Search Results (493)

Understanding gas–water two-phase flow mechanisms in deep tight gas reservoirs is critical for improving production performance and mitigating water invasion. However, the effects of pore-throat-fracture multiscale structures on gas–water flow remain inadequately understood, particularly under high-temperature and high-pressure conditions (HT/HP). In this study, we developed visualizable multiscale throat-pore and throat-pore-fracture physical nanofluidic chip models (feature sizes 500 nm–100 μm) parameterized with Keshen block geological data in the Tarim Basin. We then established an HT/HP nanofluidic platform (rated to 240 °C, 120 MPa; operated at 100 °C, 100 MPa) and, using optical microscopy, directly visualized spontaneous water imbibition and gas–water displacement in the throat-pore and throat-pore-fracture nanofluidic chips and quantified fluid saturation, front velocity, and threshold pressure gradients. The results revealed that the spontaneous imbibition process follows a three-stage evolution controlled by capillarity, gas compression, and pore-scale heterogeneity. Nanoscale throats and microscale pores exhibit good connectivity, facilitating rapid imbibition without significant scale-induced resistance. In contrast, 100 μm fractures create preferential flow paths, leading to enhanced micro-scale water locking and faster gas–water equilibrium. The matrix gas displacement threshold gradient remains below 0.3 MPa/cm, with the cross-scale Jamin effect—rather than capillarity—dominating displacement resistance. At higher pressure gradients (~1 MPa/cm), water is efficiently expelled to low saturations via nanoscale throat networks. This work provides an experimental platform for visualizing gas–water flow in multiscale porous media under ultra-high temperature and pressure conditions and offers mechanistic insights to guide gas injection strategies and water management in deep tight gas reservoirs. Full article

Gasification of residual biomass has emerged as an efficient thermochemical conversion process, applicable to a wide range of uses, such as electricity generation; chemical manufacturing; and the production of liquid biofuels, BioSNG (biomass-based synthetic natural gas), and hydrogen. Thus, gasification of biomass residues not only constitutes an important contribution toward decarbonizing the economy but also promotes the efficient utilization of renewable resources. Although a variety of gasification technologies are available, there are no clear guidelines for selecting the type of gasifier appropriate depending on the feedstock and the desired downstream products. Herein, we propose a gasifier classification model based on an extensive literature review, combined with a multi-criteria decision-making approach. A comprehensive and up-to-date literature review was conducted to gain a thorough understanding of the current state of knowledge in biomass gasification. The different features of the different types of gasifiers, in the context of biomass gasification, are presented and compared. The gasifiers were reviewed and evaluated considering criteria such as processing capacity, syngas quality, process performance, feedstock flexibility, operational and capital costs, environmental impact, and specific equipment features. A multi-criteria classification methodology was evaluated for assessing biomass gasifiers. A case study of such methodology was a applied to determine the best gasifiers for BioSNG inclusion in the natural gas distribution system in a small-scale scenario. Validation was conducted by comparing the matrix findings with commercially implemented gasification projects worldwide. Full article

Graphite nodularity is a key indicator for evaluating the microstructure quality of ductile iron and plays a crucial role in ensuring product quality and enhancing manufacturing efficiency. Existing research often only focuses on a single type of feature and fails to utilize multi-source information in a coordinated manner. Single-feature methods are difficult to comprehensively capture microstructures, which limits the accuracy and robustness of the model. This study proposes a hybrid estimation model for the graphite nodularity of ductile cast iron based on multi-source feature extraction. A comprehensive feature engineering pipeline was established, incorporating geometric, color, and texture features extracted via Hue-Saturation-Value color space (HSV) histograms, gray level co-occurrence matrix (GLCM), Local Binary Pattern (LBP), and multi-scale Gabor filters. Dimensionality reduction was performed using Principal Component Analysis (PCA) to mitigate redundancy. An improved watershed algorithm combined with intelligent filtering was used for accurate particle segmentation. Several machine learning algorithms, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), are applied to estimate graphite nodularity based on geometric features (GFs) and feature extraction. Experimental results demonstrate that the CatBoost model trained on fused features achieves high estimation accuracy and stability for geometric parameters, with R-squared (R²) exceeding 0.98. Furthermore, introducing geometric features into the fusion set enhances model generalization and suppresses overfitting. This framework offers an efficient and robust approach for intelligent analysis of metallographic images and provides valuable support for automated quality assessment in casting production. Full article

At a particular frequency, most materials and objects of interest exhibit a polarization signature, or Mueller matrix, of limited dimensionality, with many matrix elements either negligibly small or redundant due to symmetry. Robust design of a polarization sensor for a particular material or object of interest, or for an application with a limited set of materials or objects, will adapt to the signature subspace, as well as the available modulators, in order to avoid unnecessary measurements and hardware and their associated budgets, errors, and artifacts. At the same time, measured polarization features should be expressed in the Stokes–Mueller basis to allow use of known phenomenology for data interpretation and processing as well as instrument calibration and troubleshooting. This approach to partial Mueller-matrix polarimeter (pMMP) design begins by defining a vector space of reduced Mueller matrices and an instrument vector representing the polarization modulators and other components of the sensor. The reduced-Mueller vector space is proven to be identical to ${\mathbb{R}}^{15}$ and to provide a completely linear description constrained to the Mueller cone. The reduced irradiance, the inner product of the reduced instrument and target vectors, is then applied to construct classifiers and tune modulator parameters, for instance to maximize representation of a specific target in a fixed number of measured channels. This design method eliminates the use of pseudo-inverses and reveals the optimal channel compositions to capture a particular signature feature, or a limited set of features, under given hardware constraints. Examples are given for common optical division-of-amplitude (DoA) 2-channel passive and serial/DoT-DoA 4-channel active polarimeters with rotating crystal modulators for classification of targets with diattenuation and depolarization characteristics. Full article

This paper presents new fast discrete Krawtchouk transform (DKT) algorithms for input sequences of length 3 to 8. Small-sized DKT algorithms can be utilized in image processing applications to extract local image features formed by a sliding spatial window, and they can also serve as building blocks for developing larger-sized algorithms. Existing strategies to reduce the computational complexity of DKT mainly focus on modifying the recurrence relations for Krawtchouk polynomials, dividing the input signals into blocks or layers, or using different methods to approximate the coefficient values. Algorithms developed using the first two strategies are computationally intensive, which introduces a significant time delay in the computation process. Algorithms based on the approximation of polynomial coefficient values reduce computation time but at the expense of reduced accuracy. We use a different approach based on reducing the block structure of the matrix to one of the previously developed block-structural patterns, which allows us to factorize the resulting matrix in such a way that it leads to a reduction in the computational complexity of the synthesized algorithm. We describe the algorithmic solutions we have obtained through data flow graphs. The proposed DKT algorithms reduce the number of multiplications, additions, and shifts by an average of 58%, 27%, and 68%, respectively, compared to the direct computation of DKT via matrix-vector product. These characteristics were averaged across the considered input sizes (from 3 to 8). Full article

Greenery and its significance in fostering sustainable urban development constitute a fundamental theme in contemporary urban planning. This study focuses on allotment gardens as a potential means of enhancing the quality of urban living environments, seeking to establish whether this form of urban greenery—often perceived as an anachronism—continues to play a meaningful role in promoting the well-being of city residents. The objective of the article was to examine whether allotment gardens exhibit the characteristics of spaces conducive to well-being within residential contexts, drawing upon scientific knowledge and expert opinions. The research employed a literature review, qualitative data analysis of material collected through individual in-depth and focus group interviews, and a final matrix analysis to assess the extent to which existing benefits satisfy contemporary demands. The findings identify current well-being features associated with allotment gardens, addressing residents’ needs regarding the benefits they offer, including recreation and leisure, and their impact on physical and mental health, as well as the formation of social relationships. Nutrition was further characterised by the self-production of healthy, affordable, and extraordinary food. The results also underscore the importance of accessibility in shaping the well-being benefits of allotment gardens, emphasising the acquisition of new competencies, the strengthening of social relations, and opportunities for health and recreation as their primary contributions. Full article

Objectives: Lepidium meyenii Walpers (LMW), a high-altitude plant, is known to stimulate hormone release, counteract neurodegeneration, and protect against oxidative stress. Saliva is vital for oral health, and reduced production leads to xerostomia, often caused by aging, radiation, or Sjögren’s syndrome. Key pathological features include mesenchymal fibrosis and acinar atrophy, largely regulated by the TGF-β1 pathway. Current treatments are limited, with many patients relying on artificial saliva. Developing therapies to restore salivary function could offer significant benefits. Methods: In this study, we assessed the protective effects of LMW extract (LMWE) in irradiated C57BL/6J mice and TGF-β1-treated rat parotid acinar cells (Par-C10) using histological, molecular, bioenergetic, and 3D organoid analyses to evaluate salivary gland regeneration and lineage-specific differentiation. Results: LMWE significantly restored gland weight, shortened secretion lag time, and increased amylase activity in irradiated mice. Histological and molecular analyses showed reduced acinar atrophy and fibrosis, preservation of epithelial polarity, and upregulation of Mist1, AQP5, and amylase. In vitro, LMWE protected Par-C10 cells from TGF-β1-induced senescence, preserved mitochondrial membrane potential, and improved epithelial barrier function. In 3D organoid cultures of Par-C10 cells embedded in matrix, (1E,4Z)-1-(2,4-dihydroxyphenyl)-5-(3,4-dihydroxyphenyl) penta-1,4-dien-3-one (DHPPD) and (Z)-N-phenyldodec-2-enamide (E4Z-PD)-selectively enhanced acinar and ductal lineage differentiation, respectively. Conclusions: These results suggest that LMWE promotes salivary gland regeneration through antioxidative and lineage-specific mechanisms and may represent a safe and effective therapeutic strategy for xerostomia. Full article

Industrial process monitoring increasingly relies on sensor-generated time-series data, yet the lack of labels, high variability, and operational noise make it difficult to extract meaningful patterns using conventional methods. Existing clustering techniques either rely on fixed distance metrics or deep models designed for static data, limiting their ability to handle dynamic, unstructured industrial sequences. Addressing this gap, this paper proposes a novel framework for unsupervised discovery of operational modes in univariate time-series data using image-based convolutional clustering with composite internal evaluation. The proposed framework improves upon existing approaches in three ways: (1) raw time-series sequences are transformed into grayscale matrix representations via overlapping sliding windows, allowing effective feature extraction using a deep convolutional autoencoder; (2) the framework integrates both soft and hard clustering outputs and refines the selection through a two-stage strategy; and (3) clustering performance is objectively evaluated by a newly developed composite score, $S_{eva}$, which combines normalized Silhouette, Calinski–Harabasz, and Davies–Bouldin indices. Applied to over 3900 furnace melting operations from a Nordic foundry, the method identifies seven explainable operational patterns, revealing significant differences in energy consumption, thermal dynamics, and production duration. Compared to classical and deep clustering baselines, the proposed approach achieves superior overall performance, greater robustness, and domain-aligned explainability. The framework addresses key challenges in unsupervised time-series analysis, such as sequence irregularity, overlapping modes, and metric inconsistency, and provides a generalizable solution for data-driven diagnostics and energy optimization in industrial systems. Full article

Background: Phosphaturic mesenchymal tumours secreting fibroblast growth factor 23 (hereinafter referred to as FGF23+ PMT) are rare neoplasms that can cause hypophosphataemic osteomalacia, owing to excessive FGF23 production. Mast cells (MCs) play a key role in tumour biology by modulating proliferative activity of atypical cells, resistance to innate and acquired immunity, angiogenesis, and metastatic behaviour. However, MCs associated with FGF23+ PMT have not previously been investigated. This study, to our knowledge, is the first to characterise features of the tumour microenvironment through spatial phenotyping of the immune and stromal landscape, together with histotopographic mapping of intercellular MC interactions with other subcellular populations in FGF23+ PMT. Methods: Histochemical staining (haematoxylin and eosin, toluidine blue, Giemsa solution, picro-Mallory protocol, silver impregnation), as well as monoplex and multiplex immunohistochemical staining with spatial phenotyping, were performed to detect atypical FGF23-secreting cells, immune cells (CD3, CD4, CD8, CD14, CD20, CD38, CD68, or CD163), stromal components (CD31, α-SMA, or vimentin), and specific MC proteases (tryptase, chymase, or carboxypeptidase A3). Bioinformatics analysis using artificial intelligence technologies was applied for spatial profiling of MC interactions with tumour, immunocompetent, and stromal cells in the tumour microenvironment. Results: Bioinformatic analysis of the entire tumour histological section, comprising over 70,000 cells stained using monoplex and multiplex immunohistochemical protocols, enabled identification of more than half of the cell population. The most abundant were CD14+ (30.7%), CD163+ (23.2%), and CD31+ (17.9%) cells. Tumour-associated MCs accounted for 0.7% of the total pool of immunopositive cells and included both mucosal and connective tissue subpopulations, predominantly of the tryptase + chymase-CPA3-specific protease phenotype. This pattern reflected combined multidirectional morphogenetic processes in the patient’s FGF23+ PMT. More than 50% of MCs were colocalized with neighbouring cells of the tumour microenvironment within 20 μm, most frequently with monocytes (CD14+CD68+), M2 macrophages (CD68+CD163+), and endothelial cells (CD31+). In contrast, colocalization with atypical FGF23-secreting cells was rare, indicating minimal direct effects on tumour cell activity. Interaction with T lymphocytes, including CD8+, was also infrequent, excluding their activation and the development of antitumour effects. Mapping of MC histotopography validated the hypothesis of their inductive role in monocyte differentiation into M2 macrophages and probable polarisation of macrophages from M1 into M2, thereby contributing to slow tumour growth. MCs were further involved in extracellular matrix remodelling and participated in the formation of pro-osteogenic niches within the FGF23+ PMT microenvironment, leading to pathological osteoid development. Conclusions: This study demonstrated active MC participation in the evolution of the FGF23+ PMT microenvironment. The findings may be applied in translational medicine to develop novel algorithms for personalised therapy in patients with FGF23-secreting tumours, offering an alternative when surgical removal of the tumour is not feasible. Full article

Mine water inrush is a significant environmental catastrophe during the coal mining process, and the timely discrimination of the source of water inrush is the key to ensuring safe production in coal mines. This work suggests a mine water inrush—belief rule base (MWI-BRB) source discrimination model to overcome the interpretability and performance issues with conventional models. MWI-BRB firstly automatically constructs the reference values of prerequisite attributes using the Sum of Squared Errors—K-means++ algorithm, which effectively combines expert knowledge and data-driven methods, and solves the limitation of the traditional belief rule base model relying on specialist knowledge. Secondly, the hierarchical incremental structure solves the rule explosion problem caused by complex features while using XGBoost to select features. Finally, in the inference process, the model adopts an evidential reasoning algorithm to realize transparent causal inference, guaranteeing the model’s interpretability and transparency. The Penalized Covariance Matrix Adaptation Evolution Strategy algorithm optimizes the model parameters to increase the discriminative accuracy of the model even more. Experimental results on a real coal mine dataset (a total of 67 samples from Hebei, China, covering four water inrush sources) demonstrate that the proposed MWI-BRB achieves 95.23% accuracy, 95.23% recall, and 95.36% F1-score under a 7:3 training–testing split with parameter tuning performed via leave-one-out cross-validation. The near-identical values across accuracy, recall, and F1-score reflect the balanced nature of the dataset and the robustness of the model across different evaluation metrics. Compared with baseline models, MWI-BRB’s accuracy and recall are 4.78% higher than BPNN and 9.52% higher than KNN, RF, and XGBoost; its F1-score is 4.85% higher than BPNN, 10.64% higher than KNN, 10.19% higher than RF, and 9.65% higher than XGBoost. Moreover, the model maintains high interpretability. In conclusion, the MWI-BRB model can realize efficient and accurate water inrush source discrimination in complex environments, which provides a feasible technical solution for the prevention and control of mine water damage. Full article

The dried root extract of Astragalus membranaceus, also known as Astragali radix, is widely used in traditional Chinese medicine for its multiple health benefits and well-established safety profile. Astragalus root extract exhibits several bioactive properties, including anti-inflammatory, antioxidant, antiviral and hepatoprotective effects. Due to its unique features, it is being investigated in a novel application as a complementary remedy in the management of joint disorders. In this study, we evaluated the effect of Astragalus membranaceus hydroalcoholic root extract (0.01 and 0.1 mg/mL) in vitro on the HTB-94 cell line, a well-known model for studying inflammatory pathways in human chondrocytes. The mRNA modulation levels were measured by quantitative real-time polymerase chain reaction (qRT-PCR), while the protein secretion levels were assessed using an Enzyme-Linked Immunosorbent Assay (ELISA). Results obtained demonstrated that this extract is able to decrease the tumor necrosis factor-α (TNF-α)-induced inflammatory response by downregulating both the mRNA expression and release of the pro-inflammatory mediators Interleukin-6 (IL-6), Interleukin-1β (IL-1β) and Interelukin-8 (IL-8), as well as matrix metalloproteases, including Matrix Metalloprotease-3 (MMP-3), Matrix Metalloprotease-13 (MMP-13) and A disintegrin, and metalloproteinase with thrombospondin motifs 5 (ADAMTS-5). Moreover, the interleukin and matrix metalloprotease production was also assessed in non-TNF-α-stimulated cells, revealing that the extract did not alter the basal levels of these mediators. Finally, our findings highlight the potential benefits of Astragalus membranaceus extract, both in terms of its favorable safety profile and its efficacy mitigating joint inflammatory responses. These results support the potential of this extract as a nutraceutical agent for joint health support. Full article

Extracellular matrix (ECM) bioscaffolds have demonstrated therapeutic potential across a variety of clinical and preclinical applications for tissue repair and regeneration. In parallel, these scaffolds and their components have shown the capacity to modulate the immune response. Unlike synthetic implants, which are often associated with chronic inflammation or fibrotic encapsulation, ECM bioscaffolds interact dynamically with host cells, promoting constructive tissue remodeling. This effect is largely attributed to the preservation of structural and biochemical cues—such as degradation products and matrix-bound nanovesicles (MBV). These cues influence immune cell behavior and support the transition from inflammation to resolution and functional tissue regeneration. However, the immunomodulatory properties of ECM bioscaffolds are dependent on the source tissue and, critically, on the methods used for decellularization. Inadequate removal of cellular components or the presence of residual chemicals can shift the host response towards a pro-inflammatory, non-constructive phenotype, ultimately compromising therapeutic outcomes. This review synthesizes current basic concepts on the innate immune response to ECM bioscaffolds, with particular attention to the inflammatory, proliferative, and remodeling phases following implantation. We explore how specific ECM features shape these responses and distinguish between pro-remodeling and pro-inflammatory outcomes. Additionally, we examine the impact of manufacturing practices and quality control on the preservation of ECM bioactivity. These insights challenge the conventional classification of ECM bioscaffolds as medical devices and support their recognition as biologically active materials with distinct immunoregulatory potential. A deeper understanding of these properties is critical for optimizing clinical applications and guiding the development of updated regulatory frameworks in regenerative medicine. Full article

With decreasing production costs, carbon nanomaterials have become common, scalable, and cost-effective additives in high-performance composites due to the potentially significant increases in mechanical, thermal, and electrical properties. The mechanical performance of carbon nanomaterial-reinforced matrix materials under high-strain-rate compressive conditions was investigated. This study compares neat epoxy-amine with 0.1 wt.% loadings of graphene or graphite dispersed in epoxy-amine. Quasi-static and high-rate testing was conducted using an Instron load frame and Split Hopkinson Pressure Bar (SHPB), respectively, to assess the material’s response to increasing strain rates via compressive loadings. No significant change in compressive strength was observed at quasi-static strain rates, with the 0.1 wt.% graphene sample showing no significant deviation from the neat resin at high strain rates. In contrast, the 0.1 wt.% graphite sample exhibited a substantial reduction in comparative compressive strength, decreasing by ~43% at 10² s⁻¹ strain rate and ~42% at 10³ s⁻¹ strain rate. While graphene may not significantly enhance stiffness at high strain rates, its ability to preserve ductility without introducing failure-prone features makes it a more effective additive for dynamic applications. Full article

Fish condition detection, including the identification of feeding desire, biological attachments, fence breaches, and dead fishes, has become an important research frontier in fishery aquaculture. However, perception in underwater conditions is less satisfactory and remains a tricky problem. Firstly, we have developed a multimodal dataset based on Neuromorphic vision (NeuroVI) and RGB images, encompassing challenging fishery aquaculture scenarios. Within the fishery aquaculture dataset, a spike neural network (SNN) method is designed to filter NeuroVI images, and the sift feature points are leveraged to select the optimal image. Next, we propose a dual-image cross-attention learning network that achieves scene segmentation in a fishery aquaculture cage. This network comprises double-channels feature extraction and guided attention learning modules. In detail, the feature matrix of NeuroVI images serves as the query matrix for RGB images, generating attention for calculating key and value matrices. Then, to alleviate the computational burden of the dual-channel network, we replace dot-product multiplication with element-wise multiplication, thereby reducing the computational load among different matrices. Finally, our experimental results from the fishery cage demonstrate that the proposed method achieves the state-of-the-art segmentation performance in the management process of fishery aquaculture. Full article

Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity. However, traditional NMF often struggles to effectively capture and preserve topological structure information between data during low-dimensional representation. Therefore, this paper proposes an autoencoder-like sparse non-negative matrix factorization with structure relationship preservation (ASNMF-SRP). Firstly, drawing on the principle of autoencoders, a “decoder-encoder” co-optimization matrix factorization framework is constructed to enhance the factorization stability and representation capability of the coefficient matrix. Then, a preference-adjusted random walk strategy is introduced to capture higher-order neighborhood relationships between samples, encoding multi-order topological structure information of the data through an optimal graph regularization term. Simultaneously, to mitigate the impact of noise and outliers, the $l_{2,1}$-norm is used to constrain the feature correlation between low-dimensional representations and the original data, preserving feature relationships between data, and a sparse constraint is imposed on the coefficient matrix via the inner product. Finally, clustering experiments conducted on 8 public datasets demonstrate that ASNMF-SRP consistently exhibits favorable clustering performance. Full article