Search Results (2,597)

Machining chromium–nickel alloy steel is challenging due to its material properties, such as high strength and toughness. These properties often lead to tool damage and degradation of tool life, which overall impacts the production time, cost, and quality of the product. Therefore, it is essential to investigate early signs of tool damage to determine the effective machining conditions for chromium–nickel alloy steel, thereby increasing tool life and improving product quality. In this study, the early signs of tool wear were observed in a physical vapor deposition (PVD) carbide-coated tool (Seco Tools, Björnbacksvägen, Sweden) during the machining of X5CrNi18-10 steel under both dry and wet conditions. A finish turning operation was performed on the outer diameter (OD) of the workpiece with a 0.4 mm nose radius tool. At the early stage, the tool was examined from the functional side (f–side) and the passive side (p–side). The results indicate that dry machining leads to increased coating removal, more heat generation, and visible damage, such as pits and surface scratches. By comparison, wet machining helps reduce heat and wear, thereby improving tool life and machining quality. These findings suggest that a coolant must be used when machining chromium–nickel alloy steel with a PVD carbide-coated tool. Full article

Chromium–aluminum nitride (CrAlN) coatings were deposited on polished H13 tool steel substrates using direct current (DC) magnetron sputtering. The Cr/Al composition in the target was varied by inserting either four or eight chromium (Cr) plugs into cavities machined into an aluminum (Al) plate target. Nitrogen was introduced as a reactive gas to facilitate the formation of the nitride phase. Coatings were deposited at substrate bias voltages of −30 V, −50 V, and −60 V to study the combined effects of composition and ion energy on coating properties. Compositional analysis of coatings deposited at a −50 V bias revealed Cr/Al ratios of approximately 0.8 and 1.7 for the 4- and 8-plug configurations, respectively. This increase in the Cr/Al ratio led to a 2.6-fold improvement in coating hardness. Coatings produced using the eight-Cr-plug target exhibited a nearly linear increase in hardness with increasing substrate bias voltage. Cross-sectional scanning electron microscopy revealed a uniform bilayer structure consisting of an approximately 0.5 µm metal interlayer beneath a 2–3 µm CrAlN coating. Surface morphology analysis indicated the presence of coarse microdroplets in coatings with the lower Cr/Al ratio. These microdroplets were significantly suppressed in coatings with higher Cr/Al content, especially at increased bias voltages. This suppression is likely due to enhanced ion bombardment associated with the increased Cr content, attributed to Cr’s relatively higher atomic mass compared to Al. Coatings with lower hardness exhibited greater scratch resistance, likely due to the influence of residual compressive stresses. The findings highlight the critical role of both Cr/Al content and substrate bias in tailoring the tribo-mechanical performance of PVD CrAlN coatings for wear-resistant applications. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

The treatment of chronic wounds is one of the most complex therapeutic problems of modern medicine. It leads to patients’ protracted recovery, generating high treatment costs. Herbal products may be useful in the treatment of chronic wounds via a wide range of pharmacological properties and multidirectional effects on the wound healing phases. The study aims to determine the ability of selected plant extracts to modulate the processes involved in wound healing. The antimicrobial (MIC, MBC, MFC) and antioxidant (ABTS, DPPH) activities, cytotoxicity (MTT test), scratch wound test, and collagen assay were tested. R. canina (MBC 0.39 mg/mL) and V. venifera (MBC 3.13 mg/mL) extracts had bactericidal activities against P. aeruginosa and S. aureus, respectively. The V. vinifera extract showed the highest antioxidant activity in both ABTS (EC50 0.078 mg/mL) and DPPH (EC50 0.005 mg/mL) methods. The percentage of wound closure observed for C. cardunculus, R. rosea, and R. canina extracts with HaCaT, and V. vinifera extract with Hs27 cells was set as 100%. V. vinifera extract (50 μg/mL) stimulated collagen synthesis 5.16 times more strongly than ascorbic acid. Our preliminary study showed that some plant extracts may be promising modulators of the wound healing process, although further in-depth studies are necessary to determine their effectiveness in the in vivo model. Full article

Computational Thinking (CT) has been an important concept for the computer science education community in the last 20 years. In this work we performed a systematic review of the literature regarding the computational thinking of children from kindergarten to primary school. We compiled a large dataset of one hundred and twenty (120) studies from the literature. Through analysis of these studies, we tried to reveal important insights and draw interesting and valid conclusions. We analyzed various qualitative and quantitative aspects of the studies, including the sample size, the year of publication, the country of origin, the studies’ design and duration, the computational tools used, and so on. An important aspect of the work is to highlight differences between different study designs. We identified a total of 120 studies, with more than half of them (>50%) originating from Asian countries. Most studies (82.5%) conducted some form of intervention, aiming to improve their computational thinking in students. A smaller proportion (17.5%) were assessment studies in which the authors conducted assessments regarding the children’s computational thinking. On average, intervention studies had a smaller number of participants, but differences in duration could not be identified. There was also a lack of large-scale longitudinal studies. Block-based coding (i.e., Scratch) and Plugged and Unplugged activities were observed in high numbers in both categories of studies. CT assessment tools showed great variability. Efforts for standardization and reaching a consensus are needed in this regard. Finally, robotic systems have been found to play a major role in interventions over the last years. Full article

The article presents a new AM (Additive Manufacturing) process development, necessary to repair parts made from Aluminum 6061 material, with T6 treatment. The laser Directed Energy Deposition (DED) and Extreme High-Speed Directed Energy Deposition (EHLA) capabilities are evaluated for repairing Al large components. To optimize the process parameters, single-track depositions were analyzed for both laser-powder DED (feed rate of 2 m/min) and EHLA (feed rate 20 m/min) for AlSi10Mg and Al6061 powders. The cross-sections of single tracks revealed the bonding characteristics and provided laser-powder DED, a suitable parameter selection for the repair. Three damage types were identified on the Al component to define the specification of the repair process and to highlight the capabilities of laser-powder DED and EHLA in repairing intricate surface scratches and dents. Our research is based on variation of the powder mass flow and beam power, studying the influence of these parameters on the weld bead geometry and bonding quality. The evaluation criteria include bonding defects, crack formation, porosity, and dilution zone depth. The bidirectional path planning strategy was applied with a fly-in and fly-out path for the hatching adjustment and acceleration distance. Samples were etched for a qualitative microstructure analysis, and the HV hardness was tested. The novelty of the paper is the new process parameters for laser-powder DED and EHLA deposition strategies to repair large Al components (6061 T6), using AlSi10Mg and Al6061 powder. Our experimental research tested the defect-free deposition and the compatibility of AlSi10Mg on the Al6061 substrate. The readers could replicate the method presented in this article to repair by laser-powder DED/EHLA large Al parts and avoid the replacement of Al components with new ones. Full article

In real-world applications, user demands for new functionalities and activities constantly evolve, requiring action recognition systems to incrementally incorporate new action classes without retraining from scratch. This class-incremental learning (CIL) paradigm is essential for enabling adaptive and scalable systems that can grow over time. However, Wi-Fi-based indoor action recognition under incremental learning faces two major challenges: catastrophic forgetting of previously learned knowledge and uncontrolled model expansion as new classes are added. To address these issues, we propose CAREC, a class-incremental framework that balances dynamic model expansion with efficient compression. CAREC adopts a multi-branch architecture to incorporate new classes without compromising previously learned features and leverages balanced knowledge distillation to compress the model by 80% while preserving performance. A data replay strategy retains representative samples of old classes, and a super-feature extractor enhances inter-class discrimination. Evaluated on the large-scale XRF55 dataset, CAREC reduces performance degradation by 51.82% over four incremental stages and achieves 67.84% accuracy with only 21.08 M parameters, 20% parameters compared to conventional approaches. Full article

The Sprouty (Spry) proteins modulate signalling and regulate processes like cellular migration and proliferation. Here, we investigated a Spry4 alteration substituting a lysine at position 177 to an arginine, based on a mutation found in Kallmann syndrome, a genetically heterogeneous disease connected to reduced fibroblast growth factor receptor1 (FGFR) signalling. Using growth curves to evaluate proliferative and scratch assays to determine migrative capacities of the cells, in normal fibroblasts as well as in osteosarcoma-derived cells, we demonstrate that the modified Spry4^K177R version hinders both processes, which the unaltered protein cannot do under the same conditions. The inhibition of these processes was accompanied by lower relative phospho-extracellular-signal-regulated kinases (pERK) levels in response to serum induction, indicating that activation of MAPK was less efficient. In contrast to the situation in these cells of mesenchymal origin, in lung cancer-derived cell lines both variants of Spry4 were able to interfere with proliferation of tested cells, and in the cells with elevated FGFR1 expression the Spry4 proteins with an alteration at codon 177 were even more effective. In summary, these data indicate that the lysine at position 177 restricts the ability of Spry4 to inhibit signal transduction at least in cells with high FGFR1 levels. Full article

Colorectal cancer (CRC) remains a predominant cause of global cancer-related mortality, highlighting the pressing demand for innovative therapeutic strategies. Natural polysaccharides have emerged as promising candidates in cancer research due to their multifaceted anticancer mechanisms and tumor-suppressive potential across diverse malignancies. In this study, we enzymatically extracted a polysaccharide, named ERPP, from Ruditapes philippinarum and comprehensively evaluated its anti-colorectal cancer activity. We conducted in vitro assays, including CCK-8 proliferation, clonogenic survival, scratch wound healing, and Annexin V-FITC/PI apoptosis staining, and the results demonstrated that ERPP significantly inhibited HT-29 cell proliferation, suppressed colony formation, impaired migratory capacity, and induced apoptosis. JC-1 fluorescence assays provided further evidence of mitochondrial membrane potential (MMP) depolarization, as manifested by a substantial reduction in the red/green fluorescence ratio (from 10.87 to 0.35). These antitumor effects were further validated in vivo using a zebrafish HT-29 xenograft model. Furthermore, ERPP treatment significantly attenuated tumor angiogenesis and downregulated the expression of the vascular endothelial growth factor A (Vegfaa) gene in the zebrafish xenograft model. Mechanistic investigations revealed that ERPP primarily activated the mitochondrial apoptosis pathway. RT-qPCR analysis showed an upregulation of the pro-apoptotic gene Bax and a downregulation of the anti-apoptotic gene Bcl-2, leading to cytochrome c (CYCS) release and caspase-3 (CASP-3) activation. Additionally, ERPP exhibited potent antioxidant capacity, achieving an 80.2% hydroxyl radical scavenging rate at 4 mg/mL. ERPP also decreased reactive oxygen species (ROS) levels within the tumor cells, thereby augmenting anticancer efficacy through its antioxidant activity. Collectively, these findings provide mechanistic insights into the properties of ERPP, underscoring its potential as a functional food component or adjuvant therapy for colorectal cancer management. Full article

Artificial Intelligence (AI) and Generative AI are transformative yet double-edged technologies with evolving risks. While research emphasises trustworthy, fair, and responsible AI by focusing on its “what” and “why,” it overlooks practical “how.” To bridge this gap in financial services, an industry at the forefront of AI adoption, this study employs a qualitative approach grounded in existing Responsible AI and Corporate Digital Responsibility (CDR) frameworks. Through thematic analysis of 15 semi-structured interviews conducted with professionals working in finance, we illuminate nine non-technical barriers that practitioners face, such as sustainability challenges, trade-off balancing, stakeholder management, and human interaction, noting that GenAI concerns now eclipse general AI issues. CDR practitioners adopt a more human-centric stance, emphasising consensus-building and “no margin for error.” Our findings offer actionable guidance for more responsible AI strategies and enrich academic debates on Responsible AI and AI-CDR symbiosis. Full article

Background/Objectives: Alectinib, a second-generation tyrosine kinase inhibitor indicated for the treatment of non-small-cell lung cancer (NSCLC), exhibits suboptimal oral bioavailability, primarily attributable to its inherently low aqueous solubility and limited dissolution kinetics. This study aimed to enhance Alectinib’s solubility and therapeutic efficacy by formulating a G4-NH2-PAMAM dendrimer complex. Methods: The complex was prepared using the organic solvent evaporation method and characterized by DSC, FTIR, dynamic light scattering (DLS), and zeta potential measurements. A validated high-performance liquid chromatography (HPLC) method quantified the Alectinib. In vitro drug release studies compared free Alectinib with the G4-NH2-PAMAM dendrimer complex. Cytotoxicity against NSCLC cell line A549 was assessed using MTT assays, clonogenic assay, and scratch-wound assay. Xenograft effect was investigated in the H460 lung cell line. Pharmacokinetic parameters were evaluated in rats using LC–MS/MS. Results: Alectinib exhibited an encapsulation efficiency of 59 ± 5%. In vitro release studies demonstrated sustained drug release at pH 6.8 and faster degradation at pH 2.5. Anticancer activity in vitro showed comparable efficacy to free Alectinib, with 98% migration inhibition. In vivo tumor suppression studies revealed near-complete tumor regression (~100%) after 17 days of treatment, compared to 75% with free Alectinib. Pharmacokinetic analysis indicated enhanced absorption (shorter Tmax), prolonged systemic circulation (longer half-life), and higher bioavailability (increased AUC) for the dendrimer-complexed drug. Conclusions: These findings suggest that the G4-NH2-PAMAM dendrimer system significantly improves Alectinib’s pharmacokinetics and therapeutic potential, making it a promising approach for NSCLC treatment. Full article

Microcalcification clusters (MCCs) are key indicators of breast cancer, with studies showing that approximately 50% of mammograms with MCCs confirm a cancer diagnosis. Early detection is critical, as it ensures a five-year survival rate of up to 99%. However, MCC detection remains challenging due to their features, such as small size, texture, shape, and impalpability. Convolutional neural networks (CNNs) offer a solution for MCC detection. Nevertheless, CNNs are typically trained on single-resolution images, limiting their generalizability across different image resolutions. We propose a CNN trained on digital mammograms with three common resolutions: 50, 70, and 100 $\mathsf{\mu }$m. The architecture processes individual 1 cm² patches extracted from the mammograms as input samples and includes a MobileNetV2 backbone, followed by a flattening layer, a dense layer, and a sigmoid activation function. This architecture was trained to detect MCCs using patches extracted from the INbreast database, which has a resolution of 70 $\mathsf{\mu }$m, and achieved an accuracy of 99.84%. We applied transfer learning (TL) and trained on 50, 70, and 100 $\mathsf{\mu }$m resolution patches from the MEXBreast database, achieving accuracies of 98.32%, 99.27%, and 89.17%, respectively. For comparison purposes, models trained from scratch, without leveraging knowledge from the pretrained model, achieved 96.07%, 99.20%, and 83.59% accuracy for 50, 70, and 100 $\mathsf{\mu }$m, respectively. Results demonstrate that TL improves MCC detection across resolutions by reusing pretrained knowledge. Full article

Artificial Intelligence continues to demand robust and adaptable training methods for neural networks, particularly in scenarios involving limited computational resources or noisy, complex data. This study presents a comparative analysis of four training algorithms, Backpropagation, Genetic Algorithm, Black-hole Algorithm, and Particle Swarm Optimization, evaluated across both classification and regression tasks. Each method was implemented from scratch in MATLAB ver. R2024a, avoiding reliance on pre-optimized libraries to isolate algorithmic behavior. Two types of datasets were used, namely a synthetic benchmark dataset and a real-world dataset preprocessed into classification and regression formats. All algorithms were tested in both basic and advanced forms using consistent network architectures and training constraints. Results indicate that while Backpropagation maintained strong performance in smooth regression settings, the Black-hole and PSO algorithms demonstrated more stable and faster initial progress in noisy or discrete classification tasks. These findings highlight the practical viability of the Black-hole Algorithm as a competitive, gradient-free alternative for neural network training, particularly in early-stage learning or hybrid optimization frameworks. Full article

Tuberaria lignosa (Sweet) Samp. (Cistaceae) is a herbaceous species native to southwestern Europe, traditionally used to treat wounds, ulcers, and inflammatory or infectious skin conditions. This study aimed to characterize the phytochemical profile of its aqueous leaf extract and evaluate its skin-related in vitro biological activities. The phenolic composition was determined using UHPLC-HRMS/MS, HPLC-DAD, and quantitative colorimetric assays. Antioxidant activity was assessed against synthetic free radicals, reactive oxygen and nitrogen species, transition metals, and pro-oxidant enzymes. Enzymatic inhibition of tyrosinase, hyaluronidase, collagenase, and elastase were evaluated using in vitro assays. Cytocompatibility was tested on human keratinocytes and NIH/3T3 fibroblasts using MTT and resazurin assays, respectively, while wound healing was evaluated on NIH/3T3 fibroblasts using the scratch assay. Antifungal activity was investigated against several Candida and dermatophyte species, while antibiofilm activity was tested against Epidermophyton floccosum. The extract was found to be rich in phenolic compounds, accounting for nearly 45% of its dry weight. These included flavonoids, phenolic acids, and proanthocyanidins, with ellagitannins (punicalagin) being the predominant group. The extract demonstrated potent antioxidant, anti-tyrosinase, anti-collagenase, anti-elastase, and antidermatophytic activities, including fungistatic, fungicidal, and antibiofilm effects. These findings highlight the potential of T. lignosa as a valuable and underexplored source of bioactive phenolic compounds with strong potential for the development of innovative approaches for skin care and therapy. Full article

Background/Objectives: Chronic wounds represent a significant clinical and public health challenge due to impaired tissue repair and high associated morbidity. This study investigates the therapeutic potential of the secretome derived from human mesenchymal stem cells obtained from the pulp of deciduous teeth (hDP-MSCs) in promoting skin wound healing. Methods: After confirming the mesenchymal identity and multipotent differentiation potential of hDP-MSCs by using flow cytometry and histological staining, the effects of the secretome on human keratinocyte (HaCaT) cultures were evaluated. Results: Scratch assays, performed under high- and low-glucose conditions, demonstrated that the secretome significantly promoted keratinocyte migration and wound closure without compromising cell viability. Additionally, the secretome modulated the expression of key genes involved in inflammation and tissue regeneration, including IL-1β, TNF-α, TGF-β1, and VEGF-α, in a time-dependent manner. Under inflammatory conditions induced by lipopolysaccharide, co-treatment with the secretome significantly reduced TNF-α expression and increased TGF-β1 expression, suggesting an anti-inflammatory effect. Conclusions: These findings indicate the potential of the hDP-MSC-derived secretome as a promising cell-free therapeutic strategy capable of accelerating skin regeneration and modulating the inflammatory response during the wound healing process. Full article