jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenxuan Lv | Yixue Bai | Dongyang Zhu | Changzheng He | Fengjiao Bu | Yusong Luo | Ping Zhao | Yanhong Qiu | Zunzheng Wei | Jie Zhang | Shaogui Guo | Yongtao Yu | Jingfang Wang | Yi Ren | Guoyi Gong | Haiying Zhang | Yong Xu | Guang Liu | Sihui Dai | Maoying Li
Source: Nanomaterials (review, 2025-10-31)
Field: Materials Science — Materials Chemistry

Innovative Application of Nanomaterials in Vegetable Cultivation: Recent Advances in Growth Promotion and Stress Tolerance Vegetables are crucial to human diet and health. To ensure sustainable vegetable production, regulatory measures are needed to enhance seed germination, plant growth, and resilience to extreme environmental conditions. Nanomaterials (NMs), owing to their high surface area, nanoscale dimensions, and unique photocatalytic properties, exhibit remarkable biological effects, such as promoting germination and growth, as well as improving stress resistance in crops, offering novel solutions to key challenges in vegetable cultivation. This review summarizes the absorption pathways of NMs in plants, specifically through the leaves and roots of vegetables. Their uptake and translocation occur via passive diffusion, active transport, and endocytosis, with key influencing factors including particle size, chemical composition, surface charge, and surface modifications. We further evaluate the advantages of nanofertilizers and nanopesticides, in vegetable production over their traditional counterparts, focusing on improvements in seed germination rates, seedling vigor, biotic and abiotic stress tolerance, and overall yield and quality. Through this review, we aim to offer comprehensive insights into the application of NMs in vegetable crop production. Abiotic stress ; Abiotic component ; Resilience (materials science) ; Germination ; Biomass (ecology) ; Crop productivity ; Promotion (chess) ; Resistance (ecology) Nanoparticles: synthesis and applications ; Polymer-Based Agricultural Enhancements ; Carbon and Quantum Dots Applications
Authors: Jan Simonik | Petr Lapčík | Pavla Bouchalová | Pavel Bouchal
Source: Mass Spectrometry Reviews (article, 2025-12-15)
Field: Chemistry — Spectroscopy

Data‐Independent Acquisition Mass Spectrometry in Tumor Classification and Cancer Biomarker Research Cancer treatment is far from optimal also because current classification systems do not reflect the complex molecular status of the tumor and its phenotype in sufficient detail. To construct molecular tumor classifiers, omics tools provide complex molecular data reflecting many aspects from genotype to phenotype. However, the true molecular effectors in the cells are proteins which often serve as potent cancer biomarkers and therapy targets. This review summarizes the method aspects that allowed the data-independent acquisition (DIA) mass spectrometry (MS) to outperform the traditional, data-dependent acquisition (DDA) approach in recent years. DIA-MS studies have already recapitulated molecular classification of colorectal and breast cancer, provided data improving molecular classification of prostate and other cancers, and led to validated diagnostic, prognostic, predictive biomarkers and therapy targets for common solid tumors. Tissue-specific spectral libraries are important for a deep characterization of tissue proteomes. Further perspectives of current cancer proteomics lie in the fields of single-cell and spatial proteomics and their integration with clinical data. The importance of functional and clinical validation is highlighted to allow stratified and/or personalized targeted therapy. Proteomics ; Biomarker discovery ; Prostate cancer ; Cancer ; Biomarker ; Cancer biomarkers ; Colorectal cancer ; Proteogenomics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Aytaj S. Hasanova | Fatima S. Hasanova
Source: Journal of Nanotechnology and Innovative Materials (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

The Invisible Enemy: Nanoparticle Exposure and Neurodegenerative Decline Atmospheric air pollution, specifically the ultra-fine fraction of particulate matter (PM2.5) known as nanoparticles (NPs), poses serious global health threats with critical neurological implications. This review summarizes evidence indicating that inhaled nanoparticles, particularly metallic nanoparticles (MNPs), can penetrate respiratory defense mechanisms and the blood-brain barrier (BBB). This enables direct translocation into the central nervous system (CNS). Once they enter the brain, these nanoparticles act as structural scaffolds, thereby accelerating the misfolding and aggregation of amyloid-beta (Aβ) peptides. This phenomenon has been identified as a pathological hallmark of Alzheimer's disease (AD). The underlying mechanisms involve chronic neuroinflammation and excessive reactive oxygen species (ROS) generation, leading to neuronal degeneration and cognitive decline. Significantly, the physicochemical properties that make nanoparticles potentially hazardous also present therapeutic possibilities. A notable example is the use of gold nanoparticles (AuNPs) to inhibit amyloid beta aggregation, which highlights the potential of nanomedicine for targeted neuroprotection. Keywords: air pollution, particulate matter (PM2.5), nanoparticles (NPs), amyloid beta (Aβ) protein, neurotoxicity, Alzheimer’s disease (AD), reactive oxygen species (ROS) Neuroinflammation ; Nanomedicine ; Nanoparticle ; Amyloid (mycology) ; Blood–brain barrier ; Nanotoxicology ; Reactive oxygen species ; Disease Nanoparticles: synthesis and applications ; Cancer-related cognitive impairment studies ; Air Quality and Health Impacts
Authors: Changmei Liang | Jiaxiong GUAN | Tao Gao | Juxia LI | Yanwen LI | Qifeng Zhao | Pengfei Wen | Zhuming Bi | Fumin Ma
Source: INMATEH Agricultural Engineering (article, 2025-04-30)
Field: Chemistry — Analytical Chemistry

GRAPE LEAF VARIETY RECOGNITION BASED ON THE AF-SWIN TRANSFORMER MODEL Aiming at the problem of differentiated cultivation strategies for different grape varieties, the AF-Swin Transformer model is proposed in this study. Firstly, Focal Loss is used to effectively tackle data imbalance in grape leaves. Secondly, the AdamW optimizer is selected to better control model complexity and improve generalization. The results show that the training accuracy of AF-Swin Transformer model is 7.87 percentage points higher than that of the original Swin Transformer model. Precision and recall improved by 4.4 and 4.3 percentage points, respectively. This study enables accurate automated variety monitoring within vineyard cultivation systems, assisting growers in implementing targeted cultivation strategies. Transformer ; Variety (cybernetics) ; Computer science ; Engineering ; Artificial intelligence ; Electrical engineering ; Voltage Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Daisuke Itô | Shinsuke Ishikawa | Hidenori Fujimoto | Akihiko Happoya | M. Nagata
Source: IEICE Electronics Express (article, 2025-12-24)
Field: Materials Science — Polymers and Plastics

Effect of blending poly(2,6-dimethyl-1,4-phenylene sulfide) resin bearing vinyl groups on printed wiring boards To reduce transmission loss in printed wiring boards (PWBs), materials with low dielectric loss tangent, such as oligo(2,6-dimethyl-1,4-phenylene ether)-based resins, are commonly used. In this study, we fabricated PWBs blending poly(2,6-dimethyl-1,4-phenylene sulfide)-based resins (PMPS-V), in which benzene rings are connected via sulfide rather than ether bonds, and evaluated its effects. The PWBs exhibited a low loss tangent of 0.0017 and achieved UL94 V-0 flame retardancy with only one-third the conventional flame retardant content. Transmission loss and eye diagram degradation up to 30 GHz were effectively suppressed after heat testing at 150 °C. It was clarified that PMPS-V contributes to improving the thermal reliability of PWBs. Dielectric loss ; Dissipation factor ; Printed circuit board ; Dielectric ; Degradation (telecommunications) ; Bearing (navigation) ; Fire retardant ; Reliability (semiconductor) Synthesis and properties of polymers ; Recycling and Waste Management Techniques ; Nanomaterials and Printing Technologies
Authors: Hua Yin | Lei Ding | Anqi Xiong | Lu Yuan | Ming‐Hui Chen | Yilu Xu | Yinglong Wang | Hui Xiao | Wei Quan
Source: Preprints.org (preprint, 2025-11-28)
Field: Chemistry — Analytical Chemistry

A Pipeline for Mushroom Mass Estimation Based upon Phenotypic Parameter: Multiple &lt;em&gt;Oudemansiella raphanipies&lt;/em&gt; Model Estimating the mass of Oudemansiella raphanipies quickly and accurately is indispensable for optimizing post-harvest packaging processes. The traditional methods typically involve manual grading followed by weighing with a balance, which is inefficient and labor-intensive. To address the challenges encountered in actual production scenarios, in this work, we proposed a novel pipeline for estimating the mass of multiple Oudemansiella raphanipies. To achieve this goal, an enhanced deep learning (DL) algorithm for instance segmentation and a machine learning (ML) model for mass prediction were introduced. On one hand, to segment the multiple samples in the same image, a novel instance segmentation network named FinePoint-ORSeg was presented to obtain the finer edges of samples, which integrated the edge attention module for improving the fineness of the edges. On the other hand, for individual samples, a novel cap-stem segmentation approach was applied and 18 phenotypic parameters were obtained. Furthermore, the Principal Component Analysis (PCA) was utilized to reduce the redundancy among features. Combining the two aspects mentioned above, the mass was computed by Exponential GPR model with 7 principal components. In terms of segmentation performance, our model outperforms the original Mask R-CNN, the AP, the AP50, the AP75 and the APs are improved by 2%, 0.7%, 1.9%, and 0.3%, respectively. Additionally, our model outperforms other networks such as YOLACT, SOLOV2 and Mask R-CNN with swin. As for mass estimation, the results showed the average Coefficient of Variation (CV) of single sample mass in different attitude are 6.81%. Moreover, an average mean absolute percentage error (MAPE) of multiple samples is 8.53%. Overall, the experimental results indicated that the proposed method is time-saving, non-destructive and accurate. This can provide a reference for the research on post-harvest packaging technology of Oudemansiella raphanipies. Segmentation ; Principal component analysis ; Pattern recognition (psychology) ; Pipeline (software) ; Redundancy (engineering) ; Image segmentation ; Jaccard index Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Santosh Kumar Mishra | Sunil Kumar Mishra | Bhargav Appasani
Source:  (article, 2025-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integer-PSO-Optimized Checkerboard Dual-Band Terahertz Metamaterial Absorber for Biomedical Sensing Applications This paper presents a checkerboard-patterned terahertz (THz) metamaterial absorber engineered for wide-band, dual-band absorption. The absorber consists of a gold metal layer patterned on a polyimide substrate, forming a unit cell structure with dimensions of 85 µm × 85 µm. At the core of the design is a square metal patch of 67 µm × 67 µm, which is divided into a 5 × 5 grid of 25 smaller cells. An integer-coded Particle Swarm Optimization (PSO) algorithm is employed to generate the pattern, where an input value of ‘1’ retains the metal in a cell, and a ‘0’ results in the removal of metal from that cell, resulting in a digitally optimized checkerboard pattern. The substrate height is also optimized and fixed at 7 µm to enhance resonance characteristics. The PSO algorithm is run for 50 iterations, with the fitness function defined as the number of frequency points at which the absorption exceeds 90%. The finalized design achieves two distinct absorption peaks with high efficiency: 99.53% at 3.434 THz, with a 90% absorption bandwidth of 212 GHz; and 99.35% at 3.823 THz, with a bandwidth of 177 GHz. While the absorption performance is already significant, it can be further improved by increasing the number of PSO iterations, albeit at the cost of higher computational complexity. The proposed absorber demonstrates strong potential for biomedical sensing, as validated through its ability to differentiate between cancerous and non-cancerous breast and blood cells. This work paves the way for fully automated, algorithm-driven metamaterial design strategies in the THz regime, particularly for applications in non-invasive biomedical diagnostics. Terahertz radiation ; Metamaterial ; Metamaterial absorber ; Bandwidth (computing) ; Particle swarm optimization ; Grid ; Absorption (acoustics) ; HFSS Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Martina Balac | Andreja Rajković | Mihailo Milanović | Zoran Mileusnić | Rajko Miodragović | Aleksandra Dragičević | Olivera Ećim-Đurić
Source: Journal of Agricultural Engineering (article, 2025-09-04)
Field: Materials Science — Polymers and Plastics

Use of machine learning tools to estimate drying rate of apple and apricot wood discs This study compares three machine learning (ML) models -artificial neural networks (ANN), recurrent neural networks (RNN), and extreme gradient boosting (XGBoost)- for predicting the drying kinetics of wood biomass from the clearing of outdated apple and apricot orchards. The primary goal was to optimize and evaluate the performance of these models in predicting drying time and moisture content of the wood, which is crucial for improving energy efficiency in the drying process. Experiments were conducted under four temperature regimes (40°C, 50°C, 60°C, and 70°C) to simulate low- and high-temperature drying conditions. The models were trained and evaluated using statistical quality metrics such as root mean squared error (RMSE), mean absolute error MAE, and R². Results showed that all models performed with high accuracy, but XGBoost demonstrated the best statistical performance, exhibiting the lowest MAE and RMSE values. The RNN model exhibited the highest R² values and performed well in terms of MAE and RMSE, whereas the ANN model yielded slightly lower results. Despite small differences in performance, the models showed strong predictive capabilities and can be effectively used for modeling the drying process of moist wood biomass. This research emphasizes the significance of model optimization in enhancing the accuracy of drying time predictions and minimizing energy consumption in drying processes. Mathematics ; Horticulture ; Machine learning ; Agricultural engineering ; Biology ; Engineering ; Computer science Textile materials and evaluations
Authors: L. Lai | Hui Zhang | Jiahui Gu | Long Wen
Source: Applied Sciences (article, 2025-07-23)
Field: Chemistry — Analytical Chemistry

Detection of Cumulative Bruising in Prunes Using Vis–NIR Spectroscopy and Machine Learning: A Nonlinear Spectral Response Approach Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Nonlinear system ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Melkamu Workneh Bishaw | Senayt Molla Ejigu | Sefineh Getachew Tiruneh | Adis Abebaw Dessalegn
Source: Cureus Journal of Computer Science. (article, 2025-08-06)
Field: Chemistry — Analytical Chemistry

Automatic Quality Assessment of Red Haricot Beans Using Deep Learning Red haricot bean, also called Boleqe and Red Kidney bean, is one of the most important legume crops in the world. In Ethiopia, it became one of the most exported legume crops in 2020. Due to its many advantages in daily life, ensuring its quality is essential for both producers and consumers. Therefore, we developed an automatic red haricot bean defect detection system using a custom model that utilizes a convolutional neural network (CNN) with skip connections and a SoftMax classifier. Sample images of red haricot beans were collected from the Ethiopian Commodity Exchange at the Bure branch. We took 650 images, each containing a minimum of 15 kernels, and finally obtained 10,093 kernel datasets using an automatic segmentation process. Eighty percent of the dataset was used for training, and the remaining 20% was used for validation and testing. After splitting the dataset, we evaluated the custom model with different image sizes (32 × 32 and 64 × 64) and applied data augmentation to prevent overfitting. Finally, we achieved 98.5% test accuracy and 98.9% training accuracy with augmented data and an image size of 64 × 64. We also compared our best model with a CNN without skip connections, VGG16, and ResNet50, which yielded average accuracies of 96.7%, 97.5%, and 63.0%, respectively. The findings show that custom deep learning models can provide accurate and reliable quality assessments while significantly reducing the time and subjectivity involved in traditional agricultural quality control methods. Computer science ; Quality (philosophy) ; Artificial intelligence ; Philosophy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Sensory Analysis and Statistical Methods
Authors: Yiyang Liu | Qiangbo Zhang | Changwei Zhang | Mengguang Wang | Zhenrong Zheng
Source: Nanomaterials (article, 2025-07-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Planar Wide-Angle Imaging System with a Single-Layer SiC Metalens Optical systems with wide field-of-view (FOV) imaging capabilities are crucial for applications ranging from biomedical diagnostics to remote sensing, yet conventional wide-angle optics face integration challenges in compact platforms. Here, we present the design and experimental demonstration of a single-layer silicon carbide (SiC) metalens achieving a 90° total FOV, whose planar structure and small footprint address the challenges. This design is driven by a gradient-based numerical optimization strategy, Gradient-Optimized Phase Profile Shaping (GOPP), which optimizes the phase profile to accommodate the angle-dependent requirements. Combined with a front aperture, the GOPP-generated phase profile enables off-axis aberration control within a planar structure. Operating at 803 nm with a focal length of 1 mm (NA = 0.25), the fabricated metalens demonstrated focusing capabilities across the wide FOV, enabling effective wide-angle imaging. This work demonstrates the feasibility of using numerical optimization to realize single-layer metalens with challenging wide FOV capabilities, offering a promising route towards highly compact imagers for applications such as endoscopy and dermoscopy. Planar ; Optics ; Materials science ; Numerical aperture ; Acceptance angle ; Footprint ; Field of view ; Aperture (computer memory) ; Phase (matter) ; Computer science ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Coatings and Gratings
Authors: Alessandro Genoni | Mosè Casalegno | Piero Macchi | Guido Raos
Source: ChemRxiv (preprint, 2025-07-03)
Field: Chemistry — Spectroscopy

Jordan-Wigner mapping for nonorthogonal spin orbitals Quantum computing offers a promising platform to address the computational challenges inherent in quantum chemistry, and particularly in valence bond (VB) methods, which are chemically appealing but suffer from high computational cost due to the use of nonorthogonal orbitals. While various fermionic-to-spin mappings exist for orthonormal spin orbitals, such as the widely used Jordan–Wigner transformations, an analogous framework for nonorthogonal spin orbitals remains undeveloped. In this work, we propose an alternative Jordan–Wigner-type mapping tailored for the nonorthogonal case, with the goal of enabling efficient quantum simulations of VB-type wavefunctions. Our approach paves the way towards the development of chemically interpretable and computationally feasible valence bond algorithms on near-term quantum devices. Atomic orbital ; Spin (aerodynamics) ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies
Authors: Muhammad Shafique | Yash R. Bade | Achal P. Bhedurkar | Vinayak K. Rahangdale | Shubhada Ganvir | Dr M. D. Kitukale
Source: International Journal of Advanced Research in Science Communication and Technology (article, 2025-01-23)
Field: Materials Science — Biomaterials

The Impact of Nanorobotics on Enhancing Therapeutic Strategies and Disease Management in Medicine Nanorobotics has emerged as a groundbreaking medical field, offering innovative solutions to longstanding challenges in disease management and therapeutic strategies. This systematic review explores the fundamentals of nanorobotics, its applications in targeted drug delivery, diagnostics, cancer treatment, and minimally invasive surgery, as well as the associated challenges and future directions. Nanorobots, characterized by their ability to operate at nanoscale dimensions, have demonstrated significant potential in enhancing the precision and efficacy of medical interventions. In oncology, nanorobots enable targeted drug delivery, reducing systemic toxicity while improving treatment outcomes. In minimally invasive surgery, nanorobots facilitate precise interventions, minimizing collateral damage and expediting recovery. Biocompatibility, technical limitations, and ethical concerns remain critical despite these advancements. Issues like immune system interference, long-term toxicity, and difficulties in propulsion and control require innovative solutions. Ethical considerations regarding patient privacy and regulatory frameworks necessitate global collaboration for responsible implementation. The future of nanorobotics lies in its integration with emerging technologies, such as artificial intelligence, which promises to enhance navigation, predictive analytics, and personalized medicine. Advancements in biocompatible materials, scalable manufacturing, and real-time imaging will further drive the clinical translation of nanorobotics. This review underscores nanorobotics' transformative potential to revolutionize healthcare and emphasizes the need for continued interdisciplinary research to overcome existing barriers and optimize its applications Nanorobotics ; Disease ; Intensive care medicine ; Medicine ; Disease management ; Computer science ; Internal medicine ; Artificial intelligence Nanoparticle-Based Drug Delivery ; Micro and Nano Robotics ; Anesthesia and Neurotoxicity Research