jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Namgil Lee | Hojin Yoo | Juhyoung Kim | Heejung Yang
Source: BMC Bioinformatics (article, 2025-10-31)
Field: Chemistry — Spectroscopy

A shrinkage-based statistical method for testing group mean differences in quantitative bottom-up proteomics The proposed statistical method offers an effective alternative to classical approaches for differential analysis of peptides in quantitative bottom-up proteomics using DIA-MS. The R software package MDstatsDIAMS is available at https://github.com/namgillee/MDstatsDIAMS . Proteomics ; Statistical analysis ; DNA microarray ; Quantitative proteomics ; R package ; Software ; Software package ; Differential (mechanical device) Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Mass Spectrometry Techniques and Applications
Authors: Yi-Chien Wu | Drew Schwartz | Elie Abi Khalil | Aditi Upadhye | Jalees Rehman | Seung Young Lee
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-24)
Field: Chemistry — Spectroscopy

<i>NicheProt</i> : Cell-type-resolved proteomics of tissue compartments Spatial proteomics uncovers the molecular underpinnings of cellular function in intact tissues. Laser capture microdissection coupled with mass spectrometry enables comprehensive proteomic profiling of selected tissue regions, but typically does not support cell-type-specific proteomic analysis. We present <i>NicheProt</i>, a 3D optical microscopy-guided, photobleaching-mediated cell barcoding approach for isolating intact specific cell types from defined microanatomical tissue compartments or niches. Using sequential bottom-up proteomic analysis, we defined two distinct phenotypes of CD11c<sup>+</sup> dendritic cells based on their spatial locations in the inflamed mouse spleen. These two compartment-specific dendritic cell populations were characterized by proteomic signatures differing in the levels of 54 proteins. This 3D tissue microscopy-guided method offers cell-type and microregion-resolved proteomic analysis, facilitating the proteomic discovery of previously unrecognized cell subtypes and their functional roles in distinct tissue compartments. Proteomics ; Laser capture microdissection ; Proteome ; Cell ; Microdissection ; Cell type ; Phenotype ; Quantitative proteomics Advanced Proteomics Techniques and Applications
Authors: Sunday C. Anyaora | Promise C. Okoye | Bethel Mba | Ifeanyichukwu U. Onyenanu
Source: Scientific journal of engineering and technology. (article, 2025-07-09)
Field: Chemistry — Inorganic Chemistry

Synergistic Application of Dimensional Analysis to Optimize Virgin Coconut Oil Press This research explores the optimization of virgin coconut oil (VCO) extraction by integrating dimensional analysis with experimental validation. It aims to create mathematical models that predict machine efficiency, throughput capacity, and oil yield quality based on key variables such as pressure, grind size, moisture level, viscosity, feed rate, and temperature. By applying Buckingham’s π theorem, dimensionless groups were formulated to define the functional relationships that regulate oil extraction behavior. The approach employed included constructing a dimensional matrix, developing parametric models, and deriving π-terms, followed by experimental verification. The created models were evaluated against the actual performance of the machine, resulting in regression coefficients (R²) of 0.0014 for efficiency, 0.0345 for throughput capacity, and 0.4009 for oil yield quality. These values indicate that, while the oil yield model had reasonable predictive strength, the efficiency and throughput models showed little correlation under the testing conditions, indicating opportunities for improvement. Nevertheless, the models captured key trends and exhibited promise for assisting in optimization efforts. This study highlights the effectiveness of dimensional analysis as an economical approach for improving VCO extraction, especially in low-resource settings. Suggested actions include refining the π-terms, applying machine learning techniques to account for non-linear effects, and performing field validations to enhance real-world relevance. Coconut oil ; Chemistry ; Food science Coconut Research and Applications ; Mechanical Engineering and Vibrations Research ; Natural Products and Biological Research
Authors: Bing Zhang | Xinqun Zhu | Jianchun Li
Source: Sensors (article, 2025-09-12)
Field: Materials Science — Materials Chemistry

Truncated Transfer Matrix-Based Regularization for Impact Force Localization and Reconstruction Civil infrastructure, such as bridges and buildings, is susceptible to damage from unforeseen low-speed impacts during service. Impact force identification from dynamic response measurements is essential for structural health monitoring and structural design. Force identification is an ill-posed inverse problem, and the regularization technique is widely used to solve this problem using a full transfer matrix. However, existing regularization techniques are not suitable for large-scale practical structures due to the high computational cost for the inverse calculation of a high-dimensional transfer matrix, and impact excitation locations are often unknown in practice. To address these challenges, a novel two-step truncated transfer matrix-based impact force identification method is proposed in this study. In the first step, a sparse regularization-based technique is developed to determine unknown force locations using modal superposition. In the second step, the full transfer matrix is truncated by time windows corresponding to short durations of impact excitations, and a Tikhonov regularization-based technique is adopted to reconstruct the time history of impact forces. The proposed method is verified numerically on a simply supported beam and experimentally on a 10 m steel-concrete composite bridge deck. The results show that the proposed method could determine the impact locations and reconstruct the time history of impact forces accurately. Compared with existing Tikhonov and sparse regularization methods, the proposed method demonstrates superior accuracy and computational efficiency for impact force identification. The robustness of the proposed method to noise level and the number of modes and sensors is investigated. Experimental studies for both single-force and multiple-force localization and identification are conducted. The results indicate that the proposed method is efficient and accurate in identifying impact forces. Tikhonov regularization ; Inverse problem ; Regularization (linguistics) ; Modal ; Transfer matrix ; Inverse ; Robustness (evolution) ; Impact ; Structural health monitoring High-Velocity Impact and Material Behavior ; Model Reduction and Neural Networks ; Structural Health Monitoring Techniques
Authors: 智也 百瀬 | Tomoya Momose | 浩太郎 梶川 | Kotaro Kajikawa
Source: Institutional Repositories DataBase (IRDB) (article, 2025-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of thin multilayer absorbers of graphite/polymer composites for W-band millimeter wave Extremely high frequency ; Composite number ; Millimeter ; Thermoplastic composites ; Microwave Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Microwave and Dielectric Measurement Techniques
Authors: P. V. Gulyaev
Source: Devices and Methods of Measurements (article, 2025-07-02)
Field: Materials Science — Surfaces, Coatings and Films

Correction of Disturbances Influence on Results of Elongated Objects's Probe Microscopy Using Predictive Relief Estimates Correct measurement of the distorted objects length in images is an important task of probe microscopy. Existing measurement methods do not fully take into account the specifics of a given subject area. The purpose of this work was to develop an algorithm for locating the objects skeletons adapted to the images peculiarities in scanning probe microscopy and does not require significant computing resources. The limited speed of the probe microscope, contamination of the sample surface, and Non-ideality of the probe lead to typical disturbances in the form of stripes, 1/ f noise, and brightness fluctuations and cause defragmentation of the skeletons of objects and a decrease in the accuracy of length measurements. The method proposed in this paper uses predictive relief estimates to eliminate the influence of these disturbances. The forecast is calculated based on extrapolation of information from the raster columns of the already scanned part of the image. The forecast interval is equal to the discretization interval of the image. The set of forecast estimates forms a predictive image, which is subsequently used to determine the length of objects. The peculiarity of predictive images is the sharpening of areas distorted by disturbances. This made it possible to defragment the skeletons and measure their length more accurately when locating objects using curvature detectors. Studies have shown that an increase in the integral prediction error is a indication of the need for additional image filtering from low-frequency or shock interference. At the same time, the use of predictive images reduces the relative deviation of the number of unrecognized skeletons and the average deviation of the maximum measured length. It has been established that control information in the form of forecast estimates can be used in image processing in probe microscopy to detect and partially eliminate disturbances. The formation of predictive images enhances the sharpness of objects and increases the probability of their correct selection using methods based on the analysis of changes in the brightness function. Microscopy ; Predictive value ; Computer science ; Artificial intelligence ; Optics ; Physics ; Medicine ; Internal medicine Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Naoki TANI | Satoru S. Kano | Y. Zempo
Source: Journal of Computer Chemistry Japan (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Singular Spectrum Analysis for Time-Series Data of Real-time TDDFT Optical spectrum prediction based on first-principles calculations is important for the development of optical materials. In particular, Time Dependent Density Functional Theory (TDDFT) in real-time is one of the most widely used calculation methods. In real-time TDDFT, the dynamic dipole moment is used to obtain the polarizability by Fourier transform (FT). The optical spectrum can be obtained from this polarizability. However, if the time length is not sufficient, the spectrum resolution depends on the length and becomes ambiguous. To solve this problem, we introduced Dynamic Mode Decomposition (DMD). This spectral analysis technique uses both Singular Value Decomposition and Proper Orthogonal Decomposition to obtain frequencies and intensities of spectrum. The signal frequency and the intensity are directly obtained. This method was applied to TDDFT time series data for ethylene and small molecules of benzene, naphthalene, anthracene, and tetracene. Compared to conventional FT, clear spectra were obtained from a short time series data. Time-dependent density functional theory ; Polarizability ; Series (stratigraphy) ; Singular spectrum analysis ; Spectrum (functional analysis) ; Dipole ; Fourier transform ; Spectral line ; Singular value decomposition Molecular spectroscopy and chirality ; Spectroscopy and Chemometric Analyses ; Spectroscopy and Quantum Chemical Studies
Authors: Tiantian He | Chang Liu | W. Y. Tang | Dan Li | Ping Yan | Qiang Liu | Qirong Xiao
Source: Nanophotonics (article, 2025-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The longitudinal dynamics evolution of optical skyrmions via meta‐optics Skyrmions, as topologically structured light fields, have attracted considerable attention due to their unique topological properties and potential applications such as optical communication and advanced sensing technologies. However, their longitudinal evolution, as a dimension ripe for exploitation, typically remains uncontrolled and non-deterministic, hindering its in-depth exploration and application scenarios. Here, this paper presents a novel method using dielectric metasurfaces for precisely modeling the longitudinal dynamics evolution of skyrmions. We introduce a new mechanism that allows for the accurate period modulation of skyrmions stokes properties along the propagation direction by controlling the differences in numerical apertures of a zero-order right-circularly polarized beam and a first-order left-circularly polarized beam. Crucially, the evolution period can be arbitrarily designed, and the propagation distance can be expanded by increasing the waist radius of input beams. To validate this approach, we showcase this paradigm through displacement sensing applications, where single-snapshot polarization measurements directly infer absolute position within a compact metasurface-integrated platform, offering a compact and simple alternative to conventional scanning-based approaches for displacement sensing. Our approach advances the understanding of dynamically controlled topological light fields and enables compact devices for precision metrology and optical information technologies. Polarization (electrochemistry) ; Position (finance) ; Displacement (psychology) ; Metrology ; Optical communication ; Light beam ; Interferometry ; Skyrmion ; Dielectric Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Cristian T. Camañan | Joy M. Picar
Source: Journal of interdisciplinary perspectives (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Time Series Analysis of Factors Affecting Coconut Price in the Philippines Coconut remains a fundamental agricultural product in the Philippines, widely used in food production, cosmetics, and bio-industries. Despite its importance, the price of copra, its dried kernel, has experienced substantial volatility due to environmental conditions, market dynamics, and production levels. This study aims to analyze and forecast the factors influencing copra prices in the country, utilizing time series data from 2013 to 2022. Specifically, it investigates the influence of rainfall, coconut production, and market demand. Utilizing a non-experimental quantitative approach, the study employed ARIMA and SARIMA models for trend analysis and forecasting. AIC, BIC, AICc, residual variance, and log-likelihood guided model selection. Results revealed that ARIMA(0,1,1) was optimal for forecasting coconut production, ARIMA(2,1,1) for copra prices, and SARIMA(2,0,1)(1,0,1)[12] for Philippine rainfall,SARIMA(0,0,1)(2,1,1)[12] was used for Luzon, SARIMA(1,0,0)(2,0,0)[12] for Visayas, ARIMA(1,1,1) for Mindanao. Regression results confirmed that rainfall did not significantly influence coconut production, and neither coconut production nor market demand had a statistically significant effect on copra prices. The forecast indicated a potential decline in coconut production and copra prices if current conditions persist. The study concludes that time series forecasting models are essential for agricultural stakeholders, enabling data-driven planning, risk management, and policy formulation. To improve predictive accuracy and policy relevance, future studies are encouraged to incorporate historical data points on market demand and coconut production, and to adopt multivariate time series models, such as ARIMAX or Vector Autoregression (VAR), which can account for external shocks and global economic indicators. These approaches could better capture the complex interactions within the Philippine coconut industry and support more effective decision-making. Copra ; Autoregressive integrated moving average ; Time series ; Agriculture ; Volatility (finance) ; Production (economics) ; Vector autoregression ; Regression analysis ; Multivariate statistics Coconut Research and Applications ; Natural Products and Biological Research ; Global Trade and Competitiveness
Authors: Yuwei Wang | Wenjuan Dong | Delinuer Azan | Xingang Wang | Renaguli Wufuer | Hao Wang | Chao Ji | Chunwei Song | Jinlong He | Gang Li
Source: Electronics (article, 2025-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Detection Method of Residual Magnetism in Power Transformers Based on the Hysteresis Area of Magnetization Residual magnetism in the core of a power transformer can lead to an increased inrush current during closing, which may trigger relay protection malfunctions and cause equipment aging. Accurate detection of residual magnetism is crucial for grid safety. Traditional offline detection requires interrupting operation, while online methods are susceptible to interference and have limited accuracy. This paper proposes a method for detecting residual magnetism in power transformers based on the hysteresis area of magnetization. First, the magnetic flux distribution of the transformer is analyzed through finite element simulation, revealing that low-frequency excitation can make the core’s magnetic flux density distribution more uniform, and that the leakage flux and the flux inside the core have similar characteristics, which helps to determine the optimal position for flux detection. Next, the relationship between the small hysteresis loop area and residual magnetism is studied, revealing a monotonic mapping relationship between the normalized area of the negative hysteresis loop under current pulse excitation and the residual magnetism. Finally, experimental verification shows that this method effectively detects residual magnetism under different levels and operational conditions. The method is non-invasive, real-time, and highly resistant to interference, offering a new approach for residual magnetism detection in power transformers. Magnetism ; Residual ; Magnetic flux ; Transformer ; Magnetization ; Excitation ; Inrush current ; Magnetic hysteresis Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Power Systems Fault Detection
Authors: Jasmine Nguyen‐Duc | Malte Brammerloh | Melina Cherchali | Inès de Riedmatten | J. Pérot | Jonathan Rafael‐Patiño | Ileana Jelescu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-26)
Field: Materials Science — Polymers and Plastics

CATERPillar: A Flexible Framework for Generating White Matter Numerical Substrates with incorporated Glial Cells Abstract Monte Carlo diffusion simulations in numerical substrates are valuable for exploring the sensitivity and specificity of the diffusion MRI (dMRI) signal to realistic cell microstructure features. A crucial component of such simulations is the use of numerical phantoms that accurately represent the target tissue, which is in this case, cerebral white matter (WM). This study introduces CATERPillar (Computational Axonal Threading Engine for Realistic Proliferation), a novel method that simulates the mechanic of axonal growth using overlapping spheres as elementary units. CATERPillar facilitates parallel axon development while preventing collisions, offering user control over key structural parameters such as cellular density, undulation, beading and myelination. Its uniqueness lies in its ability to generate not only realistic axonal structures but also realistic glial cells, enhancing the biological fidelity of simulations. We showed that our grown substrates feature distributions of key morphological parameters that agree with those from histological studies. The structural realism of the astrocytic components was quantitatively validated using Sholl analysis. Furthermore, the time-dependent diffusion in the extra- and intra-axonal compartments accurately reflected expected characteristics of short-range disorder, as predicted by theoretical models. CATERPillar is open source and can be used to (a) develop new acquisition schemes that sensitise the MRI signal to unique tissue microstructure features, (b) test the accuracy of a broad range of analytical models, and (c) build a set of substrates to train machine learning models on. Highlights CATERPillar generates realistic synthetic voxels of white matter containing packed axons and glial cells. Synthetic axons had similar morphologies to those segmented from human electron microscopy in previous works. The morphological features of synthetic astrocytes closely matched those observed in histology. The functional form of diffusion and kurtosis time-dependence in the intraand extra-axonal spaces agreed with experimentally observed disorder power laws in voxels composed of synthetic axons. Caterpillar ; White matter ; White (mutation) ; Computer science ; Chemistry ; Biology ; Ecology ; Medicine ; Lepidoptera genitalia ; Biochemistry Conducting polymers and applications ; Neuroscience and Neural Engineering ; Quantum-Dot Cellular Automata
Authors: Konni Biegert | Stefano Modugno | Silas Föll | Magdalena Peterlin | Daniel Pichler | Christian Andergassen | Roy McCormick
Source: Plant Growth Regulation (article, 2025-09-24)
Field: Chemistry — Analytical Chemistry

Apple fruitlet drop prediction using spectral Vis/NIR and growth diameter times-series In apple production different thinning strategies are applied to produce optimal yields of marketable fruit and to reduce the risk of alternate bearing. However, the effectiveness of a chemical thinning treatment is highly variable being influenced by the tree’s physiological state and environmental conditions both before and after application. Optical sensing with a visible/near-infrared spectrophotometer is tested as a prediction tool to decide if a further thinning treatment is required and precisely thin trees in the orchards. In 2023, in Southwest Germany and in Northern Italy, the apple cultivars ‘Gala’ and ‘Braeburn’ were chemically thinned between the 8-15 mm fruit diameter growth stage. King and second lateral fruitlets, which were the target of the thinning, were measured with digital calipers and optically scanned before and up to 13 days after thinning treatments. Thinning treatments effectively reduced fruit set by one half. Receiver operating characteristic curves were tested separately on spectral indices and fruit diameter growth. Spectral monitoring recorded similar information as measuring fruit diameter growth. The obtained area under the curves were >75% on 5-8 days after treatment for both cultivars. Persisting fruitlets showed higher light absorption in the chlorophyll and water related wavelength regions and higher light reflectance from the near-infrared region (699 to 939 nm). Thinning ; Cultivar ; Reflectivity ; Calipers ; Chlorophyll ; Drop (telecommunication) ; Fruit tree Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI