Search Results (25,059)

The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation (${\mathrm{R}}_{\mathrm{CV}}^2$) = 0.914, root mean square error of cross-validation (RMSE_CV) = 0.688, the ratio of prediction to deviation of cross-validation (RPD_CV) = 3.367) and TA (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.762, RMSE_CV = 0.037, RPD_CV = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.702, RMSE_CV = 0.471, RPD_CV = 1.666), though the RPD_CV values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control. Full article

Background: Dental caries arise from a multifactorial interplay between microbial dysbiosis, host immune responses, and enamel degradation visible on radiographs. Deep learning excels in image-based caries detection; however, integrative analyses that combine radiographic, microbiome, and transcriptomic data remain rare because public cohorts are seldom aligned. Objective: To determine whether three independent deep-learning pipelines—radiographic segmentation, microbiome regression, and transcriptome regression—can be reproducible implemented on non-aligned datasets, and to demonstrate the feasibility of estimating microbiome heritability in a matched twin cohort. Methods: (i) A U-Net with ResNet-18 encoder was trained on 100 annotated panoramic radiographs to generate a continuous caries-severity score from a predicted lesion area. (ii) Feed-forward neural networks (FNNs) were trained on supragingival 16S rRNA profiles (81 samples, 750 taxa) and gingival transcriptomes (247 samples, 54,675 probes) using randomly permuted severity scores as synthetic targets to stress-test preprocessing, training, and SHAP-based interpretability. (iii) In 49 monozygotic and 50 dizygotic twin pairs (n = 198), Bray–Curtis dissimilarity quantified microbial heritability, and an FNN was trained to predict recorded TotalCaries counts. Results: The U-Net achieved IoU = 0.564 (95% CI 0.535–0.594), precision = 0.624 (95% CI 0.583–0.667), recall = 0.877 (95% CI 0.827–0.918), and correlated with manual severity scores (r = 0.62, p < 0.01). The synthetic-target FNNs converged consistently but—as intended—showed no predictive power (R² ≈ −0.15 microbiome; −0.18 transcriptome). Twin analysis revealed greater microbiome similarity in monozygotic versus dizygotic pairs (0.475 ± 0.107 vs. 0.557 ± 0.117; p = 0.0005) and a modest correlation between salivary features and caries burden (r = 0.25). Conclusions: Modular deep-learning pipelines remain computationally robust and interpretable on non-aligned datasets; radiographic severity provides a transferable quantitative anchor. Twin-cohort findings confirm heritable patterns in the oral microbiome and outline a pathway toward future clinical translation once patient-matched multi-omics are available. This framework establishes a scalable, reproducible foundation for integrative caries research. Full article

The mobile tunnel scanning system (MTSS) enables efficient tunnel inspection; however, motion blur (MB) generated at high travel speeds remains a major factor undermining the reliability of deep-learning-based crack detection. This study focuses on investigating how horizontally oriented MB in MTSS imagery affects the crack-detection performance of convolutional neural networks (CNNs) and proposes a data-centric quality-assurance framework that leverages no-reference image quality assessment (NR-IQA) to optimize model performance. By intentionally applying MB to both public and real-world MTSS datasets, we analyzed performance changes in ResNet-, VGG-, and AlexNet-based models and established the correlations between four NR-IQA metrics (BRISQUE, NIQE, PIQE, and CPBD) and performance (F1 score). As the MB intensity increased, the F1 score of ResNet34 dropped from 89.43% to 4.45%, confirming the decisive influence of image quality. PIQE and CPBD exhibited strong correlations with F1 (−0.87 and +0.82, respectively), emerging as the most suitable indicators for horizontal MB. Using thresholds of PIQE ≤ 20 and CPBD ≥ 0.8 to filter low-quality images improved the AlexNet F1 score by 1.46%, validating the effectiveness of the proposed methodology. The proposed framework objectively assesses MTSS data quality and optimizes deep learning performance, enhancing the reliability of intelligent infrastructure maintenance systems. Full article

Fully weathered phyllite is widely encountered along railway corridors in China, yet its suitability as subgrade fill remains insufficiently documented. This study provides an integrated laboratory and field evaluation of both untreated and low-dosage cement-stabilized phyllite for sustainable transport constructions. Laboratory investigations covered mineralogy, classification, compaction, permeability, compressibility, shear strength, and bearing capacity, while large-scale field trials examined the influence of loose lift thickness, moisture content, and compaction sequence on subgrade quality. Performance indicators included the degree of compaction and the subgrade reaction modulus K₃₀, defined as the plate load modulus measured with a 30 cm diameter plate. A recommended cement dosage of 3.5% (by weight of dry soil) was established based on preliminary trials to balance strength development with construction reliability. The results show that untreated phyllite, when compacted under controlled conditions, can be used in lower subgrade layers, whereas cement stabilization significantly improves strength, stiffness, and constructability, enabling reliable application in the main load-bearing subgrade layers. Beyond mechanical performance, the study demonstrates a methodological innovation by linking laboratory mix design directly with field compaction strategies and embedding these within a life-cycle perspective. The sustainability analysis shows that using stabilized in-situ phyllite achieves lower costs and approximately 30% lower CO₂ emissions compared with importing crushed rock from 30 km away, while promoting resource reuse. Overall, the findings support circular economy and carbon-reduction objectives in railway and road earthworks, offering practical guidance for low-carbon, resource-efficient infrastructure. Full article

Background. Severe left ventricular hypertrophy (SLVH) is a high-risk structural cardiac abnormality associated with increased risk of heart failure. It is typically assessed using echocardiography or cardiac magnetic resonance imaging, but these modalities are limited by cost, accessibility, and workflow burden. We introduce a deep learning framework that classifies SLVH directly from chest radiographs, without intermediate anatomical estimation models or demographic inputs. A key contribution of this work lies in interpretability. We quantify how clinically relevant attributes are encoded within internal representations, enabling transparent model evaluation and integration into AI-assisted workflows. Methods. We construct class-balanced subsets from the CheXchoNet dataset with equal numbers of SLVH-positive and negative cases while preserving the original train, validation, and test proportions. ResNet-18 is fine-tuned from ImageNet weights, and a Vision Transformer (ViT) encoder is pretrained via masked autoencoding with a trainable classification head. No anatomical or demographic inputs are used during training. We apply Mutual Information Neural Estimation (MINE) to quantify dependence between learned features and five attributes: age, sex, interventricular septal diameter (IVSDd), posterior wall diameter (LVPWDd), and internal diameter (LVIDd). Results. ViT achieves an AUROC of 0.82 [95% CI: 0.78–0.85] and an AUPRC of 0.80 [95% CI: 0.76–0.85], indicating strong performance in SLVH detection from chest radiographs. MINE reveals clinically coherent attribute encoding in learned features: age > sex > IVSDd > LVPWDd > LVIDd. Conclusions. This study shows that SLVH can be accurately classified from chest radiographs alone. The framework combines diagnostic performance with quantitative interpretability, supporting reliable deployment in triage and decision support. Full article

The in vitro production of embryos has significant potential to enhance animal productivity. However, further refining of this technology is required for its widespread adoption and cost-effectiveness. The objectives were to evaluate the effects of L-carnitine (LC) on the maturation, lipid content, and Hippo signaling of oocytes, and the cryotolerance of the resulting embryos. Abattoir-derived oocytes were in vitro matured using fetal bovine serum (FBS), bovine serum albumin (BSA), or FBS + 1.5 or 3.0 mM LC. The maturation rates did not differ among FBS (83%) and FBS with LC (1.5 or 3.0 mM; 82 and 80%, respectively). In contrast, the BSA group exhibited a significantly lower maturation rate of 71% compared to the other groups. The lipid content of matured oocytes (assessed using Nile red staining) was significantly reduced in the BSA group and in the FBS + LC groups, compared to the FBS group. The blastocyst-stage embryos were cryopreserved, and their cryotolerance was evaluated by assessing their ability to re-expand and hatch after thawing. The embryos from the FBS + LC groups showed a numerically higher re-expansion rate at 24 h (78.8%), compared to the BSA (74.0%) and FBS groups (57.7%). The expression of Hippo signaling pathway genes was not significantly affected by LC, indicating that LC enhanced cryotolerance and reduced lipid content without impacting oocyte maturation or the Hippo signaling pathway. Full article

Background: Lung adenocarcinoma (LUAD), the most prevalent and malignant form of lung cancer subtypes, is in urgent need of additional therapeutic targets and prognostic indicators. Antioxidant 1 (ATOX1) copper chaperone and RhoA/Rho kinase 1 (ROCK1) are novel anti-tumour targets in cancers. However, their prognostic value and synergistic inhibitory effect remain unclear in LUAD. Methods: We re-analyzed the open-access proteomic landscape study of LUAD in 2019 and investigated the prognostic value of ATOX1/ROCK1 expression patterns. Then we verified it immunohistochemically using an independent cohort from our hospital enrolling 35 patients with TNM stage III/IV LUAD. In vitro, double fluorescence was used to confirm the co-expression and location of ATOX1/ROCK1. The CCK—8 assay and Transwell assay were carried out to assess the changes in proliferation and migration of Lewis lung carcinoma (LLC) cells following treatment with ATOX1/ROCK1 si-RNA or inhibitory drugs. Western blot was used to confirm protein expression after si-RNA transfection. Moreover, ATOX1/ROCK1-targeted drugs’ therapeutic effects were further investigated in the LLC allogeneic transplantation model and MNU-induced tumour model. Results: Firstly, according to the ATOX1/ROCK1 expression pattern derived from proteomic data, double-low expression of ATOX1/ROCK1 indicated a better Disease Free Survival (DFS) (log-rank test p = 0.01) and Overall Survival (OS) (log-rank test p = 8.2 × 10⁻³), whose expression was also correlated with the lower expression of MCM family proteins. Further, we verified this prognostic correlation in our cohort. The IHC-defined ATOX1/ROCK1 low subtype also had the best OS (log-rank test p = 2.4 × 10⁻³). In vitro, double fluorescence confirmed that ATOX1/ROCK1 was highly expressed together in Lewis cells. Co-inhibition of ATOX1 and ROCK1 either by siRNA transfection or inhibitory drugs could lead to a significant decrease in tumour proliferation. Interestingly, transcriptional inhibition of ATOX1 can lead to the up-regulation of ROCK1, while inhibition of ROCK1 resulted in the promotion of ATOX1. Moreover, in the analysis of migration ability, a similar synergistic effect from the co-inhibition of ATOX1/ROCK1 was also observed. Finally, the Lewis and Mnu-induced allogeneic transplantation model also demonstrated a greatly improved therapeutic effect by combining targeting ATOX1 and ROCK1. Conclusions: Collectively, our results suggest that a low expression pattern of ATOX1/ROCK1 can predict better clinical outcomes in LUAD. Combining the inhibition of these two targets can reach a significantly better therapeutic effect than targeting either alone. Full article

As network infrastructure and Internet of Things (IoT) technologies continue to evolve, immersive systems such as virtual reality (VR) are becoming increasingly integrated into interconnected environments. These advancements allow real-time processing of multi-modal data, improving user experiences with rich visual and three-dimensional interactions. However, ensuring continuous user authentication in VR environments remains a significant challenge. To address this issue, an effective user monitoring system is required to track VR users in real time and trigger re-authentication when necessary. Based on this premise, we propose a multi-modal authentication framework that uses eye-tracking data for authentication, named MobileNetV3pro. The framework applies a transfer learning approach by adapting the MobileNetV3Large architecture (pretrained on ImageNet) as a feature extractor. Its pre-trained convolutional layers are used to obtain high-level image representations, while a custom fully connected classification is added to perform binary classification. Authentication performance is evaluated using Equal Error Rate (EER), accuracy, F1-score, model size, and inference time. Experimental results show that eye-based authentication with MobileNetV3pro achieves a lower EER (3.00%) than baseline models, demonstrating its effectiveness in VR environments. Full article

Since the 1970s, the understanding of cultural heritage (CH) has expanded from a focus on monumental conservation to a systemic, socially constructed concept shaped by communities and dynamic values. While recognized as a resource for sustainable development, CH remains marginal in global policy agendas, notably in the UN’s 2030 Agenda. This paper explores how the Historic Urban Landscape (HUL) approach and broader heritage systems can function as enablers of sustainable urban development, moving beyond heritage-as-object to heritage-as-process. It synthesizes conceptual advances, policy frameworks, and empirical cases—such as Capitals of Culture programs, adaptive reuse initiatives, and circular economy models—to analyze how heritage systems contribute to environmental, social, and economic sustainability. Drawing on a value-based and people-centered framework, the paper identifies key principles, models, and success factors for integrating CH into urban planning. Case studies from Europe and Asia illustrate the potential of heritage to foster inclusive governance, community resilience, and innovation, while also exposing challenges such as institutional fragmentation, gentrification, and policy–practice gaps. The findings highlight the need for holistic, cross-sectoral, and participatory strategies to embed CH meaningfully in urban transformation processes, offering concrete insights for advancing heritage-led sustainable development through the lens of systems thinking. Full article

The BN-350 is the first industrial fast neutron reactor in the history of nuclear energy. It is currently undergoing decommissioning. One of the key challenges of decommissioning is managing the solid radioactive waste that has accumulated throughout the reactor’s operational life. At the moment, the accumulated solid radioactive waste is stored in a storage facility within the BN-350 reactor complex. An analysis showed that more than ~7262 tons with 5.17 × 10¹⁴ Bq activity of various types of solid radioactive waste have been accumulated over the reactor operation. They are mainly represented by materials with low activity. At the same time, the main share of activity is comprised of highly active waste with a total mass of ~170 tons and an activity of 4.73 × 10¹⁴ Bq. A solid radioactive waste management strategy has been developed. It includes all stages from collection and classification to transportation and long-term storage. Modern technologies now offer new possibilities. Some radioactive waste can be processed and reused in other economic sectors. In particular, recycling metals and alloys can reduce the volume of solid radioactive waste. It can also return valuable materials to industrial use. Full article

The exponential increase in global solid waste generation poses significant environmental, economic, and social challenges, particularly in rapidly urbanizing regions. Traditional waste management methods that focus on handling and disposal have proven unsustainable because of their negative impacts on air, soil, and water quality, and their contribution to greenhouse gas emissions. In response, the concept of zero-waste cities, rooted in circular economy principles, has gained increasing attention in recent years. This study proposes a comprehensive and integrated waste management system designed to optimize resource recovery across four distinct waste streams: household, healthcare, green/organic, and inert. The system integrates four specialized facilities: a Secondary Sorting Facility, Energy Recovery Facility, Composting Facility, and Inert Processing Facility, coordinated through a central Primary Sorting Hub. By enabling interconnectivity between these processing units, the system facilitates material cascading, maximizes the reuse and recycling of secondary raw materials, and supports energy recovery and circular nutrient flow. The anticipated benefits include enhanced operational efficiency, reduced environmental degradation, and generation of multiple revenue streams. However, the implementation of such a system faces challenges related to high capital investment, technological complexity, regulatory fragmentation, and low public acceptance. Overcoming these limitations will require strategic planning, stakeholder engagement, and adaptive governance. Full article

Background: The role of vascular endothelial growth factor (VEGF) in intimal hyperplasia has been investigated and discussed numerous times in the literature, producing contrary results and controversial outcomes. In particular, research concerned with the effects of VEGF after catheter-mediated injuries regarding the development of neointimal hyperplasia resulted in diverging conclusions. Methods: A systematic review based on PRISMA principles using MEDLINE was conducted. In summary, 66 publications met the qualifying criteria to be included in this review. Results: VEGF can both cause and attenuate neointimal hyperplasia depending on its site of application and production. Endogenous VEGF produced in the media and adventitia promotes intimal hyperplasia after vascular injury, while exogenous VEGF delivered through drug eluting-stents or by gene therapy can ameliorate re-endothelialization and thereby inhibit intima hyperplasia. Conclusions: The understanding of post-injury released cytokines such as VEGF holds great promise for currently used therapeutic applications and potential for applications to be investigated in the future. Full article

Municipal wastewater treatment relies primarily on biological methods, yet effective disposal of residual sludge remains a major challenge. Converting sludge into biochar via oxygen-limited pyrolysis presents a novel approach for waste resource recovery. This study prepared sludge-based biochar (SBC) through one-step pyrolysis of sewage sludge and applied it to activate peroxymonosulfate (PMS) for degrading diverse contaminants. Characterization (SEM, XPS, FTIR) revealed abundant pore structures and diverse surface functional groups on SBC. Using Acid Orange 7 (AO7) as the target pollutant, SBC effectively degraded AO7 across pH 3.0–9.0 and catalyst dosages (0.2–2.0 g·L⁻¹), achieving a maximum observed rate constant (k_obs) of 0.3108 min^–1. Salinity and common anions showed negligible inhibition on AO7 degradation. SBC maintained 95% degradation efficiency after four reuse cycles and effectively degraded sulfamethoxazole, sulfamethazine, and rhodamine B besides AO7. Mechanistic studies (chemical quenching and ESR) identified singlet oxygen (¹O₂) and superoxide radicals (O₂^•- ) as the dominant reactive oxygen species for AO7 degradation. XPS indicated a 39% reduction in surface carbonyl group content after cycling, contributing to activity decline. LC-MS identified five intermediates, suggesting a potential degradation pathway driven by SBC/PMS system. ECOSAR model predictions indicated significantly reduced biotoxicity of the degradation products compared to AO7. This work provides a strategy for preparing sludge-derived catalysts for PMS activation and pollutant degradation, enabling effective solid waste resource utilization. Full article

Neuroscience education must convey 3D structure with clarity and accuracy. Traditional 2D renderings are limited as they lose depth information and hinder spatial understanding. High-resolution resources now exist, yet many are difficult to use in the class. Therefore, we developed an educational brain video that moves from gross to microanatomy using MRI-based models and the published literature. The pipeline used Fiji for preprocessing, MeshLab for mesh cleanup, Rhino 6 for target fixes, Houdini FX for materials, lighting, and renders, and Cinema4D for final refinement of the video. We had our brain models validated by two neuroscientists for educational fidelity. We tested the video in a class with 96 undergraduates randomized to video and lecture or lecture only. Students completed the same pretest and posttest questions. Student feedback revealed that comprehension and motivation to learn increased significantly in the group that watched the video, suggesting its potential as a useful supplement to traditional lectures. A short, well-produced 3D video can supplement lectures and improve learning in this setting. We share software versions and key parameters to support reuse. Full article

The dynamic contact problem describing collision of an elastic bar with a rigid obstacle, prescribed by an initial velocity, is considered in a variational formulation. The non-smooth, piecewise-linear solution is constructed analytically using partition of a 2D rectangular domain along characteristics. Challenged by the discontinuous velocity after collision, full discretization of the problem is applied that is based on a space-time finite element method. For an iterative solution of the discrete variational inequality, a primal–dual active set algorithm is used. Computer simulation of the collision problem is presented on uniform triangle grids. The active sets defined in the 2D space-time domain converge in a few iterations after re-initialization. The benchmark solution at grid points is indistinguishable from the analytical solution. The discrete energy has no dissipation, it is free of spurious oscillations, and it converges super-linearly under mesh refinement. Full article