Search Results (239)

This paper extends the data-driven computational mechanics paradigm to nonlinear materials characterized by the Duncan–Chang Elastic-Bulk (E-B) constitutive model. Unlike in linear elastic systems, geotechnical media exhibit stress-dependent tangent moduli and non-convex constitutive manifolds. We propose a recursive nested data-driven solver that dynamically adapts the phase-space distance metric to account for pressure-dependent hardening. A rigorous mathematical analysis of convergence is provided, demonstrating that the solver’s performance is governed by the local transversality between the conservation law constraint set and the nonlinear material manifold. We derive explicit error bounds that couple spatial discretization resolution with material data density. Numerical experiments using triaxial test data from a high-altitude region validate the theoretical predictions, showing that the proposed scheme demonstrates convergence in single-element tests. Full article

This study investigates the influence of fiber bridging on the interlaminar strength of carbon fiber-reinforced polymer (CFRP) made from recycled carbon staple fiber yarn (rCF), compared to CFRP made from new fibers (vCF). Double-cantilever beam (DCB) tests measure the resistance of both materials against crack formation and the corresponding energy release rate (ERR). Several microscopic tools (SEM, CT) were then used to analyze the fracture surfaces and characterize the underlying failure mechanisms of the fiber bridges. The resulting ERR of rCFRP is four times (2140 J/m² compared to 587 J/m²) higher than that of vCFRP. SEM images of the fracture surface reveal that the fracture mechanism is fiber debonding followed by fiber pull-out with constant friction. This finding is confirmed by calculating the fiber bridging stress using the mathematical formulation of this effect resulting in a fiber bridge tension of approximately 70 N/mm². The main reason for the increased ERR of rCFRP compared to vCFRP is the extensive occurrence of fiber bridges in rCFRP due to the inhomogeneity of the rCF roving. This results in a pronounced nesting effect between adjacent rCF layers. The influence of the nesting effect on the ERR was investigated by testing samples with an increased layer orientation difference of 3° and 5°. This results in an ERR decrease of 26% in rCF and 30% in vCF. The nesting effect can be eliminated in vCFRP, but in rCFRP higher layer orientation, nesting is still visible. This finding suggests that the coarse, inhomogeneous structure of the rCFRP roving causes nesting regardless of the layer orientation and leads to a pronounced tendency to form fiber bridges. Full article

Efficient sorting of resin-based CFRP composites is critical for optimizing composite recycling streams. In this work, a methodology integrating Laser-Induced Breakdown Spectroscopy (LIBS) with Machine Learning (ML)-enhanced classification models to achieve accurate material discrimination is presented. LIBS is employed to identify the chemical composition of individual compounds, producing spectrograms that are subsequently processed to group chemically similar materials based on Epoxy resin (Bisphenol-A). The grouped datasets that contain 4000 peaks and 665 features were sampled to standardize feature dimensionality and cleaned to remove noise. A statistical analysis is then conducted to select the most informative features, followed by dimensionality reduction using Linear Discriminant Analysis (LDA). Finally, classification is performed using a Support Vector Classification (SVC) model, fine-tuned to the processed data to maximize accuracy. With a 5-fold cross validation (CV), the average nested accuracy score is 0.8317 ± 0.0212. This integrated approach demonstrates the potential for advancing automated sorting technologies in composite recycling applications. Full article

Laboratory mice are the most widely used model organisms in biomedical and behavioral research, yet growing concerns regarding reproducibility and translational validity have highlighted the substantial influence of housing and husbandry conditions on experimental outcomes. Although domestication is often assumed to have rendered laboratory mice fully adapted to artificial environments, evidence from ethology indicates that many core behavioral and physiological needs remain conserved. As a result, standard laboratory housing may generate chronic stress, alter behavior, and introduce systematic bias into experimental data. This narrative review critically examines how ethological constraints persisting after domestication interact with key environmental factors, social housing, environmental enrichment, ambient temperature, and lighting regimes to shape welfare and experimental validity in laboratory mice. Rather than providing an exhaustive overview of mouse behavior, the review adopts a problem-oriented and solution-focused approach, highlighting specific welfare-related mechanisms that can distort behavioral and physiological readouts. Particular attention is given to social isolation and aggression in male mice, the role of nesting material in mitigating thermal stress, and the effects of circadian disruption under standard and reversed light–dark cycles. By integrating ethological theory with laboratory animal welfare research, this review argues that housing conditions should be regarded as integral components of experimental design rather than secondary technical variables. Addressing welfare-related bias through evidence-based refinement strategies is essential for improving reproducibility, enhancing data interpretability, and strengthening the scientific validity of mouse-based research. Full article

An algorithm named ASR-BL-SA is proposed to solve the impact of a rectangular-part nesting sequence on final material utilization. Based on the Bottom Left principle, a coefficient, k, is defined as the ratio of the shape factor to 0.785 plus the square root of the min–max-normalized area. Parts are sorted in descending order of k. To tackle the flexible adaptation of part width and height via 90° rotation for sheet size and irregular leftover space, the Bottom Left algorithm initially compares utilization of original and rotated placements, selecting the option with higher utilization at each step. Finally, simulated annealing is applied for optimization. Experiments show that in the small-batch test, the proposed algorithm improves utilization by 5.51%, 3.75%, 8.84%, 5.51%, and 3.75% compared to the three baselines; in the mass production test, the improvements are 1.74%, 7.98%, 2.6%, 1.74%, and 7.89% within an acceptable time; in general applicability Test 3, its utilization is basically higher than the five comparative algorithms, achieving certain improvements in utilization. Full article

The purpose of this paper is to examine the application and development of CAD/CAM technologies in the modern metal cutting industry, with a focus on their role in increasing production accuracy, efficiency, and sustainability. The study presents an industrial case of laser cutting of AISI 304 stainless-steel sheets, in which two approaches are compared under identical material and technological parameters: conventional manual nesting and automatic nesting based on algorithms implemented in a CAD/CAM environment. The methodology evaluates both layouts using clear technical and economic indicators, including number of parts per sheet, material utilization, cutting time, weight of scrap, and cost per sheet. For the analyzed batch, automatic nesting increases the number of parts per sheet from 44 to 76 (≈73%), reduces the unused sheet area from 61% to 39%, and shortens the cutting time from 12 to 9 min (≈25%), which leads to a reduction in material waste by about 36% and cost savings of approximately 314 EUR per sheet. As a result, the process becomes more efficient and reliable, supporting sustainable and digital manufacturing goals. The findings confirm the importance of algorithmic optimization in CAD/CAM systems for enhancing industrial competitiveness, enabling effective resource management, and facilitating the transition towards Industry 5.0. Full article

Background and Objectives: Despite substantial advances in treatment strategies for patients with type 2 diabetes mellitus (T2DM), its complication, particularly sarcopenia, has emerged as a global healthcare challenge. Pay-for-performance (P4P), an incentive-based payment scheme, has long been used to improve the quality of care; however, few studies have explored its effect on sarcopenia prevention. Therefore, we conducted a nested case–control study to investigate the association between P4P participation and the risk of sarcopenia among patients with T2DM. Materials and Methods: Using a large claims dataset, we identified individuals aged 20–70 years with newly diagnosed T2DM between 2001 and 2010 in Taiwan. All enrollees were followed up until 2013 to determine the occurrence of sarcopenia. For each case, we randomly matched two controls without sarcopenia. The risk of sarcopenia in relation to P4P participation was estimated by fitting conditional logistic regression to yield the adjusted odds ratio (aOR) and corresponding 95% confidence interval (CI). Results: A total of 3475 individuals with sarcopenia and 6948 matched controls were included. Patients enrolled in the P4P program had a lower risk of sarcopenia than their matched counterparts (aOR = 0.66; 95% CI: 0.61–0.74). Earlier P4P enrollment (within 1 year of T2DM diagnosis) and high-intensity P4P participation were associated with additional reductions in sarcopenia risk. Conclusions: Integrating P4P into routine T2DM care may help prevent sarcopenia, highlighting the importance of interdisciplinary collaboration and timely program implementation. Full article

Background: Elderly-onset sarcoidosis > 65 (EOS) is rare and occurs in patients over 65. Studies on its incidence, clinical features, and treatment are limited, and its link to malignancy remains complex. Objectives: In this study, we aimed to analyze the possible association between malignancy and the occurrence of sarcoidosis in elderly patients over 65 years old. Design: Monocentric, nested retrospective case–control study. Material and Methods: A retrospective study analyzed newly diagnosed sarcoidosis patients in the Loewenstein Lung Center, Baden-Württemberg, Germany, categorizing them into younger-onset (<65 years) and elderly-onset (≥65 years). Demographic data, smoking status, medical history, symptoms, diagnostic methods, and any prior malignancy history were collected. Results: A total of 447 patients were included (365 patients within the group of younger-onset sarcoidosis and 82 patients with EOS). The median age of the younger-onset group was 47 (47 [23–63] years), compared to 69 (69 [65–84] years), p ≤ 0.001. Female patients were more prevalent in the group of elderly-onsets (54.9%) compared to the younger-onset group (35.9%), corresponding to an odds ratio of 2.2 (95% CI: 1.3–3.5, p: 0.002). Regarding the past history of malignancy, patients who had a positive history of malignancy were more prevalent among the elderly-onset group (29.6%) compared to the younger-onset group (5%) [OR (95% CI): 8.1 (4.1–15.8), p ≤ 0.001]. In multivariable logistic regression analysis with malignancy as the outcome, increasing age at sarcoidosis diagnosis was independently associated with a higher likelihood of prior malignancy (adjusted OR 1.08 per year, 95% CI 1.04–1.12), whereas sex, smoking status, and cardiometabolic comorbidity (diabetes and/or hypertension) were not independently associated. Conclusions: Elderly-onset sarcoidosis (EOS) is a less frequent variant of sarcoidosis with limited data regarding the possible risk factors. The increased prevalence of malignancy observed among patients with elderly-onset sarcoidosis appeared to be largely driven by age rather than a distinct EOS-specific effect. Age-adjusted analyses are essential when interpreting malignancy risk in sarcoidosis, and future age-matched prospective studies are needed to clarify potential biological links and guide evidence-based screening strategies. Full article

This study introduces a refined multi-scale micromechanical framework for analyzing C/C-SiC ceramic matrix composites (CMCs) using a dedicated Parametric High-Fidelity Method of Cells (PHFGMCs). A three-level geometric model is constructed from scanning electron microscope (SEM) micrographs and computed tomography (CT) scans. Specialized dual micro-meso nested PHFGMCs are employed to accurately generate the effective properties and spatial distributions of local stress fields in the highly heterogeneous microstructure of an 8-harness C/C-SiC representative volume element (RVE). The proposed refined framework recognizes the different micro- and meso-scales, ranging from the carbon fiber and amorphous carbon matrix to intra-yarn segmentation and weave regions. All are nested in a complete 8-harness architecture. The refined PHFGMC analyses showed good agreement between predicted mechanical properties and experimental data for C/C-SiC. The model’s ability to resolve local spatial deformation in the complex microstructure of C/C-SiC CMCs is demonstrated. These findings highlight the need for a refined multi-scale analysis that captures microstructural complexity and constituent interactions influencing both macroscopic and local responses in C/C-SiC CMCs. The proposed PHFGMC-based framework provides a robust theoretical and computational foundation for the future integration of nonlinear and progressive damage models within C/C-SiC CMC material systems. Full article

This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R² > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article

A 60-year-old woman with hypertension and hyperlipidemia was referred for an incidentally detected gastric subepithelial mass during screening endoscopy. Esophagogastroduodenoscopy revealed a 10 mm dimple in the antrum, and contrast-enhanced CT showed a 2.5 cm enhancing oval lesion. Laparoscopic partial gastrectomy with intraoperative endoscopic guidance was performed. Gross examination revealed a 3.0 × 2.0 × 1.0 cm pale, firm nodule. Histology showed small round cells arranged in nests and trabeculae within the muscularis propria, with numerous vessels and focal calcification. Immunohistochemistry was negative for CD117, HMB45, and chromogranin A, but demonstrated strong smooth muscle actin positivity, weak synaptophysin reactivity, and focal CD56 staining. The findings confirmed a gastric glomus tumor with neuroendocrine features. Smooth muscle actin immunostaining is essential to distinguish gastric glomus tumors from neuroendocrine tumors when biopsy material is limited, ensuring accurate diagnosis and appropriate management. Full article

For the first time, studies have been conducted aiming at the diversity of the oribatid mites (Oribatida) that inhabit the nests of open-nesting birds of the genus thrushes (Turdus), particularly fieldfare (T. pilaris Linnaeus, 1758) and redwing (T. iliacus Linnaeus, 1766), in the taiga forests of the European north-east. Long-term observations were carried out in the green belt of the city of Syktyvkar (N 61°40′ E 50°50′) in 2021–2025. Among 168 studied thrush nests (fieldfare—138, redwing—30), 1982 specimens of oribatid mites of 35 species from 33 genera and 26 families were found. The nests of thrushes contain a mixed fauna of oribatid mites, including the following: (a) Soil species that obviously enter the nest with building materials collected by birds from the soil surface. These are epigeic species such as Eupelops plicatus, Neoribates aurantiacus, and Chamobates pusillus; hemi-edaphic species such as Heminothrus peltifer; and euedaphic species such as Oppiella nova and Quadroppia quadricarinata. (b) Tree-dwelling species that have been recorded as inhabiting epiphytic lichens in the European north-east, such as Ameronothrus oblongus, Ceratoppia quadridentata, Oribatula propinqua, Trichoribates berlesei, and Diapterobates oblongus. (c) Eurybiont species such as Tectocepheus velatus, Scheloribates laevigatus, and Oribatula tibialis. An increase in the number and diversity of oribatid mites was noted in nests collected after the end of the nesting period and the flight of chicks compared to nests collected in the spring (overwintered nests). Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

Aspergillosis includes a variety of diseases caused by species of the genus Aspergillus, ranging from non-invasive allergic diseases to chronic, invasive pulmonary infections, which are potentially fatal in immunocompromised hosts. Therefore, there is an urgent need for new diagnostic tools and the optimization of existing tests to improve patient care. This work reviews the most commonly used molecular methods for the diagnosis of aspergillosis from clinical samples, emphasizing their advantages. These methods included HTS, NTS, ISH, microarrays, PCR-RFLP, LAMP, and PCR in various modalities (qPCR, multiplex PCR, nested PCR, RT-PCR, endpoint PCR, U-dHRM, and ddPCR). The review showed that the most commonly used methods for diagnosing aspergillosis are NGS and PCR in their different modalities; however, each method has advantages and disadvantages. qPCR is the method that has demonstrated the greatest sensitivity and specificity on clinical samples (such as blood, serum, bronchoalveolar lavage [BAL], tissue, or sputum), since it detects specific sequences, and the validation of this method shows greater progress in achieving this objective. Likewise, NGS showed that BAL is the most suitable sample, with a higher fungal load than sputum or blood. On the other hand, NGS is not a targeted technique, since it sequences all the genetic material present. Additionally, the sensitivity for detecting pathogens decreases when clinical samples are used due to the high background of nucleic acids present in the human host. Full article

Furniture is an integral part of daily life. Its comfort and usability are key factors that define its success. In recent years, there has been increasing demand for applications that drive businesses toward Industry 4.0. These applications aim to improve productivity through greater automation in both 3D modeling and fabrication processes. This research aims to develop a Computer Aided Design (CAD) platform that automates the design and manufacturing of furniture. The platform is based on visual programming using Grasshopper 3D™ and provides a solid foundation for processing different geometric shapes. These shapes can be customized according to the user’s preferences. The platform’s innovation lies in its ability to process complex geometries with a fully automated algorithm. Once the initial parameters are set, the algorithm generates the results. The input data includes an initial geometry, which can be highly complex. Additionally, a set of construction parameters is introduced, leading to multiple alternative design solutions based on the same initial geometry. The designer and user can select their final choice, and all resulting design and manufacturing outcomes are automatically generated. These outcomes include 3D part models, 3D assembly files, Bill of Materials, G-code for CNC machining, and nesting capabilities for improved material efficiency. The platform ensures high-quality performance. The results of the study show that the platform successfully works with different geometries. Moreover, the study is significant as the Industry 4.0 transformation moves toward more automated design processes. Full article