jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jacson Hindersmann | Jean Michel Moura-Bueno | Gustavo Brunetto | Tales Tiecher | William Natale | Eduarda Zanon Cargnin | Eduardo Dickel Ambrozzi | João Alex Tavares Pinto | Natália Adam | Gilberto Nava | Renan Navroski | Fábio Joel Kochem Mallmann
Source: Horticulturae (article, 2026-03-02)
Field: Chemistry — Analytical Chemistry

Combining Vis-NIR Spectral Data and Multivariate Technique to Estimate Nutrient Contents in Peach Leaves Peach tree (Prunus persica L. Batsch) is a fruit species of great economic importance worldwide. Thousands of chemical leaf analyses are performed on a yearly basis to support decision-making about fertilizer application. However, traditional methods to determine nutrient content in plant tissue require a mix of strong acids, besides being time-consuming and generating polluting waste. Visible (Vis) and near-infrared (NIR) spectroscopy combined with multivariate techniques emerges as a potential solution to overcome limitations of traditional chemical analyses. The aim of the present study is to combine Vis-NIR spectral data and multivariate techniques to test strategies for the development of models to estimate nutrient content in peach leaves. The study estimated N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn content in the leaves of peach trees grown in two locations, namely: Pelotas and Pinto Bandeira, in Southern Brazil. Therefore, local and regional scale prediction models were developed by combining preprocessed Vis-NIR spectral data to both Savitzky–Golay first-derivative (SGD1d) and partial least squares regression (PLSR) multivariate technique. Most of the proposed prediction models showed average accuracy (R2 ≥ 0.50 and &lt;0.75, RPIQ ≥ 1.9 and &lt;3.0). The local-1 ‘PB’ model showed higher nutrient prediction accuracy than the regional ‘PB + Pelotas’ model and the local-2 ‘Pelotas’ model. Estimates on nutrient content in peach tree leaves subjected to local, local-1 ‘PB’ and local-2 ‘Pelotas’ models fed with data collected in the same site showed better performance than calculations based on data from other sites and/or regions. Finally, the current study allowed making updates in the refinement of more sustainable techniques to set nutrient content. Multivariate statistics ; Partial least squares regression ; Nutrient ; Tree (set theory) ; Multivariate analysis ; Fertilizer ; Regression analysis Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Banana Cultivation and Research
Authors: Grzegorz Dudek | Arkadiusz Tomczyk
Source: Electronics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Advances in Computational Intelligence and Machine Learning Models and Applications Machine learning (ML) and artificial intelligence (AI) have entered a phase of accelerated evolution, reshaping the computational landscape and influencing an ever-growing spectrum of scientific and industrial activities [...] Computational intelligence ; Computational model ; Computational learning theory ; Applications of artificial intelligence ; Artificial Intelligence System ; Artificial neural network Machine Learning in Materials Science ; Machine Learning and Data Classification ; Big Data and Digital Economy
Authors: Mathieu Decrette | Jean-François Osselin | Julie Walther | Jean-Yves Dréan
Source: Journal of Engineered Fibers and Fabrics (article, 2026-02-28)
Field: Materials Science — Polymers and Plastics

Motorised jacquard technology for interlock weaving damages reduction: Effect of shed geometry and yarn density Woven composite reinforcements usually have very specific and high performances. However, the weaving process may be aggressive and damage warp tows, due to the flexion and friction sensitivity of the used materials, for example, carbon and glass. This is a crucial point as damages, which consist of filament breakages, material loss or bonding errors, tend to cause a loss of mechanical properties and/or a bad visual aspect. Stages from tensioning to reed were quite widely studied and optimised. Nevertheless, recently since density is high the impact of yarn-to-yarn and yarn to heddle wires friction at the shedding step have been clearly established. The weaving devices used are a loom specifically adapted to interlock weaving and a UNIVAL 100 Jacquard shedding system. A UNIVAL Jacquard from Stäubli, which is a motorised technology, enables any shed geometry and individual yarn motion thanks to special parameters. Such yarn movements, which are hardly reproducible with standard shedding devices, may help towards a better understanding of yarn interactions and wear mechanisms inside the shed. To understand the damage phenomenon and to reduce it, a methodology would consist in counting damage events which occur on yarns and hamper a correct shedding according to time. Several yarn interactions were set up in order to understand the effect of shedding parameter configuration on yarn damage generation. The results clearly show the several damage evolutions in time are different according to shedding configurations and that some configurations can help to reduce warp yarn damages for high-density multilayer weaving. In addition, an algorithm for weave pattern reading and processing has also been set up to manage automatically shed geometry and tow density in the shed. Weaving ; Yarn ; Interlock ; LOOM ; Process (computing) ; Crimp ; Lock (firearm) Textile materials and evaluations ; Mechanical Behavior of Composites ; Advanced Sensor and Energy Harvesting Materials
Authors: Ahmad Batvandi | Ali Shekarifard
Source: Research Square (preprint, 2026-02-20)
Field: Chemistry — Analytical Chemistry

Geochemical controls on colloidal stability and polar compounds deposition in Cretaceous –Tertiary carbonate reservoirs using core extracts, SW Iran Asphaltene ; Carbonate ; Colloid ; Organic matter ; Pyrolysis ; Polar ; Precipitation ; Deposition (geology) Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Ebrahim Alinia-Ahandani | Sahebe Hajipour
Source: Wah Academia Journal of Health and Nutrition (article, 2026-03-30)
Field: Chemistry — Analytical Chemistry

Medicinal Plants, Environmental Contaminants, and Biomedical Innovations: A Multidisciplinary Review of Natural Products in Health and Disease Management Medicinal plants have been fundamental to human healthcare for millennia, serving as the primary source of therapeutic agents across diverse cultures. However, the simultaneous presence of environmental contaminants, particularly heavy metals, in these plants poses significant health risks that cannot be overlooked. This comprehensive review synthesizes findings from over 50 studies to provide a multidisciplinary perspective on the therapeutic potential of medicinal plants, the challenges posed by heavy metal contamination, and the emerging role of nanotechnology and phytoremediation in addressing these challenges. Key medicinal plants including Cichorium intybus, Ziziphora persica, Lavandula angustifolia, and Catharanthus roseus are examined for their pharmacological properties, with particular attention to anti-inflammatory, antimicrobial, and anticancer activities. The review critically evaluates the sources and health implications of heavy metal contamination in medicinal plants and food products, drawing on case studies from Northern Iran. Furthermore, it explores innovative approaches including Nano-formulations for enhanced drug delivery and phytoremediation strategies for soil decontamination. The integration of traditional knowledge with modern biotechnological tools presents unprecedented opportunities for sustainable healthcare and environmental management. This review identifies critical research gaps and proposes future directions for maximizing the therapeutic potential of medicinal plants while ensuring their safety and sustainability. Multidisciplinary approach ; Phytoremediation ; Human health ; Medicinal plants ; Health care ; Catharanthus roseus ; Heavy metals Heavy Metals in Plants ; Ethnobotanical and Medicinal Plants Studies ; Saffron Plant Research Studies
Authors: Kaizad Rustomji | Nasim Mohammadi Estakhri | Nooshin M. Estakhri
Source: ArXiv.org (article, 2026-02-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Coherent Virtual Absorption in Dielectric Slabs: A Temporal Analysis of Symmetric and Asymmetric Geometries Coherent virtual absorption refers to time-limited storage of optical energy in lossless configurations due to excitation of a complex zero frequency through proper temporal engineering of the incident wave. Given the dynamics underlying the effect and the storage-release mechanism occurring for finite excitation pulses, studying and understanding the associated time dynamics are crucial for enabling future applications. In this work, we carefully investigate this phenomenon in symmetric and asymmetric geometries, shedding light on practical considerations in situations when a closed-form analytical solution is not readily available. Combinations of time domain analysis and spectral filtering are used to enable systematic analysis of these structures. Our approach can be generalized to more complex structures, including multilayered and inhomogeneous cases, providing new opportunities for optimized energy storage and advanced sensing applications utilizing complex-frequency dynamics in lossless designs. Lossless compression ; Excitation ; Absorption (acoustics) ; Time domain ; Energy (signal processing) ; Bounded function ; Frequency domain ; Dielectric Metamaterials and Metasurfaces Applications ; Quantum Mechanics and Non-Hermitian Physics ; Quantum optics and atomic interactions
Authors: Yousen Zhang | Lehai Ni | Yuting Meng | Cuiju Cui | Qihao Luo | Zan Li | Guohua Sun | Yanwei Feng | Xiaohui Xu | Jie Yang | Weijun Wang
Source: Foods (article, 2026-01-20)
Field: Chemistry — Analytical Chemistry

Development of Near-Infrared Models for Selenium Content in the Pacific Oyster (Crassostrea gigas) Near-infrared (NIR) spectroscopy is a vital non-destructive analytical tool in the food and aquaculture industries. This study pioneers the application of portable NIR spectrometers for evaluating selenium (Se) content in the Pacific oyster (<i>Crassostrea gigas</i>). We developed quantitative and qualitative models to predict selenium levels in oyster tissue, representing a novel application for monitoring trace elements in marine organisms. Quantitative models were developed using partial least squares (PLS) regression on spectra collected with two portable spectrometers (Micro NIR 1700, Micro PHAZIR RX) and a benchtop FT-NIR instrument, with validation via cross-validation and an independent set. Qualitative models were also constructed to categorize Se content into three levels: 0-1, 1-3, and >3 mg/kg. For quantitative analysis, the Micro NIR 1700 model performed robustly in external validation (RP = 0.932; RMSEP = 0.392; RPD = 2.46). The Micro PHAZIR RX model achieved the highest RC (0.988) and the lowest RMSEC (0.233), yet cross-validation indicated a potential risk of overfitting. In contrast, the FT-NIR instrument yielded the best external predictive ability for powdered samples (RP = 0.954, RPD = 2.60), highlighting its high precision under laboratory conditions. For qualitative discrimination, the Micro PHAZIR RX's classification module achieved a 100% correct recognition rate (AUC = 0.937). The models based on the Micro NIR 1700 and FT-NIR instruments showed cumulative contribution rates (CCR) of 98.61% and 97.59%, respectively, with high performance indices (PI) of 89.3 and 90.2, confirming their effective discrimination capability. The models established in this study enable the rapid, on-site detection of Se content in oyster samples, underscoring the significant potential of portable NIR spectroscopy for selenium analysis in shellfish. Oyster ; Pacific oyster ; Selenium ; Partial least squares regression ; Crassostrea ; Aquaculture ; Predictive modelling ; Qualitative analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Selenium in Biological Systems
Authors: Gust Bilcke | Arthur Cleyman | Nadine Rijsdijk | Triana Forment | Thomas Eekhout | Darja Belišová | Peter Chaerle | Carolin Grones | Sien Audoor | Michiel Van Bel | Judith Porters | Nicolás Manosalva Pérez | Evelien Mylle | Daniël Van Damme | Bert De Rybel | Lieven De Veylder | Wim Vyverman | Klaas Vandepoele
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-25)
Field: Materials Science — Biomaterials

A Myb-dominated gene regulatory network universally controls sexual cell fate transitions in diatoms Summary Diatoms are the foundation of aquatic food webs and contribute about 40% of the total marine primary productivity. Yet, the regulation of their complex size-dependent life cycles remains obscure. Here, we leveraged single-cell transcriptomics and transgenic reporter lines to uncover the molecular mechanisms behind partner recognition, nuclear fusion, and the remarkable 15-fold size expansion of auxospores. Gene regulatory network inference revealed that the irreversible commitment to differentiate into gametes is controlled by Myb transcription factors, whose specific activity in the global ocean underscores their significance for ploidy transitions across diatom clades. These findings reinforce microalgae as powerful models to study cell fate transitions and provide a mechanistic framework for the life cycle dynamics that underpin the functioning of aquatic systems worldwide. Gene regulatory network ; Transcriptome ; Gene ; Regulation of gene expression ; Cell fate determination ; Transcription factor ; Gene expression ; Transcription (linguistics) ; Regulator gene Diatoms and Algae Research ; Algal biology and biofuel production ; Protist diversity and phylogeny
Authors: Mabuse Gontse Moagi | Thatiana Ferraz Ferreira | Kristof Endre | Adzani Gaisani Arda | Rini Arianti | Péter Horvatovich | Éva Csősz
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-10)
Field: Chemistry — Spectroscopy

DIA-NN EasyFilter workflow for the fast and user-friendly critical assessment and visualization of DIA-NN proteomics analysis outcome Abstract Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics, particularly data-independent acquisition (DIA), has become widely adopted across in One Health approaches for biological and clinical research for quantitative protein characterization. Among the many computational tools available, DIA-NN has demonstrated superior performance; however, the primary output of the current versions is provided as a compact, compressed PARQUET file that can be difficult to interrogate without programming expertise. To address this limitation, we developed DIA-NN EasyFilter (DEF), a fast, user-friendly, KNIME-based workflow for comprehensive protein filtering, and visualization. DEF integrates chromatographic peak-based filtering, curated contaminant libraries, and quantity-quality assessment, along with interactive modules for qualitative and quantitative data exploration. The workflow is optimized for efficient execution within the KNIME local desktop environment and is designed to support end-users in improving accuracy and interpretability without requiring coding skills. We provide detailed description on how to run DEF and demonstrate the utility and robustness of DEF using published large-scale proteomics datasets, showing high comparability across studies regardless of instrument platform or dataset size. Table of Contents graphic Workflow ; Interpretability ; Comparability ; Visualization ; Robustness (evolution) ; Data visualization ; Coding (social sciences) Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Cell Image Analysis Techniques
Authors: Xinyu Liang | Xiaorong Sun | Haiyue Sun | Runqiu Wang | Shunda Qiao | Ying He | Yufei Ma
Source: Sensors (article, 2026-02-15)
Field: Chemistry — Spectroscopy

Highly Sensitive CH4/C2H2 Dual-Component TDLAS Sensor Based on a Dual-Channel Hexagram Multi-Pass Cell A tunable diode laser absorption spectroscopy (TDLAS) sensor with a highly sensitive dual-component for methane (CH<sub>4</sub>) and acetylene (C<sub>2</sub>H<sub>2</sub>) detection is reported in this paper for the first time. A multi-pass cell (MPC) design model was established employing a vector-based ray-tracing method. A dual-channel MPC with an interlaced dual hexagonal star pattern was designed to improve gas absorption and realize real-time synchronous detection of CH<sub>4</sub> and C<sub>2</sub>H<sub>2</sub>. During the simultaneous continuous monitoring of CH<sub>4</sub> and C<sub>2</sub>H<sub>2</sub>, the sensor exhibited an excellent linear response to concentration variations. The minimum detection limit (MDL) for CH<sub>4</sub> reached 132.08 ppb, improving to 77.32 ppb when the average time was increased to 300 s. In the case of C<sub>2</sub>H<sub>2</sub>, the MDL was measured at 20.19 ppb and further reduced to 3.50 ppb under the same extended average time. Detection limit ; Tunable diode laser absorption spectroscopy ; Acetylene ; Absorption spectroscopy ; Absorption (acoustics) ; Analytical Chemistry (journal) ; Methane ; Diode Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Laser-induced spectroscopy and plasma
Authors: Yuxin Zhao | Peng Hu | X. D. Zhang | Qiang Li | Tao Xie | Yuquan Yuan | Xiaoxiao Wu | Fang Ling
Source: Scientific Reports (article, 2026-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A broadband Terahertz metamaterial absorber enabled by a water-assisted square-pyramid architecture with optical transmittance Terahertz (THz) absorbers with broad bandwidth and high absorption are significant interest for applications in electromagnetic shielding and photonic devices. In this work, we propose a broadband THz metamaterial absorber consists of square-pyramid structure with optical transmittance, composed of liquid water, polyethylene terephthalate, and indium tin oxide. The continuously varying square-pyramid configuration promotes multiple reflections and electromagnetic field superposition, in combination with the strong intrinsic THz absorption of water, which leads to efficient THz absorption. As a result, the proposed absorber achieves absorption exceeding 90% from 0.5 to 10 THz, over 95% from 0.65 THz to 10 THz, and over 99% from 1.2 THz to 10 THz. In addition, the absorber exhibits polarization-insensitive behavior and maintains a broadband absorption bandwidth of 7 THz with absorptance above 90% for incident angles up to 70°. These features highlight the proposed design as a promising platform for broadband THz absorption in practical applications where visible-band optical transmittance is desirable. Broadband ; Terahertz radiation ; Transmittance ; Metamaterial ; Metamaterial absorber ; Reflectivity Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Taeung Kim | Jeongmoo Lee | Ara Go
Source: arXiv (Cornell University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Bath Fitting for Hamiltonian-Diagonalization Dynamical Mean-Field Theory We propose a machine-learning-based initialization method to overcome the nonlinear bath-fitting bottleneck in Hamiltonian-diagonalization-based dynamical mean-field theory (HD-DMFT). In HD-DMFT, the continuous hybridization function is approximated by a finite set of bath-site energies and hybridization amplitudes, determined by minimizing a highly non-convex multivariable cost function. As the number of bath sites increases, the optimization becomes more sensitive to the initial guess and more prone to suboptimal local minima, which can slow or destabilize the DMFT self-consistency loop. We reformulate bath fitting as a supervised regression problem and train a kernel ridge regression model to predict near-optimal discrete bath parameters directly from the target hybridization function on the Matsubara axis. To ensure physical relevance and data diversity, we construct the training dataset from tight-binding Hamiltonians of layered-perovskite-like ruthenate models across systematically deformed structures, instead of relying on naive random parameter sampling, and obtain high-quality labels through fully converged conventional bath fitting. Time-reversal symmetry is explicitly incorporated in both feature and target representations to reduce effective dimensionality and enforce physical consistency. Benchmarks in the non-interacting limit show that the learned initialization systematically reduces the initial fitting error, decreases the number of conjugate-gradient iterations, and improves robustness against local minima over a wide range of bath sizes. We further demonstrate transferability to interacting DMFT calculations for $\mathrm{Sr_{2}RuO_{4}}$ solved with an adaptive-truncation impurity solver, where the ML initialization yields consistently faster convergence than a symmetry-preserving heuristic baseline while preserving the final fitted solution. Initialization ; Maxima and minima ; Curse of dimensionality ; Bottleneck ; Nonlinear system ; Kernel (algebra) ; Robustness (evolution) ; Heuristic ; Estimator Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Source: International Journal of Progressive Research in Engineering Management and Science (article, 2026-02-26)
Field: Chemistry — Analytical Chemistry

RESEARCH PAPER TOPIC: DEEP LEARNING BASED FOOD IMAGE RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS Currently, relating food particulars from images has come an important task in healthcare and diet monitoring operations.man-made identification of food from pictures is sensitive and moment eating.In this examination, a profound education predicated food picture recognition network applying Convolutional Neural net (CNN) is offered.The input food images are pre-processed by resizing and normalization before training the model.The CNN model is used to prize important features from the images and classify the food particulars into different orders.The performance of the model is estimated grounded on delicacy.The developments show off that the suggested system can recognize food pictures with alright version. Deep learning ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial neural network ; Feature (linguistics) ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Drying and Modeling