jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leila Mansoori
Source: International Journal of Research in Advanced Electronics Engineering (article, 2024-07-01)
Field: Chemistry — Analytical Chemistry

Application of sensor-based technologies for quality assessment of sesame seeds as functional foods Sesame (Sesamum indicum L.) has gained substantial recognition as a functional food due to its rich composition of essential fatty acids, lignans, proteins, minerals, and antioxidants, prompting a growing demand for technologies that ensure its quality and traceability. Traditional methods for evaluating seed quality such as chemical assays, visual inspection, and manual grading are often laborious, subjective, and inadequate for large-scale industrial applications. Sensor-based technologies, including hyperspectral imaging, near-infrared spectroscopy, machine vision, electronic nose systems, and dielectric property sensors, have emerged as advanced, rapid, and non-destructive tools capable of characterizing the physicochemical attributes of sesame seeds with high precision. These technologies facilitate the assessment of moisture content, lipid oxidation, lignan concentration, seed purity, contamination, and mechanical damage, thereby enhancing real-time decision making across the value chain. Recent advancements in feature extraction algorithms, pattern recognition models, and machine-learning-based classification have significantly enhanced the potential of sensor-based systems for quality grading, adulteration detection, and functional component estimation. When integrated with IoT platforms, these sensor technologies further allow remote monitoring and automation, supporting standardized quality protocols for the food and nutraceutical industries. Despite the promising potential, challenges persist in calibration robustness, cost feasibility, data complexity, and technology transfer to smallholder systems. Understanding the applicability, performance limits, and optimization strategies for sensor-based tools is therefore essential for strengthening sesame seed quality assurance frameworks. This article synthesizes current developments, operational principles, and applications of sensor-based technologies for the quality assessment of sesame seeds as functional foods. By focusing on compositional profiling and functional attributes such as fatty acid distribution, lignan content, antioxidant potency, and microbial safety the review highlights the transformative role of sensor-driven approaches in enabling rapid, non-invasive, and reliable quality evaluation. The article also explores future trends and research gaps to support wider adoption of intelligent sensing systems in sesame value chains. Nutraceutical ; Quality assessment ; Quality assurance ; Sesame seed ; Lignan ; Quality (philosophy) ; Grading (engineering) Spectroscopy and Chemometric Analyses ; Sesame and Sesamin Research ; Smart Agriculture and AI
Authors: Bartosz Barzdajn
Source: arXiv (Cornell University) (preprint, 2024-05-14)
Field: Materials Science — Materials Chemistry

Implementing conditional max-min designs in Python. Data-driven, machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements into predictions of energies and forces. As a result, these potentials are as good as the training data (usually results of so-called ab initio simulations) and we need to make sure that we have enough information for a model to become sufficiently accurate, reliable and transferable. The main challenge stems from the fact that descriptors of chemical environments are often sparse high-dimensional objects without a well-defined continuous metric. Therefore, it is rather unlikely that any ad hoc method of choosing training examples will be indiscriminate, and it will be easy to fall into the trap of confirmation bias, where the same narrow and biased sampling is used to generate train- and test- sets. We will demonstrate that classical concepts of statistical planning of experiments and optimal design can help to mitigate such problems at a relatively low computational cost. The key feature of the method we will investigate is that they allow us to assess the informativeness of data (how much we can improve the model by adding/swapping a training example) and verify if the training is feasible with the current set before obtaining any reference energies and forces -- a so-called off-line approach. In other words, we are focusing on an approach that is easy to implement and doesn't require sophisticated frameworks that involve automated access to high-performance computational (HPC). Transferability ; Kernel (algebra) ; Context (archaeology) ; Computer science ; Machine learning ; Kernel method ; Artificial intelligence ; Human–computer interaction ; Mathematics ; Support vector machine Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Yevgeniy Trotsenko | Olexandr Protsenko | Dmytro Myronov | Vadim Chumack | Yaroslav Haran | Julia Peretyatko
Source:  (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Application of Phase-Resolved Partial Discharge Patterns for Detecting Potentially Hazardous Partial Discharges within Insulation Systems Deviations from the sinusoidal voltage waveform and various transient processes significantly impact the longevity of electrical equipment such as induction motors and power transformers. These deviations cause excessive heating, increased electrical stress, and mechanical vibrations, leading to premature aging and equipment failure. Diagnosing the insulation condition is crucial for maintaining the reliability and efficiency of electrical systems. Partial discharges within insulation are particularly harmful, gradually eroding the material and leading to insulation breakdown and equipment failure. This paper describes the practical application of phase-resolved partial discharge patterns for detecting hazardous partial discharges within insulation systems. By analyzing the temporal and amplitude characteristics of partial discharges across different phases, this study establishes a reliable method for identifying and assessing the severity of insulation defects. Experimental results from partial discharge measurements on electrical insulating cardboard samples are presented, demonstrating the effectiveness of phase-resolved analysis. The developed software tool processes partial discharge data to correlate time-resolved partial discharge events with the phase angle of the applied alternating voltage, resulting in detailed phase-resolved partial discharge patterns. These patterns provide insights into the nature and characteristics of partial discharges, aiding in early detection of insulation degradation. Additionally, the results can be used as input data for training and testing artificial neural networks, improving the accuracy of partial discharge classification models and insulation diagnosis techniques. This approach supports the development of condition-based maintenance strategies, optimizing equipment operation, extending service life, and enhancing overall system reliability and safety. Partial discharge ; Reliability (semiconductor) ; Electrical equipment ; Transient (computer programming) ; Waveform ; Voltage ; Partial element equivalent circuit ; Electrical treeing ; Insulation system High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Dielectric materials and actuators
Authors: Lin Chen | Shu‐Ping Lin
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Investigation of Neuromorphic Behaviors in Access Regions-Integrated Van Der Waals Heterostructures The emergence of neuromorphic devices has disrupted the traditional von Neumann architecture, providing a solution to the challenges of simulating neural parallel processing and power consumption in conventional computer architectures [1]. Access regions (ARs) are strategically created and positioned in proximity to source and drain electrodes, resulting in a channel that does not overlap with the floating gate (FG) [2]. In this study, we investigated ARs integrated van der Waals heterostructural field-effect transistor (AR-VH-FET) device, constructed by vertically stacking several 2D materials. Specifically, molybdenum disulfide (MoS 2 ) served as a channel, hexagonal boron nitride (h-BN) as a tunneling layer, and graphene (Gr) as the FG. We conducted measurements on the transfer and output characteristics to characterize the electrical performance of the AR-VH-FET. Under a fixed V DS , with the application of positive and negative V BG as programming and erasing voltages respectively, we measured device retention and endurance. AR-VH-FET devices not only emulate basic neuromorphic functions but also replicate biological characteristics. References [1] Q. Wang, C. Zhao, Y. Sun, R. Xu, C. Li, C. Wang, W. Liu, J. Gu, Y. Shi, L. Yang, Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information, Microsystems &amp; nanoengineering 9(1) (2023) 96. [2] T. Sasaki, K. Ueno, T. Taniguchi, K. Watanabe, T. Nishimura, K. Nagashio, Material and device structure designs for 2D memory devices based on the floating gate voltage trajectory, ACS nano 15(4) (2021) 6658-6668. Figure 1 Neuromorphic engineering ; van der Waals force ; Heterojunction ; Materials science ; Computer science ; Condensed matter physics ; Physics ; Psychology ; Nanotechnology ; Human–computer interaction ; Chemical physics ; Artificial intelligence ; Quantum mechanics ; Artificial neural network ; Molecule Graphene research and applications ; Thermal properties of materials ; Nanowire Synthesis and Applications
Authors: Shichao Zhu | Mengmeng Wang | Mingjie Zhang | Gang Jin
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Estimation of Content and Particle Size Distribution of Glass Bead in Glass Bead-Filled Polystyrene Based on Near-Infrared Diffuse Reflectance Spectroscopy and Ann Bead ; Polystyrene ; Materials science ; Particle-size distribution ; Diffuse reflectance infrared fourier transform ; Diffuse reflection ; Infrared spectroscopy ; Particle (ecology) ; Near-infrared spectroscopy ; Spectroscopy ; Particle size ; Composite material ; Optics ; Chemical engineering ; Polymer ; Chemistry ; Catalysis ; Organic chemistry ; Geology Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Advanced Measurement and Detection Methods
Authors: Eslam Ibrahim | Yury Lysogorskiy | Ralf Drautz
Source: arXiv (Cornell University) (preprint, 2024-06-20)
Field: Chemistry — Inorganic Chemistry

Efficient parameterization of transferable Atomic Cluster Expansion for water We present a highly accurate and transferable parameterization of water using the atomic cluster expansion (ACE). To efficiently sample liquid water, we propose a novel approach that involves sampling static calculations of various ice phases and utilizing the active learning (AL) feature of ACE-based D-optimality algorithm to select relevant liquid water configurations, bypassing computationally intensive ab-initio molecular dynamics (AIMD) simulations. Our results demonstrate that the ACE descriptors enable a potential initially-fitted solely on ice structures which is later upfitted with few configurations of liquid, identified with active learning to provide an excellent description of liquid water. The developed potential exhibits remarkable agreement with first-principles reference, accurately capturing various properties of liquid water, including structural characteristics such as pair correlation functions, covalent bonding profiles, and hydrogen bonding profiles, as well as dynamic properties like the vibrational density of states, diffusion coefficient and thermodynamic properties such as the melting point of the ice Ih. Our research introduces a new and efficient sampling technique for machine learning potentials in water simulations, while also presenting a transferable interatomic potential for water that reveals the accuracy of first principles reference. This advancement not only enhances our understanding the relationship between ice and liquid water at the atomic level, but also opens up new avenues for studying complex aqueous systems. Cluster (spacecraft) ; Environmental science ; Computer science Radioactive element chemistry and processing ; Luminescence Properties of Advanced Materials ; Advanced Chemical Physics Studies
Authors: Nadine Schrode | Yang Hu | Haocheng Yu | Matthew D. Galsky | John Sfakianous | Robert Sebra | Kristin G. Beaumont | David J. Mulholland
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-07)
Field: Chemistry — Spectroscopy

Mapping of Multilineage Tumor Cell Populations in Mouse Bladder Cancer ABSTRACT In bladder cancer (BLCA), the cellular expression of lineage markers with predictive biomarker function can have important implications for tumor progression, treatment response, and survival. However, tumor heterogeneity and the coexistence of distinct tumor subpopulations can complicate the utility of using a primary, clinically assigned tumor signature. In this report, we have applied the reference atlas, Tabula Sapiens, and deep learning model, UniCell, to conduct in-depth transcriptional analysis of unique lineage marker-defined cell clusters in a carcinogen (BBN) induced bladder cancer model. UniCell deconvolution has identified tumor populations, including urothelial, adenocarcinoma, squamous cell carcinoma, and mesenchymal tumor populations, each with cell-intrinsic gene expression signatures relevant to human BLCA progression. The identified tumor clusters contain uniquely basal, luminal, stromal, or hybrid cells in cell lineage marker expression. To understand the significance of these populations during progression, we used trajectory and pseudo-time analysis to show that cells uniquely basal are plastic in lineage identity and can evolve to form tumor populations composed of other lineage marker-defined signatures. Finally, pathway and drug enrichment analysis of tumor cell clusters were used to identify therapeutics that may preferentially target the identified tumor cell populations. These data collectively define a molecular template that may uniquely profile molecular plasticity occurring during progression and response to therapy with important implications for human disease . Significance Tumor heterogeneity is a mechanism for treatment resistance. Our study defines unique tumor subpopulations having differential therapeutic sensitivities and potential for lineage plasticity. Our modeling may impact the treatment of BLCA patients. Bladder cancer ; Cancer ; Biology ; Cancer research ; Genetics Advanced Proteomics Techniques and Applications
Authors: Aleksandra Thamm | Florian Thamm | Markus Wiedemann | Jochen Bretschneider | Magdalena Sikorska | Andreas Maier
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2024-10-19)
Field: Materials Science — Materials Chemistry

Deep learning based drill wear segmentation and analysis of the wear progress Drill ; Segmentation ; Artificial intelligence ; Engineering ; Mechanical engineering ; Computer science ; Manufacturing engineering ; Engineering drawing Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Tunneling and Rock Mechanics
Authors: Hao Zheng | Zhiyan Zhao | Guoshun Wan | Mengxuan Cheng | Wen Qiao | Ruiqian Zheng | Yuxi Jia
Source:  (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Efficient and Accurate Calculation of Equivalent Thermal Conductivity of Wiring Structures in Mesoscopic Scale and Intelligent Prediction With the development of artificial intelligence and autonomous driving technologies, the complexity of the conductive layers (CDLs) in the integrated circuit (IC) is increasing, leading to severe thermomechanical reliability issues. To ensure the reliability, temperature distribution must be accurately simulated. It is impractical to capture all wiring structure features during modeling, so partition homogenization methods have been proposed. However, most current equivalence methods still have limitations when dealing with complex microstructures. Thus, the equivalent thermal conductivity (ETC) calculation method based on mesoscopic finite element analysis (MFEA) was proposed, which fully takes into account the detailed structural characteristics of each partition to batch calculate the their ETCs through automated scripts, and ultimately to achieve efficient and accurate simulation of the macroscopic temperature field. The superiority of the proposed method in predicting the temperature distribution of IC products with complex wiring structures was verified by comparing the results of the proposed method in this paper with those obtained from linear interpolation model (LIM) and series-parallel model (SPM) methods. To further improve the computational efficiency of ETC, the study also introduced Global Context Vision Transformers (GC ViT) for fast predicting ETC, which provides the possibility that this equivalent method can meet the industrial requirements for efficiency. In conclusion, the scheme in this study not only provides an efficient simulation strategy for the thermal management design of IC products, but also opens up new possibilities for the future application of deep learning techniques in material property prediction and complex system modeling in a wider range of engineering fields. Mesoscopic physics ; Thermal conductivity ; Scale (ratio) ; Conductivity ; Computer science ; Thermal ; Materials science ; Physics ; Condensed matter physics ; Meteorology ; Composite material Machine Learning in Materials Science ; Thermal properties of materials
Authors: Shengxin Li | Ziyan Zhang | Zhiran Zhang | Sen Zhou | M.L. Liu | Xichao Li | Zheng Zheng | Jie Sun
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Comparative Analysis of Spectral Variable Selection Methods for Nir-Based Multi-Component Detection of Xanthoceras Sorbifolium Bunge Seed Kernels Selection (genetic algorithm) ; Component (thermodynamics) ; Spectral analysis ; Component analysis ; Mathematics ; Feature selection ; Pattern recognition (psychology) ; Statistics ; Computer science ; Artificial intelligence ; Physics ; Spectroscopy Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Advanced Algorithms and Applications
Authors: Paul J. DesLauriers | Jan‐Hendrik Arndt | Guru Geertz | Subrajeet Deshmukh | Robert Brüll
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Quantitative Assessment of 2d-Lc Analysis of Polyolefins for Multiway Applications; Part 1: Calibration of the Log M Axis Calibration ; Chromatography ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Mathematics ; Statistics Polymer crystallization and properties ; Fuel Cells and Related Materials ; Rheology and Fluid Dynamics Studies
Authors: Christopher T. Boughter
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-09-10)
Field: Chemistry — Spectroscopy

Utilizing Protein Bioinformatics to Delve Deeper Into Immunopeptidomic Datasets Immunopeptidomics is a growing subfield of proteomics that has the potential to shed new light on a long-neglected aspect of adaptive immunology: a comprehensive understanding of the peptides presented by major histocompatibility complexes (MHC) to T cells. As the field of immunopeptidomics continues to grow and mature, a parallel expansion in the methods for extracting quantitative features of these peptides is necessary. Currently, massive experimental efforts to isolate a given immunopeptidome are summarized in tables and pie charts, or worse, entirely thrown out in favor of singular peptides of interest. Ideally, an unbiased approach would dive deeper into these large proteomic datasets, identifying sequence-level biochemical signatures inherent to each individual dataset and the given immunological niche. This chapter will outline the steps for a powerful approach to such analysis, utilizing the Automated Immune Molecule Separator (AIMS) software for the characterization of immunopeptidomic datasets. AIMS is a flexible tool for the identification of biophysical signatures in peptidomic datasets, the elucidation of nuanced differences in repertoires collected across tissues or experimental conditions, and the generation of machine learning models for future applications to classification problems. In learning to use AIMS, readers of this chapter will receive a broad introduction to the field of protein bioinformatics and its utility in the analysis of immunopeptidomic datasets and other large-scale immune repertoire datasets. Computational biology ; Computer science ; Data science ; Bioinformatics ; Biology Advanced Proteomics Techniques and Applications ; Protein Degradation and Inhibitors
Authors: M. S. V. Madampage | K. D. H. Keshan | T. Kodithuwakku | Mithila Karunarathna | B. C. Liyanapathirana | J. A. Seneviratne | W. G. C. Kumarage
Source:  (article, 2024-10-25)
Field: Materials Science — Polymers and Plastics

ADVANCES IN FLEXIBLE ORGANIC FIELD-EFFECT TRANSISTORS IN THE APPLICATION OF ARTIFICIAL SKIN Flexible organic field-effect transistors (FOFETs) represent a breakthrough in the domain of flexible electronics, encompassing roll able displays, bendable smart cards, flexible sensors, and influencing the development of artificial skin. In the realm of artificial skin, flexible electronic systems have achieved remarkable advancements for instance in stretch ability, from 30% up to 300%, through rational structural designs involving rigid inorganic matter. Recent studies highlight the practical applications of these technologies in prosthetics, robotics, and wearable health monitoring devices, particularly in the form of pressure sensors, temperature sensors, and bioelectronic interfaces. OFET can work inside a human body because of their mechanical resilience. The skin-like sensing, skin-biothermal, and self-healing properties endow them with broadband applications. Integration of machine learning and soft robotics has further improved their performance, making them more reliable and efficient in such a way that leading the path to exciting advancements in artificial skin, but it is also important to recognize that many challenges remain including long-term stability and biocompatibility. This review article provides a comprehensive overview of the state-of-the-art advancements in flexible OFETs, underscoring their transformative potential in artificial skin applications. It also addresses the current challenges in the field, including issues related to long-term stability, biocompatibility, and the need for seamless integration with biological tissues. Additionally, the article discusses the potential for future research and development, highlighting the interdisciplinary nature of this domain that bridges material science, electronic engineering, and biomedical technology. The insights provided in this review pave the way for continued innovation, fostering advancements that could revolutionize the fields of prosthetics, robotics, and wearable health technologies. Electronic skin ; Transformative learning ; Wearable technology ; Robotics ; Soft robotics ; Domain (mathematical analysis) ; Electronics ; Wearable computer Conducting polymers and applications ; Neuroscience and Neural Engineering ; Advanced Memory and Neural Computing