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Influence of Financial Attitude and Financial Socialization on Investment Behavior of Women
Women generally avoid risk when investing, often preferring traditional options such as bank fixed deposits and gold. This behavior limits their participation in NTI, like stocks and mutual funds, which typically offer higher long-term returns. Purpose: The paper discussed how financial attitude (FA) and parental financial socialization (PFS) affected the non-traditional investment behavior (NTIB) of Indian women. Design/Methodology/Approach: The study used a quantitative research design, with an online survey administered to 403 working Indian women aged 2555 years. Data was collected through convenience sampling and analyzed using SPSS 23. Findings: FA related to interest and deliberative spending significantly influenced womens non-traditional investment behavior. Parental financial behavior (FB) and direct financial teaching also had a positive impact, whereas financial anxiety and parental role modeling did not exhibit a significant influence. Practical Implications: The results emphasized the need for financial institutions and policymakers to implement targeted financial education programs, as well as for parents to provide proactive financial education, to increase the level of non-traditional investment among women. Originality/Value: The research contributed to the family financial socialization theory by providing empirical data on the joint effect of FA and PFS on the NTIB of Indian women. 2026, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Does Customer Co-Creation Influence Customer Loyalty? A Special Reference to Online Video Games
The research investigates the factors influencing consumer loyalty through co-creation in online video games in Delhi National Capital Region (Delhi- NCR). The study focuses on four independent variables, social motivation, personal motivation, utilitarian motivation, and hedonic motivation, to examine customer engagement through value co-creation, which is mediated through attitudinal and behavioral loyalty. The study collected 200 respondent data through an online survey administered to online video game players in the Delhi-NCR region and analyzed the data using the Statistical Package for the Social Sciences (SPSS) software. The study has several implications for online video game companies operating in the Delhi-NCR region to improve their co-creation strategies and enhance customer loyalty through value co-creation. The study backs to the body of knowledge on co-creation, customer engagement, and loyalty in the online video game industry. However, the stud has a limitation which include the sample size as the data has been collected only from Delhi NCR. 2025 by Apple Academic Press, Inc. -
Building Emotions Awareness in the Classroom
Emotional development begins in early childhood, with the school environment and educators playing a crucial role in shaping children's relationship with emotions. Integrating social-emotional learning (SEL) can foster the skills to manage strong emotions and build healthy relationships. Through their daily interactions and structured lessons, teachers can help children identify, regulate, and express feelings appropriately. Children who effectively manage their emotions play a vital role in promoting teachers' well-being and job satisfaction. Emotion regulation doesn't involve blanket strategies that work for everyone. This chapter will explore the variations in emotional development across the Pre-K to 12 age range, highlighting the distinct stages and characteristics unique to each phase. Therefore, the proposed strategies, with a strong focus on parent-teacher collaboration are designed to align with the child's developmental stage, ensuring maximum effectiveness and benefits. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Structural health monitoring using AI and ML based multimodal sensors data
Climatic changes, sudden or gradual, influence the structural health of buildings and bridges due to variations in temperature and humidity. Risk and disaster management plays a vital role in the decision-making process for safeguarding structures. Data analytics from sensors systems in smart structures aid in taking appropriate action in securing buildings during natural calamities. The correlation between climate and structural measuring responses can be further improved using artificial intelligence (AI)- machine learning (ML) algorithms to monitor and predict structural health and take any precautionary steps before the event of a casualty. Linear regression is an efficient tool for analyzing structural health. The proposed work's objective is to monitor and predict the structural health and inform the concerned authorities in the event of a failure in advance, using AI-ML approaches. We have analyzed various sensor data sets to predict the health of a structure based on the crack developed. From the data obtained for experimentation, mean width of the crack is observed as 2.38 cm and mean length of the crack is 63.36 cm. 2023 The Authors -
Key-Based Message Transmission to Avoid Broadcast Storm in VANET
VANET network communicates traffic information to the neighbor vehicles through low cost wireless communication technologies. ITS major task is to share the road information to the vehicles at most on time to minimize the threat of road accidents. The vehicle that receives communication from its neighbor becomes a part of VANET that controls and forward the received information to the neighbor vehicles. In this paper, a design to reduce the broadcasting storm is proposed. The approach named as key-based message broadcast for VANET (KMB-V) to reduce the broadcast storm in VANET. This approach forms a quiet a little amount of nodes (vehicles) to form a cluster with Cluster Head (CH) and creates a novel unique key to transmit before message transmission to avoid broadcast storm. This approach proves a better performance through PDR, network life and throughput parameters in comparison to previous works. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Hybrid Grayscale Image Scrambling Framework Using Block Minimization and Arnold Transform
Image disarranging is the process of randomly rearranging picture elements to make the visibility unreadable and break the link among neighboring elements. Pixel values often don't change while they are being scrambled. There has been a slew of proposed image encryption techniques recently. The two steps that most image encryption algorithms go through are confusion and diffusion. Using a scrambling technique, the pixel positions are permuted during the confusion phase, and an inverse-able function is used to modify the pixel values during the diffusion phase. A good scrambling method practically eliminates the high relationships between adjacent pixels in a picture. In the proposed scheme, XOR based minimization operator is applied on blocks of images followed by Arnold Transform. The suggested design is assessed using a matrix comprising the Structured Similarity Index and the Peak Signal to Noise Ratio. The computed PSNR value less than 10 indicates the input image and scrambled image has high variation. The SSIM value nearer to 0 indicates no similarity in the structure of the input image and scrambled image. 2024 IEEE. -
A Two-Pass Hybrid Mean and Median Framework for Eliminating Impulse Noise From a Grayscale Image
In a digital era, Image recuperation plays a vital role in the area of digital image processing. Image instauration offers more visualization on the quality of the image thereby eliminating noise. Elimination of Gaussian and impulse noise is a challenging problem in the area of image restoration. Rigorous research is pursued to restore salt-and-pepper (SAP) noise utilizing spatial filters. Mean and Median are two contributing spatial filters for eliminating impulse noise. This paper applies a two-pass hybrid mean and median framework on a corrupted grayscale image to replace salt and pepper noise. The hybrid framework is effectively restoring the image by abstracting the low, medium, and high-density impulse noise. The efficacy of the recommended strategy is evaluated by quantifying the peak signal to noise ratio and structural similarity index metric. The result obtained when compared with recent recuperation strategies outperforms to remove noise from grayscale images. 2021 IEEE -
Bioremediation of Heavy Metal Contaminated Sites Using Phytogenic Nanoparticles
Heavy metals (HMs) accumulate in milieu due to various human activities that persist leading to biomagnification in food chains and cause unpleasant effects on human health and environment. Pollutants such as organic matter and HMs are reme-diated traditionally by chemical precipitation, electrochemical treatment, adsorption, reverse osmosis, ion exchange, coagulation, and photo-catalyzation, remained inef-fective. Use of nanomaterials conjugated with various compounds showed significant reduction in several contaminated sites. However, existing implication of nanotech-nology works with nanoparticles (NPs) synthesis majorly involved the use of chem-ical raw materials and physical methods which are relatively toxic and unstable. Aforesaid difficulties made researchers and entrepreneurs to reconnoitre effective, newer, and novel synthesis approaches for the replacement over older version. During the past decade, to overcome these issues plant-derived NPs are extensively used because of its less cost, efficiency, and eco-friendly in nature. Hence, advanced alternative technology like phytoremediation using nanomaterials with innovative techniques has been a boon for HM remediation. Efficiency of green synthesized NPs is based on redox reactions which makes metals stable facilitated by flavonoids and polyphenols responding to HM-stress. Several metal complexation processes are known to produce phytochelatins or other metal-chelating peptides helping the biore-mediation of HMs. Current chapter throws light on adaptive mechanism employed by NPs coupled with plant or microbial extracts in overcoming the HM stress. Further-more, here we also focus on the possible mechanism and interaction between NPs and HM in minimizing severity of polluted sites with many examples. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Corporate Default Prediction Model: Evidence from the Indian Industrial Sector
The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions. 2021 MDI. -
Enhancing Teacher-Student Engagement: The Role of Intellectual Humility
The book chapter explores the significant role of intellectual humility in cultivating strong teacher-student engagement within the landscape of education. It proposes that teachers modelling intellectual humility by admitting their mistakes and uncertainties signal students to take intellectual risks by asking questions or expressing their perspectives. Furthermore, the chapter also highlights that intellectually humble students are more open towards diverse viewpoints, are eager to learn from new information and expand their cognitive capacity, which are pivotal for active participation. Lastly, the chapter suggests various strategies for fostering intellectual humility in both teachers and students as well as for enhancing the advancements in the educational environments. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Examining the Effectiveness of ASHA Workers in Providing Healthcare Services in Rural and Urban Areas of Bengaluru
Purpose: This study aims to evaluate the effectiveness of Accredited Social Health Activists (ASHAs) in providing healthcare services in rural and urban areas of Bengaluru. It explores their role efficacy, role clarity, job satisfaction, and social relations while identifying challenges such as workload, financial insecurity, and training deficits that impact their performance. The study provides insights into systemic improvements needed to enhance the efficiency and satisfaction of ASHAs in public healthcare. Study Design/Methodology/Approach: A mixed-method approach was employed, integrating primary and secondary data. Primary data was collected from 400 respondents (ASHAs and community members), and 286 valid responses were analyzed (122 rural, 164 urban). Structured questionnaires and focus group discussions captured qualitative and quantitative insights. Secondary data from the National Rural Health Mission (NRHM) and government reports provided contextual understanding. Data analysis utilized SPSS 27 for quantitative techniques (ANOVA, t-tests) and NVIVO for qualitative analysis. Cronbachs Alpha assessed reliability, ensuring internal consistency in role efficacy, clarity, stress, satisfaction, and social relations constructs. Findings: ASHAs serve as a crucial link between healthcare systems and communities, with rural ASHAs demonstrating strong interpersonal trust but facing infrastructure deficits. Urban ASHAs confront population density, distrust, and increased workload. Role efficacy remains stable across locations, but urban ASHAs show greater autonomy. Training deficits, workload stress, and financial insecurity significantly impact role satisfaction. Rural ASHAs exhibit greater job role confusion, while urban ASHAs report social constraints. Significant differences in stress arise from knowledge gaps and disrupted work-life balance, affecting mental health and efficiency. Enhanced training, financial incentives, and psychosocial support are critical for sustaining ASHAs' contributions. Originality/Value: This study uniquely contrasts urban and rural ASHA experiences, providing policy insights for optimizing ASHA programs in diverse settings. By identifying key stressors and systemic challenges, it offers targeted recommendations to improve training, compensation, and work conditions, ultimately strengthening Indias public health framework. Research Implications: The findings emphasize the need for structured training in digital healthcare, mental health, and non-communicable diseases. Policy enhancements should focus on increased monetary incentives, timely payments, and career advancement pathways. Addressing the rural-urban divide through community engagement programs and improved infrastructure will optimize ASHA workers impact on public health outcomes. 2025, World Scientific and Engineering Academy and Society. All rights reserved. -
Complete analysis of beam analyzing powers in d + ? ? ? n + p at near threshold energies
Focusing attention on the photon spin in d ( ? ? , n ) p at near threshold energies of interest to Big Bang Nucleosynthesis, a complete analysis of beam analyzing powers in d ( ? ? , n ) p reaction is carried out. A complete analysis of the reaction needs not only measurements using one state of linear polarization of photon but also measurements using another state of linear polarization inclined to the first at ?/4 and the two states of circular polarization of the photon. A discussion on the complete characterization of the states of photon polarization is presented. The beam analyzing powers with respect to photon polarization are discussed theoretically, using model independent irreducible tensor formalism. 2022 IOP Publishing Ltd. -
Neutron Polarization Observables in d(Formula Presented.)p at Low Energies of Interest to Astrophysics
A model-independent theoretical analysis of neutron polarization observables in (Formula Presented.) using circularly polarized photons at the range of energies of interest to Big Bang Nucleosynthesis is presented. An investigation of various spin dependent observables is carried out including the isoscalar multipole amplitudes M1s and E2s. It is suggested that the measurement of neutron polarization in the final state at near threshold energies will be very useful to assess the contribution of isoscalar amplitudes at range of energies of interest to BigBang Nucleosynthesis. 2022, The Author(s). -
Fermented aquatic weed meal (FAWM) as a protein source in Asian Catfish Clarias batrachus diets: Impacts on growth, blood chemistry profile, liver and gut morphology and economic efficiency
The global aquaculture industry is increasingly seeking sustainable alternatives to fishmeal (FM) due to its high price and shortfall in supply. In this context, fermented aquatic weed meal (FAWM) could emerge as a viable plant protein source for aquafeed. Four isoproteic diets [30 % crude protein (CP)] were formulated, incorporating 50 % total protein from FAWM comprising fermented Azolla diet (D1), Pistia diet (D2), and Eichhornia diet (D3). The control diet (D0) did not contain FAWM. At the end of the 90 days feeding trial, their growth performance, whole-body proximate composition, gut microbial load, haemato-biochemical indices, liver and gut health, and economic efficiency were determined. Fish fed with D0 had significantly (p < 0.05) improved growth performance and feed utilization compared to other treatment groups. Meanwhile, the fish supplemented with D1 diet exhibited significantly (p < 0.05) higher final weight (g), specific growth rate (%/day), weight gain (%), total biomass (g), and protein efficiency ratio among the FAWM dietary groups. The D1 group also demonstrated the significantly (p < 0.05) highest whole-body CP (64.27 0.40 %) and lower crude lipid (8.24 0.28 %) compared to other test diets. The total bacteria (TB) and lactic acid bacteria (LAB) in the fish gut were found to be significantly (p < 0.05) higher in D1 group. Furthermore, most of the hemato-biochemical indices of fish were significantly (p < 0.05) affected by FAWM inclusions, with few exceptions. The histological findings indicated that amongst the FAWM groups, D1 fish exhibited improved intestinal health. Nonetheless, the gut of the control fish demonstrated substantially (p < 0.05) lower villi width and crypt depth than other treatments. The D1 and D2 diet groups had significantly improved liver health. Moreover, FAWM inclusion enhanced economic efficiency by considerably reducing farm feed cost (US$/kg) and increasing return on investment (%). In summary, dietary inclusion of fermented Azolla pinnata (D1) protein at 50 % in aquafeed promoted feed utilization, growth, health, and farm economics of Asian catfish fingerlings compared to other FAWM diets. 2024 The Authors -
Advancements in Astronaut Health Monitoring Technologies
Astronautics also outlines unique biological and cognitive obstacles that demand advancements in health monitoring and techniques for risk prevention for space travellers. This study investigates the consequences of microgravity, space radiation and persistent confinement on astronaut well-being, focusing on cardiovascular, musculoskeletal, neurological and immune system vulnerability. Cardiovascular ailment, a major concern, is monitored using clinical prediction models (CPMs) that combine traditional risk factors, biomarkers and machine learning techniques. Additionally, AI-powered methods consisting of GPT-based models and time series transformers are required for real-time health monitoring and analytical assessment. Test-based outcomes illustrate that models such as logistic regression, random forest and support vector machines attain high designation accuracy in defining astronauts health hazards from non-astronaut data. Furthermore, wearable medical trackers and space-sourced clinical techniques are detected as an alternative solution for both space missions and terrestrial well-being. The study also highlights the need for perpetual advancements in zero gravity to protect astronauts well-being and enhance medicinal solutions for upcoming space travels. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
RP-HPLC METHOD FOR QUANTITATIVE ESTIMATION OF NAFTIFINE HYDROCHLORIDE IN FORMULATED PRODUCTS: DEVELOPMENT AND VALIDATION
Background: Naftifine hydrochloride is an allylamine antifungal agent commonly used to treat dermatophyte infections. It inhibits squalene epoxidase, a key enzyme in ergosterol biosynthesis, thereby disrupting the integrity of the fungal cell membrane. It exhibits broad-spectrum activity against dermatophytes, yeasts, and molds, and is typically formulated as a 1% topical cream or gel. Methodology: A rapid and robust reverse-phase high-performance liquid chromatography (RP-HPLC) method was developed and validated for the estimation of naftifine hydrochloride in a topical cream formulation (2% Naftifast, Zydus), in accordance with ICH and FDA guidelines. Chromatographic separation was achieved on an Inertsil ODS column using an isocratic mobile phase consisting of 35% acetonitrile, 40% methanol, 25% water, and 0.8% triethylamine (pH adjusted to 5.5 with acetic acid) at a flow rate of 1.4 mL/min. Detection was performed at 265 nm. Results and Discussion: Naftifine hydrochloride showed a retention time of approximately 4.0 minutes with a total run time of 6.0 minutes. The method displayed excellent linearity over a concentration range of 20120 g/mL (R > 0.999). Recovery studies indicated a mean recovery of 100.4%. Precision was confirmed by relative standard deviation (RSD) values of less than 2%, demonstrating the methods reproducibility. Conclusion: The proposed RP-HPLC method is simple, precise, and time-efficient. It is suitable for routine quality control of naftifine hydrochloride in pharmaceutical dosage forms due to its short analysis time and strong validation performance. 2025 The authors. -
The Role of Artificial Intelligence in Simulating, Automating, and Analyzing Business Operations
Business processes have been transformed with the advent of artificial intelligence. However, to efficiently utilize the technology and to close the gap, we reviewed the literature to find these solutions in this work. We ensured that styles worked because they allowed for extensions and replication. In these studies, we correlated patterns that assisted with task automation and helped analysts create, expand, or re-engineer business processes with the confidence to make judgments. The authors used various AI methods, including swarm intelligence, Bayesian networks, and K-means. Our analysis gives data on the approaches and issues being dealt with and indicates potential future directions. Processes for predictive business future planning and activity prediction are examples of monitoring jobs that are becoming less significant as new technologies allow for the intelligent automation of company processes. Deep learning models are used in recent work on this subject to encapsulate historical event information without further processing. The data context, which includes the dependence of conditions and particular traits, might also have an impact on the anticipated data, even though it was not taken into account in earlier research. We present a novel encoding strategy for state data, encompassing non-existent, multi-character private, and regular event states. We present the transformer and LSTM deep learning models, two new deep learning models, and two popular deep learning models. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
1-Edge contraction: Total vertex stress and confluence number
This paper introduces certain relations between 1-edge contraction and the total vertex stress and the confluence number of a graph. A main result states that if a graph G with ?(G) = k ? 2 has an edge vivj and a ?-set CG such that vi, vj ? CG then, ?(G/vivj) = k ? 1. In general, either S(G/ei) ? S(G/ej) or S(G/ej) ? S(G/ei) is true. This observation leads to an investigation into the question: for which edge(s) ei will S(G/ei) = max{S(G/ej): ej ? E(G)} and for which edge(s) will S(G/ej) = min{S(G/e`): e` ? E(G)}? 2024 Azarbaijan Shahid Madani University. -
Total induced vertex stress in barbell-like graphs
This paper introduces new parameters called induced vertex stress and total induced vertex stress in G, respectively. For graphs G and H, aspects of the maximum and minimum total induced vertex stress that can be obtained by 1-edge addition and 2-vertex merging are discussed. 2021 Journal of the Indonesian Mathematical Society. All rights reserved. -
A Cooperative Global Sequencing Algorithm for Distributed Wireless Sensor Networks
Data gathering is a very fundamental use for wireless sensor networks. The area to be monitored has sensor units distributed. They can tell how much demand there is. Temperature, pressure, humidity, sun rays, and other factors could be involved. The detected data is sent to a centralized device called a sink or just a base station. Networks are frequently distributed in character, meaning that more than one kind of instrument is placed in a particular area. There is only one kind of component in uniform networks. A tree is created and anchored at the sink after the nodes have been distributed. In distributed networks, flawless aggregation is challenging to accomplish. In contrast to uniform networks, nodes may receive and transmit multiple types of packets. Every message should be forwarded by the node to a parent so that it can be combined in order to increase the likelihood of aggregation. As a result, a node might need to choose more than one progenitor. This implies that various parameters should be taken into account while forming trees. We have improved the literature's suggested combined distributed scheduling and tree generation for distributed networks. We discover that the expanded method maximizes aggregation, schedules the network with fewer time slots, and uses less energy. Additionally, it is discovered that distributed networks require more management costs to schedule than uniform networks do. 2023 IEEE.