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Ag Ions Versus Ag Nanoparticle-Embedded Glass for Antimicrobial Activity Under Light
Incorporating silver nanoparticles (NPs) into a host material has been recognized to limit the release of Ag+ ions, yet their efficacy in neutralizing nearby microorganisms remains uncertain. This study aims to compare the toxicity of Ag+ ions versus the plasmonic effect of Ag NPs within a glass matrix, assessing their respective killing efficiency and mechanisms against microorganisms. To achieve this objective, a simple ion exchange technique was employed to embed glass with silver ions, nanoclusters (NCs), or NPs, which was confirmed by UVVis-NIR spectrometer, photoluminescence (PL), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). The biocidal action of these Ag species on model Escherichia coli (E. coli) bacteria was investigated in the absence and presence of visible light. The findings revealed that in the absence of light, plasmonic Ag NPs were less toxic to E. coli compared to Ag+ ions due to the predominant release of Ag+ ions dictating the antibacterial effect. However, exposure to visible light triggered the plasmonic effect in Ag NPs to disintegrate 100% E. coli in 1h compared to Ag+ ions (68%) owing to the localized heating around the Ag NPs, facilitated by surface plasmon resonance relaxation. The cell morphology investigated by Bio-AFM assisted in unraveling the mechanism leading to bacterial cell damage. Overall, this study demonstrates the sustained disinfection capability of Ag NPs embedded in glass without significant leaching, emphasizing their potential in prolonged antimicrobial applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
After-Sale Service Failures and Their Influence on Customer Behaviour with Reference to Home Appliances
There are continuous technological advancements, and home appliance manufacturers have developed innovative products that make customer's life effortless. The increase in the purchasing power of the customers made the industry more competitive and put an extra burden on the manufacturers to adopt new technologies that help customers solve their problems and fulfil their needs. Firms face problems and challenges in the form of after-sale service failures. After-sale services are an integral part of home appliance products, and the companies can not avoid these while serving the customers. Although the after-service structure is rich in empirical studies on different service sectors like information technology, after-sale service failure, and consumer behaviour modelling in the home appliance have not been adequately investigated in Indian services. Previous researches have relied on understanding the services and their relation to either satisfaction or loyalty. Thus, they have been unable to disentangle the phenomenon of unfavourable reactions after an after-sale service failure from satisfaction and dissatisfaction. After-sale service is an essential component of customer behavioural outcomes. Therefore, businesses need to understand how after-sale service failures influence customer behaviour. Despite service superiority's importance, the home appliance industry lacks industry-specific, widely recognized instruments for after-sale service assessment. The primary goal of this study is to find major after-sale service failures and look at how these after-sale service failures affect customers, leading to unfavourable behavioural reactions. The study used a quantitative approach to understand the issue comprehensively. This research incorporated various after-sale service failure areas discussed and analyzed by previous research. It also discussed service theories and models (Expectancy Disconfirmation Paradigm, Justice Theory, Attribution Theory) related to failures and behaviours. However, this research focuses mainly on how these service failure areas lead to customer behavioural outcomes. Firstly, to know the major after-sale service failure areas, this study prepared the questionnaire based on the literature available on after-sale service failures and customers' reviews and their experience with the after-sale service of the home appliance companies. Data is collected from customers who have experienced after- sale service failures and their subsequent behaviour. The study analyzed the reasons for after-sale service failures, the types of failures that customers encounter, and the impact of these failures on customer behaviour, including their negative word of mouth, switching behaviour, willingness to recommend the brand etc. The findings of this study provided valuable insights into how businesses can improve their after-sale service and retain their customers. The study found seven major after-sale service failures that significantly impact customer behaviours. Unreasonable charges and policy clarity issues are the most significant service failures affecting customers, leading to negative behaviours. These findings show that different types of service failure elicit different reactions. The present study is one of the few empirical studies examining the links between service failures and actual behaviours in consumer durable after-sale service failures. -
After-sale service experiences and customer satisfaction: An empirical study from the Indian automobile industry
For the growth of any industry, services play an essential role. Customers are more aware of the type of services they receive, and the expectations from the service providers are very high. Twenty-two percent total Gross Domestic Product (GDP) of the country is generated through the automotive industry. Global automotive majors have entered India and have dramatically changed the country's car production scenario. Changes to international technology design and adaptation have helped Indian car manufacturing compete globally, facing worldwide challenges. Considering services' high significance and essential role in the automobile industry, this study examined customer satisfaction with after-sales service experiences in the automobile sectorthis paper analyses customer satisfaction concerning automotive service interactions. The conceptual framework explains the impact on customer satisfaction in various car industries from various experiences, including employee behaviour, service lead time, service quality, service processes, and service costs. The respondents from Bangalore were selected. The data collection sampling approach used was convenience sampling. In a standardized questionnaire, data is collected from 400 respondents. The results demonstrate the substantial influence of service interactions on customer satisfaction. 2022 Elsevier Ltd -
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Affordable Two-Dimensional Layered Cd(II) Coordination Polymer: High-Performance Pseudocapacitor Electrode Behavior
In recent years, pseudocapacitive materials have been investigated rigorously as they provide a unique pathway for realizing high-energy and high-power densities. However, innovative approaches involving rational design and synthesis of new materials are still vital to address concerns such as degradation, low conductivity, low cycling performance, high resistance, production cost, etc. Working in this direction, we report the cost-effective synthesis, characterization, and excellent pseudocapacitive behavior of a Cd(II)-based coordination polymer (COP) abbreviated as Cd(DAB). It has been realized in quantitative yield through a facile one-pot reaction occurring among the N4-ligand, 3,3?-diaminobenzidine (DAB), and Cd(II) ions, derived from Cd(OAc)22H2O, at room temperature. The proposed structure of the COP was ascertained by subjecting it to various standard spectroscopic and electron microscopic studies; these techniques reveal the self-assembly of indefinitely long coordination strands into a two-dimensional (2D) layered structure. The electrochemical performance of Cd(DAB) was evaluated as an electrode material for supercapacitors. Owing to its high conductivity, it portrayed remarkable energy storage (pseudocapacitor) behavior; it exhibited a high specific capacitance of 1341.6 F g-1 and a long cycle life with 81% retention over 10,000 cycles at 20 A g-1. Additionally, an asymmetrical supercapacitor device was fabricated, which exhibited a specific capacitance of 428.5 F g-1 at a current density of 1 A g-1 2024 The Authors. Published by American Chemical Society. -
Affiliate Marketing and the Symbiotic Relationship in the Pharma Industry
The objective of the study is to understand the dynamic relationship between customers and the healthcare industry giants in the Indian context. The purpose revolves around how the consumer is benefitting and at the same time, how the indirect third-party affiliates also earn marginal profits along with serving the customers. The study is backed by both primary and secondary data, which were collected from 173 individuals from various fields through a questionnaire. The convenience sampling method was used to select the respondents, and the Technology Acceptance Model (TAM) was used to propose the model for the study. There exists a parallel symbiotic relationship between consumers, pharmaceutical companies, and affiliates. The application of this research can be put to use for the startups, which want to explore and excel in this industry along with the future researchers who want to forecast and study the progress of the pharma companies in the long run. The empirical evidence of this paper highlights a unique relationship between affiliates, the pharma sector, and customers, which drives customer buying behavior and a combination that has not been explored yet. The study provides a unique understanding of how feedback from customers in third-party applications can benefit and produce huge profit margins down the line. 2025 Apple Academic Press, Inc. -
Affective geographies and the anthropocene: Reading shubhangi swarups latitudes of longing
This paper is a critical reading of the affective and emotional geographies imagined in the Islands plot-line of Shubhangi Swarups novel Latitudes of Longing (2018). The paper argues that Swarup presents the case of a rethinking environmental aesthetics that conveys a deeper sense of space, time, and place. By creating an ambient poetics to negotiate human and non-human interconnectedness, the paper demonstrates the strength of novelistic traditions and their potential to generate an idea of affect that is transcorporeal as one not located only in the site of the human body, instead, emanating from a more nuanced interconnectedness between the human and the non-human world. Informed by affective ecocriticism and Zayin Cabots multiple ontologies approach that generates ecologies of participation, the paper closely reads the Islands section to establish how literary illustrations provide an instance to widen the horizons of environmental engagement and generate a narrative imagination that encompasses a larger ecosystem cutting across geological spacetimes in the Anthropocene. Swarups use of fiction is critically used to generate an ecoaesthetics that leads to a more informed ethical action towards recognizing the interconnectedness of living and non-living forms that create sustainable ecologies. 2021 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore), ISSN: 0253-7222. -
Affecting computing in multimodal mobility
Computational models that simulate human emotions have witnessed a substantial development in recent years for widening the spectrum of applications. Emotional computation is becoming crucial in human-to-computer interactions with exponential growth of artificial intelligence. Normally referred to as emotion recognition, it is widely believed that the prospective detection of a person 's emotional state of mind should be computed from their facial expressions. Face-movement combinations may express many different emotion types, for instance, hate, anger, panic, joy, grief, surprise, shock, to name a few. The goal and emphasis of this manuscript is the deployment of different algorithms and computation models for emotions. Considerable advancements in this domain of emotion recognition can be made through AI model development that discusses the challenges of the system and Facial Action Coding as an integral part of the models. 2023, IGI Global. All rights reserved. -
AeroGlan: A Smart and Sustainable Plant Species Estimator For Organic And Localized Air Filtering
Introduction: Human health is significantly compromised by air pollution, especially by local air quality. The majority of our society spends their lives in a confined geographical location, which if subjected to air pollution can expose them to long-term air contamination. It is also possible that poor air quality can pose serious health risks, especially to susceptible individuals thereby impacting their lifestyle. Air quality can be improved with appropriate plantation, but they are underutilized. Various air purification devices have been developed in response to the ever-increasing air pollution level. Methods: However, artificial means of air purification are not very viable in terms of cost, accessibility to society, and reliable tools to purify air. This research integrates traditional solutions with modern technology to counter air purification by selectively using plant species and placing them in desired locations suitable for urban settings. The study aims to measure the constituents of various air pollutants spanning across regions to identify and accumulate pollution data using IoT-based smart devices, remit, and feed this information to cloud-based storage for further processing. In addition, advanced predictive intelligence is utilized to determine the plant species that can suffice the need for air purification through organic means in a given geographical zone resulting in enhancement of Air Quality (AQ), with minimal cost, prolonged shelf life, future proof and non-detrimental consequences. Results: Implementation outcome gives a promising outcome. Accurate readings of various air pollutants are aggregated. Suitable trees are identified to tackle these pollutants and their absorbing capacity is determined. Various predictive methods are employed and the random forest model recorded the best results. The sensory units of the model successfully captured the pollutant data and any major fluctuations were reported. The prediction pipeline recorded a mean precision, recall, and f-score value of about 0.95, 0.92, and 0.94 respectively while the mean accuracy of 0.965 was also noted. The observed training and validation accuracy with our model were 0.96 and 0.93 respectively. Conclusion: Hence, the proposed AeroGlan model may be locally applied as an air pollutants monitoring device and also to suggest suitable plant species required to counter air contamination in that locality. 2025, Bentham Science Publishers -
Adverse Childhood Experiences, Psychological Well-wBeing, and Grit: A Comparative Study between LGBTQIA+ and Cis-Heterogeneous Sample of India
Adverse childhood experiences (ACEs) is a major concern that has been related to serious health consequences. Moreover, lesbian, gay, bisexual, transgender, intersex, asexual, and queer (LGBTQIA+) individuals are more likely to experience ACEs than cis-heterosexual individuals, especially in India. However, research in India has been scarce. This study compared these variables between Indian LGBTQIA+ individuals (n = 102) and cis-heterosexual individuals (n = 118) aged between 18 and 25. The findings of this comparative study reveal significant differences between LGBTQIA+ and cis-heterogeneous groups in terms of ACEs and grit levels. Notable differences were also discovered in three domains of psychological well-being: environmental mastery, positive interpersonal relationships, and self-acceptance. However, the vulnerability of LGBTQIA+ individuals in India reveals itself in descriptive statistics that report they are susceptible to negative outcomes in mental health. This study further emphasizes the importance of implementing focused interventions and support to increase psychological well-being and grit in the LGBTQIA+ community. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
Adversarial Shadows in Digital Forensics: New Insights Into File Fragment Classification Vulnerabilities and Defenses
The paper is a comprehensive survey of adversarial attacks on file fragment classification (FFC) models - a relatively unexplored area in digital forensics, given the increasing application of machine learning techniques. Unlike image or text classification adversarial attacks, adversarial attacks on FFC exploit statistical and structural properties at the byte level in systems that lack semantic or perceptual knowledge. Such properties necessitate the use of domain-specific defense strategies, as the defense strategies adopted from other domains are typically not effective for the problems of FFC. The survey comprehensively evaluates attack mechanisms relevant to FFC, including evasion and poisoning attacks, and discusses their impact on forensic reliability. It highlights the absence of domain-specific benchmarks, robust evaluation protocols, and systematic research on the adversarial robustness of FFC. The paper also discusses the different types of byte level perturbations that can happen in fragment data, and it sets specific research priorities for raising the reliability of machine learning-based digital evidence recovery and security. The paper provides building blocks for future work, offering practical insights for development in ensuring file fragment classification systems utilized in forensics are secure. 2013 IEEE. -
Adversarial networks in image generation: A detailed approach to manage datasets and to analyze discriminator and generator losses using GANs
Image production has been transformed by generative adversarial networks (GANs), which have made unprecedented realism and diversity possible. Still, there are significant hurdles in managing datasetsdatasets managing and analyzing lossesloss analysis. This book chapter focusses on dataset administration and loss analysis, while providing a thorough method for using adversarial networks for image production. A thorough approach for selecting and preparing datasets, while maintaining optimal GAN performance is put forth by researchers. The proposed research approach enables the effective training of GANs, resulting in high-quality image generationhigh-quality image generation. Experimental results demonstrate the efficacy of the current method, showcasing improved image realism and diversity. The suggested strategy also presents a fresh way to examine discriminator and generator lossesgenerator losses, offering new perspectives on the convergence and stability of GANs. This study advances the field of GAN-based image productionGAN-based image production and offers professionals and academics who wish to use adversarial networks a priceless tool. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. -
AdvanDNN: Deep Neural Network Analysis of Neuroimaging for Identifying Vulnerable Brain Regions in Autism Spectrum Disorder
Exploring the neurological framework of autism spectrum disorder (ASD) presents a significant challenge due to its diverse manifestations and cognitive impacts. This study introduces an innovative deep learning approach, employing an advanced deep neural network (AdvanDNN) model to identify and analyze brain regions vulnerable to ASD. Utilizing the AAL116 brain atlas for anatomical standardization, our model processes a comprehensive set of neuroimaging data, including structural and functional MRI scans, to discern distinct neural patterns associated with ASD. The AdvanDNN model, with its robust deep learning architecture, was meticulously trained and validated, demonstrating a notable accuracy of 91.17% in distinguishing between ASD-affected individuals and controls. This marks an improvement over the state of the art, contributing a significant advance to the diagnostic processes. Notably, the model identified a pronounced anticorrelation in brain function between anterior and posterior regions, corroborating existing empirical evidence of disrupted connectivity within ASD neurology. The analysis further pinpointed critical regions, such as the prefrontal cortex, amygdala, and temporal lobes, that exhibit significant deviations from typical developmental patterns. These findings illustrate the potential of deep learning in enhancing early detection and providing pathways for intervention. The application of the AdvanDNN model offers a promising direction for personalized treatment strategies and underscores the value of precision medicine in addressing neurodevelopmental disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Advancing Tibetan Text-to-Speech: Challenges and Innovations
This initiative aims to develop a platform for Tibetan Text-to-Speech (TTS) technology, addressing the significant demand for this technology for the Tibetan language. The main objective of this project is to create a system that is capable of converting text into natural and good quality speech. Through the compilation of Tibetan text-audio datasets, the project meets the increasing demand for technology that preserves oral traditions and allows Tibetans to communicate with other people interested in the language. The process includes the gathering of varied Tibetan text and audio samples, such as news articles, followed by processing of data through cleaning processes and statistical analysis. A benchmark dataset is created to enable the testing of models. The lack of certain resources for Tibetan TTS is addressed by the development of pre-trained machine learning models specific to acoustic modeling, using the adapted FastPitch model for waveform synthesis through the HiFi-GAN vocoder. The existing models were?further trained utilizing features particular to Tibetan phonetics and tonalities. The TTS approach is a key strategy for improving digital accessibility for Tibetan speakers and for safeguarding their cultural heritage; it finds applications in media, education, and communication, thus helping to preserve the Tibetan language in the digital era. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
ADVANCING THE FRONTIERS OF NEUROCOGNITIVE REHABILITATION: Research and Practice Ahead
This chapter explores the expanding horizons of neurocognitive rehabilitation by synthesizing emerging trends in research, technology, and practice. With a focus on translational innovation, it identifies how cutting-edge advancements, such as neurofeedback training (NFT), braincomputer interfaces, and artificial intelligence (AI)-driven diagnostics, are reshaping cognitive recovery pathways. Emphasis is placed on the growing need for culturally and contextually responsive models, particularly in low-resource settings, as well as scalable, tech-enabled delivery methods that enhance accessibility and personalization. The chapter also highlights the critical importance of long-term outcome studies, interdisciplinary collaboration, and workforce upskilling to support sustainable integration of novel tools into routine care. Ethical considerations, including data privacy, informed consent in neurotechnological interventions, and the equitable distribution of emerging therapies, are also explored. As the field advances, the convergence of neuroscience, digital innovation, and individualized care promises to transform neurocognitive rehabilitation from reactive to anticipatory, from standardized to precision-based. Ultimately, this chapter advocates for a global, equity-focused, and evidence-based framework that places individuals, not just their impairments, at the center of rehabilitation science and practice. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Advancing the Evaluation of Oral Fluency in English for Specific Classrooms: Harnessing Natural Language Processing Tools for Enhanced Assessment
A crucial component of language learning and teaching is evaluating students' speaking abilities. Natural language processing (NLP) techniques have been employed recently in language assessment to automate the evaluation process and produce more impartial and reliable findings. In this study, we offer a speaking evaluation tool based on Natural Language Processing (NLP) that assesses a learner's speaking ability in real-time using cutting-edge algorithms. The instrument is altered to assess the fundamental facet of speaking skills - Fluency. As a result of the tool's immediate feedback, learners may pinpoint their areas of weakness and focus on honing their language abilities. The usefulness of the instrument was assessed through an intervention with a sample size of 30 students of the post-graduate students of a college in Pune, India. Python libraries, including random and re, were utilized to implement the algorithm. Data preprocessing involved accurate transcription of videos using an online tool and manual checking for corrections. Despite acknowledging limitations, such as potential biases in manually inserted hesitation markers, the study serves as a pivotal step toward automated fluency assessment, presenting exciting prospects for NLP and language education advancements. 2024 IEEE. -
Advancing Sustainable Agriculture Through Artificial Intelligence: Harvesting Greener Future
The growing population of the globe is expected to reach 10 billion by 2050. Its demands have led to widespread food insecurities and hunger, which conventional farming failed to address. To meet such a grim scenario, the United Nations drafted the Sustainable Development Goals (SDGs) to end hunger (SDG 2), promote sustainable consumption and production (SDG 12), and promote life on land (SDG 15). To achieve these goals, the global farming system needs to make a substantial transition by adopting alternative methods and approaches. The concept of sustainable agriculture is an integrated system of animal and plant production practices to satisfy human food and fiber requirements. It enhances environmental quality, efficient use of energy resources, economic viability of farm operation, and quality of life of farmers and society. It has emerged as a viable alternative to meet such goals. Sustainable agriculture practice incorporates the true spirit of the abovementioned SDGs by combining the aspirations of the present and future generations. Smart technologies, which include the use of robotics, intelligent sprayers, satellite drones, Internet of Things (IoT) devices, and climate sensors, appear as supplementary tools for sustainable agriculture to mitigate the accelerated demands of food across the globe. The advancement of artificial intelligence (AI)/IoT technologies has the potential to monitor the agricultural environment to ensure high-quality products. According to the World Economic Forum (WEF), the integration of AI into the farming sector can provide an adequate food supply to the people. Such integration of smart technologies with sustainable agriculture has significant advantages for the world in achieving sustainable development goals. However, smart technologies are not free from challenges due to their novelty, complications related to the control and operation of IoT/AI machines, data sharing and management, interoperability and large amounts of data ownership, analysis, and storage. Keeping in view the above, this chapter explores the integration and interaction of AI-enabled smart technologies and smart agriculture analysing key challenges involved in this regard. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Advancing Supply Chain and Logistics With Emerging Technologies
Abstract The digital era is transforming industries, requiring businesses to innovate for cost- efficiency and scalability. This chapter explores emerging technologies like cloud computing, AI, automation, blockchain, and IoT across healthcare, retail, finance, and logistics. It examines digital transformation's impact on traditional business models, industry trends, and strategies for adaptation. Technology integration enhances competitive advantage through big data analytics, digital marketing, and continuous innovation. Sector- specific insights cover IoT and blockchain in supply chains, AI diagnostics in healthcare, omnichannel retail, and digital finance solutions. The chapter provides recommendations for fostering innovation, investing in technology, and forming digital partnerships. Concluding with key takeaways and a future outlook, it serves as a resource for executives, entrepreneurs, and industry professionals navigating digital transformation for efficiency. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Advancing Spatio-Temporal Predictive Modelling in Intelligent Transportation Systems: A Comprehensive Survey of Machine Learning and Deep Learning Approaches
In an effort to alleviate traffic and improve urban mobility, intelligent transportation systems (ITS) relies heavily on forecasting traffic. In the paper, a comprehensive survey on spatial temporal predictive modelling techniques for forecasting traffic has been presented. The focus remains on advanced machine learning and deep learning that have been developed between 2017 and 2025. With the use of state of the art technologies to forecast both in real time scenarios (short-term) traffic prediction and long term forecasting, such as transformer based models, (RNN) recurrent neural networks, convolutional networks on grids and graphs, and (GNN) graph neural networks. Former approaches were examined for strengths and limitation to capture intricate temporal dynamics and spatial interdependencies. Through the above findings, a brand-new conceptual methodology that associates attention mechanisms and graph-based learning to increase prediction accuracy with computing efficiency has been proposed. The performance improvements of newer methods over the conventional methods are also shown through a comparison of the experimental findings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Advancing Software Defect Detection and Prevention: Bridging Gaps in Early-Stage and Evolving Software Systems
Software defect prediction (SDP) is a critical method in modern software development, saving costs while ensuring the delivery of high-quality software systems. This study investigates the vital importance of SDP, focusing on its function in detecting and correcting software faults that might lead to system failures. SDP forecasts defect-proneness and optimizes software-testing processes by using software metrics such as lines of code and change information. The chapter examines the progress of SDP research since the turn of the century, emphasizing the academic emphasis on refining static characteristics and establishing efficient learning methods for building high-performance defect predictors. Recognizing the economic consequences of software flaws, particularly in major engineering projects, this chapter emphasizes the importance of SDP in limiting project failures and economic losses in the twenty-first century. Several defect prediction methods are investigated in the context of software quality, with an emphasis on ongoing attempts to prevent and discover errors early in the software development lifecycle. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.