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HumanWildlife Conflict in Kerala Conservation Policies and the Elusive Ethics of Peaceful Coexistence
The troubled humanwildlife relationship in the highlands of Kerala is a matter of growing concern due to the constant disruption of the lives and livelihoods of the people who share space with wildlife. Debates surrounding the complexities of humanwildlife conflict often persist, largely due to the divide between the environmentalist perception of conservation and the experiences of farmers confronting wildlife-related threats. This study demonstrates that the precarious social and economic circumstances of the farmers and local communities directly affected by the inter-species conflict undermine the skewed discourse promoting coexistence between humans and wildlife. 2025, Economic and Political Weekly. All rights reserved. -
Upconversion nanoparticles for detection of small biomolecules and ions
In recent years, upconversion nanoparticles (UCNPs) have resulted in substantial advances in the area of sensitive and selective detection of small biomolecules and ions. UCNPs possess a unique optical property known as lanthanide upconversion luminescence. This phenomenon enables them to absorb low-energy light from the near-infrared region and subsequently emit higher-energy light in the visible or ultraviolet part of the spectrum. This process, often referred to as the anti-Stokes shift, resists the conventional fluorescence behavior by absorbing lower-energy photons, followed by emission of higher-energy photons. This chapter provides a comprehensive overview of the mechanism of upconversion fluorescence and explores the properties of UCNPs. It then delves into the applications of UCNPs in detection of biomolecules like proteins and amino acids, nucleic acids, and tumor biomarkers, thus facilitating early diagnosis and patient care. Additionally, UCNPs are useful in the detection of ions by altering their surface chemistry to bind selectively to target ions, expanding their utility in environmental monitoring and chemical analysis. 2026 Elsevier Ltd. All rights reserved. -
An ESIPT/AIE active Schiff Base for the selective detection of Picric acid, Ammonia, and its potential applications in anticounterfeiting and latent fingerprinting
A novel ESIPT/AIE-active Schiff base fluorophore, N?1,N?6-bis((Z)-2,4-dihydroxybenzylidene)adipohydrazide (ADHB), has been designed and synthesized. ADHB exhibits remarkable selectivity and sensitivity towards picric acid in aqueous phase, as well as ammonia in both aqueous and solid phases, with LOD values of 55.5 nM and 88.7 nM respectively, facilitating its efficacy in real sample analysis. While exhibiting notable luminescence in polar solvents (? = 0.15 %), ADHB displays pronounced fluorescence enhancement in the solid state (??? = 320 nm) due to aggregation-induced emission (AIE). The molecular skeleton of ADHB incorporates two potential excited-state intramolecular proton transfer (ESIPT) active sites that exhibit distinctive, reversible halochromic properties in the solid state. The adaptability of this Schiff base as a multi-responsive fluorescent material was explored by the fabrication of a blue-emitting polyvinyl alcohol (PVA) composite film and paper-based test strips. The detection limits agree with the amount of contaminants that the U.S. Environmental Protection Agency (EPA) allows in drinking water. The sensing mechanism was elucidated through comprehensive DFT studies, NMR titration studies and Job's plot analysis. The tunable photophysical properties of this AIE-active probe facilitates practical applications in anti-counterfeiting and latent fingerprint visualization, highlighting its significance in forensic science and security authentication. These findings establish ADHB as a fluorescent platform for the sensitive detection and continuous monitoring of hazardous compounds in environmental systems. 2025 Elsevier B.V. -
ESIPT Active Schiff Base Fluorescent Sensor for Selective and Sensitive Detection of Co(II) Ions: Experimental, DFT Optimization Studies and Real Sample Analysis
A novel fluorescent Schiff base chemosensor, N1,N6-bis((E)-3,5-dibromo-2-hydroxybenzylidene)adipohydrazide (DBSA), has been developed for the detection of Cobalt (II) ions. DBSA exhibits distinct fluorescence enhancement upon interacting with Co(II) ions via photoinduced electron transfer (PET). The developed sensor demonstrates a remarkable sensitivity, with the detection limits of 9.9 nM for Co(II) ions, which aligns well with the Environmental Protection Agency (EPA) regulatory thresholds for drinking water contaminants. Structural characterization by LC-MS, FTIR coupled with Jobs plot and NMR titration studies confirm the formation of DBSA-Co complex with a binding constant of 4.61 106 M? 1. The chemo sensor exhibits a quantum yield of 0.082, highlighting its potential applicability in photochemical processes. Computation studies were used to further investigate the binding interactions with Co2+ ions. The practical utility of DBSA has been validated through successful analyses in varied aqueous matrices, including tap water, lake water and recycled water. Cytotoxicity assessment via MTT assays on SH-SY5Y cells confirms excellent biocompatibility of the probe. This work presents a significant advancement in the design of efficient molecular probes for environmental monitoring, offering a robust platform for the concurrent detection of transition-metal ions in aqueous systems. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Revolutionizing Healthcare: The Impact of Generative AI and Large Language Models
The chapter explores the transformative impact of generative AI and large language models (LLMs) in healthcare, emphasizing their potential to revolutionize patient care, clinical operations, and medical research. Generative AI, a subset of artificial intelligence, offers groundbreaking capabilities such as personalized medicine, virtual health assistants, and enhanced diagnostic accuracy. LLMs like Med-PaLM and BioBERT are fine-tuned to perform specific healthcare tasks, such as clinical note summarization and diagnostic support. These models also assist in drug discovery, clinical trials, and pandemic preparedness by analyzing complex medical data and predicting patient outcomes with high accuracy. The chapter also addresses the ethical and regulatory considerations associated with AI in healthcare, including data privacy, bias, and accountability. While the integration of AI technologies promises significant advancements, it also requires stringent regulatory oversight to ensure safety, efficacy, and fairness. The potential of generative AI to generate synthetic medical data offers a secure way to advance research without compromising patient privacy. Additionally, AI can optimize healthcare processes, enhance patient engagement, and accelerate medical research, contributing to a more efficient and personalized healthcare system. The chapter concludes by highlighting the need for continuous collaboration between AI developers, healthcare professionals, and regulators to maximize the benefits of these technologies while addressing the associated risks. 2025 selection and editorial matter, Sakshi Gupta, Umesh Gupta, Moolchand Sharma, Kamal Malik; individual chapters, the contributors. -
Celestial Image Classification Using Attention And Boosting Mechanism
Astronomical image classification is vital in the comprehension of celestial objects, but deep learning models are severely challenged by the lack of labeled datasets. The novelty of the study is two-fold - the development of the dataset and a hybrid learning method that combines both transformer-based feature extraction and gradient-boosted decision trees to improve classification performance for celestial image classification. This study is a comparison of CNNs, transformers, and hybrid models in nebulae, galaxy, and star cluster classification using the dataset collected from the Hubble Space Telescope image archive. Through progressive data augmentation, the dataset was augmented from 603 images to 4,500 high-diversity training samples to enhance model generalization. This research explores various architectures, including ResNet-50, DenseNet-121, EfficientNetV2-S, DeiT (Data-Efficient Image Transformer), and hybrid models like DeiT-RF (Data-Efficient Image Transformer - Random Forest) and DeiT-XGBoost (DXg). DXg brings a novel fusion mechanism in which DeiT learns high-level spatial representations, adaptive dimensionality reduction fine-tunes feature selection, and XGBoost best classifies celestial objects. Such a unique combination of transformers and gradient boosting improves interpretability without sacrificing state-of-the-art performance. 2025 IEEE. -
Celestial Image Classification using Ensemble Learning and Vision Transformers
Astronomical image classification plays a crucial role in understanding the universe, but deep learning models often stumble when faced with scarce labeled data. In our work, we address this gap in two key ways: first, by building a richly varied dataset from just 600 Hubble Space Telescope images and, through targeted augmentation, expanding it to 4,500 distinct training examples; and second, by introducing a hybrid learning strategy that marries transformer-driven feature extraction with gradient-boosted decision trees. We used benchmark standard convolutional architectures (ResNet-50, DenseNet-121) alongside the Data-Efficient Image Transformer (DeiT) and two novel hybrids-DeiT-RF and DeiT-XGBoost (DXg). In DXg, DeiT captures complex spatial patterns, an adaptive dimensionality reduction layer hones in on the most informative features, and XGBoost delivers the final classification. This fusion not only boosts accuracy across nebulae, galaxies, and star clusters but also enhances interpretability by revealing which transformer-derived features most influence the model's decisions. 2025 IEEE. -
IoT-Enabled Smart Security Surveillance System for Farmland and Livestock Monitoring Using Computer Vision
Agriculture is the main source of income of every country and agriculture plays a critical role in sustaining human civilization by providing food, fuel, and other essential resources. Even though, the conflict between farmers and wildlife remains a significant challenge even today. It is essential to arrange the protection of fields and farms by deterring wild animals and predators without making harm to the wildlife. This study proposes a framework that can detect and classify animals or intruders or any natural calamities. The novelty of the study is deterring the wild animals using innocuous devices and chemical components instead of the harmful electric fences and other methods. Based on the classification it will take preventive measures and will send an alert to the Farmer. The preventive measures are ensuring that will not make any harm to the wild animals. The framework incorporated four major components PIR (Passive Infrared) Sensor, Raspberry Pi Camera, Scarecrow and GSM (Global System for Mobile Communications) Module. PIR Sensor detects an object, Raspberry Pi Camera captures the images and images classify the specific object, later based on the type of the animal the scare tactics will be applied by the scarecrow and if any unusual incident is happening like the presence of an intruder, fire or tornado the alert will send to the Farmer with the help of a GSM Module. 2025 IEEE. -
Development of interactive e-content to enhance listening skill and language comprehension among secondary school students
The present study aimed to develop interactive e-content, conduct expert validation, and examine the appropriate level. The researchers used a purposive sampling technique to select the sample of 100 secondary school students and 35 teachers from the Kerala state scheme. The researchers adopted the analysis, design, development, implementation, and evaluation (ADDIE) model to develop interactive e-content. The study employed two quantitative methods. Firstly, the study administered expert validation sheets to three content and two media experts to validate developed interactive e-content. The study utilized the percentage analysis to evaluate the results of the expert validation sheets. Secondly, the study administered a survey questionnaire to 100 secondary school students and 35 teachers to examine the appropriate level of interactive e-content. The study employed the correlation method to analyze the questionnaire results, examining the strength and direction of relationships between variables. The average score of content expert validation is 95.5% and media expert validation is 91.5% confirm that the developed interactive e-content is highly valid and appropriate. A major challenge for the researchers was the insufficient internet speed in rural areas of Kerala. The study recommends that teachers have to develop interactive multimedia teaching-learning aids to improve listening, speaking, reading, and writing (LSRW) among students. 2026 Institute of Advanced Engineering and Science. All rights reserved. -
An efficient reconfigurable band tuning filter design for channelizer in transponder satellite system
For improved performance in a variety of applications, the transponder in satellite systems must be very flexible. The channelizer-dependent transponder system significantly boosts the operation of a satellite system. When channelizing wideband input signals, a digital filter bank is typically used to extract several small sub-bands. In this research, a reconfigurable band tuning (RBT) design for the channelizer in the satellite transponder system is designed and implemented. Cosine modulation, exponential modulation and IFIR filter are the techniques behind the RBT design. The RBT design facilitates the generation of many channels enabling channelization with non-uniform narrow transition width. A number of examples are presented to illustrate how well the RBT design performs. Findings indicate that there are fewer filter coefficients in the RBT design than there are in the current approaches Effective implementation of a properly designed RBT design lowers power consumption and simplifies the hardware. 2024 The Franklin Institute -
A multi-cognitive approach to empowering secondary school teachers' self-efficacy and practices related to education for sustainable development
Purpose Education for Sustainable Development (ESD) is vital for addressing global sustainability goals. However, integration in Indian schools faces challenges, particularly due to gaps in teacher preparedness. This study aimed to evaluate the effectiveness of a multi-cognitive approach (MCA) in empowering secondary school teachers' self-efficacy and ESD integration. Design/methodology/approach A quasi-experimental, one-group pretestposttest design was employed with 50 secondary school teachers from marginalized communities in Kerala, India. Participants with over 6years of experience but no prior ESD training underwent a 3-month MCA-based transformative learning program. The intervention addressed content, perspectives, processes and design. Teacher self-efficacy and ESD practices were measured pre- and immediately post-intervention, and three months later, using structured questionnaires. Findings Teachers' self-efficacy significantly improved post-intervention (52.707.61) and was sustained at three months (56.604.59), compared to baseline (49.069.69) (p<0.001). ESD-related practices also improved post-intervention (47.487.16), with further gains at three months (51.863.96), compared to pre-intervention (41.905.91). Research limitations/implications These results support incorporating the MCA into teacher training and professional development programs to foster sustainable education practices. The approach aligns with SDG 4.7 and can guide policy reforms in integrating ESD into mainstream education. Practical implications The study also presents a professional development model for schools, particularly beneficial in resource-constrained contexts, that enables teachers to embed sustainability in their practices. Furthermore, it offers policy guidance for embedding MCA-informed ESD into teacher education and national curricula, supporting Sustainable Development Goal 4.7 and NEP 2020 vision, promoting systemic education reform in sustainability. Social implications This study empirically validates an MCA as an effective framework for ESD. It highlights those engaging teachers across the cognitive, reflective, procedural and design dimensions, simultaneously enhancing their self-efficacy and sustaining ESD practices. The findings extend existing theories by showing that self-efficacy in sustainability is teachable and durable with the right interventions. Originality/value This study highlights MCA as a promising model for building teacher capacity in ESD and recommends future research on its impact on student outcomes. Emerald Publishing Limited -
Beliefs of secondary school teachers towards education for sustainable development: a statistical research
Educators are the architects of sustainable development (SD), transforming society and balancing development and sustainability. They enhance education for sustainable development (ESD) and societal transformation, driving innovative evolution and future-oriented development within the community. ESD, a millennium, and sustainable development goal (SDG), need to be implemented globally. Teachers are vital in transmitting knowledge, beliefs, and skills required for sustainability in the changing environment. This study examined secondary school teachers beliefs about ESD based on their professional qualifications, teaching experience, and position. The authors used a survey approach and collected the data using a belief assessment tool, i.e., the ESD beliefs scale. The respondents were 400 secondary school teachers in Kerala, India. The study used an item-based evaluation to achieve these objectives and calculated t-values, F-values, and percentages. The research findings indicated that teachers hold constructive opinions towards ESD. The positional status of teachers did not alter beliefs regarding ESD among secondary school teachers. In contrast, professional qualifications and years of teaching experience significantly influenced these ESD beliefs. The findings from this study enable education stakeholders to amend the current secondary education system for SD. 2026 Institute of Advanced Engineering and Science. All rights reserved. -
Optimising Education for Sustainable Development through Secondary School Teachers with Relevant Subjects, Standards and Training: Quantitative Review
India aims to become a developed nation by 2047, emphasizing the role of Education for Sustainable Development (ESD) in achieving Sustainable Development Goals. This study examines the beliefs of Kerala secondary school teachers regarding ESD, investigating how teaching standards, subjects, and prior ESD training shape their perspectives. A survey of 400 teachers utilized the revised ESD Belief Scale, incorporating demographic considerations. The research examined demographic variables, including teaching level, subject specialization, and previous ESD training. Quantitative analysis encompassed descriptive statistics, t-tests, and ANOVA to evaluate beliefs across various groupings. Findings reveal that educators predominantly recognise the significance of ESD in fostering sustainable decision-making and lifelong learning. The discipline taught, especially social sciences in contrast to science, technology, engineering, and mathematics, is the primary determinant of educators' beliefs towards ESD. Teachers recognise the benefits of ESD, although they encounter obstacles, including restricted curricular integration and implementation. This research addresses ESD within the secondary curriculum in a unique manner, filling a notable gap in both theoretical and empirical literature. The implementation of an updated belief scale and subgroup analyses provides policymakers and curriculum developers with novel perspectives. It is recommended that curriculum reform incorporate ESD throughout all courses, accompanied by specialized teacher training to enhance awareness, skills, and pedagogical techniques for the effective implementation of ESD. Secondary educators in Kerala predominantly advocate for the integration of ESD, particularly within the social sciences. Future policy and research must emphasise curricular innovation and longitudinal assessment to further Indias sustainable development objectives. 2026 Sijo VARGHESE & P.M. MATHEW. Published by the Asian Society of Human Services (ASHS). -
Socioeconomic determinants of COVID-19 in Asian countries: An empirical analysis
The spread of coronavirus disease, 2019, has affected several countries in the world including Asian countries. The occurrences of COVID infections are uneven across countries and the same is determined by socioeconomic situations prevailing in the countries besides the preparedness and management. The paper is an attempt to empirically examine the socioeconomic determinants of the occurrence of COVID in Asian countries considering the data as of June 18, 2020, for 42 Asian countries. A multiple regression analysis in a cross-sectional framework is specified and ordinary least square (OLS) technique with heteroscedasticity corrected robust standard error is employed to obtain regression coefficients. Explanatory variables that are highly collinear have been dropped from the analysis. The findings of the study show a positive significant association of per capita gross national income and net migration with the incidence of total COVID-19 cases and daily new cases. The size of net migration emerged to be a potential factor and positive in determining the total and new cases of COVID. Social capital as measured by voters' turnout ratio (VTR) in order to indicate the people's participation is found to be significant and negative for daily new cases per million population. People's participation has played a very important role in checking the incidence of COVID cases and its spread. In alternate models, countries having high incidence of poverty are also having higher cases of COVID. Though the countries having higher percentage of aged populations are more prone to be affected by the spread of virus, but the sign of the coefficient of this variable for Asian country is not in the expected line. Previous year health expenditure and diabetic prevalence rate are not significant in the analysis. Therefore, people-centric plan and making people more participatory and responsive in adhering to the social distancing norms in public and workplace and adopting preventive measures need to be focused on COVID management strategies. The countries having larger net migration and poverty ratio need to evolve comprehensive and inclusive strategies for testing, tracing, and massive awareness for sanitary practices, social distancing, and following government regulation for management of COVID-19, besides appropriate food security measures and free provision of sanitary kits for vulnerable section. 2020 John Wiley & Sons Ltd -
Borne dreamily along the River of Stories: polyphony, anthropoharmonism and the search for indigenous epistemologies in Orijit Sens graphic novel
This paper offers a close reading and analysis of Orijit Sens The River of Stories (1994), often hailed as the first Indian graphic novel. Based on the historical events of the 1990s, Sens work revolves around the Narmada Bachao Andolan, a milestone movement in the annals of Indian environmentalism that resisted the construction of dams across the Narmada River. This project led to the displacement of the tribal population residing along the river bank and destroyed their livelihood. The paper employs Bakhtins concept of polyphony to explore the richly layered nature of Sens graphic novel. The graphic novel calls upon its readers to constantly navigate the diverse, often conflicting perspectives offered by different characters as well as the complex interplay between words and images. Drawing upon the works of posthumanist philosophers like Braidotti, Hathaway and Scharper, the paper argues that, through characters like Malgu gayan, The River of Stories presents a passing vision of anthropoharmonism, i.e. harmony between mankind and the natural world. However, the overarching presence of the influential and corrupt State officials, blindly seeking progress and development underscores that it is only through the relentless quest to uncover and preserve indigenous epistemologies, comprising lost tribal cultures and folk traditions, that this harmonious vision can be realised. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Digital Testimony and Vulnerability in Reinhard Kleists An Olympic Dream: Restorying Samias Journey Through Facebook Posts
An Olympic Dream pieces together the story of the young female Somali athlete, Samia Yusuf Omar. In the absence of adequate information for her story coupled with Samias attempts to hide her identity, the digital traces left behind by the athlete serve as invaluable sources for the author. Weaving together Samias Facebook posts, text conversations, and information sourced from journalists, Reinhard Kleist recre ates the lost story of an Olympian. This chapter aims to study the role of Facebook posts in disseminating Samias story to readers and creating a narrative arc for her Olympic journey. Employing the theoretical lens of Vulnerability Studies along with Digital Postcolonialism it attempts to unpack the athletes story. Through an in-depth study of the graphic novel characterized by its tenuous use of social media posts, this chapter examines how social discrimination, surveillance and margin alization render a refugee woman athlete from the Global South highly vulnerable. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Automated Detection of Deepfakes using Integrated AI and Computer Vision Strategies
Deepfakes, or artificial intelligence-generated fake videos, are becoming a greater concern for online information trust, personal privacy, and digital content security. This paper presents a straightforward and understandable technique for automatically identifying deepfakes in order to address this significant problem. The approach makes use of conventional computer vision and machine learning methods. The model examines manually produced visual cues such as eye distance, mouth movement, and head tilt in video footage. To increase accuracy, it employs a variety of classifier types, including Random Forest, Gradient Boosting, and a soft Voting Classifier. A method known as SMOTE was used to clean and balance the data, and categorical data was transformed into a format suitable for machine learning models. With an F1-score of 0.9802 and 98% accuracy, the results demonstrate that the Voting Classifier, which combines several models, works admirably while being straightforward and effective. This method makes detection successful and simple to comprehend while offering a helpful tool for swiftly identifying deepfakes, especially on systems with constrained resources. 2025 IEEE. -
Quality of work life and work motivation among garment sector executive employees /
The International journal Of Indian Psychology, Vol.3, Issue 1, pp.111-119, ISSN No: 2349-3429. -
Optimized handwritten character recognition using artificial neural network
Handwritten character recognition (HCR) plays important role in the modern world and is one of the focused area of research in the field of image processing and pattern recognition. Handwritten character recognition refers to the process of conversion of hand-written character into printed/word file character which can immensely improve the interface between man and machine in numerous application. It is difficult to process with great variations in writing styles, different size and orientation angle of the character that are existing. Also segmentation of cursive handwritten text is difficult as the edges cant be detected easily. There are numerous approaches to recognize handwritten data. The images are acquired using a digital camera or scanner and stored in standard format like JPG, PNG etc. The second stages include pre-processing techniques like Binarization, Skeletonization, thinning, resizing the image and segmentation. In our work we mainly concentrated on extracting statistical features of alphabets like mean, variance, standard deviation, Skewness and kurtosis, which differentiates a character from another. We used feed forward algorithm to train Artificial Neural Network (ANN). The features of input character after pre-processing are fed into ANN. A database of 650 samples is created to test input samples for recognition of character by neural net-work. The Experimental results that we have achieved show 88.46 % accuracy rate with minimum time taken for training. IJSTR 2020. -
A New Economics Awaits Us
This article attempts to look into the concept of othering in the context of urban development. The major motivation for the initiation of this article came after reading Dipankar Guptas book review titled A New Sociology Awaits Us (EPW, 26 December 2020), which mainly concentrated on urban affairs. 2022 Economic and Political Weekly. All rights reserved.