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A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan
Japan is a country that suffers a lot of earthquakes and disasters because it lies across four major tectonic plates. Subduction zones at the Japanese island curves are geologically complex and create various earthquakes from various sources. Earthquake prediction helps in evacuating areas, which are suspected and could save the lives of people. Artificial neural network is a computing model inspired by biological neurons, which learn from examples and can be able to do predictions. In this paper, we present an artificial neural network with PSO-BP model for the prediction of an earthquake in Japan. In PSO-BP model, particle swarm optimization method is used to optimize the input parameters of backpropagation neural network. Information regarding all major, minor and aftershock earthquake is taken into account for the input of backpropagation neural network. These parameters are taken from Japan seismic catalogue provided by USGS (United States Geological Survey) such as latitude, longitude, magnitude, depth, etc., of earthquake. 2019, Springer Nature Singapore Pte Ltd. -
A Pathway to Better EMI Shielding Performance in Natural Rubber Through Ternary Carbonaceous Filler Systems
In the present study, we fabricated and characterized ternary hybrid fillers of conductive carbon black (CCB), carbon nanotubes (CNT), and reduced graphene oxide (RGO) reinforced natural rubber (NR) composites. The ternary filler system exhibited good filler-polymer interaction as observed from the cure characteristics and mechanical properties. We used impedance analysis to study the dielectric permittivity and associated polarization mechanisms, and the AC conductivity was fitted using the Jonsher Power law. The presence of functional groups on the ternary nanofiller surfaces caused increased filler-filler interactions, leading to the formation of an excellent conductive network. Mechanical and viscoelastic studies revealed the reinforcing effect of the CCB, CNT, and RGO fillers. The theoretical models, such as Nicolais-Narkis and Turcsanyi, were employed to predict the tensile strength. Morphological analysis confirms the homogeneous dispersion of filler in the matrix. The present system also demonstrated excellent electromagnetic interference (EMI) shielding performance, with the highest shielding effectiveness (SE) values of 37.4 and 35.3 dB at 12 GHz for the ternary composites, satisfying commercial requirements. 2026 John Wiley & Sons Ltd. -
A Penalized Maximum Likelihood Estimation for the Log-Logistic Distribution with Complete Data
Penalized maximum likelihood estimation is specified for estimating parameters of a log-logistic distribution for complete-data situations. This approach addresses the issues of Maximum Likelihood Estimation, wherein Maximum Likelihood Estimation is often unstable when sample sizes are small, and fails with heavy-tailed or asymmetric data. By adding a ridge penalty to the log-likelihood, we derive new score equations, which are solved numerically. The performance is measured for a variety of shape and scale parameters and sample sizes, with bias and Mean Squared Error as the two main measures. The simulation experiment results indicate Penalized maximum likelihood estimation consistently achieves lower bias and Mean Square Error with small sample sizes and particularly strong improvements under skewed or heavy-tailed data. With larger sample size, the differences between Maximum Likelihood Estimation and Penalized maximum likelihood estimation decrease, as we would expect. These results suggest that Penalized maximum likelihood estimation is a viable estimation method using the log-logistic distribution, especially with small or limited datasets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Performance Investigations of Modular Multilevel Inverter with Reduced Switch Count
A multilevel inverter is a special variant of converter for dc-Ac conversion in medium and high voltage and power requirements. In this paper, a novel configuration with fewer switches needed has been developed for the staircase output voltage levels. Two direct current voltage sources and eight transistors are required to synthesize five levels across the load using the conventional topology. The modular topology has two dc voltage sources, and six switches with a five-level output. Using the optimum multi-carrier pulse width modulation approach, the voltage quality is enhanced and total harmonic distortion is reduced. Furthermore, the viability of the proposed topology in contrast to the conventional cascaded H-bridged multilevel inverter with five levels is established by presenting comparable results showing reduced power losses with varied modulation indexes and increased efficiency. The simulation analysis has been carried out using the MATLAB/SIMULINK tool. 2022 IEEE. -
A perspective analysis of emotional appeal used in television advertising /
The purpose of the study is to find out whether emotional appeal is still prevailing in television advertising. The researcher focuses on the various elements used by advertisers to evoke emotional response on the audience‟s side. The advertisements decided by the researcher portray important relationships that are valued and maintained in the society. -
A perspective analysis on doodle art used in five educational materials /
Doodle Art are simple drawings that have concrete representational meaning in abstract shapes. A doodler intently shifts through information to generate substantial understandings. Doodle Art is one of the evolving styles that are attracting young audience especially through subject materials. Themain aim ofthe study is to understand whether doodling has emerged as a new trend in brand recall. -
A Perspective on Challenges and Opportunities of Supply Chain Management
Global Journal of Arts and Management Vol. 2, No. 3, pp. 227 - 231, ISSN No. 2249-2658 -
A perspective reading of photographs of the Ahmedabad city /
The present study talks about the usage of photographs as a major tool for storytelling. It shows how various photographs of various places, if shown to people and analyzed according to their perspectives and experiences can give us a rich definition and an idea about the events that had shaped the place and led to their current being. Also, it describes the place according to the way or the angle the respondents saw it to be. -
A pharmacognostic approach, including phytochemical and GC-MS analysis, targeted towards the authentication of Strobilanthes jomyi P. Biju, Josekutty, Rekha & J.R.I.Wood
The genera Strobilanthes Blume have a rich history in therapeutic culture all over the world. Asian countries like India, China, Myanmar and Thailand still use Strobilanthes genus-based medicinal preparations for various diseases. Strobilanthes jomyi is a newly discovered species from Kerala, India. Some tribal communities of Kasaragod district still use S. jomyi leaf extract as a wound healing medication. The current study aims to investigate the pharmacognostic, phytochemical and GC-MS analysis of the leaves, stems and roots of S. jomyi. The microscopic, macroscopic, organoleptic, fluorescent, phytochemicals and GC-MS analysis of the leaves, stem, and root of S. jomyi were estimated using various standard protocols. The macroscopic and microscopic characters of leaves revealed the presence of non-glandular trichomes with paracytic stomata in the leaves. The transverse section of the stem and petiole showed the presence of raphides and the root showed the presence of tannin cells. Cystoliths were observed only in the petiole. Powder morphology of leaves, stems and roots revealed the presence of fibers, trichomes, palisade cells, spiral xylem vessels, bordered pit vessels and raphides. The vegetative part of S. jomyi powder exhibited various fluorescent coloration based on numerous chemical treatments along with different tastes, smells, colors and textures by organoleptic assays. Qualitative phytochemical analysis of different vegetative parts revealed the presence of flavonoids and other phytochemicals. GC-MS study revealed that lupeol a significant bioactive compound was present in all the vegetative parts of S. jomyi. The results acquired from this study can be used for the standardization, identification, quality and purity check of plant samples. The Author(s). -
A Phased approach to solve the University Course Scheduling System
International Journal of Computational Engineering Research Vol.3, Issue 4, pp. 258-261, ISSN No. 2250-3005 -
A phenomenological exploration of Indian women's body image within intersecting identities in a globalizing nation
The goal of the study was to examine Indian women's body image experiences utilizing an intersectional framework. Using phenomenological method, the study attempted to explore how experiences of gender oppression intersect with salient social identities to produce experiences of body dissatisfaction in Indian women. Thirty-Five Indian women in the age group 1840 years participated in semi-structured interviews. Overall, women experienced and discussed their bodies in terms of physical features they liked and disliked. Three themes emerged that comprised body image experiences of Indian women- (a) Beautiful, thin and fair- three social imperatives for women, (b) Internalization and (c) Body image management. Each of these impacted women negatively and contributed to greater body monitoring, increased indulgence in unhealthy behaviours and heightened body dissatisfaction. Women also discussed coping techniques for managing such experiences. Researchers and practitioners are encouraged to take into account culturally constructed beauty norms and unique socio-cultural factors for Indian women that determine body image. Findings are interpreted in the context of evolving socio-cultural norms that have recolonised Indian women's embodiment in a globalizing nation. 2023 Elsevier Ltd -
A Phenomenological Investigation of Mens Experiences of Depression and Gender Role Socialization in Early Family Relationships in urban South India
With the overwhelming suicide rate among men over women, and family problems and illness being the major causes of suicide in India, it is vitally important that we have a better understanding of how men struggle with depression in the context of family. Based on increasing studies on depression and family in the last two decades, a number of researchers have suggested that male depression is associated with early family life experiences. As few studies on the experiences of depression and gender role socialization in early family relationships are reported in India, the present study adds credence to the concept of male depression that may relate to early family life experiences. The aim of this study is to understand the subjective experiences of depression and gender role socialization in early family relationships among Indian men. A non-clinical sample of 9 men was selected using purposive sampling from a human service organization. Theoretical sampling of biographical accounts of a clinical group (1 male client with history of clinical depression) was used for triangulation of data. Consensual Qualitative Research methodology is adopted. The subjective experiences of participants are examined by open-ended, in-depth interviews. The interviews are taped and transcribed. Identification of domains and core ideas, and cross-analyses are conducted on transcripts. A research team of three and an external auditor are employed for data analysis. The findings have contributed to new understandings of depression in the light of gender role socialization in early family relationships among Indian men. Implications suggest further studies of male depression in the family context, the challenge of family life education in India, and the importance of gender sensitivity in counseling with men. Keywords: men, depression, gender role socialization, early family relationships, consensual qualitative research. -
A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments
High-Impact Weather (HIW) events, particularly high-impact heavy rainfall, pose significant risks to urban infrastructure in Australia. Traditional forecasting approaches often struggle to resolve the complex, non-linear thermodynamic interactions that drive these infrequent events, while standard supervised machine learning models are hindered by severe class imbalance. This study presents a novel, multi-disciplinary framework that integrates synoptic climatology with unsupervised anomaly detection to classify and predict high-impact heavy rainfall events in Darwin, Sydney, Brisbane, and Perth. Using daily meteorological observations (20242025), we developed a multi-phase analytical framework comprising precursor, thermodynamic, kinematic, and system evolution phases to isolate the physical signatures of storm genesis. Exploratory analysis using Danger Rose polar histograms revealed a strong anisotropic risk pattern, with heavy rainfall predominantly associated with South-South-East (SSE) and West-South-West (WSW) vectors. Bivariate Kernel Density Estimation (KDE) revealed a distinct Thermodynamic Lock-in mechanism, where severe events are confined to narrow regimes of low pressure (< 1010 hPa), high humidity (> 60%), and compressed diurnal temperature ranges. To address the limited representation of severe events data (12.1%), we benchmarked five unsupervised anomaly detection algorithms. The results indicate that DBSCAN (Density-Based Spatial Clustering) yields the optimal performance (F1-Score: 0.319; Recall: 67.5%), significantly outperforming Isolation Forest and PCA. Topological validation via t-SNE projection confirms that high-impact heavy rainfall events form dense, cohesive clusters within the phase space rather than appearing as randomly distributed stochastic outliers. These findings prove that hybridizing physical phase-space analysis with density-based machine learning offers a robust pathway for early warning systems in data-sparse environments. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
A physics-informed neural network framework for consolidation parameter prediction using controlled clay-sand mixtures
This paper introduces a novel Physics-Informed Neural Network (PINN) model for predicting the coefficient of consolidation (Cv) in high plasticity clays. The model was trained from experimental data obtained from controlled clay-sand mixtures. The input parameters include clay content, Atterberg limits, initial void ratio, compaction energy, applied pressure and consolidation characteristics like compression index (Cc) and volumetric compressibility (mv). Additional parameters like plasticity index, porosity, activity-clay interaction and compaction efficiency were derived from feature engineering. The proposed PINN model combines data-driven loss and physics-based loss into a total loss function. The physics loss includes three constraints derived from modified Kozeny-Carman equations, activity-based mineralogical relations, and compression-volume consistency. Hyperparameter optimization identified the optimal configuration: 800 epochs, learning rate 0.001, architecture [128, 64, 32], and physics loss weights distributed as 0.7, 0.25, and 0.05. Five-fold cross-validation demonstrated robust performance (R2 = 0.9903 0.0026), significantly outperforming baseline neural networks (R2 = 0.9682 0.0126, p = 0.0116) with 73.9% reduction in Root Mean Square Error (RMSE = 6.37 10-11 m/s) and 5.71% improvement in Mean Absolute Percentage Error (MAPE = 4.48%). External validation showed the PINN (R = 0.9968) substantially outperformed empirical correlations (best R2 = 0.1636) and conventional machine learning models (best R2 = 0.9878). SHapley Additive exPlanations (SHAP) interpretability analysis validated physically meaningful decision-making, with plastic limit and activity emerging as primary drivers. This framework provides a transferable, physics-consistent solution applicable across diverse clay types for foundation design and site characterization. Copyright 2026. Published by Elsevier B.V. -
A Pilot Feasibility Study of Reconnecting to Internal Sensations and Experiences (RISE), a Mindfulness-Informed Intervention to Reduce Interoceptive Dysfunction and Suicidal Ideation, among University Students in India
Although 20% of the worlds suicides occur in India, suicide prevention efforts in India are lagging (Vijayakumar et al., 2021). Identification of risk factors for suicide in India, as well as the development of accessible interventions to treat these risk factors, could help reduce suicide in India. Interoceptive dysfunctionor an inability to recognize internal sensations in the body has emerged as a robust correlate of suicidality among studies conducted in the United States. Additionally, a mindfulness-informed intervention designed to reduce interoceptive dysfunction, and thereby suicidality, has yielded promising initial effects in pilot testing (Smith et al., 2021). The current studies sought to replicate these findings in an Indian context. Study 1 (n = 276) found that specific aspects of interoceptive dysfunction were related to current, past, and future likelihood of suicidal ideation. Study 2 (n = 40) was a small, uncontrolled pre-post online pilot of the intervention, Reconnecting to Internal Sensations and Experiences (RISE). The intervention was rated as highly acceptable and demonstrated good retention. Additionally, the intervention was associated with improvements in certain aspects of interoceptive dysfunction and reductions in suicidal ideation and eating pathology. These preliminary results suggest further testing of the intervention among Indian samples is warranted. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
A Pilot Study of the DREAMS Program: A Community Collaborative Intervention for the Psychosocial Development of Middle School Students
The purpose of this study was to pilot the DREAMS (Desire, Readiness, Empowerment, Action, and Mastery for Success) program, a community-collaborative, after-school intervention program designed specifically to address the holistic developmental needs of students at school. The author originally developed and implemented the program in Kerala, India, and later redesigned it for American school students. Combining the theories of Vygotsky and Erikson, the DREAMS model emphasizes the impact of the community on the development of children. This study evaluates the effects of a summer camp, the primary intervention of a three-year program, on the self-worth, self-esteem, and self-concept of 20 middle school students in Northeast Louisiana. After students attended the week-long program, the most significant improvements were observed in self-esteem and self-worth. Further longitudinal or comparative experimental research on the complete design would provide stronger evidence to draw more substantive conclusions. (2024), (California State University). All rights reserved. -
A Pilot Study on Detection of Microplastics for Environmental Monitoring Using Inland Lakes as Ecological Indicators
The waterbodies of a city play a major role in its biodiversity and ecological well-being. The main aspect of this study was to select lakes close to urban areas that are affected due to garbage dumping or have wastewater treatment plants inlets in them and check for microplastics (MPs) presence in them. Seetharampalya and Puttenahalli lakes in Bangalore both showed the presence of microplastics in their water and bank sediment soil samples, which were segregated by the wet peroxide oxidation process. In scanning electron microscopy (SEM) analysis, the microplastics segregated from the water of Seetharampalya lake were found to be clumped and in clusters of uneven form and shape. Microplastics extracted from the soil of Seetharampalya lake were found to have sheet, like structures with occasional dumps or clusters. The microplastics sorted out from Puttenahalli lake water were uneven and had roughly rectangular structures. The soil microplastics recovered from Puttenahalli lake were found to be sheaths of globular masses. The energy dispersive spectroscopy (EDS) analysis majorly showed presence of carbon and oxygen. In Fourier transform infrared spectroscopy (FTIR) analysis, characteristic peaks at 719/cm and 1469/cm were observed. Similarly, in x-ray diffraction (XRD), the 26 values around 20 could be seen in all four samples. This is the first reported study of microplastics in these lakes of Bangalore. 2024 - Kalpana Corporation. -
A Pilot Study on Latent Fingerprint Development Using Cow Dung-Derived Biochar
In forensic investigations, human latent fingerprints (LFPs), due to their unique characteristics, serve as crucial tangible evidence for the identification of criminals. The widely used procedure for the development of LFPs is the powder dusting approach. Several chemical powders have already been utilised for the same. However, the production processes, expenses, and toxicity-related factors limit their utilization. To address these constraints, the present work attempted to analyse the efficacy of a biowaste-derived Biochar (BC) for the development of LFPs on various surfaces. Commonly available biowaste, Cow dung (CD), was utilized as a precursor to synthesize BC in an easy, economical, environmentally friendly, and sustainable approach. The synthesis of BC was carried out by pyrolysis at 350C for 4h in a limited oxygen environment. The physical and chemical properties of BC were indicated by the SEM, FTIR, XRD, and Raman characterisation techniques. Further, the BC was tested for the development of LFPs on the selected non-porous, porous, and frequently stolen materials using the powder dusting method. Additionally, aging studies were also carried out to determine the efficiency of the material over time to ensure its relevance in a real-time forensic scenario. CD-derived BC was found to be a promising powder for the development of LFPs on different surfaces, revealing the characteristic fingerprint patterns. Results revealed that the BC material produced good contrast fingerprints on frequently stolen materials even after nine days of deposition. This pilot study presents the first report on the utilisation of CD-derived BC in forensic applications. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
A Pipeline for Speech-to-Text Summarization and Question Identification for Enhanced Chatbot Interactions
The rapid advancements in natural language processing provide strong support for the new potential application of integrating Google Speech Recognition API, BART, and BERT to create a full pipeline for speech recognition, text summarization and question answering without breaking human interaction. The research aims to develop such a holistic pipeline involves integrating the Google Speech Recognition API to perform speech-to-text, BART for text summarization, and finally BERT for question answering based on both the summary and original transcript. The system was tested under various criteria such as testing accuracy, real-time processing performance, and stress tests for scalability where the findings include an average of 60% text compression with BART, an 88% accuracy in BERT-based question answering, and scores indicating high user satisfaction (4.3/5). Real-time processing latency can be able to cater to interaction within 2-3 seconds and the capacity of the system has proven without performance loss during simultaneous users. The research done can practically find applications in areas like intelligent virtual assistants, customer service automation and e-learning applications that improve accessibility and user experience. 2025 IEEE. -
A post covid machine learning approach in teaching and learning methodology to alleviate drawbacks of the e-whiteboards
Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem. 2021 Tamkang University. All Rights Reserved.