jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Z Z Lu | Yu Li | Chong Li | T. Wang | 浩一 楠瀬 | Wang Yang | Guanghui Wang
Source: Forests (article, 2025-12-22)
Field: Chemistry — Organic Chemistry

Vision-Based System for Tree Species Recognition and DBH Estimation in Artificial Forests The species, quantity, and tree diameter at breast height (DBH) are important indicators for assessing species distribution, individual growth status, and overall health in the forest. The existing tree information collection mainly relies on manual labor, which results in low efficiency and high labor intensity. To address these issues, we propose a method for tree species identification and diameter estimation by combining deep learning algorithms with binocular vision. First, an image acquisition platform is designed and integrated with a weeding machine to capture images during weeding operation. Images of seven types of trees are captured to develop a dataset. Second, a tree species identification model is established based on the YOLOv8n network, achieving 98.5% accuracy, 99.0% recall, and 99.2% mAP. Then, an improved YOLOv8n-seg model is proposed. It simplifies the network by introducing VanillaBlock in the backbone. FasterNet with a CCFM structure is added at the neck to enhance the model’s multi-scale expression capability. The mIoU of the improved model is 93.7%. Finally, the improved YOLOv8n-seg model is combined with binocular vision. After obtaining the segmentation mask of the tree, the spatial position of the two measurement points is calculated, allowing for the measurement of tree diameter. Verification experiments show that the average error for tree diameter ranges from 4.40~6.40 mm, and the proposed error compensation method can reduce diameter errors. This study provides a theoretical foundation and technical support for intelligent collection of tree information. Tree (set theory) ; Identification (biology) ; Segmentation ; Pattern recognition (psychology) ; Tree structure ; Diameter at breast height ; Image segmentation Wood and Agarwood Research ; Smart Agriculture and AI ; Remote Sensing and LiDAR Applications
Authors: Chuyu Liu | Rui Guo | Zhongsen Li | Benqi Zhao | Zhuozhao Zheng | Xiaolei Song
Source: NMR in Biomedicine (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

<scp> <i>k</i> </scp> ‐Space and Image Domain Hybrid Reconstruction Using Low‐Rank Plus Sparse Model (KILS) for Accelerated CEST MRI As a promising in vivo metabolic imaging method, chemical exchange saturation transfer (CEST) MRI requires collecting a series of images using varied saturation frequencies (ω). The low-rankness feature was utilized for acquisition acceleration and data denoising but primarily in the image domain. Herein, we aim to utilize such features in both k-space and the image domain, for achieving faster imaging and higher quality reconstruction. According to Parseval's theorem, CEST images at each ω have equal total energy to their k-space counterparts; that is, k-space signal along ω exhibited a similar valley shape as Z-spectra in the image domain, whereas the central k-space and peripheral regions reflect different spatial-frequency components, corresponding to varied low-rankness features. Because k-space series contain sparsity and redundancy complementary to the image domain, we proposed a hybrid k-space and image domain reconstruction for CEST MRI based on low-rank plus sparse (KILS). Both retrospective and prospective experiments were conducted at 3 T, on a BSA phantom, 11 healthy volunteers and 15 glioma patients. KILS was also retrospectively evaluated in human liver at 3 T and rat brains at 9.4 T. In retrospective experiments of phantoms and human brains, KILS demonstrated the best quantitative metrics among all reconstruction methods, with acceleration factors (AFs) ranging from 2 to 8. The ablation study demonstrated that KILS well preserved both CEST contrast and image anatomy. Prospectively, a 31-offset spectral scan of 2-mm isotropic whole-brain images was demonstrated, taking 5.7 min. Additionally, KILS proved effective in retrospective reconstruction for human liver at 3 T (AF = 6) and ischemic rat brain at 9.4 T (AF = 6). KILS demonstrated accurate and robust reconstruction of undersampled CEST MRI. The complementary nature of k-space and image domain may enable KILS to be readily applicable to multiple application scenarios. Iterative reconstruction ; Magnetic resonance imaging ; Image quality ; Pattern recognition (psychology) ; Redundancy (engineering) ; Feature (linguistics) ; Acceleration ; Energy (signal processing) ; Limiting Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Juan Huo | Feng He | Changtong Lu | Meng Zhu | Yifan Bu | Di Kang | Rui Wang | Wenning Feng | Rong Ma
Source: ACS Omega (article, 2025-01-13)
Field: Chemistry — Analytical Chemistry

Nonlinear Small Sample Data Regression with a New Rational-Quadratic Minkowski Kernel for Tobacco Laser Perforation Process Tar Reduction Estimation This paper investigates the nonlinear relationship between tobacco harmful content tar reduction and laser perforation parameters. To find a model to demonstrate the relationship between the laser perforation parameters and the cigarette tar reduction level, an online platform based on Python Streamlit was built to collect and publish related data. After the initial analysis of the collected experimental data, the quadratic nonlinear regression model demonstrates a significant fit to the experimental data. However, although the nonlinear regression has much higher accuracy than the linear regression plane, the prediction normalized root mean squared error (NRMSE) is still high, over 10%, which indicates that the regression relationship is more complex than the simple quadratic function expression. On the other hand, the sample dataset used for modeling is very limited, which restricts its exploration and the development of a model comparable to those built with big data. To address this challenge for small sample size data in modeling this complex nonlinear relationship, a novel rational-quadratic Minkowski (RM)-based kernel was designed. This RM-kernel model acquires higher accuracy than other kernels in both SVM and Gaussian process regression. Furthermore, this new kernel also shows less sensitivity to hyperparameter change, the greater ability to capture complex relationships, and more flexibility than the RBF kernel and RQ kernel. Subsequently, the kernel-based RM regression model was successfully implemented for laser perforation parameter selection, yielding consistent results that align with human sensory test data. Polynomial regression ; Mathematics ; Dimensionality reduction ; Nonlinear regression ; Kernel (algebra) ; Local regression ; Statistics ; Regression analysis ; Algorithm ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Deboral Eshak | Mohanapriya Arumugam
Source: Journal of Translational Medicine (article, 2025-12-01)
Field: Materials Science — Biomaterials

Nanomaterials: an overview of current trends and future prospects in neurological disorder treatment The World Health Organization (WHO) has identified neurological disorders (NDs) as one of the major health concerns worldwide, resulting in high mortality rates. NDs are conditions affecting the central and peripheral nervous systems, including the brain, spinal cord, cranial nerves, peripheral nerves, nerve roots, neuromuscular junctions, and muscles. These neurological diseases include Alzheimer's disease, Parkinson's disease, glioma/brain cancer, Huntington's disease, amyotrophic lateral sclerosis, multiple sclerosis, neuroinfections, ischemic stroke, trauma, hypoxia/anoxia, and depression. Unfortunately, these disorders remain difficult to treat due to the limited ability of conventional drugs to cross the blood-brain barrier (BBB) and achieve significant pharmacological effects in the brain. There is an urgent need to develop methods that can enhance drug efficacy and bypass the BBB. The application of various nanomaterials represents a promising approach to address these neurological disorders. Drugs incorporated with nanomaterials help improve therapeutic outcomes, reduce toxicity, provide better stability, enable targeted delivery, and enhance drug loading capacity. Numerous types and morphologies of inorganic and organic nanomaterials are increasingly employed for treating NDs, including quantum dots, dendrimers, metal nanoparticles, polymeric nanoparticles, liposomes, carbon nanotubes, metal oxide nanoparticles, and micelles. Their exceptional properties such as sensitivity, selectivity, and potential to bypass the BBB make them suitable for both diagnosis and treatment of NDs. In this review article, we briefly summarize the etiology and pathophysiology of various NDs along with current literature highlighting the use of nanomaterials for treating neurological disorders. Etiology ; Drug ; Neurological disorder ; Peripheral nerve ; Amyotrophic lateral sclerosis ; Drug development Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Biological Research and Disease Studies
Source: Malaysian Journal of Chemistry (article, 2025-04-17)
Field: Chemistry — Organic Chemistry

Enzymatic and Microbial-Assisted Treatments for the Extraction of Agarwood Essential Oils and Authentication using FTIR-ATR Spectroscopy and Multivariate Data Analysis Agarwood essential oil is a highly prized natural product known for its unique fragrance and therapeutic properties.This study explores the use of enzymatic and microbial-assisted treatments for enhanced extraction of agarwood essential oils, aiming to improve the yield and quality while maintaining sustainability.Moreover, advanced Fourier transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR) was employed for rapid characterization through functional and fingerprint regions comprising of the wavenumber region of 4000 -400 cm -1 of the essential oils.The multivariate data analysis techniques, including discriminant (DA), partial least squares-discriminant (PLS-DA) and principal component (PCA) analyses, were applied to the FTIR-ATR spectra as to differentiate the essential oil samples based on their chemical profiles of different essential oil extractions.The findings reveal that enzymatic and microbial treatments significantly enhance the efficiency of essential oil extraction, yielding sufficient volumes of essential oils.The FTIR-ATR analysis also successfully identified the unique spectral of each treatment method, while multivariate data analysis (MVDA) provided robust classification and discrimination of oils derived from different sources and processing techniques.Herein, this integrated approach demonstrates an efficient methodology for agarwood essential oil extraction and authentication, offering valuable insights for the fragrance, pharmaceutical, and cosmetic industries.By combining advanced spectroscopic techniques with statistical modelling, this study establishes a framework for quality assurance and traceability in the production of high-value natural products. Agarwood ; Fourier transform infrared spectroscopy ; Extraction (chemistry) ; Chemistry ; Enzyme ; Chromatography ; Food science ; Biochemistry ; Chemical engineering ; Medicine ; Engineering Wood and Agarwood Research ; Biological Stains and Phytochemicals ; Cultural Heritage Materials Analysis
Authors: Tobias Eckert | Daniel de las Heras | Enrique Velasco | Yuri Martı́nez-Ratón
Source: ArXiv.org (preprint, 2025-11-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sedimentation profiles and phase stacking diagrams in polydisperse hard rounded rectangle fluids We analyze the sedimentation behavior of a polydisperse two-dimensional liquid-crystal fluid using a local density functional theory based on scaled particle theory. Polydispersity is incorporated through variations in the roundness of hard rectangular particles interacting solely via excluded area effects. Despite its simplicity, the model displays a rich phenomenology. In bulk, the fluid exhibits isotropic, nematic, and tetratic phases. In sedimentation, we obtain complex phase stacking diagrams featuring multiphasic stacking sequences with up to four stacks of different bulk phases, inverted stacking sequences such as top isotropic and bottom nematic together with top nematic and bottom isotopic, as well as stacking sequences with reentrant stacks such as tetratic and nematic stacks floating between two isotropic stacks. This phenomenology arises as a result of an intricate coupling between particle polydispersity and the effect of gravity. Our approach can be easily adapted to investigate the sedimentation behaviour of other polydisperse colloidal systems. Stacking ; Rectangle ; Liquid crystal ; Isotropy ; Dispersity ; Phase diagram ; Phase (matter) ; Colloid Liquid Crystal Research Advancements ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Ms. Hani Chotai | Rupeshkumar V. Ramani | J H Markna | Bharat Kataria
Source: Journal of Advanced Mechanical Engineering Applications (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Cu and CuO Nanoparticles in Heat Transfer Applications: A Comprehensive Review Nanoparticle ; Heat transfer ; Copper ; Enhanced heat transfer Copper-based nanomaterials and applications ; Nanomaterials and Printing Technologies ; nanoparticles nucleation surface interactions
Authors: Chukwuemeka Fortunatus Nnadozie | Chiamaka Prisca Onuoha | Emeka E. Oguzie | Enos Ihediohamma Emereibeole | E.E. Oguzie
Source: Discover Chemistry. (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Anti-corrosion effect of ethanol extract from Piper guineense leaves on mild steel in water-in-diesel emulsion Abstract The growing demand for cleaner fuel alternatives has driven significant interest in water-in-diesel emulsions to reduce nitrogen oxides and particulate matter emissions, particularly in diesel-dependent regions. However, corrosion of fuel system components remains a critical barrier to water-in-diesel emulsions adoption. This study presents the novel application of ethanol extract of Piper guineense leaves as a green corrosion inhibitor for mild steel in water-in-diesel emulsion environments while also addressing the stability of water-in-diesel emulsion with varying water content, hydrophilic-lipophilic balance, surfactant dosage and mixing time. The anti-corrosion performance was evaluated by immersing mild steel specimens in a water-in-diesel emulsion containing Piper guineense extract at concentrations of 0.5, 1.0, and 1.5 g/L, with weight loss measurements conducted over 672 h. The Piper guineense extract demonstrated a maximum inhibition efficiency of 91.47% at 1.0 g/L, significantly reducing the corrosion rate from 0.0140 to 0.0008 mm/year. Stability evaluation revealed that the maximum emulsion performance was achieved with water-in-diesel emulsion formulated with 20% water content and a 5% surfactant dosage at a hydrophilic-lipophilic balance value of 9, maintaining stability for 8 h without visible phase separation. Surface characterization using Atomic Force Microscopy revealed a 23% reduction in apparent surface roughness and a 28% decrease in overall surface roughness for treated specimens. Fourier-Transform Infrared Spectroscopy analysis identified the adsorption of Piper guineense extract bioactive compounds on the steel surface, forming a protective barrier. These findings highlight the potential of Piper guineense extract as a sustainable additive that enhances material protection while supporting the operational viability of water-in-diesel emulsion fuels. Diesel fuel ; Emulsion ; Corrosion ; Chemistry ; Pulmonary surfactant ; Adsorption ; Corrosion inhibitor ; Materials science ; Metallurgy ; Organic chemistry Corrosion Behavior and Inhibition ; Polymer composites and self-healing ; Petroleum Processing and Analysis
Authors: Chao Chen | Wangzhong Mu
Source: Crystals (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Crystallization of High Performance Metallic Materials (2nd Edition) Crystallization generally refers to the material processing in which a solid phase nucleates within a liquid or solid matrix [...] Crystallization ; Phase (matter) ; Matrix (chemical analysis) ; Solid solution ; Metal ; Work (physics) Crystallization and Solubility Studies ; Aluminum Alloy Microstructure Properties ; Metallic Glasses and Amorphous Alloys
Authors: Budi Aribowo | Aprilia Tri Purwandari | Ni’mah Tsabitah | Reudinta Zesha | Dwi Astharini
Source: Journal of Scientific Insights (article, 2025-01-13)
Field: Chemistry — Analytical Chemistry

Honey Price Classification using K-Nearest Neighbor Machine Learning The global honey market faces significant challenges due to price inconsistencies, which often do not correlate with the actual quality of the honey. This research aims to develop a honey price classification model based on quality using the K-Nearest Neighbor (K-NN) machine learning method. The contribution in this research is a machine learning model that can classify honey prices validated by measured variables of honey concentration. Data was collected on 14 types of honey, focusing on price per 100 ml, pcategorized into 'cheap' and 'expensive' classifications. Data processing includes statistical testing using T-Test to determine the significance of price differences, followed by applying the K-NN algorithm for classification. Model performance is evaluated using metrics such as accuracy, the Receiver Operating Characteristic (ROC) curve, a graph used to evaluate the performance of binary classification models, and the Area Under the Curve (AUC). The results show that the K-NN model achieves an optimal accuracy of 100% and an AUC of 1.00 when the K parameter is set to 3, indicating excellent classification ability. It is hoped that these findings will increase market transparency, set fairer price standards, and help consumers and producers in making decisions in purchasing honey. k-nearest neighbors algorithm ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Nearest neighbor search Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Yan Liu | Minghao Liu | Y. Shangguan | Ke Mei | Shiyao Zhu | Kai Liu | Ruiquan Liao
Source: Materials (article, 2025-12-06)
Field: Materials Science — Metals and Alloys

Role of Environmental Chemistry in Governing the Corrosion and Stress Corrosion Cracking Mechanism of L415 Pipeline Steel in Acidic Soils The operational integrity of L415 pipeline steel, a critical component of China's energy network, is severely threatened by the unique acidic red soil environments prevalent in Southern China. A significant knowledge gap exists regarding its specific failure mechanisms, particularly the interplay between Anodic Dissolution (AD) and Hydrogen Embrittlement (HE) in driving Stress Corrosion Cracking (SCC). This study systematically investigates the corrosion and SCC behavior of L415 steel in a simulated environment that replicates the typical soil chemistry of the Gannan region in Southern China. Results revealed that corrosion kinetics are highly dependent on environmental chemistry, with corrosion rates escalating nearly four-fold from 0.0505 mm/a to a severe 0.1949 mm/a, driven by the synergy of low pH and high SO<sub>4</sub><sup>2-</sup> concentration. This behavior is governed by the integrity of the corrosion product film, where aggressive environments form porous, unprotective layers with low charge transfer resistance. Slow strain rate tensile (SSRT) tests confirmed that the steel's susceptibility to SCC is strongly promoted by acidity. Critically, the dominant SCC mechanism was environment-dependent, transitioning from Hydrogen Embrittlement (HE) to intergranular cracking in the most acidic environment, and a mixed AD-HE mechanism causing transgranular cracking in high-chloride conditions. These findings provide a direct mechanistic link between soil chemistry and failure mode, offering a crucial scientific basis for developing environment-specific integrity management strategies for pipelines in these challenging terrains. Stress corrosion cracking ; Corrosion ; Hydrogen embrittlement ; Intergranular corrosion ; Environmental stress fracture ; Cathodic protection ; Embrittlement ; Dissolution Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis ; Corrosion Behavior and Inhibition
Authors: Soraya Shafawati Mohamad Tahier | Nurfirzana Liyana Abdul Jaafar
Source: Indonesian Journal of Chemistry (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Unveiling the Bioactive Compound Potential of Red Mariposa &lt;i&gt;Christia vespertilionis&lt;/i&gt; Using Chemometric and ATR-FTIR Spectroscopy Red Mariposa Christia vespertilionis (MCV) is a medicinal plant containing potential bioactive compounds of interest; however, these have not been fully investigated. This work focuses on the antioxidant properties of red MCV, as evaluated by attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with chemometric methods of analysis such as principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA).The highest total phenolic levels (15.91 ± 0.04 mg GAE/mL) were found in the leaf of aqueous extracts, and total flavonoid content, 15.7 ± 0.04 mg QE/mL, in hexane extracts, compared to other solvents, which is linked to better DPPH radical scavenging activity (74.24 ± 0.00%). The FTIR spectra demonstrated the presence of hydroxyl (O−H) and carbonyl (C=O) functional groups, indicating a significant abundance of polyphenols and flavonoids. The bioactive composition of the red MCV was definitively confirmed through chemometric techniques. The chemometric analysis revealed that methanol and aqueous extracts offer maximum antioxidant and optimum solvent profiles. These results show that red MCV is a feasible source for pharmaceutical and nutraceutical applications dealing with oxidative stress. Nutraceutical ; Polyphenol ; DPPH ; Fourier transform infrared spectroscopy ; Antioxidant ; Solvent ; Aqueous solution ; Flavonoid ; Chemometrics Chromatography in Natural Products ; Bioactive natural compounds ; Phytochemicals and Antioxidant Activities