Search Results (185)

With the increasing penetration of converter-based devices, harmonic distortion has become a major challenge for power quality monitoring in large-scale power systems. This study presents a systematic review of methods for modeling harmonic sources and their applicability to real-time monitoring of power distribution systems. The review was conducted following PRISMA guidelines, considering literature published between 2000 and 2026. Searches were performed across Scopus, IEEE Xplore, Web of Science, ScienceDirect, and MDPI using predefined keywords. A total of 128 peer-reviewed journal articles were included. Potential sources of bias were qualitatively assessed, including selection, retrieval, and classification bias; however, residual bias may still arise from database selection, keyword design, and study classification. A structured comparative framework is introduced, based on a six-dimension coverage scoring scheme and maturity analysis, enabling consistent evaluation across both methodological and deployment aspects. The robustness of this framework was evaluated using leave-one-out and perturbation analyses, indicating low variability in coverage scores and stable rankings across both corpora. A taxonomy of harmonic source modeling approaches is proposed. Comparative synthesis indicates that measurement-based approaches, particularly those leveraging distribution-level PMUs, show strong potential for real-time monitoring. Key challenges include D-PMU placement, data integration, and computational scalability. Future work should focus on physics-informed AI and digital twin-based monitoring. Full article

During elections, information manipulation on social media has accelerated the use of artificial intelligence, yet the evidence is difficult to interpret without an integrated view of methods, data, and evaluation. We mapped 557 English-language journal articles from Scopus and Web of Science, combining performance indicators, science mapping, and a focused full-text synthesis of highly cited papers. The literature grows sharply after 2019, peaks in 2025, and shows geographically uneven production, with collaboration structured around a small set of hubs. The thematic structure suggests that, during the pandemic era, infodemic-related research served as a catalyst, intensifying scientific attention to fake news and disinformation and expanding the associated detection and monitoring agendas. In addition, socio-political harm constructs such as hate speech, extremism, and polarization appear as recurrent and structurally central targets, highlighting that election-relevant work often extends beyond veracity assessment toward monitoring discourse risks. Blockchain also emerges as a novel and adjacent integrity theme, aligned with authenticity and provenance-oriented mitigation rather than mainstream detection pipelines. AI for electoral disinformation is not reducible to veracity classification, as influential studies also target automation and coordinated behavior, verification support, diffusion analysis, and estimation frameworks that focus on exposure and impact. Evaluation remains heterogeneous and is often shaped by benchmark settings, making high accuracy values hard to compare and potentially misleading when labeling quality, topic leakage, or context shift are not characterized. Overall, the findings motivate evaluation protocols that align operational objectives with modeling roles and explicitly address robustness to temporal and platform changes, asymmetric error costs during election windows, and representativeness across electoral contexts and languages, while also guiding future work on emerging integrity challenges and governance-relevant deployment settings. Full article

Cyber aggression and abuse (CAA) has become a major interdisciplinary research area spanning psychology, communication, public health, and computer science. Existing reviews have largely focused on detection methods and model performance, offering limited insight into how CAA research themes have evolved over time at the field level. This study addresses this gap by, to the best of our knowledge, applying Latent Dirichlet Allocation (LDA) to 2309 Web of Science–indexed publications with English-language abstracts published between 2000 and 2024, providing a large-scale, longitudinal, and multi-level analysis of the literature. The model identifies 29 latent topics, which are organized using the User–Activity–Content (UAC) framework to link psychosocial research, platform-mediated behaviors, and computational detection approaches. Temporal analysis reveals a clear methodological transition: early dominance of survey-based and psychosocial themes gradually declines in relative prominence, while computational topics related to machine learning, deep learning, and pre-trained language models exhibit sustained growth, particularly after 2010. A Hot–Cold topic classification further distinguishes emerging, stable, and declining research directions. Journal-level, disciplinary, and geographic analyses reveal systematic differentiation across venues and regions, with complementary emphases on psychosocial and computational approaches. These findings provide a structured, field-level perspective on the evolution of CAA research and offer practical value for researchers, funding agencies, journal editors, and publishers by identifying dominant, emerging, and declining themes that can inform research prioritization, editorial planning, and strategic investment. Full article

Background: Cutaneous melanoma metastases (CMM) represent a clinically relevant manifestation of advanced melanoma and may constitute the first sign of disseminated disease. Their diagnosis is challenging because CMM shows highly variable clinical and dermoscopic presentations and frequently mimic other benign or malignant skin lesions. Although dermoscopy is routinely used to improve skin lesion assessment, dermoscopic criteria specific to CMM remain poorly defined and still non-standardized. Methods: We performed a narrative review of the literature to summarize dermoscopic features reported in CMM. MedLine (via PubMed) and Web of Science were searched up to 3 December 2025 using the keywords “dermoscopy” and “melanoma metastasis,” complemented by manual reference screening. Eligible studies were English-language full-text articles in peer-reviewed journals providing a complete dermoscopic description. Extracted data included patient demographics and major dermoscopic criteria, categorized as global patterns and focal dermoscopic and vascular structures. Due to heterogeneity, results were synthesized descriptively. Results: Twenty studies were included, comprising 774 patients. Dermoscopic findings were markedly heterogeneous. Globally, lesions frequently showed homogeneous pigmentation with variable colors and included amelanotic presentations. Commonly evaluated focal features included irregular dots and globules, crystalline structures, peripheral gray dots, and lacuna-like areas. Vascular patterns were prominent, particularly serpentine and corkscrew-like vessels. Conclusions: CMM dermoscopy is characterized by substantial heterogeneity and a lack of standardized criteria. Systematic classification of recurring dermoscopic features may improve diagnostic consistency and provide an interpretable framework for future artificial intelligence-based approaches supporting non-invasive recognition of melanoma metastases. Full article

Electroencephalogram (EEG) methods for the diagnosis of Benign Epilepsy with Centrotemporal Spikes (BECTS) are reviewed. The focus is on procedures reported for EEG analysis and diagnosis in BECTS, since some recent and potential applications of artificial intelligence (AI) aim to enhance the diagnostic accuracy and time reduction process, thereby moving a step closer to advancing our knowledge of the electrical nuclei sources and dynamics of energy distribution through the scalp in patients with epilepsy. The advantages of AI classification techniques have an increasing publication rate in the specialist literature, with no clear agreement on methodology. Hence, a better understanding of the procedures, arguments, and achievements is needed. To achieve this goal, (1) we review the background knowledge of the clinical characteristics of BECTS, (2) we analyze the results and advantages of computational processing methods for source and connectivity analyses of EEG in BECTS, and finally, (3) we explore the AI methods published in specialized journals for BECTS analysis. In conclusion, we argue in favor of the combined use of a priori information, which is the basis of the clinical visual analysis of EEG, as a potential feature to be included in AI methods for the classification of epileptiform graphoelements in EEG in BECTS diagnosis. Full article

The increasing volume and diversity of scientific publications poses challenges for scalable and interpretable topic discovery and automated document categorization. This study proposes an integrated framework that combines probabilistic topic modeling with supervised classification to support large-scale scientific literature analysis. Using 3689 abstracts from the Journal of Forensic Sciences (2009–2022), Latent Dirichlet Allocation (LDA) is applied to uncover latent thematic structures, assess topic diagnosticity across forensic disciplines, and analyze temporal research trends. Bayesian model selection with repeated resampling identifies a stable topic resolution, with the number of topics T lying in the range $83–88$, yielding semantically coherent and discipline-aligned topics. The resulting document–topic representations are then used for supervised abstract classification. Across multiple models and resampling scenarios, the strongest and most stable performance is achieved under a Grouped Category configuration. In particular, XGBoost attains an Accuracy of 0.754 and a Macro-averaged F1 score of 0.737 at $T=88$, with comparable results at neighboring topic counts, indicating robustness to topic granularity. Overall, the proposed framework provides a reproducible, interpretable, and computationally efficient pipeline for literature organization, trend analysis, and metadata enhancement in scientific domains. Full article

Itaúba (Mezilaurus itauba (Meisn.) Taub. ex Mez) is an Amazonian forest tree whose high-quality timber has driven sustained commercial exploitation, leading to its classification as threatened with extinction. This systematic scoping review synthesizes the current scientific knowledge on M. itauba. A systematic search of the Web of Science, Scopus, and SciELO databases retrieved studies published in English, Portuguese, and Spanish. Sixty-eight articles were analyzed using quantitative and qualitative approaches. Publications were concentrated between 2012 and 2025, largely derived from research conducted in Brazil and disseminated mainly through national journals. Overall, the literature is dominated by studies on wood technological properties, whereas research on the ecology and silviculture of M. itauba remains limited and often methodologically insufficient to support effective conservation actions. Based on the synthesis of identified knowledge gaps, we highlight as research priorities (i) the generation of empirical data on field performance across developmental stages, from nursery based seedling production to establishment and growth under open field and managed forest conditions; (ii) advancement of knowledge on genetic attributes, including structure and adaptive potential, to support conservation strategies and the selection of planting material; and (iii) integration of ecological interactions, ecophysiological responses, and regeneration processes into applied management frameworks capable of informing evidence based public policies. Addressing these priorities is essential to support conservation planning and the sustainable management of M. itauba. Full article

Background: Advanced endometrial cancer is associated with poor survival. With the advent of molecular classification and novel systemic therapies—including immunotherapy and targeted agents—treatment regimens have become increasingly complex. While these approaches aim to improve survival, they also potentially introduce long-term toxicities and treatment burden, reinforcing the importance of incorporating health-related quality of life (HRQoL) and patient-reported outcomes (PROs) into clinical trials. Methods: A systematic review was conducted of phase III randomized controlled trials (RCTs) in advanced, recurrent, or metastatic endometrial cancer evaluating systemic treatment registered on ClinicalTrials.gov and published up to 30 November 2025. Extracted data included study characteristics, HRQoL instruments, reporting formats, adherence to CONSORT-PRO, and timing of HRQoL dissemination (relative to primary efficacy reports). Results: Eight phase III RCTs published between 2020 and 2024 were included. Although HRQoL was consistently designated as a secondary endpoint, reporting within pivotal efficacy publications was limited. Most reports presented mean changes from baseline using the EORTC QLQ-C30, QLQ-EN24, and EQ-5D-5L. None of the primary reports reported time-to-deterioration analyses or the proportions of patients improving/deteriorating. Adherence to CONSORT-PRO was low, with only a minority of items addressed. Dedicated QoL publications were delayed by up to 25 months after primary efficacy reports and typically appeared in journals with lower impact factors. Conclusions: Despite routine inclusion of HRQoL measures in trial protocols, reporting remains inconsistent, limited in scope, and often delayed. Strengthening adherence to established frameworks is essential to ensure that HRQoL endpoints are predefined, analytically robust, and disseminated alongside efficacy data—particularly in a rapidly evolving therapeutic landscape. Full article

Many states around the world create an unprotected class of migrants by legally categorizing them as “unauthorized”. Yet, we have a limited understanding of the state of knowledge that has resulted from this empirical phenomenon, particularly outside the U.S. and over time. In this article, we map the state of knowledge on unauthorized migration by analyzing the last 30 years of papers published in a leading migration journal. Articles were identified through a comprehensive keyword-based search strategy and analyzed using a computational pipeline that combines natural language processing and large language model-assisted classification. Our findings reveal a persistent empirical emphasis on Mexico–U.S. migration, with economic drivers and disparities, as well as immigration laws and policies, dominating articles’ content. Our analysis also identifies underexplored or peripheral topics, including studies on gender or the environment, highlighting the need for more diversified, cross-national research. Ultimately, by providing a detailed account of our computational mapping approach, we hope this study will serve as a blueprint for future scholars to track how migration research evolves into the future. Full article

As artificial intelligence (AI) becomes increasingly woven into educational design and decision-making, its use within initial teacher education (ITE) exposes deep tensions between efficiency, equity, and professional agency. A critical action research study conducted across three iterations of a third-year ITE course investigated how pre-service teachers engaged with AI-supported lesson planning tools while learning to design for inclusion. Analysis of 123 lesson plans, reflective journals, and survey data revealed a striking pattern. Despite instruction in inclusive pedagogy, most participants reproduced fixed-tiered differentiation and deficit-based assumptions about learners’ abilities, a process conceptualised as micro-streaming. AI-generated recommendations often shaped these outcomes, subtly reinforcing hierarchies of capability under the guise of personalisation. Yet, through iterative reflection, dialogue, and critical framing, participants began to recognise and resist these influences, reframing differentiation as design for diversity rather than classification. The findings highlight the paradoxical role of AI in teacher education, as both an amplifier of inequity and a catalyst for critical consciousness and argue for the urgent integration of critical digital pedagogy within ITE programmes. AI can advance inclusive teaching only when educators are empowered to interrogate its epistemologies, question its biases, and reclaim professional judgement as the foundation of ethical pedagogy. Full article

Over the last fifteen years, the Google Earth Engine (GEE) has become a pivotal tool for large-scale geospatial analysis, with growing applications in urban environmental monitoring. This review examines the peer-reviewed literature, published between 2015 and 2024, that utilizes GEE to evaluate urban environments through remote sensing-derived indices. The literature search strategy was guided by predefined search terms, which were applied to online databases including Scopus and Google Scholar. The inclusion criteria for this review comprised English-language publications, limited to articles only from journals, while book series, books, and conference articles were excluded. The eligibility criteria applied aimed to identify peer-reviewed studies that applied GEE to urban contexts using vegetation, thermal, greenness, or density indices. Studies without a clear urban focus or not employing GEE as a primary tool were excluded. The selection process followed a structured methodological flow, where a total of 291 studies were identified that fulfilled the applied criteria. This review indicates that key methodological trends encompass both conventional techniques, such as Random Forests (RFs), Support Vector Machines (SVMs), and classification/regression trees, as well as emerging machine learning algorithms, with Landsat, Sentinel, and MODIS as the most commonly used satellite datasets. The articles included in this review show a geographic focus, with over 44% of publications from China, 11% from the United States, and 9% from India, while the rest of the countries identified in this review contribute fewer than 5% each, suggesting that there is a significant opportunity for research in underrepresented regions. The main result of this review is that GEE proves to be an effective, scalable, and reproducible platform for urban environmental analysis, with most studies focusing on vegetation and thermal indices using Landsat, Sentinel, and MODIS data. As GEE has become one of the most widely used platforms for urban environmental monitoring, future research should focus on addressing challenges such as the standardization of indices, the consistency of methodological approaches, and the expansion of global coverage through advanced cloud-based geospatial frameworks. Full article

The widespread circulation of digital misinformation exposes a critical shortcoming in prevailing detection strategies, namely, the absence of robust mechanisms to confirm the origin and authenticity of online content. This study addresses this by introducing VeriTrust, a conceptual and provenance-centric framework designed to establish content-level trust by integrating Self-Sovereign Identity (SSI), blockchain-based anchoring, and AI-assisted decentralized verification. The proposed system is designed to operate through three key components: (1) issuing Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) through Hyperledger Aries and Indy; (2) anchoring cryptographic hashes of content metadata to an Ethereum-compatible blockchain using Merkle trees and smart contracts; and (3) enabling a community-led verification model enhanced by federated learning with future extensibility toward zero-knowledge proof techniques. Theoretical projections, derived from established performance benchmarks, suggest the framework offers low latency and high scalability for content anchoring and minimal on-chain transaction fees. It also prioritizes user privacy by ensuring no on-chain exposure of personal data. VeriTrust redefines misinformation mitigation by shifting from reactive content-based classification to proactive provenance-based verification, forming a verifiable link between digital content and its creator. VeriTrust, while currently at the conceptual and theoretical validation stage, holds promise for enhancing transparency, accountability, and resilience against misinformation attacks across journalism, academia, and online platforms. Full article