The rapid integration of artificial intelligence (AI) into advertising has introduced deepfake technology as a powerful yet controversial persuasive tool. Given limited empirical research on Nigerian audiences’ responses to deepfake advertising, this study investigated perceptions of AI-generated deepfake advertisements featuring Pastor Enoch Adeboye and journalist Seun Okinbaloye. A descriptive survey design was adopted, with a structured questionnaire administered to 192 respondents in Lagos State. Descriptive statistics and regression analysis examined perceived authenticity, trust, credibility, ethical concerns, and advertising effectiveness. The study was anchored on Source Credibility Theory, the Elaboration Likelihood Model, and Uses and Gratifications Theory. Findings indicated that audience awareness (R = 0.873, p < 0.05), content-driven perception (R = 0.610, p < 0.05), credibility (R = 0.667, p < 0.05), and ethical concerns (R = 0.734, p < 0.05) significantly influenced engagement with and acceptance of deepfake advertising. Although high-quality deepfakes may enhance attention and recall, respondents raised concerns about consent, manipulation, and misinformation. Religious figures attracted greater moral scrutiny than media professionals. The study concludes that deepfake advertising has strategic potential but may undermine brand credibility and public trust when ethical transparency is lacking.
Ade Inasa-Thomas, Olatayo Agogo, Adekunle Christiana Adetola· Zenodo (CERN European Organi...· 0 citations
The rapid integration of artificial intelligence (AI) into advertising has introduced deepfake technology as a powerful yet controversial persuasive tool. Given limited empirical research on Nigerian audiences’ responses to deepfake advertising, this study investigated perceptions of AI-generated deepfake advertisements featuring Pastor Enoch Adeboye and journalist Seun Okinbaloye. A descriptive survey design was adopted, with a structured questionnaire administered to 192 respondents in Lagos State. Descriptive statistics and regression analysis examined perceived authenticity, trust, credibility, ethical concerns, and advertising effectiveness. The study was anchored on Source Credibility Theory, the Elaboration Likelihood Model, and Uses and Gratifications Theory. Findings indicated that audience awareness (R = 0.873, p < 0.05), content-driven perception (R = 0.610, p < 0.05), credibility (R = 0.667, p < 0.05), and ethical concerns (R = 0.734, p < 0.05) significantly influenced engagement with and acceptance of deepfake advertising. Although high-quality deepfakes may enhance attention and recall, respondents raised concerns about consent, manipulation, and misinformation. Religious figures attracted greater moral scrutiny than media professionals. The study concludes that deepfake advertising has strategic potential but may undermine brand credibility and public trust when ethical transparency is lacking.
Ade Inasa-Thomas, Olatayo Agogo, Adekunle Christiana Adetola· Zenodo (CERN European Organi...· 0 citations
SignificanceAgainst the backdrop of the carbon peaking and carbon neutrality goals, power systems are integrating a high share of renewable energy. However, renewable power generation is uncertain, variable, and intermittent. This makes it difficult to maintain supply-demand balance in power systems. Therefore, power systems urgently need flexible resources that can provide effective balancing support. Supply-side flexibility still relies heavily on thermal power units. These units are increasingly constrained by carbon reduction requirements and technical limits. It is therefore necessary to further unlock the flexibility potential of demand-side resources. Industrial users are characterized by substantial electricity consumption, substantial carbon-reduction potential, considerable adjustable capacity, and mature automation and control systems. At the technical level, industrial users can improve their process routes. At the operational level, they can reschedule production batches and adjust equipment power. These measures create flexibility for power-system balancing and low-carbon operation. Therefore, industrial users have become a major focus for developing demand-side flexibility for carbon reduction. Industrial users are evolving from conventional loads into integrated resources that can function as generation, load, and energy storage. This transition gives industrial users three main resource attributes. Distributed energy, self-owned power plants, and waste-heat generation provide on-site power. Adjustable production loads can coordinate production with system dispatch. Electrical, thermal, hydrogen, and intermediate-product storage enable energy transfer across time. Therefore, this paper provides a review of the process reconfiguration of industrial users in supporting the low-carbon transition of power systems.ProgressFirst, from the perspective of the transition of industrial users from consumers to prosumers, their basic connotation and main resource classifications are summarized. Second, representative industrial scenarios are examined, including iron and steel, electrolytic aluminum, and cement. For each scenario, the process characteristics and flexibility mechanisms are analyzed. For the steel industry, special attention is given to the flexibility differences among three process routes: the blast furnace-basic oxygen furnace long-process route, the scrap-based short-process route, and the hydrogen-based direct reduced iron (H‒DRI) short-process route. This research also examines the multi-level regulation capability of electrolytic aluminum. This capability mainly comes from the thermal inertia of aluminum reduction cells. It also discusses the flexibility of the cement industry in terms of start-stop scheduling and smooth power adjustment. The industries differ in their dominant flexibility mechanisms. Long-process steelmaking mainly relies on self-generation fueled by by-product gases. Scrap-based electric-arc-furnace (EAF) production can shift loads through batch scheduling. In H‒DRI processes, electrolyzers, hydrogen storage, and intermediate-product storage can be coordinated to provide flexibility. Electrolytic aluminum provides fast frequency response and different levels of load adjustment under cell thermal constraints. Cement plants mainly adjust crushing, raw-material preparation, and grinding. Clinker kilns generally remain in continuous operation. For flexibility potential assessment based on the process reconfiguration of industrial users, this paper proposes a three-dimensional modeling framework for industrial users. The framework covers physical characteristics, economic incentives, and carbon benefits. The physical dimension focuses on coupling constraints among material flows and energy flows. The economic dimension considers the willingness of users to provide flexibility. The dimension of carbon benefit captures how carbon reduction benefits affect feasible regulation boundaries. The assessment should distinguish theoretical potential from actually available potential. Physical modeling identifies the feasible regulation region under equipment, production, material-balance, and energy-coupling constraints. Economic modeling accounts for energy costs, production adjustment losses, operational risks, and management costs. Carbon benefit modeling needs to further incorporate marginal carbon emissions, green electricity consumption, carbon market compliance, and product carbon footprint accounting. For flexibility control strategies enabled by the process reconfiguration of industrial users, coordination should be designed across short-, medium-, and long-term time scales. At short timescales, electrolytic aluminum is a representative resource for rapid frequency response. At medium timescales, batch processes such as EAF steelmaking can enable intraday load shifting. At long timescales, electrolysis combined with hydrogen storage can support cross-seasonal balancing. These decisions must also account for multiple uncertainties. These include renewable energy output, market prices, product demand, equipment states, and material supply. The interactions among multiple market mechanisms, including electricity and carbon markets, should also be considered.Conclusions and ProspectsBy participating in power system flexibility regulation, industrial users can promote renewable energy integration, reduce the carbon footprint of industrial products, and support the coordinated low-carbon transition of both the industrial and power sectors. Future research should develop a unified model that captures material flows, energy flows, and industrial production constraints. Furthermore, it is essential to promote the deep integration of artificial intelligence with industrial production and power system control. Meanwhile, credible accounting frameworks for industrial carbon emissions and product carbon footprints must be established, alongside the improvement of multi-market benefit allocation mechanisms. Ultimately, these efforts will enable the large-scale, normalized, and market-driven participation of industrial users in power system flexibility regulation.
Gengrui Chen, Hui Hongxun, GAO Hongjun et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
To review the relevant researches on the application of clinical decision support system based on artificial intelligence in emergency department from four aspects: overview,application status, challenges and future development trends,aiming to enhance the intelligent management of medical decision⁃making of emergency department, and to provide reference for the development of smart medicine.
Danlu Chen, CHEN Ru, SHAN Yawei et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
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Global food security is confronted with mounting pressures arising from continuous population growth, intensifying climate change, and increasing agro-ecological vulnerability. Coarse grain crops, characterized by strong tolerance to drought and poor soil conditions, serve as strategic reserve crops with high nutritional value in the development of diversified food systems. Nevertheless, insufficient research investment and underdeveloped breeding infrastructure have led to the long-term marginalization of many coarse-grain crop resources. Smart breeding, through the integration of biotechnology and artificial intelligence (AI), opens new pathways for breaking through the bottlenecks in coarse grain crops breeding. This review systematically outlined the evolution of smart breeding technologies and discussed the applications of biological big data and AI in the digitization of coarse-grain crop germplasm resources, high-throughput phenomics, genotype-phenotype association analysis, and intelligent decision-making systems. We synthesized representative smart breeding cases in oats, foxtail millet, buckwheat, quinoa, and other coarse grain crops, explored pathways for the industrialization of coarse-grain breeding, and identified persistent challenges—specifically, the shortfall in fundamental research, institutional frictions in the breeding innovation system, and structural constraints in human resources and funding mechanisms and proposed corresponding countermeasures. Finally, we provided an outlook on frontier directions including de novo domestication, genomic design breeding, and digital twins breeding.
Xukai Li, Yajun LI, Jiaoyan Tang et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
Soybean [Glycine max (L.) Merr.] is a strategic crop with grain, oilseed, and feed value, yet its improvement has long been constrained by slow yield gains and a narrow genetic base. For China, the high dependence on soybean imports and the low self-sufficiency rate make expanding soybean cultivation and increasing yield per unit area becoming important tasks for ensuring food security, creating an urgent demand for technologies that can improve breeding efficiency. Over the past decade, rapid advances in large-scale sequencing, high-throughput phenotyping, and artificial intelligence algorithms have provided technical conditions for the transformation of breeding approaches. At present, the main constraint on soybean smart breeding is not the insufficiency of any single technology, but the lack of stable connections among data, algorithms, and applications. Whether data can be effectively learned by models, whether prediction results can be translated into breeding decisions such as parent selection, cross screening and target design, and whether new data generated from field validation can be standardizedly fed back, and used to continuously improve models directly affect the operational effectiveness of smart breeding systems. This review summarized research progress in soybean smart breeding from the perspectives of the data layer, algorithm layer, and application layer. It further analyzed the major barriers in data-to-algorithm transfer, algorithm-to-application translation, and application-to-data feedback, and identified key challenges in five aspects: Data, algorithms, applications, platforms and governance. Finally, it discussed the conditions required for soybean breeding to move toward intelligent design breeding. Future efforts should focus on bridging disconnections between layers, making prediction, validation, and data feedback into routine processes, and shifting evaluation criteria from model prediction accuracy to realized genetic gain.
Rongsheng ZHU, Daohan Cui, Xiaoxia WU et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
The growth rate of global grain production can no longer meet the demands arising from population expansion. Meanwhile, climate change, cultivated land degradation and environmental stress are further exacerbating the vulnerability of the food production system. Ensuring food security is fundamental to maintain the stability and sustainable development of human society. Crop breeding is the pivotal technical means to achieve this goal. Developing new crop varieties with high yield, superior quality, and multiple resistances have become the core pathway to safeguard agricultural sustainable development and global food supply. However, traditional breeding techniques suffer from long cycles, low accuracy and limited efficiency, which cannot satisfy the development requirements of modern seed industry. The rapid iteration of artificial intelligence (AI) technology has injected new intelligent momentum into the innovation of crop breeding, driving the transformation of breeding technology from traditional experience-based breeding to precise, intelligent and efficient breeding. In this review, we summarized the development of crop breeding, and focused on the innovative breakthroughs of genomic selection, precision genome editing, protein design and high-throughput phenotyping driven by AI. We also further elaborated the intelligent driving effects exerted by these technologies on key breeding links involving germplasm mining, gene function analysis, directional trait improvement, intelligent phenotypic assessment and intelligent factory breeding. Finally, we discussed the challenges and future developmental prospects of AI deployment in crop breeding, aiming to provide a reference for the innovation and industrial application of intelligent breeding technologies.
Shunmei LI, Jingyi XU, Ruiying LIU et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
AI Explained: A Guide for Non-Technical Readers builds understanding of artificial intelligence from first principles rather than diving in at the top. Written by experienced policy and technical experts, the book walks through rules and logic-based approaches, statistical methods, neural networks, machine learning, and generative models in accessible, structured terms. Each major section concludes with a dedicated use-cases chapter grounding abstract concepts in practical scenarios drawn from healthcare, law, and business. Rather than teaching readers how to build or deploy AI, the book answers a more fundamental question: how do these systems achieve the outcomes they produce? Coverage of AI policy, ethics, and societal impact rounds out the treatment, informed directly by the authors' advisory roles with governments and international bodies.
Objective: Pathological invasion influences patient prognosis in lung adenocarcinoma (LUAD); however, diagnosis is often associated with high interobserver variability because non-lepidic adenocarcinoma (NLA) and cancer-related fibrosis (CRF) are intricately mixed, and CRF contains both invasive and non-invasive components. In this study, to enable quantitative and reproducible diagnosis of areas showing high intratumoral heterogeneity, we proposed an artificial intelligence (AI)-based analysis distinguishing between NLA and CRF. This approach could reduce interobserver variability in prognostic prediction and help examine the different biological meanings of NLA and CRF. Methods: To clarify the physiological structure of lung parenchyma, we used elastin staining specimens. CRF and NLA were separately annotated in the first cohort (n = 35) and used for supervised learning. The AI then analyzed whole non-lepidic areas in the second cohort (n = 188); we then examined the relationship between clinicopathological features and the AI analysis. Results: For the first cohort, the accuracy was 89.2% on average. For the second cohort, groups with high CRF ratios (>50%) in the AI analysis showed a statistical correlation with types B-C in Noguchi's classification (p < 0.001), grades 1-2 in the WHO grading system (p = 0.018), and a higher 5-year disease-free survival (DFS) rate (85.7% vs. 69.4%, p = 0.006). In groups with a low CRF ratio (n = 111), a central distribution of CRF was associated with a lower DFS rate (41.2% vs. 74.5%, p = 0.001). Conclusion: The AI engine for distinguishing between NLA and CRF enabled prognostic prediction of LUAD with reduced interobserver variability. It may also be useful for examining the biological significance of NLA and CRF.
The mathematical sciences are an essential—albeit often invisible—part of the UK's national infrastructure, woven through almost every sector of the economy and most of the systems and services on which society depends. They are fundamental to our resilience, quality of life, and prosperity: recent analysis by AcadMathSci found that people working in mathematical science occupations directly contributed nearly half a trillion pounds to the UK economy in 2024, equivalent to about 19% of total GDP. But what does this contribution mean in practice? One does not have to look far for examples. The Research Excellence Framework (REF) is the UK's system for assessing the quality and impact of research carried out in higher education institutions. Its most recent exercise, REF 2021, included many detailed impact case studies showing how university research has delivered tangible benefits far beyond academia: from improving public services and protecting lives to supporting new technologies, strengthening businesses, and informing public policy. This report distils a subset of the 176 publicly available REF 2021 impact case studies associated with the mathematical sciences$^1$ into concise, accessible summaries. The case studies reach across the economy and society, showing how mathematical ideas developed in one setting can provide the methods, evidence, and insight needed to solve important problems elsewhere. Together, they illustrate both the breadth of the UK's mathematical research and the variety of ways in which it is changing the world. This collection is necessarily a selective and time-bound snapshot, rather than a comprehensive account, of the mathematical sciences' leading-edge impact. For example, REF 2021 captured research and impact developed before the recent rapid advances in artificial intelligence (AI) capabilities: AI-related case studies were thus far less prominent than they are likely to be in future exercises. Work connected to national security is also substantially under-represented: a REF 2021 overview report noted that many highly confidential case studies related to national security (i.e., not among the 176 mathematical science case studies$^1$ in the public domain) were based on "mathematical research of the very highest calibre." Despite such constraints, these case studies point consistently to the same conclusion. When we invest in the mathematical sciences—in education, research, and people—we strengthen the UK’s capacity to innovate, respond to uncertainty, and improve lives. That investment helps build a more productive, resilient, and prosperous future, while generating knowledge and tools with benefits reaching far beyond the problems they were first developed to solve. Maximising the UK-wide impact of the mathematical sciences, and sustaining that impact into the future, requires national capacity to identify, connect, and utilise mathematical expertise. AcadMathSci is working to build that national capacity by bringing together education, academia, business, industry and government; translating evidence into forms useful for decision-makers, policymakers, and industry; and helping ensure that the mathematical sciences are recognised and supported as a strategic national asset. With sustained support, AcadMathSci will be able to advance its wider work to strengthen the mathematical sciences across the UK. Repeating and extending this exercise following REF 2029 would form one part of that work: capturing a new generation of impact case studies, strengthening the evidence base for support for the mathematical sciences, and revealing many further ways in which the mathematical sciences are transforming lives, society, and the economy.
Vinesh Rajpaul, Christie Marr, David Schley· Zenodo (CERN European Organi...· 0 citations
The mathematical sciences are an essential—albeit often invisible—part of the UK's national infrastructure, woven through almost every sector of the economy and most of the systems and services on which society depends. They are fundamental to our resilience, quality of life, and prosperity: recent analysis by AcadMathSci found that people working in mathematical science occupations directly contributed nearly half a trillion pounds to the UK economy in 2024, equivalent to about 19% of total GDP. But what does this contribution mean in practice? One does not have to look far for examples. The Research Excellence Framework (REF) is the UK's system for assessing the quality and impact of research carried out in higher education institutions. Its most recent exercise, REF 2021, included many detailed impact case studies showing how university research has delivered tangible benefits far beyond academia: from improving public services and protecting lives to supporting new technologies, strengthening businesses, and informing public policy. This report distils a subset of the 176 publicly available REF 2021 impact case studies associated with the mathematical sciences$^1$ into concise, accessible summaries. The case studies reach across the economy and society, showing how mathematical ideas developed in one setting can provide the methods, evidence, and insight needed to solve important problems elsewhere. Together, they illustrate both the breadth of the UK's mathematical research and the variety of ways in which it is changing the world. This collection is necessarily a selective and time-bound snapshot, rather than a comprehensive account, of the mathematical sciences' leading-edge impact. For example, REF 2021 captured research and impact developed before the recent rapid advances in artificial intelligence (AI) capabilities: AI-related case studies were thus far less prominent than they are likely to be in future exercises. Work connected to national security is also substantially under-represented: a REF 2021 overview report noted that many highly confidential case studies related to national security (i.e., not among the 176 mathematical science case studies$^1$ in the public domain) were based on "mathematical research of the very highest calibre." Despite such constraints, these case studies point consistently to the same conclusion. When we invest in the mathematical sciences—in education, research, and people—we strengthen the UK’s capacity to innovate, respond to uncertainty, and improve lives. That investment helps build a more productive, resilient, and prosperous future, while generating knowledge and tools with benefits reaching far beyond the problems they were first developed to solve. Maximising the UK-wide impact of the mathematical sciences, and sustaining that impact into the future, requires national capacity to identify, connect, and utilise mathematical expertise. AcadMathSci is working to build that national capacity by bringing together education, academia, business, industry and government; translating evidence into forms useful for decision-makers, policymakers, and industry; and helping ensure that the mathematical sciences are recognised and supported as a strategic national asset. With sustained support, AcadMathSci will be able to advance its wider work to strengthen the mathematical sciences across the UK. Repeating and extending this exercise following REF 2029 would form one part of that work: capturing a new generation of impact case studies, strengthening the evidence base for support for the mathematical sciences, and revealing many further ways in which the mathematical sciences are transforming lives, society, and the economy.
Vinesh Rajpaul, Christie Marr, David Schley· Zenodo (CERN European Organi...· 0 citations
Generative artificial intelligence (Gen AI) has taken the academic world by storm. This G.I.F.T.S. paper argues that personalizing academic assignments may curb students’ tendency to copy and paste content from Gen AI outputs. I demonstrate how I personalized a written assignment in one of my courses. A total of 81 paper grades were analyzed, and no significant difference was found between students who engaged in personalizing their learning and those who did not. Final course grades showed the same result. Although no statistically significant difference was detected, personalizing assignments remains a valuable way to engage students in the Gen AI era.
