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Short-chain fatty acid: An updated review on signaling, metabolism, and therapeutic effects
Fatty acids are good energy sources (9 kcal per gram) that aerobic tissues can use except for the brain (glucose is an alternative source). Apart from the energy source, fatty acids are necessary for cell signaling, learning-related memory, modulating gene expression, and functioning as cytokine precursors. Short-chain fatty acids (SCFAs) are saturated fatty acids arranged as a straight chain consisting minimum of 6 carbon atoms. SCFAs possess various beneficial effects like improving metabolic function, inhibiting insulin resistance, and ameliorating immune dysfunction. In this review, we discussed the biogenesis, absorption, and transport of SCFA. SCFAs can act as signaling molecules by stimulating G protein-coupled receptors (GPCRs) and suppressing histone deacetylases (HDACs). The role of SCFA on glucose metabolism, fatty acid metabolism, and its effect on the immune system is also reviewed with updated details. SCFA possess anticancer, anti-diabetic, and hepatoprotective effects. Additionally, the association of protective effects of SCFA against brain-related diseases, kidney diseases, cardiovascular damage, and inflammatory bowel diseases were also reviewed. Nanotherapy is a branch of nanotechnology that employs nanoparticles at the nanoscale level to treat various ailments with enhanced drug stability, solubility, and minimal side effects. The SCFA functions as drug carriers, and nanoparticles were also discussed. Still, much research was not focused on this area. SCFA functions in host gene expression through inhibition of HDAC inhibition. However, the study has to be focused on the molecular mechanism of SCFA against various diseases that still need to be investigated. 2022 Taylor & Francis Group, LLC. -
Short-Term H ? Line Variations in Classical Be Stars: 59 Cyg and OT Gem
We present the optical spectroscopic study of two classical Be stars, 59 Cyg and OT Gem obtained over a period of few months in 2009. We detected a rare triple-peak H ? emission phase in 59 Cyg and a rapid decrease in the emission strength of H ? in OT Gem, which are used to understand their circumstellar disks. We find that 59 Cyg is likely to be rapid rotator, rotating at a fractional critical rotation of ?0.80. The radius of the H ? emission region for 59 Cyg is estimated to be Rd/R? ? 10.0, assuming a Keplerian disk, suggesting that it has a large disk. We classify stars which have shown triple-peaks into two groups and find that the triple-peak emission in 59 Cyg is similar to ? Tau. OT Gem is found to have a fractional critical rotation of ?0.30, suggesting that it is either a slow rotator or viewed in low inclination. In OT Gem, we observed a large reduction in the radius of the H ? emission region from ?6.9 to ?1.7 in a period of three months, along with the reduction in the emission strength. Our observations suggest that the disk is lost from outside to inside during this disk loss phase in OT Gem. 2017, Indian Academy of Sciences. -
Should Crypto Integrate Micro-Finance option?
Purpose - The purpose of the study is to identify the readiness or acceptance by the younger population specifically, the school and university students towards the investment in cryptocurrency if the micro-finance option is included in such new asset investments. Further to this the research also focusses on the mediating factor as trustworthiness to identify the impact or influence of the variable towards the acceptance of the new asset investment.Design/methodology/approach - The research conducted through relevant literature with sufficient variables measuring with five point Likert's scale. The research also tested with hypothesis on the relationship with variables. A total of 293 valid respondents data were collected and analysed through Structural Equation model.Findings - The analysis and results suggested that the perception, awareness and trustworthiness has positive impact towards the readiness towards crypto investments. Whereas, the investment behaviour has complex acceptability towards the readiness as it failed to accept the hypothesis.Research limitations/implications - the research is limited with the younger population however the research did not focusses on the economically challenged population as they may not be afford to invest in such platforms. The future studies can also be focussed on the same area with more towards the other factors that influence the economically challenged population and identify solution their economic growth. Furthermore, the study may be game changer for the policy makers in legalising the crypto investments in the country.Originality/value - According the wider background study and with substantial literature the research is of first in its kind as per the author's knowledge to integrate the micro finance concept in crypto investments to promote the investment habit among the younger population. 2024 IEEE. -
Should we judge phcs by only iphs guidelines or probe further?
Background: Indian Public Health Standards (IPHS) evaluates supply side compliance of Primary Health Centers (PHCs). Patient Satisfaction (PS) on the other hand, assesses the demand side. Objective: Examining the supply side compliance and relating it to PS in the domain of Reproductive Health (RH). Methods: Using multistage stratified sampling, six rural and three urban PHCs in sub-districts, Ramanagara and Channapatna, in District Ramanagara, state of Karnataka, India, were chosen. Information collected using IPHS proforma for PHCs was compared with PS questionnaire (PSQ 18) data, collected from 398 patients visiting these facilities. Results: Using descriptive and inferential analysis, sub-optimal compliance levels in ease of access, physical & human infrastructure, patient data and usage of untied funds was found. Existing behavioral compliance was found to be optimal. These findings were in alignment with PS findings. Conclusion: Results call for PHC capacity building, incentivization and a crucial need to look into PS side, before passing judgement about performance standard. 2020, Indian Association of Preventive and Social Medicine. All rights reserved. -
Shrewd protective equipment to safegaurd women's automony from insolent and domestic violence /
Patent Number: 202241006814, Applicant: 1)Dr. L. Shakkeera.
According to a 2011 TrustLaw poll, India is the fourth most dangerous country for women. India is ahead of Somalia in terms of women's safety. Women's safety is a major concern in India. Even during the day, Delhi is the most dangerous city for Indian women. Women's safety is a pressing issue in today's culture. We witness a lot of incidents where a lady is discarded, and in certain circumstances, even a 5-month-old infant is not spared. -
Shrikrishna Vasudeo Kale (19242012)
S.V. Kales research encompassed a broad range of topics, notably focusing on the mental health of officers and personnel in the Indian Merchant Marine, the dynamics of small group responses to frustration influenced by leadership behaviour, attentional deficits associated with psychiatric disorders in relation to arousal and pathology, and a psycho-social study on vocational planning that emphasized aspects of choice, decision-making, and indecision. His foresight allowed him to recognize and address pertinent social issues of his time. Through his impactful articles, he pored over critical matters such as education, the advancement of psychology, and corruption, which continue to resonate today. Additionally, he edited the influential book Child Psychology and Child Guidance and created the PSYCHRON psychometric instrument. This innovative tool evaluates human reaction time, movement time, and perceptions of time by analysing responses to controlled audiovisual stimuli differentiated by frequency, amplitude, intensity, and colour. 2025 selection and editorial matter, Braj Bhushan; individual chapters, the contributors. -
SHRINKAGE BASED ESTIMATION FOR STRESS STRENGTH RELIABILITY P[Y < X < Z] FOR THE EXPONENTIAL DISTRIBUTION
Estimating stress-strength reliability of the form P(Y < X < Z) is a pivotal concern in reliability analysis, particularly when systems are subjected to both lower and upper stress limits. This paper investigates the reliability measure under the assumption that the stress variables Y and Z, as well as the strength variable X, follow exponential distributions. The analysis is conducted using both complete samples and right-censored samples to reflect realistic data collection scenarios. To improve estimation efficiency, we propose several shrinkage estimators based on distinct strategies: a constant shrinkage weight factor, a modified Thompson-type shrinkage weight, and the formulation introduced by Mehta and Srinivasan (1971). The performance of these estimators is evaluated via extensive Monte Carlo simulations and compared against the conventional maximum likelihood estimator, demonstrating the relative merits and limitations of each approach. ARF India, Gurgaon, India. -
SHRINKAGE ESTIMATION OF THE TTT OF THE LOMAX DISTRIBUTION UNDER PROGRESSIVE TYPE II CENSORING
Shrinkage estimation is a robust procedure that integrates an unbiased estimate with the current estimate using a weighted factor. Several weighted factors dynamically adjust the shrinkage factor based on the sample size and censoring proportion, ensuring that the method tailors the shrinkage to the available data rather than applying a constant factor. Despite certain real-world application limitations, the Lomax distribution exhibits flexibility as a tail-behaviour modelling technique, making it particularly suitable for reliability and survival analysis. The present study focuses on estimating the shape parameter of the Lomax distribution using PTII censored sampling within the framework of the shrinkage estimator method. Shrinkage estimators are derived based on constant and shrinkage weight factors under different weight functions. These depend on the base estimates sample size, bias, and variance to shrink the current estimate. The performance of these estimators is rigorously evaluated through a Monte Carlo simulation study. 2026 Pushpa Publishing House, Prayagraj, India. -
Shrinking Sizes, Swelling Prices: Evaluating the Ripple Effects of Inflation and Shrinkflation on Economic Growth Using Dynamic Panel Framework
This study used the novel cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model and the JuodisKaraviasSarafidis (JKS) causality test to investigate the intricate nexus between inflation, shrinkflation, and economic growth in 20 countries from 1990 to 2022. The results validated the detrimental effects of inflation and shrinkflation on economic growth, underlining price stability, and reverse or positive shrinkflation as crucial for sustained expansion. Causality analysis further revealed feedback causality between inflation and economic growth. Finally, our findings solidify the quantity-led growth or value-driven growth hypothesis. Reverse shrinkflation, or growth driven by value, drives economic growth unidirectionally. Consumption expenditure increases as the value or quantity of goods and services increase, which boosts consumption, aggregate demand, and economic growth. Hence, to stimulate sustainable economic growth, policymakers should implement prudent monetary policies to control high inflation, a major contributor to shrinkflation. Additionally, industries should be encouraged to enhance productivity and reduce manufacturing costs without sacrificing product size or quality. Lastly, it is essential to monitor pricing changes in critical industries and intervene if unjustified shrinkflation trends emerge. 2025 Emerging Markets Institute, Beijing Normal University -
Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English-Hindi Language Pairs
The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason for this is that it is difficult to represent the text from two different languages in a common semantic space. In this article, a novel Siamese architecture-based model is proposed to detect the cross-lingual plagiarism in English-Hindi language pairs. The proposed model combines the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network to learn the semantic similarity among the cross-lingual sentences for the English-Hindi language pairs. In the proposed model, the CNN model learns the local context of words, whereas the Bi-LSTM model learns the global context of sentences in forward and backward directions. The performances of the proposed models are evaluated on the benchmark data set, that is, Microsoft paraphrase corpus, which is converted in the English-Hindi language pairs. The proposed model outperforms other models giving 67%, 72%, and 67% weighted average precision, recall, and F1-measure scores. The experimental results show the effectiveness of the proposed models over the baseline models because the proposed model is very efficient in representing the cross-lingual text very efficiently. Copyright 2023, Mary Ann Liebert, Inc., publishers 2023. -
Sibling Bereavement Among Young Indian Adults
This qualitative study explores the bereavement experiences of 12 surviving siblings in India, focusing on familial, societal, and cultural influences. Six themes emerged: The Demanding Familial Role, Isolation That Accompanies the Grief, Damaging Impact of Society, Positive Role of Friends and Family, Support Systems, and Continuing Bonds. Participants often felt the burden of supporting their parents, leading to personal grief suppression and isolation, exacerbated by societal stigmas. Conversely, empathetic friends, supportive extended family, and professional resources like therapy provided crucial coping mechanisms. Continuing bonds with the deceased offered comfort and connection. The study highlights the need for comprehensive support systems tailored to cultural and societal contexts. It emphasizes the importance of public awareness and education to foster a supportive response to bereavement. Further research with larger, more diverse samples is recommended. The Author(s) 2024. -
Sibling influence on musical identity in emerging adults
A key characteristic and an important task of emerging adulthood is identity development. Music provides an important context for identity development, especially in emerging adults, and siblings in turn play an important role in the process of identity development. Using a qualitative narrative approach, this study aims to understand the role of siblings in the musical identity development of emerging adults. Five emerging adults between the age range of 1825years were recruited and interviewed individually. Thematic narrative analysis of the transcripts gave rise to 3 themes: Sibling Relationships, Early Identity and Discrepancy, and The Growth Journey. Overall, it was evident that siblings played a crucial role and had a large impact on the identity development process of the participants. This study provided support for Arnett's theory of emerging adulthood, with identity exploration being a defining feature of this developmental period. Further, the study also supported the notion of important others impacting the identity verification process according to identity control theory. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
SIDNet: A SQL Injection Detection Network for Enhancing Cybersecurity
SQL (Structured Query Language) injection is one of the most prevalent and dangerous forms of cyber-attacks, posing significant threats to database management systems and the overall security of web applications. By exploiting vulnerabilities in web applications, attackers can execute malicious SQL statements, potentially compromising the integrity and confidentiality of critical data. To combat these threats, in this study, we introduce two novel CNN models, SIDNet-1 (SQL Injection-attack Detection Network-1) and SIDNet-2 (SQL Injection-attack Detection Network-2), specifically designed for the classification of SQL injection attacks to bolster web application security. Our comprehensive evaluation includes a comparison of the performance of these customized CNN models against traditional machine learning approaches, highlighting improvements in classification accuracy and reductions in false alarm rates. The proposed models have been experimented with two publicly available dataset SQLI (SQL-Injection) and SQLV2 (SQL-Injection version2). Specifically, SIDNet-1 achieves an impressive accuracy of 98.02% on the SQLI dataset, while SIDNet-2 closely follows with 97.54%. Furthermore, on the SQLIV2 dataset, SIDNet-1 attains 97.77%, and SIDNet-2 achieves 97.83% accuracy respectively. 2013 IEEE. -
Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis
Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individuals sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individuals tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset. 2023 Tech Science Press. All rights reserved. -
Sign Language Diversity and its Impact on Global Communication: Towards a Unified Framework
Sign language is a visually expressive language conveyed through hand gestures and other visual expressions rather than spoken words. Primarily used by individuals with auditory impairments, it is also practiced by many hearing individuals to bridge the gap between the hearing-impaired and hard-of-hearing communities, fostering inclusivity in society. Currently, there are more than 300 different types of sign languages in use, practiced by over 72 million people worldwide. These languages lack a standardized framework, leading to communication, education, and professional integration challenges. This study aims to provide an extensive review of linguistic diversity in sign languages and their impact on communication. It also proposes AI-driven solutions to address these challenges, enabling policymakers, stakeholders, and educators to create an inclusive global community by working toward standardizing sign languages. 2025 IEEE. -
Sign Language Recognition Using Hand Gestures
In everyday interactions between humans and computers, recognizing hand gestures plays a crucial role as it offers a natural and easy way to control and communicate with various applications. This article delves into a detailed exploration of how MobileNet architecture can enhance the accuracy and efficiency of hand gesture detection systems. The aim is to address the limitations found in traditional models by leveraging the optimization and computational efficiency offered by MobileNet. To understand the significance of incorporating MobileNet architecture, the article begins by examining previous research methods used in hand gesture recognition. By conducting a thorough review of existing literature, the study identifies areas where improvements can be made. It then introduces a novel approach that utilizes MobileNet architecture to elevate the precision and effectiveness of hand gesture detection. This approach is tested using a well-established dataset of American Sign Language movements, providing a reliable foundation for training and evaluating the model. This work delves into the technical aspects of implementing the MobileNet-based hand gesture detection system. It explains how the model framework integrates MobileNet architecture and discusses the preprocessing techniques employed for image analysis. The experimental and analytical work conducted during the project is highlighted, emphasizing the iterative process of refining and optimizing the model to achieve optimal performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Sign Language Recognizer Using HMMs
In our day to day lives, we come across especially abled people who perform their daily chores with the aid of motivation that they get from self-confidence. There are many with hearing impairment. Sign language is the most expressed and natural way for them to communicate. Some chains of restaurants have, in fact, recruited deaf servers providing them with employment opportunities. Therefore, automatic Sign language recognition has become the crux of vision research. This paper is based on a project that builds a system that can recognize words communicated using the American Sign Language (ASL). Having been provided with a preprocessed dataset of tracked hand and nose positions extracted from the video, the set of Hidden Markov Models are trained. Using a part of this dataset, identification of individual words from test sequences is done. It provides them with the ability to communicate better, opening up a lot of opportunities. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sign reversal of the spontaneous and induced polarisation in a mixture of achiral liquid crystal host and chiral azo dopant
Achiral liquid crystal, possessing orthogonal smectic A and tilted smectic C phases in its phase sequence, was doped with a chiral photochromic azo dopant. It was found that the spontaneous and induced polarisation in the tilted smectic C* phase and in the orthogonal smectic A phase, respectively, change their sign, as well as their magnitude, under illumination with UV light. The origin of this sign reversal effect is considered to be the different sign of the molecular net dipole moment component y of trans- and cis-isomers of the photochromic azo dopant, respectively. This light-induced sign reversal effect seems to have large potential for applications in the light-light controlled photonic liquid crystal devices, based on this effect. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Signal-aware deep learningbased respiratory motion prediction for lung tumor management
Introduction: Respiratory motion management in radiotherapy for lung cancer patients remains a significant challenge, as it directly affects accurate tumor targeting. Furthermore, unaccounted tumor motion during treatment planning and delivery can lead to imaging artifacts and biased dose distributions, which compromises the accuracy of image-guided radiotherapy. This issue places clinicians in a dilemma between expanding treatment margins, which increases radiation exposure to healthy tissue or risking reduced targeting precision. Methods: In this work, a hybrid deep learning model composed of dilated convolutional layers, bidirectional long-short term memory layers, and a generative autoencoder module is proposed to jointly model the spatial and temporal characteristics of respiratory motion, while enabling reconstruction of the physiologically coherent respiratory signals. Each architectural component learns complementary motion-related patterns from respiratory signals to support tumor motion prediction. The model performs motion-range classification, captures abnormal breathing patterns across spatial and temporal domains, reconstructs physiologically coherent respiratory cycles, and predicts tumor motion within an algorithmic validation framework. Results: Experimental evaluation demonstrates high motion-range classification performance of 98.37%, including low root-mean square error in motion prediction, while maintaining stable performance across long and complex respiratory signals over multiple breathing cycles. Discussion: This study focuses on algorithmic feasibility and establishes a computational foundation for future clinically calibrated and dosimetrically validated models. The findings indicate that the proposed approach can support future motion-aware radiotherapy planning strategies by improving motion characterization at the algorithmic level. Copyright 2026 Das, J. and Medhi. -
Signature based key exchange for securing data and user from web data stealing attacks
Due to the immense technological growth, web and its related applications are becoming a major part of everyday life. The growth of the internet and technology not only increases the positive benefits but also increases negative activities such as data theft. As web applications are used frequently for many online services, it is the most common and valuable target for the adversary to host any web vulnerabilities. Data theft or data stealing attacks are quite common in the web and the internet with severe consequences. The private data are generally stored on the system which gives an opportunity for the attacker to steal the data from the storage or during transit. However, apart from stealing the critical data from the user, the attacker also steals the sensitive data from the web applications. This type of attack takes several forms for stealing perilous information from the user and web application. Unfortunately, these attacks are easy to execute as the attacker needs only the internet connection, a web server and technical knowledge which are readily available. Several prevention strategies exist to thwart the user and the application from the web attacks, however, they do not provide the complete solution. This paper presents the signature based key exchange to prevent the user as well as the web application from several variations of data stealing attacks through mutual attestation. The experimental results show that the proposed method prevents the user and application from data theft than any other existing methods. BEIESP.