Search Results (2,927)

This paper presents the development of two adaptive autopilots for the Cessna Citation X business jet aircraft. The two adaptive control strategies, including a dynamic inversion controller and a neural network controller, provide dual adaptation. The control objective consists of tracking the vertical speed, altitude, and heading commands. Dynamic inversion is applied on each output variable, and then the neural network (NN) controller is updated using adaptive law, derived from backpropagation. Dynamic inversion (DI) is achieved locally using a Recursive Least Squares (RLS) algorithm for state estimation. An inner control loop for the pitch, roll and yaw rates is integrated within the autopilots. The longitudinal states were separated from the lateral states in order to differentiate between longitudinal and lateral control. Robustness tests were conducted under turbulence and wind-gust conditions. The autopilot results were compared with flight simulation data from a Cessna Citation X research flight simulator. Results have shown that the autopilots accurately track the vertical speed, altitude and heading reference signals. The flight simulation comparison has shown that the proposed adaptive controllers were better than the one currently on board the Cessna Citation X. Full article

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes 82 studies published between 2020 and 2026, identified through keyword search and citation snowballing over OpenAlex, arXiv, and Crossref. We develop a taxonomy that clarifies the components of federated systems, deployment locations, trust boundaries, and protected assets. We further map the combined RAG+FL attack surface, summarize practical defenses and system patterns, and distill actionable guidance for secure, privacy-preserving, and efficient FedRAG deployment in real-world IoT and edge scenarios. Our synthesis highlights recurring trade-offs among robustness, privacy, latency, communication overhead, and maintainability, and identifies open research priorities in benchmark design, governance mechanisms, and cross-silo evaluation protocols for practical deployment. Full article

Artificial Intelligence (AI)-based crack detection in buildings uses computer vision and deep learning to automatically identify structural cracks from inspection images. In recent years, many studies have explored this topic, but the overall development of the field, its methodological practices, and the remaining challenges are still not fully clear. Unlike most previous reviews that focus mainly on technical methods, this study combines a large-scale scientometric mapping of the research field with a focused technical analysis of recent AI-based crack detection methods specifically applied to building structures. This study therefore provides a dual-layer review covering research published between 2015 and 2025. A total of 146 Scopus-indexed publications were analysed using Visualization of Similarities viewer (VOSviewer) to examine publication growth, thematic evolution, collaboration patterns, and citation structures. In addition, a focused technical review of 36 highly relevant studies was carried out to analyse task formulations, model families, datasets, evaluation protocols, and methodological practices. The results show a rapid increase in research activity after 2020, largely driven by advances in deep-learning and Unmanned Aerial Vehicle (UAV)-based inspections. At the same time, collaboration networks remain uneven, and citation influence is concentrated in a limited number of research communities. The technical review further shows that most studies focus on detection-level tasks, particularly You Only Look Once (YOLO)-based models, while predictive diagnostics, automated inspection reporting, and decision-oriented Structural Health Monitoring (SHM) are still rarely addressed. Current datasets and evaluation protocols also remain mostly perception-oriented, which makes it difficult to assess robustness, generalisability and long-term predictive capability. Full article

The increasing global demand for sustainable water purification technologies has accelerated research on catalytic degradation and advanced oxidation processes for the removal of refractory pollutants. This study provides a comprehensive bibliometric analysis of global research trends in catalytic water and wastewater treatment from 2010 to 2025, combining quantitative mapping with a qualitative synthesis of emerging technological directions. Bibliographic data were retrieved from the Scopus database and screened using the PRISMA framework, followed by analysis using VOSviewer (v1.6.20) and OriginPro (version 2023, OriginLab Corporation, Northampton, MA, USA) to examine publication growth, citation patterns, international collaboration networks, and thematic evolution. A total of 1550 publications, including 1265 research articles and 285 review papers, were analyzed. The results show a significant increase in research output after 2015, reflecting growing global attention to water sustainability and environmental remediation. China, the United States, and India were identified as the leading contributors, with strong international collaboration networks. Keyword co-occurrence analysis revealed three dominant research themes: photocatalytic degradation and semiconductor engineering, Fenton and Fenton-like advanced oxidation processes, and emerging hybrid catalytic systems involving carbon-based materials and metal–organic frameworks. The analysis also indicates a recent shift toward multifunctional hybrid catalysts designed to improve efficiency, stability, and performance in complex wastewater systems. These findings highlight key scientific developments and suggest future research priorities, including green catalyst synthesis, reactor and process scale-up, AI-assisted catalyst design, and life-cycle sustainability assessment to support the transition from laboratory research to practical water treatment applications. Full article

Objective: The objective of this study was to map and critically synthesize empirical evidence on ML/AI applications across surface mining unit operations, and to characterize models, validation practices, and evidence gaps. Eligibility criteria: Our eligibility criteria comprised peer-reviewed studies (2020–2025) applying ML/AI to surface mining activities, training/validating models on empirical datasets, and reporting quantitative performance metrics. Information sources: Scopus, ScienceDirect, Dimensions, and Web of Science were our information sources, last searched December 2025 and supplemented by website and citation snowballing. Risk of bias: Risk of bias was assessed using an adapted domain-based approach based on PROBAST, used to interpret findings without excluding studies. Synthesis method: Our research employed a narrative synthesis (no meta-analysis due to heterogeneity in datasets, algorithms, contexts, and metrics), grouped by application domain. Results: From 5317 records, 57 studies were included, concentrated in blasting (43), followed by load and haul (6), post-dismantling management (4), extraction (2), and overall exploitation (2). Studies predominantly reported statistical metrics (e.g., R², RMSE, and MAE), with limited operational performance indicators; validation was frequently site-specific. Dataset sizes were not reported consistently across studies. Limitations: This study’s limitations were database coverage, restricted timeframe, and incomplete reporting (e.g., software/tooling). Conclusions: ML/AI shows strong potential, especially in blasting, but scalable deployment is constrained by site specificity, inconsistent reporting, and heterogeneous validation; standardized reporting and operational indicators are priorities. Registration: The systematic review protocol was registered in OSF with DOI 10.17605/OSF.IO/5UMKB. Funding: EU Erasmus+ STRIM project (1010832727). Full article

The Cite Frequency Approach (CFA) is an accepted method for identifying potential factors influencing bid/no-bid decisions, yet no study has systematically validated its theoretical foundation. The aim of this study was to elucidate the theoretical basis of CFA and ascertain whether citation frequency truly reflects factor importance. Through rigorous screening undertaken using the PRISMA method, 24 journal articles with bid/no-bid decision (BNBD) factors’ RII were extracted for analysis. By constructing a 276-times pairwise comparisons of these articles, a matrix of 121 factors’ RII was built. Based on the matrix data, 2380 Spearman correlation coefficients (rhos) between cite frequency and RII were calculated. Significant level rhos of medium and high strength account for only 15.59%. This demonstrates that the cite frequencies of factors found in previous studies are not highly representative of their importance. Cite frequencies are therefore shown to be unreliable in identifying the potential factors affecting BNBD. Moreover, the Meta-Analyses approach (MAA) is proposed as a superior factor selection method (MAA), and this was verified to be more representative and effective than CFA. This study provides the first systematic and global validation of CFA’s core assumption supported by robust and generalizable findings. It enriches the methods for identifying potential factors affecting decisions, including but not limited to BNBD. Full article

This scoping review summarizes current computational image analysis and artificial intelligence (AI) approaches for the assessment of hair and scalp disorders, with emphasis on quantitative trichoscopy and operator-independent evaluation. A deep Medline search was performed using a citation network-based approach using MeSH terms and complementary keywords covering diagnostic imaging, trichoscopy/videodermoscopy, image processing, algorithms, AI, and mobile/smartphone-based workflows. Overall, relatively few studies assess algorithms in real-world clinical pathways, and much of the retrieved literature is predominantly pre-clinical or methodology-driven. In parallel, commercially available AI-assisted trichoscopy platforms have little or no traceable peer-reviewed evidence; their validation methods and underlying datasets are often proprietary, undisclosed, and not directly comparable, limiting independent verification and cross-platform benchmarking. The most mature academic applications focus on follicular unit quantification (hair density, shaft diameter distribution, vellus-to-terminal ratio, and severity mapping), mainly using convolutional neural networks with object detection and instance segmentation. In conclusion, AI-assisted trichoscopy may support a shift toward standardized quantitative outputs, but clinical translation remains early and constrained by small or proprietary datasets, heterogeneous acquisition/annotation protocols, limited external validation, and scarce prospective studies. Full article

Using a panel of Chinese A-share listed companies from 2007 to 2022, this study examines how firms’ structural positions in patent citation networks affect innovation efficiency. We construct a firm-level patent citation network and use betweenness centrality to capture firms’ brokerage-oriented positions in knowledge flows. Based on firm- and year-fixed-effects models, instrumental-variable estimation, and robustness checks, we find that stronger brokerage positions significantly improve innovation efficiency. Mechanism analyses show that this effect operates through two channels: cross-domain knowledge recombination and organizational boundary spanning. Firms in stronger brokerage positions are more likely to access technologically heterogeneous external knowledge and interact with a wider range of external knowledge-bearing entities, thereby improving the efficiency with which innovation inputs are transformed into patent-based outputs. We further find that digital transformation negatively moderates the relationship between brokerage centrality and innovation efficiency. This suggests that digital transformation reduces firms’ marginal dependence on external brokerage positions by strengthening internal data-processing, coordination, and knowledge-integration capabilities. Additional analyses show that the positive effect of brokerage centrality is broadly shared across ownership groups. Regional heterogeneity is more evident in the stronger brokerage premium observed in the western region than in the eastern region. Full article

Generative AI systems increasingly mediate how students retrieve literature and generate citations, shifting methodological rigor toward the maintenance of an auditable evidence trail. This study audits the search stage of AI-assisted literature review work, focusing on retrieval performance and citation traceability rather than downstream screening or synthesis. Four widely accessible tools were compared across two retrieval postures, and Boolean queries were executed against Scopus and evaluated against a DOI-verified librarian baseline built from Scopus, Web of Science, and Google Scholar. Using a canonical prompt and a bounded top-k capture rule (k = 20), each bibliographic record was evaluated for DOI traceability, DOI resolution integrity, metadata accuracy, and run-to-run drift. Records were screened through staged title/abstract and full-text eligibility review, and the final set included 37 studies after quality appraisal was 37 studies. Across sixteen audit runs, natural-language prompting frequently produced under-target yields, recurrent integrity failures, and low overlap with the librarian benchmark. Boolean translation improved run completion and increased the proportion of auditable records, but reproducibility remained unstable across repeated runs. These findings show that correctness at the record level does not ensure stability at the evidence-set level. Limitations include the bounded tool set, the search-stage focus, and the absence of downstream screening or synthesis evaluation. Retrieval posture, therefore, emerges as a practical governance lever for AI-assisted literature review workflows and supports the use of a student-facing verification checklist anchored in DOI verification and transparent protocol capture. This research received no external funding. OSF registration: Open Science Framework, 10.17605/OSF.IO/U8NHT. The manuscript reports the final included set as n = 37, states no external funding, and lists the OSF registration DOI. Full article

Simple shear testing is a widely used method in geotechnical engineering for evaluating soil liquefaction susceptibility, deformation characteristics, and shear strength under controlled loading conditions. This study presents a bibliometric analysis of research trends in simple shear testing based on 367 publications indexed in the Scopus database between 2000 and 2024, analyzed using VOS-viewer. It appears that the current research output on this topic has greatly increased lately. The number of research articles reached a peak in 2024 with a total of 42 research articles. The most frequently cited journals on this topic are Soil Dynamics and Earthquake Engineering, with a total of 48 research articles (1173 citations); the Journal of Geotechnical and Geo-environmental Engineering, with a total of 34 research articles (772 citations); and the Canadian Geotechnical Journal, with a total of 10 research articles (250 citations). This indicates substantial research interest in earthquake engineering and soil mechanics. The output shows that there is a major emphasis on research done in the USA, with a total of 104 research articles (1215 citations). The highest average citations per document belong interestingly to the research done by Taiwanese, with a total of 36.73 citations. Similarly, it appears that there is a good impact on soil liquefaction studies. The research findings show that confining pressure, strain rates, and volume ratio affect the shear strength of the soil. Advances in boundary control and shear testing techniques have improved the reliability of experimental results. The study underscores the growing need for more sophisticated numerical modeling techniques and field verification to bridge the gap between laboratory findings and real geotechnical applications. These findings contribute to improving soil characterization methods, which enable safer and more efficient geotechnical designs for infrastructure development. Full article

Background: Tendons adapt to mechanical loading by increasing cross-sectional area (CSA), stiffness, and matrix organization, with structural remodeling critical for both rehabilitation and performance. Collagen supplementation has been proposed to enhance this process by supplying key amino acids for collagen synthesis; however, inconsistent results across trials have limited its clinical and athletic application. Methods: A systematic review of randomized controlled trials evaluating collagen supplementation in humans was conducted. PubMed, EMBASE, CINAHL, and Web of Science were searched from database inception through May 2025. Risk of bias was assessed using the PEDro scale (≥6/10 classified as good-to-excellent quality). Due to substantial heterogeneity in supplementation protocols, training modalities, and outcome measures, results were synthesized narratively without meta-analysis. Data extraction included collagen type, dose, training modality, intervention duration, and outcome measures. Results: Of 887 unique citations, eight RCTs (n = 257; ages 18–52; 246 M:11 F) met the inclusion criteria. All studies incorporated resistance or plyometric training (3–15 weeks). Three of four studies reported significantly greater increases in tendon CSA in collagen groups versus placebo. Four studies investigated tendon stiffness and Young’s modulus; the two using higher doses (15–30 g/day) demonstrated significant between-group improvements favoring collagen, while lower-dose studies (~5 g) showed only within-group effects. Muscle strength improved with training in all trials, but no additive effects of collagen were observed. One study reported improvements in eccentric rate of force development and deceleration impulse with collagen, though gross explosive metrics (e.g., jump height) were unaffected. Conclusions: Collagen supplementation (15–30 g) with vitamin C (≥50 mg) may enhance tendon remodeling when combined with high-intensity resistance training (≥70% 1 RM). The current literature suggests strong evidence (GRADE A) for increases in tendon CSA and stiffness, strong evidence (GRADE A) against an effect on muscle strength, and conflicting evidence (GRADE C) for muscle cross-sectional area and physical performance. Limitations include small sample sizes, heterogeneous protocols, and short intervention durations. Full article

Urban rail transit (URT) train-door failures are safety-critical and can cause cascading service disruptions, yet existing emergency operation schemes (EOSs) are often static, difficult to adapt to evolving fault patterns, and hard to verify against updated regulations. This study proposes a retrieval-augmented large language model (LLM) framework for executable and evidence-traceable EOS generation. Multi-source heterogeneous incident evidence (structured work orders, operational impact records, and unstructured maintenance/dispatch narratives) is normalized into a structured incident representation, and a hybrid retriever (dense + BM25) with cross-encoder reranking selects compact regulatory clauses and historical cases under a fixed context budget. The generator is fine-tuned with structured objectives to enforce schema compliance, role assignment, and citation grounding. Experiments on 776 passenger-door incidents from Shanghai URT (2019–2024) show that Hybrid + rerank achieves the best retrieval quality (Recall@5 = 0.78; Coverage@B = 0.71; FirstHit/B = 0.46). For generation, the full setting improves operational usability, reaching SchemaPass = 0.88, RoleAcc = 0.91, CiteCov = 0.73, and UsableAns = 0.83, compared with 0.15 UsableAns for a pure LLM baseline and 0.26 for prompting with RAG only. These results indicate that combining high-utility retrieval with structure- and citation-aware fine-tuning substantially improves the executability and verifiability of safety-critical operation schemes. Full article

There has been a recent increase in the use of social media platforms (SMPs), as well as a large increase in scientific journals and academic article publications. We need to study if and how much academics, scholars and researchers trust SMPs as sources, i.e., citations, for writing their research articles. The purpose of this research is to explore the relationship between SMPs and bibliographic article citations for ten years between 2010 and 2019, with 31 December marking the official identification of COVID-19, a milestone that affected the whole world, including academic publishing. By using a citation retrieval tool written in Java, the citations referring to the URLs of 6432 articles from 14 Q1 open access education journals ranked by the SCImago platform were extracted. The retrieved URLs were stored in a relational database, preprocessed and cleaned, and analyzed using SQL queries to identify and quantify citations originating from SMPs. The findings showed that there were 112 instances, which corresponds to 1.8% of the articles, of an SMP post being used as a citation. Out of the 17 SMPs checked, eight were used, with the most popular being YouTube, having a percentage of 68% of the aforementioned 112 citations, followed by Twitter (now X) with approximately 13.5% and then by Facebook with around 7%. Most of these in-text citations were found at the Introduction and the Design/Methodology sections of the papers. Other important findings of this study were that about 2% of the URL citations referred to blogs and wikis and that one in 100 articles used Wikipedia in the bibliography. Also, for a 26-year period from 1999 to 2024, it was observed that the number of journals increased by 82.8%, while the number of open access journals showed an impressive 552.14% increase. The findings of this study could lead to changes in the metadata design of bibliographic databases, like the way of searching them, and to a review of the life cycle duration of sustainable access to the content of the cited SMPs. Full article

Background/Objectives: Paediatric renal tumours, particularly Wilms tumours, have good survival outcomes following multimodal therapy; however, long-term morbidity related to nephrectomy and adjuvant treatment remains a major concern. As treatment paradigms increasingly prioritize nephron preservation and minimization of late effects, there is growing interest in technologies that can enhance intraoperative precision. Methods: A scoping review following the PRISMA guidelines was performed. We analysed articles on fluorescence for childhood renal tumours on 1 November 2025. Case reports, opinion articles, and narrative reviews were excluded. An Ovid Medline search with search terms “Kidney neoplasm” AND “Fluorescent Dyes”, along with a Cochrane trials registry search for “kidney” AND “neoplasm” AND “Fluorescent Dye”, was performed, along with a hand search of citations. Results: The Ovid Medline search yielded 21 results, and the Cochrane trials search gave 4 results. Following review, five papers were included, of which one was an ex vivo study and one was a randomised, controlled trial that is currently recruiting. Conclusions: There is a lack of evidence around the use of near-infrared fluorescence in paediatric renal tumour surgery. This review summarizes the key current findings and future perspectives. Full article

The accelerated expansion of advanced technologies—particularly artificial intelligence, intelligent systems, and interactive digital environments—is influencing contemporary media ecosystems and contributing to changes in educational practices. This study provides a systematic and descriptive bibliometric mapping of recent scientific production on artificial intelligence in education, algorithmic ethics, and digital inequality. A total of 229 Scopus-indexed documents published between 2021 and 2026 were analyzed using Biblioshiny and VOSviewer to examine publication patterns, influential authors and sources, and the conceptual structure of the field. Results indicate a marked increase in research output since 2024, with an annual growth rate of 47.58%, an average of 8.68 citations per document, and an international co-authorship rate of 24.45%. These indicators reflect an expanding and increasingly collaborative research landscape, accompanied by a diversification of thematic priorities within the field. The analysis identifies five thematic clusters: (1) the technical foundations of AI and digital transformation; (2) intelligent and immersive learning environments; (3) personalized and adaptive learning systems; (4) AI literacy and pedagogical integration; and (5) ethical considerations, including algorithmic bias and educational robotics. The findings highlight the need for explicit justification of database selection, strengthened critical AI literacy, and context-sensitive strategies that address disparities in access, skills, and institutional capacity. Overall, this study offers a coherent overview of a research area that is currently expanding and undergoing conceptual reorganization, providing evidence-informed insights for future research, policy development, and the design of equitable AI-driven educational technologies. Full article