Search Results (1,336)

The aetiology of invasive candidiasis is undergoing substantial changes; traditionally, these mycoses have been considered to originate from endogenous reservoirs; however, the increasing prevalence of non-Candida albicans species, such as Candida parapsilosis and Candida auris (also named Candidozyma auris), is a cause of concern as they demonstrate significant exogenous transmission. This challenges the long-standing paradigm of endogenous origin in hospital settings. Unlike previous reviews primarily focused on clinical epidemiology, this work adopts a multidisciplinary perspective combining microbiological evidence with biomaterials science. We analyse how surface roughness, hydrophobicity, and polymer composition within the hospital “plastisphere” influence Candida adhesion and the formation of dry surface biofilms (DSBs). In this specific context, in contrast to C. albicans, primarily associated with mucosal colonisation, C. auris and C. parapsilosis exhibit distinctive adaptations that promote survival in healthcare environments, including pronounced cell surface hydrophobicity and the capacity to form dense cellular aggregates, which facilitate prolonged adherence to synthetic polymers used in medical devices. We also explore the biological mechanisms underlying this resilience, with particular emphasis on the development of dry surface biofilms and viable but non-culturable states. These phenotypic traits confer tolerance to desiccation and resistance to conventional disinfectants, raising concerns that standard hygiene and decontamination protocols may be inadequate to prevent transmission. Understanding these mechanisms is essential for designing effective infection control strategies and mitigating the risk of invasive disease caused by these highly persistent species. Full article

In this study, the process of preparing ABS-ZnO (Acrylonitrile Butadiene Styrene-Zinc Oxide) composite materials as FDM printing materials was elaborated, and the influence of printing process parameters on the tensile properties and surface roughness of the materials was analyzed. It was concluded through orthogonal experiments that among all the parameters studied, the infill rate had the most significant effect on the tensile strength, followed by layer thickness and layer width, while the printing speed had the least effect. When the printing parameters were set as follows: infill rate (90%), layer thickness (0.2 mm), layer width (0.4 mm), and printing speed (200 mm/s), the tensile strength of the sample reached the maximum value of 48.37 MPa. Scanning electron microscopy (SEM) analysis revealed that a high infill rate could make the internal structure of the material denser and the bonding between fibers more sufficient. In contrast, with the increase in layer thickness and layer width, the internal structure of the material exhibited a porous morphology, which led to a decrease in tensile properties. By investigating the effects of printing temperature and layer thickness on the surface roughness of the samples, the optimal surface roughness was achieved when the printing temperature was set at 230 °C, and the layer thickness was 0.3 mm. Full article

The roughness of rock fractures has complex features that affect how fluids move through them. This research looks at how gas and water flows change in rough fractures when they are moved using a model based on fractal geometry. Rough surfaces are created using a method called fractional Brownian motion. When the surfaces are moved, the space in the fractures becomes uneven. By using a level-set method together with a fluid flow model, the study explores how the speed the fluid enters, the roughness of the surface, and the movement of the surfaces affect the change between bubble, slug, and ring-like flow. The results indicate that more roughness and movement make the flow less stable, which causes a reverse change from ring-like flow to slug and bubble flow. A framework based on pressure is built, showing that the outlet pressure decreases quickly with fluid speed, rises steadily with roughness, and changes in a square relation with movement. A single prediction formula is made with R² = 0.98, allowing precise identification of the flow types using pressure change limits. This research gives insights into flow changes in fractured reservoirs and offers a way to predict flow in real-time. Full article

This paper proposes a theoretical framework for studying rough intuitionistic fuzzy filters within the structure of BE-algebras. Building on rough set theory and intuitionistic fuzzy set theory, we introduce rough intuitionistic fuzzy filters via lower and upper approximation operators induced by congruence relations. To further generalize the framework, we define set-valued homomorphisms on BE-algebras and use them to formulate $\Gamma$-rough intuitionistic fuzzy filters. Several structural properties and characterization results are established, including stability under approximation operators, relationships with classical intuitionistic fuzzy filters, and preservation under homomorphic mappings. The proposed approach provides an algebraic mechanism for modeling uncertainty, hesitation, and imprecision in implication-based systems, with potential relevance to uncertainty-aware reasoning in artificial intelligence, decision-support systems, and medical diagnosis. Full article

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

Dry machining of AISI 1045 steel is attractive for sustainable manufacturing but makes it more challenging to control surface roughness R_a. This work investigates dry turning of AISI 1045 using a 2³ factorial design with three center points (11 runs) and compares a traditional power-law correlation with a quadratic response surface model (RSM). The power-law fit on log-log data explains only about 20% of the variance, whereas the quadratic RSM achieves R² ≈ 0.98 with a root-mean-square error (RMSE) of 0.62–0.77 µm based on leave-one-out cross-validation and bootstrap resampling. Feed rate S is identified as the dominant factor, while cutting speed V and depth of cut t have secondary but non-negligible interactive effects. Sobol global sensitivity indices confirm that S and S² account for more than half of the output variance. The optimized setting within the tested domain (V ≈ 83 m/min, S = 0.60 mm/rev, t = 0.10 mm) yields a predicted R_a ≈ 5.3 µm, appropriate for semi-roughing prior to grinding. The proposed framework combines small-sample RSM, Lasso regularization, uncertainty quantification and Sobol analysis to provide an uncertainty-aware model for optimizing dry-turning parameters of AISI 1045 steel. Full article

The dynamic migration of mineral particles within spiral separators and its control via structural parameters are not yet fully understood, hindering efficiency improvements. To this end, a set of spiral separators with systematically adjusted structural parameters was designed. Extensive sampling of a 1–0.25 mm coal slurry yielded 120 samples from 6 separators, across 5 turns and 4 radial streams. Sink-float analysis revealed a well-defined three-stage separation mechanism: the roughing stage involves rapid segregation of light and heavy particles, while intermediate-density particles remain widely distributed; the intensified cleaning stage governs the radial migration of intermediate-density particles while simultaneously enriching the high-density and low-density fractions; and the final cleaning stage stabilizes the particle distribution and redirects misplaced particles. The influence of key structural parameters was also quantified: the composite cross-section outperformed cubic parabolic and elliptical profiles, markedly enhancing the separation of high-density and medium-high-density particles from the lighter product; increasing the trough inclination angle significantly promoted the radial inward migration of medium-high-density particles; a reduced pitch-to-diameter ratio effectively concentrated high-density and medium-high-density particles within inner and middle regions. Based on these insights, a “process intensification” strategy was proposed and materialized in a novel spiral separator design featuring stage-optimized, multi-parameter coordination. Performance evaluation demonstrated a separation efficiency of 94.74% under equivalent product quality constraints, a substantial improvement over conventional design. This work provides a fundamental, stage-specific understanding of particle separation dynamics and establishes a practical basis for the advanced design of high-efficiency spiral separation systems. Full article

Surface roughness is a critical quality attribute in laser cutting, directly influencing edge integrity, dimensional accuracy, and post-processing requirements. While most studies address surface roughness through forward modeling and optimization, practical manufacturing tasks often require solving inverse parameter selection problems, where process parameters must be chosen to satisfy prescribed surface quality requirements. In this study, surface roughness control in laser cutting is formulated within an inverse target-tracking framework based on response surface methodology (RSM). A quadratic response surface model is established using a Box–Behnken experimental design, with cutting speed, laser power, and assist-gas pressure as input factors. The fitted response surface provides an explicit forward mapping within a bounded operating window and serves as a local surrogate for methodological demonstration of target-oriented parameter estimation. Based on this surrogate model, a model-predicted feasible roughness range within the investigated design space is identified as R_a = 1.952–4.212 μm. For prescribed roughness targets within this interval, an inverse least-squares target-tracking formulation is employed to compute model-based parameter estimates. The inverse results are presented as continuous set-point maps and tabulated operating conditions, accompanied by a target-versus-predicted consistency check performed at the model level. Owing to the statistically significant lack-of-fit of the forward response surface, the inverse results presented in this study should be interpreted as theoretical, model-based estimates intended to illustrate the proposed framework rather than as experimentally validated process set-points. The proposed approach highlights both the potential and the limitations of inverse target-tracking strategies based on response surface models and underscores the need for statistically adequate models and independent experimental validation for industrial application. Full article

This paper analyzes different approaches for the mathematical modeling and optimization of process parameters in the hard turning process of 42CrMo4 steel using a hybrid approach combining response surface methodology (RSM), multi-criteria decision making (MCDM), and machine learning through, support vector regression (SVR) with one-factor-at-a-time (OFAT) sensitivity analysis. Controlled process parameters such as cutting speed, depth of cut, feed, and insert radius are applied to conduct the experiments based on a full factorial experimental design. RSM was used to develop models that describe the effect of controlled parameters on surface roughness and cutting forces. Special emphasis was placed on the analysis of standardized residuals to evaluate the predictive capabilities of the RSM-developed model on an unseen data set. For all four outputs considered, analysis of the standardized residuals shows that over 97% of the points lie within ±3 standard deviations. A multi-criteria optimization technique was applied to establish an optimal combination of input parameters. The SVR model had high performance for all outputs, with coefficient of determination values between 89.91% and 99.39%, except for surface roughness on the test set, with a value of 9.92%. While the SVR model achieved high predictive accuracy for cutting forces, its limited generalization capability for surface roughness highlights the higher complexity and stochastic nature of surface formation mechanisms in the turning process. OFAT analysis showed that feed rate and depth of cut have been shown to be the most important input variables for all analyzed outputs. Full article

This study employs a multi-objective particle swarm optimization (MOPSO) algorithm to address the dual-objective challenge in the robotic polishing of Ti-6Al-4V. The aim is to determine optimal parameters that minimize surface roughness while maximizing the material removal rate (MRR), thereby improving both surface quality and processing efficiency. First, a material removal depth model for end-face polishing is established based on Preston’s equation and theoretical analysis, from which the MRR model is derived. Subsequently, orthogonal experiments are conducted to investigate the influence of process parameters and their interactions on surface roughness, followed by the development of a quadratic polynomial roughness prediction model. Analysis of variance (ANOVA) and model validation confirm the model’s reliability. Finally, the MOPSO algorithm is applied to obtain the Pareto optimal solution set, yielding the optimal parameter combination. Experimental results demonstrate that at a normal contact force of 7.58 N, a feed rate of 4.52 mm/s, and a spindle speed of 5851 rpm, the achieved MRR and Ra values are 0.2197 mm³/s and 0.291 μm, respectively. These results exhibit errors of only 5.64% and 2.65% compared to model predictions, validating the proposed method’s effectiveness. Full article

Using the concept of category, we provide some insight into and prove an intrinsic property of the category AprS of approximation spaces and continuous functions. We also introduce rough closure and rough interior operators to characterize clopen topologies. Our main result proves the equivalence of several categories, including the category of equivalence relations and relation-preserving functions, the category of rough interior spaces and continuous functions, the category of rough closure spaces and continuous functions, and the category AprS. This work provides a deeper understanding of the interplay among rough set theory, information systems, and category theory. Full article

Pavement roughness is a critical indicator for road maintenance decisions and driving safety assessment. Existing methods primarily rely on multi-source explicit features, which have limited capability in capturing implicit information such as spatial topology between road segments. Furthermore, their accuracy and stability remain insufficient in cross-regional and small-sample prediction scenarios. To address these limitations, we propose a Graph-Fused Natural Gradient Boosting framework (GF-NGB), which combines the spatial topology modeling capability of graph neural networks with the small-sample robustness of natural gradient boosting for high-precision cross-regional roughness prediction. The method first extracts an 18-dimensional set of multi-source features from the U.S. Long-Term Pavement Performance (LTPP) database and derives an 8-dimensional set of implicit spatial features using a graph neural network. These features are then concatenated and fed into a natural gradient boosting model, which is optimized by Optuna, to predict the dual objectives of left and right wheel-track roughness. To evaluate the generalization capability of the proposed method, we employ a spatially partitioned data split: the training set includes 1648 segments from Arizona, California, Florida, Ontario, and Missouri, while the test set comprises 330 segments from Manitoba and Nevada with distinct geographic and climatic conditions. Experimental results show that GF-NGB achieves the best performance on cross-regional tests, with average prediction accuracy improved by 1.7% and 3.6% compared to Natural Gradient Boosting (NGBoost) and a Graph Neural Network–Multilayer Perceptron hybrid model (GNN-MLP), respectively. This study reveals the synergistic effect of multi-source texture features and spatial topology information, providing a generalizable framework and technical pathway for cross-regional, small-sample intelligent pavement monitoring and smart maintenance. Full article

Copper has become more important owing to its eco-friendliness and persistent efficacy against infections. Furthermore, copper has benefits such as safety in use and durability. This study aimed to develop and assess the antibacterial efficacy of stainless steel coated with a composite layer, which is nanostructured and incorporates copper, to create antibacterial surfaces with good adherence and good corrosion resistance. The composite coating was produced using anodic oxidation, with an external copper layer applied via pulse electroplating. The homogenous cauliflower-like covering showed important characteristics, like increased surface roughness, boosted surface free energy, reduced contact angle, and higher hardness. Additionally, the adherence between the composite covering and the substrate was exceptional. Electrochemical experiments indicated aggressive corrosion behavior in chloride-containing settings. Antibacterial tests were conducted on four prevalent bacterial strains: Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Salmonella typhimurium—microorganisms often linked to healthcare and environmental pollution. The coating exhibited enhanced antibacterial efficacy relative to untreated steel and anodized steel. Results indicated that the composite coating is an effective and possibly cost-efficient method for controlling the surface proliferation of the mentioned pathogens. Full article

Variable precision fuzzy rough sets (VPFRSs) and multi-granulation fuzzy rough sets (MGFRSs) are both significant extensions of rough sets. However, existing variable precision models generally lack the inclusion property, which poses potential risks in applications. Meanwhile, multi-granulation models tend to emphasize either optimistic or pessimistic scenarios but overlook compromise situations. A generalized fuzzy remote neighborhood system is a symmetric union-fuzzified form of the neighborhood system, which can extend the fuzzy rough set model to a more general framework. Moreover, semi-grouping functions eliminate the left-continuity required for grouping functions and the associativity in t-conorms, making them more suitable for information aggregation. Therefore, to overcome the limitations of existing models, we propose an optimistic (OP), pessimistic (PE), and compromise (CO) variable precision fuzzy rough set (OPCAPFRS) based on generalized fuzzy remote neighborhood systems. The semi-grouping function and its residual minus are employed in the OPCAPFRS. We discuss the basic properties of the OPCAPFRS and prove that it satisfies the generalized inclusion property (GIP). This partially addresses the issue that a VPFRS cannot fulfill the inclusion property. A novel methodology for addressing multi-attribute decision-making (MADM) problems is developed through the fusion of the proposed OPCAPFRS framework and the VIKOR technique. The proposed method is applied to the problem of selecting an optimal CPU. Subsequently, comparative experiments and a parameter analysis are conducted to validate the effectiveness and stability of the proposed method. Finally, three sets of experiments are performed to verify the reliability and robustness of the new approach. It should be noted that the new method performed ranking on a dataset containing nearly ten thousand samples, obtaining both the optimal solution and a complete ranking, thereby validating its scalability. Full article

Soil liquefaction remains a critical geotechnical hazard during earthquakes, posing significant risks to infrastructure and urban resilience. Traditional empirical methods, while practical, often fall short in capturing complex parameter interactions and providing interpretable outputs. This study presents an interpretable machine learning (IML) framework for soil liquefaction assessment using Rough Set Theory (RST) to generate a transparent, rule-based predictive model. Leveraging a standardized SPT-based case history database, the model induces IF–THEN rules that relate seismic and geotechnical parameters to liquefaction occurrence. The resulting 25-rule set demonstrated an accuracy of 86.2% and strong alignment (93.8%) with the widely used stress-based semi-empirical model. Beyond predictive performance, the model introduces scenario maps and parameter interaction diagrams that elucidate key thresholds and interdependencies, enhancing its utility for engineers, planners, and policymakers. Notably, the model reveals that soils with high fines content can still be susceptible to liquefaction under strong shaking, and that epicentral distance plays a more direct role than previously emphasized. By balancing interpretability and predictive strength, this rule-based approach advances site-adaptive, explainable, and technically grounded liquefaction assessment—bridging the gap between traditional methods and intelligent decision support in geotechnical engineering. Full article