jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiangfeng Si | Maoqun Shen | Bing Yu | Yongtao Jin | GuangSheng Cai | Qifeng Bian | Tong Bai | Huanmin Yao | Haibao Mu
Source: Electronics (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Analysis and Adaptive Separation of IGBT Switching Noise in PD Monitoring of Flexible HVDC Valves: An Evolutionary Perspective The high-frequency switching noise of insulated-gate bipolar transistors (IGBTs) limits the sensitivity of online partial discharge (PD) monitoring in ultra-high-voltage flexible DC (VSC-HVDC) transmission systems. To address this challenge, this study investigates the underlying mechanisms and evolution of this interference and develops an anti-interference signal separation method. Simulation and experimental results indicate that the energy of IGBT switching noise is concentrated in the 30–180 MHz range, which significantly overlaps with the ultra-high-frequency (UHF) band used for PD detection. This research further reveals the pronounced modulation effect of device aging on the interference spectrum: bond wire aging triggers “spectral reconstruction” via altered parasitic parameters, where severe collector aging leads to an abnormal surge in turn-off interference amplitude. In contrast, gate oxide layer degradation manifests as characteristic “global spectrum attenuation” and a shift in peak frequency toward lower bands. Confronted with the challenges of strong interference and spectrum drift induced by aging, this paper proposes an adaptive signal separation method based on feature optimization of the time–frequency cumulative energy function. This method constructs novel characteristic parameters—namely, oblique intercept width and morphological gradient steepness—to effectively capture the fundamental differences in the energy accumulation process of the signals. Experimental verification demonstrates that even under conditions of varying interference characteristics, the proposed method achieves high-precision separation of PD signals from IGBT noise, outperforming traditional equivalent time–frequency and wavelet principal component analysis methods. This research provides crucial theoretical and technical support for insulation condition monitoring and device aging diagnosis in VSC-HVDC converter valves. Insulated-gate bipolar transistor ; Interference (communication) ; Noise (video) ; Energy (signal processing) ; Sensitivity (control systems) ; Separation (statistics) ; SIGNAL (programming language) ; Electromagnetic interference ; Transistor High voltage insulation and dielectric phenomena ; Silicon Carbide Semiconductor Technologies ; Electrostatic Discharge in Electronics
Authors: Xueqing Mei | Zhaosheng Li | Huawei Wu | Xiaobo Wu | D.-Q. Zhang
Source: World Electric Vehicle Journal (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Crack Propagation of Ground Insulation in Electric Vehicle Drive Motor End-Winding Based on Electromechanical Coupling Phase Field Model Grounding insulation is a key component of electric vehicle drive motors, and cracks may appear during the manufacturing process and assembly. In this paper, the novel method of coupling phase field, mechanic field and electric field is proposed to investigate the coupled propagation characteristics of electromechanical damage in stator end-wingding insulation. The crack propagation model is derived by using the phase field method, where the maximum historical variable is introduced to ensure the forward propagation of the crack damage in insulation. According to the crack evolution states, the electric potential distributions in the insulation domain are determined and the electrical damage variable is defined to quantitatively describe the dynamical evolution mechanism of electric damage with the variation in mechanical damage. The results in this research will contribute to understanding the electrical performance degradation and electromechanical failure of the end-winding insulation in electric vehicle drive motors, which also provides the basis for the mechanism of insulation damage, insulation fault diagnosis and residual life prediction of electrical machines. Stator ; Electric field ; Coupling (piping) ; Electric vehicle ; Residual ; Phase (matter) ; Transient (computer programming) ; Fault (geology) High voltage insulation and dielectric phenomena ; Numerical methods in engineering ; Vibration Control and Rheological Fluids
Authors: Yu-Jie Jiang | Yupeng Li | Xujin Wang | Jie-Yun Yan
Source: Photonics (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

A Robust and Tunable Splitter–Filter Based on a Hybrid Photonic Crystal–Quasicrystal Waveguide We propose a design of a composite splitter–filter by replacing the traditional periodic arrays with Fibonacci rod chains along both sides of the output channel of a T-junction photonic crystal waveguide. This integrated structure concurrently realizes the dual functions of a power splitter and an optical filter. The coexistence and effectiveness of these two functions are verified through numerical simulations. Furthermore, the proposed device exhibits excellent robustness against three types of defects and enables strong tunability of its operating wavelength window. Owing to these superior characteristics, this hybrid photonic crystal–quasicrystal structure holds significant application potential in photonic integrated circuits and high-performance optical communication systems. Splitter ; Photonic integrated circuit ; Photonic crystal ; Photonics ; Fibonacci number ; Robustness (evolution) ; Electronic circuit ; Waveguide ; Optical communication ; Wavelength Quasicrystal Structures and Properties ; Photonic Crystals and Applications ; Metamaterials and Metasurfaces Applications
Authors: Amir Gildor | Sariel Hodisan | Shahar Kvatinsky | Yoav Kalcheim
Source: arXiv (Cornell University) (article, 2026-04-06)
Field: Materials Science — Polymers and Plastics

Harnessing the VO2 Phase Transition for Automatic Gain Control in Transimpedance Amplifiers Transimpedance amplifiers (TIAs) are essential in sensor electronics, converting input currents into output voltages. Conventional TIAs utilize fixed-gain resistors, which saturate under high input currents and consequently result in undesirable recovery times. To overcome this limitation, volatile resistive switching devices have emerged as a promising alternative, offering intrinsic automatic gain control (AGC). Among these, vanadium dioxide (VO2) devices stand out for their reversible insulator-metal transition (IMT), producing abrupt, energy-efficient resistance changes near the transition temperature (67 C). In this work, a switching device was fabricated by sputtering a VO2 thin film and patterning 200 nm electrode gaps atop it. Before integrating this device into the TIA circuit, its switching dynamics were characterized under electrical pulse excitation. Slightly exceeding the temperature-dependent IMT threshold voltage (Vth) yielded fast and reproducible switching. Complementary pump-probe measurements showed that operating well below TC effectively suppresses short-term memory effects linked to the stochastic nature of the first-order transition. Leveraging these insights, a custom VO2-based TIA was developed, demonstrating variable gain and AGC functionality. Furthermore, applying a constant DC current bias during switching induced self-sustained oscillations (2 pJ per spike) with frequencies up to 60 MHz, consistent with the thermal timescale of the VO2 devices. Overall, these results provide a detailed understanding of VO2 switching dynamics and demonstrate their potential for enabling compact, energy-efficient AGC in high-speed TIAs for advanced sensing applications. Transimpedance amplifier ; Amplifier ; Automatic gain control ; Voltage ; Vanadium dioxide ; Resistive touchscreen ; Resistor ; Thermal ; Sputtering Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Andrés Felipe Aristizábal Pachón | July Katherine Rodríguez Ariza
Source: Revista Colombiana de Hematología y Oncología (article, 2026-02-17)
Field: Chemistry — Spectroscopy

Nuevas fronteras en la oncología de precisión: la transcriptómica y la proteómica La integración de la transcriptómica y la proteómica en el estudio del cáncer ha cambiado significativamente la comprensión de la biología tumoral, además de abrir nuevas posibilidades para el diagnóstico y la clasificación de pacientes. La transcriptómica facilita la identificación de perfiles de expresión génica, fusiones y el papel regulador de distintos ARN no codificantes en la progresión neoplásica; por otro lado, la proteómica brinda una aproximación más directa a la función celular, al evidenciar proteínas activas y sus modificaciones postraduccionales, aspectos cruciales en los mecanismos de señalización y de resistencia a tratamientos. Si bien ambas disciplinas han dado origen a herramientas clínicas ya consolidadas en la oncología de precisión, aún persisten limitaciones en términos de reproducibilidad, estandarización y accesibilidad, particularmente en entornos con recursos restringidos. Frente a ello, los abordajes integrativos de tipo multiómico, apoyados en inteligencia artificial y en tecnologías emergentes como las ómicas de célula única y las espaciales, se consolidan como una vía prometedora para reflejar la complejidad biológica del cáncer y avanzar hacia diagnósticos dinámicos y estrategias terapéuticas personalizadas. Environmental crisis ; Poison control ; Military government Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Bioinformatics and Genomic Networks
Authors: Mohamed Badrawy | Omar M. El-Abassy | Israa M. Nour | Amr Eissa
Source: Scientific Reports (article, 2026-02-02)
Field: Chemistry — Analytical Chemistry

Spectroscopic methods for the simultaneous determination of amlodipine besylate and Hydrochlorothiazide in their binary mixture and pharmaceutical dosage form The development of environmentally friendly analytical methods is important in the current scientific landscape. This study presents an investigation into several green spectrophotometric methods for the determination of amlodipine besylate and hydrochlorothiazide in both their pure binary mixtures and pharmaceutical formulations. A spectrophotometric method is proposed for the selective determination of amlodipine besylate in the presence of hydrochlorothiazide at 366 nm, where no interference from hydrochlorothiazide was observed. Additionally, a novel five different spectrophotometric approaches were applied for the determination of hydrochlorothiazide: the dual wavelength method, first derivative method, ratio difference method, and ratio derivative method, as well as Fourier Self-Deconvolution method. Amlodipine besylate's linearity range was 5-30 µg/mL, whereas hydrochlorothiazide's was 3-18 µg/mL reaching limits of detection of 1.238, 0.906, 0.825, 0.540, 0.779 and 0.805 µg/mL, respectively for all the proposed methods. The proposed methods were validated in accordance with ICH international guidelines, including full statistical analysis and comprehensive environmental evaluation using multiple assessment tools as The Analytical GREEnness Calculator, the Modified Green Analytic Procedure Index, spider diagram, and green analytical selection tool. Hydrochlorothiazide ; Amlodipine ; Dosage form ; Derivative (finance) ; Binary number ; Pharmaceutical formulation ; Accuracy and precision Analytical Methods in Pharmaceuticals ; Dye analysis and toxicity ; Pharmacological Effects and Assays
Authors: Martin Zouhar | Šárka Mikmeková | Jan Hovjacký | Petra Váňová
Source: Materials (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

CLM App: Interlamellar Distance of Pearlite via CLM Revisited and Automated Pearlitic (stainless) steel is used in automotive, aerospace, and other industries where high strength, hardness, and wear resistance are required. Its quality control can be performed using mechanical tests or by examining the lamellar microstructure, namely, determining interlamellar distance. One of the related approaches is the circular line method (CLM). This paper reviews the challenges to automate employment of the CLM using custom Python code in order to reduce human time costs during image-based quality assessment of pearlite. The goal is to perform intersection counting automatically once the human operator has configured the application and selected the locations of measuring circles. Performance assessment using manually processed data from some 465 images is performed. We divide the imaged pearlite microstructures into different "types" when the code performs well or, respectively, not so well. We conclude with possible extensions of the work presented here. Python (programming language) ; Pearlite ; Lamellar structure ; Intersection (aeronautics) ; Code (set theory) ; Microstructure ; Quality (philosophy) Machine Learning in Materials Science ; Mineral Processing and Grinding ; Image and Object Detection Techniques
Authors: G. Keshava Datta | Asha Verma | Pooja Singh | Sreedhar Jadapalli | Neha K. Saini | Shivesh Tripathi
Source: Research Square (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Machine Learning–Assisted Performance Prediction of Graphene–Silicon Twin-Port Band-Notched Wideband Antenna in THz Domain Antenna (radio) ; Terahertz radiation ; HFSS ; Wideband ; Radiation pattern ; Antenna efficiency ; Coupling (piping) ; Coaxial antenna ; Antenna measurement Graphene research and applications ; Plasmonic and Surface Plasmon Research ; Antenna Design and Analysis
Authors: Xin Yu | Z. Li | Jiaxiang Liu | Haichun Xu | Junfang Miao | C H Wang | Yongqing Fang | Ying Pan | Yonghua Fang
Source: Photoacoustics (article, 2026-02-25)
Field: Chemistry — Spectroscopy

Differential photoacoustic-stimulated Raman spectroscopy (DPA-SRS) for high-sensitivity hydrogen detection To detect non-polar, infrared-inactive hydrogen, a Differential Photoacoustic-Stimulated Raman Spectroscopy (DPA-SRS) method is proposed. Utilizing the SRS process, a portion of the pump light is converted into intense Stokes light corresponding to the hydrogen Raman shift, eliminating complex dual-laser configurations. The nonlinear thermoacoustic effect is excited by this dual-color light field, endowing Photoacoustic Spectroscopy with the capability for hydrogen fingerprint identification. Raman cell pressure was optimized to achieve a synergistic enhancement of the Stokes conversion efficiency and the Four-Wave Mixing effect. Furthermore, an acoustic mode-optimized differential H-type resonant photoacoustic cell was designed, which effectively enhances anti-interference capability through the differential detection mechanism. Distinct from traditional lock-in amplification methods, a time-frequency transformation algorithm was employed to precisely extract the frequency-domain photoacoustic signal from the broadband time-domain acoustic signal. Experimental results demonstrate that the DPA-SRS system exhibits excellent linearity and achieves a Limit of Detection of 0.65 ppm under atmospheric conditions. Raman spectroscopy ; Photoacoustic spectroscopy ; Detection limit ; SIGNAL (programming language) ; Photoacoustic effect ; Analytical Chemistry (journal) ; Mixing (physics) ; Photoacoustic imaging in biomedicine ; Hydrogen Spectroscopy and Laser Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: PRAKASH R | RAJENDRAN SD | Sowmya Gopalan | DIVYA DEVI G | Vinodhini Chandrasekhar
Source: Asian Journal of Pharmaceutical and Clinical Research (article, 2026-01-07)
Field: Chemistry — Analytical Chemistry

EVALUATION OF COMPARATIVE BIOAVAILABILITY AND BIOEQUIVALENCE ANALYSIS OF TERAZOSIN HYDROCHLORIDE Terazosin hydrochloride is a selective α₁-adrenergic receptor antagonist used to treat high blood pressure and benign prostatic hyperplasia. Due to interest by patients, prescribers, and payers for cost-effective and therapeutically equivalent generic formulations, the evaluation of bioavailability and Bioequivalence (BE) has been a significant focus area for regulatory authorities and pharmaceutical companies. Regulatory authorities, including the United States Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have established guidelines related to the requirements for BE studies, as well as requiring the use of validated bioanalytical methods to quantify drug concentrations in biological matrices. BE studies focus on pharmacokinetic parameters such as the maximum plasma concentration (Cmax), time to Cmax (Tmax), and area under the curve (AUC) from the plasma concentration vs. time curve, which gives an estimate of the rate and extent of drug absorption. This article discusses current regulatory frameworks and important considerations in the design and conduct of BE studies recently provided by the FDA and EMA (e.g., validated bioanalytical methods and pharmacokinetic parameters such as Tmax, Cmax and AUC). The extent to which the industry adheres to BE study design and conduct regulations, as well as the issues around studying population, dosage form and statistical analysis of the results, will also be explored. Discussion will also include potential biowaivers for BCS Class I and III drugs, which are important for development, and still maintaining safe therapeutic equivalence. The alignment of regulatory requirements and scientific advancement permits navigation through the safe and effective development and marketing of generic terazosin hydrochloride products. Bioequivalence ; Bioavailability ; Bioanalysis ; Cmax ; Pharmacokinetics ; Biopharmaceutical ; Drug ; Pharmaceutical industry Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety ; Computational Drug Discovery Methods
Authors: NUR UL HAYDER CHOWDHURY
Source: Journal of Textile Engineering and Fashion Technology (article, 2026-03-30)
Field: Materials Science — Polymers and Plastics

Industrial Sewing Stitch Classification: A Technical Review Aligned with ISO 4915 and ASTM D6193 Standards Stitch classification is a critical factor in determining seam behavior, garment longevity, and production consistency within apparel manufacturing. Variations in stitch selection often create quality deviations and mechanical weaknesses. This study reviews the six stitch classes defined in ISO 4915 and interpreted through ASTM D6193, examining how thread paths, interlacing mechanisms, and fabric interactions shape mechanical performance. The comparison shows that lockstitches provide controlled structural strength, chainstitches and coverstitches deliver elasticity for dynamic fabrics, and overedge stitches support effective edge stabilization. Results indicate that selecting the appropriate stitch depends on fabric properties, seam function, and expected load. The review offers a practical engineering perspective that supports more reliable seam construction and improved process efficiency. Interlacing ; Consistency (knowledge bases) ; Clothing ; Process (computing) ; Manufacturing process ; Thread (computing) ; Quality (philosophy) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Mechanical Behavior of Composites
Authors: Jian Gu | Aldeida Aleti | Chunyang Chen | Hongyu Zhang
Source: ArXiv.org (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Rethinking Weight Tying: Pseudo-Inverse Tying for Stable LM Training and Updates Weight tying is widely used in compact language models to reduce parameters by sharing the token table between the input embedding and the output projection. However, weight sharing does not guarantee a stable token interface: during training, the correspondence between encoding tokens into hidden states and decoding hidden states into logits can drift, worsening optimization sensitivity and making post-training interventions such as editing, patching, and lightweight adaptation less predictable. We propose Pseudo-Inverse Tying (PIT), which synchronizes embedding and unembedding as coupled projections of a shared latent token memory, guaranteeing a pseudo-inverse-consistent interface throughout training. PIT maintains an orthonormal shared memory, obtained by thin polar decomposition for teacher initialization or random orthonormal initialization from scratch, and introduces a fully learned symmetric positive definite hidden-space transform parameterized via a Cholesky factor. The output head applies this transform to hidden states before the vocabulary projection, while the embedding applies the inverse transform to token vectors using stable triangular solves, avoiding explicit pseudo-inverse recomputation and any vocabulary-sized auxiliary parameters. We evaluate PIT on on-device models spanning 256M-1.3B parameters across pretraining and adaptation, and consistently observe improved training stability, stronger layerwise semantic consistency, and substantially reduced side effects. Initialization ; Orthonormal basis ; Security token ; Embedding ; Tying ; Set (abstract data type) ; Decoding methods ; Cholesky decomposition Machine Learning in Materials Science ; Advanced Neural Network Applications ; Topic Modeling
Authors: Fadil Abdullah | Afriani Kusumadewi | Tina Martina | - Kuswinarti | Fandi Achmad
Source: Texere (article, 2026-01-09)
Field: Materials Science — Polymers and Plastics

Analytical Data for Sewing Production Efficiency: A Model Based on Artificial Neural Networks (ANNs) The labor-intensive apparel manufacturing sector is continually focused on meeting output goals, necessitating continuous improvements in production efficiency. Achieving targets at the lowest feasible cost is crucial for production management efficiency, especially in clothing production. The sewing component plays a vital role in enhancing the usefulness of clothing through a series of steps to produce ready-made garments. To optimize this process, we developed a model using an artificial intelligence (AI)-based method, specifically Artificial Neural Networks (ANNs), to enhance sewing production efficiency. The model focused on optimizing parameters that significantly influenced efficiency. Our results demonstrate that the ANNs model, with 1000 iterations, successfully replicates empirical data with an R-squared value of 0.98. The research introduces the novel use of an ANNs model with a five-node configuration and 1000 iterations, proving effective in optimizing sewing process parameters. This AI-based approach is a powerful tool for improving production efficiency in the textile industry, making significant theoretical and practical contributions. The findings offer substantial practical implications for practitioners in the textile industry and provide a robust framework for optimizing sewing production process parameters to achieve higher efficiency. Production (economics) ; Artificial neural network ; Clothing ; Process (computing) ; Component (thermodynamics) ; Textile industry ; Clothing industry Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; 3D Shape Modeling and Analysis