Search Results (1,348)

Chronic atrophic gastritis and intestinal metaplasia (IM) are gastric precancerous conditions (GPCs) associated with an increased risk of gastric cancer. Early detection and accurate characterization of GPC are therefore crucial for risk stratification and the implementation of preventive strategies. In the absence of clear mucosal changes observed through white-light imaging (WLI) or virtual chromoendoscopy, endocytoscopy can help unveil the presence of GPC by enabling in vivo assessment of nuclear and cellular structures at ultra-high magnification. Endocytoscopy is typically performed using WLI following dye-based staining of the mucosa. In this case, we demonstrate that combining endocytoscopy with the texture and color enhancement imaging (TXI) mode substantially improves the assessment of the gastric mucosa. In a 61-year-old man undergoing esophagogastroduodenoscopy, WLI showed multifocal erythema in the stomach, without clearly visible lesions on either WLI or narrow-band imaging. Conventional endocytoscopy revealed multiple small spots of IM with characteristic changes in glandular structures, which were even more evident when using the TXI mode. Histological analysis of targeted biopsies confirmed small foci of IM in both the antrum and corpus. The patient was enrolled in a surveillance program because of his clinical background. The combination of endocytoscopy with the TXI mode significantly enhances the delineation of mucosal and cellular architecture, supporting a more accurate optical diagnosis. Full article

This study details how 3-(naphthalen-2-yl)-1-phenylprop-2-en-1-one (3NPEO) behaves in terms of photophysics when exposed to different solvents. The solvatochromic effect study reveals significant polarity shifts in the excited states of the 3NPEO compound, likely due to an intramolecular proton transfer mechanism. Measurements of dipole moments provide insight into their resonance structures in both ground and excited states. Electrochemical analysis revealed a reversible redox process, indicating a favorable charge transport potential. HOMO and LUMO energies of the compound were computed via oxidation and reduction potential standards. 3NPEO exhibits optimal one-photon and two-photon absorption characteristics, validating its suitability for visible wavelength laser applications in photonic devices. Furthermore, molecular docking and dynamics simulations demonstrated strong interactions between 3NPEO and the progesterone receptor enzyme, supported by structure–activity relationship (SAR) analyses. In vitro cytotoxicity assays on the MDAMB-231 breast cancer cell line showed moderate tumor cell inhibitory activity. Apoptosis studies confirmed the induction of both early and late apoptosis. These findings suggest that 3NPEO holds promise as a potential anticancer agent targeting the progesterone receptor in breast cancer cells. Overall, the findings highlight the substantial influence of solvent polarity on the photophysical properties and the design of more effective and stable therapeutic agents. Full article

Functional flours, high in bioactive compounds, have garnered increasing attention, driven by consumer demand for alternative ingredients and the nutritional limitations of wheat flour. This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) spectroscopy (350–2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. The full spectral range yielded high classification accuracy (0.98, 0.98, and 0.99, respectively), and an explainability framework revealed the wavelengths most relevant for each class. To address concerns regarding color as a confounding factor, a targeted spectral refinement was implemented by sequentially excluding the visible region. Models trained on the 1000–2500 nm and 1400–2500 nm ranges showed minor reductions in accuracy, suggesting that classification is not solely driven by visible characteristics. Results indicated that legume and wheat flours retain higher total phenolic content (TPC) under mild thermal conditions, whereas grape seed flour (GSF) and olive stone flour (OSF) exhibited notable thermal stability of TPC even at elevated temperatures. These first findings suggest that the proposed non-destructive spectroscopic approach enables rapid classification and quality assessment of functional flours, supporting future applications in precision food formulation and quality control. Full article

This paper analyzes the historical genealogy of conspiracy theories about a global supergovernment by focusing on one period of American history in which it attained particular visibility. The formation of the United Nations in 1945 and the onset of the Cold War galvanized speculation on the political margins that a shadowy, malevolent international government was seeking world domination by targeting the United States and its political culture. At the same time, mainstream United States foreign policy was marked by a desire to both reshape international institutions to resist Soviet influence while also avoiding any domestic changes that might result from international engagement. This paper suggests that conspiracy theory functioned as a mechanism resolving the vicious circle occasioned by these competing foreign policy priorities. Through a narrative analysis of conspiratorial sentiments in North Dakota Representative Usher L. Burdick’s warnings about the United Nations as a threat to American liberty and sovereignty, this article highlights the continuities between mainstream American exceptionalism and conspiratorial ideas. Full article

Infrared thermography is a common approach used in building inspection for identifying building envelope thermal anomalies that cause energy loss and occupant thermal discomfort. Detecting these anomalies is essential to improve the thermal performance of energy-inefficient buildings through energy retrofit design and correspondingly reduce operational energy costs and environmental impacts. A thermal bridge is an unwanted conductive heat transfer. On the other hand, an infiltration/exfiltration anomaly is an uncontrollable convective heat transfer, typically happening around windows and doors, but it can also be due to a defect that comprises a building envelope’s integrity. While the existing literature underscores the significance of automatic thermal anomaly identification and offers insights into automated methodologies, there is a notable gap in addressing an automated workflow that leverages building envelope component segmentation for enhanced detection accuracy. Consequently, an automatic thermal anomaly identification workflow from visible and thermal images was developed to test it, utilizing segmented building envelope information compared to a workflow without any semantic segmentation. Therefore, building envelope images (e.g., walls and windows) were segmented based on a U-Net architecture compared to a more conventional semantic segmentation approach. The results were discussed to better understand the importance of the availability of training data and for scaling the workflow. Then, thermal anomaly thresholds for different target domains were detected using probability distributions. Finally, thermal anomaly masks of those domains were computed. This study conducted a comprehensive examination of a campus building in Syracuse, New York, utilizing a drone-based data collection approach. The case study successfully detected diverse thermal anomalies associated with various envelope components. The proposed approach offers the potential for immediate and accurate in situ thermal anomaly detection in building inspections. Full article

There is growing interest in the use of herbicide for the silvicultural practice of tree thinning (i.e., chemical thinning or e-thinning) in New Zealand. Potential benefits of this approach include improved stability of the standing crop in high winds, and safer and lower-cost operations, particularly in steep or remote terrain. As uptake grows, tools for monitoring treatment effectiveness, particularly during the early stages of stress, will become increasingly important. This study evaluated the use of UAV-based multispectral and hyperspectral imagery to detect early herbicide-induced stress in a nine-year-old radiata pine (Pinus radiata D. Don) plantation, based on temporal changes in crown spectral signatures following treatment with metsulfuron-methyl. A staggered-treatment design was used, in which herbicide was applied to a subset of trees in six blocks over several weeks. This staggered design allowed a single UAV acquisition to capture imagery of trees at varying stages of herbicide response, with treated trees ranging from 13 to 47 days after treatment (DAT). Visual canopy assessments were carried out to validate the onset of visible symptoms. Spectral changes either preceded or coincided with the development of significant visible canopy symptoms, which started at 25 DAT. Classification models developed using narrow band hyperspectral indices (NBHI) allowed robust discrimination of treated and non-treated trees as early as 13 DAT (F1 score = 0.73), with stronger results observed at 18 DAT (F1 score = 0.78). Models that used multispectral indices were able to classify treatments with a similar accuracy from 18 DAT (F1 score = 0.78). Across both sensors, pigment-sensitive indices, particularly variants of the Photochemical Reflectance Index, consistently featured among the top predictors at all time points. These findings address a key knowledge gap by demonstrating practical, remote sensing-based solutions for monitoring and characterising herbicide-induced stress in field-grown radiata pine. The 13-to-18 DAT early detection window provides an operational baseline and a target for future research seeking to refine UAV-based detection of chemical thinning. Full article

UAVs are essential for forest fire detection due to vast forest areas and inaccessibility of high-risk zones, enabling rapid long-range inspection and detailed close-range surveillance. However, aerial photography faces challenges like multi-scale target recognition and complex scenario adaptation (e.g., deformation, occlusion, lighting variations). RGB-Thermal fusion methods integrate visible-light texture and thermal infrared temperature features effectively, but current approaches are constrained by limited datasets and insufficient exploitation of cross-modal complementary information, ignoring cross-level feature interaction. A time-synchronized multi-scene, multi-angle aerial RGB-Thermal dataset (RGBT-3M) with “Smoke–Fire–Person” annotations and modal alignment via the M-RIFT method was constructed as a way to address the problem of data scarcity in wildfire scenarios. Finally, we propose a CP-YOLOv11-MF fusion detection model based on the advanced YOLOv11 framework, which can learn heterogeneous features complementary to each modality in a progressive manner. Experimental validation proves the superiority of our method, with a precision of 92.5%, a recall of 93.5%, a mAP50 of 96.3%, and a mAP50-95 of 62.9%. The model’s RGB-Thermal fusion capability enhances early fire detection, offering a benchmark dataset and methodological advancement for intelligent forest conservation, with implications for AI-driven ecological protection. Full article

Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused by Verticillium dahliae by integrating multispectral imaging with machine learning and deep learning techniques. Multispectral and chlorophyll fluorescence images were collected from leaves of the inbred eggplant line 11-435, including data on image texture, spectral reflectance, and chlorophyll fluorescence. Subsequently, we established a multispectral data model, fusion information model, and multispectral image–information fusion model. The multispectral image–information fusion model, integrated with a two-dimensional convolutional neural network (2D-CNN), demonstrated optimal performance in classifying early-stage Verticillium wilt infection, achieving a test accuracy of 99.37%. Additionally, transfer learning enabled us to diagnose early leaf wilt in another eggplant variety, the inbred line 14-345, with an accuracy of 84.54 ± 1.82%. Compared to traditional methods that rely on visible symptom observation and typically require about 10 days to confirm infection, this study achieved early detection of Verticillium wilt as soon as the third day post-inoculation. These findings underscore the potential of the fusion model as a valuable tool for the early detection of pre-symptomatic states in infected plants, thereby offering theoretical support for in-field detection of eggplant health. Full article

This study examines the regional disparities in public perceptions of decarbonization and the acceptance of the bioeconomy within Western Macedonia, a Greek region undergoing structural economic change. While the environmental benefits of decarbonization, such as reduced carbon emissions and improved air quality, are widely acknowledged, perceptions of economic and social outcomes, including investments, new business development, and policy support, vary significantly across sub-regions. To this end, a structured survey was conducted among 765 residents, utilizing Likert-scale items to assess attitudes, with demographic data providing a contextual framework. Statistical analyses, incorporating techniques such as one-way analysis of variance (ANOVA), Kruskal–Wallis, and multiple regression, were employed to explore spatial variations and identify the primary drivers of bioeconomy acceptance. The results indicate that perceived government action, visible investment, new enterprises, and a positive view of public sentiment are all significant predictors of acceptance, with institutional support showing the strongest influence. The findings reveal that certain areas feel less engaged in the transition, expressing skepticism about its benefits, while others report more optimism. This disparity in perception underscores the necessity for targeted policy interventions to ensure inclusive and equitable participation. The study emphasizes the necessity for regionally responsive governance, enhanced communication strategies, and tangible local development initiatives to cultivate public trust and support. The study makes a significant contribution to the broader discourse on just transitions by emphasizing the role of place-based perceptions in shaping sustainable change. Full article

As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications. Full article

Spectral features of photoionization of various levels of C III are reported. These include characteristics of Rydberg and Seaton resonances, low and high excited levels, lifetimes, and total and partial cross sections. Calculations are performed in the relativistic Breit–Pauli R-matrix method with close-coupling approximation, including damping effects on the resonance structure associated with the core-excited states produced by the electron excitation of C IV and photoionization of C III. For bound channel contribution, the close-coupling wavefunction expansion for photoionization includes ground and 14 excited states of the target ion CIV and 105 states configurations of C III. Extensive sets of atomic data for bound fine-structure levels, resulting in 762 dipole-allowed transitions, radiative probabilities, and photoionization cross sections out of J^π = 0^± − 4^± fine-structure levels are obtained. The ground-level photoionization cross section smoothly decreases with increasing energy, showing a very narrow, strong Rydberg resonance converging to the CIV 1s²2p threshold. The work shows that prominent Seaton resonances for 2sns states with n ≥ 5, caused by photoexcitation of the core electron below the 2p threshold, visibly contribute to photoabsorption from excited states of C III. The present results provide highly accurate parameters of various model applications in plasma spectroscopy. Full article

Background/Objectives: Mpox re-emerged in 2022 as a global health concern. Between 2022 and 2025, Portugal experienced three distinct outbreak waves, highlighting the critical role of laboratory surveillance and public health interventions. This study describes the epidemiological trends, diagnostic performance, and key lessons learned to improve outbreak preparedness. Methods: A total of 5610 clinical samples from 2802 suspected cases were analyzed at the National Institute of Health Doutor Ricardo Jorge using real-time PCR methods. Positivity rates and viral loads (Ct values) were assessed across different clinical specimen types, including lesion, anal, oropharyngeal swabs, and urine samples. Results: Mpox was confirmed in 1202 patients. The first outbreak accounted for 79.3% of cases (n = 953), followed by a significant reduction in transmission during subsequent waves. Lesion and rectal swabs provided the highest diagnostic sensitivity (95.1% and 87.9%, respectively). Oropharyngeal swabs contributed to diagnosis in cases without visible lesions, while urine samples showed limited utility. Conclusions: This study underscores the importance of sustained laboratory surveillance and adaptive public health strategies in controlling mpox outbreaks. Optimizing specimen collection enhances diagnostic accuracy, supporting early detection. Continuous monitoring, combined with targeted vaccination and effective risk communication, is essential to prevent resurgence and ensure rapid response in non-endemic regions. Full article

The flexible transparent conductive films (TCFs) of a ZnS/Cu/Ag/TiO₂ multilayered structure were deposited on a flexible PET substrate with high surface roughness using magnetic sputtering, and the effects of structural characteristics on the performance of the films were analyzed. The TCFs with TiO₂/Cu/Ag/TiO₂ and ZnS/Cu/Ag/ZnS symmetric structures were also prepared for comparison. The TCF samples were deposited using ZnS, TiO₂, Cu and Ag targets, and they were analyzed using scanning electronic microscopy, atomic force microscopy, grazing incidence X-ray diffraction, spectrophotometry and a four-probe tester. The TCFs exhibit generally uniform surface morphology, excellent light transmittance and electrical conductivity with optimized structure. The optimal values are 84.40%, 5.52 Ω/sq and 33.19 × 10⁻³ Ω⁻¹ for the transmittance, sheet resistance and figure of merit, respectively, in the visible spectrum. The satisfactory properties of the asymmetric multilayered TCF deposited on a rough-surface substrate should be mainly attributed to the optimized structure parameters and reasonable interfacial compatibilities. Full article

Windows are a major contributor to energy loss in buildings, particularly in hot climates where solar radiation heat gain significantly increases cooling demand. An ideal energy-efficient window must maintain high visible light transmittance while effectively blocking ultraviolet and near-infrared radiation, presenting a significant challenge for material design. We propose a plasma silica aerogel window utilizing the local surface plasmon resonance effect of plasmonic nanoparticles. This design incorporates indium tin oxide (ITO) nanospheres (for broad-band UV/NIR blocking) and silver (Ag) nanocylinders (targeted blocking of the 0.78–0.9 μm NIR band) co-doped into the silica aerogel. This design achieves a visible light transmittance of 0.8, a haze value below 0.12, and a photothermal ratio of 0.91. Building simulations indicate that compared to traditional glass, this window can achieve annual energy savings of 20–40% and significantly reduce the economic losses associated with traditional glass, providing a feasible solution for sustainable buildings. Full article

Geoethics emphasizes responsible human interaction with the Earth, promoting ethical practices in the geosciences to ensure sustainability for current and future generations. UNESCO Global Geoparks (UGGps) are designated areas that support sustainable development by integrating geoconservation, geoeducation, and community engagement, thereby raising awareness of geological heritage. This quantitative study employed an online questionnaire (n = 798) to assess geoethical awareness among residents of all nine Hellenic UGGps, with the aim of profiling environmental engagement and perceptions. The results indicate a generally high level of geoethical awareness, with Sitia UGGp exhibiting the highest average mean score (M⁻ = 8.98, SD⁻ = 1.34), reflecting strong community support and effective outreach efforts. In contrast, Lavreotiki UGGp (M⁻ = 8.48, SD⁻ = 1.15) and Psiloritis UGGp (M⁻ = 8.33, SD⁻ = 1.36) scored lower in areas such as community engagement and geotourism, suggesting opportunities for targeted improvement. Regional differences suggest that management, visibility, and local context significantly influence public perceptions. Cluster analysis identified four respondent profiles: (a) highly engaged environmental stewards (28.7%), (b) supportive but selective advocates (40.5%), (c) moderately indifferent participants (26.9%), and (d) disengaged or critical respondents (3.9%). Demographic factors such as age, residence, prior visits to Hellenic UGGps, and education significantly differentiated these groups. Mapping geoethical awareness provides a valuable tool for assessing societal benefits and enhancing the governance of UGGps. Overall, the findings underscore the need to shift from an anthropocentric to a more geocentric worldview that prioritizes the well-being of both humanity and Earth’s systems. Full article