Search Results (336)

Background: The Detection and localization of breast lesions remain challenging in mammography and digital breast tomosynthesis (DBT) due to tissue overlap and information loss during volumetric reconstruction. Sinograms preserve the full angular projection data acquired during scanning, enabling analysis of tissue structure without reconstruction. Methods: This study proposes a direct segmentation approach for mammography and DBT sinograms using a U-Net architecture. Experiments were conducted on 1082 annotated mammography mass images from the CBIS-DDSM dataset (521 benign, 561 malignant) and 272 annotated DBT images from the Breast Cancer Screening DBT dataset (136 benign, 136 malignant). Dataset splitting was performed at the patient level to prevent data leakage, and all reported quantitative results correspond to the independent test set, with the validation set used solely for model selection and early stopping. Three input configurations were evaluated: mammography sinograms, DBT sinograms, and a combined model. Results: The mammography model achieved the highest performance (Dice: 0.94 training, 0.90 test), outperforming DBT alone (0.77 training, 0.70 test). Multimodal fusion improved DBT results (Dice: 0.84 test). Centroid analysis showed 99.11% correspondence with reference annotations (average distance: 2.83 pixels), and partial back-projection reconstructions confirmed anatomical consistency. Compared with YOLOv5x, the proposed approach provided superior lesion localization, particularly for small or multiple lesions. Conclusions: Direct sinogram segmentation is an efficient, clinically viable strategy for breast lesion detection and localization. Full article

In the context of global sustainability agendas and the rapid transformation of the media industry, cultivating new media professionals equipped with language proficiency, cross-cultural communication skills, and sustainability awareness has become a crucial educational imperative. This study implemented a pedagogical framework integrating Content-Based Instruction (CBI) and Project-Based Learning (PBL) at Communication University of Shanxi, centering on authentic media projects. A mixed-methods approach (questionnaires, N = 204; semi-structured interviews, n = 50) was employed to evaluate its effectiveness. Under this model, students demonstrated positive gains in linguistic knowledge and skills, media literacy, self-directed learning, critical thinking, and teamwork. Positive outcomes were also observed in intercultural competence and innovative thinking. Comparative analysis of pre- and post-test academic performance indicated significant improvement across all participating majors. The integrated CBI-PBL model provides a promising teaching pathway for sustainability-oriented foreign language education within similar instructional contexts. It contributes to achieving United Nations Sustainable Development Goal 4 (SDG 4) and offers theoretical and practical insights for aligning media education with the evolving sustainable demands of the industry Full article

Background: Breast cancer is among the most common cancers in women, and early diagnosis is critical for better treatment outcomes and reduced mortality. Efficient computer-aided diagnostic (CAD) systems play a crucial role in enhancing diagnostic accuracy and facilitating timely clinical decisions. Methods: This study proposes an automated CAD system for detecting cancerous tumors in mammograms, consisting of four stages: preprocessing, feature extraction, feature selection, and classification. In preprocessing, the region of interest (ROI) is extracted, followed by noise suppression and contrast enhancement to improve image quality. Shape, histogram, and tissue-related features are then computed from each ROI. An Improved Simulated Annealing (ISA) algorithm is employed to adaptively select the most informative features through a flexible process and composite fitness function, effectively reducing dimensionality while preserving high classification accuracy. Finally, classification is performed using a Support Vector Machine (SVM) to distinguish between malignant and benign masses. Results: Evaluation on the CBIS-DDSM and MIAS datasets showed the system achieved accuracies of 99.67% and 98%, sensitivities of 99.33% and 98%, and F1-scores of 99.66% and 97.9%, respectively. These results indicate notable improvements over traditional SA and full-feature approaches. Conclusions: The findings confirm the effectiveness of the ISA algorithm in selecting relevant features, thereby enhancing the performance of breast cancer detection. Full article

Traditional burn severity assessment methods have predominantly leveraged optical remote sensing data, yet such methods often overlook critical vegetation structural information inherent to post-fire ecosystems. Synthetic Aperture Radar (SAR) data offer structural information but are hindered by non-uniform scattering in fire-affected areas, limiting the utility of conventional decomposition techniques. Here, we introduced a metric that quantifies scattering non-uniformity by jointly considering canopy burn and ground condition non-uniformity. From this metric, we derived quantitative polarimetric features that enhance SAR-based severity estimation and demonstrated the potential to assess burn severity, with an R of 0.77 and a RMSE of 0.58. Initially, six decomposition features were extracted with the covariance matrix and then 14 feature groups were formed through metric and combination. Subsequently, sensitivity analyses were conducted for the first nine feature groups with the Composite Burn Index (CBI) values. Following this, the 14 feature groups were employed as inputs and the CBI values as outputs for random forest learning at a 7:3 training ratio to assess burn severity and generate burn severity maps. This study used the Jinyun Mountain fire in Chongqing as the primary case and eight fires in the United States as supplemental data to discuss the general applicability of the quantitative polarimetric features in assessing burn severity. Notably, the developed methodology showcased superior results within all wildfires, offering a new outlook for future burn severity assessments utilizing vegetation structure information. Full article

Enzymatic browning is a major challenge in maintaining the quality and shelf life of fresh-cut fruits, and in this context, plant-derived hydrocolloids are increasingly recognized as sustainable alternatives to synthetic additives due to their ability to retard browning while supporting quality retention. Therefore, in the present study, Cucumis sativus L. mucilage was extracted using microwave irradiation, yielding 24.56% freeze-dried irregular particles with an average size of 194.5 nm and −19.8 mV zeta potential. Various characterization techniques confirmed the amorphous structure and the presence of polysaccharides functional group. The mucilage was primarily composed of glucose (32.27%), along with arabinose, galactose, xylose, mannose, rhamnose, and minor uronic acids, reflecting a glucose-rich heteropolysaccharide. Functionally, the mucilage exhibited notable water retention (8.46 g/g), oil retention (3.21 g/g), foaming capacity (52.13%) with stability (30.46%), emulsifying capacity (90.45%) with stability (91.62%), and solubility (90.14%). Antioxidant assays revealed strong ferric reducing power (5.1 mM FeSO₄ at 10 mg/mL), DPPH scavenging (67.50%; IC₅₀ = 1.798 mg/mL), and ABTS scavenging (60.14%, IC₅₀ = 8.038 mg/mL). Anti-inflammatory evaluation indicated enhanced macrophage viability (1.38-fold at 25 mg/mL) with reduced nitric oxide production, while tyrosinase inhibition reached 60.40% (monophenolase) and 68.50% (diphenolase) at 2 mg/mL. Furthermore, when applied as an edible coating on fresh-cut apple slices, Cucumis sativus L. mucilage effectively delayed enzymatic browning in a dose-dependent manner, with 2 mg/mL maintaining apple slice brightness (L* value; 71.08) and minimizing color change (ΔE = 4.54). Overall, these findings highlight Cucumis sativus L. mucilage as a multifunctional biopolymer with promising applications in food systems and edible coatings. Full article

This study aims to develop an innovative electrochemical genosensor based on activated biochar (ABC) derived from the biomass of the seaweed Sargassum spp. The synthesis process begins with the pyrolysis of Sargassum spp. at 500 °C to obtain biochar (BC), which is chemically activated with nitric acid (HNO₃). The physicochemical properties of the resulting material, such as morphology and surface area, were characterized using techniques including scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and the Brunauer–Emmett–Teller (BET) method for surface area. BET results showed an increase in surface area from 22.9367 ± 0.0879 m²/g (BC) to 159.2915 ± 2.2641 m²/g (ABC). For the development of the genosensor, a hydrolyzed collagen gel matrix enriched with ABC is created. This nanostructured, biocompatible mixture is used to immobilize a DNA probe on a graphite electrode, employing the large surface area of ABC and the formation of a functional HC-based coating. The system’s viability was evaluated by cyclic voltammetry (CV), which showed changes in the maximum anodic peak current (Ipa) during fabrication: 27.78 ± 1.87 μA for the bare electrode, 35.25 ± 1.24 μA for ABC 30%, and 39.25 ± 1.84 μA for HC + ABC 30%. After ssDNA immobilization and hybridization to dsDNA, Ipa decreased to 28.81 ± 1.565 μA and 23.10 ± 1.25 μA, respectively. Finally, hematoxylin (Hx) was used as an intercalating indicator from hybridization, reducing the maximum anodic peak current to 15.51 ± 1.13 μA, consistent with additional interfacial limitations associated with dsDNA formation. Overall, the developed system demonstrates a sustainable, promising platform for molecular diagnostics in electrochemical DNA biosensor development. Full article

The study explored the impact of Microwave-Assisted Extraction (MAE) on the physicochemical, structural, functional, and antioxidant properties of protein concentrates from white pea (Lathyrus sativus), red gram (Cajanus cajan), and black gram (Vigna mungo). The objective was to evaluate the efficiency of MAE as a sustainable green extraction technique compared to the conventional method. Total amino acid content increased in MAE protein from 69.23 to 72.78 g/100 g powder in white pea protein (WPP), 69.41 to 72.39 g/100 g powder in red gram protein (RGP), and 65.56 to 70.30 g/100 g powder in black gram protein (BGP). Functionally, MAE significantly improved solubility and emulsifying capacity and water- and oil-holding capacities. Bioactive evaluation showed a significant increase in total phenolic and flavonoid contents, followed by improved DPPH, ABTS, and FRAP activities. A reduction in tannins and phytic acid correlated with enhanced in vitro protein digestibility. These enhanced MAE-derived proteins further demonstrated superior performance when incorporated into wheat flour, improving its nutritional and functional properties. Overall, MAE protein demonstrated improved structural integrity, antioxidant potential, and digestibility, highlighting white pea protein as the most responsive legume to MAE, followed by red and black gram. Full article

As the scale of the Industrial Internet continues to expand, the number of network connections and data traffic are experiencing explosive growth. Security threats and attack types targeting the Industrial Internet are becoming increasingly complex, rendering traditional firewalls and encryption/decryption technologies inadequate for addressing diverse and sophisticated attack scenarios. Furthermore, traffic characteristics within the Industrial Internet environment exhibit significant asymmetry, such as a highly imbalanced distribution between benign and malicious traffic. To address this challenge, this paper proposes CBiNet—a hybrid deep learning model that integrates a one-dimensional convolutional neural network (1D-CNN) with a bidirectional long short-term memory network (BiLSTM). Designed to effectively learn and leverage such asymmetric spatio-temporal patterns, experimental validation demonstrates that the CBiNet model can efficiently tackle complex traffic identification tasks in industrial internet environments. It provides a highly accurate, scalable intrusion detection method for securing industrial internet gateways. Full article

This study examines the determinants of inflation volatility in Tunisia, focusing on central bank independence (CBI), economic transparency, and macroeconomic fundamentals. Although CBI is widely regarded as essential for monetary credibility, its effectiveness depends on its institutional framework. Our contribution is twofold. First, we develop a theoretical framework based on game theory to illustrate how the effectiveness of economic transparency and CBI shapes the welfare of both the central bank and the private sector in the presence (or not) of fiscal policy. Second, we use a binary threshold nonlinear autoregressive distributed lag (NARDL) model to capture long-run relationships and a Markov-switching GARCH (MS-GARCH) framework to model volatility dynamics. As a continuous measure, CBI has no significant impact on volatility. Paradoxically, high de jure independence in a binary regime is associated with a slight increase in inflation fluctuations. This indicates that legal independence alone is insufficient without fiscal discipline or effective coordination between the monetary and fiscal authorities. Notably, under fiscal pressure, greater CBI substantially reduces inflation volatility, highlighting the need for a coherent macroeconomic framework. Economic transparency generally increases short-term volatility but stabilizes inflation when supported by credible fiscal signals. Among the macroeconomic fundamentals, volatility in broad money is strongly destabilizing, whereas fluctuations in industrial production and the real exchange rate are largely insignificant. Government spending and exposure to external shocks, including import prices and geopolitical risks, further amplify this volatility. The observed negative trend over time reflects gradual improvements owing to policy reforms. Policy recommendations emphasize the establishment of genuinely independent and credible monetary institutions, enhancing coordination with fiscal policy, improving communication strategies, and strengthening risk management. Full article

Background: Burnout is a syndrome resulting from long-term, unmanaged work-related stress. This study aimed to evaluate the validity and reliability of the Serbian versions of BAT among fifth-year medical students at the University of Belgrade Faculty of Medicine. Methods: This cross-sectional study, which included a total of 431 students at the Faculty of Medicine, University of Belgrade, was conducted during the last week of November 2024. The study instruments used were the Burnout Assessment Tool (BAT) and the Copenhagen Burnout Inventory (CBI). Results: Cronbach’s alpha for the entire BAT scale was α = 0.946; for core burnout symptoms, it was α = 0.938; for the exhaustion scale, α = 0.865; for mental distance, α = 0.858; for cognitive impairment, α = 0.907; for emotional impairment, α = 0.878; for secondary symptoms, α = 0.863; for psychological distress, α = 0.791; and for psychosomatic complaints, α = 0.801. The EFA showed six factors that explained a total of 63.76% of the variance. Factor 1 explained 35.71% of the variance; factor 2 explained 9.81%; factor 3, 5.785%; factor 4, 5.415%; factor 5, 3.956%; and factor 6 explained 3.076% of the variance. After the elimination of the three items with the lowest loadings, the EFA showed five factors that explained a total of 63.347% of the total variance. Factor 1 explained a total of 36.637% of the variance; factor 2, 10.544%; factor 3, 6.345%; factor 4, 5.612%; and factor 5 explained a total of 4.209%. Conclusions: This study showed that the Serbian version of the BAT exhibits excellent reliability, clear factorial validity, and strong convergent and discriminative performance. Full article

Assessing post-fire disturbance in Mediterranean ecosystems is essential for quantifying ecological impacts and guiding restoration strategies. This study evaluates fire severity following an extreme wildfire event (~28,000 ha) in northwestern Spain using vegetation indices (VIs) derived from PRISMA hyperspectral imagery, validated against field-based Composite Burn Index (CBI) measurements at the vegetation, soil, and site levels across three vegetation formations (coniferous forests, broadleaf forests, and shrublands). Hyperspectral VIs were benchmarked against multispectral VIs derived from Sentinel-2. Hyperspectral VIs yielded stronger correlations with CBI values than multispectral VIs. Vegetation-level CBI showed the highest correlations, reflecting the sensitivity of most VIs to canopy structural and compositional changes. Indices incorporating red-edge, near-infrared (NIR), and shortwave infrared (SWIR) bands demonstrated the greatest explanatory power. Among hyperspectral indices, DVIRED, EVI, and especially CAI performed best. For multispectral data, NDRE, CIREDGE, ENDVI, and GNDVI were the most effective. These findings highlight the strong potential of hyperspectral remote sensing for accurate, scalable post-fire severity assessment in heterogeneous Mediterranean ecosystems. Full article

Background: Anatomic pathology laboratories operate under conditions requiring high precision, strict documentation, biosafety protocols, and tight turnaround times. Evidence from Greece is limited, and joint assessment of burnout and leadership in this setting is rare. Objective: The aim of this study was to estimate burnout levels among anatomic pathology personnel in Attica and examine their association with perceived leadership style. Methods: A cross-sectional survey of public and private laboratories was carried out. The questionnaire included demographics and work characteristics, the Copenhagen Burnout Inventory (CBI), and the Multifactor Leadership Questionnaire Form 5X (MLQ-5X). Results: Burnout levels were moderate to low overall, with personal burnout highest, work-related intermediate, and colleague-related lowest. Women and employment type were associated with personal burnout (p < 0.05). Passive/avoidant leadership (including management by exception–passive and laissez-faire) showed positive associations with burnout, whereas transformational leadership and favorable leadership outcomes—particularly, perceived effectiveness and satisfaction with the leader—were inversely associated; transactional leadership followed the same direction but less robustly (p < 0.05 where supported). Conclusions: Burnout among anatomic pathology personnel in Attica is non-trivial and varies across domains. Leadership dimensions display differential links with burnout, indicating potentially modifiable organizational targets for intervention. Significance: To our knowledge, this is the first study in Greece and among the first in Europe to jointly apply CBI and MLQ-5X in anatomic pathology laboratories, offering practical evidence to inform leadership-oriented interventions. Full article

Selective catalytic reduction (SCR) of NO using various reducing agents is a critical area of research for mitigating environmental pollution. In this study, the influence of active phase loading was investigated in four bimetallic Pt-Ag/Al₂O₃-WO_x catalysts, one monometallic Ag/Al₂O₃-WO_x catalyst, and one Pt-Ag/Al₂O₃-WO_x catalyst subjected to high-severity air-SO₂ pretreatment. All catalysts were supported on cordierite monoliths, and their performance in NO SCR was evaluated using H₂, C₃H₈, and CO as reducing agents. An increase in the active phase loading (Pt-Ag/Al₂O₃) from 10.7 wt% to 17.4 wt% resulted in higher conversions of NO, C₃H₈, and H₂, as well as improved N₂ selectivity. However, CO conversion decreased as the active phase loading increased, which was attributed to competitive reduction by H₂, since both reactions occur within the same temperature range (100–200 °C). The presence of N₂O below 6 ppm was observed in some catalysts. Furthermore, higher active phase loadings led to increased carbon deposition; the Ag/Al₂O₃-WO_x catalyst exhibited the highest carbon content (5 wt%). The deposited carbon was identified as ordered graphitic carbon. In the Pt-Ag catalysts, the presence of Ag⁺ and Agⁿδ⁺ species, as well as the Ag° plasmon, was identified by UV-Vis spectroscopy. STEM analysis showed Ag-Pt crystallites with an average size of 24 nm, which may have contributed to the higher NO conversion observed at 350 °C and the improved N₂ selectivity at 100 °C in the Pt-Ag bimetal catalysts, compared to the activity of the Ag/Al₂O₃-WOx catalyst. Full article

Background and Objectives: Breast cancer remains a leading cause of cancer-related morbidity and mortality worldwide, and robust, interpretable artificial intelligence (AI) pipelines are increasingly being explored to support mammography-based detection. This study combines a PRISMA 2020-compliant systematic review with an original experimental validation to characterize current evidence and address identified gaps in reproducibility and interpretability. Materials and Methods: A PRISMA 2020-guided systematic review and an original experimental study were conducted. The review searched PubMed and Scopus/ScienceDirect for studies using convolutional neural networks (CNNs), support vector machines (SVMs) or eXtreme Gradient Boosting (XGBoost) for breast cancer detection in mammography and related imaging modalities, and identified 45 eligible articles. In parallel, we implemented and evaluated representative CNN (ResNet-50, EfficientNetB0 and MobileNetV3-Small) and classical machine learning (SVM and XGBoost) pipelines on the CBIS-DDSM dataset, following a CRISP-DM-inspired workflow and using Grad-CAM and SHAP to provide image- and feature-level explanations within a reproducible machine-learning-operations (MLOps)-oriented framework. Results: The systematic review revealed substantial heterogeneity in datasets, preprocessing pipelines, and validation strategies, with a predominant reliance on internal validation and limited use of explainable AI methods. In our experimental evaluation, ResNet-50 achieved the best performance (AUC-ROC 0.95; sensitivity 89%), followed by XGBoost (AUC-ROC 0.90; sensitivity 74%) and SVM (AUC-ROC 0.84; sensitivity 66%), while EfficientNetB0 and MobileNetV3-Small showed lower discrimination. Grad-CAM produced qualitatively plausible heatmaps centered on annotated lesions, and SHAP analyses indicated that simple global image-intensity and size descriptors dominated the predictions of the classical models. Conclusions: By integrating systematic evidence and large-scale experiments on CBIS-DDSM, this study highlights both the potential and the limitations of current AI pipelines for mammography-based breast cancer detection, underscoring the need for more standardized preprocessing, rigorous external validation, and routine use of explainable AI before clinical deployment. Full article

Background: Breast cancer is a leading cause of cancer-related mortality among women, and earlier diagnosis significantly improves treatment outcomes. However, traditional mammography-based systems rely on single-modality image analysis and lack integration of volumetric and clinical context, which limits diagnostic robustness. Deep learning models have shown promising results in identification but are typically restricted to 2D feature extraction and lack cross-modal reasoning capability. Objective: This study proposes SwinCAMF-Net, a multimodal cross-attention Swin transformer network designed to improve joint breast lesion classification and segmentation by integrating multi-view mammography, 3D ROI volumes, and clinical metadata. Methods: SwinCAMF-Net employs a Swin transformer encoder for hierarchical visual representation learning from mammographic views, a 3D CNN volume encoder for lesion depth context modelling, and a clinical projection module to embed patient metadata. A novel cross-attentive fusion (CAF) module selectively aligns multimodal features through query–key attention. The fused feature representation branches into a classification head for malignancy prediction and a segmentation decoder for lesion localization. The model is trained and evaluated on CBIS-DDSM and RTM benchmark datasets. Results: SwinCAMF-Net achieved accuracy up to 0.978, an AUC-ROC of 0.998, and an F1-score of 0.944 for classification, while segmentation reached a Dice coefficient of 0.931. Ablation experiments confirm that the CAF module improves performance by up to 6.9%, demonstrating its effectiveness in multimodal fusion. Conclusion: SwinCAMF-Net advances breast cancer analysis by providing complementary multimodal evidence through a cross-attentive fusion, leading to improved diagnostic performance and clinical interpretability. The framework demonstrates strong potential in AI-assisted screening and radiology decision support. Full article