jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qianqiu Shao | Songhai Fan | Zongxi Zhang | Fenglian Liu | Jie Lu | Zhengzheng Fu | Pinlei Lv
Source: Energies (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Multi-Parameter Assessment and Validation of Cable Insulation Using Game Theory and Fuzzy Comprehensive Evaluation Accurate assessment of high-voltage cable insulation condition is essential for safe operation in complex tunnel environments. Traditional methods relying on single diagnostic indicators and fixed weighting schemes often suffer from limited accuracy and adaptability. This paper proposes a multi-parameter assessment method integrating game theory with fuzzy comprehensive evaluation. Five types of online monitoring data, namely cable surface temperature, sheath grounding current, partial discharge, tunnel humidity, and ambient temperature, are selected as diagnostic parameters. Subjective and objective weights are first derived using the analytic hierarchy process and the entropy weight method, and then optimally integrated through a game-theoretic framework. Fuzzy membership functions are applied to construct an evaluation matrix, enabling quantitative and graded assessment of insulation condition. A case study on 110 kV tunnel high-voltage land cables in Zhejiang, China, verifies the effectiveness of the approach. Results show that the proposed method more accurately reflects actual operating conditions and provides higher diagnostic precision and robustness compared with single-feature and traditional weighting methods. By combining expert knowledge with real monitoring data, this study develops a scientific and practical framework for insulation condition assessment, offering reliable support to real-time insulation monitoring and predictive maintenance applications of high-voltage power transmission systems. Weighting ; Analytic hierarchy process ; Fuzzy logic ; Robustness (evolution) ; Entropy (arrow of time) ; Power transmission ; Evaluation methods ; Process (computing) High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Transformer Diagnostics and Insulation
Authors: Alfredo Bonini Neto | Maikon V. da Silva Lira | Guilherme Constantino Meirelles | Luiz Felipe de Melo Santos | Carolina dos Santos Batista Bonini | Reges Heinrichs
Source: Engenharia Agrícola (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

ARTIFICIAL NEURAL NETWORKS FOR PREDICTING SUGARCANE STALK AND TOTAL BIOMASS YIELD BASED ON MICRONUTRIENT RATES APPLIED IN THE PLANTING FURROW AND TO THE LEAVES ABSTRACT Sugarcane is pivotal in the global bioeconomy, providing a renewable resource for products such as ethanol, sugar, bioenergy, animal feed, and bioplastics. Its versatility makes it an essential crop for industries seeking sustainable alternatives to fossil fuels. This study presents an advanced approach that uses artificial neural networks (ANNs), specifically a multilayer perceptron model, to accurately estimate sugarcane productivity and biomass. The model incorporates the effects of micronutrient applications, both in the planting furrow and on the leaves, effectively capturing the complex interactions that influence crop yield. During training, the ANN demonstrated high precision, achieving a mean squared error (MSE) of 0.000097 and an R2 of 0.98, closely aligning the predicted outputs with experimental results. In the validation phase, using previously unseen data, it maintained strong performance, with an MSE of 0.0008796. This performance supports the model's ability to generalize beyond the training set, reliably estimating sugarcane yield and biomass under varying conditions. These findings highlight the potential of ANN-based approaches to enhance agricultural management, offering a robust tool to optimize crop performance and improve resource allocation in real-world farming scenarios. Stalk ; Micronutrient ; Sowing ; Yield (engineering) ; Biomass (ecology) ; Agronomy ; Artificial neural network ; Mathematics ; Biology ; Horticulture ; Computer science ; Chemistry ; Artificial intelligence ; Materials science Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement
Authors: Zhaoping Che | Ruilian Zhao | Weiwei Wang
Source:  (article, 2025-02-14)
Field: Materials Science — Materials Chemistry

Empirical Study on Machine Learning Testing Based on Mutation Testing In the context of machine learning projects, the inherent randomness of machine learning algorithms and the intuitive nature of developer test assertions frequently result in unstable and ineffective test cases. Mutation testing is a proven method to enhance the efficacy of test cases. However, the implementation of mutation testing for machine learning is confronted with challenges such as the generation of a substantial number of mutants and suboptimal execution efficiency. This paper undertakes a comprehensive analysis of six prominent open-source machine learning projects, optimises their test cases through the utilisation of mutation testing, and conducts a detailed investigation into the performance of various mutants. The objective is to provide recommendations and assistance in the selection of effective mutants in machine learning projects. The primary conclusions of this study are as follows: 1) The predominant cause of the failure of machine learning mutants is the substantial semantic discrepancies between the mutant program statements and the original program. 2) In existing machine learning projects, the proportion of mutants eliminated by test assertions is minimal, with an average of merely 4.39% of mutants being eliminated. 3) Mutation operators involving numeric and operator types are highly effective in adjusting the boundaries of test assertions. Computer science ; Mutation testing ; Software testing ; Test strategy ; Machine learning ; Empirical research ; Artificial intelligence ; Mutation ; Programming language ; Software ; Statistics ; Mathematics ; Genetics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Scientific Computing and Data Management
Authors: МИРЭА-Российский технологический университет | Федор А. Федулов | Дмитрий В. Савельев | Екатерина В. Болотина | Михаил В. Джапаридзе | I. K. Fetisov
Source: Radioelectronics Nanosystems Information Technologies (article, 2025-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synaptic behavior of a composite multiferroic heterostructure with remanent magnetization at resonant excitation Недавний прогресс в области глубокого обучения расширяет возможности искусственного интеллекта для различных практических задач, делая нейронные сети крайне популярным и полезным инструментом. Однако даже самые мощные современные нейронные сети работают на чипах, построенных на архитектуре фон Неймана. На данный момент для кардинального улучшения эффективности обучения и работы нейронных сетей ведутся обширные исследования по созданию устройств не-фон Неймановской (non-von Neumann architecture) архитектуры, к которым относятся нейроморфные вычислительные системы и синаптические устройства. В работе продемонстрировано синаптическое поведение композитной мультиферроидной гетероструктуры на основе пьезокерамики цирконата-титаната свинца и никеля при резонансном возбуждении магнитоэлектрического (МЭ) эффекта и приложении импульсов магнитного поля различной амплитуды и полярности. МЭ коэффициент αE рассматрен в качестве аналога синаптического веса, а выходное электрическое напряжение гетероструктуры интерпретировано как постсинаптический потенциал. Показана возможность управления МЭ коэффициентом за счёт изменения остаточной намагниченности никеля при приложении импульсов магнитного поля. Продемонстрирована возможность имитации долгосрочного возбуждения и угнетения в МЭ гетероструктуре, а также синаптической пластичности, зависящей от времени прихода спайка. Multiferroics ; Excitation ; Materials science ; Condensed matter physics ; Remanence ; Magnetization ; Nuclear magnetic resonance ; Composite number ; Physics ; Magnetic field ; Ferroelectricity ; Optoelectronics ; Composite material ; Quantum mechanics Multiferroics and related materials ; Theoretical and Computational Physics
Authors: Л. М. Мосула | N. I. Vynnytska
Source: Biopolymers and Cell (article, 2025-07-21)
Field: Chemistry — Organic Chemistry

Computational prediction of biological activity of 5-(2-oxoindolin-3-ylidene)-substituted of 3-(benzo[d]thiazol-2-ylamino)-2-thioxothiazolidin-4-ones Aim.To carry out in silico research of potential affinity of 5-isatinylidene derivatives of 3-(benzo[d]thiazol-2-ylamino)-2-thioxothiazolidin-4-one to biotargets and determine their possible belonging to ATC classes.Methods.Web tool Su-perPred 3.0.Results.The predominant antitumour activity of the compounds was determined.The potential biological activity of derivatives 5-19 was compared with the in vitro efficacy of the core heterocycle, previously synthesized compounds 1-4 and the known effect of the antineoplastic drug Sutent (Pfizer Inc., USA).The compounds exhibited the hignest group efficiency against two targets: Cathepsin D and Casein kinase II alpha/beta (Model accuracy are 98.95 and 99.23%, respectively).The important role of the isatinylidene moiety on the biological activity of the derivatives was determined.Conclusions.The highest probability of structural similarity to drugs with ATC code L01XE is predicted for compound 2 (38.18%).Compounds 15 and 19 are considered potential multi-hitters.The hi ghest affinity is predicted for compounds: 19 (99.80% to Cath-D) and 1 (93.56% to CK2).These values exceed those predicted for Sutent.The results obtained herein provide a platform for structure-based optimization of these derivatives. Chemistry ; Stereochemistry ; Mathematics ; Combinatorial chemistry Synthesis and biological activity ; Quinazolinone synthesis and applications ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Rafael A Monteiro | Camila S. Oliveira | Taís Coelho Ferreira | Mariana C. Duarte | Pedro M. S. Carvalho
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-15)
Field: Materials Science — Materials Chemistry

Dual-Functional Biomimetic Nanoclusters for Sustained Antifibrotic Therapy and Vascular Protection in Fibrosis Management Abstract Fibrosis represents a critical pathological hallmark underlying multiple chronic diseases, yet conventional pharmacological interventions are hampered by limited bioavailability, rapid systemic clearance, and insufficient vascular protection. In this study, we developed a biomimetic nanocluster system designed to overcome these therapeutic bottlenecks. The nanoclusters were fabricated via a self-assembly approach using PLGA–PEG and cholesterol matrices to co-encapsulate pirfenidone (antifibrotic) and resveratrol (vascular protective agent). Physicochemical characterization confirmed uniform particle morphology, stable size distribution, and sustained colloidal stability. Pharmacokinetic evaluation revealed that nanoclusters achieved a delayed peak time and prolonged plateau phase, maintaining therapeutic drug concentrations in serum and hepatic tissue for up to 72 h, in contrast to the rapid clearance observed in micelle and free-drug formulations. Mechanistic investigations suggested that biomimetic surface engineering and electrostatic coupling contributed to suppressed burst release, enhanced retention in fibrotic microenvironments, and receptor-mediated uptake. Functional assays further demonstrated significant inhibition of hepatic stellate cell activation, reduction of collagen deposition, and improved endothelial tube formation. In a CCl□-induced hepatic fibrosis mouse model, nanocluster treatment markedly decreased fibrotic area and enhanced CD31-positive vessel density without detectable systemic toxicity. The finding highlight the dual antifibrotic and vascular protective effects of biomimetic nanoclusters, which substantially outperform conventional nanocarriers in both therapeutic durability and safety. The present work not only introduces a long-acting drug delivery paradigm for fibrotic disease management but also provides an engineering framework adaptable to broader applications in translational nanomedicine. Nanocarriers ; Hepatic stellate cell ; Drug delivery ; Nanoclusters ; Pirfenidone ; Fibrosis ; Drug ; Hepatic fibrosis Nanocluster Synthesis and Applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Lu, Jinshuai | Zhang,  Shuhao | Ma,   Jin | You,  Wenying
Source: Journal of Combinatorial Mathematics and Combinatorial Computing (article, 2025-04-16)
Field: Chemistry — Analytical Chemistry

Application of Least Squares Support Vector Regression Analysis to Green Concrete with Oil Palm Husk Aggregate Least Squares Support Vector Regression (LSSVR) machine has the advantages of small sample, nonlinearity and high dimensionality, which can solve the problem of predicting the compressive strength of green concrete with oil palm shell aggregate.In this paper, the error sum of squares instead of the error sum is used as the objective function, IFFA is used to find the optimization of the kernel function parameters and penalty factors of LSSVR, and the PWLCM-based chaotic search is used to initialize the population, and ultimately the improved auricular fox algorithm is realized for the optimization of the least squares support vector regression algorithm, which makes it have strong fitting and generalization abilities, and significantly reduces the burden of computation, thus improving the Computational efficiency.Application of the designed combined algorithm for compressive strength prediction of concrete reveals that the R, MAPE and RMSE values obtained by this paper's model on the training dataset are 98.71%, 5.92% and 1.0823 MPa, respectively.The correlation coefficients predicted by the model are much closer to 1 as compared to that of the baseline model, which suggests that this paper's model possesses a superior generalization capability, making it more effective in dealing with complex and invisible data.The adopted method is practical and innovative, and is of guiding significance for practical engineering. Husk ; Aggregate (composite) ; Palm oil ; Palm ; Partial least squares regression ; Mathematics ; Regression analysis ; Support vector machine ; Statistics ; Materials science ; Computer science ; Composite material ; Chemistry ; Botany ; Food science ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses
Authors: Devi S | R V Ishitha Reddy | A John | Vishal Vinod Naik
Source: International Journal of Latest Technology in Engineering Management & Applied Science (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

The Effects of Intermediate Targets on Bullet Trajectory ok Trajectory ; Computer science ; Physics ; Astronomy High-Velocity Impact and Material Behavior ; Automotive and Human Injury Biomechanics ; Electromagnetic Launch and Propulsion Technology
Authors: Javier Guerra | Shamayeeta Ray | Rachelle Gaudet
Source: Biophysical Journal (article, 2025-02-01)
Field: Chemistry — Physical and Theoretical Chemistry

BPS2025 - Determining the structural basis of metal selectivity in Eggerthella lenta Nramp-like proteins Chemistry ; Metal ; Selectivity ; Biophysics ; Biochemistry ; Biology ; Organic chemistry thermodynamics and calorimetric analyses
Authors: Maryna Kachaeva | Oksana Bahrieieva | Oleksandr Kobzar | Yurii Shulha | A. V. Golovchenko | Oksana I. Golovchenko | Iryna Nizhenkovska | О. V. Мykhailenko | Stepan Pilyo | Victor Zhirnov | В. С. Броварец
Source: SynOpen (article, 2025-11-07)
Field: Chemistry — Organic Chemistry

Synthesis, Anticancer Screening, and Virtual Analysis of 5-S-Substituted Derivatives of 1,3-Oxazol-4-ylphosphonates and 1,3-Oxazole-4-carbonitriles Abstract Ten 5-(arylsulfanyl), 5-(arylsulfinyl), and 5-(arylsulfonyl) derivatives of 1,3-oxazol-4-ylphosphonates and 1,3-oxazole-4-carbonitriles were synthesized and characterized using IR, 1H NMR, 13C NMR, 31P NMR spectroscopy, elemental analysis, and mass spectrometry. Their anticancer activity was assessed against NCI-60 human tumor cell lines using a single-dose assay. Diethyl [2-phenyl-5-(phenylsulfonyl)-1,3-oxazol-4-yl]phosphonate, diethyl [5-(4-chlorophenylsulfonyl)-2-(4-methylphenyl)-1,3-oxazol-4-yl]phosphonate, and 5-(4-methylphenylsulfonyl)-2-phenyl-1,3-oxazole-4-carbonitrile, which demonstrated the highest anticancer activity, were selected for five-dose screening. The two phosphonates showed selectivity (SIr &gt; 3 by TGI and LC50) against most leukemia lines, while the 1,3-oxazole-4-carbonitrile derivative was selective against renal (63%), colon (57%), and breast cancer (50%). One compound demonstrated selectivity against the entire leukemia subpanel. A comparison analysis revealed that no standard drug exhibited a high degree of similarity to compounds across all potency vectors. This suggests that the molecular mechanisms may be unique. Possible targets such as cannabinoid receptor 2, adenosine A3 receptor, and cyclin-dependent kinase 2 have been proposed based on in silico studies. The parameters of druglikeness predicted by the ADMET analysis were within the rational range for all compounds. The two phosphonates are expected to be preferable for the development of antileukemia agents, while 5-(4-methylphenylsulfonyl)-2-phenyl-1,3-oxazole-4-carbonitrile is considered promising for agents targeting renal, colon, and breast cancers. Potency ; Selectivity ; In silico ; Leukemia ; Virtual screening ; Cancer ; In vitro ; Drug ; Cytotoxicity Synthesis and Reactivity of Sulfur-Containing Compounds ; Organophosphorus compounds synthesis ; Synthesis and Reactions of Organic Compounds
Authors: Patrick Onochie | D. Ekanem | Francis Onoroh | S. O. Osigbemeh | H.I. Owamah | Henry Oghenero Orugba | Sunday Chukwuyem Ikpeseni | G. I. Okolotu | Solomon Oyebisi | Collins Chike Kwasi-Effah
Source: NIPES Journal of Science and Technology Research (article, 2025-06-22)
Field: Chemistry — Inorganic Chemistry

Palm Kernel Shell and Coconut Shell Pellets Cofiring: Effect of Optimization Ratio on the Combustion Emission CO2 and SO2 Biomass is considered one of the best options for mitigating greenhouse gas (GHG) emissions and substitutes for fossil fuels.This study investigated the effect of optimization ratios on the combustion emissions of co-fired pellets produced from coconut shell (CS) and palm kernel shell (PKS) using waste paper as a binding agent.Ten pellets were produced mechanically using a manual screw press machine.Two optimization models were adopted: Model I includes ratios of 90%C:10%P, 80%C:20%P, 70%C:30%P, 60%C:40%P, and 50%C:50%P, while Model II includes ratios of 90%P:10%C, 80%P:20%C, 70%P:30%C, 60%P:40%C and 50%P:50%C .The optimized pellets were characterized through proximate and ultimate analyses in accordance with ASTM standards.Data obtained was used to analyse the combustion emissions.Results obtained revealed that, on both wet and dry bases, the 50%:50% optimization ratio in Models I and II had better potential for mitigating combustion CO2 and SO2 emissions than other optimization ratios, which exhibited relatively higher values.Similarly, models demonstrated more favourable emissions on a wet basis than on dry basis.Comparatively, Model I showed greater potential in CO2 mitigation, while Model II was more effective in SO2 mitigation.All optimized pellets in both models exhibited low CO2 and SO2 emissions on wet and dry composition analyses, as recommended by the Bureau of Energy Efficiency.The findings from this study underscore the potential of varying CS and PKS ratios in optimizing combustion emissions in a Biomass Power Plant, highlighting the effectiveness of CS in SO2 reduction and PKS in CO2 reduction. Palm kernel ; Pellets ; Shell (structure) ; Cofiring ; Combustion ; Materials science ; Pellet ; Pulp and paper industry ; Environmental science ; Waste management ; Composite material ; Chemistry ; Engineering ; Agroforestry ; Organic chemistry ; Palm oil Coconut Research and Applications ; Thermochemical Biomass Conversion Processes ; Agricultural and Environmental Management
Authors: Michael K. Lawson | Emily J. Carter | D. Mitchell | Jamie Turner
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-05)
Field: Materials Science — Biomaterials

Design and Evaluation of PLGA-Based Nanocarriers for Targeted and Sustained Drug Delivery in Vascular Disorders Abstract Vascular diseases, including atherosclerosis and vascular inflammation, have high incidence and mortality rates worldwide. Current drug therapies are limited by short circulation time, broad non-specific distribution, insufficient efficacy, and significant side effects. This study aimed to develop and optimize a targeted nanodrug delivery system based on poly(lactic-co-glycolic acid) (PLGA) to improve drug accumulation and therapeutic outcomes at vascular lesion sites. Nanoparticles prepared by the solvent evaporation–self-assembly method had an average diameter of 145.6 ± 12.3 nm, a zeta potential of −21.4 ± 3.7 mV, and showed uniform spherical morphology. The encapsulation efficiency (EE%) was 82.3 ± 4.5%, and the drug loading (DL%) was 9.6 ± 1.1%, indicating good drug-carrying ability. For surface modification, conjugation of ligands to the ends of PEG chains balanced the conflict between “stealth” and “affinity,” maintaining circulation stability while restoring effective binding to vascular endothelial cells. Drug release experiments demonstrated a biphasic release profile in PBS (pH 7.4): about 40% was released within 0–12 h, and cumulative release reached 76.5 ± 3.2% at 72 h. Kinetic analysis fitted the Higuchi model (R 2 = 0.983), suggesting diffusion as the main driving mechanism. This predictable and controllable release behavior can provide both rapid effect in the acute phase and sustained therapy in the chronic phase. Overall, the PLGA nanocarrier system proposed in this study showed clear advantages in physicochemical properties, surface functionalization, and drug release kinetics. It can achieve prolonged circulation, high targeting efficiency, and controlled release in the treatment of vascular diseases. This work provides experimental evidence to overcome the limitations of conventional drug therapy and lays the foundation for developing multifunctional and clinically translatable nanodrug delivery platforms for personalized treatment. Nanocarriers ; PLGA ; Drug delivery ; Drug ; Targeted drug delivery ; Nanotechnology ; Pharmacology ; Medicine ; Materials science ; Nanoparticle Nanoparticle-Based Drug Delivery ; Protease and Inhibitor Mechanisms ; Connective tissue disorders research
Authors: Xingao Gong
Source: AI Materials (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

AI Materials: A New Open Access Journal for Artificial Intelligence and Materials Science We stand at a transformative moment in scientific history, where artificial intelligence and materials science are engaged in a mutually reinforcing cycle of advancement [...] Transformative learning ; Applications of artificial intelligence ; Moment (physics) ; Open science Machine Learning in Materials Science ; Nanoporous metals and alloys ; Artificial Intelligence in Healthcare and Education