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Silenced, Scarred & Shattered: Unmasking the Wounds of Child Sexual Abuse in Select American Memoirs
The research brings to light the marginalized voices of three American women who have written about their sexual abuse in their respective memoirs Roxane Gay, Hunger: A Memoir of my Body (2017), Nikki Dubose, Washed Away: From Darkness to Light (2016) and Neesha Arter Controlled: The worst Night of my Life and its Aftermath (2015). Using these memoirs as primary data and using thematic analysis the study identified three themes which were further classified into different subthemes. Firstly, the research discovered the challenges faced by the survivors in expressing and communicating about sexual abuse due to fear and shame, the survivors do not come forward because of threats, because of rape stereotypes that permeate the society and the fear of what parents and others might think. Secondly, the research explores the various impact of trauma that is caused by sexual abuse which include shame, guilt and self blame, unworthy self, uncontrollable rage, disruption of safety and trust, isolating themselves from everyone, hostility towards body, destructive behaviours which include eating disorder from Anorexia Nervosa to Binge eating disorder, it also includes self harm and substance abuse. Thirdly, the research focuses on the recovery aspect on how the survivors learn to live with the wounds caused by sexual abuse. It focuses on how the survivors came in terms with the abuse, the conflicting feelings of forgiveness and revenge and how they sought redemption through writing their journey. 2025 Sciedu Press. All rights reserved. -
Portrayal of African Americans in hollywood movies released from 2006-2013 /
Racial stereotypes in American movies have mirrored our general public's predominant philosophies and have impacted our belief systems since the film business started. This study relates to the portrayal of how African Americans are projected in the certain movies from a particular time period from 2006-2013. Generalizations of blacks as lethargic, imbecilic, absurd, apprehensive, easygoing, reckless, infantile, rough, sub-human, and creature like, are widespread in today's general public. These debasing generalizations are fortified and upgraded by the negative depiction of blacks in the media. -
QSPR ANALYSIS OF ANTI-ASTHMATIC DRUGS USING REDEFINED FIRST ZAGREB POWER INDEX
In this paper, we introduce a novel degree-based topological in-dex, the Redefined First Zagreb Power Index (ReZG1P I(G)). Explicit formu-lae for ReZG1P I(G) are derived for several standard graphs. Furthermore, we investigate the quantitative structureproperty relationship (QSPR) of anti-asthmatic drugs. The study reveals a strong correlation between the physicochemical properties of these drugs and their corresponding ReZG1P I(G), reflecting the structural representation of molecules as graphs. Finally, we establish linear and quadratic regression models between the proposed molec-ular descriptor and the physicochemical properties of anti-asthmatic drugs. 2026, MUK Publications and Distribution. All rights reserved. -
Attentional Deep Learning with Inverse Transform Sampling for Robust Respiratory Sound Classification
The necessity for efficient breathing sound classification systems originates from respiratory diseases, which impair oxygen-carbon dioxide exchange and impact lung function. Feature extraction and pattern categorization are general components of such systems. Because of their effectiveness with big datasets, deep neural networks have acquired popularity recently in the category of breathing sounds. Enhancing medical care requires cooperation amongst researchers, medical professionals, and patients. An attentional deep learning model with inverse transform sampling is presented in this study to classify respiratory diseases from audio data. Robust models were developed to classify and detect respiratory elements using the Respiratory Sound dataset. The primary objectives include effectively determining lung sounds and determining respiratory illnesses. The architectures of CNN, VGG16, and ResNet50 were developed to extract features and categorize data. Also, the pre-trained models ResNet50 and VGG16 identify critical characteristics in spectrum pictures more accurately. Inverse transfer sampling is used to rectify class imbalance in respiratory datasets. The models achieved 98% accuracy with the CNN model, 83% accuracy with VGG16, and 95% accuracy with ResNet50. Moreover, LSTM and CRNN models offer more information on how respiratory illnesses are classified. 2026, Hemanth K S, Harisha Naik T, N Kartik, N Nanda kumar, S Senthilkumar and Ramya R. -
Hormone Balancing Through Nutrition:Nutritional Strategies and AI Tools to Balance Hormones Associated with PCOS
Polycystic ovary syndrome (PCOS) causes a worldwide disturbance in hormonal equilibrium, leading to a growing need for individualized, non-pharmaceutical therapies. Studies on AI-driven technologies in PCOS have uncovered a groundbreaking approach to regulating hormones through dietary changes. AI algorithms examine individual data, integrating genetics, biomarkers, and dietary habits to provide a distinct metabolic and hormonal profile. Suggested options consist of adaptable meal plans that can adapt to immediate fluctuations in blood sugar and hormones, maximizing nutrition to regulate hormones, reduce inflammation, and enhance general well-being. Additionally, these recommendations provide predictive insights to enable proactive alterations to ones diet. The comprehensive evaluation of limitations and moral considerations about AI highlights the necessity for human expertise and safeguarding of data privacy. Concrete instances from reality and current scientific investigation demonstrate the capacity of AI to enhance the capabilities of women suffering from polycystic ovary syndrome (PCOS). Proposing the integration of AI into evidence-based dietary practices anticipates a future in PCOS care that is tailored, proactive, and data-driven, with the ability to maximize hormonal equilibrium and improve womens well-being. This concise investigation provides significant perspectives for healthcare practitioners engaged in the treatment of PCOS and associated endocrine problems. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Pediococcus pentaceous-mediated fermentation of Gracilaria corticate: A sustainable reutilisation of renewable resource to enhance its nutritional profile, optimised through response surface methodology for improved growth and pathogenic resistance in Oreochromis niloticus
Seaweed, as a functional food and a sustainable alternative to synthetic additives, is gaining attention. It can enhance the nutritive value, improve antioxidant properties and mitigate oxidative stress induced by pathogens. This study investigates the utilisation of fermented seaweeds in feed formulations to reduce oxidative stress, improve fish health and enhance disease resistance. Seaweeds Gracilaria corticate, rich in bioactive compounds such as polyphenols and antioxidants, were fermented using probiotic Pediococcus pentosaceus MK459540. Nile tilapia (Oreochromis niloticus) was fed a diet supplemented with fermented seaweed, which indicates lower levels of Superoxide Dismutase (SOD), Glutathione (GSH) and Glutathione-S-Transferase (GST) activities compared to control and non-fermented seaweeds when challenged with Vibrio harveyi, Aeromonas hydrophila and a mixture of both pathogens. These findings highlight the potential of seaweed, a sustainable and renewable marine resource in advancing aquaculture practices by promoting fish health and immunity. 2026, World Researchers Associations. All rights reserved. -
Benefits of AI in the Food Industry
Artificial Intelligence is increasingly becoming a transformative force in the food industry, reshaping how food is produced, processed, and delivered. The current research on the impact of Artificial Intelligence on improving operational effectiveness in the food industry. Firstly, it emphasizes how AI technologies enhance production processes and optimize supply chain management while prioritizing food safety. By employing predictive analytics and automation, AI contributes to minimizing waste, maximizing resource use, and upholding high-quality standards throughout the production cycle. Secondly, the study investigates how AI boosts customer interaction by offering personalized experiences and streamlined service delivery. By evaluating consumer preferences and behaviors, AI allows food enterprises to customize their offerings, ultimately resulting in heightened customer satisfaction and loyalty. Through this investigation, the research seeks to provide practical insights and best practices for food sector stakeholders aiming to exploit AI for better operational results. 2026, IGI Global Scientific Publishing. All rights reserved. -
Evaluation of mechanical properties of e-glass and coconut fiber reinforced with polyester and epoxy resin matrices
Composite manufacturing is the novel branch of science, which finds its immense applications in various industries such as sporting, automotive, aerospace and marine industries. The superior properties of composites such as stiffness, better mechanical properties, low density and light weight make it a candidate in engineering applications. The need for seeking alternate materials with increased performance in the field of composites revived this research, to prepare fiber reinforced composites by hand layup method using E-glass and coconut fibers with length 5-6 mm. The resin used in the preparation of composites was epoxy and polyester. Fiber reinforced composites were synthesized at 18:82 fiber-resin weight percentages. Samples prepared were tested to evaluate its mechanical and physical properties, such as tensile strength, flexural strength, impact strength, hardness and scanning electron microscope (SEM). Scanning electron microscope analysis revealed the morphological features. E-glass fiber reinforced epoxy composite exhibited better mechanical properties than other composite samples. The cross linking density of monomers of the epoxy resin and addition of the short chopped E-glass fibers enhanced the properties of E-glass epoxy fiber reinforced composite. TJPRC Pvt. Ltd. -
Impact of Carbon Nanofiber Fillers on Surface Finish and Dimensional Accuracy in FDM-printed Polymer Composites Parts
This paper examines the effect of carbon nanofiber (CNF) reinforcement on the surface finish and dimensional accuracy of the polylactic acid (PLA) composite produced using fused deposition modeling (FDM). Twin-screw compounding was used to prepare PLA-CNF composite filaments with 03wt.% CNF with extrusion of filaments (L/D ratio 30:1, 120rpm). To prepare rectangular specimens (10 10 20mm), the filaments were printed with the nozzle size of 0.4 mm, the layer thickness of 0.1 mm, the infill density of 100%, and the print speed of 5 mm/s. Dimensional accuracy was assessed by a digital vernier caliper with a resolution of 0.01 mm, and surface roughness (Ra) was measured by a digital surface tester that was stylus-based. Every measurement was averaged three times, and the statistical examination of percent deviation and variance analysis of compositions was done. Findings show that CNF addition was important with respect to the dimensional stability and surface finish. The volume variation of the print decreased to 0.20% of PLA/3 wt.% CNF compared to 0.30% of neat PLA, which is a 33% smaller variation of the dimensional accuracy. The surface roughness also reduced as 0.70 ?m of neat PLA was reduced to 0.42 ?m of PLA/3 wt.% CNF, which is a 40% decrease. It was statistically tested that trends of consistent improvement were observed between compositions. The findings indicate that CNF reinforcement results in a higher level of geometric accuracy and less surface morphology in FDM-printed polymer composites. 2026 World Scientific Publishing Company. -
Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning
This study explores the developing relationship between health care and technology, with a special emphasis on the use of machine learning (ML) algorithms to detect early stage osteoarthritis (OA) in the hip and knee joints. OA, a substantial worldwide health problem, requires improved diagnosis techniques. In this analysis, we illuminate the limitations of traditional methods, emphasizing the inherent subjectivity of clinical assessments and the delay in detection using routine imaging techniques. The research investigates the potential of ML to bring about significant changes. It focuses on combining various algorithms with extensive datasets and highlights the need to select relevant features and prepare the data to improve the accuracy of the models. The use of ML is closely connected to ethical issues, which include the protection of data privacy and the capacity to comprehend the models used. To bridge the gap between theory and practice, the chapter presents concrete examples of ML's practical use in detecting OA, opening possibilities for customized therapy and enhanced patient results. The chapter also highlights potential areas for future study, emphasizing the urgent requirement for additional progress in ML-based early detection techniques to alleviate the worldwide impact of OA. 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Advancing Nutrient Removal and Resource Recovery Through Artificial Intelligence: A Comprehensive Analysis and Future Perspectives
The increasing difficulties associated with effectively controlling wastewater treatment operations while simultaneously satisfying the imperatives of nutrient removal and resource recovery have necessitated the use of advanced technology. This book chapter provides a comprehensive analysis of the use of artificial intelligence (AI) methods within this complex context. Utilizing a vast array of scholarly investigations and real-world implementations, this study explores the intricate domain of wastewater treatment, providing a comprehensive understanding of how artificial intelligence algorithms are used to enhance the efficiency of nutrient removal procedures and expedite the recovery of valuable resources. This chapter presents a thorough examination of the impact of artificial intelligence (AI) on sustainable innovations in wastewater treatment facilities. It accomplishes this through a comprehensive analysis of relevant data and the inclusion of real-world case studies. The findings of this research highlight the transformative effect of AI on conventional approaches to wastewater treatment, enabling the adoption of environmentally friendly and resource-efficient practices. The integration of artificial intelligence (AI) with wastewater management offers a fascinating story that highlights the shifting paradigm in the field of environmental engineering and the efficient exploitation of resources. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Assessment of diversity, abundance, and seasonal variatons of bird species in Bengaluru District, India during COVID-19 lockdown
The study investgates bird populaton dynamics in Bengaluru, India, post-lockdown, focusing on occurrence, seasonal abundance, species diversity, richness, dominance, and evenness. It covers 55 bird species across 52 genera, grouped into 32 families within 13 orders, with a notable peak in winter. Various indices, including Shannon Wiener, Margalefs, Pielous, and Simpsons, reveal signifcant seasonal diferences in bird populaton characteristcs. The Rock Pigeon Columba livia dominates, while the Black-headed Ibis Threskiornis melanocephalus is less prevalent. The study identfes Near Threatened species like Black-headed Ibis and Oriental Darter Anhinga melanogaster, along with Least Concern species per the IUCN Red List. Common species include Rock Pigeon, Large-billed Crow Corvus macrorhynchos, House Crow Corvus splendens, Black Drongo Dicrurus macrocercus, Brown Shrike Lanius cristatus, Common Myna Acridotheres trists, Jungle Myna Acridotheres fuscus, Red-whiskered Bulbul Pycnonotus jocosus, and Streak-throated Swallow Petrochelidon fuvicola. The study aims to inform improved management and conservaton strategies for Bengalurus diverse bird species. Hemanth et al. 2024. Creatve Commons Atributon 4.0 Internatonal License. JoTT allows unrestricted use, reproducton, and distributon of this artcle in any medium by providing adequate credit to the author(s) and the source of publicaton -
Green Marketing and AI-Driven Branded Entertainment: Ethics and Opportunities
This chapter explores the fusion of green marketing and AI-driven branded entertainment, focusing on ethical, strategic, and creative innovations in sustainable advertising. As brands increasingly adopt green marketing to resonate with environmentally conscious consumers, artificial intelligence (AI) is transforming the way branded content is produced, personalized, and delivered. The chapter discusses ethical concerns, such as transparency, data privacy, and potential manipulation, while highlighting the strategic benefits of AI in promoting sustainability. It also examines the evolving role of advertising professionals, emphasizing the new competencies required to navigate this AI-driven landscape. Case studies of successful campaigns illustrate how AI can effectively engage audiences and drive sustainable consumer behavior. 2025, IGI Global Scientific Publishing. -
Cognitive IoT-Integrated Real-Time Transaction Monitoring System for Financial Security
As financial transactions become more complex in terms of the digitally connected environment, smart and real-time monitoring systems are required to fight off fraud and guarantee safety. The paper proposes a Cognitive IoT based Real-Time Transaction Monitoring System which is a proposed system to the financial sector. The proposed model resorts to the Z-Score normalization as a means of pre-processing the data and Recursive Feature Elimination with Random Forest as a method of selecting the most relevant features affecting the behavior of transactions. As the main type of classifier, a hybrid LSTM-Autoencoder model is used that makes it possible to reliably detect anomalies both using sequential and reconstruction-based learning. Developed on top of TensorFlow and its federated learning extensions, the system maintains the privacy of its user data by supporting decentralized training of edge devices. Experimental tests indicate higher results over the detection accuracy, which reduces false positives, and real-time reactivity. It is a scalable, safe, flexible system that can be used to monitor financial transactions efficiently, using the combined potential of the cognitive computer, the IoT environment, and powerful machine learning to respond to the dynamic nature of financial security issues. 2025 IEEE. -
Empowering women through livelihood interventions: Case studies from an impoverished community
This chapter looks at the influence of a livelihood project in empowering women belonging to an impoverished community from one of the most backward regions of the state of Karnataka in Southern India. Jamkhandi taluq of Bagalkote district is one of the poorer taluqs in the state, with a sex ratio of 938 and a female literacy rate of 50.75%. The Centre for Social Action began working in the area around a decade ago. The livelihood project was an offshoot of a project on Population displacement that was undertaken in the region. CSA adopted the Self-Help Approach (SHA) to meet the needs of this community. This gave the necessary impetus for the creation of a livelihood project for disadvantaged women. The central theme of the chapter is to study the extent of empowerment that is evident among the project's women beneficiaries. This chapter presents the evidence of empowerment using the qualitative case study methodology using ten cases. The theoretical framework provided by the 'Three Dimensional Model of Women Empowerment' is used to present the analysis. Document review and in-depth interviews are the prominent data sets used to present the study's major findings. The hybrid approach to coding and thematic analysis is used to integrate the insights from the theory used as well as observations from the study. Both within-case analysis and cross-case analysis are used for analysis. The personal, relational, and societal dimensions of empowerment are presented through themes emerging from the data. The implication of the chapter is the reiteration of the efficacy of the model in empowering women. This model can be replicated in other project areas, and livelihood strategies can be adopted extensively. 2024 Nova Science Publishers, Inc. -
Yield management in hotel industry: An exploratory study on selected northern states of India: A time series analysis
Asian Journal of Research in Business Economics and Management, Vol. 7, Issue 3, pp. 37-63, ISSN No. 2249-7307. -
Autonomous AI in Automotive Safety: Rethinking Tortious Liability Paradigms
This paper seeks to examine the application of tort law to the Automated Vehicles. Section I deals with a brief overview of the impact of AVs and tort law and the application of tortious principles. Section II discusses the concept of fault and the multiple parties to whom fault can be attributed. Furthermore, it discusses who should be blamed and to what extent. Section III discusses the tortious principles such as product liability, vicarious liability and apportionment of liability and its application to the AVs. Section IV proposes directions for future research, conclusion and recommendations for the legal challenges posed by the AVs. 2026 by IGI Global Scientific Publishing. All rights reserved. -
AI and data-driven digital platforms: the case for establishing global minimum standards in competition law
AI and data-driven platforms have the potential to enable foreclosures, exclusionary and exploitative practices, resulting in the distortion of competition in the digital markets. The inherent conundrum of boundary-less AI and data-driven systems and territorial application of competition laws has created problems in cross-border digital markets. Countries have adopted divergent approaches in enforcing competition law, with some following the per se rule to categorically prohibit MFN clauses and others employing the rule of reason, assessing the anti-competitiveness of such agreements based on effects. Moreover, the countries have adopted either ex-post or ex-ante or a combination of both to regulate digital markets, deepening the divergence in competition, allowing AI and data-driven practices to enable regulatory arbitrage. To establish divergences in regulating the digital markets, the research adopts a comparative approach, analysing the competition law statutes and case laws across jurisdictions. Although competition laws must duly account for domestic market conditions, bringing harmonisation in their enforcement across jurisdictions remains imperative. Adopting a global minimum standard for competition law is a necessary step towards bringing consistency in the application of the competition laws across countries and equipping the competition law enforcement body to confront AI and data-driven market distortions in cross-border digital platforms. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Social Identity of Kodavas Understanding Evolution and Transitions
The Kodavas of Kodagu district in Karnataka have a distinct social structure and follow a set of unique social codes and values peculiar to the community. Various influences have resulted in shifting social identities, which maybe a potential indicator of an identity crisis within the group. The present study follows a Constructivist Grounded Theory approach to inquiry, to arrive at an analytical schema of the process of social identity formation of the Kodavas. The analysis of data collected from forty-one middle and older adults, highlight the core traditional attributes of the Kodava identity, factors contributing to identity transition and its reflection in contemporary times. 2023 Tata Institute of Social Sciences. All rights reserved. -
Protection of Artificial Intelligence Autonomously Generated Works under the Copyright Act, 1957-An Analytical Study
Artificial Intelligence (AI) is not new anymore; it has become a new normal. In the present 3A era (Advanced, automated and autonomous), the Next Rembrandt paintings, Shimons lyrics and songs and Bot Dylans Irish folk songs are the works generated by the AI without any considerable human contribution. In the US, the Copyright Act, 1976 does not protect the works generated independently by the AI without human intervention and thus dropping such works in the public domain immediately after their creation. However, in the UK, the Copyright, Patents and the Designs Act, 1988 under Section 9 (3) attributes copyright to the person by whom the arrangements necessary for the creation of the work are undertaken in case of AI generated works. India has taken a giant leap by considering AI as the joint author along with the human responsible for the creation of work. However, there is not much comprehensive literature available that focuses on the impact of AI being considered as a joint author. This paper aims to create a concrete foundation by emphasising such impact under the Copyright Act, 1957. Furthermore, the paper considers the stance of the US, UK and Australia in protecting AI generated works to suggest measures to the current copyright regime in India. 2023, National Institute of Science Communication and Policy Research. All rights reserved.