jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Louwrens C. Hoffman | Wencong Wu | Shuxin Zhang | Michel Mubiayi Beya | Daniel Cozzolino
Source: Foods (article, 2025-04-21)
Field: Chemistry — Analytical Chemistry

The Effect of the Level of Goat Liver Addition to Goat Minced Meat on the Near-Infrared Spectra, Colour, and Shelf Life of Samples This study aimed to evaluate the utilisation of near-infrared (NIR) spectroscopy combined with chemometric techniques to identify the addition of goat liver to goat minced meat and to monitor the shelf life of the samples up to 8 days of storage. Mix samples were created by adding goat liver to goat meat in different ratios (0%, 2%, 4%, 6%, and 8% w/w), and after mincing, the samples were stored under chilled (2-4 °C) conditions for 8 days. The NIR spectra, CIELab parameters, and pH of the mixture samples were collected at the start of the study and after 2, 4, 6, and 8 days of storage. The mince became darker with the increase in days of storage, while the pH value was not affected by days of storage. Partial least squares (PLS) regression was used to develop calibration models for the CIELab parameters to predict the level of liver addition to minced meat and to predict days of storage. The standard error in cross-validation (SECV) and the coefficient of determination in cross-validation (R<sup>2</sup><sub>cv</sub>) were 0.10 (SECV: 3.3), 0.63 (SECV: 1.5), and 0.60 (SECV: 0.90) for L*, a*, and b*, respectively. The R<sup>2</sup><sub>CV</sub> and SECV were 0.32 (SECV: 2.4%) and 0.92 (SECV: 0.98 days) to predict the level of liver addition to minced meat and days of storage, respectively. The NIR calibration models developed to predict the CIELab parameters and level of addition of liver to minced meat were inadequate for predicting new samples. On the other hand, the PLS models developed could predict the days of storage, R<sup>2</sup><sub>cv</sub> 0.92 (SECV: 0.98 days). Compared with traditional methods such as CIELab or pH measurements, NIR spectroscopy can yield results more rapidly. However, the variability in the data set should be increased to allow the development of more reliable models. Partial least squares regression ; Shelf life ; Chemistry ; Food science ; Chromatography ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Maciej Kalka | Piotr Pigoń | B. J. Spisak
Source: Entropy (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Phase-Space Approach for Topological Phase Transitions in Silicene Silicene is a two-dimensional silicon monolayer with a band gap caused by relatively strong spin-orbit coupling. This band gap can be steered using a vertical electric field. In turn, the change in this electric field value leads to a transition from a topological insulator to a bulk insulator regime. This study aims to develop a phase-space approach to detecting the topological phase transitions in silicene induced by the presence of parallel magnetic and electric fields with the aid of the concept of topological quantum number based on the Wigner-Rényi entropy. A reinterpreted definition of the Wigner distribution function is employed to determine this indicator. The topological phase transition in silicene as a function of the electric field in the presence of the magnetic field is confirmed through the use of the topological quantum number determined for the one-half, Shannon and collision entropies. Silicene ; Topological order ; Topological insulator ; Electric field ; Condensed matter physics ; Physics ; Topology (electrical circuits) ; Quantum phase transition ; Phase transition ; Quantum ; Quantum mechanics ; Mathematics Graphene research and applications ; Topological Materials and Phenomena ; Fullerene Chemistry and Applications
Authors: Fredrika Wannberg | María Bueno Álvez | Alvida Qvick | Tamás Pongrácz | Katherina Aguilera | Emma Adolfsson | Louise Essehorn | Max Gordon | Mathias Uhlen | Gisela Helenius | Viktoria Hjalmar | Mikael Åberg | Axel Rosell | Charlotte Thålin
Source: Nature Communications (article, 2025-12-29)
Field: Chemistry — Spectroscopy

Plasma protein profiling predicts cancer in patients with non-specific symptoms Cancer detection is challenging, especially in patients with diffuse symptoms that overlap with non-malignant conditions. Here we show that plasma protein profiling can identify cancer among patients with non-specific symptoms. Using proximity extension assay-based proteomics of 1463 plasma proteins from 456 patients presenting with non-specific symptoms sampled prior to cancer diagnostic work-up and diagnosis, we identify 29 proteins associated with new cancer diagnoses. We develop a model able to stratify 160 cancer cases and 296 non-cancer cases with an area under the curve of 0.80, maintaining performance (0.82) in an independent replication cohort of 238 patients. The model also distinguishes cancer from autoimmune, inflammatory and infectious diseases. Designed as a triage tool, our model based on a blood test could help prioritize patients at higher cancer risk for rapid and highly sensitive diagnostic modalities such as positron emission tomography-computed tomography. These findings emphasize the potential of blood proteome profiling to support timely diagnosis and transform clinical medicine. Cancer ; Proteome ; Blood proteins ; Triage ; Proteomics ; Cohort ; Cancer screening ; Positron emission tomography Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; SARS-CoV-2 detection and testing
Authors: Ian W. Hamley | Valeria Castelletto
Source: Journal of Applied Crystallography (article, 2025-06-20)
Field: Materials Science — Biomaterials

Small-angle scattering studies on diverse peptide-based nanotube and helical ribbon structures reveal distinct form and structure factors Peptide-based nanotubes are bio-based self-assembled nanostructures with intriguing structural and functional properties. The structure of such nanotubes can be probed in detail using small-angle scattering experiments due to the typical length scales, <i>i.e.</i> diameter and wall thickness of the nanotubes, which span the range accessible in small-angle X-ray scattering (SAXS) or small-angle neutron scattering (SANS) studies. Here, we present SAXS data for several classes of peptide and lipopeptide systems previously studied by our group, as well as newly reported data for model short lysine-sequence lipopeptides. Previous data are re-examined using more accurate models for data plotted on Kratky plots, which emphasizes fine details of nanotube structure. In some cases, consideration of structure-factor effects is necessary to allow for the coexisting structures, and a lamellar structure factor is used to describe this. In other cases, such as several examples of surfactant-like peptides, only a form factor has to be considered to accurately fit the measured SAXS data. In these cases, a form factor for hollow nanotubes with a Gaussian bilayer profile to represent the layered peptide ordering in the nanotube walls is used to model the data. A general expression for the cross section scattering form factor is provided, which can be used for any scattering density profile (electron density for SAXS or scattering length density for SANS) across the wall. This is analysed along with the form factor for multishell (multiwall) nanotube structures with a series of slabs to represent the scattering density profile. For lipopeptides C<sub>16</sub>-KFK and C<sub>16</sub>-K (C<sub>16</sub> indicates a hexa-decyl lipid chain), SAXS data show aperiodicity in the form-factor oscillations, as well as apparent broad structure-factor peaks. These features cannot be fitted using solely nanotube form-factor models, this being ascribed to the presence of coexisting structures. Lastly, for comparison, the form factors for helical ribbon and cochleate (scroll) structures are evaluated for several examples, since in many cases electron microscopy of peptide- and lipopeptide-based nanotube systems reveals the coexistence of nanotubes with such structures, related to nanotubes. Ribbon ; Nanotube ; Scattering ; Materials science ; Crystallography ; Peptide ; Nanotechnology ; Chemical physics ; Chemistry ; Optics ; Physics ; Carbon nanotube ; Nuclear magnetic resonance ; Composite material Supramolecular Self-Assembly in Materials
Authors: Sebastian Sonntag | Janosch Luttmer | Arun Nagarajah
Source:  (article, 2025-08-17)
Field: Materials Science — Materials Chemistry

Did You Understand the Problem? – Evaluating Fine-Tuned Language Models for Identifying AI-Related Tasks in Product Development Abstract Recent advancements in machine learning (ML) offer significant potential to support product development. However, adoption in industry remains limited due to challenges in the initial step of translating domain-specific product development problems into ML-related tasks. This process requires domain-specific knowledge in both product development and ML, which is often lacking. Prior research investigated the use of large language models (LLMs) as recommender systems for suggesting suitable ML algorithms based on product development-related problem formulations. However, the results indicated only moderate success, primarily due to a lack of understanding of the underlying product development tasks, which often led to incorrect identification of the corresponding ML problem types. Therefore, this study investigates the use of small, fine-tuned language models (LMs) for classifying product development-related problem formulations into ML problem types as a preliminary step toward identifying suitable ML algorithms for the respective task. A curated dataset of 400 problem statements, extracted from abstracts of peer-reviewed publications identified through a systematic literature review, was expanded to 2,400 entries using lexical and semantic data augmentation. This dataset was subsequently used to fine-tune and evaluate both encoder-only and decoder-only models. The findings indicate that decoder-only models surpass encoder-only models in performance, reaching F1-scores of up to 83.9% on the augmented dataset. However, performance drops significantly when controlled data splits are applied, indicating a tendency toward overfitting rather than generalization. Future research should focus on expanding the dataset and exploring prompt engineering techniques to enhance model performance. Overfitting ; New product development ; Identification (biology) ; Process (computing) ; Product (mathematics) ; Focus (optics) ; Data modeling Machine Learning in Materials Science ; Ethics and Social Impacts of AI ; Topic Modeling
Authors: Mukhtar Hadi | Muhammad Kodrat | Siska Atmawan Oktavia
Source: TESLA Jurnal Teknik Elektro (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

A REVIEW OF ANTENNA DESIGN OPTIMIZATION FOR PARTIAL DISCHARGE DETECTION IN HIGH VOLTAGE EQUIPMENT Partial discharge (PD) merupakan indikator utama degradasi isolasi pada peralatan tegangan tinggi seperti kabel daya, transformator, dan sambungan (joint). Aktivitas PD yang tidak terdeteksi dapat mempercepat kerusakan isolasi dan menyebabkan gangguan sistem. Di antara berbagai metode deteksi, pendekatan berbasis antena memiliki keunggulan karena bersifat non-intrusif dan mampu mendeteksi sinyal elektromagnetik PD dari jarak jauh. Penelitian ini menyajikan kajian komprehensif terhadap desain antena untuk deteksi PD selama periode 2013–2023. Sebanyak 67 model antena dari 22 peneliti dianalisis, meliputi 182 data hasil simulasi dan pengukuran dengan parameter return loss (RL), voltage standing wave ratio (VSWR), dan bandwidth (BW). Hasil menunjukkan bahwa antena bowtie dan microstrip mendominasi karena memiliki lebar pita yang luas dan bentuk yang kompak. Tren performa memperlihatkan peningkatan signifikan, dengan nilai RL mencapai –30 dB, VSWR menurun hingga &lt;1,3, dan bandwidth melebihi 3 GHz. Kajian ini mengidentifikasi empat fase inovasi utama, dari optimasi bentuk dasar hingga desain multi-layer dan hybrid adaptif. Rekomendasi pengembangan ke depan meliputi desain hibrid bowtie–microstrip, penggunaan substrat fleksibel atau ber-loss rendah, serta integrasi dengan sistem pemrosesan sinyal dan mechine learning. Inovasi ini diharapkan mendukung pengembangan sistem deteksi PD cerdas generasi berikutnya yang lebih sensitif dan efisien. Bandwidth (computing) ; High voltage ; Voltage ; Standing wave ratio ; Transformer High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Lightning and Electromagnetic Phenomena
Authors: Zixi Liu
Source: Applied and Computational Engineering (article, 2025-07-03)
Field: Materials Science — Polymers and Plastics

AI-Driven Dynamic Life Cycle Assessment and Modular Design Strategies for Sustainable Textiles: Enhancing Repairability, Upgradability, and End-of-Life Recovery This study combines artificial intelligence and modular design to build a dynamic monitoring and evaluation system for the entire life cycle of textile products. By addressing the problems of traditional static and lagging environmental protection evaluation methods, a dynamic monitoring model based on the collaboration of dual GRU and CNN networks is proposed, which corrects environmental impact prediction by collecting real-time clothing wear data. At the same time, through the application of modular models such as removable clips and standardized interfaces, the efficiency of recycling and dismantling old clothing has been significantly improved. Measured data show that this scheme maintains the carbon footprint prediction error within 5%, and the fiber recovery rate of a single jacket can reach 97%, providing a new idea for the green transformation of the fast fashion industry. However, industrialization and implementation still face practical bottlenecks such as data silos and a lack of standards, such as the difficulty of communicating between fabric databases of different brands. In the future, the integrated application of blockchain traceability and reinforcement learning can be explored. By building a cross-brand data alliance, the environmental protection data of each garment, from production to recycling, can be recorded on the chain in real time. When an old T-shirt enters the recycling station, the system can automatically identify the fabric composition and generate the optimal dismantling plan—this could be the new image of the circular economy in the textile industry. Modular design ; Life-cycle assessment ; Engineering ; Computer science ; Economics Textile materials and evaluations ; Environmental Impact and Sustainability ; Additive Manufacturing and 3D Printing Technologies
Authors: Jonathan Paige | Robert S. Walker | Briggs Buchanan | Alastair Key | J. Thomas Romans | Metin I. Eren
Source: Forensic Sciences Research (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Elliptical Fourier analysis of bullet ricochet impact site plan-view shape in concrete Bullet ricochets often leave impact sites that can be useful for shooting incident reconstruction. Here, we assess the proposal that these sites can potentially provide information about the design and composition of the projectile and possibly the corresponding firearm. We produced impact sites in concrete from five bullet types fired at two distances, for a total of 10 experimental conditions. Then, using elliptical Fourier analysis, we assessed the 297 bullet ricochet impact sites' plan-view outline shapes. Of the different bullet types, the 45 calibre Hollow Points exhibited the greatest variability in ricochet impact site shape, and the greatest potential for diagnosis. Overall, statistical assessment of robust sample sizes of ricochet impact sites suggests that there is too much overlap between different bullets' ricochet sites' plan-view form for confident identification of bullet design and composition, much less identification of the firearm from which it originated. We conclude with an extended discussion about future experiments involving bullet ricochet impact sites, including the employment of "paradigmatic classification", which can both organise the testing of experimental variables as well as illustrate the tremendous amount of experimental work that still needs to be conducted before researchers or firearm examiners draw firm conclusions. Key PointsBullet ricochet impact sites often occur in shooting incidents.We analysed bullet richochet impact site shapes in concrete using elliptical Fourier analysis.Results suggest different bullet types' ricochet impact shape morphospaces overlap with each other.This overlap obscures conclusions about the design and composition of the projectile and possibly the responsible firearm. Plan (archaeology) ; Geology ; Forensic engineering ; Engineering High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads
Authors: Biagio Morrone | Petronia Carillo | Mohammed El Ganaoui | Souad Morsli | Rachid Bennacer
Source: Fluid dynamics & materials processing (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Foreword to the International Conference on Materials and Energy (ICOME 2023) Engineering physics ; Environmental science ; Engineering Machine Learning in Materials Science ; Nanoporous metals and alloys ; 2D Materials and Applications
Authors: Robert Richer | Julia Jorkowitz | Sebastian Stühler | Luca Abel | Miriam Kurz | Marie Oesten | Stefan G. Grießhammer | Nils C. Albrecht | Arne Küderle | Christoph Ostgathe | Alexander Kölpin | Tobias Steigleder | Nicolas Rohleder | Bjoern M. Eskofier
Source:  (preprint, 2025-01-23)
Field: Chemistry — Spectroscopy

PEPbench -- Open, Reproducible, and Systematic Benchmarking of Automated Pre-Ejection Period Extraction Algorithms The pre-ejection period (PEP) is a widely used cardiac parameter in psychophysiological research that reflects the duration between the onset of ventricular depolarization and the opening of the aortic valve and is an established marker of sympathetic nervous system activity. While many algorithms for automated PEP extraction from electrocardiography (ECG) and impedance cardiography (ICG) signals have been proposed in the literature, they have not been systematically compared against each other. This lack of standardized algorithm comparisons is due to the absence of open-source algorithms and annotated datasets for evaluating PEP extraction algorithms. To address this issue, we introduce PEPbench, an open-source Python package with different Q-peak and B-point detection algorithms from the literature that can be combined to form comprehensive PEP extraction pipelines, and a standardized framework for evaluating PEP extraction algorithms. We use PEPbench to systematically compare 90 different algorithm combinations. All combinations are evaluated on datasets from two different studies with manually annotated Q-peaks and B-points, which we make publicly available as the first datasets with reference PEP annotations. Our results show that the algorithms can differ vastly in their performance and that the B-point detection algorithms introduce a considerable amount of error. Thus, we suggest that automated PEP extraction algorithms should be used with caution on a beat-to-beat level as their error rates are still relatively high. This highlights the need for open and reproducible benchmarking frameworks for PEP extraction algorithms to improve the quality of research findings in the field of psychophysiology. With PEPbench, we aim to take a first step towards this goal and encourage other researchers to engage in the evaluation of PEP extraction algorithms by contributing algorithms, data, and annotations. Ultimately, we hope to establish a community-driven platform, fostering innovation and collaboration in the field of psychophysiology and beyond. Benchmarking ; Computer science ; Period (music) ; Algorithm ; Economics ; Physics ; Management Analytical Chemistry and Chromatography
Authors: Edwin Kamalha | Mackenzie Tuhirirwe | Ildephonse Nibikora
Source: Journal of Textile Science and Technology (article, 2025-12-26)
Field: Materials Science — Polymers and Plastics

Data-Driven Frameworks for Circular and Traceable Banana Fibre Textiles: Opportunities and Challenges The textile industry is one of the most resource-intensive and polluting sectors worldwide, accounting for approximately 10% of global carbon emissions and nearly 20% of industrial wastewater. The growth of fast fashion has further intensified these challenges, generating about 92 million tons of textile waste annually, with projections indicating a rise to 134 million tons by 2030. This has created an urgent need for sustainable and renewable alternatives to conventional fibers such as cotton and synthetics. Banana fiber, derived from agricultural waste in the form of pseudostems, presents a viable solution due to its biodegradability, low water footprint, and high tensile strength. This review synthesizes current research on banana fiber textiles, with an emphasis on traceability, circularity, and technological innovations that can enhance sustainability. Despite its potential, less than 5% of banana pseudostems are currently processed into fiber. The barriers include fragmented supply chains, limited infrastructure, and a lack of digital traceability systems. However, emerging technologies such as blockchain, the Internet of Things (IoT), and Digital Product Passports (DPPs) have the potential to transform these limitations by improving supply chain transparency, product authentication, and lifecycle monitoring. In addition, Life Cycle Assessment (LCA) studies indicate that banana fiber has a significantly lower carbon and water footprint compared to widely used fibers such as polyester and cotton, strengthening its case as a sustainable textile material. The review further identifies critical research and policy gaps. These include the need for integrated frameworks that combine traceability and circularity, comprehensive LCAs, inclusion of smallholder farmers in the value chain, and national policies supportive of ecofriendly fiber production. Future research should aim at building data-driven frameworks that integrate traceability tools, circular economy models, and supportive policies to unlock banana fiber’s full potential in the global market. By bridging these gaps, banana fiber can emerge as a catalyst for sustainable textile innovation, fostering environmental conservation, supporting rural livelihoods, and contributing to the achievement of the United Nations Sustainable Development Goals (SDGs 12 and 13). Traceability ; Carbon footprint ; Supply chain ; Sustainability ; Life-cycle assessment ; Product (mathematics) ; Agriculture ; Textile Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Sustainable Supply Chain Management