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Escape velocity backed avalanche predictor neural evidence from nifty /
International Journal of Recent Technology And Engineering, Vol.8, Issue 4, pp.486-490, ISSN No: 2277-3878. -
Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy
Marine microplastic contamination presents a significant risk to ocean health, necessitating precise spatiotemporal predictions for effective marine policy development. This study introduces a transparent deep learning model to examine and forecast microplastic levels in global oceans by leveraging historical sampling data, seasonal variations, and climatic factors. A comprehensive global dataset is curated and analyzed, integrating environmental indices such as ENSO, PDO, NAO, and MEI to model the influence of large-scale ocean-atmosphere interactions. Temporal decomposition, Mann-Kendall trend testing, Theil-Sen regression, and seasonal analysis reveal statistically significant monthly and interannual variations in microplastic concentration. Correlations with climate drivers underscore the dynamic environmental control on pollutant distribution. By incorporating interpretable environmental modeling, the proposed framework supports data-driven marine pollution mitigation and policy strategies aligned with UN Sustainable Development Goal 14 (Life Below Water). This work establishes a foundation for future extensions involving LSTM- and Transformer-based time series forecasting combined with SHAP-based explainability for enhanced decision-making. Furthermore, anomaly detection employing Prophet residuals and Isolation Forest reveals sudden increases in pollutants, providing early warning systems for disturbances to marine ecosystems. High-risk areas that need focused regulatory actions are further identified using clustering analysis. All things considered, the model makes it possible to forecast marine plastic pollution in a comprehensive, comprehensible, and scalable manner-a crucial component of sustainable ocean governance. 2025 IEEE. -
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Microplastic pollution is a growing environmental concern, threatening aquatic ecosystems and human health. This study presents a dual deep learning approach for microplastic detection and classification using two datasets. For water microplastics, YOLOv8 and YOLOv11 were employed for object detection. InceptionV3, VGG19, ResNet50, ResNet152, DenseNet121, EfficientNetB0, and a custom CNN were applied for classification, classifying three distinct microplastic types in non-aquatic environments. Experimental findings display high accuracy, and indicate the potential of AI-enabled solutions for environmental monitoring. This research contributes to SDG 6 Clean Water and Sanitation, promoting sustainable management of water. 2025 IEEE. -
Dreamscapes and Virtual Realms: Exploring VRs Impact on Dream Patterns
In recent years, psychologists have been exploring the impacts of virtual reality on mental health, discovering its potential toward curing diseases and therapeutic tools for many such mental conditions. In conventional exposure therapy, the ability of patients to successfully visualize particular feared stimuli is a prerequisite for imaginal exposures. On the flip side, various VR-related tasks have been conducted to test the impact on dreams. This study examines the impact of VR on dreams and analyze the changes in its patterns. By reviewing the recent papers, we concluded that the potential harm caused by VR is well established, with negative side effects reported since the early 1990s. Seven of these twenty-three studies either did not report global incorporation rates or failed to provide sufficient data to determine them. The side effect profile associated with the clinical use of VR and AR remains largely unknown; therefore, we systematically reviewed available evidence of their adverse effects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Financial Vulnerability in Households: Dissecting the Roots of Financial Instability
The phenomenon of household financial vulnerability, defined by unexpected shocksin income and expenditures, carries major implications for both individual households and the overall economy of a nation. For a considerable time, household debt has been widely acknowledged as the primary determinant of household financial vulnerability. This study aims to extend the analysis beyond the scope of household debt. Middle-income households may experience financial difficulties when faced with unexpected changes in income and expenses. These challenges can arise from several circumstances, including the inability to engage in discretionary activities such as dining out or vacations. For a very long time, it has been posited that low-income households exclusively experience financial vulnerability. Hence, it is imperative to thoroughly examine the concept of household financial vulnerability and its underlying factors to enhance households' ability to withstand adversities and better clarify the matter. In light of the prevailing economic recession triggered by the global pandemic and the ongoing confrontation between Russia and Ukraine, the significance of the matter is further underscored. This study aims to comprehensively define household financial vulnerability and examine its relationship with financial capability, digitalized payments, financial stress, and financial socialization. The current study anticipates establishing a foundational framework for future research endeavors in this specific field. Moreover, this paper also explores potential avenues for future research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Aritificial intelligence in investment and wealth management
Artificial Intelligence (AI) has emerged as a transformative force in various industries, revolutionizing the fields of investment and wealth management. This study explores how AI technologies, including machine learning, natural language processing, and robotic process automation, have enhanced decision-making processes, risk management, and portfolio optimization within financial services. Early developments in AI were limited by rule-based systems, but advancements in deep learning and access to large datasets have enabled sophisticated real-time analysis and personalized financial solutions. However, challenges related to data privacy, algorithmic bias, and ethical considerations persist, necessitating ongoing innovation in AI system transparency and accountability. This research analyzes the impact of AI on investment strategies, compares AI-driven portfolios with traditional approaches, and evaluates AI's role in reducing market volatility and improving return on investment. 2025, IGI Global Scientific Publishing. -
Water purification membranes: state of the art, fundamentals, challenges, and opportunities
The rising global demand for clean and safe water has intensified the necessity for effective and sustainable purification technologies. This chapter thoroughly overviews membrane-based water purification systems, highlighting their basic principles, material types, operational mechanisms, and evolving roles in addressing water scarcity. It begins with the historical progression of membrane technology, discussing the various membrane types alongside essential separation processes including ultrafiltration, nanofiltration, reverse osmosis, and membrane distillation. The text also covers recent innovations in nanocomposite membranes, cutting-edge material design, and membrane module configurations, focusing on enhancing performance and energy efficiency. Special focus is placed on membrane fouling, its causes, effects, and strategies for mitigation, backed by computational modeling and machine learning insights. The chapter begins by exploring emerging trends, such as the development of fit-for-purpose membranes, their integration into zero liquid discharge systems, and scalable fabrication methods. Together, this information highlights the transformative capacity of membrane technology in tackling global water issues. 2026 Elsevier Inc. All rights reserved.. -
Early Warning System for Engine Failure Detection in Aircraft Engines Using Machine Learning
Aviation has a problem with engine defects which are a major concern. Unforeseen causes might render them expensive on the ground and hazardous in the air. We present a system that signals when an aircraft engine is about to fail. Our AdvancedModelTrainer checks a collection of models - Random Forest, XGBoost, Gradient Boosting, LightGBM, Ridge, Lasso, ElasticNet, and a simple neural network - through a dataset of 10,000 engine cycles along with 25 engineered features. Hyperparameter tuning and Remaining Useful Life (RUL) metrics help to select the top two (Gradient Boosting and XGBoost, RMSE 39.99, R2=0.7715). A complete MLOps structure keeps an eye on the drift, initiates the retraining process, and sets up dashboards that are user-friendly for the mechanics. The system has detected on 1,433 new engines, 1,126 were classified as Safe, 106 as Warning, and 201 as Critical, which is indicating the coverage of 93.44The dataset used was completely anonymized in order to safeguard sensitive operational data and to not conflict with the aviation data privacy regulations. 2025 IEEE. -
Ensemble Hybrid LSTM Architectures for Robust Multi-Currency Forex Forecasting
The analysis of financial time series presents a longlasting obstacle regarding currency exchange rate forecasting because volatility and nonlinearity and non-stationarity characterize currency markets. The research presents an ensemble forecasting system which combines various deep learning and hybrid predictive models such as LSTM and GRU-LSTM and CNN-LSTM and Attention-LSTM and XGBoost-LSTM for scalable integration. The ensemble methodology follows a dynamic weighted averaging technique which bases its priority on assigning weights through the reciprocal calculation of Mean Squared Errors from individual models to identify accurate forecasters. A representative study based on the EUR/USD exchange rate took place as part of extensive evaluations that spanned various currency pairs. The standalone XGBoost-LSTM model proved most effective in terms of MSE and R2 values at 0.000088 and 0.9778 respectively. The ensemble model proved to be highly robust and generalizable through its outcomes which produced an MSE of 0.000142 along with MAE of 0.009204 and R2 of 0.9643. The ensemble approach stands as an effective and reliable method to increase both stability and predictive power of forex forecasting systems. The conceptual structure offers sound potential applications for algorithmic trading as well as financial risk management and multi-currency strategic decision-making systems. 2025 IEEE. -
Efficient Pathfinding in a Maze to overcome Challenges in Robotics and AI Using Breadth-First Search
Efficient pathfinding in a maze is a key obstacle in robotics, computer science, and artificial intelligence. The article is proposing a strategy using the Breadth-First Search (BFS) algorithm to establish the shortest path for a robot navigating from the top-left to the bottom-right corner of a maze depicted as a two-dimensional grid. The maze comprises open pathways and obstructions, signified by 0 and 1, respectively. The robot's permissible actions include up, down, left, and right, restricted by the boundaries of the grid and the position of obstacles. BFS, an approach well-suited for unweighted graphs, sequentially examines all available routes, ensuring that the first observed path to the goal is the shortest. A visited set removes redundant cell visits, reducing infinite loops and inefficient processing. The algorithm's efficiency is dramatically upgraded by harnessing a queue structure to maintain live routes and their associated steps. This approach assures effectiveness and extensiveness for grid-based navigation problems, making it especially appropriate for real-world robotic applications where minimizing traversal cost is critical. Additionally, the paper discusses the algorithm's execution, complexities, and potential upgrades for larger grids or dynamic environments. Experimental results demonstrate BFS's resilience and efficacy in solving pathfinding challenges in various maze configurations. This work contributes to developing stable navigation techniques, integral to advancing autonomous robotic navigation and related fields. 2025 IEEE. -
Data linearity using Kernel PCA with Performance Evaluation of Random Forest for training data: A machine learning approach
In this study, Kernel Principal Component Analysis is applied to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Performance Evaluation of Random Forest on various data sets has been compared to understand accuracy and various statistical measures of interest. 2016 IEEE. -
A multilevel analysis of hiv1-miR-H1 miRNA using KPCA, K-means, Random Forest and online target tools
The goal of this study was to propose a workflow using machine learning to identify and predict the miRNA targets of Human Immunodeficiency virus 1. miRNAs which is ~21 nt long are attained from larger hairpin RNA precursors and is maintained in the secondary structure of their precursor relatively than in primary chain of successions. The proposition approach for identification and prediction of miRNA targets in hiv1-miR-H1is based on secondary structure and E-value through machine learning. Data Linearity of Length and e-value for sequence match with hiv1-mir-H1 is verified using Kernel PCA. miRNA targets were grouped into clusters thereby indicating similar targets using K-means algorithm. Classification model using Random Forest was implemented regards to each secondary features variable considering feature relevance. A learning methodology is put forward that assimilate and integrate the score returned by various machine learning algorithms to predict cellular hiv1-miR-H1 targets. Gene targets results using TargetScan, miRanda, PITA, DIANA microT and RNAhybrid are also explored for multiple parameters. 2021 Inderscience Enterprises Ltd. -
From Text to Action: NLP Techniques for Washing Machine Manual Processing
This scientific research study focuses on the advancements in Natural Language Processing (NLP) driven by large-scale parallel corpora and presents a comprehensive methodology for creating a parallel, multilingual corpus using NLP techniques and semantic technologies, with a particular focus on washing machine manuals. The study highlights the significant progress made in NLP through the utilization of large-scale parallel corpora and advanced NLP techniques. The successful creation of a parallel, multilingual corpus for washing machine manuals, coupled with the integration of semantic technologies and ontology modeling, demonstrates the broad applicability and potential of NLP in diverse domains.The research covers various aspects, including text extraction, segmentation, and the development of specialized pipelines for question-answering, translation, and text summarization tailored for washing machine manuals. Translation experiments using fine-tuned models demonstrated the feasibility of providing washing machine manuals in local languages, expanding accessibility and understanding for users worldwide. Additionally, the study explored text summarization using a powerful transformer-based model, which exhibited remarkable proficiency in generating concise and coherent summaries from complex input texts. The implementation of a question-answering pipeline showcased the effectiveness of various language models in handling question-answering tasks with high accuracy and effectiveness.Additionally, the article discusses the processes of data collection, information preparation, ontology creation, alignment strategies, and text analytics. Furthermore, the study addresses the challenges and potential future developments in this field, offering insights into the promising applications of NLP in the context of washing machine manuals. 2024 Elsevier B.V.. All rights reserved. -
Understanding stigma and burnout among HIV/ AIDS health care workers Implications for counselling
The article examines the association between burnout and stigma among Health Care Workers (HCWs) and highlights the need for counselling services in the care of the HCWs. Stereotypes of HIV/AIDS and burnout in HCWs caring for people living with HIV/AIDS (PLHIV) were assessed using self-report methods. Stereotypes about AIDS Scale (SAAS) and Maslach Burnout Inventory MBI were completed by 120 staff from 8 community care centres for PLHIV across south India. Results of SAAS showed that about 33 percent respondents manifested high level of stigma while 35 percent exhibited moderate levels. The results of MBI showed high level of burnout in about 31 percent and moderate in 35 percent respondents. -
Counselling and psychological wellbeing of people living with HIV in Kerala
There is a dearth in the documentation of the benefits of HIV-counseiling in India. This article deals with how HIV-counselling facilitates the psychological wellbeing of Persons Living with HIV (PLHIV) in Kerala, India. About 269 PLHIV participated in the study. Meaning in Life Questionnaire, Illness Perception Questionnaire and Psychological Wellbeing Scale were used. It was noticed that counselling did not impact the scores on subscales such as Timeline, Emotional Representation and Consequences, while the scores on Self-Acceptance and Autonomy did not differ even with counselling. Findings call for a reconsideration of the way HIV-counselling is provided. -
Study on Spray Dried Yttria Stabilized Zirconia Dental Implants
Medical implants are devices, tissues or supports that are positioned in a suitable manner on any defective part of the human body to facilitate its smooth functioning again. Known as 'prosthetics', they may be used to offer support to a specific organ or tissues, distribute medication, or observe the body condition. While many of the implants are made from skin, bone or other tissues removed from the body itself, the artificial ones are made from engineering materials which could be any of the compatible metals, plastics, ceramics or even composites. The high end technologically advanced implant material is expected to withstand severe barriers and compatibility issues when in contact with the human body. One such application is dental implants, where, the materials must possess superior mechanical properties, exhibit good hydro-chemical and low thermal degradation characteristics. They are also required to possess characteristics such as low friction, strong wear resistance, good wettability and biocompatibility, when placed in the mouth. The only materials that come close to meeting the needs are ceramics, limited by the associated high fracture rate. Stabilized zirconia (stabilized with yttria, ceria etc.) has provided potential solution. Among the two stabilizers, ceria stabilized zirconia may be a better alternative to yttria stabilized zirconia. Other alternatives are alumina, apatites: but their use are constrained based upon technological and cost considerations. Implant product is a highly demanding technology. Spray drying is a suitable process methodology to obtain free flowing powders with uniform morphology and chemical composition, essential for an implant production. This paper presents (i) results from spray drying 8% Y2O3-stabilized ZrO2 and (ii) a review of published literature pertaining to dental implant materials, the various processing methodologies, with special reference to stabilized zirconia and spray drying. Published under licence by IOP Publishing Ltd. -
Future Inclusive Education
The United Nations (UN) Sustainable Development Goals (SDGs) ensure inclusive and equitable quality education for promoting lifelong learning. Inclusive education fosters an environment for access to quality education by addressing diversity and barriers that can cause exclusion. COVID-19 has reimagined Higher Education with new challenges and opportunities for the present and future. Digital divide, gender inequality, addressing specially-abled students, and a non-inclusive learning environment are the major barriers to inclusive education. Inclusive education ensures that no one leaves behind, and higher education institutes can enhance their capacity building to promote inclusivity for the common good. Employability is one of the key concepts in higher education that builds the workforce and contributes to nation-building. With COVID-19, nature of work has seen radical changes; hence, graduate attributes have evolved with the 21st-century skills. The chapter emphasizes the role of inclusive education and reimagining higher education with suggestions to using existing strategies in life-long and futuristic inclusive learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2024. -
The Need for Universal Design for Learning in Higher Education for the Specially-AbledAn Essay
Educators at any grade level or subject area can apply Universal Design for Learning (UDL), which is a set of principles for curriculum development that attempts to give all students an equal opportunity to learn. The provision of instructional alignment between objectives, instructional design, methods of delivery and assessment of learning outcomes, which could be individualized and which works for all is blueprinted in a UDL framework. The approaches and methods for instruction in UDL are adaptable and not the same for all the learners or it is not one size fits all approach according to the National Center for Universal Design for Learning (Harper, 2018). The guiding principles of UDL include acceptance and practice of various means of equivalent representation or acquiring information, various means of equivalent expression or demonstrating the learning and various means of equivalent engagement to enhance learning. Given the multiple potentials of specially-abled (SA) students, inclusive learning through UDL provides an environment of diversity and unison. The key attempt is to provide instructional delivery of the same topic to different learners with different learning abilities and approaches in the same course, resulting in comparable outcomes. This chapter highlights the various strategies of UDL that may be extended to assist SA students transition through the pandemic, some of which include customizing learning contents with assured accessibility, individualizing learning goals as per student potential, flexible/customized assessments, and qualitative grading. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2024. -
Interplay of illustration narration and metaphor in kolam art with reference to South Indian traditions
The goal of this research was to understand Kolam, from a three layer perspectives: illustration, narration and metaphor, which is practiced for centuries by women in Tamil Nadu, India. The elements of Kolam are dots and lines made with rice flour at the labyrinth of a house every morning. Although the art appears simple in structure when analyzed layer after layer it unfolds unfathomable encoded meanings of a living culture. Previous studies undertaken overlooked the correlation of illustration, narration and metaphor in Kolam which is argued in this study to be the heart of this art. The study used qualitative multimethod approach throughout the research process. Data was collected from multiple sources of selected rural and urban areas of Tamil Nadu and Karnataka with multiple methods. The description, analysis and interpretations lead to the conclusion that a Kolam narrates deep seated legends and living experiences passed over from generation to generation through an art form. Kolam is not just an art practiced in society, but an art that binds together the life of a society. An insider lives and continues the culture through it; an outsider learns to appreciate and respect the culture by it. -
The path to resilience: Exploring household financial vulnerability
Household financial vulnerability represents a significant financial challenge, predominantly impacting low and middle-income households when faced with sudden changes in income or expenses. At the household level, this vulnerability might arise as short-term liquidity issues or long-term solvency concerns. While household debt is a primary factor contributing to this vulnerability, elements like financial capability and the use of digital payments also play roles. The repercussions of household financial vulnerability encompass financial stress and potential bankruptcy, underscoring the critical need to comprehend its dynamics. Thus, this chapter aims to extensively explore household financial vulnerability, including its determinants, theoretical frameworks, assessment methodologies, and strategies for mitigation. 2024 by IGI Global. All rights reserved.