Search Results (193)

Rough sets and fuzzy sets are two complementary approaches for modeling uncertainty and imprecision. Their integration enables a more comprehensive representation of complex, uncertain systems. However, existing rough fuzzy sets models lack the expressive power to fully capture the interactions among structural uncertainty, cognitive hesitation, and multi-level granular information. To address these limitations, we achieve the following: (1) We propose intuitionistic fuzzy covering rough membership and non-membership degrees based on maximal description and construct a new single-granulation model that more effectively captures both the structural relationships among elements and the semantics of fuzzy information. (2) We further extend the model to a multi-granulation framework by defining optimistic and pessimistic approximation operators and analyzing their properties. Additionally, we propose a neutral multi-granulation covering rough intuitionistic fuzzy sets based on aggregated membership and non-membership degrees. Compared with single-granulation models, the multi-granulation models integrate multiple levels of information, allowing for more fine-grained and robust representations of uncertainty. Finally, a case study on real estate investment was conducted to validate the effectiveness of the proposed models. The results show that our models can more precisely represent uncertainty and granularity in complex data, providing a flexible tool for knowledge representation in decision-making scenarios. Full article

Concept-cognitive learning reveals the principle of human cognition by simulating the brain’s process of learning and processing concepts. Nevertheless, for neighborhood similarity granules, the average information of objects regarding all attributes is not considered, which may lead to unbalanced acquisition of knowledge. On the other hand, there are some unnecessary concepts in the extension of fuzzy concepts, which results in poor classification learning. To tackle these challenges, we present a forgetting-based concept-cognitive learning model for classification in a fuzzy formal decision context. Firstly, the fuzzy concept space is established based on the the correlation coefficient matrix. Then, to delete unnecessary objects that are in the zone of proximal development, we construct the forgetting fuzzy concept space by selecting the concept corresponding to the maximum similarity. Subsequently, a forgetting-based fuzzy concept model (FCCLM) mechanism is proposed. In the end, experimental results on eight datasets validate the feasibility and efficiency of the proposed learning mechanism through classification performance assessment. Full article

Cardiac fibrosis following myocardial infarction plays a critical role in the formation of scar tissue and contributes to ventricular arrhythmias, including ventricular tachycardia and sudden cardiac death. Current clinical diagnostics use electrical and structural markers, but lack precision due to low spatial resolution and absence of molecular information. In this paper, we employed line scan Raman microspectroscopy to classify sheep myocardial tissue into muscle, necrotic, granulated, and fibrotic tissue types, using collagen as a molecular biomarker. Three spectral regions were evaluated: region A (600–2960 cm⁻¹), region B (600–1399 cm⁻¹ and 1751–2960 cm⁻¹), and region C (1400–1750 cm⁻¹), which includes the prominent collagen-associated peaks at 1448 cm⁻¹ and 1652 cm⁻¹. Linear and nonlinear principal component analysis (PCA) and support vector machines (SVMs) were applied for dimensionality reduction and classification, with nonlinear models specifically addressing the nonlinearity of collagen formation during fibrogenesis. Histological validation was performed using Masson’s trichrome staining. Raman bands associated with collagen in region C consistently outperformed regions A and B, achieving the highest explained variance and best class separation in both binary and multiclass PCA models for both linear and nonlinear approaches. The ratio of collagen-related peaks enabled stage-dependent tissue characterization, confirming the nonlinear nature of fibrotic remodeling. Our findings highlight the diagnostic potential of collagen-associated Raman bands for characterizing myocardial fibrosis. The proposed PCA-SVM framework demonstrates robust performance even with limited sample size and has the potential to lay the foundation for real-time intraoperative diagnostics. Full article

The cement industry is responsible for approximately 7–8% of global CO₂ emissions, primarily due to the energy-intensive production of clinker. In response to growing environmental concerns and the pressure to decarbonize the construction sector, the composition of cement has been evolving toward more sustainable alternatives. This article presents a review of the recent literature and EPD reports concerning changes in cement composition and their environmental impact, as assessed through Life Cycle Assessment (LCA) methodologies. This paper reviews the literature of recent LCA studies on cement with alternative materials. For a thorough analysis, VOSviewer_1.6.18 was used to find the research gap in this field. The companies’ EPD reports were analyzed to compare the most relevant information. The data that was extracted from the reports concerns carbon footprint, energy consumption, and system boundaries. The analysis reveals a clear trend toward reducing clinker content by incorporating supplementary cementitious materials (SCMs) such as fly ash, ground granulated blast furnace slag, natural pozzolans, and limestone. These modifications significantly lower key LCA indicators, particularly Global Warming Potential (GWP). Despite the growing number of studies on individual SCMs, there is a lack of integrated reviews comparing their environmental performance within a standardized LCA framework. This study addresses this gap by systematically comparing the environmental profiles of various low-clinker cement types and highlighting the critical role of supplementary cementitious materials selection. The findings confirm that changes in cement formulation are not only occurring but are essential for reducing the environmental footprint of construction materials. Full article

The accurate prediction of remaining useful life (RUL) is crucial in order to reasonably and efficiently utilize lithium-ion batteries (LiBs). In this paper, a construction method of periodic time series is applied to the degradation data of LiBs to address the issues of insufficient training data and smooth degradation in the RUL interval prediction method based on trend filtering segmentation and fuzzy information granulation. The construction method for periodic time series is used to form a new dataset from the original data, based on which the fusion model, by combining the variational mode decomposition (VMD) and gated recurrent unit (GRU), is used as the RUL interval prediction model of LiBs. Moreover, the effectiveness and advantage of the RUL interval prediction method proposed in this paper was verified and analyzed by utilizing the CALCE battery dataset and NCA battery dataset. Full article

Stress granules (SG), dynamic cytoplasmic condensates formed via liquid-liquid phase separation (LLPS), serve as a critical hub for cellular stress adaptation and antiviral defense. By halting non-essential translation and sequestering viral RNA, SG restrict viral replication through multiple mechanisms, including PKR-eIF2α signaling, recruitment of antiviral proteins, and spatial isolation of viral components. However, viruses have evolved sophisticated strategies to subvert SG-mediated defenses, including proteolytic cleavage of SG nucleators, sequestration of core proteins into viral replication complexes, and modulation of stress-responsive pathways. This review highlights the dual roles of SG as both antiviral sentinels and targets of viral manipulation, emphasizing their interplay with innate immunity, autophagy, and apoptosis. Furthermore, viruses exploit SG heterogeneity and crosstalk with RNA granules like processing bodies (P-bodies, PB) to evade host defenses, while viral inclusion bodies (IBs) recruit SG components to create proviral microenvironments. Future research directions include elucidating spatiotemporal SG dynamics in vivo, dissecting compositional heterogeneity, and leveraging advanced technologies to unravel context-specific host-pathogen conflicts. This review about viruses and SG formation helps better understand the virus-host interaction and game process to develop new drug targets. Understanding these mechanisms not only advances virology but also informs innovative strategies to address immune escape mechanisms in viral infections. Full article

Missing data introduce uncertainty in data mining, but existing set-valued approaches ignore frequency information. We propose unsupervised attribute reduction algorithms for multiset-valued data to address this gap. First, we define a multiset-valued information system (MSVIS) and establish $\theta$-tolerance relation to form the information granules. Then, $\theta$-information entropy and $\theta$-information amount are introduced as uncertainty measures. Finally, these two UMs are used to design two unsupervised attribute reduction algorithms in an MSVIS. The experimental results demonstrate the superiority of the proposed algorithms, achieving average reductions of 50% in attribute subsets while improving clustering accuracy and outlier detection performance. Parameter analysis further validates the robustness of the framework under varying missing rates. Full article

In this paper, we propose a novel fuzzy time series forecasting model that integrates fuzzy C-means (FCM) clustering, the principle of justifiable granularity (PJG), and particle swarm optimization (PSO), with a focus on leveraging symmetry in subinterval partitioning to enhance model interpretability and forecasting accuracy. First, the FCM method is employed to partition the universe of discourse, generating an initial division of subintervals. To ensure symmetric information representation, triangular fuzzy information granules are constructed for these subintervals in accordance with the principle of justifiable granularity. Then, an objective function is formulated for the entire universe of discourse, and the PSO algorithm is utilized to optimize the subinterval division, resulting in the final optimal partition. This process ensures that the subintervals achieve a balance between coverage and specificity, thereby introducing a form of symmetry in the partitioning of the universe of discourse. Leveraging the optimized symmetric partition, the framework of the fuzzy time series model is implemented for forecasting. Finally, the proposed approach is carried out on the Taiwan Weighted Stock Index (TAIEX) datasets and the Shanghai Composite Index (SHCI) datasets. The forecasting results demonstrate that the proposed approach achieves higher prediction accuracy and semantic accuracy compared with other methods. Full article

The chemical interaction of salicylic acid, formaldehyde, and sulfuric acid produced a disalicylic ligand (3,3′-dicarboxy-2,2′-dihydroxydiphenylmethane, DCM), which was then allowed to coordinate with copper (II) ions. The solid compounds’ chemical structures were determined using elemental analysis, UV-Vis, FT-IR, MS, ¹H-NMR, PXRD, SEM, TEM, magnetic studies, as well as molecular modeling based on DFT (density functional theory) calculations. It was proposed that the ligand coordinates in a tetradentate fashion with the copper ion to give a square-planar binuclear complex. A significant difference in the diffraction patterns between Cu(II)–DCM (amorphous) and DCM (crystalline) was displayed using an X-ray diffraction analysis. Spherical granules were identified throughout through morphology analysis using SEM and TEM. UV-Vis spectra were used to quantify the optical characteristics such as the energy gap, optical conductivity, refractive index, and penetration depth. The band gap values that lie within the semiconductor region suggested that the compounds could be used for electronic applications. The optimized structure of the synthesized Cu(II)–DCM complex was investigated using DFT and TD-DFT (time-dependent density functional theory) at the B3LYP/6-31G(d, p) level, with the LANL2DZ basis set for Cu in an ethanol solvent and the gas environment modeled by CPCM. The experimental data suggest a square-planar geometry of the Cu(II) binuclear complex. The theoretical calculations support the proposed structure of the compound. The cytotoxicity of the DCM against HCT–116 (human colon cancer) cells was tested, and the outcome exhibited good inhibitions of growth. A molecular docking (MD) examination was carried out to illustrate the binding mode/affinity of the prepared compounds (DCM and Cu(II)–DCM) in the active site of the receptor protein [CDK2 enzyme, PDB ID: 6GUE]. The compounds formed hydrogen bonds with the amino acid residues of the protein, increasing the binding affinity from −7.2 to −9.3 kcal/mol through the coordination process. The information from this current study, particularly the copper complex, is beneficial for exploring new compounds that have anticancer potential. Full article

The quantity of construction demolition waste (CDW) has been increasing due to the demolition of many old buildings throughout the world. So far, all the statistics indicate that there is a very large generation of CDW, which increases annually. The increasing amount CDW in landfills will cause a scarcity of landfill space and will also increase pollution and cost due to transportation. Recycled brick aggregate concrete (RBAC) incorporating polystyrene (EPS) aggregate beads has emerged as an alternative lightweight material with numerous obvious sustainable benefits, suitable for a future circular economy. The goal of this paper is to assess the feasibility of obtaining lightweight aggregate concrete of structural grade with recycled brick aggregate (RBA) as a coarse aggregate and the incorporation of polystyrene beads in a certain percentage by conducting an experimental study on the dry and apparent density, compressive strength, split-tensile strength and elasticity modulus. In addition, the effects of the w/c ratio and cement content on these properties were studied to provide useful information for the performance optimization of this concrete with RBA and polystyrene (EPS) beads. The properties were investigated for two cement contents, 400 and 360 kg/m³, and two ratios between water and cement, 0.43 and 0.39, respectively. The RBAC mixtures containing EPS beads in 15%, 25% and 35% replacement percentages were evaluated through a comprehensive test program based on the European standards. The results showed that, in general, the use of polystyrene (EPS) beads decreased the mechanical properties of the recycled brick aggregate concrete; however, the outcome indicates the potential for producing lightweight concrete of different grades, including structural classes. It was found that the developed lightweight concrete presents a uniform distribution of the polystyrene granules in the hardened volume of concrete. Also, it was found that the recycled brick aggregate with a 16 mm maximum size did not negatively influence the uniform distribution of the EPS beads, avoiding concentrations of beads. With the increase in the percentage of EPS beads, the properties of the recycled brick aggregate concrete were found to be less sensitive to the water-to-cement ratio. Full article

Background/Objectives: This study applies a Physiologically Based Biopharmaceutics Modeling (PBBM) framework to predict the bioavailability (BA) and bioequivalence (BE) of apixaban, a borderline BCS Class III/IV drug. It investigates how formulation factors, such as particle size, granulation method, and dissolution conditions, affect apixaban’s in vivo behavior under fasting conditions. Methods: A PBBM approach was developed by integrating physicochemical, formulation, and drug-related parameters to simulate dissolution and absorption using a middle-out strategy for combining in silico, in vitro, and in vivo data. The Noyes–Whitney equation was used to predict dissolution influenced by particle size, granulation type, and in vitro dissolution conditions. This information was added to a compartmental absorption model of the gastrointestinal track connected to a classical compartmental model characterizing apixaban’s disposition. Results: The study validated the apixaban PBBM predictions by comparing simulated and observed pharmacokinetic profiles across several doses and immediate release formulations (solution and tablets) administered through the oral route. Results demonstrated acceptable prediction accuracy for BA and BE under various conditions. The model’s simulations identified a dissolution safe space, enabling regulatory and development insights into acceptable formulation characteristics. Conclusions: These findings highlight the potential of PBBM in streamlining drug development, reducing clinical studies, and supporting regulatory decisions. Specifically, for apixaban, the study demonstrated that particle sizes below 120 µm ensure BE with reference formulations, while formulations with faster dissolution rates, such as smaller particle sizes, align closely with BCS biowaiver criteria. This research emphasizes PBBM as a valuable tool for optimizing drug quality and lifecycle management. Full article

Intermittent faults occur randomly, last for short durations, and ultimately lead to permanent failures, threatening the safety and stability of analog circuits. Additionally, these faults are often hard to differentiate from noise-induced anomalies, resulting in incorrect disassembly and complicating circuit maintenance. To address these challenges, we propose a novel fault diagnosis method. The method uses an adjustable sliding window to extract multiscale fuzzy entropy features, mitigating the impact of normal data on entropy calculations for intermittent faults. The coarse granulation strategy of sliding point by point is applied to avoid information loss in short time series. The raw signal is then segmented and transformed into four statistical features, which are fused into comprehensive amplitude features via a self-attention mechanism. This comprehensive feature better captures amplitude variations than individual statistical features. Finally, the two features are fed into a convolutional neural network for diagnosis. The method is applied to two typical analog circuits. Ablation studies confirmed its effectiveness. Although the proposed method does not have the lowest diagnostic cost and the fastest detection time, the differences with state-of-the-art methods are minimal, and the proposed method achieves higher classification accuracy. Taken together, these findings demonstrate the superiority of the proposed method. Full article

To efficiently reduce the dimensionality of time series and enhance the efficiency of subsequent data-mining tasks, this study introduces cloud model theory to propose a novel information granulation method and its corresponding similarity measurement. First, we present an information granulation validity index of time series (IGV) based on the entropy and expectation of the cloud model. Taking IGV as the granulation target for time series, an adaptive information granulation algorithm for time series (CMAIG) is proposed, which can transform a time series into a granular time series consisting of several normal clouds without pre-specifying the number of information granules, achieving efficient dimensionality reduction. Then, a new similarity measurement method (CMAIG_ECM) is designed to calculate the similarity between two granular time series. Finally, the hierarchical clustering algorithm based on the proposed time series information granulation method and granular time series similarity measurement method (CMAIG_ECM_HC) is carried out on some UCR datasets and a real stock dataset, and experimental studies demonstrate that CMAIG_ECM_HC has superior performance in clustering time series with different shapes and trends. Full article

Unlike many other fungicides, strobilurins are applied several times during the growing season for prophylactic purposes, thus heightening the risk of environmental contamination. In the EU, the dimoxystrobin approval period lasted for 17 years. It has been classified as moderately toxic to birds and highly toxic to earthworms, and it is suspected to be carcinogenic to humans. However, it is still commercialized in several countries. The effects of dimoxystrobin are still largely underexplored, with only three studies reporting sublethal alterations in fish. Here, we evaluated for the first time the effects of dimoxystrobin on zebrafish liver after short-term exposure (96 h) to two sublethal and environmentally relevant concentrations (6.56 and 13.13 μg/L), providing evidence of morphological, functional, and ultrastructural modifications. We revealed severe alterations encompassing three reaction patterns: circulatory disturbance, regressive and progressive changes, which also showed a dose-dependent trend. Furthermore, we revealed that dimoxystrobin induced a significant increase in lipid content, a decrease in glycogen granules and affected the defensive response against oxidative stress through a significant downregulation of SOD and CAT. The information presented here demonstrates that the hazardous properties of dimoxystrobin may result from several pathological events involving multiple targets. Our results also emphasize the importance of the combined use of morphological, ultrastructural and functional investigation in ecotoxicological studies. Full article

Quantitative transcriptomics offers a new way to obtain a detailed picture of freshly isolated cells. By direct isolation, the cells are unaffected by in vitro culture, and the isolation at cold temperatures maintains the cells relatively unaltered in phenotype by avoiding activation through receptor cross-linking or plastic adherence. Simultaneous analysis of several cell types provides the opportunity to obtain detailed pictures of transcriptomic differences between them. Here, we present such an analysis focusing on four human blood cell populations and compare those to isolated human skin mast cells. Pure CD19⁺ peripheral blood B cells, CD14⁺ monocytes, and CD4⁺ and CD8⁺ T cells were obtained by fluorescence-activated cell sorting, and KIT+ human connective tissue mast cells (MCs) were purified by MACS sorting from healthy skin. Detailed information concerning expression levels of the different granule proteases, protease inhibitors, Fc receptors, other receptors, transcription factors, cell signaling components, cytoskeletal proteins, and many other protein families relevant to the functions of these cells were obtained and comprehensively discussed. The MC granule proteases were found exclusively in the MC samples, and the T-cell granzymes in the T cells, of which several were present in both CD4⁺ and CD8⁺ T cells. High levels of CD4 were also observed in MCs and monocytes. We found a large variation between the different cell populations in the expression of Fc receptors, as well as for lipid mediators, proteoglycan synthesis enzymes, cytokines, cytokine receptors, and transcription factors. This detailed quantitative comparative analysis of more than 780 proteins of importance for the function of these populations can now serve as a good reference material for research into how these entities shape the role of these cells in immunity and tissue homeostasis. Full article