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Exploring character strength in the functioning and well beings of religious leaders
Positive psychology is the scientific study of optimal functioning, flourishing and well-being of individuals and organizations. The backbone of positive psychology, the character strengths are significant in effective leadership functioning. The current study explored the character strengths development and character strengths utilization in the functioning and well-being of religious leaders (consecrated nuns and priests). There were 17 participants, nine female and eight male consecrated Catholic religious leaders. The study used the mixed design. The Values in Action Tests was administrated to identify leaders top strengths and a phenomenological approach was used to explore character strengths development as well as the usage of character strengths in the functioning of the religious leaders. The findings illustrated that the most prevalent character strengths of leaders are honesty, gratitude, teamwork, fairness, and kindness. The least prevalent strengths are love of learning, humour, appreciation of excellence, zest, judgement and creativity. Results showed that the influencing factors of character strengths development are family influences, experiences at school, formative programmes in the religious formation, critical events and factors enhancing strength. The strength of wisdom and knowledge were used mainly at organizational and administrative level of leadership functioning. Strengths of courage manifested at the implementation level. The strength of humanity is identified as the most striking character strength in leader-member exchange. The strength of temperance has the role of controller in leadership functioning. The strength of justice is seen as a catalyst in promoting cohesion in the community. The leaders pivotal manifestation of the strengths of transcendence is in their intimacy with God that gives higher purpose and meaning in leadership, that is, do the Will of God. Character strengths were found in promoting wellness through achievements, facilitating total engagement, giving a great purpose in leader life and in promoting better leader-follower interactions. The highlighted character strengths that promote well-being were gratitude and appreciation. The study has brought out an ongoing leadership training programme for religious leaders that can be completed in three phases. -
Exploring challenges in online higher education for AI integration using MICMAC analysis
The consequence of Covid-19 has affected the traditional higher education system. Acknowledging the significant role of online education in national development for accessibility and quality education, countries around the world have understood its importance in current digital era. Indian policymakers have been giving due importance to enhancing the education quality, however the progress made by the country in higher education is not adequate. Amidst all the inadequacies of traditional education system, artificial intelligence (AI) technologies are bringing new ray of hope to democratize education system. This chapter is subjected to identify the challenges in online education and suggest specific ways to address each of them. The challenges are categorized into internal and external challenges/barriers. These challenges have been modeled with the expertise of educationalist's opinions and interpretive structural modeling to create a hierarchy of the barriers using MICMAC analysis and categorize these barriers into four clusters. 2024, IGI Global. All rights reserved. -
Exploring Caregiving Experiences and Needs of Mothers of Children with Cerebral Palsy
Parents play a major role while caring for a child with Cerebral Palsy (CP). Each child with CP and their caregivers needs constant professional support in terms of medical care, psycho-education, guidance and support in order to achieve maximum functioning. Mothers of children with cerebral palsy are vulnerable because the caregiving may affect their personal and marital life, work, finances, relationships, and other responsibilities. Therefore, it is important to understand their experiences this study explores caregiving experiences and needs of mothers of a child with cerebral palsy. This qualitative exploratory study used semi-structured open-ended interviews for data collection from 25 mothers of children with Cerebral Palsy who attended regular clinical assessments at Unit of Hope OP clinic. The data were analyzed in Atlas. ti 8 trial version using thematic analysis. Six major themes emerged from the thematic analysis which includes: pathways of care, challenges in taking care of the child, impact (subjective and objective) on mothers and their family, coping mechanism and psycho-social needs. Mothers expressed that they experience unpreparedness; unsupportive interaction; insecurity/uncomfortable on caregiving by others; challenges in decision making, finding the right care, meeting individual family member???s needs; they had inappropriate expectations of improvement, difficulties in treatment adherence and lack of knowledge, lack of respite, lack of support from family members/relatives, changes in the family system, changes in personal life. Mothers had caregiver burden and emotional challenges. The mothers adopted both maladaptive and adaptive coping strategies. In this study, mothers expressed various needs like the need for professional support, the need for respite care, and the need for family support. In conclusion, having a child with cerebral palsy negatively affect the mothers. During the caregiving process, they have some unmet needs which need to be addressed. The findings of the study emphasize that it is important to understand the caregiving experiences and needs of mothers of children with cerebral palsy to plan interventions to support these mothers in caring for their child. -
Exploring boronate-appended hyperbranched amino-functionalized dendrimer-empowered sensors for the potential recognition of FSH in age-categorized human plasma samples
Boronic acids can act as ideal saccharide receptors as they possess a high affinity for diols and readily form cyclic-boronate esters when reacting in an aqueous medium. Here, we present hydrophilic amino-functionalized boronic acid dendrimer (Af-BAD) for the first time, with significantly enhanced sensitivity towards Follicle Stimulating Hormone (FSH) detection. In this study, newly synthesized Af-BAD was dip-coated on a gold substrate to create an impedance-type sensing working electrode. The effects of Af-BAD coating on the gold chip, the sensing properties for FSH recognition, sensitivity, and stability were measured by the charge transfer resistance across the electrochemical setup. The impedimetric measurements were conducted in the presence of [Fe(CN)6]3-/[Fe(CN)6]4- redox reporter at pH 7.4. The increments in the charge-transfer resistance were monitored upon increasing the FSH concentrations from 25 fg/mL to 100 pg/mL. The device achieved good sensitivity with a calculated detection limit of 4.01 fg/mL and acceptable linearity. The observed behavior was linear concerning the tested concentrations. An attempt at a real application to serum samples was also successfully conducted. Meanwhile, the level of tolerance of boronic acid dendrimer with other competing glycoproteins and monosaccharides was also tested. In this study, we also compared human plasma FSH levels in female oral cancer patients and normal controls using the Af-BAD modified device and the clinically used ELISA method. With a sound understanding of boronate materials and their affinity, amino functionalized multi-boronic acid dendrimer was developed as a highly selective conjugate toward glycoprotein FSH detection. Copyright 2025. Published by Elsevier Ltd. -
Exploring Bio Signals for Smart Systems: An Investigation into the Acquisition and Processing Techniques
Bio signals play a vital role in terms of communication in the absence of normal communication. Bio signals were automatically evolved from the body whenever any actions took place. There are lots of different types of bio signal based research going on currently from several researchers. Signal acquisition, processing the signals and segmenting the signal were totally different from one technique to another. Placing electrodes and its standard measurements were varied. The signals gathered from each subject may be varied due to their involvement. Each and every trial of signals can generate different patterns. Each and every pattern generated from the activities also has a different meaning. In this study we planned to analyze the basic measurement techniques handled to record the bio signals like Electrooculogram. 2023 IEEE. -
Exploring best practices in mobile app design patterns and tools: A user-centered approach
Design patterns are reusable solutions to common design problems that provide a consistent user experience across different apps. This article explores the best practices in mobile app design patterns and tools with a focus on the user-centered approach to design. Design patterns such as navigation bars, tab bars, list views, and card views are discussed, along with design tools such as Sketch, Figma, Adobe XD, and InVision. The problem is to ensure that mobile app design is centered around the needs and preferences of the user, rather than the designer or the technology, and that the right design patterns and tools are used to create interfaces that are familiar and easy to use. The chapter emphasizes the importance of conducting user research to understand the needs and preferences of the target audience and using design patterns and tools to create interfaces that are familiar and easy to use. Mobile apps have become an integral part of our lives, and designing a successful mobile app is a challenging task that requires a thorough understanding of user needs and preferences. 2023, IGI Global. All rights reserved. -
Exploring BERT and Bi-LSTM for Toxic Comment Classification: A Comparative Analysis
This study analyzes on the classification of toxic comments in online conversations using advanced natural language processing (NLP) techniques. Leveraging advanced natural language processing (NLP) techniques and classification models, including BERT and Bi-LSTM models to classify comments into 6 types of toxicity: toxic, obscene, threat, insult, severe toxic and identity hate. The study achieves competitive performance. Specifically, fine-tuning BERT using TensorFlow and Hugging Face Transformers resulted in an AUC ROC rate of 98.23%, while LSTM yielded a binary accuracy of 96.07%. The results demonstrate the effectiveness of using transformer-based models like BERT for toxicity classification in text data. The study discusses the methodology, model architectures, and evaluation metrics, highlighting the effectiveness of each approach in identifying and classifying toxic language. Additionally, the paper discusses the implementation of a userfriendly interface for real-time toxic comment detection, leveraging the trained models for efficient moderation of online content. 2024 IEEE. -
Exploring artificial intelligence techniques for diabetic retinopathy detection: A case study
There is a notable increase in the prevalence of Diabetic Retinopathy (DR) globally. This increase is caused due to type2 diabetes, diabetes mellitus (DM). Among people, diabetes leads to vision loss or Diabetic Retinopathy. Early detection is very much necessary for timely intervention and appropriate treatment on vision loss among diabetic patients. This chapter explores how Artificial Intelligence (AI) methods are helpful in automated detection of diabetic retinopathy. In this chapter deep learning algorithm is proposed that is used to extract important features from retinal images and classify the images to identify the presence of DR. The model is evaluated using various metrics like specificity, sensitivity etc. The results of the case study provide an AI driven solution to existing methods used to identify DR and this can improve the early detection and appropriate treatment at the right time. 2024, IGI Global. All rights reserved. -
Exploring ARIMA Models with Interacted Lagged Variables for Forecasting
Including interactions among the explanatory variables in regression models is a common phenomenon. However, including interactions existing among lagged variables in autoregressive models has not been explored so far. In this paper, Autoregressive Integrated Moving Average (ARIMA) model with interactions among the lagged variables is proposed for improving forecast accuracy. The methodology for identifying the interacted lagged variables and including them in the ARIMA model is suggested. Using five different data sets of different types, the paper explores the effect of interacted lagged variables in ARIMA model. The experimental results exhibit that when interactions do actually exist, ARIMA model with interactions improves the forecast accuracy as compared to ARIMA model without interactions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Exploring Applications, Datasets, Algorithms, and Technologies in Satellite Image Processing
Amidst an era filled with complex local and global problems, satellite data presents itself as a revolutionary tool with unmatched potential to tackle practical problems in a variety of fields. This article investigates how satellite imagery, which is available through open data programs and repositories, is a valuable tool for applications including wildlife conservation, urban planning, precision agriculture, and disaster management. It highlights the unique perspective that satellite data offers. Various sources for data acquisition, the applications that are suitable for a chosen satellite data and commonly used algorithms and techniques are discussed. Through case studies, the paper demonstrates how quick and reliable data provided by satellites can be used to solve complex real-world problems. The benefits of satellite data are emphasized, including its affordability, ability to monitor in real-time, and ability to support sustainable behaviours and policy-making. The study explores cutting-edge technologies, highlighting cloud computing and GIS integration as well as machine learning algorithms to build robust solutions using satellite data. The immense potential of satellite data is accompanied by challenges, including data integration, computational complexity, and ethical considerations. These challenges underscore the need for standardization and continuous efforts to fully realize the potential of satellite data in sustainable development and informed decision-making. 2025 Bijeesh TV, Bejoy BJ, Michael Moses Thiruthuvanthan and Raju G. -
Exploring AI-Driven Economic Decision Making and Role in Promoting Green Investment
Artificial Intelligence has assumed a disruptive role in the sphere of economic decision-making, specifically in the field of capital allocation towards green investments that would meet global sustainability requirements. Using machine-learning algorithms, neural networks, and big-data analytics, AI can offer greater accuracy in predicting economic patterns and risk assessment of the environment, and using AI can diversify portfolios with low-carbon assets, commercializing the old dichotomy between the financial value of profit and the eco-friendliness. This study discusses the transformations that AI-based tools are ready to make to the traditional economic paradigms, including the predictive analytics in terms of renewable-energy valuation, natural-language processing that would analyze sustainability reporting, or both in combination, a means of creating a paradigm shift where green investments would no longer be considered an act of charity, but rather a data-driven necessity of constructing long-term values. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Exploring AI-Driven Accessibility Solutions: A Comprehensive Study on Assistive Tools for the Visually Impaired
Artificial intelligence (AI) has significant access to visually impaired persons, which enables more freedom in digital interactions. This article examines the role of AI-operated equipment, including chatbots, speech recognition, and natural language processing (NLP) models, to improve communication, education, and navigation for blind users. It undergoes the largest progress of AI-driven accessibility solutions, especially in examination and interactive virtual scriptures. Despite the remarkable progress, challenges in speech remain accreditation accuracy, user interface targeted, and real -time treatment efficiency. The study highlights the ongoing research trends, identifies significant intervals, and emphasizes the need for better training data, adaptive AI interfaces, and improved user experience to promote more inclusion in education and the professional environment. 2026 IEEE. -
Exploring AI and ML Strategies for Crop Health Monitoring and Management
This chapter offers a thorough examination of machine learning (ML) and artificial intelligence (AI) approaches designed especially for agricultural crop health monitoring. The story starts with a basic introduction to AI and ML ideas and then covers supervised and unsupervised learning approaches, the fundamentals of reinforcement learning, and the significance of high-quality data preparation in agricultural settings. This chapter explores the use of deep learning architectures and neural networks, explaining how they can be used to simulate human brain activity and how they can be used in picture identification to identify crop diseases. A detailed analysis is conducted of the practical aspects of ML for agriculture, encompassing feature engineering and model assessment methodologies. Additionally, the chapter highlights the ethical issues involved in the proper application of AI/ML models in agricultural contexts. These kinds of applications. In conclusion, the chapter discusses obstacles, offers predictions for future developments, and discusses new lines of inquiry for AI and ML research related to crop health monitoring. Through this thorough research, the chapter seeks to offer insightful information on the transformative potential of AI/ML approaches in supporting efficient and sustainable agriculture practices for improved crop health management. (Publisher name) (publishing year) all right reserved. -
Exploring Advances in Machine Learning and Deep Learning for Anticipating Air Quality Index and Forecasting Ambient Air Pollutants: A Comprehensive Review with Trend Analysis
India and the rest of the world are growing more and more worried about polluted atmosphere on a daily basis. A comprehensive prevision and prognostication of air quality parameters is vital due to the major harm that air pollution causes to both the environment and public health, causing concern on a global scale. In-depth analyses of the methods for predicting ambient air pollutants, like carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter with diameters less than 10? (PM10) and less than 2.5? (PM2.5), and ozone (O3), are provided in this work in tandem with the modeling of the Air Quality Index (AQI).To further enhance the anticipated precision and applicability of these models, the assessment additionally employs trend analysis to determine precedents and new trends in air quality. This paper offers insights into recent advances in algorithms using deep learning and machine learning for anticipating AQI and forecasting pollutant concentrations by combining current research in this topic. In order to inform policy decisions and measures aimed at reducing air pollution and its adverse effects on public health, trend analysis integration affords a more thorough comprehension of the dynamics of air quality. 2024 IEEE. -
Exploring advancements in space object detection through computer vision
[No abstract available] -
Exploring a GE/Nafion/Co-MOF nanosheets/CuO NPs/GOx powered electrochemical biosensor for ultrasensitive detection of rebaudioside A
Rebaudioside A (Reb A) is a natural, non-nutritive sweetener highly prevalent in the global sweetener market and widely preferred by consumers. In this study, an advanced electrochemical biosensor was developed for sensing Reb A, using a modified graphite rod electrode extracted from discharged ZnC batteries. The electrode was fabricated using a layer-by-layer strategy with Nafion, Co-MOF nanosheets, CuO NPs, and glucose oxidase (GOx) enzyme. The nanomaterials were characterized by UV-vis, FTIR, DLS, zeta potential measurements, XRD, Raman, SEM, TEM, EDS, and XPS techniques. Electrochemical characterization via Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) revealed a significant enhancement in electrical conductivity and increased electroactive surface area. The designed biosensor exhibited a sharp oxidation peak at 0.16 V due to ester bond cleavage in Reb A, which was further amplified in the presence of GOx, resulting from hydroxyl oxidation and hydrogen peroxide generation. Differential pulse voltammetry (DPV) demonstrated a linear response over a concentration range of 2.014 M (R2 = 0.993) with a limit of detection (LOD) of 0.23 M. The sensor displayed excellent analytical performance, with repeatability, reproducibility (RSD = 3.9%), and stability. Additionally, recovery studies confirmed its accuracy, ranging from 97% to 98.17%. Further, the molecular docking studies confirmed strong Reb AGOx interactions (?7.26 kcal mol?1), supporting the biosensor's specificity. The developed biosensor demonstrates excellent analytical performance, making it highly suitable for routine laboratory analysis of sweeteners in complex food matrices. This journal is The Royal Society of Chemistry, 2026 -
Exploratory Architectures Analysis of Various Pre-trained Image Classification Models for Deep Learning
The image classification is one of the significant applications in the area of Deep Learning (DL) with respective to various sectors. Different types of neural network architectures are available to perform the image classification and each of which produces the different accuracy. The dataset and the features used are influence the outcome of the model. The research community is working towards the generalized model at least to the domain specific. On this gesture the contemporary survey of various Deep Learning models is identified using knowledge information management methods to move further to provide optimal architecture and also to generalized Deep Learning model to classify images narrow down to the sector specific. The study systematically presents the different types of architecture, its variants, layers and parameters used for each version of Deep Learning model. Domain specific applications and limitations of the type of architecture are detailed. It helps the researchers to select appropriate Deep Learning architecture for specific sector. 2024 by the authors. -
Exploratory analysis of legal case citation data using node embedding
Legal case citation network is primary tool to understand mutable landscape of the legal domain. These networks are also used to study legal knowledge transfer, similar precedents and inter-relationship among laws of a judiciary. These networks are often very huge and complex due to the multidimensional texture of this domain. In recent years, network embedding using deep learning emerges as a promising breakthrough for analyzing networks. This paper presents a novel approach of learning vector representation for a legal case based on its citation context in the network using node2vec algorithm. These vector embedding are further used in understanding similarities between cases. Paper highlights that the tSNE reduced representation of the obtained vectors facilitates visual exploration and provides insights into the complex citation network. Suitability of node embedding for application of machine learning algorithm is demonstrated by clustering the node vectors for finding similar cases. ICIC International 2019. -
Exploratory Analysis of Anthropometric and Demographic Factors Influencing PCOS: A Study on BMI, Weight, and Waist Ratio
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age, often leading to hormonal imbalances, obesity, and an increased risk of metabolic and cardiovascular diseases. This study examines the impact of PCOS on anthropometric and demographic variables such as Body Mass Index (BMI), weight, height, and waist ratio, using a comprehensive dataset. By comparing these factors between individuals with and without PCOS, the study aims to identify significant differences and correlations, thereby contributing to a deeper understanding of PCOS and informing clinical management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Exploratory Analysis and Pattern Recognition in Energy Production and Demand: A Data-Driven Approach Using Multi-Source Energy Metrics
Hydropower is a leading renewable energy source due to its high efficiency and low operational costs. However, it still faces significant environmental, operational, and forecasting challenges. This paper explores the use of machine learning (ML) models such as SARIMA, Random Forest (RF), and Neural Basis Expansion Analysis for Time Series (NBEATS) to optimize hydropower operations. By analyzing diverse data sets, including hydrometeorological data, plant operations, sensor inputs, and other energy production and demand metrics such as solar, wind, coal, nuclear, and storage, ML enhances decision-making in areas such as inflow forecasting, predictive maintenance, and environmental sustainability. The paper presents an exploratory analysis of 48 -hour energy production and demand patterns across multiple sources (Hydro, Coal, Solar, Wind, Nuclear, and Storage), offering insights into interdependencies and system behavior. It also reviews current ML applications in hydropower, highlights challenges such as data quality and model interpretability, and discusses emerging technologies such as reinforcement learning, explainable AI (XAI), and digital twins as promising future directions. 2025 IEEE.