxicological evaluation of Calotropis gigantea (L.) W. T. Aiton (Apocynaceae) stem extract in zebrafish: A chronic exposure study
Calotropis gigantea is widely used in traditional medicine across rural and tribal regions for treating various ailments. The safety profile of this plant especially in concerning long term or high dose exposure, remains inadequately studied. This study aims to scientifically assess the toxicity of C. gigantea stem extract using zebrafish (Danio rerio) as a model organism. Chronic exposure over 10, 20 and 30 days revealed significant oxidative stress, mitochondrial impairment and histopathological alterations in vital organs. Key antioxidant enzymes glutathione reductase (GR), glutathione S-transferase (GST), succinate dehydrogenase (SDH), catalase (CAT) and superoxide dismutase (SOD) were analysed in the liver, gills, brain and muscles. Enzyme activity has initially increased but declined by the 30th day, indicating progressive oxidative damage. The liver and gills exhibited the most substantial biochemical and structural changes. The histological analysis confirmed cellular degeneration, inflammation and necrosis. These findings highlight the potential risks associated with unregulated therapeutic use of C. gigantea and emphasize the need for scientific validation and public awareness to ensure safe application. The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/) -
Toxicity analysis of endocrine disrupting pesticides on non-target organisms: A critical analysis on toxicity mechanisms
Endocrine disrupting compounds are the chemicals which mimics the natural endocrine hormones and bind to the receptors made for the hormones. Upon binding they activate the cascade of reaction which leads to permanent activating of the signalling cycle and ultimately leads to uncontrolled growth. Pesticides are one of the endocrine disrupting chemicals which cause cancer, congenital birth defects, and reproductive defects in non-target organisms. Non-target organisms are keen on exposing to these pesticides. Although several studies have reported about the pesticide toxicity. But a critical analysis of pesticide toxicity and its role as endocrine disruptor is lacking. Therefore, the presented review literature is an endeavour to understand the role of the pesticides as endocrine disruptors. In addition, it discusses about the endocrine disruption, neurological disruption, genotoxicity, and ROS induced pesticide toxicity. Moreover, biochemical mechanisms of pesticide toxicity on non-target organisms have been presented. An insight on the chlorpyrifos toxicity on non-target organisms along with species names have been presented. 2023 Elsevier Inc. -
Toxic waste colonialism : A re-evaluation of global management of transboundary hazardous waste /
Journal On Environment Law Policy And Development, Vol.3, pp.85-119, ISSN: 2348-7046. -
Toxic text classification
The users of the Internet increase every moment with increasing population and accessibility of the Internet. With the increase in the number of users of the Internet, the number of controversies, arguments and abuses of all kinds increases. It becomes necessary for social media and other sites to identify toxic content amongst a large number of content being posted by the users of the sites every second. The traditional algorithms that depend on users reporting toxic content for it to be deleted and necessary actions to be taken against the users posting the content would take a long time, within which it would have gained media attention and would have lead to huge fights over the content. Thus, it becomes important for the content to be evaluated for toxicity at the time it is posted in order to stop it from being posted. Therefore, we have designed and trained a deep learning model that can be read through the textual content given through it and determine if it is toxic or not. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Toxic heavy metal ion detection by fluorescent nanocarbon sensor derived from a medicinal plant
In the twenty-first century, the importance of environmental pollution sensing cannot be overstated. Cadmium is a well-known poisonous heavy metal that seriously endangers human health. In terms of screening for poisons and diagnosing illnesses, the sensitive and focused detection of cadmium in cells is crucial. In this work, we developed Green fluorescent Carbon nanomaterial (Carbon nanomaterial) synthesized from a novel precursor, Justicia Wynaadensis, by the most eco-friendly, cost-effective hydrothermal method, which acts as a fluorescent probe for Cadmium fluorescence sensing technique with the concentration range of 1 nM1 M. The sensor displays remarkable linear detection with a 5.235 nM detection limit. 2022 The Author(s) -
Toxic Effects of Nanoparticles on Fish Embryos
Nanoparticles are used widely in the in-drug delivery, diagnostics, cosmetics, paints, electronics, fabrics, solar cells, medicines etc. Their wide application is due to their special properties which include minute size, high reaction rate, increased surface area and quantum effect. Despite their multiple applications, nanoparticles have harmful effects too due to their improper disposal causing their entry into the aquatic environment greatly threatening the ecological security as well as our health. Zebra fishes (Danio rerio) are used more commonly for the evaluation of toxicity and are considered a promising animal model. Studies on Salmon and Labeo rohita have also been used for toxicity evaluation. Nanoparticles affect the embryo more easily than the adult since the embryo are more sensitive. Hence it becomes important for us to study the effect of the nanoparticles on the embryo of the fishes. These nanoparticles have the ability to cross the chorion layer and affect the developing embryo. Since the fishes are a part of the food chain, when these organisms get affected they will eventually harm the humans too. This review focuses on the effect of metal nanoparticles (NPs) like gold(Au), silver(Ag), copper(Cu), platinum and metal oxides nanoparticles like titanium dioxide, aluminium oxide, copper oxide, nickel oxide zinc oxide on the embryonic development of fish embryos. When compared to the other nanoparticles (NPs) like silver, copper and platinum, it has been observed that the gold nanoparticles (Au NPs) showed no toxicity to embryos of zebrafish though few studies contradict this. Aluminium nanoparticles showed no toxicity and role of reactive oxygen species (ROS) in enhancing the toxicity of nanoparticles have also been discussed. 2021 World Research Association. All rights reserved. -
Towards Visibility: Subaltern Counterpublics in Paul Chirakkarodes Pulayathara
Christianity has always been celebrated as a catalyst towards modernity for the Dalits of Kerala. Though missionary accounts and ethnographic studies confirm the progress of the community, there was rampant casteism and separatism too. This is succinctly revealed in Dalit Christian texts. Pulayathara by Paul Chirakkarode stands as a testimony to the Dalit Christian dilemma and traces the history of the Kuttanadan Pulaya community in the pre- and post-conversion scenarios. Conversions could not change the existing public sphere of Kerala, where upper castes were the dominant party. They (Dalits) continued to be marginalized and subordinated and lacked a class consciousness. The article highlights the limitations in the public sphere that emerged in Kerala as part of the missionary endeavours in accommodating the converted Dalits. The article attempts to trace the emergence of subaltern counterpublics among the Dalit Christians to oppose the continued oppression and casteism by situating Pulayathara at the centre of the analysis. 2022 Indian Institute of Management, Ahmedabad. -
Towards various applications of Big Data and related issues and challenges
A new trend in feature abstraction is Big Data Analysis combined with computational techniques. This includes gathering knowledge from reputable data sources, analyzing information quickly, and forecasting the future. Big data entails vast amounts of data that are challenging to analyze using typical database and software approaches. When using big data applications, a technological hurdle arises when transporting data across several locations, which is quite expensive and necessitates a huge primary memory for storing data for processing. Big data refers to the transaction and interaction of datasets whose size and complexity transcend the usual technical capabilities of acquiring, organizing, and processing data in a cloud environment. This article provides an in depth study of various applications of big data. It also provides a detailed view on various problems and challenges in Big Data. 2021 IEEE. -
Towards the underlying theories of artificial intelligence in customer engagement: A review and future research agenda
Artificial Intelligence (AI) and customer engagement have grabbed the attention of many business organizations in the recent past, especially with the advancement of machine and deep learning. Though there is significant development in this area, in reality customer engagement and customer experience has received less attention. Since there is less theoretical and empirical evidence related to AI application in customer engagement, the objective of this research is to lay down the theoretical underpinning for artificial intelligence and customer engagement research and to propose a future research agenda. In this context, the present study aims to full fill the gap based on 20 different theories and model identified from 53 A Star and A categories of Australian Business Deans Council (ABDC) journals (2011-2023), and result are analyzed using classifier variables. In associating the presence research aim, four prominently used theories like value co-creation theory, theory of planned behaviour, SOR (Stimulus- Organism-Response) theory, Relationship Marketing theories are selected to review. The selected theories are compared based on key attributes and outcomes to give a clear direction towards an optimum theory development. By validating the various theoretical perspective, the present study also gives first-hand insight to the practising manager to formulate organization strategy and researcher to explore the future research directions. 2025 Arabinda Bhandari and Mudita Sinha. All rights reserved. -
Towards sustainable resource management: A short and long-run dynamics of mineral production on ecological footprint
The effect of mineral production on ecological footprint is examined in this study while controlling for economic growth, renewable energy consumption, and trade openness as additional determinants for Pakistan. On the empirical front, the study uses the Dynamic Autoregressive Distributed Lag (DYNARDL) simulations for the data collected between 1990 and 2021. The result portrays movement to the long-run equilibrium relationship when considering the ecological footprint as the outcome variable amidst mineral production, economic growth, renewable energy consumption, and trade openness as the covariates. Further, the finding shows temporal dynamics of mineral production on environmental quality with a short-term degradation versus long-term amelioration, which suggests that mineral production can be conducted more sustainably over time with an implication towards taking measures such as technological advancements, improved efficiency, and better waste management practices. Additionally, it failed to find evidence for the conventional Environmental Kuznets Curve, implying a need for policy reevaluation, reassessment of economic development models and accounting for environmental externalities in economic decision-making. Besides, as expected, the outcome demonstrates that using renewable energy lowers the ecological footprint both in long and short terms, which indicates that utilization of renewable energy sources reduces reliance on fossil fuels, resulting in decreased environmental degradation, thereby fostering the need for emphasis on the importance of continued technological innovation in renewable energy technologies to reduce the ecological footprint further. Moreover, it shows that trade openness improves the environmental quality in the short run (worsens it in the long run), thereby highlighting that trade openness may lead to short-term environmental benefits by promoting cleaner technologies and increasing resource efficiency. However, in the long term, trade openness can exacerbate environmental degradation due to economic priorities often taking precedence over environmental concerns. 2024 Elsevier Ltd -
Towards Sustainable Living through Sentiment Analysis during Covid19
Artificial intelligence is the process of the machine to perform with the simulation of human intelligence. Computing within the field of emotions paves the recognitions to sentiment analysis. Sentiment analysis is the method of capturing the emotions behind a text whether or not it's positive, negative or neutral. Sentiment Analysis (SA) or Opinion Mining (OA) is the process to provide computational treatment to unstructured data to categorize and identify the sentiments or emotions expressed in a piece of text. It combines Natural Language Processing Techniques and Machine Learning Techniques. This technology is additionally referred to as opinion mining or feeling computing. Sentiment Analysis uses the ideas of machine learning alongside an AI based process called NLP to extract and analyse the data, emotions, information from the text. This work explores the impact of social media during covid 19 and possible link between sustainable living and health care with the usage of sentiments. This paper address the sustainable development goal 3 (good health and wellbeing) of SDG 2030 and a possible way towards sustainable living through sentiment analysis. The Electrochemical Society -
Towards Sustainable Finance: Understanding Green Banking Adoption Among Indian Young Adults
This study explores the factors influencing the adoption of green banking among young adults in India, drawing on the Theory of Planned Behaviour. Key determinants include environmental concern, social factors, perceived behavioural control, subjective norms, and attitude toward behaviour. The moderating role of effort expectancy is also examined. Based on data from respondents aged 1835, the findings provide practical insights for banks to develop targeted strategies that encourage green banking practices among youth. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Towards sustainable business: Review of sentiment analysis to promote business and well-being
Sustainability in business is expected considering the growth in the long run. Sustainable development goals are important for our sustainability on this planet. In case of a business, it is essential to ensure sustainable processes and sustainability of the existing customers. Sustainable customers can in turn contribute to improving the process by providing constructive suggestions to the business. This paper is an attempt to review sentiment analysis techniques to improve the customer experience of a business. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Towards Smarter Warehouse Layouts: Simulation-Driven Insights on Congestion and Forklift Flow Patterns
In high-mix, make-to-order warehouse environments, slotting decisions in constrained warehouse settings affect flow dynamics, yet their behavioral implications remain underexplored. This study employs discrete event simulation (DES) using the software FlexSim to evaluate three slotting strategies: baseline reflecting current operations, a benchmark informed by heuristic frequency-based clustering, and a proposed layout based on a learned configuration within a forklift-operated warehouse characterized by narrow aisles, unidirectional traffic, and spatial contention. Instead of emphasizing throughput alone, the study takes a closer look at how forklifts operate on a day-to-day basis, specifically how their time is divided between active movement, waiting due to blockages, and idling with no task assigned. Each strategy was tested over 20 simulation replications. Notably, the proposed layout cut blocked time by more than 30% and allowed forklifts to remain idle (and ready) more often, without reducing overall utilization. These patterns held consistently across runs. Statistical analysis confirmed that the differences in forklift behavior were significant, and Levenes tests showed that performance didnt become more erratic. These findings demonstrate that the improvements are systematic, not random. The work presents a simulation-based method for diagnosing layout effectiveness by looking at behavior, not just outputs, connecting slotting choices to real operational flow and system stability. This approach supports more resilient warehouse designs in settings with limited space and high product mix. 2025 IEEE. -
Towards Smarter Transit Systems: An Artificial Intelligence based IoT Approach
Transportation today is paramount, and difficulties such as unreliable bus schedules and overcrowding are still found due to inadequate managerial practices. While cities are confronted with rapid urbanization and population growth, public transit remains a strong reliance of the middle class, especially in India. Individuals are subsequently subjected to overcrowded, and unreliable modes of transit, which lead them to seek private solutions that ultimately leads to increased private vehicle usage, which is directly related to more congestion and pollution. Therefore, utilising an IoT/machine learning based solution which provides commuters with updated bus locations and occupancy via their mobile phones to make more informed travel decisions, thus reducing wait times is proposed. Accurately tracking the buses via gps, is beneficial for providing timely information, where sensors are used for estimating occupancy based on passenger counts. The traffic prediction provided to users is generated from a Random Classifier machine learning model that would otherwise improve commuting efficiency and urban mobility. The model is found to have 98% accurate on cross-validation and 99% on test data, while the average F1-score over various traffic situations is 0.99. The described solution assists transit users by providing up to date service information improving the passengers quality of travel, heightened their sense of safety, and creates a more integrated urban experience, which promotes long-term sustainable development to meet the interconnectedness challenges cities confront with rapid urban expansion. 2025 IEEE. -
Towards resilience: navigating local knowledge in flood risk management strategies in Majuli Island, Assam
Scientific knowledge of climate change and its latent effects is important. But it often lacks the expertise of local communities. This paper emphasises the importance of understanding local knowledge within the dynamics of vulnerability and resilience. It also offers insights into the applicability of these knowledge systems, providing valuable lessons and challenges on local knowledge in flood risk management. The study was conducted within a qualitative framework, utilising a case study design, in Majuli Island. Data were collected through 20 key informant interviews. Findings reveal that the diverse dimensions of local knowledge among Indigenous communities strengthen mitigation, coping, and adaptation strategies, enabling them to endure recurring floods. The evidence presented can guide government and non-governmental organisations (NGOs) in Majuli in integrating local knowledge into their interventions. By documenting and critically analysing existing practices, this paper adds to the growing literature on local knowledge in disaster research and practice. 2026 Informa UK Limited, trading as Taylor & Francis Group. -
Towards reading song in performance as aural narrative : Reading a sense of spatiality in select albums of mark knopfler using henri lefebver's notions of space
One of the chief objectives of this study is to explore and offer a strategy to read song in performance as Aural Narratives. This was necessary because verbal texts can be read in multiple ways. Perhaps the chief claim of a text s possibility to be literary lies in its inherent potential to be discursive. Therefore sound texts require a way of understanding sound as an element of storytelling. Shifts in Humanities necessitate an expanding notion of textuality. One of the chief concerns and burdens of the writer of literature is the evocation of a sense of spatiality which can be perceived as an outcome of spatial practice. Spatial practice in turn is defined by social codes and practices. Spaces can be read therefore through the life experiences of the inhabitants of a space as spatial practices are dependent on particular spaces. This study explores the use of Sound in creating a sense of Spatiality. Singer, songwriter and guitarist, Mark Knopfler creates songs that are arranged around a central character s lived experience. Thus the perception and conception of a sense of space that is thus evoked can be negotiated using Henri Lefebvre s triadic notions of newlinespatiality as a reading strategy. -
Towards Optimal ?-Binding Functions of (2K1?K2)-Free Graphs and (P3?K1)-Free Graphs
A function f:N?R is called a ?-binding function for a hereditary family G of graphs, if ?(G)?f(?(G)) for every G?G where ?(G) and ?(G) denote the chromatic number and clique number respectively. In his influential work, Gya?fa? (1987) showed that the family of (2K1?K2)-free graphs and the family of (P3?K1)-free graphs are ?-bounded. Randerath and Schiermeyer (2004) improved the ?-binding functions of both these classes to x+12. In this paper, we further improve the ?-binding function of both these classes to x22 for x?3. Furthermore, we obtain a tight chromatic bound for (P3?K1)-free graphs with clique number 4. The Author(s), under exclusive licence to Springer Nature Japan KK 2025.