jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hendrik Vater | Oliver Schweins | Lukas Hölsch | Wilhelm Kirchgässner | Till Piepenbrock | Oliver Wallscheid
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

RHINO-MAG: Recursive H-Field Inference based on Observed Magnetic Flux under Dynamic Excitation Driven by the MagNet Challenge 2025 (MC2), increased research interest is directed towards modeling transient magnetic fields within ferrite material. An accurate time-resolved and temperature-aware H-field prediction is essential for optimizing magnetic components in applications with quasi-stationary / non-stationary excitation waveforms. Within the scope of this investigation, a selection of model structures with varying degrees of physically motivated structure are compared. Based on a Pareto investigation, a rather black-box gated recurrent unit (GRU) model structure with a graceful initialization setup is found to offer the most attractive model size vs. model accuracy trade-off, while the physics-inspired models performed worse. For a GRU-based model with only 325 parameters, a sequence relative error of 8.02 % and a normalized energy relative error of 1.07 % averaged across five different materials are achieved on unseen test data. With this excellent parameter efficiency, the proposed model won the first place in the performance category of the MC2. Initialization ; Excitation ; Transient (computer programming) ; Sequence (biology) ; Inference ; Model selection ; Magnetic field ; Magnet ; Selection (genetic algorithm) Magnetic Properties and Applications ; Electromagnetic Simulation and Numerical Methods ; Magnetic properties of thin films
Authors: Seul-Ki-Chan Jeong | Seonmin Lee | Hayeon Jeon | Seokhee Han | Soeun Kim | Minkyung Woo | Pil Nam Seong | Samooel Jung | Kyung Jo
Source: Food Science of Animal Resources (article, 2026-02-11)
Field: Chemistry — Analytical Chemistry

Prediction of pork belly cooking loss by hyperspectral imaging analysis of semispinalis capitis muscle This study evaluated the performance of a partial least squares regression (PLSR) model in predicting the cooking loss of pork belly slices using hyperspectral images of the semispinalis capitis (SC) muscle from the pork shoulder butt. Pork shoulder butts and whole belly cuts were obtained from 70 half-right gilt carcasses at 24 h postmortem. Pork belly slices were obtained from the 6th and 11th thoracic vertebrae and the 4th lumbar vertebra. Reflectance hyperspectral images of the SC muscle were captured. Cooking loss was measured in SC muscle and pork belly slices. A significant correlation was observed between the cooking loss of pork belly slices and SC muscle. In the PLSR model for predicting the cooking loss of belly slices, standard normalized variate pre-processing and wavelength selection did not enhance the model accuracy. In contrast, first-derivative pre-processing showed the highest accuracy with R<sup>2</sup>v values ranging from 0.73 to 0.80. These results suggest that the cooking loss of pork belly slices can be nondestructively predicted using the hyperspectral image of SC muscle exposed on the half-carcass surface. Moreover, this study can provide a foundation for future research on nondestructive and real-time prediction of pork quality in production lines. Hyperspectral imaging ; Partial least squares regression ; Thermography ; Back muscles Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Behavior and Welfare Studies
Authors: Phạm Xuân Bách | Nguyễn Thu Trang | Vu Bao Khanh
Source: Tạp chí điện tử Khoa học và Công nghệ Giao thông (article, 2026-03-29)
Field: Materials Science — Polymers and Plastics

Phân tích các yếu tố ảnh hưởng đến độ kim lún của nhựa đường biến tính Polyethylene dựa trên tiếp cận học máy Nghiên cứu này trình bày một cách tiếp cận tiên tiến dựa trên học máy để phân tích các yếu tố ảnh hưởng đến độ kim lún của nhựa đường biến tính polyethylene (PE). Một bộ dữ liệu thực nghiệm toàn diện bao gồm 204 mẫu đã được tổng hợp từ các nghiên cứu trước đó, bao quát 7 biến đầu vào quan trọng: hàm lượng PE, điểm nóng chảy, loại PE, nhiệt độ trộn, thời gian trộn, tốc độ trộn và độ kim lún của nhựa đường gốc. Hiệu suất của năm thuật toán học máy (ExtraTrees, LGBM, Gradient Boosting, Bagging và AdaBoost) đã được đánh giá thông qua 30 mô phỏng Monte Carlo và xác thực chéo 5 lớp để đảm bảo tính ổn định và khách quan. Kết quả cho thấy mô hình ExtraTrees đạt hiệu suất vượt trội nhất với hệ số xác định R2 = 0.982 và sai số RMSE = 6.473 trên tập dữ liệu kiểm tra. Kỹ thuật trí tuệ nhân tạo có khả năng giải thích thông qua biểu đồ phụ thuộc từng phần (PDP) đã làm rõ cơ chế tác động phi tuyến tính của các biến; trong đó, độ kim lún nhựa gốc (X7) và hàm lượng PE (X1) là hai yếu tố có ảnh hưởng quyết định nhất đến độ kim lún của nhựa đường biến tính PE. Cuối cùng, một giao diện người dùng trực quan đã được phát triển, cung cấp công cụ dự báo nhanh chóng và chính xác, giúp tối ưu hóa quy trình thiết kế nhựa đường cải tiến trong thực tiễn. Polyethylene ; Monte Carlo method ; Table (database) ; Die (integrated circuit) ; Square (algebra) Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Polymer Science and PVC
Authors: Fatimah Zachariah Ali | Norfazlina Mohd Nawi | Wijenthiran Kunasekaran | Tan Li Jin | Lim Chia Ee | Nazia Abdul Majid
Source: International Journal of Molecular Sciences (article, 2026-04-08)
Field: Chemistry — Spectroscopy

Future Perspectives: Mass Spectrometry for Spatial Localisation of Anti-Angiogenic Oil Palm Compounds Angiogenesis is a spatially regulated hallmark of colorectal cancer (CRC) progression, yet current analytical frameworks fail to resolve how nutraceutical bioactive compounds interact with angiogenic signalling within the heterogeneous tumour microenvironment. This review advances a central hypothesis: that the spatial localisation of palm oil mill effluent (POME)-derived bioactive compounds within CRC tumour tissues is predictive of their functional anti-angiogenic activity. POME—the largest waste stream of palm oil processing—contains a chemically diverse array of bioactives, including tocotrienols, phenolics, carotenoids, and fatty acids, with reported antioxidant, anti-inflammatory, and anti-angiogenic properties. However, the existing evidence is predominantly derived from bulk in vitro analyses, limiting mechanistic conclusions about compound behaviour within spatially organised tumour architectures. To address this gap, we propose an integrated framework positioning mass spectrometry imaging (MSI)—across matrix-assisted laser desorption/ionisation (MALDI), desorption electrospray ionisation (DESI), and secondary ion mass spectrometry (SIMS) platforms—as the analytical bridge between compound localisation and angiogenic function. By enabling the label-free, spatially resolved co-localisation of POME-derived compounds with key angiogenic mediators, including VEGF, HIF-1α, and NF-κB, within intact CRC tissues, MSI provides a mechanistic platform that transcends the limitations of conventional molecular analyses. A four-component translational roadmap is outlined, encompassing POME bioactive profiling, spatial compound mapping, angiogenic co-localisation analysis, and functional validation. Critically, the existing evidence on oil palm-derived bioactives is appraised with respect to study quality, mechanistic depth, and translational limitations, identifying the most analytically tractable candidate compounds for spatial investigation. Collectively, this framework positions POME valorisation within a precision nutraceutical oncology paradigm, offering a spatially informed strategy for anti-angiogenic intervention in CRC while simultaneously addressing the environmental burden of palm oil processing waste. Nutraceutical ; Mass spectrometry ; Limiting ; Valorisation ; Polyphenol ; Desorption electrospray ionization ; Tandem mass spectrometry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Edible Oils Quality and Analysis
Authors: Daniel Raw
Source: International Journal of Applied Science (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Probabilistic Learning Framework for Inverse Material Design Inverse material design, which seeks microstructures yielding desired macroscopic properties, is inherently ill-posed due to non-uniqueness and the existence of unattainable targets. While data-driven generative models offer powerful empirical solutions, they often lack rigorous theoretical guarantees. This paper establishes a formal probabilistic learning framework to address these challenges. We first prove that the forward homogenization map, which predicts effective properties from a microstructure descriptor field, is Frechet differentiable and locally Lipschitz continuous under physically reasonable assumptions. This foundational result justifies the use of gradient-based methods and sensitivity analysis. Building upon this, we demonstrate the probabilistic well-posedness of the Bayesian inverse problem: the posterior distribution over microstructures is well-defined and depends continuously on the target property data. Furthermore, we prove Bayesian consistency, showing that as observational uncertainty vanishes, the posterior measure concentrates on the true set of solutions. This theoretical foundation validates advanced probabilistic inference techniques (e.g., MCMC, variational inference) for robustly exploring the solution manifold and quantifying uncertainty, thereby enabling a more complete and practical exploration of the material design space. Probabilistic logic ; Lipschitz continuity ; Inverse ; Differentiable function ; Posterior probability ; Bayesian inference ; Uncertainty quantification ; Inverse problem ; Bayesian probability ; Generative grammar Machine Learning in Materials Science ; Advanced Mathematical Modeling in Engineering ; Topology Optimization in Engineering
Authors: Okky Husain | Teresa Liliana Wargasetia | Julia Windi Gunadi | Angkasa Ramatuan Hamdan
Source: Health Science Reports (article, 2026-03-01)
Field: Materials Science — Biomaterials

Quantitative Histomorphometric Analysis of Collagen Bundles in Masson's Trichrome Stained Rat ( <i>Rattus norvegicus</i> ) Skin: A Methodological Study This work provides a validated framework for the objective and reproducible quantification of collagen in rat skin. By bridging traditional histology with computational analysis, this protocol offers a robust tool for future studies on collagen dynamics in skin pathologies and wound healing, with potential applications in evaluating therapeutic strategies. Stain ; Trichrome ; Trichrome stain ; Masson's trichrome stain ; Staining ; Digital image analysis ; Histology ; Quantitative analysis (chemistry) Collagen: Extraction and Characterization ; Skin Protection and Aging ; Morphological variations and asymmetry
Authors: Hao Shi | Linjun Lei | Ting Wang | Junhao Ou
Source: Journal of Physics Conference Series (article, 2026-02-01)
Field: Materials Science — Biomaterials

Data-driven optimization of performance parameters of artificial leather: Performance improvement strategies combined with multiple statistical analysis Abstract This study aims to analyze and optimize the process parameters in the production process of artificial leather to improve product performance. First, data preprocessing is carried out to explore the influence of process parameters and performance through typical correlation analysis, and principal component analysis. The influence of single process parameters and multi-parameter interactions on performance is examined, and the key combinations of process parameters affecting different performance characteristics are identified. Process (computing) ; Principal component analysis ; Component (thermodynamics) ; Key (lock) ; Performance improvement ; Performance prediction ; Production (economics) ; Product (mathematics) Collagen: Extraction and Characterization ; Food Supply Chain Traceability ; Fault Detection and Control Systems
Authors: Mahdiyeh Zahrabi | Mine Altunbek | Süleyman Çelik | Mina Namvari | Bahattin Koç
Source: Biofabrication (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Reply to comment on ‘3D melt electrowritten MXene-reinforced scaffolds for tissue engineering applications’ Yang<i>et al</i>comment on our article by Zahrabi<i>et al</i>(2025<i>Biofabrication</i><b>17</b>045011 10.1088/1758-5090/adf803) titled '3D melt electrowritten MXene-reinforced scaffolds for tissue engineering applications' reporting the first demonstration of melt electrowritten (MEW) MXene/PCL scaffolds and their pro-osteogenic cellular response without exogenous growth factors. Here, we respond by clarifying MXene's role during MEW processing and its contribution to tissue scaffold properties by defining future research directions. Importantly, the MXene within the PCL scaffolds does not exhibit high electrical conductivity as pristine 2D MXene films do, since the investigated loadings are below the electrical percolation threshold. Therefore, bulk conductivity is not expected to dominate scaffold behavior. Instead, we attribute improved print resolution and stability to MXene-enabled interfacial and thermal effects that can stabilize the MEW jet and enhance filament definition. In addition, favorable interactions between MXene surface terminations and PCL strengthen interfacial adhesion and influence crystallization and degradation kinetics. We further discuss the surface functionalization strategies (e.g. APTES functionalization) that can improve MXene-polymer compatibility and may reduce oxidation susceptibility. Building on these points, we envision next steps including (i) investigation of osteoinductive signaling pathways mechanism, (ii) complementary<i>in vivo</i>assessment in standard bone-defect models, (iii) fabrication of scalable structures by the development of hybrid manufacturing routes combining with MEW such as extrusion/hydrogel casting or electrospinning, and (iv) AI-guided optimization using existing material composition and process-structure models as a design constraint. Scaffold ; Surface modification ; Tissue engineering ; Fabrication ; Compatibility (geochemistry) ; Thermal conductivity ; Casting ; Encapsulation (networking) MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Graphene and Nanomaterials Applications
Authors: Naranjo, Juan | Thi Ha Kyaw | Gaurav Saxena | Kevin Ferreira | Jack S. Baker
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Designing quantum technologies with a quantum computer Interacting spin systems in solids underpin a wide range of quantum technologies, from quantum sensors and single-photon sources to spin-defect-based quantum registers and processors. We develop a quantum-computer-aided framework for simulating such devices using a general electron spin resonance Hamiltonian incorporating zero-field splitting, the Zeeman effect, hyperfine interactions, dipole-dipole spin-spin terms, and electron-phonon decoherence. Within this model, we combine Gray-encoded qudit-to-qubit mappings, qubit-wise commuting aggregation, and a multi-reference selected quantum Krylov fast-forwarding (sQKFF) hybrid algorithm to access long-time dynamics while remaining compatible with NISQ and early fault-tolerant hardware constraints. Numerical simulations demonstrate the computation of autocorrelation functions up to $\sim100$ ns, together with microwave absorption spectra and the $\ell_1$-norm of coherence, achieving 18-30$\%$ reductions in gate counts and circuit depth for Trotterized time-evolution circuits compared to unoptimized implementations. Using the nitrogen vacancy center in diamond as a testbed, we benchmark the framework against classical simulations and identify the reference-state selection in sQKFF as the primary factor governing accuracy at fixed hardware cost. This methodology provides a flexible blueprint for using quantum computers to design, compare, and optimize solid-state spin-qubit technologies under experimentally realistic conditions. Quantum computer ; Quantum simulator ; Quantum information ; Quantum ; Quantum error correction ; Quantum network ; Computation ; Quantum circuit ; Quantum technology ; Quantum gate Diamond and Carbon-based Materials Research ; Quantum Computing Algorithms and Architecture ; Quantum Information and Cryptography
Authors: Chunxue Wei | Tianxiang Liu | Baowei Zhang | Xiao Wang
Source: Algorithms (article, 2026-04-01)
Field: Materials Science — Polymers and Plastics

A Feature Selection Method for Yarn Quality Prediction Based on SHAP Interpretation This study developed an interpretable framework, RFE-SHAP, designed for yarn quality prediction. It integrates Recursive Feature Elimination (RFE) with SHapley Additive exPlanations (SHAP) theory to refine feature selection and mitigate data redundancy in small-sample environments. With Support Vector Regression (SVR) serving as the foundational evaluator, the RFE process iteratively identifies critical variables. Distinct from conventional methods, our approach employs SHAP values to quantify both the primary effects of individual features and the complex synergistic interactions among variables. This yields a transparent and intuitive strategy for identifying optimal feature subsets for two key quality indicators: yarn strength and hairiness H-value. To assess performance, a comparative analysis was performed between the traditional SVR-RFE method and the proposed RFE-SHAP method, using both as inputs for a Back-Propagation Artificial Neural Network (BP-ANN). The experimental results based on authentic production data demonstrate that the RFE-SHAP-BP model significantly enhances prediction reliability. Notably, compared to the baseline SVR-RFE-BP model, the proposed approach reduced the Mean Absolute Percentage Error (MAPE) by 0.73 and 1.01 percentage points for yarn strength and hairiness H-value, respectively. The final MAPE values reached 2.10% and 2.78%, confirming the model’s superior precision. These findings indicate that the RFE-SHAP method is highly feasible and effectively elevates prediction performance in data-limited industrial scenarios. Feature selection ; Redundancy (engineering) ; Yarn ; Artificial neural network ; Pattern recognition (psychology) ; Mean absolute percentage error ; Feature (linguistics) ; Interpretation (philosophy) ; Regression Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Dyeing and Modifying Textile Fibers
Authors: Ding Zhang | Peng Tang | Liqiao Jing | Xincheng Yao | Bo Zhou | Enzong Wu | Ying Wai Li | Evgueni T. Filipov | Hongsheng Chen | Zuojia Wang
Source: ArXiv.org (article, 2026-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-locking non-volatile coding metasurfaces via origami-based mechanical bits Digital coding metasurfaces have revolutionized electromagnetic (EM) manipulation, yet typical tunable approaches based on active components suffer from the "volatility" bottleneck. While mechanical modulation provides a potential solution, current implementations generally lack inherent state-locking capability, rendering them vulnerable to environmental disturbances and actuation errors. Inspired by the concept of mechanical bits (MBs), this paper presents a self-locking non-volatile coding metasurface platform enabled by Kresling origami-based MBs, where the continuous mechanical deformation of individual meta-atoms is discretized into robust binary geometric states protected by intrinsic energy barriers. The bistable states are strictly mapped to 1-bit EM coding phases via tailored metallic patterns integrated onto a multimaterial 3D printed Kresling origami array. Building upon this concept, both transmission- and reflection-type prototypes are proposed and experimentally demonstrated, exhibiting exceptional wavefront manipulation capabilities through near-field holographic imaging and far-field beam steering. In addition, the lightweight origami unit (1.5 g) exhibits an exceptional load-bearing capacity, supporting over 100 times its own weight. These results bridge mechanical logic with EM information processing, establishing a universal physical paradigm for constructing low-power, highly robust coding metasurfaces resilient to extreme environments. Bistability ; Coding (social sciences) ; Rendering (computer graphics) ; Holography ; Wavefront ; Binary number ; Metamaterial Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Materials and Mechanics
Authors: Nesma M. Fahmy | Reem H. Obaydo | Hayam M. Lotfy
Source: Scientific Reports (article, 2026-03-01)
Field: Chemistry — Analytical Chemistry

Artificial intelligence versus traditional approaches in multicomponent spectral analysis This study explores the use of AI-assisted data handlingin spectrophotometric method development, providing a flexible and globally accessible alternative to traditional manual software algorithms.Quadriderm cream combines four active ingredients: Clioquinol (CLIO), Betamethasone (BETA), Tolnaftate (TOL), and Gentamicin (GEN) with the preservative Chlorocresol (CC). Building on our previous research on complex pharmaceutical mixtures with challenging ratios, this study applied established protocols for CLIO and GEN while focusing on the more analytically demanding ternary subsystem (TOL, BETA, and CC).The integration of AI-enhanced spectral handling and interpretation reduces operator-dependent variability and streamlines the analytical workflow. This includes generating calibration graphs and regression equations, as well as effectively handling scanned spectral data via consecutive prompts. Validation data such as accuracy and precision are assessed to ensure reliability. Furthermore, the system enables intelligent, simultaneous analysis of laboratory mixtures and pharmaceutical formulations, enhancing both efficiency and accuracy. The AI strategy, trained on spectral data supplied and monitored by the expertiseanalyst, can automatically predict optimal wavelengths with minimal interference, while manual handling strategy rely on analyst-driven selection. Two novel approaches were developed: the factorized derivative ratio extraction using double divisor (MAN-[DD- DDE])via Spectra Manager® software and the automated double divisor derivative ratio (AUTO-[DD-DD]) via AI tools and for resolving ternary mixtures with severely overlapping UV spectra and comparing the results with those of(MAN-[DD- DD])at coincidence points. Linear working ranges were 0.5–5.0 µg/mL (TOL), 3.0–30.0 µg/mL (BETA), and 2.0–20.0 µg/mL (CC); LODs were 0.09, 0.09, and 0.26 µg/mL, respectively. AI-driven data processing strategy matched the accuracy and reproducibility of traditional strategy manipulation while reducing subjective steps and effort. Finally, the UV-spectrophotometric method for pharmaceutical cream analysis was evaluated using the MA Tool (2025) to assess sustainability across green, white, and AI-driven criteria. AI-assisted scoring via Microsoft Copilot enabled rapid, reproducible assessment, yielding a Whiteness Score of 60.9% and providing actionable recommendations for greener and more efficient workflows. Software ; Ternary operation ; Calibration ; Chemometrics ; Pattern recognition (psychology) ; Parametric statistics Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Metabolomics and Mass Spectrometry Studies
Authors: Thomas Husmann
Source: Research Square (preprint, 2026-03-20)
Field: Materials Science — Materials Chemistry

Fibonacci Band Structure of the Aubry–André–Harper Spectrum and Its Correspondence with Atomic Shell Degeneracies and Radius Ratios Fibonacci number ; Hamiltonian (control theory) ; Eigenvalues and eigenvectors ; Golden ratio ; Lattice (music) ; Ground state ; Spectral line Quasicrystal Structures and Properties ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research