jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mirfan | Billy Eden William Asrul | Mila Jumarlis | Juliani Juliani
Source: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (article, 2025-10-11)
Field: Chemistry — Analytical Chemistry

Automated Ripeness Detection of Oil Palm Fruit Using a Hybrid GLCM-HSV-KNN Model Accurately determining the ripeness of oil palm fruit is crucial for ensuring the quality of palm oil. However, traditional manual methods are often time-consuming and less accurate. This study aimed to develop an automated system for detecting the ripeness of oil palm fruit by combining the Hue Saturation Value (HSV) model, Gray Level Co-occurrence Matrix (GLCM), and K-Nearest Neighbor (KNN) algorithms. This system utilizes K-Nearest Neighbors to classify the relationship between color features extracted using the HSV model and texture features derived from GLCM analysis to categorize fruit ripeness. The color features represent the fruit's chromatic characteristics associated with ripeness, while the texture features provide information regarding surface patterns related to ripeness. The color features represent the fruit's color characteristics associated with ripeness, whereas the texture features provide information about the surface patterns related to ripeness. The results indicate that the system can classify oil palm fruit into four distinct categories: Over-Ripe, Ripe, Half-Ripe, and Raw. The dataset was divided with an 80:20 ratio, where 80% was allocated for training data and the remaining 20% for test data. An accuracy rate of 85% was achieved. The results of this study demonstrate that the developed system effectively classifies oil palm fruit images based on ripeness levels. This system supports a sustainable automated palm oil production model through accurate ripeness detection, thereby reducing reliance on manual methods and enhancing consistency and productivity in palm oil processing. These findings indicate that the proposed hybrid method is feasible for integration into an automated classification system to support decision-making in oil palm harvesting Ripeness ; Hue ; Machine vision ; Palm oil ; Pattern recognition (psychology) ; Palm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Oil Palm Production and Sustainability
Authors: Sutha Loidueanchai | Apichart Artnaseaw | Achira Artnaseaw
Source: Open Engineering (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Sustainable cocoon waste epoxy composite solutions: Novel approach based on the deformation model using finite element analysis to determine Poisson’s ratio Abstract Poisson’s ratio is essential in composite analysis and design. This study presents a novel method for determining the modulus of elasticity and Poisson’s ratio of cocoon waste epoxy composites (CWECs) using FEA, validated through tensile testing. The deformation curve was first established experimentally, followed by Latin Hypercube Sampling (LHS) in MATLAB to generate 20 data pairs, enhancing result reliability. In the subsequent phase, detailed structural models of CWEC were constructed using PowerShape, and simulations were performed in ANSYS to analyze mechanical behavior. Validation was achieved by comparing the simulated deformations with empirical test data, with RMSE used as the primary accuracy metric. The results yielded an RMSE of 0.2181, indicating a strong correlation between the FEA model and experimental observations. This confirms the reliability of the derived values for modulus of elasticity (565.57 MPa) and Poisson’s ratio (0.4564). Further validation of Poisson’s ratio for CWEC was conducted by comparing the simulation outcomes with experimental data in accordance with ASTM standards. The findings revealed a high degree of correlation between the two methods, with a similarity of 94.21%. Latin hypercube sampling ; Finite element method ; Ultimate tensile strength ; Young's modulus ; Composite number ; Epoxy ; Tensile testing ; Deformation (meteorology) ; Elasticity (physics) Natural Fiber Reinforced Composites ; Polymer composites and self-healing ; Advanced Cellulose Research Studies
Authors: Lechen Dong | Fang Liu
Source: Journal of Chemical Education (article, 2025-08-09)
Field: Materials Science — Materials Chemistry

Streamlining Coding Assignments and Grading on the Cloud: A Preconfigured JupyterHub Image for Chemistry Education Integrating coding skills into chemistry education is crucial for preparing students to meet the demands of modern research. However, the technical challenges associated with installing computational tools often discourage chemistry educators from incorporating programming exercises into their courses. To tackle these challenges, we developed a preconfigured image on Jetstream2, a U.S. academic cloud platform supported by the National Science Foundation. This image, shared with the community, allows chemistry instructors to effortlessly deploy a JupyterHub platform for their classrooms, facilitating the teaching of programming skills. Integrated with the automatic grading package nbgrader, the JupyterHub Web site created from this image enables seamless assignment creation, distribution, and automatic grading on a cloud-based platform. Students can access assignments directly through their web browsers without the need to install software or configure their local machines. This tool empowers educators to equip future scientists with essential coding skills, enabling them to tackle interdisciplinary challenges and drive chemical discoveries. To ensure the accessibility of this approach worldwide, we created a preconfigured image on the Google Cloud Platform and provided detailed instructions for setting up this nbgrader-integrated JupyterHub instance from scratch. Cloud computing ; Computer science ; Grading (engineering) ; Coding (social sciences) ; Installation ; Software ; Multimedia ; World Wide Web ; Software engineering ; Engineering ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Online Learning and Analytics
Authors: Alexis Cocoyutla-González | Daniela Aroche-Orta | E.M. López-Méndez
Source: Avances en ciencias e ingeniería (article, 2025-09-30)
Field: Materials Science — Biomaterials

Evaluación de la vida útil de gelatina de cacahuate con edulcorantes no-calóricos mediante análisis de riesgo de Weibull El presente estudio evaluó la vida útil de gelatina de cacahuate (Arachis hypogaea) con edulcorantes no calóricos (aspartame/acesulfame K) y sin conservadores. El producto, almacenado a 10 °C, fue analizado microbiológica (mesófilos, coliformes, hongos/levaduras), fisicoquímica (humedad, pH, aw) y sensorialmente (color, olor, sabor, textura y aceptación general). La vida útil sensorial se determinó mediante el análisis de riesgo de Weibull, basado en el rechazo de 30 consumidores (criterio: puntuación &lt;7 sobre 9 puntos). Los recuentos microbiológicos estuvieron dentro de los límites normativos hasta el día 8. El producto mostró alta actividad de agua (aw=0.998-0.973) y pH neutro (6.3). La vida útil estimada (50% de rechazo) fue de 6.6-6.8 días (r²=0.851). Los parámetros de Weibull (β=3.41-4.61, α=7.34-7.36 días) confirmaron una tasa de fallo creciente. La vida útil de la gelatina sería de una semana. Una alta aw fue el factor limitante crítico, indicando la necesidad de reformular el producto y asegurar la estabilidad. Life style ; Statistical analysis ; Electrodiagnosis Nanocomposite Films for Food Packaging ; Botanical Research and Applications ; Food Chemistry and Fat Analysis
Authors: Brandon Yee | William D. Collins | Caden Wang | Mihir Tekal
Source: Research Square (preprint, 2025-10-31)
Field: Materials Science — Materials Chemistry

Prometheus: Unsupervised Discovery of Phase Transitions and Order Parameters in the Two-Dimensional Ising Model Using Variational Autoencoders Ising model ; Phase transition ; Monte Carlo method ; Unsupervised learning ; Consistency (knowledge bases) ; Magnetization ; Representation (politics) Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: H. Shan | Howard S. Ying | Peihong Cheng | Lingxiang Hu | Jingrui Wang | Zijun Ye | Fei Zhuge
Source: Chinese Journal of Luminescence (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advances in All-optically Controlled Oxide Synapses Abstract Brain-like neuromorphic computing is expected to overcome the bottleneck of traditional von Neumann computing architecture achieving low power consumption and highly efficient information processing thereby ad vancing artificial intelligence technology.Artificial synapses are key hardware for building neuromorphic systems Materials science ; Nanotechnology ; Oxide ; Engineering physics ; Optoelectronics ; Metallurgy ; Engineering Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions ; Photorefractive and Nonlinear Optics
Authors: Priyanka P. Singh | G Nath
Source: Research Square (preprint, 2025-07-22)
Field: Materials Science — Polymers and Plastics

Valorisation of luffa cylindrica and corn husk waste into multifunctional bio composites for sustainable acoustic applications Husk ; Valorisation ; Agricultural waste ; Pulp and paper industry ; Materials science ; Composite material ; Waste management ; Botany ; Engineering ; Biology Natural Fiber Reinforced Composites ; Acoustic Wave Phenomena Research ; Advanced Cellulose Research Studies
Authors: Lennard L. Bohlender | Juliana Parsons | Antonia Mitgau | Sebastian N. W. Hoernstein | Giovanna Grigolon | Bernhard Henes | Eva L. Decker | Ralf Reski
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-28)
Field: Materials Science — Biomaterials

Secretion-based production of prolyl-hydroxylated human type III collagen in scalable Physcomitrella photobioreactors Abstract Collagens are structural proteins of the extracellular matrix essential for skin elasticity and integrity. They are widely used in dietary supplements and cosmetics. Conventional collagens of animal origin raise concerns regarding ethics, safety, and sustainability. As a vegan alternative, we report on the production of a 30 kDa prolyl-hydroxylated human collagen polypeptide from Physcomitrella moss plants. For secretion-based production and formulation compatibility, a hydrophilic region encompassing 334 amino acids from human type III collagen was selected, which includes four protein domains involved in cell adhesion, collagen binding, integrin recognition and wound healing. Transgenic moss lines were generated via protoplast transformation. Immunodetection identified collagen producing lines, and mass spectrometry validated the product and detected prolyl-hydroxylation on 23 sites. The presence of this important post-translational modification underscores the high biomimetic quality of the product. To enable industrial-scale production, the transformants were quantitatively analysed at the genomic, transcript, and protein levels. The most productive lines were forwarded to process development, where culture conditions including CO 2 supplementation, pH, and light intensity were optimized. Upscaling to 5 L photobioreactors established a robust, light- and biomass-dependent production regime that yielded nearly 1 mg/L of secreted collagen polypeptide in the culture supernatant after 11 days of cultivation. Taken together, this study presents the first scalable moss-based production of a post-translationally modified human collagen and offers a sustainable and vegan alternative to conventional collagens for cosmetic formulations. This highlights the versatility of Physcomitrella as a production host for high-quality proteins with industrial applicability that also meet consumer requirements. Key message Scalable moss bioreactors enable the production of high-quality recombinant prolyl-hydroxylated human collagen without heterologous P4H expression, offering a sustainable and vegan alternative to conventional collagens derived from animals. Physcomitrella patens ; Extracellular matrix ; Photobioreactor ; Cell culture ; Type I collagen ; Biopolymer Collagen: Extraction and Characterization ; Enzyme Production and Characterization ; Protein Hydrolysis and Bioactive Peptides
Authors: Mohadese Kamrani-Mehni | Behnam Behrooznam | Abdolkarim Ejraei | Kavous Ayazpour
Source: Journal of the Saudi Society of Agricultural Sciences (article, 2025-11-14)
Field: Materials Science — Biomaterials

Maintaining postharvest quality of tomato fruits through biopolymer coatings of chitosan and carboxymethyl cellulose Abstract Tomato ( Solanum lycopersicum L.) is a widely consumed vegetable known for its taste and nutritional value, but it faces significant postharvest losses. This study evaluated the effects of various coating methods using carboxymethyl cellulose (CMC) at levels of 0.5%, 0.75%, and 1%, and chitosan (CS) at 0.5%, 1%, and 1.5%, alongside untreated and water-washed controls, on the physicochemical properties of postharvest tomatoes during storage. Significant differences were observed in fruit weight loss, firmness, lycopene content, total phenols, flavonoids, antioxidant activity, hydrogen peroxide (H 2 O 2 ) levels, vitamin C content, and pH across the treatments over 40 days. Notably, the 1.5% CS treatment was the most effective, resulting in a 35% reduction in weight loss, 20% higher firmness, and significantly increased lycopene and vitamin C levels compared to controls. CMC treatments also reduced weight loss but were less effective in maintaining firmness and antioxidant levels. Overall, CS coatings demonstrated superior benefits in preserving fruit quality, indicating their potential as effective postharvest treatments. Postharvest ; Lycopene ; Carboxymethyl cellulose ; Hydrogen peroxide ; Chitosan ; Antioxidant ; Vitamin C ; Biopolymer Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Polymer-Based Agricultural Enhancements
Authors: Alessandro Colombo | Riccardo Busetto | Luca Innocenti | Matteo Corno | Andrea Maria Zanchettin | Sergio M. Savaresi
Source: IFAC-PapersOnLine (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Vision based robotic portion control for granular food The food industry is increasingly adopting robotic automation for food preparation, driven by the need for precision and efficiency. Vision systems play a pivotal role in enabling robots to execute tasks effectively in these dynamic and complex environments. This work proposes two vision-based methods for real-time estimation of the mass of food collected by a robotic manipulator, allowing precise portion control for granular foods, such as rice. Specifically, we investigate the usage of 2D and 3D vision algorithms. The former estimates the quantity of food based on the area of the collection tool that is occluded by food, while the latter obtains the estimate from the volume of food collected. Experimental results demonstrate that the 2D algorithm excels at higher speed, while the 3D algorithm offers superior accuracy at lower speed, highlighting the strengths and limitations of each approach for different operating conditions. Automation ; Robot ; Machine vision ; Control (management) ; Robot vision ; Food industry ; Robotics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Robot Manipulation and Learning
Authors: Hailong Fan | Yang Hu
Source: Chinese Science Bulletin (Chinese Version) (article, 2025-10-15)
Field: Materials Science — Biomaterials

Design of underwater adhesive hydrogels: from biomimetic experience to data-driven approaches 水下黏附材料是一类能够在潮湿或水下环境中保持强力黏附的功能材料，在海洋工程、应急修复以及高端生物医用器械等领域具有重要价值。然而，复杂水环境中水分子形成的水合层阻碍了界面作用，同时水分子的渗透也会导致材料逐渐失效，使得高性能水下黏附材料的制备长期面临挑战。自然界的黏附蛋白为人类提供了重要启示，早期研究主要通过模仿多酚、带电基团等官能团实现了初步突破，但受限于聚合物序列不可控和经验依赖，性能提升空间有限。近年来，材料科学逐渐从经验仿生走向数据驱动，研究者尝试通过蛋白质数据库提炼共性规律，并结合人工智能算法探索最优材料配方。这一思路不仅推动了水下黏附材料性能的大幅提升，也展示了未来功能软物质研发的新范式，即融合自然启发与数据智能的方法论。 Underwater ; Adhesive ; Feature (linguistics) ; Focus (optics) Diatoms and Algae Research ; Marine Biology and Environmental Chemistry
Authors: Daniel S. Papp | Hanrong Wen | David Scheich | Nico Lingg | Goran Mitulović | Sebastiaan Eeltink
Source: Journal of Separation Science (article, 2025-12-01)
Field: Chemistry — Spectroscopy

A Protocol for Robust Discovery Proteomics Using Nano Liquid Chromatography Using Pillar‐Array Column Technology and High‐Resolution Mass Spectrometry With Data‐Independent Acquisition Nano liquid chromatography-mass spectrometry has come to be a key enabling technology in shotgun proteomics due to the combination of exceptional separation power, sensitivity, and comprehensiveness. However, the know-how of setting up proteomics methods to deliver robust, reliable, and meaningful results to large-scale life science experiments has remained somewhat ambiguous. This protocol outlines guidance for establishing nano-LC-MS/MS workflows focusing on comprehensive and untargeted deep proteome profiling, using state-of-the-art column technology and mass spectrometry. Employing a second-generation micropillar-array column, a trade-off is demonstrated between analysis time and chromatographic resolving power, which in turn impacts peptide and protein identification scores from a commercial HeLa reference standard. Furthermore, a straightforward workflow to develop a data-independent acquisition (DIA)-parallel accumulation-serial fragmentation (PASEF) analytical method is proposed, with a special focus on the optimization of the ESI source settings. Besides the method development, the study discusses the use of segmented gradients, and an MS-compatible surfactant in the sample diluent is also explored. Finally, the robustness of the developed method is demonstrated through consistently identifying 7558 protein groups (CV = 0.3%) as maintaining high repeatability peptide retention times (mean CV = 0.2%) and system pressure (CV = 0.4%) over 21 consecutive analyses. Mass spectrometry ; Shotgun proteomics ; Proteomics ; Repeatability ; Workflow ; Proteome ; Quantitative proteomics ; Sample preparation ; Fragmentation (computing) ; Analytical technique Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Kien Xuan Ngo | Takashi Sumikama | Rémi Vuillemot | Han Gia Nguyen | Taro Q.P. Uyeda | Ngan Le | Sergei Grudinin
Source: Research Square (preprint, 2025-05-10)
Field: Chemistry — Physical and Theoretical Chemistry

Atomic Conformational Dynamics and Actin-Crosslinking Function of Alpha-Actinin Revealed by SimHS-AFMfit Actin ; Actinin ; Function (biology) ; Biophysics ; Alpha (finance) ; Chemistry ; Cell biology ; Physics ; Biochemistry ; Biology ; Cytoskeleton ; Mathematics ; Cell thermodynamics and calorimetric analyses ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: V.N. Kabanov | D.V. Shilov
Source: Russian Journal of Building Construction and Architecture (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Assessment of the Reliability of the Estimated Value of the Duration of the Construction Process Statement of the problem. The discrepancy between the values of the standard duration of work when calculated using the standards established by ENiR and GESN in the organizational and technological design of construction processes is considered. Results. As a result of the study, a scientific task was set, which consists in determining approaches to determining the reliable value of the duration of reinforcement work under the conditions of current standards. Conclusions. The use of current standards to determine the duration or required amount of labor resources does not provide the required level of reliability. To solve the scientific problem, it is proposed to build a digital twin of the technological process for the production of reinforcement works. Duration (music) ; Reliability (semiconductor) ; Process (computing) ; Task (project management) ; Work (physics) ; Production (economics) Structural mechanics and materials ; Mechanical Systems and Engineering ; Aerospace, Electronics, Mathematical Modeling
Source: Chemistry International (article, 2025-07-01)
Field: Chemistry — Physical and Theoretical Chemistry

Collaborative development and sustainability of digital chemical data standardsDigSustain2: A community workshop, April 2025, Germany Article Collaborative development and sustainability of digital chemical data standardsDigSustain2: A community workshop, April 2025, Germany was published on July 1, 2025 in the journal Chemistry International (volume 47, issue 3). Sustainability ; Environmental planning ; Engineering ; Business ; Environmental resource management ; Political science ; Environmental science ; Ecology ; Biology History and advancements in chemistry ; Chemistry and Chemical Engineering ; Various Chemistry Research Topics
Authors: Akashamrut M Patel | N. R. Sardar | J. P. Rathod | K.V. Vala | N. Chavda
Source: International Journal of Research in Agronomy (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Rice variety identification using artificial neural network Rice is considered staple food as it is eaten in large quantity in all over world. And it serves as a crucial source of energy and nutrition. With the diverse array of rice varieties cultivated globally, the need for accurate and efficient identification methods becomes paramount. As all rice varieties are not equally valuable unscrupulous persons become tempted to mix up lower cost variety in costly one. Physical attributes of rice like size, shape, cooking characteristics etc. offer lot of avenues for identification of such practices. Among physical attributes is image analysis. This study was conducted with objective of using image analysis using artificial neural network to identify Basmati and Gujarat 17 verities. It was found in this study that rice variety identification is quite feasible using simple feed forward neural network particularly for those verities which differ in visual parameters i.e. like Basmati and Gujarat 17 used in this study. Artificial neural network ; Variety (cybernetics) ; Identification (biology) ; Artificial intelligence ; Computer science ; Nervous system network models ; Machine learning ; Time delay neural network ; Biology ; Types of artificial neural networks ; Ecology Spectroscopy and Chemometric Analyses