jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziqi Chen | Bo Peng | T. Zhai | Daniel Adu-Ampratwum | Xia Ning
Source: ArXiv.org (preprint, 2025-02-09)
Field: Chemistry — Spectroscopy

Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSMol to facilitate drug development. DiffSmol generates 3D binding molecules based on the shapes of known ligands. DiffSMol encapsulates geometric details of ligand shapes within pre-trained, expressive shape embeddings and then generates new binding molecules through a diffusion model. DiffSMol further modifies the generated 3D structures iteratively via shape guidance to better resemble the ligand shapes. It also tailors the generated molecules toward optimal binding affinities under the guidance of protein pockets. Here, we show that DiffSMol outperforms the state-of-the-art methods on benchmark datasets. When generating binding molecules resembling ligand shapes, DiffSMol with shape guidance achieves a success rate 61.4%, substantially outperforming the best baseline (11.2%), meanwhile producing molecules with novel molecular graph structures. DiffSMol with pocket guidance also outperforms the best baseline in binding affinities by 13.2%, and even by 17.7% when combined with shape guidance. Case studies for two critical drug targets demonstrate very favorable physicochemical and pharmacokinetic properties of the generated molecules, thus, the potential of DiffSMol in developing promising drug candidates. Diffusion ; Statistical physics ; Psychology ; Physics ; Thermodynamics Analytical Chemistry and Chromatography ; Mathematical Biology Tumor Growth
Authors: Emmanuel A. Mensah | Abrar Faiyaz | Giovanni Schifitto | Md Nasir Uddin
Source: International Journal of Molecular Sciences (review, 2025-11-15)
Field: Materials Science — Materials Chemistry

Chemical Exchange Saturation Transfer Imaging in Neuroinflammation: Methods, Challenges, and Recommendations Chemical Exchange Saturation Transfer (CEST) imaging has emerged as a promising non-invasive molecular MRI technique for investigating neuroinflammation. It offers unique insights into metabolic and molecular alterations in the brain. This review presents a comprehensive overview of CEST principles, methodological developments, and translational applications in neuroinflammation. It covers the basic mechanisms, pulse sequence designs, readout strategies, and various CEST contrasts used to probe molecular changes associated with inflammation. Recent advancements in fast CEST imaging, including optimized undersampling strategies and accelerated reconstruction methods are discussed. Improvements in post-processing and quantification techniques are also highlighted. The growing role of artificial intelligence (AI) in CEST imaging for image reconstruction, artifact correction, and biomarker extraction, is examined. Preclinical and clinical studies show CEST's potential to detect neuroinflammation across neurological disorders. The impact of high-field MRI on enhancing CEST sensitivity and specificity are also discussed. Despite notable progress, several challenges remain. These include sensitivity to field inhomogeneities, lack of acquisition standardization, and limited clinical validation. We outline current limitations, translational barriers, and provide recommendations for improving reproducibility, facilitating clinical adoption, and integrating AI-based approaches for robust molecular characterization. Overall, CEST imaging shows great potential as a non-invasive biomarker for neuroinflammation. It can deepen understanding of the molecular and metabolic mechanisms underlying neurological diseases, while addressing technical and translational challenges remains key for its broader clinical implementation. Molecular imaging ; Translational research ; Biomarker ; Undersampling ; Magnetic resonance imaging ; Neuroimaging Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Sulfur Compounds in Biology
Authors: Carlos Alberto Arias | Camilo Baldovino | José A. Gómez | B Restrepo Restrepo | Sergio Sánchez
Source: Transactions on Energy Systems and Engineering Applications (article, 2025-07-04)
Field: Chemistry — Analytical Chemistry

Deep learning model for recognizing fresh and rotten fruits in industrial processes The detection of fruit condition is essential to ensure quality control in industrial processes. Currently, this task is often performed manually, which is inefficient and time-consuming for operators. Therefore, it is crucial to implement emerging technologies that reduce human effort, costs, and production time while enabling more effective defect detection in fruits. In this context, this work presents the implementation of an artificial intelligence model based on computer vision to identify the condition of fruits. Various models were compared, including YOLOv8, YOLOv11, Detectron2, and Fast R-CNN, trained on a dataset that classifies fruits into two categories: ripe and rotten. The models were evaluated in terms of accuracy, speed, and robustness under different lighting and background conditions to select the most suitable for real-time applications. The results showed that YOLOv8 achieved the best generalization, reaching a mAP@50 of 83.8% and an accuracy of 77.3%. Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Mostafa Sadeghian | Arvydas Palevičius | Giedrius Janušas
Source: Mathematics (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

A Review of Theories and Numerical Methods in Nanomechanics for the Analysis of Nanostructures Nanostructures, such as carbon nanotubes (CNTs), graphene, nanoplates, etc., show behaviors that classical continuum theories cannot capture. At the nanoscale, size effects, surface stresses, and nonlocal interactions become important, so new models are needed to study nanostructures. The main nanomechanics theories that are used in recently published papers include nonlocal elasticity theory (NET), couple stress theory (CST), and nonlocal strain gradient theories (NSGTs). To solve these models, methods such as finite elements, isogeometric analysis, mesh-free approaches, molecular dynamics (MD), etc., are used. Also, this review categorizes and summarizes the major theories and numerical methods used in nanomechanics for the analysis of nanostructures in recently published papers. Recently, machine learning methods have enabled faster and more accurate prediction of nanoscale behaviors, offering efficient alternatives to traditional methods. Studying these theories, numerical models and data driven approaches provide an important foundation for future research and the design of next generation nanomaterials and devices. Nanomechanics ; Carbon nanotube ; Numerical analysis ; Elasticity (physics) ; Nanostructure ; Finite element method ; Continuum mechanics ; Nanoscopic scale Nonlocal and gradient elasticity in micro/nano structures ; Carbon Nanotubes in Composites ; Advanced Physical and Chemical Molecular Interactions
Authors: Lei Zhang | Yang Zhang | Lizhe Zhang | Yanlin Zhu | Dongying Wang | Hongxiao Chao | Yu Lan | Jin Xiang
Source: Chinese Optics Letters (article, 2025-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Snapshot hyperspectral image segmentation based on metasurfaces In this Letter, we propose, to our knowledge, a novel snapshot hyperspectral image segmentation system based on metasurfaces.This system combines a metasurface with a monochrome camera sensor chip to create a snapshot hyperspectral sensor, encoding the target hyperspectral image into an intensity pattern.A deep learning network is proposed for snapshot segmentation, which is capable of extracting spatial-spectral features of the measurement and directly predicting the segmentation results of the scene.The proposed system obviates the necessity of obtaining or reconstructing the hyperspectral image of the original scene.By doing so, it can not only simplify the optical system but also avoid the error that might occur during the hyperspectral image reconstruction.The results demonstrate that the proposed snapshot segmentation system achieves a prediction accuracy exceeding 99% in the context of land cover. Hyperspectral imaging ; Snapshot (computer storage) ; Segmentation ; Image segmentation ; Image processing Metamaterials and Metasurfaces Applications ; Optical Polarization and Ellipsometry ; Random lasers and scattering media
Authors: Sungho Yoon | Ik‐Su Kwon | Jin-Seok Lim | Byung Bae Park | Seung-Won Lee | Hae-Jong Kim
Source: Energies (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Enhanced Clustering of DC Partial Discharge Pulses Using Multi-Level Wavelet Decomposition and Principal Component Analysis Partial discharge (PD) is a critical indicator of insulation degradation in high-voltage DC systems, necessitating accurate diagnosis to ensure long-term reliability. Conventional AC-based diagnostic methods, such as phase-resolved partial discharge analysis (PRPDA), are ineffective under DC conditions, emphasizing the need for waveform-based analysis. This study presents a novel clustering framework for DC PD pulses, leveraging multi-level wavelet decomposition and statistical feature extraction. Each signal is decomposed into multiple frequency bands, and 70 distinctive waveform features are extracted from each pulse. To mitigate feature redundancy and enhance clustering performance, principal component analysis (PCA) is employed for dimensionality reduction. Experimental data were obtained from multiple defect types and measurement distances using a 22.9 kV cross-linked polyethylene (XLPE) cable system. The proposed method significantly outperformed conventional time-frequency (T-F) mapping techniques, particularly in scenarios involving signal attenuation and mixed noise. Propagation-induced distortion was effectively addressed through multi-resolution analysis. In addition, field noise sources such as HVDC converter switching transients and fluorescent lamp emissions were included to assess robustness. The results confirmed the framework’s capability to distinguish between multiple PD types and noise sources, even in challenging environments. Furthermore, optimal mother wavelet selection and correlation-based feature analysis contributed to improved clustering resolution. This framework supports robust PD classification in practical HVDC diagnostics. The framework can contribute to the development of real-time autonomous monitoring systems for HVDC infrastructure. Future research will explore incorporating temporal deep learning architectures for automated PD-type recognition based on clustered data. Cluster analysis ; Principal component analysis ; Partial discharge ; Wavelet ; Pattern recognition (psychology) ; Redundancy (engineering) ; Waveform ; Wavelet transform ; Noise (video) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Daya Shankar Verma | Jitendra K. Mishra | Ankit Kumar | Abdul Khader Jilani Saudagar | Shambhu Mahato
Source: International Journal of Intelligent Systems (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

VQ‐Rice: Integrating Variational Quantum Models for Intelligent Rice Disease Classification This study presents a novel hybrid quantum‐classical framework for rice disease diagnosis, leveraging variational quantum circuits (VQCs) to address the limitations of traditional and deep learning models in precision agriculture. The proposed Quantum Variational Rice Disease Network (QVRDN) integrates quantum feature encoding, variational quantum processing, and adaptive optimization to achieve superior classification accuracy, efficiency, and robustness. Using a curated dataset of 3000 annotated rice leaf images spanning major disease categories, the QVRDN framework applies dimensionality reduction and quantum angle encoding to transform the image features into quantum states, which are then processed by parameterized quantum circuits for disease classification. Experimental results demonstrate that QVRDN outperforms classical models, including SVM, random forest, CNN, and ResNet50‐achieving, the highest accuracy of 97.8%, faster inference times, and greater resilience to noise and limited data. The compact design of the framework enables edge deployment without GPU dependency, making it suitable for resource‐constrained agricultural environments. By demonstrating the feasibility and advantages of quantum machine learning in crop health monitoring, this study establishes a foundation for quantum‐enhanced, data‐efficient agricultural diagnostics and paves the way for future advances in intelligent, field‐ready quantum geoinformatics systems. Quantum machine learning ; Dimensionality reduction ; Quantum ; Quantum computer ; Benchmark (surveying) ; Artificial neural network ; Feature (linguistics) ; Plant disease Spectroscopy and Chemometric Analyses ; Machine Learning in Bioinformatics ; Fractal and DNA sequence analysis
Authors: Bin Chen | Wei Tao | Bo Guo | Li Zhou | Junhua Song | J.C. Wu | Z. Zheng | Minghao Duan
Source: Journal of Food Measurement & Characterization (article, 2025-04-01)
Field: Chemistry — Analytical Chemistry

Improved STA-BiLSTM for tea withering moisture content: detection and prediction model Accurately predicting the moisture content of tea leaves during the withering process is crucial for ensuring process stability and maintaining tea quality. However, the withering process exhibits complex spatiotemporal characteristics and environmental interferences, posing significant challenges to deep learning methods. To address this, we propose a novel Spatiotemporal Attention-based Bidirectional Long Short-Term Memory (STA-BiLSTM) model integrated with XGBoost for moisture content prediction. First, a portable dielectric constant-based detection device was developed to acquire feature data. Compared to hyperspectral or near-infrared spectroscopy, this device offers advantages in cost-effectiveness (using low-cost capacitive and temperature/humidity sensors), portability (310 mm × 210 mm × 25 mm, 3D-printed resin casing), and industrial applicability (real-time monitoring with a capacitance range of 10–200 pF and ± 0.1% RH humidity accuracy). Its reliability was validated through extensive experiments, achieving a moisture content detection range of 9%–28% with bulk density compensation. The maximum relevance minimum redundancy (mRMR) algorithm identified seven key features (e.g., capacitance, temperature, bulk density) from high-dimensional data. A BiLSTM network extracted temporal dependencies, while a spatiotemporal attention mechanism adaptively focused on critical features and time steps. Finally, XGBoost was integrated as a post-processing ensemble layer to enhance robustness against noise and outliers in industrial environments. Specifically, XGBoost processes the STA-BiLSTM output by leveraging its regularization and gradient-boosted trees to suppress sensor noise and improve generalization. Experiments on a real-world dataset demonstrated that STA-BiLSTM-XGBoost achieved superior performance (RMSE: 0.022, R2: 0.967) over baseline models, validating its effectiveness for industrial tea withering applications. Water content ; Environmental science ; Horticulture ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Jingjun Cao | Xiaoqing Xian | Minghui Qiu | Xin Li | Yajie Wei | Wanxue Liu | Guifen Zhang | Lihua Jiang
Source: Agronomy (article, 2025-06-26)
Field: Chemistry — Analytical Chemistry

Automatic Potato Crop Beetle Recognition Method Based on Multiscale Asymmetric Convolution Blocks Five beetle species can occur in potato fields simultaneously, including one quarantine pest (the Colorado potato beetle (CPB)), one phytophagous pest (the 28-spotted potato ladybird beetle), and three predatory ladybird beetles (the 7-spotted lady beetle, the tortoise beetle, and the harlequin ladybird beetle). The timely detection and accurate identification of CPB and other phytophagous or predatory beetles are critical for the effective implementation of monitoring and control strategies. However, morphological identification requires specialized expertise, is time-consuming, and is particularly challenging due to the dark brown body color of these beetles when in the young larval stages. This study provides an effective solution to distinguish between phytophagous and/or quarantine and predatory beetles. This solution is in the form of a new convolutional neural network architecture, known as MSAC-ResNet. Specifically, it comprises several multiscale asymmetric convolution blocks, which are designed to extract features at multiple scales, mainly by integrating different-sized asymmetric convolution kernels in parallel. We evaluated the MSAC-ResNet through comprehensive model training and testing on a beetle image dataset of 11,325 images across 20 beetle categories. The proposed recognition model achieved accuracy, precision, and recall rates of 99.11%, 99.18%, and 99.11%, respectively, outperforming another five existing models, namely, AlexNet, MobileNet-v3, EfficientNet-b0, DenseNet, and ResNet-101. Notably, the developed field investigation mini-program can identify all the developmental stages of these five beetle species, from young larvae to adults, and provide timely management (or protection) suggestions to farmers. Our findings could be significant for future research related to precise pest control and the conservation of natural enemies. Colorado potato beetle ; Convolution (computer science) ; Crop ; Computer science ; Environmental science ; Agricultural engineering ; Pattern recognition (psychology) ; Agronomy ; Artificial intelligence ; Mathematics ; Biology ; Botany ; PEST analysis ; Engineering Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: H.M. Cekirge
Source: American Journal of Artificial Intelligence (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Algebraic Cekirge Method for Deterministic and Energy-efficient Transformer Language Models This paper introduces a unified deterministic algebraic framework for transformer-style language modeling, extending the σ-Based (Cekirge) methodology toward energy-efficient and interpretable computation. The approach constructs σ-regularized, nonsingular matrices for queries, keys, values, and output weights (Q, K, V, W), enabling attention and decoding to be computed in closed form without iterative stochastic gradient descent. By enforcing σ-regularization, matrix invertibility and numerical stability are guaranteed, allowing direct algebraic determination of weights from paired input–output examples rather than optimization through back propagation. Analytical and numerical experiments, including a controlled five-token model, demonstrate that the deterministic algebraic solution reproduces the predictive behavior of gradient descent while eliminating randomness and reducing computation time by more than sixty-fold. The framework unifies several complementary formulations—the Four-Cluster Deterministic Map, Frozen-Library Forward Training, σ-Matrix Fast Learning, and Inverse Deterministic Energy-Saving Training (IDEST)—each contributing to a closed, energy-saving learning process that transforms optimization into deterministic algebraic resolution. Conceptually, the Cekirge Method parallels perceptual refinement: just as John remains himself while seen through blue eyeglasses but becomes obscured behind a wooden mask. The model’s internal mappings preserve identity under small deterministic perturbations (ε) but lose interpretability under stochastic noise. This analogy captures the method’s central philosophy—learning through controlled perturbation that reveals structure without destroying equilibrium. Finally, the study situates deterministic computation within a sustainability perspective. Global energy consumption, intensified by iterative AI training, has surpassed ecological thresholds. The Cekirge framework reconceives learning as a finite algebraic equilibrium rather than an energy-intensive iterative loop, aligning computational intelligence with thermodynamic and social efficiency. It proposes that future AI systems should pursue mathematical determinism and ecological responsibility in parallel, ensuring progress that is both computationally exact and energetically sustainable. A supplementary real-data experiment confirms that deterministic algebraic decoding achieves comparable accuracy to gradient descent while operating with markedly lower computational energy. Algebraic number ; Interpretability ; Computation ; Randomness ; Gradient descent ; Iterative and incremental development ; Iterative method ; Invertible matrix ; Nondeterministic algorithm Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Pooya Sajjadi | Farshid Hajati | Alireza Rezaee | Shahadat Uddin
Source: Biomedical Signal Processing and Control (article, 2025-05-03)
Field: Chemistry — Analytical Chemistry

Classification of direct microscopic fungi images using optimized graph networks Fungal infections pose a significant threat to immunocompromised patients due to their variable antifungal susceptibility and the limitations of conventional identification methods. Current approaches relying on microscopy, histopathology, and culture demand specialized training, often leading to delays and increased computational costs. To overcome these limitations, we introduce a deep learning approach for identifying uncultured fungi patches. Our model leverages optimized graph-based neural networks to classify images of direct microscopic examination, addressing the challenges of low data availability and lengthy identification processes. To further enhance the model’s performance, its hyperparameters are optimized using various swarm-based optimization methods. Our model can potentially eliminate the need for expert mycologists and lengthy biochemical identification processes. By employing few-shot learning and hyperparameter optimization, the model achieves remarkable accuracy in identifying direct, uncultured fungal images, reducing identification time from 10-14 days to hours. To the best of our knowledge, our approach achieves the highest accuracy on the DeFungi (93.1 %) and DIFaS (100 %) datasets, surpassing the closest benchmark methods, which report accuracies of 86.8 % on DeFungi and 93.9 % on DIFaS. Computer science ; Graph ; Artificial intelligence ; Pattern recognition (psychology) ; Theoretical computer science Spectroscopy and Chemometric Analyses ; Cell Image Analysis Techniques ; Smart Agriculture and AI