jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Asif Khan | Qamar Din | Walid Abdelfattah
Source: match Communications in Mathematical and in Computer Chemistry (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Codimension-Two Bifurcation of a Fractional-Order Schnakenberg Chemical Reaction System This article explores the dynamic behavior of a fractional-order Schnakenberg chemical reaction model. Specifically, we conduct an analysis of codimension-two bifurcation associated with 1:2, 1:3, and 1:4 resonances. To achieve these results, we utilize the normal form method and bifurcation theory. The findings are illustrated through detailed numerical simulations, including visualizations like two-parameter bifurcation diagrams and maximum Lyapunov exponent plots. These simulations effectively explore the system's behavior under the influence of two varying parameters within a three-dimensional space. Additionally, the simulations vividly demonstrate the theoretical results and offer valuable insights into the underlying dynamics. Codimension ; Bifurcation ; Order (exchange) ; Mathematics ; Pure mathematics ; Mathematical analysis ; Physics ; Nonlinear system ; Economics ; Finance ; Quantum mechanics thermodynamics and calorimetric analyses ; Nonlinear Dynamics and Pattern Formation ; Fractional Differential Equations Solutions
Authors: Davide Filippozzi | Arash Rahimi‐Iman
Source: ArXiv.org (article, 2025-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A NEAT Approach to Evolving Neural-Network-based Optimization of Chiral Photonic Metasurfaces: Application of a Neuro-Evolution Pipeline The design of chiral metasurfaces with tailored optical properties remains a central challenge in nanophotonics due to the highly nonlinear relationship between geometry and chiroptical response. Machine-learning-assisted optimization pipelines have recently emerged as efficient tools to accelerate this process, yet their performance strongly depends on the choice of neural-network (NN) architecture. In this work, we integrate the NeuroEvolution of Augmenting Topologies (NEAT) algorithm into an established deep-learning optimization framework for dielectric chiral metasurfaces. NEAT autonomously evolves both network topology and connection weights, enabling task-specific architectures without manual tuning, whereas the reinforcement-learning strategy in our framework evolves knowledge of the solution space and fine-tunes a model's weights in parallel. Using a pipeline-produced dataset of 9,600 simulated GaP metasurface geometries, we evaluate NEAT under varying input dimensionalities, feature-scaling methods, and data sizes. With standardized feature scaling yielding the most consistent performance for both examined output dimensionalities, the relatively compact NEAT-evolved NN models, when integrated into the full optimization pipeline, achieve similar or improved predictive accuracy and generalization compared to initially employed dense few-layer perceptrons. Accordingly, these resource-efficient models successfully perform inference of metasurfaces exhibiting strong circular dichroism in the visible spectrum, allowing for transfer learning between simulated and experimental data. This approach demonstrates a scalable path toward adaptive, self-configuring machine-learning frameworks for automated photonic design both standalone and as building block for agentic artificial intelligence (AI). Pipeline (software) ; Network topology ; Scalability ; Photonics ; Topology (electrical circuits) ; Generalization ; Scaling ; Realization (probability) ; Topology optimization Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Photonic Crystals and Applications
Authors: Д.С. Ванус | С.А. Посохов | И.А. Ухов
Source: Modern Economy Success (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Расчет экономической эффективности применения различных видов и процентов содержания композитной арматуры на прочность в современном строительстве часто поднимается вопрос замены одного материала на другой с целью повышения эффективности, долговечности и снижения стоимости. Перспективным материалом является композит, так как он имеет ряд преимуществ перед металлом, а также позволяет возводить сооружения легче, быстрее и максимально экономично. Оценка экономической эффективности применения композитной арматуры в железобетонных конструкциях требует комплексного подхода, учитывающего как технические характеристики материала, так и его влияние на общую стоимость строительства и эксплуатации объекта. Композитная арматура, изготовленная из стекло-, базальто- или углеродных волокон, обладает рядом преимуществ по сравнению с традиционной стальной арматурой, включая повышенную коррозионную стойкость и меньший вес. Эти характеристики могут существенно влиять на прочность, долговечность и экономическую целесообразность железобетонных конструкций. Применение композитной арматуры в несущих железобетонных конструкциях – это актуальный вопрос, который требует дополнительных расчетов и экспериментального обоснования. in modern construction, the issue of replacing one material with another is often raised in order to increase efficiency, durability and reduce cost. Composite is a promising material, as it has a number of advantages over metal, and also allows you to build structures easier, faster and as economically as possible. Evaluating the economic efficiency of using composite reinforcement in reinforced concrete structures requires an integrated approach that takes into account both the technical characteristics of the material and its impact on the total cost of construction and operation of the facility. Composite reinforcement made of glass, basalt or carbon fibers has a number of advantages over traditional steel reinforcement, including increased corrosion resistance and lower weight. These characteristics can significantly affect the strength, durability and economic feasibility of reinforced concrete structures. The use of composite reinforcement in load – bearing reinforced concrete structures is an urgent issue that requires additional calculations and experimental justification. Computer science Structural mechanics and materials ; Structural Behavior of Reinforced Concrete ; Nanotechnology research and applications
Authors: Aastha Aastha | Leonardo de Macêdo Filho | Michael Woolman | Vladimir Ignatchenko | Alexander Keszei | Gabriela Remite-Berthet | Alireza Mansouri | Thomas Kislinger
Source: Research Square (preprint, 2025-07-15)
Field: Chemistry — Spectroscopy

Systematic evaluation of analytical methods for CSF proteomics Proteomics ; Computer science ; Computational biology ; Medicine ; Chemistry ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Biosensors and Analytical Detection
Authors: Roman Tutko | Vasyl Lozynskyi
Source: Energies (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Oscillations of the Oil Pipeline Axis with Consideration of the Inertia Component When Pumping Diesel Fuel This study examines a single-span beam crossing without longitudinal deformation compensators during diesel fuel pumping. In addition to static forces, namely, the weight of the pipeline and the transported product, the analysis considers vertical components of inertial forces acting on the oil product and the pipeline itself. These forces are directed perpendicularly to the abscissa axis connecting the endpoints of the crossing. The inertial effects cause significant vertical oscillations of the pipeline, which have not been sufficiently addressed in previous research. This work aims to study these oscillations to determine the displacements of points along the pipeline axis, the magnitudes of the inertial forces, and the resulting bending moments at the crossing. A classical Fourier series method is applied to solve the formulated boundary value problem. The results show that oscillations occur in the vertical plane, are symmetrical relative to the center of the span, and are undamped. The maximum vertical displacement reaches approximately 57 mm at the midpoint of the crossing, and the oscillation period is around 0.415 s. Inertial force distribution and bending moments are also symmetric about the center. A detailed analysis with small time steps confirmed that the oscillations are strictly periodic, exhibiting equal displacements in the upward and downward directions. The results highlight that fatigue loads arise during the operation of such crossings, which is important for assessing the strength and stability of oil pipeline structures under real operating conditions. Diesel fuel ; Pipeline (software) ; Component (thermodynamics) ; Inertia ; Automotive engineering ; Petroleum engineering ; Environmental science ; Engineering ; Marine engineering ; Mechanical engineering ; Physics ; Thermodynamics ; Classical mechanics Material Properties and Failure Mechanisms ; Industrial Engineering and Technologies ; Engineering Diagnostics and Reliability
Authors: Weiming Shi | Xing Jin
Source: ArXiv.org (preprint, 2025-04-14)
Field: Materials Science — Materials Chemistry

Heimdall: test-time scaling on the generative verification An AI system can create and maintain knowledge only to the extent that it can verify that knowledge itself. Recent work on long Chain-of-Thought reasoning has demonstrated great potential of LLMs on solving competitive problems, but their verification ability remains to be weak and not sufficiently investigated. In this paper, we propose Heimdall, the long CoT verification LLM that can accurately judge the correctness of solutions. With pure reinforcement learning, we boost the verification accuracy from 62.5% to 94.5% on competitive math problems. By scaling with repeated sampling, the accuracy further increases to 97.5%. Through human evaluation, Heimdall demonstrates impressive generalization capabilities, successfully detecting most issues in challenging math proofs, the type of which is not included during training. Furthermore, we propose Pessimistic Verification to extend the functionality of Heimdall to scaling up the problem solving. It calls Heimdall to judge the solutions from a solver model and based on the pessimistic principle, selects the most likely correct solution with the least uncertainty. Taking DeepSeek-R1-Distill-Qwen-32B as the solver model, Pessimistic Verification improves the solution accuracy on AIME2025 from 54.2% to 70.0% with 16x compute budget and to 83.3% with more compute budget. With the stronger solver Gemini 2.5 Pro, the score reaches 93.0%. Finally, we prototype an automatic knowledge discovery system, a ternary system where one poses questions, another provides solutions, and the third verifies the solutions. Using the data synthesis work NuminaMath for the first two components, Heimdall effectively identifies problematic records within the dataset and reveals that nearly half of the data is flawed, which interestingly aligns with the recent ablation studies from NuminaMath. Solver ; Correctness ; Generalization ; Scaling ; Satisfiability modulo theories ; Benchmark (surveying) ; Problem solver Machine Learning in Materials Science ; Computability, Logic, AI Algorithms ; Explainable Artificial Intelligence (XAI)
Authors: Licheng Zhang | Brian Le | Naveed Akhtar | Siew-Kei Lam | Tuan Ngo
Source: arXiv (Cornell University) (preprint, 2025-05-13)
Field: Materials Science — Materials Chemistry

Large Language Models for Computer-Aided Design: A Survey Large Language Models (LLMs) have seen rapid advancements in recent years, with models like ChatGPT and DeepSeek, showcasing their remarkable capabilities across diverse domains. While substantial research has been conducted on LLMs in various fields, a comprehensive review focusing on their integration with Computer-Aided Design (CAD) remains notably absent. CAD is the industry standard for 3D modeling and plays a vital role in the design and development of products across different industries. As the complexity of modern designs increases, the potential for LLMs to enhance and streamline CAD workflows presents an exciting frontier. This article presents the first systematic survey exploring the intersection of LLMs and CAD. We begin by outlining the industrial significance of CAD, highlighting the need for AI-driven innovation. Next, we provide a detailed overview of the foundation of LLMs. We also examine both closed-source LLMs as well as publicly available models. The core of this review focuses on the various applications of LLMs in CAD, providing a taxonomy of six key areas where these models are making considerable impact. Finally, we propose several promising future directions for further advancements, which offer vast opportunities for innovation and are poised to shape the future of CAD technology. Github: https://github.com/lichengzhanguom/LLMs-CAD-Survey-Taxonomy Intersection (aeronautics) ; Workflow ; Key (lock) ; Forcing (mathematics) ; Core (optical fiber) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Design Education and Practice
Authors: Carolina Blanch-Perez-del-Notario | Murali Jayapala
Source: Sustainability (article, 2025-09-09)
Field: Chemistry — Analytical Chemistry

Genetic Algorithm Based Band Relevance Selection in Hyperspectral Imaging for Plastic Waste Material Discrimination Hyperspectral imaging, in combination with microscopy, can increase material discrimination compared to standard microscopy. We explored the potential of discriminating pellet microplastic materials using a hyperspectral short-wavelength infrared (SWIR) camera, providing 100 bands in the 1100–1650 nm range, in combination with reflection microscopy. The identification of the most relevant spectral bands helps to increase system cost efficiency. The use of fewer bands reduces memory and processing requirements, and can also steer the development of sustainable, cost-efficient sensors with fewer bands. For this purpose, we present a genetic algorithm to perform band relevance analysis and propose novel algorithm optimizations. The results show that a few spectral bands (between 6 and 9) are sufficient for accurate (&gt;80%) pixel discrimination of all 22 types of microplastic waste, contributing to sustainable development goals (SDGs) such as SDG 6 (‘clean water and sanitation’) or SDG 9 (‘industry, innovation, and infrastructure’). In addition, we study the impact of the classifier method and the width of the spectral response on band selection, neither of which has been addressed in the current state-of-the-art. Finally, we propose a method to steer band selection towards a more balanced distribution of classification accuracy, increasing its applicability in multiclass applications. Hyperspectral imaging ; Pattern recognition (psychology) ; Relevance (law) ; Spectral signature ; Classifier (UML) ; Spectral bands ; Pixel Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Сергій Вадимович Ковалевський
Source: Обробка матеріалів тиском (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Швидкодіючі моделі штучного інтелекту для реального часу в цифрових двійниках процесів обробки металів тиском Ковалевський С. В. Швидкодіючі моделі штучного інтелекту для реального часу в цифрових двійниках процесів обробки металів тиском У статті представлено комплексну методику створення цифрового двійника технологічних процесів обробки металів тиском, який поєднує класичне моделювання методом скінченних елементів і високошвидкісні моделі штучного інтелекту. Метою дослідження було забезпечити інференс із затримкою не більше ніж сто мілісекунд, прискорити повний цикл симуляцій утричі з натяком, але в реальності в понад п’ятдесят разів порівняно з традиційними FEM‑розрахунками, а також досягти прогнозної достовірності вище дев’яноста відсотків. Для цього було сформовано навчальний набір із понад дванадцяти тисяч скінченно‑елементних випадків з широкою варіацією геометричних, матеріальних та технологічних параметрів. На основі цих даних розроблено гібридну нейромережну архітектуру, яка чергує тривимірні згорткові шари й рекурентні LSTM‑блоки, що дозволило відтворювати складні поля напружень і переміщень за допомогою воксельних тензорів. Адаптивне керування реалізоване через агент навчання з підкріпленням, здатний у процесі роботи динамічно коригувати параметри преса з урахуванням багатокомпонентної винагороди, що враховує якість поверхні, енерговитрати та продуктивність. Валідація системи на незалежних наборах даних показала високу стабільність: середня абсолютна помилка прогнозу не перевищує двох відсотків, а максимальна похибка залишається нижче восьми сотих частки одиниці. Крім того, модель витримує введення випадкового шуму до п’ятнадцяти відсотків без значної втрати точності. Енергозбереження в процесі штампування склало понад десять відсотків, а якість оброблених деталей підвищилася на п’ятнадцять відсотків порівняно з класичними режимами. Розроблена платформа відзначається масштабованістю завдяки модульній архітектурі й чітким інтерфейсам до MES та ERP, а також забезпечує прозорість роботи ШІ-модулів через візуалізацію ключових атрибутів вхідних даних. Реалізація технік переносу знань для вбудованих контролерів та оптимізація обчислювальних ресурсів залишаються пріоритетними напрямами подальших досліджень. Identification (biology) ; Process (computing) ; Product (mathematics) Material Properties and Failure Mechanisms ; Environmental and Industrial Safety ; Advanced Scientific Research Methods
Authors: Chaozheng Wang | Zezhou Yang | Shuzheng Gao | Cuiyun Gao | T. Y. Peng | Hailiang Huang | Yuetang Deng | Michael R. Lyu
Source: ArXiv.org (preprint, 2025-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

RAG or Fine-tuning? A Comparative Study on LCMs-based Code Completion in Industry Code completion, a crucial practice in industrial settings, helps developers improve programming efficiency by automatically suggesting code snippets during development. With the emergence of Large Code Models (LCMs), this field has witnessed significant advancements. Due to the natural differences between open-source and industrial codebases, such as coding patterns and unique internal dependencies, it is a common practice for developers to conduct domain adaptation when adopting LCMs in industry. There exist multiple adaptation approaches, among which retrieval-augmented generation (RAG) and fine-tuning are the two most popular paradigms. However, no prior research has explored the trade-off of the two approaches in industrial scenarios. To mitigate the gap, we comprehensively compare the two paradigms including Retrieval-Augmented Generation (RAG) and Fine-tuning (FT), for industrial code completion in this paper. In collaboration with Tencent's WXG department, we collect over 160,000 internal C++ files as our codebase. We then compare the two types of adaptation approaches from three dimensions that are concerned by industrial practitioners, including effectiveness, efficiency, and parameter sensitivity, using six LCMs. Our findings reveal that RAG, when implemented with appropriate embedding models that map code snippets into dense vector representations, can achieve higher accuracy than fine-tuning alone. Specifically, BM25 presents superior retrieval effectiveness and efficiency among studied RAG methods. Moreover, RAG and fine-tuning are orthogonal and their combination leads to further improvement. We also observe that RAG demonstrates better scalability than FT, showing more sustained performance gains with larger scales of codebase. Scalability ; Code (set theory) ; Coding (social sciences) ; Embedding ; Adaptation (eye) ; Field (mathematics) ; Domain (mathematical analysis) Copper Interconnects and Reliability
Authors: Yanhao Li | Xin Zhou | Feng Zhong | Wei Han | Yue Han | Yanman Chen | Lingyun Huang
Source: Sensors and AI (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Multi-Sensor Fusion and Data Analytics for Power Cable Condition Monitoring and Predictive Maintenance Power cables are critical infrastructure for electric power transmission, and their reliability directly affects supply security. The expansion of cable deployment has increased the difficulty of condition monitoring, while more complex operating environments intensify the challenge of early identification of insulation aging and latent defects. This paper systematically analyzes power-cable condition-monitoring and intelligent operation-and-maintenance (O&amp;M) technologies, comparing electrical monitoring methods—such as partial discharge, sheath current, and dielectric loss—with non-electrical parameter monitoring of temperature, humidity, and strain, thereby clarifying the performance characteristics and applicable scopes of each approach. We also examine O&amp;M methods including intelligent inspection, supervisory control and data acquisition (SCADA) system integration, and data-fusion algorithms. The results show that establishing a multi-parameter collaborative sensing framework, platform-based integration, and an artificial intelligence (AI)-driven O&amp;M system enables accurate condition assessment and predictive maintenance of cables. This study provides a technical pathway for transitioning power-cable O&amp;M from periodic maintenance to predictive maintenance and offers practical value for enhancing grid reliability. Predictive maintenance ; Condition monitoring ; Software deployment ; Condition-based maintenance ; Reliability (semiconductor) ; Identification (biology) ; Sensor fusion ; Power cable ; Maintenance actions ; Electric power system High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Electrical Fault Detection and Protection
Authors: Jia Liu | J.A.L. van Kan | Xiao‐Ming Chen | Laixiang Xu | Xueli Zheng | Mohammad Nazir Ahmad | Junmin Zhao
Source: Frontiers in Plant Science (article, 2025-10-16)
Field: Chemistry — Analytical Chemistry

RTCB: an integrated deep learning model for garlic leaf disease identification The proposed approach provides a scalable technical reference for the engineering application of automatic disease monitoring and control in intelligent agriculture. The current strategy is conducive to the deployment of edge computing equipment and has extensive significance and application potential in plant leaf disease detection. Deep learning ; Identification (biology) ; Software deployment ; Disease ; Scalability ; Plant disease Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Anish Pandey | Shobha Kulshreshtha
Source: IARJSET (article, 2025-10-08)
Field: Chemistry — Analytical Chemistry

Design of an Intelligent Fuzzy Controller for Drug Dosage Optimization in ICU Patients This paper presents the design and implementation of an intelligent fuzzy control system for optimizing drug dosage in intensive care unit (ICU) patients.The controller continuously monitors key physiological parameters such as heart rate (HR), mean arterial pressure (MAP), and drug concentration to determine the optimal infusion rate.By integrating fuzzy logic with adaptive tuning mechanisms, the proposed system effectively manages nonlinearities, uncertainties, and interpatient variability inherent in physiological systems.Simulation results show that the intelligent fuzzy controller significantly reduces overshoot, settling time, and steady-state error compared to conventional PID and basic fuzzy controllers.The approach ensures smoother control actions, enhances patient safety, and maintains hemodynamic stability during critical care drug administration. Fuzzy logic ; Controller (irrigation) ; Drug ; Control theory (sociology) ; Fuzzy control system Analytical Methods in Pharmaceuticals