Search Results (162,511)

Background: The aim of this study was to compare four recently introduced LLMs (ChatGPT-5, Grok 4, Gemini 2.5 Flash, and Claude Sonnet-4). Experienced endodontists evaluated the accuracy, completeness, and readability of the responses given to open-ended questions about iatrogenic events in endodontics. Methods: Twenty-five open-ended questions related to iatrogenic events in endodontics were prepared. The responses of the four LLMs were evaluated by two specialist endodontists using a Likert scale for accuracy and completeness, and the Flesch Reading Ease Score (FRES), Flesch–Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Simplified Measure of Gobbledygook (SMOG), and Coleman–Liau Index (CLI) for readability. Results: The accuracy score of ChatGPT-5’s responses to open-ended questions (4.56 ± 0.65) was found to be significantly higher than those of Gemini 2.5 Flash (3.64 ± 0.95) and Claude Sonnet-4 (3.44 ± 1.19) (p = 0.009, and p = 0.002, respectively). Similarly, the completeness score of ChatGPT-5 (2.88 ± 0.33) was higher than those of Claude Sonnet-4, Gemini 2.5 Flash, and Grok 4 (p < 0.001, p = 0.002, and p = 0.007, respectively). In terms of readability measures, ChatGPT-5 and Gemini 2.5 Flash achieved better FRESs than Claude Sonnet-4 (p = 0.003, and p < 0.001, respectively). Conversely, FKGL scores were higher for Claude Sonnet-4 and Grok 4 compared to ChatGPT-5 (p < 0.001, and p = 0.008, respectively). Correlation analyses revealed a strong positive association (r_s = 0.77; p < 0.001) between accuracy and completeness, a weak negative correlation (r_s = −0.19; p = 0.047) between completeness and FKGL, and a strong negative correlation between (r_s = −0.88; p < 0.001) FKGL and FRES. Additionally, ChatGPT-5 demonstrated lower GFI and CLI scores than the other models, while its SMOG scores were lower than those of Gemini 2.5 Flash and Grok 4 (p = 0.001, and p < 0.001, respectively). Conclusions: Although differences were observed between the LLMs in terms of the accuracy and completeness of the responses, ChatGPT-5 showed the best performance. Even with high scores of accuracy (excellent) and completeness (comprehensive), it must not be forgotten that incorrect information can lead to serious outcomes in healthcare services. Therefore, the readability of responses is of critical importance, and when selecting a model, readability should be evaluated together with content quality. Full article

Background/Objectives: Atopic dermatitis (AD) is a common chronic inflammatory skin disease that may influence cancer risk through immune dysregulation and chronic inflammation. The association between AD and colorectal cancer (CRC) remains unclear, with previous studies reporting conflicting results. Evidence from East Asian populations, where CRC incidence is rapidly rising, is particularly limited. Methods: We conducted a nested case–control study using the Korean National Health Insurance Service–National Sample Cohort (2002–2019). A total of 9920 incident CRC cases were identified and matched 1:4 with 39,680 controls by age, sex, income, and residential region. AD was defined using diagnostic codes and prescription records. Overlap propensity score weighting was applied to minimize confounding, and weighted logistic regression was used to estimate odds ratios (ORs) with 95% confidence intervals (CIs). Results: AD was not significantly associated with CRC risk (adjusted OR = 0.97, 95% CI: 0.91–1.04). The null association was consistent across subgroups stratified by age, sex, comorbidity burden, and allergic comorbidities. Sensitivity analyses yielded similar results. Conclusions: In this large, nationwide, population-based study, AD did not exhibit a significant connection with the risk of CRC. This null association remained consistent across multiple subgroups and sensitivity analyses, suggesting that AD may not play a substantial role in colorectal carcinogenesis. However, the observational design and lack of detailed lifestyle information may limit causal interpretation. Full article

Optical imaging technologies expand gastrointestinal endoscopy beyond white-light endoscopy (WLE), improving visualization of mucosal, vascular, and subsurface features. They are applied to the detection of neoplastic and premalignant lesions, inflammatory diseases, and small bowel and pancreatic disorders, though their validation and readiness for routine practice vary. This review critically evaluates both guideline-endorsed and investigational optical imaging techniques across major gastrointestinal indications, highlighting diagnostic performance, level of validation, current guideline recommendations, and practical challenges to adoption. In Barrett’s esophagus, narrow-band imaging (NBI) is guideline-endorsed, while acetic acid chromoendoscopy is validated in expert centers. For gastric intestinal metaplasia and early gastric cancer, magnifying NBI achieves diagnostic accuracies exceeding 90% and is guideline-recommended, with acetic acid chromoendoscopy aiding in margin delineation. In inflammatory bowel disease, dye-spray chromoendoscopy is the reference standard for dysplasia surveillance, with virtual methods such as NBI, FICE, and i-SCAN serving as practical alternatives when dye application is not feasible. In the colorectum, NBI supports validated optical diagnosis strategies (resect-and-discard, diagnose-and-leave), while dye-based chromoendoscopy improves detection of flat and serrated lesions. Capsule endoscopy remains the standard for small bowel evaluation of bleeding, Crohn’s disease, and tumors, with virtual enhancement, intelligent chromo capsule endoscopy, and AI-assisted interpretation emerging as promising adjuncts. Pancreaticobiliary applications of optical imaging are also advancing, though current evidence is still preliminary. Investigational modalities including confocal laser endomicroscopy, optical coherence tomography, autofluorescence, Raman spectroscopy, and fluorescence molecular imaging show potential but remain largely restricted to research or expert settings. Guideline-backed modalities such as NBI and dye-based chromoendoscopy are established for clinical practice and supported by robust evidence, whereas advanced techniques remain investigational. Future directions will rely on broader validation, integration of artificial intelligence, and adoption of molecularly targeted probes and next-generation capsule technologies, which together may enhance accuracy, efficiency, and standardization in gastrointestinal endoscopy. Full article

Escalating Human–Wildlife Conflict (HWC), particularly involving protected large carnivores such as the wolf, poses a significant challenge in Europe. This problem, exacerbated by ecological pressure, necessitates the development of innovative, non-lethal, and effective prevention methods that overcome the limitations of current passive solutions, such as habituation. This article presents the design and implementation of a prototype for an autonomous, multi-sensory preventive system. Its three-layer architecture is based on a decentralized network of sensory-deterrent nodes that utilize Edge AI for real-time species detection and adaptive selection of deterrent stimuli. During field validation, the prototype’s biological efficacy as a proof-of-concept was confirmed in a crop protection scenario against the European roe deer (Capreolus capreolus). The system’s deployment led to a near-total elimination of damages. The paper also presents key technical performance metrics (e.g., response time, energy consumption) and the accuracy of the implemented AI detection model, verified using both field and historical data. The positive test results demonstrate that the developed platform provides an effective and flexible foundation for preventive systems. Its successful validation on a common herbivore species represents a crucial, measurable step toward the target implementation and further research on the system’s effectiveness in providing protection against large carnivores. Full article

When renewable resources such as biomass, waste, or carbon dioxide together with renewable electrical energy are used, Fischer–Tropsch (FT) synthesis is a promising option for the sustainable production of fuels and petrochemicals conventionally derived from crude oil. As such renewable resources generally do not occur in large point sources like fossil fuels, future sustainable FT facilities will likely be substantially smaller in scale than their fossil counterparts, which will have a significant impact on their design. A core topic in the reimagination of such smaller-scale facilities will be the reduction in complexity of the FT gas loop. To this end, three simple gas loop designs for the conversion of syngas from biomass gasification were conceived, simulated in DWSIM, and compared regarding their performance. Concepts only employing an internal recycle were found to be inherently limited in terms of efficiency. To achieve high efficiencies, an external recycle with a tail gas reformer and high tail gas recycling ratios (>3) were required. Thereby, the carbon dioxide content of the syngas had a considerable influence on the required syngas H₂/CO ratio, making the separation efficiency of the carbon dioxide removal unit a suitable control parameter in this respect. Full article

Background/Objectives: Although the U.S. Food and Drug Administration (FDA) has approved one antiviral treatment and authorized others for emergency use, there is no fully effective antiviral therapy for coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Assays detecting virus-specific immunoglobulins (Ig) or nucleic acids in large-scale epidemiological, vaccine, and drug development studies remain limited due to high costs, reagent accessibility, and cumbersome protocols. Methods: A multiplex bead-based assay was developed to simultaneously detect human IgM, IgG, and IgA antibodies against the SARS-CoV-2 spike receptor binding domain (RBD) in serum using flow cytometry. Assay performance was evaluated for sensitivity, specificity, reproducibility, and cross-reactivity and compared to another immunoassay platform. Results: The assay enabled simultaneous measurement of three antibody isotypes across 624 samples within 2 h. Intra-plate coefficients of variation (CVs) ranged from 3.16 to 6.71%, and inter-plate CVs ranged from 3.33 to 5.49%, demonstrating high reproducibility. The platform also quantified background noise from nonspecific binding, facilitating straightforward data interpretation. Conclusions: This novel, flexible multiplex bead-based assay utilizing a well-established platform provides a rapid and reproducible approach for detecting SARS-CoV-2-specific antibodies. Its high throughput capacity and low variability make it well suited for large-scale epidemiological, vaccine, and therapeutic studies. The platform’s adaptability further supports application to other infectious diseases, offering an ideal tool for broad immunological surveillance. Full article

Water distribution networks (WDNs) are characterized by complex challenges in management and optimization, especially in ensuring efficiency, reducing losses, and maintaining infrastructure performances. The recent advancements in Artificial Intelligence (AI) techniques based on Large Language Models, particularly ChatGPT 4.0 (a chatbot based on a generative pre-trained model), offer potential solutions to streamline these processes. This study investigates the ability of ChatGPT to perform the clustering phase of WDN partitioning, a critical step for dividing large networks into manageable clusters. Using a real Italian network as a case study, ChatGPT was prompted to apply several clustering algorithms, including k-means, spectral, and hierarchical clustering. The results show that ChatGPT uniquely adds value by automating the entire workflow of WDN clustering—from reading input files and running algorithms to calculating performance indices and generating reports. This makes advanced water network partitioning accessible to users without programming or hydraulic modeling expertise. The study highlights ChatGPT’s role as a complementary tool: it accelerates repetitive tasks, supports decision-making with interpretable outputs, and lowers the entry barrier for utilities and practitioners. These findings demonstrate the practical potential of integrating large language models into water management, where they can democratize specialized methodologies and facilitate wider adoption of WDN managing strategies. Full article

Accurate prediction of structural displacements in hydropower stations is essential for the safety and long-term stability of large-scale water-related infrastructure. To address this challenge, this study proposes an AI-assisted monitoring framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with Gated Recurrent Units (GRUs) for temporal sequence modeling. The framework leverages long-sequence prototype monitoring data, including reservoir level, temperature, and displacement, to capture complex spatiotemporal interactions between environmental conditions and dam behavior. A parameter optimization strategy is further incorporated to refine the model’s architecture and hyperparameters. Experimental evaluations on real-world hydropower station datasets demonstrate that the proposed CNN–GRU model outperforms conventional statistical and machine learning methods, achieving an average determination coefficient of R² = 0.9582 with substantially reduced prediction errors (RMSE = 4.1121, MAE = 3.1786, MAPE = 3.1061). Both qualitative and quantitative analyses confirm that CNN–GRU not only provides stable predictions across multiple monitoring points but also effectively captures sudden deformation fluctuations. These results underscore the potential of the proposed AI-assisted framework as a robust and reliable tool for intelligent monitoring, safety assessment, and early warning in large-scale hydropower facilities. Full article

We conducted a systematic literature review, following PRISMA guidelines, to assess whether existing species distribution models for Aedes vexans reflect its known ecological requirements. This mosquito is closely associated with temporary floodwaters, making hydrological dynamics a critical factor for accurate modelling. From 28 peer-reviewed studies, we extracted 477 environmental and ecological variables and organized them into a hierarchical classification scheme with four main categories: weather, land characteristics, water characteristics, and population. We analysed patterns in variable usage and the reported importance of each variable. Our results show that flood-related variables were largely absent, despite the species’ reliance on ephemeral water bodies for reproduction. This may possibly reduce the predictive utility of existing Aedes vexans species distribution models. In contrast, urban-landscape variables were frequently used and often ranked as highly predictive, but such results were primarily found in studies that did not account for sampling bias. Full article

Offshore wind energy is rapidly expanding as a critical resource for global carbon neutrality, with 10.8 GW of new capacity added in 2023, raising the worldwide total to 75.2 GW. However, large-scale integration of offshore wind farms introduces power quality challenges due to the characteristics of inverter-based resources, particularly harmonic distortion, which can threaten system stability. This study quantitatively investigates the influence of short circuit ratio (SCR) on voltage and current harmonic distortion during offshore wind farm integration. A 500 MW offshore wind farm was modeled, and MATLAB/Simulink simulations were performed for 345 kV and 154 kV systems to evaluate the impact of varying SCR on total harmonic distortion (THD) and individual harmonic orders. Furthermore, the harmonic assessment based on the IEC 61400-21-2 summation method was compared with the simulation results, demonstrating the limitations of the simple summation approach and underscoring the importance of simulation-based evaluation. The results reveal that, under certain SCR conditions, parallel resonance caused by system impedance and line parameters produces unexpectedly high distortion in the 345 kV system, contrary to expectations based solely on voltage level. This resonance phenomenon and SCR dependency were also validated using short circuit capacity data from actual offshore wind farm candidate sites. Overall, the study emphasizes the necessity of comprehensive power quality assessments that account for SCR conditions, voltage levels, and harmonic emission characteristics, providing practical guidance for site selection, substation design, and harmonic mitigation in offshore wind integration. Full article

Genome sequencing has possibly been the greatest step in the development of advanced tools for animal genetic improvement: knowledge of gene sequences and use of haplotype markers for productivity traits can provide important improvements in yield production and optimisation of reproductive program. Next-generation and, more recently, third-generation sequencing techniques enormously increased the ability to produce sequences from single individuals and increased the interest in exome or whole-genome sequencing as an alternative to SNP chips in breeding programs as these techniques allowed for the capture of a wider range of variations, including characterisation of rare variants, structural variations, and copy number changes. Here, we present a procedure, based on fast de novo assembly and a scaffolding step, to quickly build an almost complete genome starting from long reads obtained in a single sequencing run. The procedure, applied to sequences from five water buffaloes, was able to independently build, for each individual, an almost complete high-quality genome with highly continuous chromosome sequences; in most cases, over 90% of the length of the reference chromosome was covered by less than ten long contigs. Unlike other pipelines based on slower assemblers or which require many sequencing data, in 1–2 days, the proposed procedure can go from a single run to continuous genome assembly, supporting fast analysis of large chromosome structures, potentially useful for improving animal breeding and productivity. Full article

Focused ultrasound-stimulated microbubble (MB + FUS) therapy is a promising radiation enhancement strategy, utilizing vascular disruption to enhance radiation efficacy. However, its mechanistic effects in large tumour volumes and clinical translatability remain insufficiently characterized. This study evaluates the synergistic impact of MB + FUS combined with radiation therapy (XRT) in a large-scale, immunosuppressed rabbit xenograft model using a clinically adaptable, MRI-guided 6144-element focused ultrasound (MRgFUS) system. Tumours were treated with MB + FUS, XRT, or both, with real-time image-guided MB activation and evaluation of treatment effects on vascular integrity, proliferation, and cellular stress responses. Assessments included Power Doppler ultrasound, histology, and immunohistochemistry targeting TUNEL, ASMase, Ki-67, Factor VIII, HIF-1α, and VEGF. Combination therapy induced significant vascular collapse, reduced perfusion, and decreased Factor VIII expression, alongside increased nuclear condensation, TUNEL positivity, and ASMase expression, consistent with ASMase-mediated endothelial apoptosis and vascular disruption. Upregulation of HIF-1α and VEGF indicated hypoxia-driven angiogenic signalling, while Ki-67 suppression reflected sustained tumour growth inhibition. Although immune responses were limited by host immunosuppression, the larger tumour burden provided clinically relevant constraints. The MRgFUS platform enabled precise and reproducible MB activation, reinforcing MB + FUS as a potent radio-enhancement modality. These findings support the continued development of MB + FUS toward clinical translation and highlight its potential as a complementary strategy to radiation therapy. Full article

The development of organic light-emitting diodes (OLEDs) for high-resolution, large-area displays relies on effective encapsulation technology. Accordingly, this study proposes a novel multilayer structure utilizing a cyclo-olefin polymer-based film. This solution significantly reduces process time and cost while achieving remarkable barrier performance. Optimization involved presenting various models and enhancing substrate–film adhesion via ultraviolet or plasma treatment, consequently improving water vapor transmission rate. Furthermore, the optimized structure’s feasibility as an OLED encapsulation layer was confirmed. These results promise to enhance core technological capabilities, improving production yield and minimizing costs—key factors for next-generation displays. Full article

Soil organic carbon (SOC) is a critical indicator of soil health, directly influencing crop productivity, soil structure, and environmental sustainability. Existing SOC estimation techniques using satellite reflectance data are effective for large-scale applications; however, their accuracy is reduced due to various types of noisy samples caused by vegetation interference, sensor-related anomalies, atmospheric effects, and other spectral distortions. This study proposes a robust data refinement framework capable of handling any soil sample, whether clean or noisy, by identifying and correcting noisy samples to enable more accurate SOC estimation outcomes. The approach first explores complex global relationships among spectral bands to understand and represent subtle patterns in soil reflectance using the Transformer network. To remove redundancy and retain only essential information of the transformed features, we apply a dimensional reduction technique for efficient analysis. Building upon this refined representation, noisy samples are detected without relying on strict data distribution assumptions, ensuring effective identification of noisy samples in diverse conditions. Finally, instead of excluding these noisy samples, the proposed framework corrects their reflectance through a conditional Generative Adversarial Network (cGAN) to align with expected soil spectral characteristics, thereby preserving valuable information for more accurate SOC estimation. The proposed approach was evaluated on benchmark satellite datasets, demonstrating superior performance over existing noise correction techniques. Experimental validation using the Landsat 8 dataset demonstrated that the proposed framework improved SOC estimation performance by increasing $R^2$ by 1.52%, reducing RMSE by 4.45%, and increasing RPD by 5.14% compared to the best baseline method (OC-SVM + Kriging). These results confirm the framework’s effectiveness in enhancing SOC estimation under noisy conditions. This scalable framework supports accurate SOC monitoring across diverse conditions, enabling informed soil management and advancing precision agriculture. Full article

The compounding effects of hydrometeorological hazards are being driven by climate change. As urban areas expand, this leads to degradation of the surrounding environment and exposes more people to hazards. Growing losses show that conventional approaches to addressing these issues can compound these problems. Over the last few decades, nature-based solutions (NBSs) have become an increasingly popular alternative. These measures, inspired by natural processes, have shown potential for reducing hazards by complementing traditional approaches and providing co-benefits in the form of eco-system services. With the adoption of NBSs becoming a more mainstream approach, there is a need for tools that support the planning and implementation of interventions. Geospatial suitability assessment is a part of this planning process. Existing tools are limited in their application for large-scale measures. This paper intends to improve this by building upon a multi-criteria analysis (MCA)-based approach that incorporates biophysical and land use criteria and conditions for mapping the suitability of large-scale NBSs. The methodology was developed and tested on six sites to assess the suitability of floodplain restoration, retention or detention, afforestation, and forest buffer strips. The resulting suitability maps also show potential for combining two or more measures for greater risk reduction. Full article