Search Results (563)

The article deals with the analysis of the mechanical properties of the newly designed aluminum alloy Al-Si7CrMnCu2.5. The research was carried out in order to map a new alloy with a certain addition of chromium and manganese from the point of view of mechanical properties and their changes after heat treatment (hardening, artificial aging) with defined parameters. Specifically, properties such as strength limit, yield strength, ductility, hardness, and microhardness were analyzed, both in the cast state and after heat treatment. The alloy was designed as an alternative to the standard Al-Si alloys already used in practice (AlSi7Mg, AlSi7Mg0.3, AlSi8Cu2Mn, AlSi8Cu3), which are mainly used in the production of engine parts and other components for the automotive and aviation industries. As can be seen from the presented results, the experimental AlSi7CrMnCu2.5 alloy exceeds the properties of the other selected alloys by tens of percent already in the cast state in many parameters. After heat treatment, the results achieved are comparable to the mentioned alloys, and in most cases, their values exceed them, especially in terms of ductility and hardness. Full article

Deep learning algorithms have significantly reduced the computational time and improved the spatial resolution of particle image velocimetry (PIV). However, the models trained on synthetic datasets might have degraded performances on practical particle images due to domain gaps. As a result, special residual patterns are often observed for the vector fields of deep learning-based estimators. To reduce the special noise step by step, we employ a denoising diffusion model (FlowDiffuser) for PIV analysis. And a data-hungry iterative denoising diffusion model is trained via a transfer learning strategy, resulting in our PIV-FlowDiffuser method. Specifically, we carry out the following: (1) pre-training a FlowDiffuser model with multiple optical flow datasets of the computer vision community, such as Sintel and KITTI; (2) fine-tuning the pre-trained model on synthetic PIV datasets. Note that the PIV images are upsampled by a factor of two to resolve small-scale turbulent flow structures. The visualized results indicate that our PIV-FlowDiffuser effectively suppresses the noise patterns. Therefore, the denoising diffusion model reduces the average endpoint error ($AEE$) by $59.4\%$ over the RAFT256-PIV baseline on the classic Cai’s dataset. In addition, PIV-FlowDiffuser exhibits enhanced generalization performance on unseen particle images due to transfer learning. Overall, this study highlights transfer-learning-based denoising diffusion models for PIV. Full article

Novel research data in different cancer types indicate that mutations within PIK3CA might serve as a biomarker of an improved response to immune therapy. Therefore, the aim of this study was to evaluate and examine possible differences in the tumor microenvironment composition and PD-L1 expression as well the prognostic significance of CD4, CD8, CD68, and CD163 in PIK3CA mutated and non-mutated hormone receptor positive and HER2 negative (HR+/HER2−) breast carcinoma. Breast carcinoma tissue was analyzed by Cobas PIK3CA mutation test for the presence of PIK3CA mutation and immunohistochemistry was applied to assess PD-L1 expression and CD4, CD8, CD68, and CD163 infiltration within tumor. Statistically significant association was observed between PD-L1 expression and the presence of PIK3CA exon 20 mutation (p = 0.044), with PD-L1–positive patients predominantly harboring this mutation. Tumors harboring PIK3CA mutations exhibited moderate to strong statistically significant positive correlations between PD-L1 expression and infiltration by CD8 cells (rs = 0.462, p = 0.0027), CD68 cells (rs = 0.398, p = 0.0134), and CD163 cells (rs = 0.617, p < 0.0001). In patients with PIK3CA mutation and exon 20 PIK3CA mutation there was statistically significant longer survival without recurrence (p = 0.026 and p = 0.041, respectively). Research regarding PD-L1 expression, immune cells and PIK3CA mutations might have an impact on how to determine therapeutic approaches for patients with HR+/HER2− breast carcinoma. Full article

Teak (Tectona grandis L.f.) is a leading tropical plantation species valued for high-quality timber and carbon (C) storage. This study assessed stand growth across ages and sites, quantified biomass and C by tree component and stand, and developed DBH-based allometric equations for biomass and C estimation. Six stand ages (5, 6, 9, 11, 14, and 17 years) were assessed in three municipalities of Nayarit, Mexico. Dendrometric inventories in permanent plots and destructive sampling of 35 trees provided calibration data for leaves, branches, stem, and roots. C concentration was determined with an elemental analyzer, and nonlinear regression models were adjusted and validated. Stand biomass and C increased with age, peaking at ages 11–14 (>130 Mg ha⁻¹; >60 Mg C ha⁻¹), with lower values at age 17. San Blas and Rosamorada accumulated significantly more than Tuxpan, reflecting site quality. C concentration was stable across sites and ages, with stem and roots consistently ranging between 48% and 50%, and leaves and branches averaging 45%–46%. Allometric equations were most accurate for stem and total biomass/C (R² = 0.73–0.79), while foliage showed higher variability. On average, 60%–70% of biomass was allocated to the stem and 15%–20% to roots. Indicators were stable, with an aboveground-to-belowground ratio (A:B) ≈ 4.9 and a biomass expansion factor (BEF) ≈ 1.5. The current annual increment (CAI) presented two main peaks: ~20 Mg ha⁻¹ yr⁻¹ at ages 5–6 and ~11 Mg ha⁻¹ yr⁻¹ at ages 9–11, followed by a decline after age 14. Teak in western Mexico reaches peak productivity at ages 6–11, with belowground biomass essential for accurate C accounting. Full article

Background/Objectives: Mesothelioma is a rare and aggressive form of cancer that primarily affects the lining of the lungs, abdomen, or heart. It typically arises from exposure to asbestos and is often diagnosed at advanced stages. Limited datasets and complex tissue structures contribute to delays in diagnosis. This study aims to develop a novel hybrid model to improve the accuracy and timeliness of mesothelioma diagnosis. Methods: The proposed approach integrates automatic image segmentation, transformer-based model training, class-based feature extraction, and image transformation techniques. Initially, CT images were processed using the segment anything model (SAM) for region-focused segmentation. These segmented images were then used to train transformer models (CaiT and PVT) to extract class/type-specific features. Each class-based feature set was transformed into an image using Decoder, GAN, and NeRV techniques. Discriminative score and class centroid analysis were then applied to select the most informative image representation for each input. Finally, classification was performed using a residual-based support vector machine (SVM). Results: The proposed hybrid method achieved a classification accuracy of 99.80% in diagnosing mesothelioma, demonstrating its effectiveness in handling limited data and complex tissue characteristics. Conclusions: The results indicate that the proposed model offers a highly accurate and efficient approach to mesothelioma diagnosis. By leveraging advanced segmentation, feature extraction, and representation techniques, it effectively addresses the major challenges associated with early and precise detection of mesothelioma. Full article

This study develops and empirically validates a framework integrating cultural factors into sustainable supply chain management (SSCM) for emerging economies. We introduce the Cultural Affinity Index (CAI), a multi-dimensional construct quantifying cultural compatibility between supply chain partners based on language compatibility, regional trust, trade networks, and historical trade patterns. Using publicly available data from UN COMTRADE, the World Bank, and Hofstede Insights, we analyze 850 supplier–manufacturer dyads across five Southeast Asian countries (2019–2023). Through Monte Carlo simulation with empirically calibrated parameters, we demonstrate that high cultural affinity (CAI > 0.7) shows positive associations with economic performance (+18.0%), environmental compliance (+12%), and social sustainability (+32%) compared to baseline scenarios. We test both linear and interaction models, finding that language compatibility and regional trust exhibit synergistic effects ($\beta$ = 0.15, p < 0.01). Multi-objective optimization reveals Pareto-optimal solutions achieving simultaneous improvements across all triple bottom line dimensions. Sensitivity analysis confirms robustness across varying cultural weights (±20%) and institutional contexts. The framework’s effectiveness varies by institutional quality, with stronger associations in weaker institutional environments (correlation = −0.92). While focused on manufacturing, we discuss adaptations for service sectors. This research provides both theoretical contributions to the SSCM literature and practical tools for organizations managing culturally diverse supply chains in emerging markets. Full article

The Transformer architecture has been the foundational cornerstone of the recent AI revolution, serving as the backbone of Large Language Models, which have demonstrated impressive language understanding and reasoning capabilities. When pretrained on large amounts of data, Transformers have also shown to be highly effective in image classification via the advent of the Vision Transformer. However, they still lag in vision application performance compared to Convolutional Neural Networks (CNNs), which offer translational invariance, whereas Transformers lack inductive bias. Further, the Transformer relies on the attention mechanism, which despite increasing the receptive field, makes it computationally inefficient due to its quadratic time complexity. In this paper, we enhance the Transformer architecture, focusing on its above two shortcomings. We propose two efficient Vision Transformer architectures that significantly reduce the computational complexity without sacrificing classification performance. Our first enhanced architecture is the EEViT-PAR, which combines features from two recently proposed designs of PerceiverAR and CaiT. This enhancement leads to our second architecture, EEViT-IP, which provides implicit windowing capabilities akin to the SWIN Transformer and implicitly improves the inductive bias, while being extremely memory and computationally efficient. We perform detailed experiments on multiple image datasets to show the effectiveness of our architectures. Our best performing EEViT outperforms existing SOTA ViT models in terms of execution efficiency and surpasses or provides competitive classification accuracy on different benchmarks. Full article

The design of multitarget drugs is a growing strategy to address complex and multifactorial diseases, and heterocycles play a major role in this approach. This review aims to critically analyze the role of heterocyclic scaffolds in the development of human carbonic anhydrase inhibitors (hCAIs), emphasizing their versatility as core chemotypes, linkers, and secondary pharmacophores. By examining advances from the last 10 years, we highlight how heterocycle-based designs contribute to modulating potency and selectivity toward hCAs, as well as to the creation of hybrid molecules with enhanced therapeutic profiles. Understanding these strategies is essential for guiding future drug discovery efforts targeting hCAs and related pathologies. Full article

Computer-assisted implant surgery (CAIS) using 3D-printed surgical templates has become a preferred approach for improving implant placement accuracy. Despite its clinical advantages over conventional freehand techniques, CAIS remains limited by the presence of cone beam computed tomography (CBCT) metal artefacts, which compromise the 3D data alignment during implant planning and guide fabrication. This narrative review aims to explore the impact of metal artefacts on the accuracy of 3D-printed surgical implant templates and to evaluate current approaches and modifications in implant planning workflows. This article reviews accuracy studies, case reports and technology research on CAIS from the past 5 years. It summarised the CAIS clinical decision framework and data alignment methods to provide alternatives for guided implant therapy in the future. Studies indicate that metal artefacts can distort anatomical data, leading to potential misalignment in 3D data superimposition during surgical guide designs and fabrication. However, various strategies have shown promise in reducing these distortions. Accurate implant planning and template fabrication are essential to ensure clinical success. Special consideration should be given to artefact management during data acquisition. Modified workflows that account for the presence of metal artefacts can enhance guide precision and improve patient outcomes. Full article

Assessing the impact of land use and land cover (LULC) on water quality (WQ) is central to land-based environmental research. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this study analyses recent trends using LULC maps to assess stream WQ within buffers, focusing on papers published between 2020 and 2024. It identifies relevant remote sensing practices for LULC mapping, landscape metrics, WQ physicochemical parameters, statistical techniques for correlating LULC and WQ, and conventions for configuring buffers. Materials include Scopus, Web of Science, and Atlas.ti, which support both qualitative data analysis and Conversational Artificial Intelligence (CAI) tasks via its integration with OpenAI’s large language models. The methodology highlights creating a bibliographic database, coding, CAI, and validating prompts. Official maps and visual or digital interpretations of optical imagery provided inputs for LULC. Classifiers from earlier generations have shaped LULC cartography. The most employed WQ parameters were phosphorus, total nitrogen, and pH. The three most referenced landscape metrics were the Largest Patch Index, Patch Density, and Landscape Shape Index. The literature mainly relied on Redundancy Analysis, Principal Component Analysis, and alternative correlation approaches. Buffer configurations varied in size. CAI facilitated an agile systematic review; however, it encountered challenges related to a phenomenon known as hallucination, which hampers its optimal performance. Full article

Background and Objectives: Chronic ankle instability (CAI), a prevalent injury among female volleyball players, can negatively affect functional performance and increase the risk of further injury. The aim of this study was to compare functional movement quality, dynamic balance, vertical jumping performance, hip muscle strength, and risk of injury between adolescent female volleyball players with unilateral CAI and those without CAI. Materials and Methods: This cross-sectional study included 46 adolescent female volleyball players, divided into CAI (n = 23) and control (n = 23) groups based on predefined criteria. Functional movement quality was assessed using the Functional Movement Screen (FMS), and dynamic balance was evaluated with the Y-Balance Test (YBT). Maximal isometric strength of the hip muscles (flexors, extensors, abductors, adductors, and internal and external rotators) was measured using hand-held dynamometry, and vertical jumping performance was assessed using countermovement jump tests. Injury risk was classified based on established cut-off values for the FMS-composite and YBT-anterior reach asymmetry scores. Results: The CAI group demonstrated significantly lower FMS-composite scores (p = 0.007), reduced anterior reach on the YBT (p = 0.004), and decreased strength in the hip flexors (p = 0.007) and hip adductors (p = 0.044), supported by moderate effect sizes. No significant group differences were observed in the other YBT directions, vertical jump tests, or the other hip muscles (p > 0.05). A greater proportion of athletes in the CAI group were classified as high risk for injury based on both FMS-composite (p = 0.022) and YBT-anterior reach asymmetry (p = 0.001) cut-off values, supported by moderate and relatively strong effect sizes, respectively. Conclusions: Adolescent female volleyball players with unilateral CAI showed impaired movement quality, balance deficits, hip muscle weakness, and increased injury risk. These results highlight the importance of targeted interventions and broader investigations into CAI in adolescent athletes. Full article

Background/Objectives: Although considerable progress has been made in understanding lateral ankle sprains (LAS) and chronic ankle instability (CAI), recurrent injury rates remain high. This highlights the need to explore additional contributors such as comprehensive lower-limb gait analysis, including multisegmented foot models and proximal joint kinematics and strength. This study aimed to assess multisegmented foot and lower-limb kinematics throughout the gait cycle in individuals with CAI compared to healthy controls. Additionally, associations between hip abductor strength and frontal plane ankle kinematics were examined. Methods: Fifty males (25 with CAI and 25 healthy controls) participated in this cross-sectional study. Gait analysis was conducted using a BTS SMART 3D motion capture system to assess multisegmented foot and proximal joint kinematics. Isometric hip strength was measured using a Biodex dynamometer. Statistical Parametric Mapping (SPM) was used to assess group differences, and correlations were calculated between hip abductor strength and ankle kinematics. Results: The CAI group demonstrated significantly greater calcaneus abduction relative to the shank in the transverse plane between 88% and 93% of the gait cycle (MD = −3.50°, 95% CI [−5.60, −1.40], d = −0.95, p = 0.037). No other statistically significant between-group differences in hip, knee, or foot segment kinematics were detected. Furthermore, correlations between hip abductor strength and ankle frontal plane kinematics were not significant. Conclusions: Males with CAI demonstrated altered rearfoot kinematics in the transverse plane during the terminal swing phase. The multisegmented foot model was valuable in detecting subtle deviations and emphasized the importance of including the swing phase. Hip abductor strength was not associated with ankle kinematics, suggesting that its potential role in CAI may involve other mechanisms. These findings may support clinical gait assessment and rehabilitation planning by highlighting the importance of evaluating all foot segments and the entire lower limb, rather than focusing solely on the ankle joint. Segment-specific deviations, particularly those emerging during the swing phase, may help guide targeted interventions aimed at improving foot positioning in males with CAI. Full article

To meet the demand for sustainable pavement infrastructure, reclaimed asphalt pavement (RAP) has become a key strategy to enhance material circularity. This study investigates the coupled mobilization and blending behaviors between virgin and aged asphalt mastic in RAP systems. Fourier-Transform Infrared Spectroscopy (FTIR) was utilized to quantify the mobilization rate (MR) of aged mastic on RAP aggregate surfaces using the Composite Aging Index (CAI). Scanning Electron Microscopy (SEM) and Fluorescence Microscopy (FM), combined with digital image analysis, were employed to assess the blending interface and quantify the degree of blending (DoB). A 3D model was developed to describe the nonlinear relationship between MR and DoB. The results show that regeneration is dominated by physical diffusion, while mixing temperature has a stronger effect on MR than time. The binder interface displays a smooth transition, whereas the mastic interface exhibits a gear-like structure. DoB in the binder system is higher than that in the mastic system under the same condition, with early-stage temperature elevation playing a key role. Even near 100%, MR does not lead to full blending due to interfacial saturation. These insights are valuable for guiding the design of RAP and optimizing mixing conditions to enhance recycling efficiency in practical applications. Full article