Era Of Innovation Research Articles

Transforming Libraries: The Impact of Artificial Intelligence

Artificial intelligence (AI) is revolutionising library services and enhancing user experience, creating a new era of efficiency, accessibility, and innovation. This editorial explores how AI reshapes the library landscape, ushering in a new generation of efficiency, accessibility, and innovation. The AI’s most notable contribution to libraries is the automation of routine tasks, cataloguing and organising; librarians can now rely on AI algorithms to streamline. It saves librarians valuable time engaging in intellectually stimulating activities and ensures a more accurate and organised library system. AI-driven systems can efficiently analyse vast amounts of data, leading to improved search functionalities and more seamless information retrieval for library patrons. Moreover, AI is instrumental in the personalisation of library services. Through machine learning algorithms, libraries can analyse user behaviour, preferences, and historical data to provide customised recommendations. This enhances the user experience by offering tailored suggestions for reading materials, resources, and services, making libraries more relevant and engaging for diverse user groups. Another essential role of AI in libraries is the development of virtual assistants. These digital assistants, powered by natural language processing and machine learning, can help library patrons navigate the catalogue system, answer queries, and even provide Realtime language translation service. Virtual assistants help libraries become more inclusive and accessible, reaching more audiences with diverse language backgrounds and information needs.

Confined mass transport in two-dimensional capillary

Abstract Over the past decade, nanofluidics has undergone significant expansion, propelled by advances in crafting artificial channels at nanometric and sub-nanometric scales with diverse geometries. Central to this domain, two-dimensional capillaries have risen as a pivotal research platform, marked by their angstrom-level precision, unparalleled wall surface smoothness, and clearly defined surface charge states. Their advent has profoundly deepened our understanding of mass transport dynamics, spanning gases, water molecules, and ions, shedding light on the complex interactions among various influencing factors and revealing a range of previously undiscovered physical phenomena. This review delves into the development of 2D capillaries, the principal fluid transport phenomena observed within, and the critical elements that affect these processes. We also touch on a fascinating discovery - the quantum liquid friction seen in water moving over carbon surfaces. In anticipation of future explorations in nanofluidics, we envision a trajectory aimed at emulating the efficiency levels of biological protein channels, setting the stage for a new era of scientific inquiry and technological innovation.

Artificial general intelligence for the upstream geoenergy industry: A review

Artificial General Intelligence (AGI) is set to profoundly impact the traditional upstream geoenergy industry (i.e., geothermal energy, oil and gas industry) by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the upstream geoenergy landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the upstream geoenergy industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream geoenergy industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.

Perspectives on the future of ecology, evolution, and biodiversity from the Council on Microbial Sciences of the American Society for Microbiology.

The field of microbial ecology, evolution, and biodiversity (EEB) is at the leading edge of understanding how microbes shape our biosphere and influence the well-being of humankind and Earth. To that end, EEB is developing new transdisciplinary tools to analyze these ecologically critical, complex microbial communities. The American Society for Microbiology's Council on Microbial Sciences hosted a virtual retreat in 2023 to discuss the trajectory of EEB both within the Society and microbiology writ large. The retreat emphasized the interconnectedness of microbes and their outsized global influence on environmental and host health. The maximal potential impact of EEB will not be achieved without contributions from disparate fields that unite diverse technologies and data sets. In turn, this level of transdisciplinary efforts requires actively encouraging "broad" research, spanning inclusive global collaborations that incorporate both scientists and the public. Together, the American Society for Microbiology and EEB are poised to lead a paradigm shift that will result in a new era of collaboration, innovation, and societal relevance for microbiology.

Migration Perspectives of Water Sector Cybersecurity

The use of digital technologies in the water sector has proved revolutionary in water management but also opens up critical infrastructure to cyber threats, with extreme risks to public health, economic instability, and even forced migration. Therefore, this paper will address how cybersecurity vulnerabilities intersect with migration through case studies from various regions like Norway, Israel, and the United States. By looking into the above incidents, the research speaks of the cascading effects of cyber-attacks on water systems and the urgency for all-encompassing policy frameworks addressing cyber and migration. On a similar note, it points out that the intensification of cyber threats is partly due to the technological innovation era and, on the other hand, the weaknesses in cybersecurity postures. It further highlights how these emerging technologies—specifically artificial intelligence and blockchain—can improve resiliency and provide practical recommendations for implementation even in resource-constrained settings. Through this multidisciplinary approach, the research contributes to a better understanding of protecting communities and critical infrastructure from the growing threat of cyber-induced displacement.

Modeling the significance of corporate social responsibility on green capabilities and sustainability performance

This study examines the complex interplay among corporate social responsibility (CSR), green dynamic capabilities, green innovation capabilities, and organizations' economic, environmental, and social performance in the current era of innovation and sustainability. The proposed framework was tested using data from 634 medium- and large-sized Chinese manufacturing firms. Partial least squares structural equation modeling was employed to analyze the data. The findings demonstrate that CSR to external and internal stakeholders positively influences green dynamic capabilities. Meanwhile, CSR to external stakeholders and green dynamic capabilities are positively associated with green innovation capabilities, subsequently influencing the economic, environmental, and social performance of manufacturing firms. Moreover, the results reveal the mediating effect of green dynamic capabilities on the association between CSR and green innovation capabilities. Green innovation capabilities also mediate the association between CSR to external stakeholders and green dynamic capabilities on manufacturing organizations’ economic, environmental, and social performance. This study offers a novel exploration of the intricate interplay among CSR, green dynamic capabilities, green innovation capabilities, and organizational performance within manufacturing organizations. These findings provide valuable insights for managers and offer opportunities for further research that will ultimately contribute to a deeper understanding of the roles of sustainability and innovation in modern corporations.

Exploring the impact of AI-driven and blockchain-enabled tax filing systems on smes in the era of technological innovation: A review of benefits, challenges, and adoption barriers

The emergence of blockchain technology and artificial intelligence (AI) presents transformative opportunities for tax compliance, especially for small and medium-sized enterprises (SMEs). These technologies promise to enhance tax system accuracy, efficiency, and transparency. However, their adoption is accompanied by significant challenges and barriers. This review analyzes the benefits, challenges, and future prospects of integrating AI and blockchain technologies into tax systems, with a focus on their impact on SMEs. It seeks to provide insights into how these technologies can reshape tax compliance and identify areas requiring further research. The review revealed that AI enhances accuracy and compliance through advanced predictive models and explainable AI, while blockchain ensures transparency and trust with its immutable ledger and smart contracts. Despite these benefits, SMEs face technical and financial constraints, such as high implementation costs and integration complexities. Regulatory and legal challenges, including evolving tax laws and data privacy requirements, further complicate adoption. Additionally, resistance to new technologies and skill gaps within SMEs hinder widespread implementation. Scalability and customization issues also pose significant barriers. In conclusion, AI and blockchain technologies substantially improve tax compliance but come with notable challenges. These technologies promise enhanced accuracy and efficiency for SMEs, yet overcoming barriers related to cost, integration, and regulation is essential for successful adoption. It was recommended that future research should focus on developing SME-specific solutions, addressing regulatory adaptations, and conducting comparative analyses of traditional versus AI-driven tax systems. By tackling these areas, stakeholders can better support SMEs in leveraging AI and blockchain technologies for more effective and transparent tax compliance.

Advent of machine learning in autonomous vehicles

The advent of Machine Learning has significantly transformed the landscape of automation, heralding a new era of efficiency, precision, and innovation. This literature review explores the pivotal role of machine learning in advancing automation across various industries. By examining the evolution of machine learning algorithms and their integration into automated systems, the paper highlights key developments and breakthroughs that have enabled machines to perform complex tasks with minimal human intervention. The review delves into case studies from manufacturing, healthcare, finance, and driver-less vehicles, illustrating how machine learning-driven automation has improved productivity, enhanced decision-making, and reduced operational costs. Furthermore, the paper discusses the challenges and ethical considerations associated with the widespread adoption of machine learning in automation, such as data privacy, job displacement, and algorithmic bias. By synthesizing findings from recent research, this review provides a thorough study of the current state and future potential of machine learning in automation. The insights gained underscore the huge impact of machine learning technologies, along with the need for continuous innovation and regulation to harness their full potential while mitigating associated risks. This paper serves as a valuable resource for academics, industry professionals, and policymakers aiming to navigate and contribute to the rapidly evolving field of machine learning in automation.

Integrating "Talking Images" in Education: A Case Study

The era of innovation in education is well underway. Effective practices demonstrate the power of technology in motivating students and improving learning outcomes. This paper examines the integration of the talking image, a new and innovative technology, into the educational process. It also sets out the methodology and the educational benefits resulting from its use. This technology combines visual and auditory elements and is an effective tool for language education, suitable for students with learning difficulties. It can be applied in various educational contexts, from science and mathematics to art, and can enrich a simple narrative or provide personalized content. This paper is a significant contribution to the field, serving as a valuable resource for educators interested in integrating cutting-edge technologies into their pedagogical practices. New research in this direction should be done on the pedagogical use of talking pictures in education and the new frameworks for their integration.

Chemical sensors: From fundamentals to the future—A review

<p>Chemical sensors bridge the gap between the chemical and electrical/optical domains, offering a powerful tool for analyzing our environment. These ingenious devices, with detection limits reaching parts-per-billion (ppb) for some analytes, rely on interactions between a specific material and the target molecule. This interaction, which can involve changes in electrical current, light emission, or mass, is translated into a measurable signal. This review delves into the core working principles of various sensor types, highlighting their diverse applications. From environmental monitoring (tracking air and water pollutants at concentrations as low as 10 ppb) to medical diagnostics (detecting biomarkers for early disease identification), chemical sensors play a crucial role in shaping a safer and healthier future. Recent advancements, such as miniaturization and integration with nanomaterials, promise even greater sensitivity, portability, and affordability, paving the way for a new era of sensor-driven innovation. This review article explores these advancements and their potential impact on various fields, inspiring further development and exploration of this transformative technology.</p>

An Overview of the Components, Techniques, and Innovation Used for Fast-Dissolving Films.

Fast-dissolving films (FDFs) stand as a cornerstone in modern pharmaceutical innovation, heralded for their unrivaled convenience, patient adherence, and the swift onset of action. Within the realm of this paper lies a meticulous exploration of FDFs, delving deep into their intricate formulation intricacies, manufacturing methodologies, and diverse therapeutic applications. The formulation journey traverses the meticulous selection of film-forming polymers, plasticizers, taste-masking agents, and active pharmaceutical ingredients (APIs), each element meticulously calibrated to achieve optimal efficacy. Diverse production techniques, spanning solvent casting, hot melt extrusion, and spray drying, are scrutinized with precision, shedding light on their inherent advantages and nuanced limitations. Beyond the realm of formulation and manufacturing, this review embarks on an expansive voyage into the vast therapeutic landscape that FDFs traverse. From the treatment areas of central nervous system disorders to the realms of cardiovascular diseases, allergies, and pain management, the versatility and adaptability of FDFs emerge as a beacon of hope for patients and practitioners alike. Yet, amidst the brilliance of FDFs, inherent challenges linger, from stability concerns to the intricate dance of bioavailability and the ever-watchful eye of regulatory scrutiny. However, within these challenges lies the fertile ground for future research and development endeavors, beckoning forth a new era of innovation and advancement in the realm of rapidly dissolving films. This study stands as an indispensable compass, guiding researchers, pharmaceutical scientists, and practitioners toward the forefront of FDF development and implementation.

Welcome from the New Editor-in-Chief: Leading Pediatric Neurology into a New Era of Innovation and Progress

Welcome from the New Editor-in-Chief: Leading Pediatric Neurology into a New Era of Innovation and Progress

Structure and process: A theoretical model of intelligent elementary Chinese vocabulary teaching

With the rapid development of artificial intelligence technology, Chinese vocabulary teaching is gradually entering an era of innovation, offering vast potential for more intelligent and personalized teaching models. Existing research mostly focuses on the auxiliary role of AI technology in second language teaching, with less attention given to the implementation of intelligent second language teaching. To address this issue, this study, based on the perspective of Artificial Intelligence Generated Content (AIGC), attempts to construct an elementary Chinese vocabulary teaching (ECVT) theoretical model, presenting the development process of an ECVT system. From the existing research literature on Chinese vocabulary teaching, a total of 17 viewpoints on teaching structures and 19 viewpoints on teaching processes for ECVT are outlined. Based on this, the study first establishes the fundamental macro-level phases of ECVT, then delves into the micro-level structures and processes of each phase, ultimately deducing a theoretical model for ECVT oriented towards intelligent teaching. Integrating the perspectives of artificial intelligence deconstruction and generation into the research on Chinese vocabulary teaching not only offers front-line teachers a reference for optimizing teaching models but also provides a more forward-looking and scientifically grounded framework for Chinese language teaching, even other language teaching. This hence propels innovation in second language teaching models.

Rural Community Engagement In Higher Education Institutions – Revisiting the Existing Literature

In India, Social work education has been mainly Western-centric and failed to address societal needs and their problems. There is a growing realization of limitations of the Western model in the Indian context that calls for Indigenous social work practice. It is because of the borrowed teaching content from Western literature, and there is hardly any relevance in the Indian context. We are living in the era of global research and innovation, where one could not underestimate scale and important of the HIEs. To achieve goals of socio-economic development, HIEs can contribute effectively, through community engagement program. In 2018, UGC launched an ambitious Quality Mandate that emphasis to improve the societal linkages of HEIs which could be pivotal for students to become socially productive. In addition, there is constant need in the Higher Institute to develop better understanding and incorporating the issues which are very happening in the society and it could be achievable by bring in social responsibility and community in their vision and mission itself, in result of this definitely there will be improvements in the standard of research as well teaching. This paper center on works on indigenous knowledge reflects how the criteria for involvement to rural community could be pivotal within the local context by exploring vivid literature on indigenization and their influence on the paradigm of social work and its curriculum specially which is related to rural community engagement in Higher Education Institution.

Click Chemistry for the Generation of Combination of Triazole Core and Thioether Donor Site in Organosulfur Ligands: Applications of Metal Complexes in Catalysis.

During the last two decades, organosulfur compounds have been used in the field of transition metal catalysis. Some of such compounds are known for their ability to withstand their exposure to air and moisture. These compounds are very important ligands. They may be obtained using simple and smooth modular synthetic protocols which include nucleophilic substitution reactions. The development of click chemistry represents a new era of innovation. It is a lighthouse of reliable and efficient reactions. In recent past, click chemistry has also been applied for the synthesis of such organosulfur ligands specifically suited for the dynamic field of transition metal catalysis. In order to synthesize novel compounds containing sulfur and triazole ring, click chemistry is an advantageous methodology over other approaches. This article covers the general features and uses of this methodology for the development of catalytically active organosulfur compounds. The significant advances in the design of transition metal catalytic systems utilizing such ligands, their use in the catalysis of many chemical transformations are also covered in this article. Effort has also been made to present a comparative overview of the performances of such catalysts vis-à-vis the catalysts designed commonly used ligands. Catalytic performances have been discussed thoroughly in order to identify the impact of ligand architecture on efficacy of the catalyst. Effect of reaction conditions (such as time, temperature etc.) and mechanistic aspects have also been rationalized.

A New Era of Innovation with Cloud Storage

A New Era of Innovation with Cloud Storage

The Impact Of Using Technology And Innovation In Law Enforcement In The Era Of Digitalization

In an era of rapid digitalization, technology and innovation have fundamentally transformed the landscape of law enforcement. This article examines the profound impact of information technology on various aspects of law enforcement in the digital age. It offers a comprehensive review of how technology has reshaped evidence collection, surveillance, rule enforcement, and dispute resolution processes. Additionally, the article discusses the challenges and opportunities the legal system faces in adapting to these technological advancements. Utilizing a literature review methodology, the research analyzes case studies and conceptual frameworks to provide a deeper understanding of how technology enhances the effectiveness and efficiency of law enforcement while considering its broader implications for justice and human rights. Furthermore, this study lays the groundwork for future research, encouraging further empirical exploration of these critical issues. This research examines the impact of technology on the accessibility of justice, highlighting both its potential to democratize legal services and the challenges it poses, such as privacy concerns and security risks. By exploring these dynamics, the study lays the groundwork for future research on the evolving role of technology in law enforcement and legal systems.

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

Unveiling Consumer Perceptions and Preferences in the Era of Disruptive Innovation of New Energy Vehicles Market in China

This study examines Chinese consumers' perceptions and responses to disruptive innovations within the New Energy Vehicles (NEVs) market, utilizing a questionnaire survey to explore awareness, perceived value of technological advancements, and the impact of these perceptions on adoption decisions. The survey, encompassing a diverse demographic of 154 respondents, highlights a female majority with a significant representation from the "25-34" age group, indicating a young, educated demographic with a considerable interest in NEVs. Analysis reveals a moderate familiarity with NEVs, a high valuation of technological progress, and a clear recognition of charging infrastructure availability as critical factors influencing adoption. Primary barriers identified include the high initial cost and limited charging infrastructure, with the data suggesting a strong potential for NEVs market growth if these concerns are addressed. Additionally, the survey uncovers optimistic future expectations among consumers, closely tied to government policies, technological advancements, and evolving market trends. This research contributes to understanding consumer attitudes in the rapidly changing NEVs landscape, offering insights for stakeholders to foster adoption and align strategies with consumer preferences and needs, underscoring the necessity for focused educational and marketing efforts to mitigate existing barriers and leverage consumer interest in technology.