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The Prevalence of Alcohol and Tobacco Use Among Adolescents aYoung Adults in Bengaluru, India
Background: Substance use can affect scholastic performance. The present study aimed to estimate the prevalence of alcohol and tobacco use and associated outcomes of their use among adolescents and young adults. Methods: We carried out a cross-sectional study in Bengaluru, India, over the period of September 2017 to September 2021. The study participants included students enrolled in pre-university courses, undergraduate colleges, and higher secondary classes (11th and 12th grades). Upon obtaining consent/assent, the study participants completed a pre-tested semi-structured questionnaire covering basic demographics and history of alcohol and tobacco use and their associated characteristics. Data were summarized using frequencies, medians (IQR), and proportions with 95% CI; sex-wise differences were assessed using Chi-square test and odds ratios, with a significance level set at P<0.05. Results: A total number of 4093 students participated in the study including 54.41% (n=2227) male and 45.59% (n=1866) female students. Lifetime alcohol use was reported by 33.33% (95% CI: 31.8834.79; n=1364) and tobacco use by 17.84% (95% CI: 16.6719.04; n=730) of the study participants. Curiosity was the most commonly reported reason for initiating alcohol (55.2%) and tobacco use (48.8%). Among alcohol consumers, 12.4% reported having sought help to quit, with no significant sex-wise difference (OR 1.234; 95% CI 0.8861.719; P=0.213). In contrast, 34.2% of tobacco users reported having sought help to quit, which was significantly more common in men than women (OR 1.483; 95% CI 1.0802.037; P=0.015). Conclusions: Alcohol and tobacco use appeared more common in Indian adolescents in our study compared with previously reported rates in the countrys general population. Therefore, appropriate interventions should be planned in early school/college days by the concerned authorities. 2026, International Journal of School Health. -
Peak-to-Average Power Ratio (PAPR) Aware Signal Shaping in UFMC: An Adaptive Orthogonal Precoding and Gaussianized Companding Approach
Universal Filtered Multi-Carrier (UFMC) is a promising waveform for 5G and beyond wireless systems due to its superior spectral localization and low-latency characteristics; however, its high Peak-to-Average Power Ratio (PAPR) significantly degrades power amplifier efficiency and causes nonlinear distortion and out-of-band (OOB) emissions. To address these limitations, this paper proposes an Adaptive Hybrid Orthogonal Precoding with Gaussianized Companding (AHO-PCG) framework for PAPR-aware UFMC transmission. The proposed approach integrates adaptive orthogonal precoding, Gaussianized Dynamic Companding (GDC), and Selective Subcarrier Companding (SSC) within a joint optimization loop to balance PAPR reduction, signal fidelity, and spectral containment. Gaussianization reshapes the signal amplitude distribution to suppress extreme peaks, while adaptive precoding dynamically selects suitable orthogonal transforms based on signal characteristics, and SSC limits nonlinear processing to dominant peak-contributing subcarriers. Simulation results under 3GPP LTE-compliant parameters using QPSK, 16-PSK, and 64-QAM demonstrate that AHO-PCG achieves up to 4.4 dB PAPR reduction at a CCDF of 10-3, reduces EVM to 2.9%, suppresses OOB emissions to 42 dBm, and improves BER by up to an order of magnitude compared to baseline UFMC. These gains are achieved with a modest processing overhead, making the framework suitable for real-time 5G baseband implementations and extensible to future 6G waveform designs. 2026 Mahendrapublications.com, All rights reserved. -
Analysis of skip-lot sampling plan of Type 3 with multiple reference criteria
In the modern quality control segment, the skip-lot sampling plan is still significant among all others plans due to rising production volumes and the demand for cost-effective inspection methods that will yield high-quality outputs. Unlike other sampling plans, while inspecting a submitted lot, a skip-lot plan is economically advantageous and ensures high quality. The skip-lot sampling plan utilizes single sampling plan (SSP) or double sampling plan (DSP) as the reference plan during both normal and skipping inspections. However, using these plans as the reference can lead to bias, favouring either the producer or the consumer. In this paper, a novel approach is illustrated where the skip-lot sampling plan of type 3 is having the provision of two different reference plans in the normal and skipping phases. The proposed plan is termed as the Multi-Reference Skip-lot Sampling Plan of type 3 (MR-SkSP-3). The plan is then compared with the help of performance measures such as operational characteristic (OC) function and average sample number (ASN). The comparison is done between the proposed plan and existing skip-lot sampling plans which use single sampling plan or double sampling plan as reference plan in both inspection phases. The comparison is made based on performance measures with graphical and tabulated illustrations. The comparative analysis proves that the proposed plan successfully balances the satisfaction of both producers and consumers. By leveraging the strengths of conventional skip-lot sampling plans that use single reference plans, it achieves superior performance. 2026 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved. -
RESPONSIBLE AI-READINESS IN HIGHER EDUCATION: VALIDATING A DUAL-MODEL FRAMEWORK FOR FACULTY DEVELOPMENT
Aim/Purpose This study validates a responsible artificial intelligence (AI) framework designed to strengthen AI-readiness among higher education faculty in India. Background With the increasing use of AI in education, faculty require structured support; however, faculty development models for responsible AI integration remain limited. Methodology A mixed-methods pilot study with ten humanities and performing arts faculty used a dual-model evaluation approach. Three sessions from the framework were implemented, and data were collected through knowledge-attitude-practice (KAP) surveys, cognitive-affective-psychomotor (CAP) performance rubrics, and reflective responses. Contribution This study validates a dual-model framework for faculty development regarding responsible AI readiness. Findings The adapted KAP survey demonstrated strong reliability, with higher AI knowledge associated with more positive attitudes. However, knowledge did not consistently translate into practice, highlighting the need for structured hands-on learning. CAP-based performance assessments and reflections indicated improved ethical awareness, critical engagement, and foundational AI-integration skills. Recommendations for Practitioners Institutions should embed structured AI-training for faculty, with authentic instructional tasks. Recommendations for Researchers Future research should test this approach across larger and more diverse institutional contexts. Impact on Society Developing AI-ready faculty can foster ethical and future-focused learning environments. Future Research Future studies should expand across disciplines and examine longer-term outcomes. (2025), (Informing Science Institute). All rights reserved. -
FROM CLASSROOMS TO CLICKS: EXPLORING STUDENT ATTITUDES AND CHALLENGES IN THE SHIFT TO DIGITAL LEARNING IN HIGHER EDUCATION
Aim/Purpose This study is a cross-sectional survey that explores attitudes towards online classroom engagement, online assessments, exams, and challenges in digital learning. Background Educational institutions have adopted digital platforms with varying degrees of success. The COVID-19 pandemic has prompted researchers and academics to reflect on digital interventions and their impact on pedagogy, learning, and assessment. However, in the present circumstances, online teaching, learning, and assessment will require learners to bring their own ethics, unrestrained by external institutional rules. Methodology The sample consisted of 1,017 students in higher education across India. The researchers developed a tool, the Digital Learning Scale, which was built on three factors: attitude towards online classroom engagement, attitude towards online assessments, and challenges in digital learning. This tool was used to collect data. Contribution The study results prove the effect of online learning and online assessments in higher education institutions. Findings The significant findings of this study are: (1) approximately 50% of the students prefer that there should be a balance between online and offline teaching, learning and evaluation; (2) there is significant positive high correlation between the last online exam and last offline exam scores; (3) there is significant positive correlations between attitudes towards online class engagement, attitude towards online assessments and exams and challenges in digital learning; (4) 65% of the students agree that digital learning increases social isolation; (5) 55% of the students agree that incidence of cheating by the students in online exams is evident. Recommendations Educational institutions should invest in robust strategies to promote academic for Practitioners integrity, provide technical support, and offer training programs that equip students with the necessary digital skills for achieving successful learning outcomes. Impact on Society There is a pressing need to prioritise digital literacy and integrity initiatives. Future Research Five years post-pandemic, research should be conducted in higher education institutions on the impact of online learning. (2025), (Informing Science Institute). All rights reserved. -
THE INFLUENCE OF STEM ATTITUDE, 21ST CENTURY SKILLS, AND TECHNOLOGY USE ON STUDENT OUTCOMES: A MEDIATION MODEL ANALYSIS
Aim/Purpose This study aims to investigate the relationships between students attitudes toward STEM subjects, 21st-century skills, and technology use, and how these factors influence STEM career interest and subjective well-being among adolescents in the 21st-century classroom. Background While positive attitudes, skill acquisition, and digital learning have been individually studied, their combined effect on students career aspirations and subjective well-being remains underexplored. Addressing this gap can show how these factors work together to support adolescent development. Methodology This study was grounded in established theories of learning and motivation. It employed a statistical method, Structural Equation Modeling (SEM), to test a conceptual model. Data were collected from 1,302 students (grade levels VIII-XII) across 30 schools in Kerala, India. Also analysed were the mediating effects of mathematics and science engagement, teacher efficacy, and teacher leadership. Contribution This research offers a combined model that reveals how students attitudes, skills, technology use, and classroom teaching practices are connected. These factors are shown to influence both career motivation and well-being. Together, the findings provide a broader and clearer picture of modern education. Findings Students with positive attitudes toward STEM showed more engagement in learning. Effective and supportive teachers also influenced them. This influence shaped their career interest. Their well-being was improved indirectly through this engagement and career motivation, but their direct impact on well-being was limited. The model demonstrated good fit indices, supporting its structural validity. By addressing the gap in integrative models that link learner attitudes, competencies, technology use, and instructional mediators to both career and well-being outcomes, this study contributes a holistic framework for understanding adolescent development in modern classrooms. Recommendations Teachers should focus on fostering STEM-positive attitudes. They can enhance for Practitioners engagement through inquiry-based methods. They should also integrate 21st-century skills into everyday classroom activities. Teacher professional development should include leadership training and technology-enhanced pedagogy. Purposeful and regulated technology integration should be prioritized to avoid negative impacts on student focus and well-being. Recommendations Future research should explore the longitudinal effects of these variables across for Researchers diverse educational contexts and age groups. Alternative indicators of academic success beyond grades should be considered. These indicators could examine how digital and thinking skills apply across different subjects. Impact on Society By identifying pathways to both academic and emotional development, the study supports policy and curriculum reforms. These reforms aimed at preparing future-ready learners capable of contributing to innovation-driven economies while maintaining adaptability. Future Research Investigations should extend to virtual or immersive environments, examine differential impacts across demographic groups, and develop standardised tools for measuring digital literacy and well-being in diverse settings. (2025), (Informing Science Institute). All rights reserved. -
TRANSFER LEARNING TECHNIQUES AND APPROACHES FOR PREDICTIVE MODELING OF DISEASE OUTCOMES
Aim/Purpose In this research work, we have developed a predictive model that focuses on utilizing knowledge from the related domains. Background A serious public health issue, especially in tropical and subtropical regions, is dengue fever, a viral infection passed by mosquitoes. Accurate early prediction of disease outcomes is essential for both efficient patient management and ef-fective use of resources. More complex methods are required since conven-tional prediction models could be faulty with limited labeled data and complex feature interactions. Methodology We propose a new strategy integrating deep attention mechanisms with trans-fer learning to enhance prediction modeling of dengue disease outcomes. First pre-trained on a large, linked dataset of common viral illnesses, a deep neural network enables the model to learn generic properties. We then iteratively im-prove our pre-trained model using a specific dengue dataset. Incorporating a deep attention mechanism allows for the focus on the most relevant features, improving interpretability and accuracy. Contribution Among logistic regression, random forests, and basic deep learning methods, current models reveal poor accuracy and dependability in forecasting dengue disease outcomes. These models sometimes fail to sufficiently depict the com-plicated interactions among clinical variables, especially under conditions with limited data. Findings The proposed method outperforms more traditional models pretty strongly. Our model acquired in the training phase an accuracy of 0.92, precision of 0.91, recall of 0.90, and F1-score of 0.90. It maintained high performance on testing with an accuracy of 0.91, precision of 0.90, recall of 0.89, and an F1-score of 0.89. Similar patterns were indicated by an accuracy of 0.90, precision of 0.89, recall of 0.88, and an F1-score of 0.88 validation results. The model also demonstrated a lowered loss (0.21, 0.23, 0.24 in training, testing, and vali-dation, respectively), higher true positive rates (0.90, 0.89, 0.88), and lower false positive rates (0.10, 0.11, 0.12). Deep attention methods and transfer learning offer a robust and effective strategy for predictive modeling of dengue disease outcomes, therefore considerably boosting accuracy and dependability. This approach offers considerable possibilities for dengue-endemic patient manage-ment and resource allocation. Recommendations for Researchers Investigations should prioritize the validation of the algorithm in various healthcare environments to assess its efficacy in clinical application. Future Research In future research, this work can be enhanced using several deep learning algo-rithms to achieve better accuracy and performance. This article is licensed to you under a Creative Commons Attribution-NonCommercial 4.0 International License. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes. -
Financial Freedom, social Capital, and the Development of Rural Women Entrepreneurship in India; [?????????? ???????, ?????????? ??????? ? ???????? ??????????????????? ????? ???????? ?????? ? ?????]
Human resource development can only be achieved by promoting female entrepreneurship. There is a very low level of female entrepreneurship in India, especially in rural areas, which has recently been a cause for concern. Women are now aware of their existence, privileges, and employment circumstances.The subject of this research is female entrepreneurs in rural India, their contribution towards society, problems faced by women entrepreneurs in India, and initial steps taken by the administration for their development in Indias rural region. The research is explanatory. The primary data is used in the paper. The self-structured questionnaire was circulated to the women entrepreneurs in rural India. The data collected was analysed using a targeted sampling method in the Statistical Package for Social Sciences programme, followed by a study of the statistical results. During the survey, 44 respondents were interviewed. The results showed that among the most significant challenges were womens family responsibilities, gender inequality, financial difficulties, low risk inclination and competition between men and women. It was concluded that the challenges faced by women entrepreneurs could be addressed through appropriate incentives, training, encouragement, social recognition of their entrepreneurial capabilities and appropriate family support. Victor M., Elangovan N., Halaswamy D., Sonia M., 2024. -
PERFORMANCE EVALUATION OF IPE AND IE-AFFECTED PATIENTS USING A MODIFIED PSO AND ANFIS
Epilepsy, a complex neurological disorder, is particularly challenging to diagnose and manage when driven by genetic factors. This study focuses on the analysis of Idiopathic Partial Epilepsy (IPE) and Idiopathic Epilepsy (IE) in both children and women, using a novel approach combining Modified Particle Swarm Optimization (MPSO) with a 9-rule Adaptive Neuro-Fuzzy Inference System (ANFIS). Four feature extraction techniquesDiscrete Wavelet Transform (DWT), Shearlet Transform (SLT), Contourlet Transform (CLT), and Stockwell Transform (SWT)are employed to process electroencephalogram (EEG) signals. The performance of the proposed MPSO-ANFIS model is evaluated and compared with existing methods. Results indicate that the SWT-ANFIS-MPSO method achieves superior classification accuracy for both IE and IPE patients, highlighting its potential to improve epilepsy diagnosis and treatment strategies. 2025, Institute of Mechanics of Continua and Mathematical Sciences. All rights reserved. -
FEATURE SELECTION AND CLASSIFICATION OF LEUKEMIC CELLS USING IOT AND MACHINE LEARNING
Machine learning and the Internet of Things (IoT) have affected every step of the leukemia process, from diagnosis to understanding to therapy. Consequently, this study delves into the planning of an innovative system that employs IoT and machine learning techniques to precisely differentiate leukemic cells. Depending on the patient's samples, the system uses different ways to feature selection and cell classification. To pick the most informative collection of features that enables stable and accurate cell categorization into suitable categories, the offered research relies on strong machine-learning approaches for feature selection. Next, a classification model is used to classify cells based on their properties using the attributes that have been chosen. There is evidence that the suggested approach can classify leukemic cells with an identification rate of up to 99%, which is greater than the current methods. As a novel strategy for managing massive volumes of biological and medical samples, the suggested method will be an invaluable tool for doctors treating leukemia patients. The system's ability to process data from various Internet of Things (IoT) sources should aid its ability to learn and adapt to real-world clinical settings. With the results of this study in hand, we may be able to detect leukemia sooner, with greater precision, and maybe use more tailored treatments for each patient, leading to better results while reducing healthcare expenditures. 2025, Institute of Mechanics of Continua and Mathematical Sciences. All rights reserved. -
Understanding the drivers of intermittent fasting adoption in middle adulthood
Over the past decade, intermittent fasting has gained into the mainstream limelight as a prevalent method for weight management and to maintain metabolic health. However, the number of studies exploring the determinants affecting its approval, especially among individuals in middle adulthood, is still comparatively under-examined. Hence, the present study has been conducted to gain better insights about the factors influencing the adoption of intermittent fasting among middle-aged adults. By employing snowball sampling, a sample of 260 respondents was surveyed to comprehend the drivers of intermittent fasting, challenges faced in adhering to it and its impact on physiological and psychological health. Factor analysis was employed to group the factors influencing the respondents adherence to intermittent fasting into three categories: longevity-enhancing incentives for intermittent fasting, wellness incentives for intermittent fasting and cognitive enhancement incentives for intermittent fasting. In order to examine the significant differences among the ages of the respondents among the three factors, ANOVA and post-hoc test were conducted. The post-hoc test results provide insight into how motivational factors for intermittent fasting vary across different age groups. For respondents in the age group between 35 to 45 years, the post-hoc results show statistically significant differences in the first factor with the group aged 18-25 and 25-35 years, respectively at 5 % level of significance. The insights garnered from this research are contributory in understanding the factors influencing how and why individuals in middle adulthood embrace intermittent fasting practices. 2025 The Authors. -
BRICS VS. G7: A COMPARATIVE ANALYSIS OF ECONOMIC AND POLITICAL EFFICIENCY IN SHAPING GLOBAL ORDER
The global distribution of power is increasingly shaped by the competing influences of two major blocs: BRICS (Brazil, Russia, India, China, and South Africa) and the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This paper investigates how BRICS and the G7 shape the emerging multipolar global order. Using comparative analysis of key indicators: GDP, trade flows, investment patterns, diplomatic engagement, and strategic alliances. The paper examines each blocs structure and internal cohesion. The analysis underscores the G7's historical supremacy, which stems from its economic strength and political unity, in contrast to BRICS rising role as a representative for the Global South and a platform for alternative governance models. Important metrics include trade flows, investment trends, diplomatic efforts, and strategic alliances. The research also assesses the internal dynamics within each bloc, including challenges to cohesion and the effectiveness of decision-making. By comparing the advantages and drawbacks of BRICS and G7, this paper provides insights into their respective functions in a multipolar world order, evaluating their ability to promote transformative global agendas. Lastly, the paper concludes that both alliances embody divergent approaches to global governance, reflecting deeper shifts in international collaboration, competition, and the balance of power. 2025, Observare. All rights reserved. -
Impact of Ionic Liquids on the Crystal Growth and Surface Morphology of Ruthenium-doped TiO2 Nano Heterojunction Structures for Improved Photocatalytic Degradation of Evans Blue Dye and the Associated Antibacterial Activities
Novel Ru-doped TiO2 nanocomposites (Ru/TiO2 NCs) were synthesized at a 130 C temperature and 24-h incubation period using hydrothermal methods with and without ionic liquids (ILs). NCs were synthesized using 1-butyl-2,3-dimethylimidazolium tetrafluoroborate as the ILs and titanium(IV) isopropoxide and ruthenium(III) nitrate as the precursors. The presence of Ru in the NCs was analyzed using different characterization techniques. Powder X-ray diffraction and transmission electron microscopy confirmed the presence of anatase and rutile phases as well as the nanocrystalline texture of the prepared Ru/TiO2 NCs. The presence of Ru, Ti, and O was confirmed via energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy. The optical properties and bandgap energies of Ru/TiO2 NCs were determined via ultraviolet (UV)visible (Vis) diffuse reflectance spectroscopy; the optical properties exhibited a redshift in the optical response toward the visible region owing to the reduced bandgap energy of Ru/TiO2 NCs in the visible region after doping Ru into the TiO2 nanocrystalline structure. Scanning electron microscopy images revealed a highly voluminous and porous network of Ru/TiO2 NCs. Moreover, different concentrations of Ru were doped into the TiO2 matrix to investigate the photocatalytic and antibacterial activities. Among all the synthesized NCs, 0.3-wt% Ru/TiO2 NCs exhibited high photocatalytic degradation efficiency and was therefore considered the optimum concentration. Moreover, it exhibited the highest BET surface area and quantum efficiency compared with other Ru/TiO2 NCs. Results revealed that Ru/TiO2 NCs synthesized via IL-assisted hydrothermal method, i.e., R3TL, exhibited considerably enhanced photocatalytic and antibacterial activities compared to the NCs synthesized without the ILs, i.e., R3TH. The inhibition pattern showed an excellent zone of inhibition (p < 0.001) in several strains with both NCs (R3TL and R3TH). However, Gram-positive Staphylococcusaureus exhibited a remarkably increased zone of inhibition (35 mm) compared with all the other strains used for R3TH. In contrast, Bacillus sp. exhibited the second largest zone of inhibition (21 mm) for R3TL after S. aureus (34 mm). In summary, this study emphasized the role of ILs and reaction mechanisms. The author(s) 2025. -
Fuzzy Computational Intelligence in Personalized Medicine and Diagnosis
The development of fuzzy computational intelligence (FCI) has emerged as an effective method for personalized medicine and diagnosis. FCI effectively handles uncertainty and imprecision in medical data, facilitating patient-specific treatment recommendations. Conventional diagnostic and treatment methods typically rely on fixed threshold-based approaches, which fail to account for individual variations in patient responses, leading to suboptimal treatment outcomes. This study proposes the personalized treatment recommendation using fuzzy logic (PTR-FC) framework for diabetes (DB) patients to address these challenges. The framework integrates patient-specific data such as blood glucose levels, diet, exercise, and medication history into the fuzzy inference system (FIS), supporting personalized treatment recommendations. The treatment plans are dynamically adapted based on individual patient outcomes using linguistic factors and fuzzy rules (FR). The proposed method dynamically adjusts recommendations in real time, potentially enhancing personalized treatment and improving decision-making in DB management. Additionally, it promotes lifestyle modifications while reducing the risk of medication-induced complications. The effectiveness of the proposed method was compared to conventional methods, demonstrating improved treatment accuracy, increased patient adherence, and reduced adverse health risks. The PTR-FC framework offers a more adaptive and effective approach to DB management, ensuring better patient outcomes. 2009 Tsinghua University Press. -
An Efficient Fuzzy Logic-Integrated Hybrid Deep Learning Framework for Medical Diagnosis
Medical diagnosis involves analyzing symptoms, test results, and patient histories, but uncertainty from vague symptoms and incomplete records complicates the process. Fuzzy logic-based systems address this issue but often depend on manual rule creation, which is time-consuming. This research proposes a hybrid approach integrating fuzzy logic with deep learning techniques (FL-DLT) for intelligent diagnosis. The framework combines adaptive neuro-fuzzy inference system (ANFIS) for handling uncertainty with convolutional neural networks (CNNs) for extracting features from medical images like X-rays and MRIs. ANFIS models relationships between symptoms, results, and diagnoses, while CNNs analyze medical images. Experimental results show high accuracy and reliability, even with noisy or incomplete data. The proposed approach can improve diagnostic accuracy and efficiency, supporting clinicians in decision-making. Key contributions include the development of the FL-DLT framework and its evaluation using a large dataset of patient records and medical images. Additionally, the research offers insights into the application of fuzzy logic and deep learning in medical diagnosis, highlighting their potential to enhance diagnostic outcomes and efficiency in clinical practice. 2009 Tsinghua University Press. -
COSMOLOGICAL DIAGNOSTICS OF BIANCHI TYPE-II BARROW HOLOGRAPHIC DARK ENERGY UNIVERSE; [????????I??? ?I????????? ????????I???? ?????? ?????I? ?????I?? ?????? ???? ?I???I-II]
In this paper, we investigate a Bianchi type II anisotropic cosmological model in the framework of Barrow holographic dark energy, considering both the Hubble horizon and GrandaOliveros scale as infrared cutoffs. To obtain exact solutions of the Einstein field equations, we assume a suitable relation between the metric potentials. Using Hubble cosmic chronometer data, we constrain the model parameters and obtain the best-fit values b4= ?0.091+0.013 ?0.012and H0= 72.32.7 km s?1Mpc?1The H(z) fit shows excellent agreement with observational data and overlaps with ?CDM at low redshifts, with mild deviations at higher z. The physical behaviour of the model is examined through a detailed analysis of cosmological parameters. The deceleration parameter q(z) reveals a smooth transition from an early decelerating phase to the present accelerating epoch. The equation of state parameter ?deshows quintom-like dynamics, evolving across the cosmological constant boundary and entering the phantom regime, consistent with late-time acceleration. Stability is tested using the squared sound speed vs2, which remains positive in the recent Universe, ensuring classical stability. The ?de?dephase plane indicates that both models lie in the freezing region, corresponding to faster acceleration. The statefinder diagnostics (r,s) and (r,q) further confirm the transition from the standard cold dark matter dominated phase to a de Sitter-like attractor, with trajectories showing clear deviations from ACDM. U.Y.D. Prasanthi, D. Tejeswararao, Diddi Srinivasa Rao, Y. Aditya, D. Ram Babu, 2026. -
Precise cervical cancer cell boundary denoising and segmentation with adaptive wavelet-spectral enhancement
Accurate segmentation of cell nuclei in cervical cytology images is crucial for automated cervical cancer screening, yet existing methods struggle with blurred boundaries, noise-induced degradation, and topologically implausible predictions. The current research proposes Cell-Seg Tool, a novel triplet-branch diffusion AI tool that synergistically integrates three innovations to address these limitations. The Wavelet-Enhanced Contour Refinement Branch employs a learnable multi-scale discrete wavelet transform with adaptive coefficient attention to dynamically enhance boundary features across horizontal, vertical, and diagonal orientations. The Adaptive Spectral Noise Suppression module performs dual-domain processing using DCT-based filtering and uncertainty-guided fusion, coupled with bidirectional anchor semantic feedback to couple cross-branch information. The Topology-Aware Hybrid Loss integrates a focal Tversky loss, a persistent homology loss, a directional boundary loss, a skeleton completeness loss, and a diffusion-noise MSE loss for multi-objective optimization. Comprehensive experiments on multiple datasets demonstrate superior performance, achieving 94.45% Dice coefficient and 19.2% reduction in boundary localization error compared to state-of-the-art methods. Unlike prior work that applies these techniques independently, this work demonstrates that their adaptive, synergistic integration within a diffusion-based framework yields substantial improvements in boundary accuracy and topological correctness. 2026 The Author(s). -
BERT-Enhanced Bi-LSTM with weighted cross-entropy for multilingual sentiment classification
With the increasing volume of multilingual user-generated content across social media platforms, effective sentiment analysis (SA) becomes crucial, especially for low-resource languages. However, traditional models relying on context-independent embeddings, such as Word2Vec, GloVe, and fastText, struggle to handle the complexity of multilingual sentiment classification. To address this, we propose an Automatic Multilingual Sentiment Detection (AMSD) framework that leverages the contextual capabilities of BERT for feature extraction and a Bidirectional Long Short-Term Memory (Bi-LSTM) network for classification. Our method, termed Elite Opposition Cross-Entropy Weighted Bi-LSTM (EOCEWBi-LSTM), integrates elite opposition-based learning to optimize hyperparameters and enhance classification accuracy. A weighted cross-entropy loss function further refines the model's sensitivity to class imbalance, thereby improving its performance. The model is trained and evaluated on the NEP_EDUSET corpus, comprising 45,434 tweets in English, Hindi, and Tamil. Experimental results demonstrate notable improvements in precision, recall, F1-score, and accuracy, highlighting the effectiveness of EOCEWBi-LSTM in multilingual sentiment analysis, especially across both high-resource and low-resource languages. The experimental results show that the proposed EOCEWBi-LSTM achieves a high F1-score ratio of 93.83% and an accuracy ratio of 93.83% compared to other existing methods. EOCEWBi-LSTM provides an effective solution for multilingual sentiment analysis, especially for languages with limited resources. 2025 The Author(s). -
Edge criticality in signed graphs admitting a Roman dominating function
A Roman dominating function(RDF) on a signed graph S = (G, ?) is a function f: V (S) ? {0, 1, 2} such that f(N[v]) ? 1 for every vertex v ? V (S) and any vertex v with f(v) = 0 has a neighbour u ? N + P (v) having f(u) = 2, where f(N[v]) = f(v) + ?u?N(v) ?(uv)f(u). The weight of an RDF is ?(f) = ?v?V f(v) and the minimum weight among all the RDFs on S is called the Roman domination number, ?R(S). In this article we explore the concept of edge criticality in signed graphs admitting an RDF by examining the signed graphs S such that ?R(S+uv) < ?R(S), for any pair of non-adjacent vertices u and v of S, such that the edge uv is positive. This work is licensed under https://creativecommons.org/licenses/by/4.0/ -
EXPLORING THE REGULATION OF CHARACTER STRENGTHS: A SCOPING REVIEW ON BALANCING UNDERUSE, OVERUSE, AND OPTIMAL USE FOR WELL-BEING AND PERFORMANCE
Character strengths play a fundamental role in psychological well-being, resilience, and personal development. Traditionally, research in positive psychology has focused on the benefits of character strengths, emphasizing their role in enhancing life satisfaction, fostering positive relationships, and promoting professional success. However, recent studies highlight the critical importance of balance in strength utilization. Specifically, underuse, overuse, and optimal use of strengths can lead to varying consequences across different life domains. This scoping review systematically examined the imbalance of character strengths and its impact on mental health, leadership, and social interactions. A systematic search was conducted across Google Scholar, PubMed, Scopus, and Web of Science, following PRISMA-SCR guidelines. The findings revealed that underuse of strengths is associated with low engagement, passivity, and decreased motivation, while overuse can lead to rigidity, interpersonal conflicts, and burnout. The review further identified contextual moderators (e.g., personality traits, cultural factors, and situational demands) that influence how strengths are applied in everyday life. Based on the findings, this review proposes the Balanced Strength Utilization Model (BSUM), integrating theoretical perspectives and empirical evidence to offer a comprehensive framework for character strength regulation. Future research should focus on longitudinal studies and intervention-based approaches to help individuals optimize strength balance and apply their strengths effectively across different domains. The Author(s). All articles are licensed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY 4.0 http://creativecommons.org/licenses/by/4.0/).