Search Results (478)

Background: Acute kidney injury (AKI) remains one of the most common perioperative complications, carrying substantial mortality and healthcare burden. Traditional diagnostic criteria relying on serum creatinine and urine output are limited by delayed detection and inability to characterize the underlying injury phenotype. This scoping review examined the current state of novel AKI biomarker research in perioperative care, evaluated their clinical implementation, and identified knowledge gaps. Methods: A systematical search was performed for studies investigating novel AKI biomarkers in surgical settings. Biomarkers were categorized as functional, stress, or damage markers. Data extraction focused on diagnostic performance, clinical outcomes, regulatory approval status, and implementation barriers. A narrative synthesis was organized by biomarker category and thematic areas. Results: Several biomarkers demonstrated superior early diagnostic performance compared to traditional ones, including PENK or CCL-14, showing promising accuracy for AKI detection and outcome prediction. TIMP-2*IGFBP-7 and NGAL achieved regulatory approval, and biomarker-guided KDIGO care bundles significantly reduced AKI incidence in surgical populations. However, substantial heterogeneity exists in assays, cutoff values, and clinical validation across different clinical settings. Conclusions: Novel AKI biomarkers offer a promise for early detection and risk stratification in perioperative care, yet widespread clinical adoption requires addressing standardization challenges, establishing cost-effectiveness, and validating implementation strategies. Full article

Background/Objectives: Primary progressive aphasia (PPA) is characterised by progressive decline in language and communication. However, existing diagnostic frameworks and assessment tools are largely based on Indo-European languages, which limits their applicability to Chinese bilingual speakers whose linguistic profiles differ markedly in tonal phonology, logographic writing, and bilingual organisation. This review aimed to (a) describe how PPA presents in Chinese bilingual speakers, (b) evaluate how well current speech–language and neuropsychological assessments capture these impairments, and (c) identify linguistically and culturally informed strategies to improve clinical practice. Methods: A systematic review was conducted in accordance with the PRISMA-ScR guidelines. Four databases (PubMed, Scopus, Web of Science, PsycINFO) were searched, complemented by backward and forward citation chaining. Eight empirical studies met the inclusion criteria. Data were extracted on participant characteristics, PPA variant, language background, speech–language and writing profiles, and assessment tools used. Thematic analysis was applied to address the research questions. Results: Across variants, Chinese bilingual speakers demonstrated universal PPA features expressed through language-specific pathways. Mandarin speakers exhibited tone-segment integration errors, tonal substitution, and disruptions in logographic writing. Lexical-semantic degradation reflected homophony and compounding characteristics. Bilingual individuals showed parallel or asymmetric decline influenced by dominance and usage. Standard English-based naming, repetition, and writing assessments did not reliably capture tone accuracy, radical-level writing errors, or bilingual patterns. Sociocultural factors, including stigma, delayed help-seeking, and family-centred care expectations, further shaped diagnostic pathways. Conclusions: Chinese PPA cannot be meaningfully assessed using tools designed for Indo-European languages. Findings highlight the need for tone-sensitive repetition tasks, logographic writing assessments, bilingual diagnostic protocols, and culturally responsive communication-partner support. This review provides a comprehensive synthesis to date on Chinese bilingual PPA and establishes a foundation for linguistically inclusive diagnostic and clinical models. Full article

Background/Objectives: As the primary cause of cancer-related death globally, lung cancer highlights the critical need for early identification, precise staging, and individualized treatment planning. By enabling automated diagnosis, staging, and prognostic evaluation, recent developments in artificial intelligence (AI) and machine learning (ML) have completely changed the treatment of lung cancer. The goal of this narrative review is to compile the most recent data on uses of AI and ML throughout the lung cancer care continuum. Methods: A comprehensive literature search was conducted across major scientific databases to identify peer-reviewed studies focused on AI-based imaging, detection, and prognostic modeling in lung cancer. Studies were categorized into three thematic domains: (1) detection and screening, (2) staging and diagnosis, and (3) risk prediction and prognosis. Results: Convolutional neural networks (CNNs), in particular, have shown significant sensitivity and specificity in nodule recognition, segmentation, and false-positive reduction. Radiomics-based models and other multimodal frameworks combining imaging and clinical data have great promise for forecasting treatment outcomes and survival rates. The accuracy of non-small-cell lung cancer (NSCLC) staging, lymph node evaluation, and malignancy classification were regularly improved by AI algorithms, frequently matching or exceeding radiologist performance. Conclusions: There are still issues with data heterogeneity, interpretability, repeatability, and clinical acceptability despite significant advancements. Standardized datasets, ethical AI implementation, and transparent model evaluation should be the top priorities for future initiatives. AI and ML have revolutionary potential for intelligent, personalized, and real-time lung cancer treatment by connecting computational innovation with precision oncology. Full article

Rainfall simulators are crucial devices in erosion research, enabling the controlled reproduction of precipitation characteristics for both laboratory and field investigations. This study presents a comprehensive characterization of a rainfall simulator originally designed to assess the erosive effects of precipitation on heritage surfaces. The simulator, installed at the University of León, was evaluated using volumetric methods and disdrometric techniques, employing a Parsivel² optical disdrometer. Simulations were conducted with a falling height of 10 m and high-intensity rainfalls. Spatial uniformity was assessed through thematic mapping and the Christiansen Uniformity (CU) coefficient, revealing limited uniformity across the full wetted area, but an improved performance within the central zone (CU up to 80%). Disdrometric data provided detailed insights into drop size and velocity distributions, enabling the estimation of rainfall intensity, kinetic energy, and momentum, as well as the spatial uniformity of the energetic parameters. Empirical models to estimate the raindrop’s fall velocity were tested against disdrometric measurements, confirming the simulator’s ability to generate rainfall with velocity characteristics comparable to those of natural precipitation. Moreover, the findings underscore the importance of integrating multiple measurement approaches to enhance the reliability and accuracy of rainfall simulator characterization. Full article

This systematic review focuses on intelligent navigation as a core enabler of autonomy in smart warehouses, where mobile robots must dynamically perceive, reason, and act in complex, human-shared environments. By synthesizing advancements in AI-driven decision-making, SLAM, and multi-sensor fusion, the study highlights how intelligent navigation architectures reduce operational uncertainty and enhance task efficiency in logistics automation. Smart warehouses, powered by mobile robots and AGVs and integrated with AI and algorithms, are enabling more efficient storage with less human labour. This systematic review followed PRISMA 2020 guidelines to systematically identify, screen, and synthesize evidence from 106 peer-reviewed scientific articles (including pri-mary studies, technical papers, and reviews) published between 2020–2025, sourced from Web of Science. Thematic synthesis was conducted across 8 domains: AI, SLAM, sensor fusion, safety, network, path planning, implementation, and design. The transition to smart warehouses requires modern technologies to automate tasks and optimize resources. This article examines how intelligent systems can be integrated with mathematical models to improve navigation accuracy, reduce costs and prioritize human safety. Real-time data management with precise information for AMRs and AGVs is crucial for low-risk operation. This article studies AI, the IoT, LiDAR, machine learning (ML), SLAM and other new technologies for the successful implementation of mobile robots in smart warehouses. Modern technologies such as reinforcement learning optimize the routes and tasks of mobile robots. Data and sensor fusion methods integrate information from various sources to provide a more precise understanding of the indoor environment and inventory. Semantic mapping enables mobile robots to navigate and interact with complex warehouse environments with high accuracy in real time. The article also analyses how virtual reality (VR) can improve the spatial orientation of mobile robots by developing sophisticated navigation solutions that reduce time and financial costs. Full article

Urban heritage, when enhanced by digital technologies, can become a living laboratory. This study explores the Art Nouveau Path, a mobile augmented reality game implemented in Aveiro, Portugal, as part of the EduCITY Digital Teaching and Learning Ecosystem. Designed as a circular path of eight georeferenced points of interest, it integrates narrative cartography, multimodal media, and sustainability competences framed by GreenComp, the European Sustainability Framework. A DBR approach guided the study, combining four interconnected datasets: the game’s structured curriculum review by 3 subject specialists (T1-R), gameplay logs from 118 student groups (4248 responses), post-game reflections from 439 students (S2-POST), and in-field observations from 24 teachers (T2-OBS). Descriptive statistics and thematic coding were triangulated to examine attention to architectural details, the mediational role of AR, spatial trajectories, and reflections about sustainability. The results present overall accuracy (85.33%), with particularly strong performance on video items (93.64%), stable outcomes on AR tasks (85.52%), and lower accuracy in denser urban contexts. Qualitative data highlight AR as a catalyst for perceiving hidden features, collaboration, and connecting heritage with sustainability. The study concludes that location-based AR games can generate semantically enriched geoinformation. They also act as cartographic interfaces that embed narrative and competence-oriented learning into urban heritage contexts. Full article

The integration of optical sensors and deep learning technologies in smart agriculture represents a critical intersection between technological innovation and agricultural economic sustainability, yet comprehensive assessments of their economic impact remain limited. This study applies a scientometric approach to 135 documents indexed in Scopus and Web of Science between January 2017 and June 2025, using Bibliometrix Bibliometrix (R package version 4.5.2), VOSviewer version 1.6.20, and Voyant Tools to examine publication trends, leading contributors, collaboration patterns, thematic structures, and reported economic outcomes. The analysis shows a strong upward trajectory with an estimated 66.48% annual increase in publications, identifying Xiukang Wang and Shaowen Wang as leading contributors among 791 authors from diverse institutions. Thematic analysis reveals three interconnected clusters: (i) precision agriculture and remote sensing as the sensing backbone; (ii) prediction and soil analysis as data-driven decision-support mechanisms; and (iii) vegetation indexes and productivity as measurement tools linking spectral information to yield and input use. Economic evidence includes high disease-detection accuracy (up to 95%), notable pesticide-use reductions (around 40%), improved autonomous-navigation precision (<6 cm error), and crop-detection performance exceeding 99%. However, adoption challenges persist, including technological heterogeneity, high implementation costs, limited model transferability, and varying levels of digital readiness across regions. Overall, the findings indicate that optical sensors and deep learning are transitioning from experimental applications to technologies with measurable economic impact, offering guidance for researchers, policymakers, technology developers, and agricultural producers seeking economically viable precision-agriculture solutions. Full article

The shift from syntagmatic to paradigmatic associations is a developmental process occurring from approximately the ages of six to nine years and plays an important role in language development. Syntagmatic relationships refer to words that co-occur due to their mutual dependency connection (e.g., “The dog barks”). Paradigmatic relationships are words within the same category (e.g., cat, kitten). In Study 1, we tested 244 Spanish-English bilingual children in grades 1 to 3 (M age = 7.87 years, 54.5% female) enrolled in dual language programs in California, USA. Children completed a matching task in both English and Spanish featuring both syntagmatic and paradigmatic lexical associations. Results showed significantly higher accuracy for older students than for younger students, higher accuracy in English than in Spanish for both paradigmatic and syntagmatic associations, and higher accuracy in paradigmatic associations in English and syntagmatic associations in Spanish. In Study 2, we conducted cognitive interviews with a separate sample of 13 Spanish-English bilingual children (M age = 8.96 years, 46.15% female) to explore how they reasoned through their word pair choices when completing the task. Children primarily relied on paradigmatic associations, using strategies like synonymy, antonymy, and category overlap, while also employing syntagmatic associations and thematic relatedness as less frequent but important reasoning strategies. Implications for early language development are discussed. Full article

The Caucasus Mountains are recognized as a global center of biodiversity but currently face significant risks of degradation due to intensified economic development and the effects of climate change. Forest inventory and mapping are essential for biodiversity conservation in the Caucasus region. Geospatial modeling is a common method of thematic mapping, but its reliability depends heavily on the initial classification of reference data used for model training. Modern vegetation science features various classification approaches, most of which were developed independently of digital mapping practices and are rarely assessed for their suitability in geospatial modeling. To fill this gap, we classified the same dataset of vegetation relevés from mixed and broadleaf forests in the northwestern Caucasus using two approaches, based on floristic and dominant concepts, and compared the predictive performance of geospatial models trained on these datasets. We considered multiple types of geospatial variables, including optical satellite imagery, a digital elevation model (DEM), and bioclimatic and soil features, to evaluate their informativeness for spatial differentiation of the resulting forest types and to identify optimal variable combinations for modeling via multistage feature selection. We trained several models using different variable sets and machine learning methods for both classifications and evaluated their accuracy via nested cross-validation. The forest types produced by the two approaches scarcely matched, and the selected variable sets for model training differed accordingly. Unexpectedly, bioclimatic and soil variables were more effective than DEM- and satellite-derived variables, despite their coarser spatial resolution. Floristic-based geospatial models outperformed dominant-based models in terms of forest-type separability and predictive accuracy. Therefore, a floristic classification approach may be preferable for forests with complex species composition, both ecologically and in terms of the reliability of geospatial modeling and the derived mapping results. Full article

With the acceleration of urbanization and the continuous expansion of building stock, building maintenance plays a critical role in ensuring structural safety, extending service life, and promoting sustainable development. In recent years, the application of artificial intelligence (AI) in building maintenance has expanded significantly, markedly improving detection accuracy and decision-making efficiency through predictive maintenance, automated defect recognition, and multi-source data integration. Although existing studies have made progress in predictive maintenance, defect identification, and data fusion, systematic quantitative analyses of the overall knowledge structure, research hotspots, and technological evolution in this field remain limited. To address this gap, this study retrieved 423 relevant publications from the Web of Science Core Collection covering the period 2000–2025 and conducted a systematic bibliometric and scientometric analysis using tools such as bibliometrix and VOSviewer. The results indicate that the field has entered a phase of rapid growth since 2017, forming four major thematic clusters: (1) intelligent construction and digital twin integration; (2) predictive maintenance and health management; (3) algorithmic innovation and performance evaluation; and (4) deep learning-driven structural inspection and automated operation and maintenance. Research hotspots are evolving from passive monitoring to proactive prediction, and further toward system-level intelligent decision-making and multi-technology integration. Emerging directions include digital twins, energy efficiency management, green buildings, cultural heritage preservation, and climate-adaptive architecture. This study constructs, for the first time, a systematic knowledge framework for AI-enabled building maintenance, revealing the research frontiers and future trends, thereby providing both data-driven support and theoretical reference for interdisciplinary collaboration and the practical implementation of intelligent maintenance. Full article

Hydroxypropyl cellulose (HPC) is a versatile cellulose ether with two standardized forms: highly substituted (H-HPC), which is water-soluble and thermoresponsive, and low-substituted (L-HPC), which is insoluble but swellable. This systematic review with bibliometric analysis aimed to map the global HPC research landscape (2005–2024), focusing on publication trends, research impact, and thematic directions. Original research articles and conference proceedings indexed in Scopus were included, while reviews and non-research items were excluded. The database was searched on 7 July 2025 using predefined strategies and analyzed using Excel for descriptive statistics and VOSviewer for network visualization. Risk of bias assessment was not applicable; data accuracy was ensured through duplicate removal and the use of standardized bibliometric indicators. A total of 1273 H-HPC and 92 L-HPC publications were analyzed. H-HPC research dominates multidisciplinary applications in drug delivery, 3D printing, thermochromic, and energy materials, whereas L-HPC remains focused on pharmaceutical disintegration and binding. Nevertheless, the field is constrained by reliance on commercial grades and a narrow application focus, leaving broader material innovations underexplored. HPC is positioned as a strategic polysaccharide derivative with expanding translational potential. Future studies should emphasize greener synthesis, advanced functionalization, and industrial scale-up. Funding: Supported by BRIN. Systematic review registration: INPLASY202590019. Full article

This study conducts a bibliometric analysis of the scientific literature on digital, automated, and AI-assisted accounting systems. The data include documents listed in the Web of Science and Scopus databases. The analysis identifies the main authors, countries/territories, sources, and thematic trends. The results reveal that the scientific output within this research field has increased since 2018, emphasising the integration of artificial intelligence (AI), robotic process automation, and blockchain technologies in accounting. The findings also suggest that automation enhances efficiency, accuracy, and reliability while also raising concerns about ethics, cybersecurity, and job displacement. This study evaluates the accounting research from early discussions on information systems and automation to current topics such as digital transformation, sustainability, and intelligent decision-making. Furthermore, it contributes to the understanding of the scientific development of digital accounting and addresses future research directions involving AI and machine learning for predictive analytics and fraud detection, blockchain for secure and transparent accounting systems, sustainability through the integration of ESG reporting, and interdisciplinary collaboration between accounting, computer science, and business management to develop intelligent financial systems. The findings provide insights for academics and practitioners aiming to understand the ongoing digital transformation of accounting systems. Full article

Background: Artificial Intelligence (AI) is a transformative innovation designed to enable machines to perform tasks typically requiring human intelligence. Among various medical fields, cardiology—and particularly electrophysiology—has seen rapid integration of AI technologies. The ability of AI to analyze large and complex datasets is reshaping diagnostic and therapeutic approaches. Objectives: This review aims to provide a comprehensive overview of AI models and their applications in cardiac electrophysiology. The focus is on understanding how AI contributes to clinical practice through ECG interpretation, arrhythmia detection, atrial mapping, and catheter ablation, while also exploring its limitations and future potential. Methods: The review discusses various AI approaches, including Machine Learning (ML) and Deep Learning (DL), and highlights relevant literature illustrating their implementation in electrophysiological settings. Key clinical applications are examined thematically, with a narrative synthesis of current capabilities, technologies, and outcomes. Results: AI-based tools have demonstrated effectiveness in identifying supraventricular arrhythmias like atrial fibrillation (AF) and atrial flutter (AFL), as well as complex conditions such as ventricular tachycardias (VTs) and long QT syndrome (LQTS). In procedural contexts, AI enhances electro-anatomical mapping, reduces operative time, and supports tailored post-ablation management. Discussion: While AI offers clear advantages in diagnostic accuracy and procedural efficiency, challenges remain regarding data security, ethical transparency, and clinical adoption. Addressing these limitations will be crucial for integrating AI into routine electrophysiology and maximizing its potential in future cardiology practice. Full article

Background: Advances in stereotactic neurosurgery rely on precise brain mapping, which allows the identification of functional regions for safer and more effective surgical interventions. The aim of this systematic review was to assess the effectiveness, challenges, and clinical applicability of algorithms used for multimodal data integration. Methodology: Databases were searched for studies published in the last 13 years. Studies that integrate fMRI and EEG data for brain mapping, quantitatively assess the performance of algorithms, and have potential applications in stereotactic neurosurgery were included. Heterogeneity among studies was assessed using the I² statistic, and the results were analyzed by thematic synthesis and meta-analysis. Results: The average accuracy of the algorithms was 90.2% (±5.0%). Key challenges include computational requirements, susceptibility to artifacts, and limited clinical applicability. Heterogeneity analysis showed significant methodological variability (I² = 71.90%), with greater heterogeneity among highly relevant algorithms (I² = 79.64%). Conclusions: Advanced algorithms offer significant potential to improve precision, safety, and applicability in stereotactic neurosurgery. Key recommendations include standardization of protocols, expansion of clinical validation, and optimization of algorithms for real-time application. Full article

Hydrogel microneedles (HMNs) act as non-invasive devices that can effortlessly merge with the human body for drug delivery and diagnostic purposes. Nonetheless, their improvement is limited by intricate and repetitive issues related to material composition, structural geometry, manufacturing accuracy, and performance enhancement. At present, there are only a limited number of studies accessible since artificial intelligence and machine learning (AI/ML) for HMN are just starting to emerge and are in the initial phase. Data is distributed across separate research efforts, spanning different fields. This review aims to tackle the disjointed and narrowly concentrated aspects of current research on AI/ML applications in HMN technologies by offering a cohesive, comprehensive synthesis of interdisciplinary insights, categorized into five thematic areas: (1) material and microneedle design, (2) diagnostics and therapy, (3) drug delivery, (4) drug development, and (5) health and agricultural sensing. For each domain, we detail typical AI methods, integration approaches, proven advantages, and ongoing difficulties. We suggest a systematic five-stage developmental pathway covering material discovery, structural design, manufacturing, biomedical performance, and advanced AI integration, intended to expedite the transition of HMNs from research ideas to clinically and commercially practical systems. The findings of this review indicate that AI/ML can significantly enhance HMN development by addressing design and fabrication constraints via predictive modeling, adaptive control, and process optimization. By synchronizing these abilities with clinical and commercial translation requirements, AI/ML can act as key facilitators in converting HMNs from research ideas into scalable, practical biomedical solutions. Full article