Search Results (1,007)

A significant wildfire event occurred in Korinthos, Greece, on 22 July 2020, releasing large amounts of smoke into the atmosphere. This episode provided the opportunity to develop and apply the methodology described in this work, where the synergistic use of ground data, satellite remote sensing data and dispersion modeling is utilized to demonstrate highly accurate source detection, emission transport, and dispersion of the smoke plumes. The Fire Radiative Power (FRP) data from SEVIRI, on board Meteosat Second Generation, are used to estimate hourly fire top-down emissions. These emissions are used as input for the FLEXPART Lagrangian particle dispersion model, driven by GFS meteorological data. Simulated smoke transport is compared with TROPOMI satellite CO observations and lidar profiles from the PANhellenic GEophysical observatory of Antikythera (PANGEA) station. The model includes key atmospheric processes such as advection and deposition, providing a framework for assessing wildfire impacts on air quality and transport. The results highlight the effectiveness of combining high temporal resolution FRP data with the WARM START configuration of FLEXPART versus the Standard FLEXPART Simulation. Full article

Background: Artificial intelligence (AI) can improve efficiency, documentation, and diagnostic quality in ophthalmology. This study examined clinical AI adoption, institutional readiness, perceived utility, trust, ethical concerns, and educational needs among Swiss ophthalmologists and residents. Methods: In May 2025, an anonymous online survey was distributed to board-certified ophthalmologists and residents across Switzerland. The structured questionnaire addressed clinical AI use, institutional infrastructure, perceptions of diagnostic utility, trust, ethical–legal concerns, and educational preparedness. Responses were recorded on five-point Likert scales. Results: Of 106 respondents (mean age 42.4 ± 11.4 years, 48.1% female), 20.8% reported current clinical AI use. Willingness to use AI exceeded 65% across all 10 diagnostic scenarios, but active use remained ≤12.1%. Institutional readiness was low: 6.6% reported AI-related guidelines, 26.4% had access to an institutional AI contact person, and 28.3% received supervisor support (more often among residents). While 80% agreed that AI can support diagnostics, only 12.1% trusted AI recommendations as much as those from colleagues; 87.9% critically reviewed the results, and 93.9% endorsed the use of AI in an assistive but not independently decision-making role. Ethical–legal concerns included unresolved liability (74.8%), informed consent (66.7%), and data protection adequacy (49.5%). Structured AI education was supported by 77.8%, yet only 15.1% felt prepared, and two-thirds (66.7%) indicated they would use AI more with better training. Conclusions: Ophthalmologists and residents in Switzerland express strong interest in the clinical use of AI and recognize its diagnostic potential. Major barriers include insufficient institutional structures, lack of regulatory clarity, and inadequate educational preparation. Addressing these deficits will be essential for responsible AI integration into ophthalmologic practice. Full article

The selection of tree species for afforestation in Mediterranean environments involves challenges related to adaptability, impact on soil properties, and overall environmental quality. Cupressus lusitanica has been recognized for its rapid growth, environmental resilience, and versatile applications, positioning it as a promising candidate for these regions. Although it has been used for afforestation in Northeast Portugal since the 1990s, no comprehensive studies have evaluated its performance under local conditions. To address this knowledge gap, this study assessed a 14-year-old C. lusitanica stand in Northeast Portugal. The wood’s anatomical, physical, chemical, and mechanical properties, as well as biomass production, were evaluated. The species showed superior radial growth and adaptability compared with other species under similar environmental conditions. Despite exhibiting lower fiber length (1.6 mm) and basic wood density (404 kg/m³), shrinkage values fell within the typical range for softwoods. Nevertheless, a marked tendency for warping was observed. The extractive content was relatively high (5.1%), with the ethanol-soluble fraction being predominant (3.6%). Mechanical tests revealed low values for both Modulus of Elasticity (MOE) (3592.5–3617.1 MPa) and Modulus of Rupture (MOR) (57.7–68.9 MPa), with both properties significantly influenced by knot presence. Given the results obtained, the species C. lusitanica, despite its low wood density and potential limitations in use, exhibits remarkable growth and adaptability, which confer a high potential for biomass production and carbon sequestration, as well as potential applications of its wood in reconstituted panels and fiber- or particle-based boards. Full article

In recent years, machine learning (ML) techniques have gained significant attention in time series classification tasks, particularly in industrial applications where early detection of abnormal conditions is crucial. This study proposes an intelligent monitoring framework based on a multimodal convolutional neural network (CNN) to classify normal and abnormal copper ion (Cu²⁺) concentration states in the etching process in the printed circuit board (PCB) industry. Maintaining precise control Cu²⁺ concentration is critical in ensuring the quality and reliability of the etching processes. A sliding window approach is employed to segment the data into fixed-length intervals, enabling localized temporal feature extraction. The model fuses two input modalities—raw one-dimensional (1D) time series data and two-dimensional (2D) recurrence plots—allowing it to capture both temporal dynamics and spatial recurrence patterns. Comparative experiments with traditional machine learning classifiers and single-modality CNNs demonstrate that the proposed multimodal CNN significantly outperforms baseline models in terms of accuracy, precision, recall, F1-score, and G-measure. The results highlight the potential of multimodal deep learning in enhancing process monitoring and early fault detection in chemical-based manufacturing. This work contributes to the development of intelligent, adaptive quality control systems in the PCB industry. Full article

With globalization and technology advancement, traditional teaching models are facing challenges due to the diverse needs of modern learners. It is necessary to enhance learner engagement and motivation, and incorporating Internet of Things (IoT)-assisted teaching tools has become a major concern for educators. However, the time it takes to develop new teaching tools and integrate IoT technology must be shortened by combining educational content with game mechanics seamlessly. Therefore, we developed a gamified teaching model by incorporating IoT technology. We used the “System, Indicators, Criteria” framework to develop a three-tiered board game evaluation and development model. Based on this framework, a teaching tool was designed to provide personalized learning experiences with IoT technology. The tool provides abstract knowledge, fosters interaction and collaboration among learners, and thus enhances engagement. To ensure a rigorous design and evaluation process, we employed quality function deployment (QFD), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). The developed model facilitates the integration of IoT technology with innovative design concepts and enhances the application value of teaching tools in education. The model also enhances intelligence, interactivity, and creativity for traditional education to revitalize learning experiences. Full article

A good way to produce large-sized wood products from small-diameter logs is by using laminated boards. The lamina undergoes an impregnation pretreatment to improve its quality before being formed into laminated boards (LBs). This research was performed to analyze the effects of an impregnation treatment on Jabon lamina with citric acid, boric acid, and polystyrene solutions on the physical and mechanical properties of Jabon LB. The Jabon lamina was first pretreated with citric acid, boric acid, and polystyrene by vacuuming for 30 min and pressing for 30 min at a pressure of 6.6 bar. The laminas were glued using isocyanate adhesive with a spreading rate of 280 g/m², consisting of three layers, which were cold pressed for 24 h. LB’s physical and mechanical properties were affected by the nature of the impregnating agent. Impregnating the lamina with citric acid and boric acid increased the density and moisture content of the laminated board, decreasing its mechanical properties. On the contrary, polystyrene-impregnated LB improved. After soaking in hot water, no LB displayed delamination, indicating high bonding performance. The best impregnating agent for lamina pretreatment was polystyrene, followed by boric acid and citric acid. The chemical compound, functional group, and degree of crystallinity of treated Jabon LB all changed due to the impregnation process. Full article

In this study, poplar boards were bleached and treated with two types of urea–formaldehyde-coated ternary system thermochromic microcapsules (UF@TS), which were mixed with UV primer. The bleached poplar boards were manually painted with two layers of primer and topcoat. Coating samples with varying microcapsule contents were prepared and evaluated based on factors such as glossiness, reflectivity, and other surface properties. The experimental results showed that bleaching treatment significantly increased the whiteness of poplar wood, with an improvement rate of up to 17%. Among the two microcapsule types, the coating containing #2 microcapsules exhibited superior surface quality compared to #1 microcapsules. As the microcapsule content increased, the coating glossiness showed an overall decreasing trend and a certain degree of fluctuation, and the #2 microcapsule showed lower reflectivity values. The addition of UF@TS microcapsules negatively affected the coating adhesion but had little effect on hardness. The #2 microcapsule enhanced the impact resistance of the coating to a certain extent and increased surface roughness. Regarding thermochromic performance, the #1 microcapsule exhibited higher color-changing temperature and larger color difference, while the #2 microcapsule showed color-changing temperature closer to room temperature. Despite a decline in thermochromic performance and glossiness during aging, the 1# microcapsule showed slightly better stability. The coating containing 10% #2 microcapsules demonstrated the best comprehensive performance on bleached poplar wood, with glossiness of 2.1 GU, reflectivity of 67.95%, adhesion grade of 1, hardness of 6 H, impact resistance grade of 4, and surface roughness of 0.681 μm. The ΔE in the range of −20 °C to 50 °C was 7.434. After aging, ΔE was 5.846, and the light loss rate was 9%, with excellent comprehensive performance. Full article

The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and unstable video delivery. This paper presents a novel approach based on the Graphical Evaluation and Review Technique (GERT) for modeling the transmission of video frames from UAVs over uncertain network paths with probabilistic feedback loops and lognormally distributed delays. The proposed model enables both analytical and numerical evaluation of key Quality-of-Service (QoS) metrics, including mean transmission time and jitter, under varying levels of channel variability. Additionally, the structure of the GERT-based framework allows integration with artificial intelligence mechanisms, particularly for adaptive routing and delay prediction in urban conditions. Spectral analysis of the system’s characteristic function is also performed to identify instability zones and guide buffer design. The results demonstrate that the approach supports flexible, parameterized modeling of UAV video transmission and can be extended to intelligent, learning-based control strategies in complex smart city environments. This makes it suitable for a wide range of applications, including traffic monitoring, infrastructure inspection, and emergency response. Beyond QoS optimization, the framework explicitly accommodates security and privacy preserving operations (e.g., encryption, authentication, on-board redaction), enabling secure UAV video transmission in urban networks. Full article

Discrete Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistors (SiC MOSFETs) are characterized by their lower parasitic parameters and single-chip design, enabling them to achieve even faster switching speeds. However, the rapid rate of change in voltage (dv/dt) and current (di/dt) can lead to overshoot and oscillation in both voltage and current, ultimately limiting the performance of high-frequency operations. To address this issue, this paper presents a high-switching-frequency motor controller that utilizes discrete SiC MOSFETs. To achieve a high switching frequency for the controller while minimizing current oscillation and voltage overshoot, a novel electronic system architecture is proposed. Additionally, a passive driving circuit is designed to suppress gate oscillation without the need for additional control circuits. A new printed circuit board (PCB) laminate stack featuring low parasitic inductance, high current conduction capacity, and efficient heat dissipation is also developed using advanced wiring technology and a specialized heat dissipation structure. Compared to traditional methods, the proposed circuit and bus design features a simpler structure, a higher power density, and achieves a 13% reduction in current overshoot, along with a 15.7% decrease in switching loss. The silicon carbide (SiC) controller developed from this research has successfully undergone double-pulse and power testing. The results indicate that the designed controller can operate reliably over extended periods at a switching frequency of 50 kHz, achieving a maximum efficiency of 98.2% and a power density of 9 kW/kg (10 kW/L). The switching frequency and quality density achieved by the controller have not been observed in previous studies. This controller is suitable for use in the development of new energy electrical systems. Full article

Wire Arc Additive Manufacturing holds promise for on-board metal part production in maritime settings, yet its implementation remains limited due to the vibrational instability inherent to shipborne environments. This study addresses this critical technological barrier by analyzing the effects of marine vibrations on process stability and proposing an integrated solution based on adaptive process control, gyrostabilized platforms, and real-time monitoring systems. The research establishes specific technical requirements for WAAM instrumentation under maritime conditions and evaluates the capabilities and limitations of existing hardware and software tools. A set of engineering recommendations is presented for improving digital modeling, thermal–mechanical monitoring, and feedback control systems. Additionally, the study highlights material-related challenges by examining the influence of alloy properties on print quality under dynamic loads. The proposed approach enhances WAAM process resilience, laying the groundwork for reliable, high-quality additive manufacturing at sea. These findings are particularly relevant to shipboard maintenance, repair, and remote fabrication tasks, marking a significant step toward the industrial adoption of WAAM in marine engineering. Full article

In printed circuit board (PCB) assembly, the volume of dispensed glue is closely related to the PCB’s durability, production costs, and the overall product reliability. Currently, quality inspection is performed manually by operators, inheriting the limitations of human-performed procedures. To address this, we propose an automatic optical inspection framework that utilizes convolutional neural networks (CNNs) and post-hoc explainable methods. Our methodology handles glue quality inspection as a three-fold procedure. Initially, a detection system based on CenterNet MobileNetV2 is developed to localize PCBs, thus, offering a flexible lightweight tool for targeting and cropping regions of interest. Consequently, a CNN is proposed to classify PCB images into three classes based on the placed glue volume achieving 92.2% accuracy. This classification step ensures that varying glue volumes are accurately assessed, addressing potential quality issues that appear early in the production process. Finally, the Deep SHAP and Grad-CAM methods are applied to the CNN classifier to produce explanations of the decision making and further increase the interpretability of the proposed approach, targeting human-centered artificial intelligence. These post-hoc explainable methods provide visual explanations of the model’s decision-making process, offering insights into which features and regions contribute to each classification decision. The proposed method is validated with real industrial data, demonstrating its practical applicability and robustness. The evaluation procedure indicates that the proposed framework offers increased accuracy, low latency, and high-quality visual explanations, thereby strengthening quality assurance in PCB manufacturing. Full article

This research focuses on analyzing the determinants of financial performance for the companies included in the Standard & Poor’s 500 index over the period from 2014 to 2023. To guide managerial decisions aimed at enhancing company performance, this study examines, as key drivers, the main financial indicators, core corporate governance characteristics, and U.S. public governance indicators. The investigation begins with a retrospective review of the specialized literature, highlighting the findings of previous studies in the field and providing the basis for selecting the variables used in the present empirical analysis. The research method employed is fixed-effects panel-data regression. The dependent variables are financial performance measures, such as the EBITDA margin, EBIT margin, net profit margin, and ROA. This study’s main results show that the price-to-book ratio, liquidity, sales growth, CEO duality, board gender diversity, ESG score, and U.S. regulatory quality exert a positive influence on financial performance. In contrast, the price-to-earnings ratio, net debt, capital intensity, R&D intensity, weighted average cost of capital, board independence, and the COVID-19 pandemic crisis have a negative impact on the financial performance of U.S. companies. The findings of this investigation could serve as benchmarks for supporting managerial decisions at the company level regarding the improvement of their financial performance. Full article

Background: Large language models (LLMs) such as ChatGPT, Google Gemini, and Microsoft Copilot are increasingly used by patients seeking medical information online. While these tools provide accessible and conversational explanations, their accuracy and safety in emotionally sensitive scenarios—such as an incidental cancer diagnosis—remain uncertain. Objective: To evaluate the quality, completeness, readability, and safety of responses generated by three state-of-the-art LLMs to common patient questions following the incidental discovery of a kidney tumor. Methods: A standardized use-case scenario was developed: a patient learns of a suspicious renal mass following a computed tomography (CT) scan for back pain. Ten plain-language prompts reflecting typical patient concerns were submitted to ChatGPT-4o, Microsoft Copilot, and Google Gemini 2.5 Pro without additional context. Responses were independently assessed by five board-certified urologists using a validated six-domain rubric (accuracy, completeness, clarity, currency, risk of harm, hallucinations), scored on a 1–5 Likert scale. Two statistical approaches were applied to calculate descriptive scores and inter-rater reliability (Fleiss’ Kappa). Readability was analyzed using the Flesch Reading Ease (FRE) and Flesch–Kincaid Grade Level (FKGL) metrics. Results: Google Gemini 2.5 Pro achieved the highest mean ratings across most domains, notably in accuracy (4.3), completeness (4.3), and low hallucination rate (4.6). Microsoft Copilot was noted for empathetic language and consistent disclaimers but showed slightly lower clarity and currency scores. ChatGPT-4o demonstrated strengths in conversational flow but displayed more variability in clinical precision. Overall, 14% of responses were flagged as potentially misleading or incomplete. Inter-rater agreement was substantial across all domains (κ = 0.68). Readability varied between models: ChatGPT responses were easiest to understand (FRE = 48.5; FKGL = 11.94), while Gemini’s were the most complex (FRE = 29.9; FKGL = 13.3). Conclusions: LLMs show promise in patient-facing communication but currently fall short of providing consistently accurate, complete, and guideline-conform information in high-stakes contexts such as incidental cancer diagnoses. While their tone and structure may support patient engagement, they should not be used autonomously for counseling. Further fine-tuning, clinical validation, and supervision are essential for safe integration into patient care. Full article

This study empirically investigates the relationship between board composition, focusing on board size and board independence, and firm performance. It further examines how national governance quality moderates this relationship. Using a panel dataset of 1604 firms from 41 developed and emerging economies, the study employs pooled ordinary least squares (OLS) as the baseline regression method, alongside two-stage instrumental variable regression and system generalised method of moments (GMM) to address potential endogeneity concerns. Firm performance is measured using return on equity (ROE) and Tobin’s Q. Board size is captured by the number of directors on the board, while board independence is measured by the proportion of non-executive directors. The findings indicate that while board size and independence are positively associated with firm performance, the strength of these relationships weakens in countries with high governance quality. Our findings remain robust after controlling for dynamic endogeneity and unobserved time-invariant heterogeneity inherent in the corporate governance–performance nexus. Full article

Background: Premature atherosclerosis (PreAS) is generally defined as a disease affecting those under the age of 50 and has an outsized impact on quality-adjusted life years. We sought to better understand what individuals are at the highest risk for PreAS by examining differences in demographics and comorbidities compared to traditional atherosclerosis (TradAS). Study Design: An Institutional Review Board (IRB) approved retrospective study was conducted using retrospective data from a large regional health system. Patients who received a diagnosis of cerebrovascular disease (CeVD), coronary artery disease (CAD) or peripheral arterial disease (PAD) between 2012 and 2023 were included. Results: The review identified 136,328 patients in which 17,008 or 13% presented with PreAS (diagnosed from age 18 up to, and including, age 50). Rates of comorbidities were as follows (PreAs/TradAS): hypertension 63%/86%, diabetes 29%/35%. hyperlipidemia 45%/67%, chronic kidney disease 15%/26%, tobacco use 52%/60% and substance use 25%/9%. Differences in race, ethnicity and gender were as follows (PreAS/TradAS): White 59%/80%, Black 22%/10% and Latinx 17%/6%; male 51%/55%, and female 49%/45%. Conclusions: Patients with PreAS had lower rates of diseases that typically progress with aging, including hypertension, hyperlipidemia, chronic kidney disease, and diabetes. Tobacco use was less prevalent in the PreAS group and there was a significantly higher rate of illicit substance use in the PreAS population. Race and ethnicity were notably different with Black and Hispanic patients representing a significantly larger proportion of those with PreAS relative to TradAS. Our findings suggest risk factors beyond those classically described may play key roles in causing patients to develop PreAS. Full article