jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Feryal H. Alharthy
Source: International Journal of Communication and Information Technology (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Leveraging advanced imaging and sensor technologies for real-time fruit quality monitoring: Implications for smart agriculture The rapid advancement of imaging and sensor technologies in agriculture has led to a significant transformation in the way fruit quality is assessed. Real-time monitoring is becoming essential for optimizing harvest timing, improving food safety, reducing waste, and increasing overall agricultural efficiency. This review explores the application of cutting-edge imaging and sensor technologies, such as hyperspectral imaging, near-infrared spectroscopy (NIRS), and electronic nose (e-nose) sensors, in the context of fruit quality monitoring. The paper provides a comprehensive analysis of how these technologies are applied for non-invasive, real-time detection of parameters such as ripeness, color, texture, and spoilage. The review synthesizes findings from studies published between 2015 and 2023, highlighting the technological innovations and their integration into smart agricultural practices. In addition to technological advancements, the review examines the challenges faced in the widespread adoption of these technologies, such as cost, data interpretation complexity, and scalability in resource-limited settings, particularly in India. Furthermore, it presents the opportunities these technologies offer to farmers, emphasizing their potential to optimize post-harvest management and minimize food losses. Based on a detailed analysis of the literature, this review also identifies gaps in the existing research, including the need for more field-based studies and the exploration of hybrid sensor systems. The paper concludes with recommendations for future research directions to enhance the integration of these technologies into practical, cost-effective solutions for smart agriculture. Context (archaeology) ; Emerging technologies ; Agriculture ; Quality (philosophy) ; Precision agriculture ; Scalability Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Valerio Francioni | Anna Beltramini | Lisha Fan | Mark T. Harnett
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-04-15)
Field: Chemistry — Spectroscopy

CLOOSE: An open-source platform for optical closed-loop experiments Abstract Brain-Computer Interfaces (BCI) have catalyzed advancements in both clinical applications and basic neuroscience research. However, technical barriers such as steep learning curves and complex synchronization requirements often impede their widespread adoption. In response to the increasing demand for optical BCI experiments and to the technical challenges associated with their implementation, we introduce CLOOSE (Closed-loop Optical Open-Source Experiments), a versatile platform designed to standardize, simplify, and accelerate closed-loop experiments with functional imaging data. CLOOSE interfaces easily with any frame-based imaging system via TCP/IP, allowing for real-time data streaming and distributed computation. Benchmark tests validate CLOOSE’s real-time accuracy in image registration, signal processing, and analysis at imaging frequencies ≥1 kHz, making it the first optical BCI compatible with voltage indicators. Throughout the paper we showcase CLOOSE’s versatility in supporting several neurofeedback paradigms: from single neuron to population dynamics, multiplane imaging, and online (and offline) z-tracking. CLOOSE’s functionality is easily accessible to users with minimal coding experience through an intuitive graphical interface for experiment setup and real-time performance monitoring. By significantly lowering the barrier to performing technically demanding closed-loop experiments, CLOOSE advances the streamlining, standardization, and democratization of online image analysis and neurofeedback BCI paradigms, opening new avenues for precise manipulation and real-time readout of neuronal activity in health and disease. Open source ; Closed loop ; Loop (graph theory) ; Open-loop controller ; Computer science ; Operating system ; Mathematics ; Engineering ; Control engineering ; Combinatorics ; Software Spectroscopy and Laser Applications
Authors: Bin Li | Eizo Taira | Tetsuya Inagaki
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-10-03)
Field: Chemistry — Analytical Chemistry

Enhanced PLS subspace-based calibration transfer method for multiple spectrometers using small standardization sample sets Near-infrared spectroscopy (NIRS) calibration transfer faces significant challenges when deploying models across multiple instruments from different manufacturers, particularly because the inherently low molar absorptivity makes spectral data highly sensitive to minor variations in optical setup. This study presents two enhanced calibration transfer methods (ICTWM1 and ICTWM2) operating within the PLS latent variable space, utilizing dimensionality reduction to preserve analytically relevant variance while reducing noise interference. ICTWM1 employs spectral space transformation (SST) to correct PLS component scores between different instruments, while ICTWM2 selectively corrects the regression coefficients of the principal components with the highest variability. The methods were validated using wheat protein analysis (7 secondary instruments from manufacturers A and B) and industrial sugarcane Brix determination (8 secondary instruments across geographically distributed facilities). ICTWM1 demonstrated superior performance, achieving 79.3% relative performance compared to the primary instrument model using only 10 standardization samples on the wheat dataset, with improved cross-instrument consistency (standard deviations of 3.4%-6.9%) compared to traditional methods (>15%). The method exhibited no manufacturer-dependent performance bias and maintained consistent performance across sample sizes ranging from 10 to 110. Under severely constrained sugarcane dataset with only 5 training samples, both ICTWM1 and ICTWM2 achieved good performance with mean RMSEP values of 0.14°Bx and 0.15°Bx, respectively, outperforming traditional calibration transfer methods. ICTWM1 demonstrates improved sample efficiency and cross-manufacturer robustness through optimized transformation within PLS subspace. These characteristics make it a practical method for industrial NIRS applications requiring reliable calibration transfer with minimal standardization samples. • Enhanced PLS subspace methods for calibration transfer with minimal standardization samples • Comprehensive validation across 7-8 secondary instruments using public and industrial datasets • Superior accuracy and cross-instrument consistency under minimal sample conditions • Practical method for sample-efficient multivariate calibration transfer in NIRS Calibration ; Principal component analysis ; Robustness (evolution) ; Standardization ; Subspace topology ; Transformation (genetics) ; Chemometrics ; Sample (material) ; Partial least squares regression Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Olga Chernousenko | Dmitro Viktorovich Rindyuk | Vitaliy Anatoliyovich Peshko
Source: Theory and Building Practice (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

TECHNICAL ASSESSMENT OF DISTRICT HEATING PIPELINES UNDER VARIABLE THERMAL LOADS WITH CONSIDERATION OF INSTALLATION DEFORMATIONS This study presents a methodology for assessing the technical condition of district heating pipelines under high wear, variable thermal loads, and limited modernization resources. Emphasis is placed on installation defects, cyclic temperature and pressure fluctuations, and corrosion-induced degradation. A case study of a return water pipeline in Kremenchuk, featuring a 3° installation misalignment and 10 cm flange displacement, demonstrated stresses of 220–250 MPa, exceeding the steel’s yield strength in critical zones. Real operational conditions, including daily temperature fluctuations and load variations, were incorporated into the modeling to ensure accuracy. The approach enables prioritization of reconstruction zones, supports data-driven decision-making, and enhances the long-term operational efficiency of district heating networks under constrained budgets. Pipeline transport ; Pipeline (software) ; Flange ; Variable (mathematics) ; Thermal ; Water heating Material Properties and Failure Mechanisms ; Integrated Energy Systems Optimization ; Geotechnical Engineering and Underground Structures
Authors: O. A. Ivanov | V. N. Sidorov | P. I. Novikov
Source: Vestnik MGSU (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Control of parameters of the calculation model of a spatial structure based on the results of full-scale tests Introduction. In numerical modelling of mechanical systems, the proximity of the computational model to the actual behaviour of the object is largely determined by the adequacy of the specified boundary conditions. In practice, the support conditions of structural elements often differ from the idealized assumptions adopted in computational models, which leads to discrepancies between calculated and experimental data. Materials and methods. This study considers an approach to optimizing the boundary conditions of a finite element model using the evolutionary algorithm CMA-ES [1]. The object of investigation was a spatial frame structure, for which experimental studies of dynamic characteristics — natural frequencies and free vibrations — were conducted. The comparison of calculated and experimental frequencies and vibration modes was performed using the modal assurance criterion (MAC) and the relative error between the calculated eigenfrequencies of the model and their experimentally determined values. Based on these metrics, the objective function of the optimization problem was formulated. Results. The optimization made it possible to determine the stiffness coefficients of the support connections in the lower chord of the structure in six degrees of freedom. The results showed that the application of the evolutionary approach significantly reduced discrepancies between model and experimental data, thereby improving the accuracy of the computational model. Conclusions. The accuracy of finite element modelling of structures can be improved by aligning the model and experimental dynamic characteristics through the use of evolutionary algorithms. Finite element method ; Modal ; Vibration ; Stiffness ; Boundary value problem ; Boundary (topology) ; Modal analysis ; Evolutionary algorithm ; Function (biology) Structural mechanics and materials ; Heat Transfer and Mathematical Modeling ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: George‐John E. Nychas | Chrysoula C. Tassou | Fady Mohareb
Source:  (article, 2025-04-04)
Field: Chemistry — Analytical Chemistry

Vibrational Spectroscopy Instruments in Tandem with Data Science can be used to Serve Food Safety The DiTECT project ("Digital Technologies as an Enabler for a Continuous Food Waste Management System") leverages advanced software technologies and non-invasive sensors to enhance food safety in the EU and China. Focusing on maize, poultry, cattle, and fish, the project monitors microbiological hazards across the food chain through rapid detection methods. A comprehensive database integrates contamination maps, spectroscopy data, and microbiological analyses. The SorfML cloud platform applies machine learning to interpret this data, enabling real-time food safety assessments. DiTECT aims to enhance early hazard detection, reducing risks and strengthening consumer confidence in food safety between the two regions. Tandem ; Spectroscopy ; Computer science ; Data science ; Engineering ; Physics ; Aerospace engineering Spectroscopy and Chemometric Analyses
Authors: Rui Wang | Yang Yang | Yihong Liu
Source: Journal of Physics Conference Series (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Automatic simulation and optimization of the energy absorption box driven by MO-SAC Abstract This study proposes a reinforcement learning-based multi-objective optimization framework for automotive energy-absorbing box structures, aiming to improve specific energy absorption (SEA) and reduce peak crushing force (PCF). A parametric model with adjustable folding angle, layer number, and multi-wall thicknesses is developed and integrated with automated modeling, simulation, and evaluation. An improved MO-SAC algorithm enables joint optimization of W2W and W2C structures. The agent explores optimal designs based on simulation feedback. Results show that the optimal 9-layer W2W structure with a 122.46° folding angle and wall thicknesses of 1.35 mm, 1.29 mm, and 1.47 mm achieves a 21.28% increase in SEA and a 12.59% decrease in PCF. This method offers a data-efficient and interpretable solution for structural optimization. Energy (signal processing) ; Parametric statistics ; Folding (DSP implementation) ; Joint (building) ; Layer (electronics) ; Absorption (acoustics) ; Automotive industry ; Parametric model Catalytic Processes in Materials Science ; Advanced Battery Technologies Research ; Fiber-reinforced polymer composites
Authors: Ziyu Li | Yujian Hu | Zhengyao Ding | Yiheng Mao | Haitao Li | Yi Fan | Hongkun Zhang | Zhengxing Huang
Source: ArXiv.org (preprint, 2025-05-06)
Field: Materials Science — Materials Chemistry

Phenotype-Guided Generative Model for High-Fidelity Cardiac MRI Synthesis: Advancing Pretraining and Clinical Applications Cardiac Magnetic Resonance (CMR) imaging is a vital non-invasive tool for diagnosing heart diseases and evaluating cardiac health. However, the limited availability of large-scale, high-quality CMR datasets poses a major challenge to the effective application of artificial intelligence (AI) in this domain. Even the amount of unlabeled data and the health status it covers are difficult to meet the needs of model pretraining, which hinders the performance of AI models on downstream tasks. In this study, we present Cardiac Phenotype-Guided CMR Generation (CPGG), a novel approach for generating diverse CMR data that covers a wide spectrum of cardiac health status. The CPGG framework consists of two stages: in the first stage, a generative model is trained using cardiac phenotypes derived from CMR data; in the second stage, a masked autoregressive diffusion model, conditioned on these phenotypes, generates high-fidelity CMR cine sequences that capture both structural and functional features of the heart in a fine-grained manner. We synthesized a massive amount of CMR to expand the pretraining data. Experimental results show that CPGG generates high-quality synthetic CMR data, significantly improving performance on various downstream tasks, including diagnosis and cardiac phenotypes prediction. These gains are demonstrated across both public and private datasets, highlighting the effectiveness of our approach. Code is availabel at https://anonymous.4open.science/r/CPGG. Cardiac magnetic resonance ; Generative model ; Autoregressive model ; Cardiac imaging ; Cardiac magnetic resonance imaging ; Magnetic resonance imaging Machine Learning in Materials Science ; Cardiac Valve Diseases and Treatments
Authors: Katarzyna Kulej | Asher Preska Steinberg | Jinxin Zhang | Gabriella Casalena | Eliyahu Havasov | Sohrab P. Shah | Andrew McPherson | Alex Kentsis
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-28)
Field: Chemistry — Spectroscopy

End-to-end proteogenomics for discovery of cryptic and non-canonical cancer proteoforms using long-read transcriptomics and multi-dimensional proteomics ABSTRACT Modern mass spectrometry now permits genome-scale quantitative measurements of biological proteomes. However, current approaches remain hindered by incomplete representation of biological variability of protein sequences in canonical reference proteomes, limiting studies to already annotated proteoforms. This, combined with reliance on tryptic peptides and limited sample complexity reduction techniques render detection of variant proteoforms missing from current reference annotations virtually impossible. Here, we report a method for end-to-end proteogenomics which integrates (i) sample-specific transcriptome assembly using long-read sequencing with (ii) multi-protease, multi-dimensional (MPMD) trapped ion mobility spectrometry (TIMS) parallel accumulation-serial fragmentation (PASEF) mass spectrometry. Termed ProteomeGenerator3, we benchmark its sensitivity and specificity for unbiased human cancer proteome discovery. Using long-read transcriptome sequencing of human Ewing sarcoma cancer cells, we detected over 40,000 full-length transcript isoforms, including aberrantly expressed neogenes, splice isoforms, and gene fusions. Combined with MPMD-TIMS-PASEF mass spectrometry, our proteogenomics workflow enabled the detection of 12,189 unique proteoforms with an average sequence coverage exceeding 50%, including 3,216 putative, non-canonical proteoforms, constituting one of the most complete human proteomes to date. Likewise, we report the proteogenomic performance metrics for current state-of-the-art implementations of DIA-NN, Spectronaut and diaTracer-MSFragger algorithms for data-independent acquisition (DIA) and PASEF mass spectral data, including optimal Double Rainbow-diaPASEF parameters for non-tryptic proteomes. In all, end-to-end integrative ProteomeGenerator3 provides a versatile platform for deep sample-specific proteogenomic discovery, suitable for the identification of disease biomarkers and therapeutic targets, and is available open-source from https://github.com/kentsislab/proteomegenerator3 . Proteogenomics ; Proteome ; Computational biology ; Proteomics ; Human proteome project ; Biology ; Computer science ; Bioinformatics ; Genome ; Genomics ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Isaac D. Juárez | Dmitry Kurouski
Source: Frontiers in Plant Science (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

Raman spectroscopy for agricultural applications Over the last decade, we observe a significant interest in Raman spectroscopy expressed by plant biologists and other agriculturalists. However, many of these people have limited experience in Raman analyses. To this end, we wrote an extensive method-focused manuscript in which we critically discuss key steps in analyses for spectroscopic data. We also conveyed the major differences between analysis of certain peaks vs the full spectra, as well as discussed how both types of spectral analyses could complement each other. We also focused on chemometric analysis of data, including supervised methods, such as PLS-DA. Using our own data, we demonstrated a contribution of spectral preprocessing, model parameters and validation in the performance of supervised methods in data classification. We also critically discussed practical applications of peak fitting and 2-D correlation spectroscopy and demonstrated how these approaches can be useful to extract valuable information about biological processes that take place in plants. Raman spectroscopy ; Agriculture ; Spectroscopy ; Environmental science ; Chemistry ; Materials science ; Biology ; Physics ; Ecology ; Optics ; Astronomy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Date Palm Research Studies
Authors: Shenghua Zhou | Shengnan Dai
Source: Journal of Physics Conference Series (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Physics-based CUDA optimization strategies for three-phonon scattering calculations Abstract Three-phonon scattering processes are crucial for studying thermal transport properties of materials, but their O(N 3 ) computational complexity leads to extremely long calculation times for large material systems. This paper presents a set of physics-based CUDA optimization strategies for the three-phonon scattering calculation module in the homemade WP3_phonopy program. Our optimization methods include frequency-dependent filtering, Bose-Einstein factor pre-computation, temperature-related optimizations, energy conservation optimizations, as well as computational physics-specific strategies like shared memory utilization. Testing results show that for material system Zr 4 Ni 4 Sn 4 (36 phonon modes, 6 × 6 × 6 grid), the optimized calculation time decreased from 39,101 seconds to 15 seconds, achieving a 2,607× speedup; for material system ZrNiSn (9 phonon modes, 15 × 15 × 15 grid), the calculation time was reduced from 1,747,590 seconds to 407 seconds, achieving a 4,294× speedup. Cross-architecture testing demonstrates consistent performance improvements, validating the generalizability of our optimization strategies. These results provide an efficient solution for phonon scattering calculations in large material systems and offer new insights for GPU acceleration of other computational physics problems. CUDA ; Scattering ; Phonon ; Set (abstract data type) ; Acceleration ; Computational complexity theory ; Energy (signal processing) ; Thermal Machine Learning in Materials Science ; Nuclear Physics and Applications ; Semiconductor materials and devices
Authors: Mary M. Hofle | Nusrat Farheen | Mathew Zachary Shumway | Evan D. Mosher | Keyave C. Hone | Marco P. Schoen
Source: Data (article, 2025-10-14)
Field: Chemistry — Analytical Chemistry

Comparative Data Analysis of Non-Destructive Testing for Hollow Heart in Potatoes Hollow heart, and other crop defects, can be devastating to farmers. Hollow heart is not a disease but a physiological disorder affected by temperature, soil moisture, plant density, and other factors. These defects can cause substantial annual losses for farmers. Currently, potatoes are shipped and inspected from producers to shipping points and markets. At these facilities, samples are inspected for defects. Detection of hollow heart consists of halving potatoes and visually inspecting for defects. The defect size is compared to USDA hollow heart classification charts for acceptance or rejection. An automatic, non-destructive system to identify hollow heart has the potential to improve quality. Two methods have been developed to collect data for such a system: acoustic signal capture and visual/vibration signal capture. Data is collected and stored for one potato at a time. The procedure includes the collection of weight, proportional size, and volume, as well as the generation of an acoustic sound signal through a drop test and a motion signal captured through a vision system. To simulate hollow heart, potatoes are cored and retested by producing a new set of data. Each potato is manually cut and inspected for true hollow heart. The generated data includes over 1000 samples, each comprising proportional volume, weight, proportional size, motion, and acoustic data. Such a dataset does not exist in the current literature and can serve for the development of machine learning algorithms to detect hollow heart nondestructively. In this paper, the data is also analyzed in terms of its statistical properties, as applied for possible feature engineering in machine learning. SIGNAL (programming language) ; Feature (linguistics) ; Data set ; Pattern recognition (psychology) ; Statistical analysis ; Test data ; Signal processing Spectroscopy and Chemometric Analyses