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A comparative study on sintering behavior and microstructure of copper-cobalt-alloy using conventional sintering and FAST
Full text linkPublisher Copyright: © 2025 The AuthorsIn this study, Copper-Cobalt-based granulated and non-granulated powders were investigated using Field-Assisted Sintering Technology (FAST) and conventional sintering. Phase composition analysis via Rietveld refinement of X-Ray Diffraction data demonstrated that the sintering method critically influenced the microstructural evolution. Conventional sintering of one of the investigated compositions (BOND1H) led to the formation of the FeCo intermetallic due to a prolonged exposure to high temperatures. On the other hand, FAST processing preserved discrete iron and cobalt phases. The FAST process exhibited superior efficacy for non-granulated powders, achieving 91.4 % theoretical density in one of the samples (BOND2R) with minimal defects, compared to granulated systems where binder decomposition induced porosity. Remarkably, FAST-processed BOND2R composition attained hardness values (245 ± 32 HRB) equivalent to the conventional counterparts (240 ± 10 HRB) despite differences in densification. These results underscore the dual influence of sintering strategy and powder morphology on microstructural control, defect formation, and mechanical performance in Copper-Cobalt-based systems.Peer reviewe
Atomic Design for MLOps: A Modular Approach to Scalable and Reusable ML Pipelines
No full textPublisher Copyright: © 2025 University of Split, FESB.The increasing complexity of machine learning pipelines presents significant challenges in terms of maintainability, scalability, and reusability. This paper explores the application of atomic design principles - widely used in software engineering - to MLOps pipelines, structuring ML workflows into modular and reusable components. By decomposing AI pipelines into hierarchical elements, such as atoms, modules, stages, templates, and pipelines, this approach aims to improve efficiency, reproducibility, and collaboration in machine learning development. We describe the conceptual framework and its implementation in time series applications, including forecasting, classification, and anomaly detection. Preliminary results demonstrate the feasibility of this methodology, highlighting its potential benefits for streamlining ML workflows and reducing development overhead.Peer reviewe
Adipose tissue-derived ECM hydrogels as a 3D platform for neural differentiation and brain diseases
Full text linkPublisher Copyright: © 2025 The Royal Society of Chemistry.The interplay between the extracellular matrix and cells significantly impacts cellular survival, proliferation, and differentiation. Cell growth within 3D scaffolds, particularly hydrogels that mimic cellular microenvironments, offers more relevant insights into tissue development compared to traditional 2D systems. This study explores the behavior of neural stem cells and their differentiation within 3D pure adipose tissue derived-ECM (adECM) hydrogels. These hydrogels provide both physical and biochemical cues that closely resemble the 3D microarchitecture of native tissues. Encapsulating neuroectodermal NE-4C cells in adECM hydrogels at different concentrations revealed intriguing divergent cellular responses. While variations in the fiber structure and pore formation between hydrogels did not significantly affect cell survival, they notably influenced the differentiation process. Analysis of neural-lineage-specific markers, such as tubulinβIII and GFAP, demonstrated divergent differentiation outcomes. This biologically derived, tissue-specific 3D platform enables in vitro study of neural differentiation and lays the groundwork for future neural models relevant to regenerative medicine and neurodegenerative research.Peer reviewe
JustRAIGS: Justified Referral in AI Glaucoma Screening Challenge
Full text linkPublisher Copyright: © IEEE. 1982-2012 IEEE.A major contributor to permanent vision loss is glaucoma. Early diagnosis is crucial for preventing vision loss due to glaucoma, making glaucoma screening essential. A more affordable method of glaucoma screening can be achieved by applying artificial intelligence to evaluate color fundus photographs (CFPs). We present the Justified Referral in AI Glaucoma Screening (JustRAIGS) challenge to further develop these AI algorithms for glaucoma screening and to assess their efficacy. To support this challenge, we have generated a distinctive big dataset containing more than 110,000 meticulously labeled CFPs obtained from approximately 60,000 patients and 500 distinct screening centers in the USA. Our objective is to assess the practicality of creating advanced and dependable AI systems that can take a CFP as input and produce the probability of referable glaucoma, as well as outputs for glaucoma justification by integrating both binary and multi-label classification tasks. This paper presents the evaluation of solutions provided by nine teams, recognizing the team with the highest level of performance. The highest achieved score of sensitivity at a specificity level of 95% was 85%, and the highest achieved score of Hamming losses average was 0.13. Additionally, we test the top three participants' algorithms on an external dataset to validate the performance and generalization of these models. The outcomes of this research can offer valuable insights into the development of intelligent systems for detecting glaucoma. Ultimately, findings can aid in the early detection and treatment of glaucoma patients, hence decreasing preventable vision impairment and blindness caused by glaucoma.Peer reviewe
On the Possibility of Extending the Crack Length Criterion in the Master Curve Methodology
No full textPublisher Copyright: Copyright © 2025 by ASME.The Master Curve (MC) methodology is a well-known approach utilized to characterize the ductile-to-brittle transition region (DBTR) of ferritic–pearlitic steels. This methodology was initially standardized in ASTM E1921 in 1997 and has undergone continuous evolution and improvement since its origin. However, the validity criterion for the crack aspect ratio (0.45 ≤ a0/W ≤ 0.55) has remained unchanged since its inception. It is worth noting that this criterion was originally established in accordance with standard ASTM E399, which characterizes fracture conditions under linear-elastic plane strain conditions, apparently for historical precedents rather than any scientific rationale. Furthermore, ASTM E1820, which is employed to characterize the fracture behavior of metallic materials in elastic–plastic conditions, permits a maximum crack length-to-width ratio of 0.70. In this context and considering that ASTM E1921 measures KJc (elastic–plastic) values of the fracture toughness, our research seeks to empirically demonstrate that the crack length-to-width criteria established in ASTM E1921 can be increased up to 0.60, without compromising the accuracy of the reference temperature calculations, at least for the specific datasets used in this work. Such a correction would offer significant advantages, especially when dealing with mini-C(T) specimens. Their subsize dimensions may result in the discarding of numerous specimens that could otherwise be effectively employed for reference temperature calculations. More research is recommended to provide additional validation of the crack aspect ratio upper limit proposed here, and even to explore the possibility of further extensions.Peer reviewe
BARRERAS PARA LA IMPLEMENTACIÓN EFECTIVA DE LA IA EN LA PREDICCIÓN DE OBRAS PÚBLICAS
No full textPeer reviewe
On the Capability of Cascaded H-Bridge Converter-Based Energy Systems to Tolerate Intraphase Active Power Imbalance
Full text linkPublisher Copyright: © 2020 IEEE.The cascaded H-bridge (CHB) converter is an attractive topology to interface energy sources, such as battery energy storage or photovoltaic systems, with the medium-voltage grid. The submodules in CHB converter-based energy systems unavoidably process different active power during operation. However, there exist inherent limits that prohibit an arbitrarily imbalanced intraphase active power distribution. Failing to consider such limits can result in output current distortion and capacitor voltage deviation, which can jeopardize the CHB converter. Crucially, these limits vary depending on the modulation method utilized, whose selection is, thus, of utmost relevance. Accordingly, this article derives and compares the feasible intraphase active power imbalance range of existing modulation methods. The analysis is experimentally corroborated in a 1-kVA single-phase CHB converter with six submodules per phase. Based on the comparison, suggestions on which modulation methods are preferred for different CHB applications are provided.Peer reviewe
A keyword extraction model study in the movie domain with synopsis and reviews
No full textPublisher Copyright: © The Author(s) 2025.The use of keywords is increasingly being applied across diverse domains, including the movie industry, whose main platforms are adopting advanced natural language processing techniques. Algorithms for automatic extraction of keywords can provide relevant information in this domain. The most novel approaches covering several categories (statistics, graphs, word embedding, and hybrid) have been considered in a model study framework. They have been implemented, applied, and evaluated with standard datasets. In addition, a movie dataset with gold standard keywords, based on textual metadata from synopses and reviews, has been specifically developed for this scope. Keyword extraction models have been evaluated in terms of F-score and computation time. Furthermore, content analysis, both quantitative and qualitative, of the extracted keywords in the movie context has been performed. Results show a great variability in model performance and computation time among the different models. Qualitative results, in addition to F-score and computation time, demonstrate that keyword extraction works better with synopses than with reviews. The quantitative content analysis revealed that EmbedRank effectively reduces redundancy and limits the use of proper nouns, leading to high-quality keywords.Peer reviewe
Evaluation Framework of Next Generation Electric Trucks
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.To measure the success of a research and development project and assess its impact, an evaluation methodology has to be established. The proposed methodology in the NextETRUCK project follows best practices in validation and draws on insights from previous support projects such as CONVERGE and FESTA. It outlines a set of research hypotheses and associated goals for different innovation aspects of the project, forming the basis for evaluation. Key elements of this evaluation plan include defining Key Performance Indicators (KPIs). A total of 28 KPIs have been identified, covering areas such as vehicle and charging performance, digital tools, driver/fleet operator experiences, and market/total cost of ownership considerations. These KPIs can be measured during the demonstration and digital twin operations, using both quantitative and qualitative measures. Objective data, subjective evaluations, and inputs such as vehicle data, questionnaires, and driver interviews can all contribute to the assessment. The evaluation plan takes a structured approach, detailing each KPI’s description, assessment methods, parameters, and necessary information to address potential risks and challenges during the evaluation phase.Peer reviewe
Improving of the Decision-Making Process Towards Climate Change Adaptation in Transport Infrastructures
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Transport infrastructure (TI) is one of the most vulnerable assets to climate change (CC) impacts. With the dramatic increase in climate induced hazards, the crucial role of LS to society, economy and environment has put this area as a significant topic in need of improvement. There are several methodologies and climatic data that exist in research for mitigation of climate change impact, but it is often perceived difficult to translate this data into a simplified integrated process that can be followed by infrastructure managers ensuring effective asset management. Accordingly, the need for implementing climate adaptation measures can be justified through a criterion for having quantifiable key performance indicators to facilitate comparison of different solutions and simplify the decision making including different stakeholder engagement, resilience assessment, sustainability consideration and opens doors for the acknowledgement of Nature-Based Solutions as viable options for consideration. This paper aims to present a review for some of the recent current decision-making practices towards climate resilient Transport infrastructures and possibilities for in depth assessment and long-term monitoring and improvement.Peer reviewe