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When will we learn to prioritise safety?
The government cannot absolve itself. It approved the event. It knew the scale of sentiment, emotional pitch, and logistical nightmare, yet it pressed ahead. -
When cloth colours become markers of caste identities: ritual performance and caste politics in contemporary Kerala
Keralas caste hierarchy, characterised by untouchability and social exclusion, historically provoked protests demanding temple entry and basic rights for marginalised Hindus. In 2024, during the centenary of the 1924 Vaikom Satyagraha, the annual Ashtami festival at the Vaikom Mahadeva Temple featured women participating in the Poothalam ritual procession and wearing colour-coded blouses that signified caste identities. The temple re-emerges as a ritual space where caste distinctions become publicly visible, which transforms a commemorative event into a site for expressing caste identities. This article argues that the colour-coded blouses worn in the ritual procession reconfigure caste as a performative and embodied marker of identity. The article further demonstrates how ritual practices re-inscribe historical hierarchies in contemporary Kerala. The study analyses media narratives and ritual symbolism to connect the historical struggle for equality with contemporary representations of caste in Kerala and draws on theories of social construction, cultural memory, and subaltern identity. 2026 Informa UK Limited, trading as Taylor & Francis Group. -
Wheat Yield Prediction using Temporal Fusion Transformers
In precision framing, Machine Learning models are an essential decision-making tool for crop yield prediction. They aid farmers with decisions like which crop to grow and when to grow certain crops during the sowing season. Many Machine Learning algorithms have been used to support agriculture yield prediction research, but it is observed that Deep Learning models outperform the benchmark Machine Learning algorithms with a significant difference in accuracy. However, though these Deep Learning models perform better, they are not preferred or widely used in place of Machine Learning models. This is because Deep Learning methods are black box methods and are not interpretable, i.e., they fail to explain the magnitude of the impact of the features on the output, and this is unsuitable for our use case.In this paper, we propose using Temporal Fusion Transformer (TFT), a novel approach published by Google researchers for wheat yield prediction viewed as a Time Series Forecasting problem statement. TFT is the state-of-the-art attention-based Deep Learning architecture, which combines high-performance forecasting with interpretable insights and feature importance. We have used TFT to perform wheat yield prediction and compare its performance with various Machine Learning and Deep Learning algorithms. 2023 IEEE. -
Wheat Grain Identification Using Explainable Artificial Intelligence
This research delves into the innovative application of Explainable Artificial Intelligence (XAI) within wheat grain identification. Employing sophisticated machine learning models augmented by XAI techniques, the study aims to enhance the transparency and comprehensibility of decision-making processes associated with classifying wheat grains. Key objectives include refining model accuracy, imparting insights into critical identification-influencing characteristics, and developing an intuitive user interface tailored for end users, particularly farmers. Through a methodical analysis, the research underscores the significance of XAI in detecting flaws and fine-tuning the model, ultimately bolstering its reliability. The findings of this investigation carry implications for advancing agricultural practices, fostering stakeholder trust, and adapting to the ever-evolving dynamics of the environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Wheat Disease Diagnosis using Transfer Learning on Convolutional Neural Networks
Wheat disease identification is essential for agricultural output and food security. Traditional diagnostic approaches are slow, ineffective, and need expert assistance, restricting their ability to grow in agriculture. Suggested innovative diagnostic method uses transfer learning on convolutional neural networks (CNNs) to effectively identify and classify wheat leaf diseases. To increase model predictions, high-quality image datasets from open-access platforms are normalised, resized, and augmented. The proposed CNN model performed best with 98.90% accuracy, 98.87% precision, and 98.80% recall. Transfer learning improved model performance by recycling knowledge from pre-trained CNN architectures, reducing training time and enhancing feature extraction. The results show improved precision as well as strength over standard methods and before. This technology helps farmers and agricultural professionals make timely disease management and crop management decisions. To improve disease recognition, future study may use a wider dataset range and other CNN designs. 2025 IEEE. -
Whats the best time to give work? A study on relationship between employee moods and performance at different time intervals
Employees change their behavior many times in a day due to many factors. It is not uncommon to see any employee being agitated over petty issues at work place. This paper aims at identifying meaning, relationships between moods and performance. Employees decision-making abilities depend on mood and his mood depend on his personality, work environment variables such as protocols, procedures, work events, dynamics of formal and informal communication. We equate this daily swing to three-time frames named morning, afternoon and evening. Our attempt has been to try to establish a relationship between moods and emotions and employee performance which can increase the productivity level of the employee. This research intends to establish a more robust relationship and involves better evaluative and interpretive models to cope with the non-linearitys related to the complexity of the model and facilitate better decision-making with more accurate and intricate or comprehensive yet simple approach. Development of such relationship will help managers in dealing with the employees and take measures to increase productivity by adopting suggestions and conclusions from this study. IJSTR2019. -
What We Think Others Think and Do About Climate Change: A Multicountry Test of Pluralistic Ignorance and Public-Consensus Messaging
Most people believe in human-caused climate change, yet this public consensus can be collectively underestimated (pluralistic ignorance). Across two studies using primary data (n = 3,653 adult participants; 11 countries) and secondary data (ns = 60,230 and 22,496 adult participants; 55 countries), we tested (a) the generalizability of pluralistic ignorance about climate-change beliefs, (b) the effects of a public-consensus intervention on climate action, and (c) the possibility that cultural tightness-looseness might serve as a country-level predictor of pluralistic ignorance. In Study 1, people across 11 countries underestimated the prevalence of proclimate views by at least 7.5% in Indonesia (90% credible interval, or CrI = [5.0, 10.1]), and up to 20.8% in Brazil (90% CrI = [18.2, 23.4]. Providing information about the actual public consensus on climate change was largely ineffective, except for a slight increase in willingness to express ones proclimate opinion, ? = 0.05 (90% CrI = [?0.02, 0.11]). In Study 2, pluralistic ignorance about willingness to contribute financially to fight climate change was slightly more pronounced in looser than tighter cultures, highlighting the particular need for pluralistic-ignorance research in these countries. The Author(s) 2025. -
What People Still Get Wrong About
NegotiationsMost executives leave value on the negotiating table, for two main reasons: First, many executives mistakenly believe that they’re negotiating over a fixed pie and that gains for one side necessarily mean losses for the other. Second, they focus exclusively on how to claim value for themselves (by taking as much as they can of that mythical fixed pie) rather than coming up with ways to increase the size of the pie -
What Numbers Never Revealed: Tracing Dalit Christian Modernity Through Malayalam Literature
Kerala has a long-standing history of Christianity as well as conversions. Conversions can be dated back to the fifteenth and sixteenth centuries, which saw a large number of slave caste conversions. For the slave castes of KeralaPulayas and ParayasChristianity offered a salvation from the circle of pollution. Scriptures provided the slave castes new vistas of knowledge which they encultured to form a counter discourse against the public sphere set up by the dominant castes. The public sphere of the Malayalee psyche was formed by the ideas of caste pollution, which restricted the slave castes from accessing the social space. A new Dalit perspective on the religious consciousness of the converted Christians will show the role of the Bible, Original Sin, and Repentance on their daily lives. Dalit Christian literature becomes the primary source where Christianity metamorphoses into an oppositional force in resisting oppression as well as in creating a social space with agency. 2022 Indian Institute of Management, Ahmedabad. -
What Keeps Secondary School Teachers Motivated? A Qualitative Study from Urban Indian Classrooms
Teacher motivation is widely recognized as central to instructional quality and teacher retention, yet limited research has examined how it is experienced and sustained within policy-driven, high-pressure school systems. This qualitative study explores how secondary school teachers in Bengaluru, India, understand and maintain motivation in their everyday professional practice. Drawing on six in-depth interviews and reflexive thematic analysis, five interrelated drivers were identified: student engagement, emotional connection, instructional autonomy, collegial support, and recognition. Teachers described motivation not as a stable trait, but as a dynamic process continually shaped through relationships, daily pedagogical decisions, mentoring roles, creative planning, and small acts of professional agency. By foregrounding teachers lived experiences, the findings complement large-scale motivation research and offer insight into how motivation is relationally constructed and negotiated within structural constraints. The study underscores the importance of school environments that protect autonomy, acknowledge emotional labour, and cultivate trust as conditions for sustaining long-term teacher engagement. The Author(s) 2026 -
What is Remembered in Pandemic: A Commentary on the Mediated Memories of Piety in COVID-19
The paper explores how the experiences of the present pandemic are shaped by the memories of popular religious piety during past pandemics and epidemics. Taking insights from the works of Astrid Erll and Reinhart Koselleck, the process remembering-imagining system within the context of the pandemic is discussed by tracing the reemergence of pandemic deities and narratives of piety in India. Using digitally documented and disseminated narratives on piety emerging during COVID-19, an attempt is made to understand how these narratives shape the experiences, responses, and collective memory of the pandemic. Through a discussion of the shift in the imagination of political leadership and the moral responsibilities of the community, an attempt is made to highlight the mode in which the narratives on piety shape the contours of a time that is otherwise unimaginable. The mediated memories of popular religious piety make it possible to remember similar crisis times and to imagine and reinstate the social order that is threatened by this sudden unimaginable crisis. The paper thus argues that within the context of India, popular religious piety, though often overlooked, becomes a significant part of making sense and shaping the experiences of the pandemic time. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
What is beautiful is good: An evalutation of effectiveness of attractiveness in celebrity endorsements
The studies in the field of marketing have shown that characteristics of the source will influence persuasiveness of an advertisement. Attractiveness is one such celebrity characteristic that is widely studied by researchers in the field of marketing. However, still, literature failed to explain how attractiveness of celebrity endorsers influenced purchase intentions. This study tried to fill this gap by modelling the influence of celebrity attractiveness on purchase intention. It also evaluated the effect of respondents' gender on the model. The data collection for the study were carried out during March - June 2017. The study found that the effect of celebrity attractiveness on purchase intention was mediated by celebrity brand fit, attitude towards the advertisement, and attitude towards the brand. The study also evaluated the moderating effect of respondents' gender using chi- square analysis, which found no significant model difference among male and female respondents. These findings indicated that celebrity attractiveness created purchase intention in a mediated manner among the respondents, irrespective of their gender. 2018, Associated Management Consultants Private Limited. -
What Influences Companies to Go Beyond Mandatory Corporate Social Responsibility Rule? Empirical Evidence from India
This study aims to empirically investigate how corporate governance (CG) characteristics influence firms to go beyond the mandatory minimum corporate social responsibility (CSR) expenditure rule to contribute towards sustainable development. It employs the panel regression technique for analysis of top seventy-five listed companies of the NIFTY100 index at Indian National Stock Exchange (NSE) for the years from 201415 to 202021. The empirical results revealed that CG attributes like large board size, large independent directors, and female directors significantly influence the CSR performance of the companies. However, no significant evidence was found in case of the impact of board meeting frequency and CSR practices of the companies. This chapter enables a better understanding of self-induced CSR practices to policymakers, regulators, practitioners, and other stakeholders. The findings suggest that various stakeholders should concentrate on specific CG attributes to focus on CSR performance. It is one of the first studies that determines what influence the adoption of self-induced CSR practices especially against the backdrop of major CG mechanism and CSR reforms in India. It provides additional empirical evidence to the extant body of literature on the CG and CSR practices from the perspective of emerging economies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
What fuels the employees in startups?: Data on hybrid/colocated/virtual working environment towards efficiency
Purpose: This article examines the concepts of workplace satisfaction and productivity using data. The data will be used to investigate the variables contributing to employee satisfaction to achieve optimum efficiency through various startup working environments. Design/ Methodology/ Approach: Descriptive causal investigation. A structured instrument scale questionnaire via the internet to 256 employees working for highly organized organizations in Bangalore, India, using Qualtrics. The researcher adopted a simple random sampling method. Findings: The respondents in the data believed that the pre-covid workplace was advantageous. The hybrid model's prevalence of autonomy and flexibility increases work productivity. When employees are given more responsibility, their job satisfaction and productivity increase. Research Limitations/ Implications: Collecting data in a startup was extremely difficult due to the difficulty of obtaining permission, and through the analysis, it was determined that businesses have a responsibility to provide supplemental benefits to remote employees, which may increase the level of job satisfaction and enjoyment experienced by these individuals. 2023 The Author(s) -
What drives the wheels of evolution in NGC 1512?: A UVIT study
Context. Environmental and secular processes play a pivotal role in the evolution of galaxies. These can be external processes such as interactions or internal processes linked to the action of bar, bulge, and spiral structures. Ongoing star formation in spiral galaxies can be affected by these processes. By studying the star formation progression in the galaxy, we can gain insights into the role of different processes that regulate the overall evolution of a galaxy. Aims. The ongoing interaction between the barred-spiral galaxy NGC 1512 and its satellite NGC 1510 offers an opportunity to inves- tigate how galactic interactions and the presence of a galactic bar influence the evolution of NGC 1512. We aim to understand the recent star formation activity in the galaxy pair and thus gain insight into the evolution of NGC 1512. Methods. The UltraViolet Imaging Telescope (UVIT) on board AstroSat enables us to characterise the star-forming regions in the galaxy with a superior spatial resolution of ?85 pc in the galaxy rest frame. We identified and characterised 175 star-forming regions in the UVIT far-ultraviolet (FUV) image of NGC 1512 and correlated with the neutral hydrogen (Hi) distribution. Extinction correc- tion was applied to the estimated photometric magnitude. We traced the star-forming spiral arms of the galaxy and studied the star formation properties across the galaxy in detail. Results. We detect localised regions of star-formation enhancement and distortions in the galactic disc. We find this to be consistent with the distribution of Hi in the galaxy. This is evidence of past and ongoing interactions affecting the star formation properties of the galaxy. We studied the properties of the inner ring. We find that the regions of the inner ring show maximum star-formation-rate density (log(SFRDmean[M yr?1 kpc?2]) ? ?1.7) near the major axis of the bar, hinting at a possible crowding effect in these regions. The region of the bar in the galaxy is also depleted of UV emission. This absence suggests that the galactic bar may have played an active role in the redistribution of gas and quenching of star formation inside the identified bar region. We therefore suggest that both secular and environmental factors might be influencing the evolution of NGC 1512. The Authors 2023. -
What drives Generation Z to choose green apparel? Unraveling the impact of environmental knowledge, altruism and perceived innovativeness
This study proposes to determine the influence of Environmental Knowledge (EK), Altruism (Atr), Consumer Confidence (CC) and constructs of Theory of Planned Behaviour (TPB) like Attitude (Atd), Subjective Norm (Sub) and Perceived behavioural control (Pbhc) on consumers intention to purchase Green Apparel Products (GAPI). Moreover, the moderating effect of Perceived Innovativeness (PInn) on the relationship between Attitude (Atd), Subjective Norm (Sub), Perceived behavioural control (Pbhc), EK, Atr and CC was studied. To test the research model and hypothesis, a survey of 349 Generation Z consumers (1826 years) was conducted. Cronbachs alpha and a Confirmatory Factor Analysis (CFA) were used to determine the scales reliability and validity. Structural Equation Modelling (SEM) validated the given model and hypotheses. In this research, six hypotheses were tested, and it was found that three hypotheses showed a direct relationship. Specifically, the result of SEM showed that Atd, Sub and CC were positively related to GAPI. Also, six hypotheses were formulated testing the moderating role of PInn. The results established that PInn moderated the relationship between Atd, Sub, CC and GAPI significantly. This research provides a novel framework to explore the relationship between the EK, Atr and CC and Generation Z consumers GAPI. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
What drives fish production Climatic indicators or economic indicators? Empirical evidence from India
Purpose This study examined the relative roles of climate and economic factors in driving fish production across Indian states from 2000 to 2020, with a disaggregated focus on inland and marine systems. It also explored the multivariate causal relationships between fish production, CO2 emissions, temperature, rainfall, GDP and fish consumption. Design/methodology/approach To investigate the interactions between fish production, climatic and economic indicators, we used two novel techniques, namely a two-stage instrumental variable approach (2SIV) and a JKS causality test. Findings Results showed that rising temperatures and carbon dioxide emissions significantly reduce fish production, while rainfall, state GDP and per capita fish consumption enhance it. A disaggregated analysis revealed that all variables of interest had a considerable effect on marine fish production, comparable with the results for overall production; however, rainfall has a negligible effect on inland fish production. This discrepancy reflects system-specific dynamics: monsoonal rainfall has a direct impact on marine fisheries through nutrient enrichment and stock availability, whereas inland aquaculture is predominantly influenced by managed economic inputs rather than rainfall variability. Furthermore, the findings demonstrated that marine production is more sensitive to climatic factors, whereas inland production is more elastic to economic variables. The JKS test revealed that incorporating climatic and economic indicators improves the accuracy of fish production predictions than relying solely on its past values. Research limitations/implications For the foreseeable future, these findings have significant policy ramifications. In addition to strengthening water resource management and encouraging climate-resilient practices, fisheries departments should allocate a larger percentage of their GDP to infrastructure development. Additional stimulation of production can be achieved by demand-side measures like nutrition campaigns and the inclusion of fish in public food programs. To maintain the sustainable growth of both marine and inland fisheries, a comprehensive policy framework that concurrently addresses climatic, economic and consumer aspects is necessary in light of the established multivariate causation. Finally, it is prudent for policymakers and other stakeholders to adopt climate-adaptive strategies for marine fisheries and direct investments and technological support towards inland aquaculture to align interventions with system-specific production drivers. Originality/value We contribute to the literature by integrating annual data for an empirical analysis across 32 Indian states and union territories. In addition, this study empirically disentangles the system-specific dynamics of Indian inland and marine fisheries. This aspect is often overlooked in existing literature because fisheries are often portrayed as a homogeneous industry. Moreover, the paper offers actionable insights for designing ecologically appropriate fisheries policies, while advancing academic debates on climateeconomyproduction relationships. 2026 Emerald Publishing Limited -
Whale Optimization Based Approach toCompress andFasten CNN forCrop Disease andSpecies Identification
In recent years deep learning and machine learning have been widely researched for image based recognition. This research proposes a simplified CNN with 3 layers for classification from 39 classes of crops and their diseases. It also evaluates the performance of pre-trained models such as VGG16 and ResNet50 using transfer learning. Similarly traditional Machine Learning algorithms have been trained and tested on the same dataset. The best accuracy using proposed CNN was 87.67% whereas VGG16 gave best accuracy of 91.51% among Convolution Neural Network models. Similarly Random Forest machine learning method gave best accuracy of 93.02% among Machine Learning models. Since the pre-trained models are having huge size hence in order to deploy these solutions on tiny edge devices compression is done using Whale Optimization. The maximum compesssion was obtained with VGG16 of 88.19% without loss in any performance. It also helped betterment of inference time of 44.13% for proposed CNN, 56.76% for VGG16 and 63.23% for ResNet50. 2023, Springer Nature Switzerland AG. -
Whale Optimization and AutoML for Precise Phishing Detection
Online fraud and social engineering tactics frequently use phishing websites as platforms. Phishers often modify the source code of the web pages they exploit in their attacks to create the illusion that alterations were made to authentic websites. A solitary response is insufficient to mitigate phishing due to the many methods employed in its execution. This study examines machine learning algorithms and evaluates their efficacy when trained on datasets including attributes that differentiate secure websites from phishing sites. Automated algorithms facilitate real-time fraud protection by swiftly detecting suspicious URLs, domain names, and website content. This study aims to identify the optimal method for detecting a prevalent category of cyberattacks. This would enhance the security and privacy of all internet users by facilitating the identification and blocking of malicious websites. Nonetheless, there is an urgent desire for automated models that provide rapid and precise detection. This research introduces a regression-based assessment method for phishing detection to address this demand. Our approach employs a whale optimization algorithm for feature selection. An AutoML framework subsequently utilizes the selected feature subsets as input. The model showed good accuracy in its predictions with very small errors on the test data, shown by an RMSE of 0.1079, an MSE of 0.0116, and an R2 value of 0.9534. These results demonstrate the reliability of our feature selection and modeling methods. 2025 River Publishers