jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Владимир Ерофеев | Victor Afonin | Tatyana Tyuryakhina | Marina M. Zotkina | В. П. Алехин
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Optimization approach to assessing the physical and mechanical properties of polymer composites It has been shown that one of the most significant components of composite building materials (CCM) are fillers, which help improve their structural and operational characteristics. The stages of the processes of structure formation of CSM are given. The role of energy characteristics, dispersion of fillers and degree of filling in the formation of optimal structures is emphasized. Existing approaches for theoretical assessment of the properties of composites, based on solving the Lamé problem for a thickwalled sphere, are presented.This article is devoted to an experimental study of the properties of epoxy composites with fillers having different elasticplastic and strength properties. Glass, ceramic, and chalk powders were considered as fillers; their elastic moduli were, respectively, greater, approximately equal, and less than those of pure polymer.The studies were carried out using mathematical methods of experiment planning. A full factorial experiment was carried out with the construction of a planning matrix and relative values of response functions. Explanatory graphical dependencies have been constructed.The research results can be used to predict the properties of CCM based on the properties of the initial components, as well as to clarify the extreme indicators of properties. Composite material ; Materials science ; Filler (materials) ; Composite number ; Ceramic ; Polymer ; Dispersion (optics) ; Factorial experiment ; Matrix (chemical analysis) ; Epoxy ; Material properties ; Computer science Structural mechanics and materials ; Innovations in Concrete and Construction Materials ; Material Properties and Applications
Authors: Azanu Mirolgn Mequanenit | Aleka Melese Ayalew | Ayodeji Olalekan Salau | Eyerusalem Alebachew Nibret | Million Meshesha
Source: Heliyon (article, 2024-12-01)
Field: Chemistry — Analytical Chemistry

Prediction of mung bean production using machine learning algorithms Agriculture is the backbone of the Ethiopian economy. It plays a significant role in the growth of the national economy. Among these, mung bean production takes the highest level of income for most smallholder farmers in all regions of Ethiopia who cultivate mung bean crops. Therefore, this study aims to predict mung bean production using a machine learning algorithm. For this study, the datasets were collected from the Central Statistical Agency of Ethiopia database. A total of 10273 instances were used for the experiment. Python machine learning tool was used to conduct the experiment and build an optimal model. To achieve the objective of this study, different experiments were conducted using Random Forest, Gradient Boosting, and Xgboosting algorithms. In addition, the predictive performances of the classifiers are evaluated and compared using accuracy, precision, recall, F1-score, and confusion matrix. Experimental result shows that the Xgboosting classifiers algorithm achieves the best performance with 98.65 % test accuracy and 99.8 % train accuracy. As a result, the Xgboosting classifier was selected for implementing the model to predict mung bean production. The findings of this study show that the main determinant factors for mung bean production include Meher season, use of extension program, fertilizer used, and fertilizer type. Therefore, the outcome of this research is essential to support the decision-making process of experts and it will enable them take corrective measures on the factors affecting mung bean production. Production (economics) ; Algorithm ; Mung bean ; Machine learning ; Computer science ; Artificial intelligence ; Biology ; Economics ; Horticulture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Vikas Khullar | Mohit Angurala | Abhineet Anand | Jagdish Chandra Patni | Harjit Singh
Source: Journal of Autonomous Intelligence (article, 2024-02-07)
Field: Chemistry — Analytical Chemistry

Intelligent fruit quality classification system using transfer learning &lt;p&gt;Amidst the burgeoning demands of fruit agriculturists and grading companies for enhanced fruit quality classification, this research presents a cutting-edge approach to binary fruit quality assessment. We built a portable device for exact fruit quality inspection using transfer learning, a deep learning approach, resulting in a decrease in both human and machine labor. The performance of the system is validated and evaluated under real-time situations, with an emphasis on end-user applicability. This paper rigorously validates and assesses the system’s performance in real-world scenarios, with a strong focus on its practicality for end-users. The model is trained on an online picture dataset that is divided into two categories: ‘good’ and ‘poor’ fruits. On dataset 1, our numerical findings show outstanding classification accuracies of 99.49% and 99.75% for the first and second models, respectively. Meanwhile, on dataset 2, the first and second models attain accuracies of 85.43% and 96.75%, respectively, highlighting the efficacy of our technique.&lt;/p&gt; Transfer of learning ; Quality (philosophy) ; Computer science ; Artificial intelligence ; Machine learning ; Physics Spectroscopy and Chemometric Analyses
Authors: Melike Çolak | Özgü Özkan | Nergis Pervan Akman | Ali Berkol | Murat Olgun | Zekiye Budak Başçiftçi | Nazife Gözde Ayter | Okan Sezer | Murat Ardıç
Source: Journal of Advanced Research in Natural and Applied Sciences (article, 2024-12-31)
Field: Chemistry — Analytical Chemistry

Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers Sunn pest damage is one of the most crucial types of agricultural damage. Authorities and farmers are working together to find a cost-effective solution for separating the damaged crops from the healthy ones. This challenge can be tackled cost-effectively with emerging technology. Over time, the number of researchers focusing on this problem by using various machine learning algorithms and image processing techniques has increased. This paper presents an approach using a recurrent neural networks-based transformer to identify different varieties of wheat grain that have been sunn pest-damaged and healthy. First, wheat grains were separated from each other using YOLOv8. Then, the dataset was enriched with different data augmentation techniques, and data-efficient vision transformers were used to classify sunn pest-damaged and healthy grains. Conversely, a high accuracy score of 98.61% was achieved on the augmented dataset while surpassing the accuracy score of 93.36% in the raw dataset. This paper's contributions to literature can be divided into three categories. In contrast to the previous research, perfectly shaped, broken, and half-wheat grains are used to better fit findings in real-life environments such as factory production lines. Moreover, this study employs a combination of augmentation techniques, implying that two separate augmentation techniques, texture-based and one morphological, were applied to the same image. Finally, no study in the available literature uses a vision transformer to classify healthy and sunned pest-damaged wheat grains. That leads to using a data-efficient vision transformer algorithm and achieving a high accuracy score of 98.61%. PEST analysis ; Agronomy ; Transformer ; Biology ; Engineering ; Botany ; Electrical engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Aaron L. Zaugg | Elliot Sherren | Rebekah Yi | Alex Farnsworth | Fetutasi Pauga | Anna Linder | Lauren E. Kelly | Meg Takara | McKenna Hoather | Sierra Stump | Christine Behunin | Boston Boyack | Mark R. Tolley | Kayla M. Holland | Mandy K. Salmon | Shenglou Deng | James E. Patterson | Paul B. Savage
Source: Antibiotics (article, 2024-10-24)
Field: Chemistry — Organic Chemistry

Incorporation of Ceragenins into Medical Adhesives and Adhesive Scar Tape to Prevent Microbial Colonization Common in Healthcare-Associated Infections <b>Background/Objectives:</b> Healthcare-associated infections involving surgical sites, skin trauma, and devices penetrating the skin are a frequent source of increased expense, hospitalization periods, and adverse outcomes. Medical adhesives are often employed to help protect compromised skin from infection and to secure medical devices, but adhesives can become contaminated by pathogens, exposing wounds, surgical sites, and medical devices to colonization. We aimed to incorporate ceragenins, a class of antimicrobial agents, into silicone- and polyacrylate-based adhesives with the goal of reducing adhesive contamination and subsequent infections. <b>Methods:</b> Three adhesives were developed and evaluated for the release of ceragenins, antimicrobial efficacy, adhesive strength, and dermal irritation. <b>Results:</b> Elution profiles over two weeks showed a high initial release followed by steady, long-term release. Standard microbial challenges of the adhesives by methicillin-resistant <i>Staphylococcus aureus</i>, <i>Pseudomonas aeruginosa</i>, or <i>Candida albicans</i> demonstrated microbial reduction for 6 to 68 days. Lap shear adhesive strength was not reduced for polyacrylate adhesives containing ceragenins, and no dermal irritation was observed in an in vivo model. <b>Conclusions:</b> Ceragenin-containing adhesive materials appear well suited for prevention of bacterial and fungal infections associated with medical devices and bandages. Colonization ; Adhesive ; Medicine ; Dermatology ; Microbiology ; Materials science ; Composite material ; Biology Antimicrobial agents and applications ; Antifungal resistance and susceptibility ; Bacterial biofilms and quorum sensing
Authors: Xiaobing Ren | Yang Yang | Zhizhi Xu | Yuanchao Ji | Tianyu Ma | Chang Liu | Liqiang He | Ye Yuan | Qian Yu | Andong Xiao | Wenjia Wang
Source: Research Square (preprint, 2024-03-08)
Field: Materials Science — Materials Chemistry

A polymer-like ultrahigh-strength metal alloy Alloy ; Materials science ; Metal ; Polymer ; Metallurgy ; Composite material Titanium Alloys Microstructure and Properties ; Shape Memory Alloy Transformations ; Intermetallics and Advanced Alloy Properties
Authors: Wanyi Wang | Zixi Zhang | Jingqi Liu | Lingtan Kong | Wanhe Wang | Chung‐Hang Leung | Jing Wang
Source: Biosensors (article, 2024-07-29)
Field: Chemistry — Spectroscopy

Development of a NIR Iridium(III) Complex-Based Probe for the Selective Detection of Iron(II) Ions As a commonly used metal ion, iron(II) (Fe<sup>2+</sup>) ions pose a potential threat to ecosystems and human health. Therefore, it is particularly important to develop analytical techniques for the rapid and accurate detection of Fe<sup>2+</sup> ions. However, the development of near-infrared (NIR) luminescence probes with good photostability for Fe<sup>2+</sup> ions remain challenging. In this work, we report a novel iridium(III) complex-based luminescence probe for the sensitive and rapid detection of Fe<sup>2+</sup> ions in a solution based on an Fe<sup>2+</sup>-mediated reduction reaction. This probe is capable of sensitively detecting Fe<sup>2+</sup> ions with a limit of detection (LOD) of 0.26 μM. Furthermore, this probe shows high photostability, and its luminescence remains stable under 365 nm irradiation over a time period of 30 min. To our knowledge, this is first iridium(III) complex-based NIR probe for the detection of Fe<sup>2+</sup> ions. We believe that this work provides a new method for the detection of Fe<sup>2+</sup> ions and has great potential for future applications in water quality testing and human monitoring. Iridium ; Detection limit ; Luminescence ; Ion ; Metal ions in aqueous solution ; Chemistry ; Near-infrared spectroscopy ; Luminescent Measurements ; Irradiation ; Analytical Chemistry (journal) ; Photochemistry ; Materials science ; Optoelectronics ; Catalysis ; Optics ; Chromatography ; Physics Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques ; Analytical Chemistry and Sensors
Authors: Carsten Speckmann | Andrea Angeletti | Lukáš Kývala | David Lamprecht | Felix Herterich | Clemens Mangler | Lado Filipovic | Christoph Dellago | Cesare Franchini | Jani Kotakoski
Source: Advanced Materials Interfaces (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

Electron‐Beam‐Induced Adatom‐Vacancy‐Complexes in Mono‐ and Bilayer Phosphorene Abstract Phosphorene, a puckered 2D allotrope of phosphorus, has sparked considerable interest in recent years due to its potential especially for optoelectronic applications with its layer‐number‐dependant direct band gap and strongly bound excitons. However, detailed experimentalcharacterization of its intrinsic defects as well as its defect creation characteristics under electron irradiation are scarce. Here, the creation and stability of a variety of defect configurations under 60 kV electron irradiation in mono‐ and bilayer phosphorene are reported including the first experimental reports of stable adatom‐vacancy‐complexes. Displacement cross section measurements in bilayer phosphorene yield a value of 7.7 ± 1.4 barn with an estimated lifetime of adatom‐vacancy‐complexes of 19.9 ± 0.7 s, while some are stable for up to 68 s under continuous electron irradiation. Surprisingly, ab initio‐based simulations indicate that the complexes should readily recombine, even in structures strained by up to 3%. The presented results will help to improve the understanding of the wide variety of defects in phosphorene, their creation, and their stability, which may enable new pathways for defect engineered phosphorene devices. Phosphorene ; Materials science ; Vacancy defect ; Bilayer ; Band gap ; Chemical physics ; Exciton ; Electron ; Ab initio ; Molecular physics ; Condensed matter physics ; Nanotechnology ; Optoelectronics ; Chemistry ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Yingying Chu | Ling Sun | Jing Wang | Zhaoyang Han | Chenyu Wei | Changbao Han | Hui Yan
Source: Nanomaterials (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

Carbon Nanotube Films with Fewer Impurities and Higher Conductivity from Aqueously Mono-Dispersed Solution via Two-Step Filtration for Electric Heating With the intensification of global climate problems, electric heating has recently attracted much attention as a clean and low-carbon heating method. Carbon nanotubes (CNTs) are an ideal medium for electric heating applications due to their excellent mechanical, electrical, and thermal properties. The preparation of electrothermal films based on an aqueous CNT dispersion as a raw material is environmentally friendly. However, in the traditional one-step filtration method, the residual excess dispersant and the small aspect ratio of the CNTs in the preparation process limit the performance of electrothermal CNT films. In this paper, we report a two-step filtration method that removes the free dispersant and small CNTs in the first filtration step and obtains denser CNT films by controlling the pores of the filter membrane in the second filtration step. The results suggest that, compared to the CNT<sub>1</sub> film obtained from one-step filtration, the CNT<sub>1-0.22</sub> film, obtained from two-step filtration using 1 and 0.22 μm membranes, has a smoother and flatter surface, and the surface resistance is 80.0 Ω sq<sup>-1</sup>, which is 29.4% lower. The convective radiation conversion efficiency of the CNT<sub>1-0.22</sub> film is 3.36 mW/°C, which is 36.1% lower. We anticipate that such CNT films could be widely applied in building thermal insulation and underfloor heating. Carbon nanotube ; Filtration (mathematics) ; Impurity ; Electrical resistivity and conductivity ; Materials science ; Conductivity ; Chemical engineering ; Carbon fibers ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry ; Electrical engineering ; Physical chemistry Carbon Nanotubes in Composites ; Thermal properties of materials ; Graphene research and applications
Authors: Bin Liu | Hui Zhou | Yuchen Liu | Shaoxun Li | Kaili Chu | Zhixue Qu | Wenxian Li | Yiran Li
Source: Journal of the American Ceramic Society (article, 2024-12-17)
Field: Materials Science — Ceramics and Composites

Structural origin of anisotropic mechanical/thermal behavior in La <sub>2</sub> SrAl <sub>2</sub> O <sub>7</sub> and Nd <sub>2</sub> SrAl <sub>2</sub> O <sub>7</sub> perovskites Abstract Rare earth strontium aluminates have attracted much attention due to their excellent properties and widely functional applications. In this work, the bonding characteristics, mechanical/thermal properties, and phonon behavior of La 2 SrAl 2 O 7 and Nd 2 SrAl 2 O 7 with layered structure are investigated using first‐principles calculations. The weak chemical bonds within the rock‐salt layer lead to the anisotropy of elastic moduli, tensile, and shear strength, benefiting their damage tolerance. It is also found that the low‐frequency phonons present much lower scattering rates, making themselves vital contributors to heat conduction. Owing to the enhanced anharmonicity, Nd 2 SrAl 2 O 7 exhibits lower thermal conductivity than La 2 SrAl 2 O 7 . Moreover, the lower thermal conductivities are observed along the z direction, which is attributed to the anisotropic chemical bonding. These results clarify the role of the weak bonds within layered structures in modulating their mechanical and thermal performance, which is expected to shield light on the development of perovskites with layered Ruddlesden–Popper structures. Materials science ; Anisotropy ; Anharmonicity ; Thermal conductivity ; Chemical bond ; Phonon ; Mineralogy ; Phonon scattering ; Condensed matter physics ; Composite material ; Chemistry ; Optics Advanced ceramic materials synthesis ; Thermal Expansion and Ionic Conductivity ; Microwave Dielectric Ceramics Synthesis
Authors: Joseph D. Paulsen | Nathan C. Keim
Source: arXiv (Cornell University) (preprint, 2024-05-13)
Field: Materials Science — Materials Chemistry

Mechanical memories in solids, from disorder to design Solids are rigid, which means that when left undisturbed, their structures are nearly static. It follows that these structures depend on history -- but it is surprising that they hold readable memories of past events. Here we review the research that has recently flourished around mechanical memory formation, beginning with amorphous solids' various memories of deformation and mesoscopic models based on particle rearrangements. We describe how these concepts apply to a much wider range of solids and glassy matter -- and how they are a bridge to memory and physical computing in mechanical metamaterials. An understanding of memory in all these solids can potentially be the basis for designing or training functionality into materials. Just as important is memory's value for understanding matter whenever it is complex, frustrated, and out of equilibrium. Psychology ; Materials science Shape Memory Alloy Transformations
Authors: Qiuyan Chen | Shuxia Zhang | Wenxiu Liu | Xiao Sun | Yun Luo | Xiaobo Sun
Source: Frontiers in Neurology (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Application of emerging technologies in ischemic stroke: from clinical study to basic research Stroke is a primary cause of noncommunicable disease-related death and disability worldwide. The most common form, ischemic stroke, is increasing in incidence resulting in a significant burden on patients and society. Urgent action is thus needed to address preventable risk factors and improve treatment methods. This review examines emerging technologies used in the management of ischemic stroke, including neuroimaging, regenerative medicine, biology, and nanomedicine, highlighting their benefits, clinical applications, and limitations. Additionally, we suggest strategies for technological development for the prevention, diagnosis, and treatment of ischemic stroke. Medicine ; Stroke (engine) ; Intensive care medicine ; Disease ; Ischemic stroke ; Neuroimaging ; Burden of disease ; Pathology ; Internal medicine ; Ischemia ; Psychiatry Advanced Nanomaterials in Catalysis ; Acute Ischemic Stroke Management ; Neuroinflammation and Neurodegeneration Mechanisms
Authors: Junhua Lu | Mei Zhang | Yusha Hu | W. F. Mader | Zhiwei Tian | Hongsen Liao | Jiawei Chen | Yuxin Yang
Source: Agronomy (article, 2024-10-16)
Field: Chemistry — Analytical Chemistry

From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70–99.8%, the accuracy of external defect grading is 88–95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56–100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future. Globular cluster ; Infrared ; Horticulture ; Artificial intelligence ; Biology ; Computer vision ; Mathematics ; Computer science ; Physics ; Astronomy ; Stars Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Listeria monocytogenes in Food Safety
Authors: Myung Hwan Na | In Seop Na
Source: Legume Research - An International Journal (article, 2024-05-02)
Field: Materials Science — Polymers and Plastics

Detection and Classification of Wilting in Soybean Crop using Cutting-edge Deep Learning Techniques Background: This paper employs deep learning in the classification of soybean wilting, a plant health indicator affected by external pressures, using a Convolutional Neural Network (CNN) with a pre-trained model. It highlights the promise of deep learning in agriculture by examining the relevance of wilting, evolution in the agricultural sector and applications in crop wellness monitoring. Methods: A CNN is used in the study to classify soybean withering, with special attention to the VGG16 pre-trained model. Deep learning’s ability to interpret complex data patterns is harnessed for intelligent and accurate wilting detection. A smart detection system tailored for soybean wilting is developed, incorporating recent advancements and addressing associated challenges. Result: The CNN model, notably VGG16, achieves 76% overall accuracy in distinguishing healthy and wilted soybean leaves, signifying a transformative shift in soybean crop health management. The approach offers a precise, efficient and sustainable solution supported by state-of-the-art CNN technology, advancing soybean cultivation practices. Wilting ; Crop ; Enhanced Data Rates for GSM Evolution ; Veterinary medicine ; Agronomy ; Artificial intelligence ; Biology ; Medicine ; Biotechnology ; Computer science Textile materials and evaluations ; Material Properties and Processing