jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyi Yu | Yunyun Niu | Zhuxian Zhou | Bo Lu | Kaiqiang JIN | Rongyang Wang
Source: INMATEH Agricultural Engineering (article, 2025-04-30)
Field: Chemistry — Analytical Chemistry

IMPROVED YOLOV8-ALGORITHM FOR SORTING FRESH WHITE TEA: COMBINING FEATURE ENHANCEMENT AND ATTENTION MECHANISM In this paper, an improved intelligent sorting algorithm for YOLOv8 white tea fresh leaves is proposed to solve the problems of unclear tea grades and uneven product levels caused by mechanical picking. The algorithm introduces the Dynamic Snake Convolution module (DSConv) for feature enhancement and adds an attention mechanism module, the Multi-Head Self-Attention mechanism (MHSA). Experiments show that the YOLOv8-DsConv-MHSA algorithm has an average accuracy of 96.4% and an average detection rate of 126.6 FPS per second, which is the best algorithm for white tea fresh leaf sorting in the comprehensive comparison. After deploying the proposed YOLOv8-DsConv-MHSA algorithm onto the developed tea sorting machine and conducting experimental comparisons with existing tea sorting machines, it is evident that the screening rate has been enhanced by 10.7%, and the operational efficiency has increased by 20%. Sorting ; Mechanism (biology) ; Feature (linguistics) ; Algorithm ; White (mutation) ; Computer science ; Artificial intelligence ; Green tea ; Pattern recognition (psychology) ; Food science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses
Authors: Tanwa M. Iwayemi | Stanley O. Tomomewo | Siddharth Choudhary | Daniel Kelly Boakye-Danquah
Source: International Journal of Latest Technology in Engineering Management & Applied Science (article, 2025-07-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid Energy Storage Systems for Renewable Integration: Combining Batteries, Supercapacitors, and Flywheels Abstract—Renewable-energy integration into power grids is constrained by the variable output of solar and wind resources. This paper proposes a Hybrid Energy Storage System (HESS) that couples lithium-ion batteries, supercapacitors, and flywheels and governs them with a Unified Mathematical Method (UMM) combining moving-average filtering with threshold-based cut-off logic. The architecture is modelled in HOMER Pro for the Grand Forks, ND (USA) resource profile and bench-marked against “Grid+Renewables” and “Grid+Renewables+Battery” baselines. The full three-storage configuration supplies 1 032 320 kWh yr−1 of useful energy—an increase of 77 % over the no- storage case—and eliminates 1.36 Mt CO2 yr−1 of emissions, a 245 % improvement relative to renewables alone. Valued at the Social Cost of Carbon (US$51 t−1) and the 45Q tax credit (US$85 t−1), the avoided emissions translate to annual economic benefits of US$69 000–US$116 000. The UMM reduces false cut-off events by more than 30 %, prolonging component life and enhancing overall system reliability. These results confirm that a tri-technology HESS managed by a unified control layer delivers superior technical performance, environmental gains, and financial returns compared with single-storage or no-storage configurations. Supercapacitor ; Flywheel ; Renewable energy ; Energy storage ; Computer science ; Automotive engineering ; Engineering ; Electrical engineering ; Chemistry ; Capacitance ; Power (physics) ; Physics Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Electric and Hybrid Vehicle Technologies
Authors: Tommaso Ocari | Emilia A. Zin | Muge Tekinsoy | Timothé Van Meter | Mélissa Desrosiers | Chiara Cammarota | Deniz Dalkara | Takahiro Nemoto | Ulisse Ferrari
Source: Nucleic Acids Research (article, 2025-08-20)
Field: Chemistry — Spectroscopy

Optimal sequencing depth for measuring the concentrations of molecular barcodes In combinatorial genetic engineering experiments, next-generation sequencing (NGS) allows for measuring the concentrations of barcoded or mutated genes within highly diverse libraries. When designing and interpreting these experiments, sequencing depths are thus important parameters to take into account. Service providers follow established guidelines to determine NGS depth depending on the type of experiment, such as RNA sequencing or whole genome sequencing. However, guidelines specifically tailored for measuring barcode concentrations have not yet reached an accepted consensus. To address this issue, we combine the analysis of NGS datasets from barcoded libraries with a mathematical model taking into account the polymerase chain reaction amplification in library preparation. We demonstrate on several datasets that noise in the NGS counts increases with the sequencing depth; consequently, beyond certain limits, deeper sequencing does not improve the precision of measuring barcode concentrations. We propose, as rule of thumb, that the optimal sequencing depth should be about ten times the initial amount of barcoded DNA molecules before any amplification step. Biology ; Computational biology ; DNA sequencing ; Genetics ; DNA Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Advanced Biosensing Techniques and Applications
Authors: A.K. Rai | Ludovic Grossard | Tristan Chabrol | François Reynaud | Mathieu Chauvet | Florent Bassignot | Rodolphe Krawczyk
Source: Optics Express (article, 2025-09-15)
Field: Chemistry — Spectroscopy

Cryoless MIR spectroscopy via pump wavelength scanning using PPLN ridge waveguide upconversion This article presents what we believe to be a novel room-temperature mid-wave Infrared spectroscopy (MWIR) technique utilizing upconversion in a 2 cm-long PPLN ridge waveguide. Addressing limitations of costly, cryo-dependent FTIR, our "non-linear spectrometer" samples MWIR spectra by sweeping a low-power, continuous wave monochromatic pump laser, enabling detection with sensitive silicon detectors. Experimental validation with a channelled spectrum demonstrated accurate thermal source spectral modulation recovery, showing a 3% relative error. This robust approach offers a promising pathway for miniaturized, practical MIR spectroscopic tools. Photon upconversion ; Spectroscopy ; Ridge ; Monochromatic color ; Wavelength ; Lithium niobate ; Silicon ; Infrared ; Imaging spectroscopy Spectroscopy and Laser Applications
Authors: Polina A. Strelnikova | А. С. Кононихин | Natalia V. Zakharova | Anna E. Bugrova | Maria I. Indeykina | Ya. B. Fedorova | И. В. Колыхалов | Аnna Morozova | Alisa Andryushchenko | Elena Fedoseeva | Marina Emelyanova | Dmitry Gryadunov | Gavrilova Si | Vladimir A. Mitkevich | George P. Kostyuk | Yulia A. Chaika | Alexander Makarov | Е. Н. Николаев
Source: International Journal of Molecular Sciences (article, 2025-12-19)
Field: Chemistry — Spectroscopy

Plasma Protein Panel for Assessing the Risk of Alzheimer’s Disease by MRM-MS Analysis: The Study of Two Independent Clinical Cohorts Early recognition of a risk of Alzheimer's disease (AD) remains a global challenge, and blood proteomic markers are of particular interest for wide-scale diagnostic use. Quantitative multiple reaction monitoring (MRM) approach demonstrates good reproducibility in the characteristic changes in the levels of reported candidate biomarkers (CBs) in different cohorts in AD. Following up on our previous study, we performed a joint analysis of 331 blood plasma samples from two different clinical cohorts of participants, comprising a total of 95 samples from patients with AD, 136 samples from patients with mild cognitive impairment (MCI), and 100 samples from controls. The obtained results confirm the significance of 37 CBs. A logistic regression-based algorithm was used to build protein classifiers, and a total of 21 important proteins were selected, 13 of which (ORM1, APOA4, LBP, HP, FN1, BCHE, APOE, PZP, A1BG, TF, SERPINA7, TTR, and F12) formed a universal panel that demonstrated strong classification performance in distinguishing AD patients from controls (ROC-AUC = 0.90) and in separating stable and progressing patients with MCI (ROC-AUC = 0.81). Overall, the analysis confirms the high potential of the MRM method for validating CBs in independent cohorts. Disease ; Logistic regression ; Biomarker ; Clinical significance ; Reproducibility ; Blood proteins Advanced Proteomics Techniques and Applications ; Alzheimer's disease research and treatments ; Metabolomics and Mass Spectrometry Studies
Authors: August Hoel | Fredrik Hoel | Sissel E. Dyrstad | Henrique Chapola | Ingrid G. Rekeland | Kristin Risa | Kine Alme | Kari Sørland | Karl A. Brokstad | Hans‐Peter Marti | Olav Mella | Øystein Fluge | Karl Johan Tronstad
Source: medRxiv (preprint, 2025-05-31)
Field: Chemistry — Spectroscopy

Charting the Circulating Proteome in ME/CFS: Cross System Profiling and Mechanistic insights Summary Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating condition often triggered by infections. The underlying mechanism remains poorly understood, and diagnostic markers and effective treatments are presently lacking. We performed aptamer-based serum proteomics in 54 ME/CFS patients and 27 healthy controls and identified 1823 of 7326 aptamers reporting differences between the groups (845 after false discovery rate (FDR) correction). Distinct patterns of tissue- and process-specific changes were seen. There was a broad increase in secreted proteins, while intracellular proteins, e.g. from skeletal muscle, particularly showed reduction. Immune cell-specific signatures indicated immune reprogramming, including a distinct reduction in neutrophil-associated proteins. Focused secretome analysis supported intensified regulatory interactions related to immune activity, inflammation, vasculature, and metabolism. Validation of measurements using antibody-based methods confirmed findings for a selection of proteins. The uncovered serum proteome patterns in ME/CFS patients help clarify a multifaceted pathophysiology and offer a foundation for future therapy and biomarker discovery efforts. Profiling (computer programming) ; Proteome ; Computational biology ; Computer science ; Data science ; Biology ; Bioinformatics Advanced Proteomics Techniques and Applications
Authors: Andrej Hlinčík | Tünde Fülöp | Peter Herich | Jozef Kožı́šek | Karol Lušpai | Lukáš Bučinský
Source: IUCrJ (article, 2025-05-23)
Field: Chemistry — Physical and Theoretical Chemistry

On the flexibility of the multipole model refinement. A DFT benchmark study of the tetrakis(μ-acetato)diaquadicopper model system In this study, the flexibility of the multipole Hansen-Coppens (HC) model refinement is investigated on calculated structure factors from a DFT reference for the tetrakis(μ-acetato)diaquadicopper model system (CCDC reference 1811668). The effect of the resolution [sin(θ)/λ], the Cu scattering factor, the inclusion of anisotropic displacement parameters and the positions of the atoms in the refinement are considered in terms of statistical error analysis, residual and deformation density maps, Atoms In Molecules parameters, d-orbital populations, and others. The choice of the neutral Cu scattering factor in the HC refinement is found to give the most satisfactory results for most of the monitored parameters, despite the formal Cu<sup>2+</sup> nature of copper in the studied complex. In order to put the difference between the HC model and the reference DFT (BLYP functional) results on the right scale, several computational chemistry methods (B3LYP, Hartree-Fock, Møller-Plesset perturbation theory and Coupled Clusters Singles and Doubles) were compared with the chosen DFT reference. Differences in the magnitudes of the structure factors and AIM parameters are presented, including considerations of relativistic effects and periodic boundary conditions, i.e. a comparison of a molecular crystal calculation versus an isolated molecule in the crystal. Multipole expansion ; Chemistry ; Density functional theory ; Computational chemistry ; Ab initio ; Perturbation theory (quantum mechanics) ; Molecule ; Crystallography ; Molecular physics ; Physics ; Quantum mechanics Crystallography and molecular interactions ; Crystal structures of chemical compounds ; X-ray Diffraction in Crystallography
Authors: Apiwat Junto | Thitima Phanomsophon | Sneha Sharma | Kannapot Kaewsorn | Jiraporn Sripinyowanich Jongyingcharoen | Cheewanun Dachoupakan Sirisomboon | Panan Rerngsamran | Panmanas Sirisomboon | Pimpen Pornchaloempong | Norhashila Hashim | Prabhas Chongstitvatana | Anupun Terdwongworakul
Source: BMC Plant Biology (article, 2025-12-16)
Field: Chemistry — Analytical Chemistry

Postharvest detection of anthracnose (Colletotrichum asianum) on mango fruit (Mangifera indica L. cv Namdokmai Sithong) using near-infrared response Anthracnose disease, caused by fungi of the genus Colletotrichum, poses a major threat to mango production and export industries, with Colletotrichum asianum being among the most significant pathogenic species. This work proposes the hypothesis that the simple difference in absorption between anthracnose-infected and noninfected mangoes illustrated by the average near-infrared (NIR) spectra obtained from hyperspectral images could be used for simple differentiation of the two groups. The method of depositing fungal spores by spraying the spores over the fruit surface, not a small area or specific point, allows for the number of spores per unit area to be harmonized and to detect infected or noninfected spores on every pixel of the mango surface using a hyperspectral imaging camera. Important wavelengths for differentiation included water bands of 970, 1190, and 1200 nm which resulted in the greatest difference in absorbance, and bands of chitin, the major component of the fungal cell wall; 1195 nm was the most important band. In addition, the vibration bands of 868 (protein in the fungal cell wall), 1134 (sugar and starch of the mango substrate), 1320 (NIR absorbers in the fungus-sprayed and mango substrate, not specifically defined) and 1069 nm (crystallinity and N-acetyl methyl groups in the fungal chitin and constituents of the mango), differed from each other. These wavelengths can be used for modelling, which can lead to high performance in quantifying the concentration of anthracnose and classifying the strength levels of anthracnose infection. The microbiological mechanism of anthracnose growth on infected mangoes corresponding to changes in the NIR spectrum during the 4 days after spore infection is comprehensively discussed. These results can aid in enhancing early detection and classification techniques for anthracnose-infected mangoes from noninfected mangoes using hyperspectral image sensors. Spore ; Postharvest ; Hyperspectral imaging ; Fungus ; Inoculation ; Starch Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Postharvest Quality and Shelf Life Management
Authors: Monika Janaszek-Mańkowska | Dariusz R. Mańkowski
Source: Processes (article, 2025-10-27)
Field: Chemistry — Analytical Chemistry

Hyperspectral Classification of Kiwiberry Ripeness for Postharvest Sorting Using PLS-DA and SVM: From Baseline Models to Meta-Inspired Stacked SVM The accurate and non-destructive assessment of fruit ripeness is essential for post-harvest sorting and quality management. This study evaluated a meta-inspired classification framework integrating partial least squares discriminant analysis (PLS-DA) with support vector machines (SVMs) trained on latent variables (sSVM) or on class probabilities (pSVM) derived from multiple PLS-DA components. Two kiwiberry varieties, ‘Geneva’ and ‘Weiki’, were analyzed using variety-specific and combined datasets. Performance was assessed in calibration and prediction using accuracy, F05, Cohen’s kappa, precision, sensitivity, specificity, and likelihood ratios. Conventional PLS-DA provided reasonably good classification, but pSVM models, particularly those with an RBF kernel (pSVM_R), consistently outperformed other approaches and ensured higher stability across all datasets. Unlike sSVMs, which were prone to overfitting, pSVM_R models achieved the highest accuracy of 92.4–96.9%, Cohen’s kappa of 84.8–93.9%, and precision of 89.1–94.2%, clearly surpassing both score-based SVM and PLS-DA. Contrasting tendencies were observed between cultivars: ‘Geneva’ models improved during prediction, while ‘Weiki’ models declined, especially in specificity. Combined datasets provided greater stability but slightly reduced peak performance than single-variety models. These findings highlight the value of probability-enriched stacking models for non-invasive ripeness discrimination, suggesting that adaptive or hybrid strategies may further enhance generalization across diverse cultivars. Ripeness ; Support vector machine ; Pattern recognition (psychology) ; Sorting ; Stability (learning theory) ; Hyperspectral imaging ; Kernel (algebra) Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Berry genetics and cultivation research
Authors: Mike Heddes | Adel Javanmard | Kyriakos Axiotis | Gang Fu | MohammadHossein Bateni | Vahab Mirrokni
Source: ArXiv.org (preprint, 2025-02-10)
Field: Materials Science — Materials Chemistry

DeepCrossAttention: Supercharging Transformer Residual Connections Transformer networks have achieved remarkable success across diverse domains, leveraging a variety of architectural innovations, including residual connections. However, traditional residual connections, which simply sum the outputs of previous layers, can dilute crucial information. This work introduces DeepCrossAttention (DCA), an approach that enhances residual learning in transformers. DCA employs learnable, input-dependent weights to dynamically combine layer outputs, enabling the model to selectively focus on the most relevant information in any of the previous layers. Furthermore, DCA incorporates depth-wise cross-attention, allowing for richer interactions between layers at different depths. Our language modeling experiments show that DCA achieves improved perplexity for a given training time. Moreover, DCA obtains the same model quality up to 3x faster while adding a negligible number of parameters. Theoretical analysis confirms that DCA provides an improved trade-off between accuracy and model size when the ratio of collective layer ranks to the ambient dimension falls below a critical threshold. Transformer ; Residual ; Computer science ; Electrical engineering ; Engineering ; Voltage ; Algorithm High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Gaber Begeš | Domen Hudoklin
Source: Applied Sciences (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Material Composition Testing Related to Measurement Instrument Enclosure Design and Safety The polymeric insulating materials widely used in measuring instrument enclosures must meet specific flammability requirements. In this study, we systematically assessed the impact of minor compositional changes, such as pigments and fillers, on horizontal burning (HB) classification according to the EN 60695-11-10 standard. We tested 64 polymer combinations at thicknesses of 1.5 mm and 3.0 mm, classifying samples into HB, HB40 or HB75 categories. The results demonstrated that additives significantly influenced the HB classifications more than thickness. Specifically, we classified 31 samples as HB, 16 as HB40, 15 as HB75, while two were unclassifiable. Several material groups consistently achieved specific HB classifications regardless of minor additive variations. These findings offer manufacturers clear guidance for selecting polymer-additive systems, facilitating informed decisions and enabling a streamlined “worst-case” testing strategy. Ultimately, this approach enhances manufacturers’ ability to efficiently achieve product safety compliance, reducing certification costs without compromising safety. Enclosure ; Engineering ; Electrical engineering Thermal and Kinetic Analysis ; Polymer Nanocomposites and Properties ; Flame retardant materials and properties
Authors: Thanh Xuan Hoang | Ayan Nussupbekov | Jie Ji | Daniel Leykam | Jaime Gomez Rivas | Yuri S. Kivshar
Source: arXiv (Cornell University) (preprint, 2025-11-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mie-tronics supermodes and symmetry breaking in nonlocal metasurfaces It is usually believed that symmetry breaking in photonic systems leads to weaker optical confinement, such as in the case of metasurfaces when bound states in the continuum are replaced by quasi-bound states with lower quality factors (Q factors). Here we show that symmetry breaking can instead enhance light trapping by strengthening in-plane nonlocal coupling pathways. We consider finite-size arrays of optical resonators supporting Mie resonances (a Mie-tronics platform) and employ diffraction and multiple-scattering analyses. We demonstrate that diffractive bands and Mie-tronics supermodes originate from the same underlying Mie resonances but differ fundamentally in their physical nature. Finite arrays exhibit Q-factor enhancement driven by redistributed radiation channels, and reversing the trends predicted by infinite-lattice theories. We reveal that controlled symmetry breaking opens new electromagnetic coupling channels, enabling polarization conversion in nonlocal metasurfaces. These novel findings establish a unified wave-physics platform linking both scattering and diffraction theories. Also, they outline the design principles for multi-functional metasurfaces that exploit nonlocality for advanced light manipulation, computation, and emission control. Symmetry breaking ; Polarization (electrochemistry) ; Diffraction ; Scattering ; Quantum nonlocality ; Resonator ; Photon ; Reflection symmetry ; Coupling (piping) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Kenan Kılıç
Source: Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi (article, 2025-10-14)
Field: Chemistry — Organic Chemistry

Wood Type Classification Based on Hybrid Feature Integration with Optimized Bagging Ensemble Approach Images of oak (Quercus petrea L.), chestnut (Castanea sativa M.) and Scots pine (Pinus sylvestris L.) tree species, which are widely used in Türkiye and around the world, were obtained in this study using mobile devices. The primary objective of this study is to automatically and reliably distinguish these wood species using image processing techniques and statistical classification methods, thereby enabling tree species identification at the genus level. In this context, colour and edge-based features such as HSV (Hue, Saturation, Value), LAB (Lightness, A (green–red), B (blue–yellow)), LBP (Local Binary Pattern) and Sobel (Sobel Edge Detection Operator) were extracted from the images. These features were evaluated using Random Forest, XGBoost, CatBoost, and Extra Trees algorithms to test classification performance. The experimental results show that colour-based features such as HSV and LAB achieved 97.5% accuracy with the Extra trees algorithm, while 100% accuracy was achieved with an optimisation-based bagging ensemble approach using all features together. Achieving such high accuracy on real-world data collected in the field using mobile devices demonstrates that the proposed method can be used as a reliable species identification tool in practical applications. Pattern recognition (psychology) ; Scots pine ; Random forest ; Sobel operator ; Identification (biology) ; Tree (set theory) ; Feature extraction ; Feature (linguistics) ; Decision tree Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Smart Agriculture and AI