jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maria Zofia Lisiecka
Source: Libri Oncologici Croatian Journal of Oncology (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Evaluation of the safety profile of CT and MRI contrast agents based on iodine and gadolinium Objectives: The purpose of the study was to investigate the safety of contrast agents in the field of medical radiology. Methods: We conducted a comprehensive literature review through databases such as PubMed, Google Scholar, Scopus, and ScienceDirect, with the selection of relevant articles based on keywords, publication in English, study design, and publication criteria from 2020 to 2024. Results: In the course of the study, we determined that iodine containing contrast agents are often used in medical imaging, but their safety is a concern, especially in patients with renal impairment and a tendency to allergic reactions. It is recommended to observe a 4-12 hours interval between procedures to minimise the risk of contrast-induced nephropathy. Iodised contrasts are more likely to cause adverse reactions compared to gadolinium-based contrasts, but serious cases such as anaphylaxis remain rare (0.06%). Low concentration iodine-containing contrasts for cerebral angiography have shown high safety, reducing toxic effects. Barium contrast agents have demonstrated a high level of safety when used in diagnostics, including procedures such as oral barium test. Special attention was paid to reducing radiation exposure and compliance with safety protocols during the COVID-19 pandemic, which prevented the spread of infections. Contrast agents based on linear gadolinium more often contribute to its deposition, which could be associated with potential long-term risks, although their clinical consequences have not been fully examined. In contrast, macrocyclic gadolinium chelates show better stability and less often cause such effects, which makes them preferable in clinical practice. Conclusions: The introduction of strict recommendations on the use of gadolinium-containing contrast agents has reduced the incidence of nephrogenic systemic fibrosis, but further studies on their accumulation in the body remain relevant. Gadolinium ; Contrast (vision) ; Iodine ; Safety profile ; Patient safety ; Adverse effect ; Nephrogenic systemic fibrosis ; Angiography Lanthanide and Transition Metal Complexes ; Radioactive element chemistry and processing ; Radiation Dose and Imaging
Authors: Jin Sima | Chao Pan | S. Kasra Tabatabaei | Alvaro G. Hernandez | Charles M. Schroeder | Olgica Milenković
Source: ArXiv.org (preprint, 2025-05-06)
Field: Materials Science — Surfaces, Coatings and Films

DNA Tails for Molecular Flash Memory DNA-based data storage systems face practical challenges due to the high cost of DNA synthesis. A strategy to address the problem entails encoding data via topological modifications of the DNA sugar-phosphate backbone. The DNA Punchcards system, which introduces nicks (cuts) in the DNA backbone, encodes only one bit per nicking site, limiting density. We propose \emph{DNA Tails,} a storage paradigm that encodes nonbinary symbols at nicking sites by growing enzymatically synthesized single-stranded DNA of varied lengths. The average tail lengths encode multiple information bits and are controlled via a staggered nicking-tail extension process. We demonstrate the feasibility of this encoding approach experimentally and identify common sources of errors, such as calibration errors and stumped tail growth errors. To mitigate calibration errors, we use rank modulation proposed for flash memory. To correct stumped tail growth errors, we introduce a new family of rank modulation codes that can correct ``stuck-at'' errors. Our analytical results include constructions for order-optimal-redundancy permutation codes and accompanying encoding and decoding algorithms. Encoding (memory) ; Decoding methods ; Limiting ; ENCODE ; Permutation (music) ; DNA ; Error detection and correction ; Extension (predicate logic) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Jinghan Dong | Weijie Nie | Arthur C. Cardoso | Haichen Zhou | Jingrui Zhang | John Rarity | Alex S. Clark
Source: APL Photonics (article, 2025-11-01)
Field: Chemistry — Spectroscopy

Open-path methane sensing via backscattered light in a nonlinear interferometer Nonlinear interferometry has widespread applications in sensing, spectroscopy, and imaging. However, most implementations require highly reflective mirrors and precise optical alignment, drastically reducing their versatility and usability in outdoor applications. This work is based on stimulated parametric downconversion, demonstrating methane absorption spectroscopy in the mid-infrared (MIR) region by detecting near-infrared photons using a silicon-based CMOS camera. The MIR light, used to probe methane, is diffusely backscattered from a Lambertian surface, experiencing significant transmission loss. We implement a single-mode confocal illumination and collection scheme, using a two-lens system to mode-match the interfering beams to achieve background methane detection at a distance of 4.6 m under a 57 dB loss. Our method is also extended to real-world surfaces, such as glass, brushed metal, and a leaf, showing robust background methane sensing with various target materials. Interferometry ; Methane ; Absorption (acoustics) ; Transmission (telecommunications) ; Spectroscopy ; Parametric statistics ; SIGNAL (programming language) Spectroscopy and Laser Applications ; Advanced Fiber Optic Sensors ; Advanced Fiber Laser Technologies
Authors: Kamel Jebreen | Hassan Kanj | Inad Nawajah | Nancy Chendeb | Rami M. Amro
Source: European Journal of Pure and Applied Mathematics (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Data Analysis and Irregularity Measurements in the Identical Structures of Carbon nanocones $\boldsymbol{C N C}_{\boldsymbol{t}}(\mathrm{m})$ Irregularity indices are topological indices by nature. They are highly helpful for determining the quantitative topography of nonregular graphs' molecular structures. Both the quantitative structure-property relationship (QSPR) and the quantitative structure-activity relationship (QSAR) depend heavily on the computation of abnormalities in a graph. It is made up of several chemical and physical characteristics, including resistance, enthalpy, entropy, toxicity, melting and boiling points, and entropy. This paper examines the application of different irregularity indices to identify irregularity measurements (IMs) in the network of carbon nanocone molecules $C N C_t(m)$, for $t=4,5$, and $t$. We have used different irregularity indices such as $\operatorname{Irdif}\left(\xi_t\right)$ $, \operatorname{Al}\left(\xi_t\right), $ $ \operatorname{Irl}\left(\xi_t\right), \operatorname{Irlu}\left(\xi_t\right), \operatorname{Irlf}\left(\xi_t\right), \operatorname{Irf}\left(\xi_t\right), \operatorname{Irla}\left(\xi_t\right)$, $\operatorname{Ird} 1\left(\xi_t\right), \operatorname{Ira}\left(\xi_t\right), \operatorname{Irga}\left(\xi_t\right), \operatorname{Irb}\left(\xi_t\right) \&amp; \operatorname{Irr}{ }_t\left(\xi_t\right)$. Comparative graphic measures of irregularity in $C N C_4(\mathrm{~m}), C N C_5(\mathrm{~m})$ and $C N C_t(\mathrm{~m})$ have also been examined and presented. We are interested in creating new formulas to gain a better understanding of irregularity measures in carbon nanocones using the indices described above. Mathematics ; Combinatorics Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Fullerene Chemistry and Applications
Authors: Liang Li | Xu Wang | Yang Yu | Lili Guo | Fei Li
Source: Modern Agriculture (article, 2025-09-19)
Field: Chemistry — Analytical Chemistry

Cotton Yield Prediction Model Based on Hyperspectral Feature Fusion and Machine Learning ABSTRACT Cotton, as a globally significant economic crop, necessitates accurate yield prediction for field management and market regulation. This study aims to enhance the precision of cotton yield prediction by integrating hyperspectral feature fusion techniques with machine learning modelling approaches. A method widely used in finance and stock markets (MF‐DFA) is introduced into the agricultural sector to extract multifractal features from cotton canopy spectra. These features are fused with other spectral characteristics, and machine learning algorithms are employed to construct yield prediction models. The results indicate that (1) building prediction models based on fused features effectively improves the accuracy and stability of cotton yield predictions, outperforming models based on single features; (2) the fusion of Vegetation Index (VI) and Fractal Parameters (FP) features yields the best prediction results during the flower and boll period, with R 2 values of 0.6681 and 0.7009 for two consecutive years, respectively; (3) the Random Forest modelling approach exhibits higher prediction accuracy, and feature fusion parameters such as VI&amp;FP and VI&amp;FP&amp;TR also perform well when applied to other model methods, demonstrating excellent generalisation capabilities. In conclusion, the prediction model based on hyperspectral feature fusion and machine learning provides a new and effective approach for cotton yield prediction. Hyperspectral imaging ; Predictive modelling ; Support vector machine ; Random forest ; Feature (linguistics) ; Pattern recognition (psychology) ; Fusion ; Stability (learning theory) Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Remote-Sensing Image Classification
Authors: Maman I. Salissou | Usman Hassan | Ibrahim Ayuba | A. Inuwa
Source: International Journal of Petroleum Technology (article, 2025-10-03)
Field: Chemistry — Analytical Chemistry

Genetic Algorithm Optimization of Crude Oil Pipeline Operations for Wax Control: A Case Study of CNPC-Niger Petroleum Company Wax formation and deposition in crude oil pipelines pose a very significant challenge, such as flow restriction, increased cost of maintenance and potential shutdowns. The goals of this study are to reduce wax accumulation in the China National Petroleum Corporation-Niger Petroleum (CNPC-NP) pipeline network, which connects the Agadem oil fields to the SORAZ refinery, by optimizing critical operating conditions. The Reliability of Aspen HYSYS for analyzing wax behavior was validated by the simulation model that closely matched the real-world data, with a simulated flow rate of 182.49 m³/h with, only 0.27% higher than the actual 182 m³/h. The study suggested changing the operating condition using a genetic algorithm method of optimization, which indicates a slight increase in the pressure from 0.6 MPa to 0.65 MPa, while decreasing in temperature from 51°C to 48.5°C and a potential increase in the flow rate to 187.49 187.49 m³/h. Furthermore, the results of the optimization led to a decrease in wax thickness from 0.058 mm to 0.0409 mm, which indicated an improvement in pipeline operating conditions. Also, the economic analysis revealed, the total capital investment was roughly $3.3 million, and the annual operating expenses were estimated to be $2.4 million. The financial indicators include an Internal Rate of Return (IRR) of 9%, a Net Present Value (NPV) of $2.14 million and a Profitability Index (PI) of 1.99, all of which were higher than the IRR of the current CNPC-Niger Petroleum, which was 8%. The results show that the economic performance of the crude oil pipeline system can be improved, and wax formation risk can be effectively decreased by combining simulation-driven decision-making with strategic operational parameter adjustment. Profitability index ; Pipeline transport ; Petroleum ; Crude oil ; Pipeline (software) ; Internal rate of return ; Volumetric flow rate ; Investment (military) ; Wax Petroleum Processing and Analysis ; Oil and Gas Production Techniques ; Reservoir Engineering and Simulation Methods
Authors: Natsuko Ishii | Ryuichi Matsubara | Seiji TAKIHANA | H Ishida
Source: Progress in Nuclear Science and Technology (article, 2025-09-30)
Field: Chemistry — Inorganic Chemistry

Development of a Non-contact Chemical Analysis Method for Process Management in Reprocessing Plants Process (computing) ; Process development ; Process analysis ; Work in process ; Management process Radioactive element chemistry and processing ; Graphite, nuclear technology, radiation studies ; Extraction and Separation Processes
Authors: Johannes G. Volpini | Stefan Schwarzmüller | Hubert Huppertz
Source: Zeitschrift für Naturforschung B (article, 2025-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The barium oxolithogallate Ba <sub>2</sub> [LiGaO <sub>4</sub> ] as an ordered variant of the BaZnO <sub>2</sub> -type structure Abstract Ba 2 [LiGaO 4 ] was prepared via a solid-state reaction in a weld-shut tantalum ampoule at T = 1,073–1,123 K. The quaternary barium oxolithogallate crystallizes in the trigonal crystal system and the crystal structure was solved and refined in the non-centrosymmetric space group P 3 2 21 (no. 154) with the lattice parameters a = 5.8361(4) Å and c = 13.320(2) Å. The structure can be constructed from vertex-sharing LiO 4 and GaO 4 tetrahedra forming a three-dimensional tetrahedron [LiGaO 4 ] 4− substructure. The charge of this anionic network is compensated by the Ba 2+ ions in eightfold coordination, forming irregularly shaped BaO 8 polyhedra. Ba 2 [LiGaO 4 ] was found to crystallize isotypically to the known barium oxolithoaluminate Ba 2 [LiAlO 4 ], both of which are ordered variants of the BaZnO 2 -type structure. The structure elucidated from single-crystal X-ray diffraction is representative for the powder sample and the element ratio of Ba:Ga is supported by the results of an EDX analysis. Barium ; Crystal structure ; Tetrahedron ; Tantalum ; Ampoule ; Trigonal crystal system ; Group (periodic table) ; X-ray crystallography ; Lattice (music) Crystal Structures and Properties ; Thermal Expansion and Ionic Conductivity ; Microwave Dielectric Ceramics Synthesis
Authors: Hichem Ykrelef | Younes Chiba | Mounir Zirari | Ahmed Benyekhlef | Abdelali Boukaoud | Djamel Sebbar | Abdelkrim Kherkhar | Hayati Mamur
Source: International Journal of Air-Conditioning and Refrigeration (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Thermal design of a thermoelectric refrigerator operating near room temperature using artificial neural network Abstract The current study aimed to design and test a prototype of a thermoelectric cooler (TEC) using thermoelectric modules (TEM) operating near room temperature. The thermoelectric cooler utilized in this investigation has a maximum cooling power of 46 W and dimensions of 40 mm × 40 mm × 3.6 mm. After a series of measurements, the device temperature decreased from an ambient temperature of 19.6 °C to 1.6 °C, with a notable coefficient of performance around 0.9, achieved through the utilization of both serial and parallel connections. The secondary objective of this research was based on an artificial neural network (ANN) approach. An ANN model was constructed using an experimental database acquired from our thermoelectric refrigeration device. The input parameters of humidity, time, performance coefficient, cooling power, and heat dissipation were introduced into the model to enhance the cold temperature as the output. Using a multilayer perceptron (MLP), the experimental dataset was used for training, testing, and validating the ANN. The precision of the model was evaluated using three established statistical metrics: mean squared error (MSE), mean absolute percentage error (MAPE), and R -squared ( R 2 ). Refrigerator car ; Artificial neural network ; Thermoelectric effect ; Thermoelectric cooling ; Thermal ; Materials science ; Environmental science ; Mechanical engineering ; Computer science ; Thermodynamics ; Engineering ; Artificial intelligence ; Physics Advanced Thermoelectric Materials and Devices ; Refrigeration and Air Conditioning Technologies ; Heat Transfer and Optimization
Authors: Duanyin Shi | Chunyan Zhao
Source: Metalurgija (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Distribution Influence ofFunctional Gradient Graphene Reinforced Composites in Solving DifferentialEquations To investigate the effects of different graphene block distribution modes on the vibration characteristics of functionally graded graphene-reinforced composite materials, this study first establishes the motion control differential equations based on the Halpin–Tsai micromechanical model and Hamilton’s principle. The nonlinear and free vibration behaviors of the materials are also analyzed. The results indicate that different graphene mass fractions and distribution patterns have varying degrees of influence on the resonance characteristics and mechanical properties of functionally graded graphene layer–reinforced composites. Among them, the cross distribution pattern exhibits the most effective vibration suppression under specific conditions. These findings provide a theoretical foundation for the development of lightweight, high-strength materials and contribute to understanding the inherent relationship between the distribution of graphene blocks and material properties. Graphene ; Vibration ; Nonlinear system ; Distribution (mathematics) ; Composite number ; Equations of motion ; Differential equation ; Mass distribution Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Aeroelasticity and Vibration Control
Authors: Robert C. Mawhinney
Source: Research Square (preprint, 2025-11-18)
Field: Chemistry — Spectroscopy

Comparative Analysis of Molecular Conformers Using Localization–Delocalization Matrices and Frobenius Distance Conformational isomerism ; Similarity (geometry) ; Delocalized electron ; Ring (chemistry) ; Pairwise comparison ; Molecule ; Point (geometry) ; Density functional theory Molecular spectroscopy and chirality ; Advanced Chemical Physics Studies ; Organic Chemistry Cycloaddition Reactions
Authors: Abhijith Jayan Abhijith Jayan | SreeragS SreeragS | Thanuja T Thanuja T | VipinJoseph VipinJoseph | Girish Kumar K Girish Kumar K
Source: International Journal of Pharmaceutical Research and Applications (article, 2025-04-01)
Field: Chemistry — Spectroscopy

Advanced Instrumental Method of Analysis in Analytical Toxicology Laboratoryfor Toxicological Analysis Majoradvances in analytical toxicology followed the introduction of spectroscopic andchromatographic techniques. Thin layer chromatography remains important togetherwith some spectrophotometric and other tests. However, gasand high-performanceliquid chromatography together with variety of enzyme immunoassay techniques are nowwidely used.Advanced Instrumentation techniques that include UPLC and its hyphenated systems with MS ,UHPLC and its hyphenated systems with MS, GC and its hyphenated systems with MS, headspace and FID. Analytical Toxicology being emerging as an emergency critical care service, these analytical techniques are to be used for better patient care in diagnosis and treatment of various poisoned victims. Toxicology ; Biochemical engineering ; Engineering ; Biology Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety
Authors: Bauyrzhan Kairatuly | Adai Shomanov
Source: Scientific Journal of Astana IT University (article, 2025-06-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

BALANCING SPEED AND PERFORMANCE WITH LAYER FREEZING STRATEGIES FOR TRANSFORMER MODELS In this paper, we evaluated different approaches to freezing BERT-base layers and analyzed their impact on the quality and speed of training in the task of named entity recognition in two languages. Layer freezing is an optimization technique in deep neural network training in which specific layers of a model remain fixed. This means their weights do not change during the backpropagation process. By not updating these layers, the overall number of parameters requiring adjustment is reduced, which results in lower computational demands and faster training times. Partial freezing of layers proved to be an effective way to preserve key representations of the model and ensure its adaptation to new tasks. Experimental results showed that freezing from three to six layers allows to achieve stable model performance regardless of the training language. Unlike standard approaches, our method highlights cross-linguistic applicability and promotes energy-efficient training. We personally designed the experimental setup, implemented the freezing scenarios, and carried out all performance evaluations. This study aims to evaluate the effectiveness of layer freezing in a pre-trained BERT model when performing the named entity recognition task. Two variants of the freezing strategy are considered: in the first one the upper layers of the model are fixed, in the second one the lower layers remain unchanged. The analysis is based on two corpora, the English language CoNLL 2003 and the Kazakh language KazNERD. Our experiments showed that freezing three to six layers provides the best balance between training speed and model quality. On the CoNLL-2003 dataset, the training time decreased from 266 to 167 seconds and the Macro F1 score remained at 87%. On KazNERD, learning accelerated from 1609 to 958 seconds with an accuracy of 94-95 % and Macro F1 in the range of 71-72 %. Full freezing of all 12 layers caused a dramatic drop in quality, with Macro F1 dropping to 50 % on CoNLL and to 7 % on KazNERD. This emphasises the importance of limited freezing and fine-tuning of the model architecture. The study further examines how the choice of layers to freeze influences the model’s ability to adapt to new linguistic patterns and domain-specific terminology. These findings offer useful insights for researchers and practitioners aiming to enhance the efficiency of fine-tuning large language models while ensuring robust performance across different languages and datasets. The results also highlight the potential for optimizing resource usage in various NER applications without compromising critical language understanding. Transformer ; Computer science ; Materials science ; Engineering ; Electrical engineering ; Voltage Magnetic Properties and Applications ; Neural Networks and Applications ; Power Transformer Diagnostics and Insulation