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Cloud and IOT based smart forest fire detection and warming system /
Patent Number: 202141048693, Applicant: Arumugam Ranjith.
The development of modern industrial civilizations has caused in the establishment of manufacturing plants, office buildings, and housing blocks throughout urban parts. Because of the combustible substances contained in these facilities, there are gas and oil tanks all over these areas. Because of the densely packed buildings, extreme heat and smoke, and the possibility of explosives, putting out a fire in one of these places is nearly impossible. -
Cloud and IOT based alcohol and health monitoring system /
Patent Number: 202141022529, Applicant: Dr.AR.Sivakumaran.
Wireless alcohol and health monitoring system that can monitor a human 24x7. Vehicle driving to manufacturing plants, Offices, Hospitals, Military, and other such ventures need to screen their staff/faculty follow all hard-working attitudes that incorporate, not coming to premises affected by liquor. This guarantees legitimate hard-working attitudes are followed. Our proposed framework takes into consideration liquor and wellbeing checking in addition to a detailing framework that screens this and reports it to concerned staff distantly over the web. -
Clonning and Characterization of An Exported Protein Present in the RD7 Region of Clinical Isolates of Mycobacterium Tuberculosis
The bacterium Mycobacterium tuberculosis is responsible for causing the disease newlinetuberculosis in mammals, which is regarded as one of the oldest diseases haunting the human race. The only available tuberculosis vaccine Bacillus Calmette-Guerine (BCG), is effective against childhood tuberculosis but is regarded as having low efficacy in conferring protection in the case of tuberculosis in adults. A comparison of the M. tuberculosis H37Rv strain and clinical isolates from Kerala had earlier revealed that the clinical strains have a distinctive 4.5 kb genomic sequence that is lacking from the H37Rv strain in the RD7 region. The RD7 is a distinctive genomic region that is absent in M. tuberculosis H37Rv and Mycobacterium bovis BCG strain. The 4.5 kb genomic sequence is projected to include 6 potential ORFs by newlineNCBI ORF prediction tool, one of which Novel Hypothetical Protein (NHP2) is anticipated to encode an exported protein with a length of 268 amino acids. Studies demonstrate that Mycobacterium tuberculosis secretory proteins such as the Ag85 complex, the ESAT-6 family protein, and the PE-PPE family proteins were newlineeffective vaccine candidates because they trigger T cells. Here, we present an indepth analysis of the exported protein, which is 268 amino acids long. The putative exported protein with a gene 807 bp long was PCR amplified and cloned in the expression vector pET-32a for expression. The protein was over expressed using Isopropyl D-1-thiogalactopyranoside (IPTG) and was isolated and purified using column chromatography. Bioinformatics studies were conducted to study the characteristics of the expressed protein. A novel putative mycobacterial protein discovered by subtractive hybridization was studied for its potential as a vaccine candidate using cutting-edge computer technologies. -
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Clitoria ternatea flower extract assisted synthesis of Pluronic F127 and l-histidine coated SrO2 as a multimodality nanocomposite for anti-cancer, anti-oxidant, and antimicrobial activities
Hepatocellular carcinoma (HepG2) is a highly aggressive liver cancer with poor prognosis, limited treatment options, and high mortality rates, making it a serious global health concern that demands urgent development of more effective and safer therapeutic approaches. In this context, the present study focuses on the green synthesis of SrO2 nanoparticles using Clitoria ternatea flower extract, followed by surface modification with Pluronic F127 (PF127) and L-histidine (LH), to fabricate SrO2-PF127-LH nanocomposites aimed at evaluating their potential anticancer efficacy against the HepG2 cell line. Various analytical techniques were used to characterize the nanocomposite, and then their anticancer activity against HePG2 liver cancer cells, antioxidant properties, and antimicrobial activity against the bacteria mentioned above were evaluated. XRD revealed the crystalline nature of SrO2 with atetragonal phase. FTIR spectrum confirmed the SrO stretching band at 573cm?1 for SrO2-PF127-LH nanocomposite. UVvisible analysis revealed the band gap energies of 4.13eV for SrO2 and 4.07eV forSrO2PF127LH nanocomposite. The surface defects including oxygen vacancies of SrO2-PF127-LH nanocomposite were investigated using PL analysis. The SrO2PF127LH nanocomposite exhibited excellent antibacterial and antioxidant activities when compared to SrO2 nanoparticles alone. In addition, the SrO2PF127LH nanocomposite had enhanced anticancer activity against liver cancer (HePG2) cell lines. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Clinical Text Classification of Medical Transcriptions Based on Different Diseases
Clinical text classification is the process of extracting the information from clinical narratives. Clinical narratives are the voice files, notes taken during a lecture, or other spoken material given by physicians. Because of the rapid rise in data in the healthcare sector, text mining and information extraction (IE) have acquired a few applications in the previous few years. This research attempts to use machine learning algorithms to diagnose diseases from the given medical transcriptions. Proposed clinical text classification models could decrease human efforts of labeled training data creation and feature engineering and for designing for applying machine learning models to clinical text classification by leveraging weak supervision. The main aim of this paper is to compare the multiclass logistic regression model and support vector classifier model which is implemented for performing clinical text classification on medical transcriptions. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Clinical Study Macular Oedema
Prior to the development of the ophthalmoscope, macular oedema remained mostly unknown. Macular oedema is caused by fluid buildup in the retinal layers around the fovea. It causes vision loss by changing the functional cell connection in the retina and stimulating an inflammatory reparative response. The clinical profile, aetiology, and varied types of Macular Oedema are hence the focus of research, and also to investigate the aetiology of macular oedema as well as the various forms of macular oedema in patients attending Krishna Hospital in Karad. The male to female ratio among the 60 participants was 2.53:1. Macular oedema is the major cause for loss in vision which is common vitreo retinal diseases, with diabetes being the most prevalent cause (35% of cases) in our study. Its early detection and treatment are critical for preventing blindness. It is consequently critical to understand the aetiology, pattern, and chronicity of macular oedema in order to customize treatment and monitor response to it. RJPT All right reserved. -
Clinical Pattern Mining for Early Detection of Chronic Kidney Disease: A Data-Driven Diagnostic Framework
Early diagnosis of the Chronic Kidney Disease (CKD) is essential to avoid irreversible damage of the kidneys, but it is clear that the traditional threshold-based techniques of the diagnosis are not always able to detect a subtle pattern of biochemical changes, which indicate the early appearance of the disease. This paper provides an interpretable and data-intensive diagnostic model which incorporates clinical state transformation, frequent and contrast pattern mining, and phenotype-based clustering to reveal hidden signs of CKD progression. Continuous laboratory variables are discretized into clinically meaningful states, enabling transparent rule extraction and comparative analysis between CKD and non-CKD cohorts. The mined contrast patterns reveal distinctive early-stage abnormalities, including mild creatinine elevation, reduced urine specific gravity, albuminuria, and increased urea levels, which consistently differentiate diseased patients from healthy controls. Furthermore, K-means clustering identifies three clinically relevant renal phenotypes corresponding to early, moderate, and advanced biochemical deterioration. Sensitivity and comparative analyses demonstrate the robustness of the extracted patterns across varying support thresholds and against standard machine learning classifiers. The proposed framework offers a clinically interpretable and computationally efficient decision-support tool for early CKD detection and patient stratification using routinely collected clinical data. 2026 IEEE. -
Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements
Deep reinforcement learning (DRL) is showing a remarkable impact in the healthcare and biomedical domains, leveraging its ability to learn complex decision-making policies from raw data through trial-and-error interactions. DRL can effectively extract the characteristic information in the environment, propose effective behavior strategies, and correct errors that occurred during the training process. Targeted toward healthcare professionals, researchers, and technology enthusiasts, this chapter begins with notable applications of DRL in healthcare, including personalized treatment recommendations, clinical trial optimization, disease diagnosis, robotic surgery and assistance, mental health support systems, chronic disease management and scheduling, and a few more. It also delves on challenges such as data privacy, interpretability, regulatory compliance, validation, and the need for domain expertise to ensure safe and effective deployment. Next, the chapter seamlessly transitions into DRL algorithms contributing to the biomedical field which are gaining traction due to their potential to provide timely and personalized interventions. Over time, the research community has proposed several methods and algorithms within the field of deep reinforcement learning that help agents learn optimal policies from rich data. Healthcare data is often complex, high-dimensional, and unstructured, such as medical images, genomics data, and patient records. The healthcare-suitable DRL algorithms such as Q-learning, SARSA, Bayesian, actor-critic, reinforcement learning (RL), Deep-Q-Networks (DQN), and Monte Carlo Tree Search (MCTS) are highlighted. In addition, the section offers guidelines for the application of DRL to healthcare and biomedical problems, aiming at providing indications to the designer of new applications in order to choose among different RL methods. Furthermore, a case study is included to fully realize the revolutionary benefits of DRL in healthcare environments, aiming to bridge the gap between theory and practice. The case study presents a remarkable impact on categories such as precision medicine, dynamic treatment regime, medical imaging, diagnostic systems, control systems, chat-bots and advanced interfaces, and healthcare management systems. 2024 Scrivener Publishing LLC. -
Clinical implications of chromosomal polymorphisms in congenital disorders
Alterations in the DNA sequence are generally seen in the general population at >1%, and these alterations can be deletions or insertions. Classically, chromosomal polymorphisms (CPMs) are alterations with no significant phenotypic distinctions. However, few studies have shown that the presence of CPM can lead to congenital disabilities, which can be fatal. These variants in the DNA can happen in the form of single nucleotide polymorphisms (SNPs). The human genome is considered full of SNPs, and they are responsible for causing pathological phenotypes and provide insight into pathogenesis, a therapeutic approach to the pathology. About 100 million SNPs are observed in humans for an average of 300 nucleotides. These polymorphisms are detected by using molecular techniques. These polymorphisms are not just restricted to the coding region. The CPMs are first recognized on the chromosomes through molecular techniques, followed by detection of the polymorphism. The CMPs are generally the SNPs, deletions/duplications, and presence of microsatellite DNAs. Here we have summarized the implications of CMPs in a few congenital disorders and the method of diagnosis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Clinical hypnosis and Patanjali yoga sutras
The trance states in yoga and hypnosis are associated with similar phenomena like relaxation, disinclination to talk, unreality, misrepresentation, alterations in perception, increased concentration, suspension of normal reality testing, and the temporary nature of the phenomena. While some researchers consider yoga to be a form of hypnosis, others note that there are many similarities between the trance in yoga and the hypnotic trance. The present study aimed to find similarities between the trance states of hypnosis and Patanjali?s yoga sutras. The trance states were compared with the understanding of the phenomena of trance, and the therapeutic techniques and benefits of both. An understanding of the concept of trance in Patanjali?s yoga sutras was gained through a thematic analysis of the book Four Chapters on Freedom by Swami Satyananda Saraswati. This led to an understanding of the concept of trance in the yoga sutras. The obtained concepts were compared to the concepts of trance in hypnosis (obtained through the literature on hypnosis) to investigate whether or not there exist similarities. The findings of the study show that there are similarities between the trance in hypnosis and the trance in Patanjali?s yoga sutras in the induction and deepening of the trance states in hypnosis and that of Samadhi, the phenomena present in hypnosis and the kinds of siddhis that are obtained through Samadhi, and the therapeutic techniques and the therapeutic process in Patanjali?s yoga sutra and hypnosis. -
Climate, agriculture, and farmer's mental health: Unravelling the nexus in Wayanad, Kerala
A sizable majority of the population works in the primary sector in Kerala's Wayanad district, where agriculture is the backbone of the local economy. However, dynamic issues including climate change, fluctuating soil quality, crop diseases, and related economic consequences pose difficulties for this industry. The complicated linkages between agricultural practices and climate change are discussed using qualitative data from in-depth interviews with 15 Wayanad farmers. Agricultural productivity and revenue are strongly impacted by unpredictable rainfall, which is exacerbated by strong winds, natural disasters, wildlife intrusions, and crop diseases. The failure of farmers to adjust to these climate changes is a remarkable finding, frequently brought on by fear and unstable financial situations. This resistance causes anxiety, a sense of powerlessness, and a sense of responsibility for circumstances that are out of their control. In order to help farmers manage the unforeseeable effects of climate change, the study emphasizes the urgent need for policy initiatives in areas like Wayanad. Cooperative farming and knowledge-sharing platforms are examples of strategies that could improve farmers' psychological resilience and general well-being. Given that agriculture accounts for a substantial portion of the region's income and that resources and knowledge are scarce, climate change has a considerable impact on agricultural outputs and farmers' psychological well-being. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Climate-Smart Livelihood - A Case Study of Dodaballapura Taluk of Bangalore Rural District
More than a billion farmers around the world are on the frontier of climate change. These farmers' livelihoods are directly and indirectly affected by the impact of climate change. Climate smart livelihood explains the practices in agriculture sector which sustainably contributes to productivity and income. This study tries to explore the adaptation of climate smart livelihood techniques by the farmers in the Doddaballapur taluk of Bangalore rural district. The data was collected primarily from the five villages and 50 households of Doddaballapur taluk. The survey revealed that 81.67% of the respondents faced problems during adaptation of climate smart agriculture was due to poor support of local and national authorities with climate related issues and ranked it one of the major constraints. This was followed by lack of financial constraints, lack of knowledge about adaptive practices (78.50%), non-availability of agriculture inputs in time (76.17%), lack of education about the adaptation strategies (75.33%), unavailability of new technologies (78.83%), higher cost of the agricultural inputs used for the practices (71.17%), lack of improved communication facility about the climate change (71 %), migration of youth due to urbanization and better employment (70.83%), lack of knowledge about post-harvest technology (68.83%), lack of awareness about climate change issues (59.83 %). The study reveals that as most farmers believe they have low capacity to adapt to climate-smart agriculture due to lack of availability of resources. Government can help farmers through National Agricultural Extension Project (NAEP), Krishi Prashasthi, etc. 2022 - Kalpana Corporation. -
Climate Risks in an Unequal Society: The Question of Climate Justice in India
Over the past few decades, India has witnessed the brunt of climate change impacts in multiple dimensions. Notably, recent years' experiences prove that there has been a substantial increase in the intensity, frequency, and duration of climate-related risks and extreme events, resulting in an acceleration of the nexus between climate change and inequities. Despite the growing advancements of socioeconomic research on current and future climate change risks in India, the explorations through the lens of legal perspectives are still limited and have not met the demands. This chapter argues for rethinking legal perspectives of climate justice in India by drawing insights from two recent climate extreme events. To begin with, this chapter briefly reviews the historical background of the global actions to combat climate inequities and injustices and identifies the ways in which climate injustices perpetuate. For this, it adopts three main principles of climate justice, consisting of equity, a rights-based approach, and sustainability. Following this, it discusses India's climate policy and the existing institutional framework and actions to respond to climate change at the national and state levels. Then, by focusing on the climate change impacts on India, it introduces two recent climate-related risk events in India, and it discusses the unequal structuration of climate risks and the resulting more vulnerable and precarious situation of the marginal sections of the society that already faces multiple social injustices of Indian society. At the end of the cases, it briefly offers a critique of the climate change action plans of the respective states. This chapter concludes by outlining a few strategies to create a more sustainable and equitable approach toward climate governance and justice by strengthening the legal and institutional dispensations of the climate regime in India. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Climate Risks and Financial Resilience
Climate threats are drastically altering the risk environment for businesses, which has a significant impact on their creditworthiness and valuation. The observable effectsdisturbed supply chains, damaged assets, and decreased consumer demanddirectly result in worse financial statements and a reduced ability to pay off debt. It is no longer possible for lenders and investors to overlook the systemic risk indicated by this decline in financial health. Therefore, the stability of the larger financial system depends on climate resilience, which is crucial for individual businesses as well. Financial stability and economic resilience are increasingly at risk due to the rising frequency and severity of catastrophic weather events and long-term ecological changes. The study uses the Scopus database, one of the most extensive bibliometric sources, to map worldwide research patterns and determine future directions in the field of climate risk and financial resilience in order to comprehend and manage these issues. The results show that supply chains are disrupted, asset prices are impacted, and operating expenses rise as a result of climate concerns. As a result, businesses and financial institutions are gradually implementing resilience planning, climate risk assessment frameworks, and sustainable investment strategies. The shift to low-carbon portfolios is a noteworthy trend that proactively integrates climate risk into financial planning to guard against possible losses and open up opportunities. This study enables a thorough knowledge of the components of climate hazard, which is crucial information for lawmakers, financial institutions, and business decision-makers. This clarity makes it easier to create forward-thinking, doable initiatives, such as strengthening public-private collaboration, funding resilient infrastructure, and incorporating climate issues into financial frameworks. Building long-term financial resilience against an unpredictable environmental backdrop and proactively addressing climate-induced shocks requires these integrated methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Climate Risk, Commodity Prices, and Sectoral Dynamics in Indian Financial Markets: A Systematic Literature Review
According to the authors, this chapter provides a comprehensive review of more than the last twenty years empirical research on the relationship between commodity prices, sectoral stock indices and climate change induced financial risk in India. The chapter discusses the historical evolution of analytical techniques from simple linear econometric analysis to more recent machine learning algorithms by integrating 67 articles published in peer-reviewed journals between 2000 and 2025 using the PRISMA methodology. Key thematic results are large transmission effects of volatility between commodities and from these to sectoral markets, varying sectoral sensitivities to climate stress, and new trends in fintech and ESG commodity trading. Issues in climate risk integration, data innovation and policy frameworks are highlighted in the chapter. It provides a sound basis for climate-f riendly financial strategies to help develop more resilient and sustainable capital markets in India. 2026 by IGI Global Scientific Publishing. -
Climate Risk, Commodity Prices, and Sectoral Dynamics in Indian Financial Markets: A Systematic Literature Review
According to the authors, this chapter provides a comprehensive review of more than the last twenty years empirical research on the relationship between commodity prices, sectoral stock indices and climate change induced financial risk in India. The chapter discusses the historical evolution of analytical techniques from simple linear econometric analysis to more recent machine learning algorithms by integrating 67 articles published in peer-reviewed journals between 2000 and 2025 using the PRISMA methodology. Key thematic results are large transmission effects of volatility between commodities and from these to sectoral markets, varying sectoral sensitivities to climate stress, and new trends in fintech and ESG commodity trading. Issues in climate risk integration, data innovation and policy frameworks are highlighted in the chapter. It provides a sound basis for climate-f riendly financial strategies to help develop more resilient and sustainable capital markets in India. 2026 by IGI Global Scientific Publishing. -
Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy
Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Ni Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggestedstudy concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictorsas mentioned above. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.