jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehral Aslam | Sadia Iftikhar | Mazia Amber | Jawad Amin | Khazeema Yousaf | Qurat ul Ain | Erum Naz
Source: Journal of Microbiological Sciences (article, 2024-12-25)
Field: Materials Science — Biomaterials

Bridging Drug Delivery and Molecular Biology: A Synergistic Alliance between Liposomes and Bioinformatics Liposomes, since their discovery have emerged as valuable nanocarriers in drug delivery and molecular biology, significantly enhancing the therapeutic index of many drugs. They were first coined in early 1960s by discovery by Bangham and co-workers. Beyond their conventional applications, a growing interest lies in integrating liposome technology with bioinformatics. Bioinformatic tools plays an important role in predicting, analyzing and simulating complex biological processes. These tools offer deeper insights into liposomal interactions with biological systems. Thanks to this advanced computational modeling, scientists can now design liposomes with improved targeting abilities, optimized stability, and enhanced therapeutic potential. One such model i.e Quantitative Structure–Activity Relationship (QSAR) is used for the precise prediction of liposome–drug and liposome–cell interactions, ultimately contributing to higher drug efficacy and reduced adverse effects. Many studies conducted till date throw light upon the promising synergy between liposomes and bioinformatics, highlighting its potential to transform drug delivery. This review explores this integration, focusing on how computational approaches can advance liposomal design, precision, and efficiency in therapeutic applications Liposome ; Nanocarriers ; Drug delivery ; Drug discovery ; Bridging (networking) ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques ; RNA Interference and Gene Delivery
Authors: Yuezhou Luo | Andrew J. Flewitt
Source: arXiv (Cornell University) (preprint, 2024-11-10)
Field: Materials Science — Materials Chemistry

Order in disorder: increased carrier mobility of downscaled amorphous semiconductors for high-speed thin film transistors in flexible electronics Amorphous semiconductors are important channel semiconductors in thin film transistors (TFTs) which serve not only active-matrix displays, but also flexible electronics for Internet of Things (IoT) applications. Nevertheless, a great limitation of amorphous semiconductors is their low carrier mobilities relative to their monocrystalline counterparts. Based on a recently established band fluctuation framework [Y. Luo and A. Flewitt, Phys. Rev. B 109, 104203 (2024)], this paper shows that the intrinsic carrier mobility of amorphous semiconductors can significantly increase simply through device downscaling, without any material-level optimizations. Specifically, it is revealed that the intrinsic electron mobility of hydrogenated amorphous silicon in a 10-nm long gap can increase by around 12 times, and does not compromise device-to-device uniformity. This mobility improvement is a result of reduced localized band tail states due to the ultra-short gap length relative to the band fluctuation length scale before downscaling; the latter is determined by the short- and medium-range structural order of the amorphous semiconductor. Finite element method ; Semiconductor ; Amorphous semiconductors ; Amorphous solid ; Materials science ; Element (criminal law) ; Optoelectronics ; Structural engineering ; Engineering ; Chemistry ; Political science ; Silicon ; Crystallography Solidification and crystal growth phenomena ; Metallic Glasses and Amorphous Alloys
Authors: Electronics and Communication RV college of Engineering
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2024-06-10)
Field: Chemistry — Analytical Chemistry

Deep Learning Based System for of Grading Cashew Kernels Cashew nuts are cultivated widely across the globe and are categorized into various classes according to their size and quality. Currently, cashew kernels are sorted and graded manually, which is time-consuming and labor-intensive. This research proposes hybrid models to automate and accelerate the cashew classification process by integrating classifiers with deep convolutional neural networks (DCNNs). The proposed hybrid models categorize cashew kernels into five groups: W180, W210, W300, W400, and W500. Various hybrid models, including RCNN + OpenCV, were implemented and evaluated based on specificity, accuracy, and other metrics. The results demonstrated that the RCNN model paired with OpenCV achieved a max- imum accuracy of 90%. This study’s findings highlight that automating cashew grading can be significantly enhanced by combining DCNNs with classifiers in hybrid models. These hybrid models improve efficiency by enabling automatic, effective, and precise classification of cashews. By leveraging advanced machine learning techniques, the process becomes faster, more accurate, and less dependent on manual labor. Overall, the integration of deep learning technologies with traditional classifiers offers a promising approach to modernizing and optimizing the cashew grading industry. Future research could further refine these hybrid models to achieve even higher accuracy and efficiency, potentially revolutionizing agricultural sorting processes beyond cashew nuts. Index Terms—DCNN, OpenCV, RCNN, Hybrid Models Artificial intelligence ; Computer science ; Convolutional neural network ; Machine learning ; Categorization ; Deep learning ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Neurological Disease Mechanisms and Treatments ; Smart Agriculture and AI
Authors: Michele Laurelli
Source: arXiv (Cornell University) (preprint, 2024-10-14)
Field: Materials Science — Materials Chemistry

AI-Driven Autonomous Control of Proton-Boron Fusion Reactors Using Backpropagation Neural Networks Proton-boron (p-11B) fusion presents a promising path towards sustainable, neutron-free energy generation. However, its implementation is hindered by extreme operational conditions, such as plasma temperatures exceeding billions of degrees and the complexity of controlling high-energy particles. Traditional control systems face significant challenges in managing the highly dynamic and non-linear behavior of the plasma. In this paper, we propose a novel approach utilizing backpropagation-based neural networks to autonomously control key parameters in a proton-boron fusion reactor. Our method leverages real-time feedback and learning from physical data to adapt to changing plasma conditions, offering a potential breakthrough in stable and efficient p-11B fusion. Furthermore, we expand on the scalability and generalization of our approach to other fusion systems and future AI technologies. Backpropagation ; Artificial neural network ; Boron ; Fusion ; Proton ; Fusion power ; Computer science ; Control (management) ; Artificial intelligence ; Plasma ; Physics ; Nuclear physics Nuclear Materials and Properties ; Fault Detection and Control Systems ; Nuclear reactor physics and engineering
Authors: Avadh K. Shah | Heer Patel | Arjun Kanjarpane | Michael F. Summers | Jan Marchant
Source: ChemRxiv (preprint, 2024-12-12)
Field: Chemistry — Spectroscopy

Relaxation optimized heteronuclear experiments for extending the size limit of RNA nuclear magnetic resonance The application of NMR to large RNAs has been limited by the inability to perform heteronuclear correlation experiments essential for resolving overlapping 1H NMR signals, determining inter-proton distance restraints and inter-helical orientations for structure calcula-tions, and evaluating conformational dynamics. Approaches exploiting 1H-13C correlations that are routinely applied to proteins and small RNAs of ~60 nucleotides or fewer are impractical for larger RNAs due to rapid dipolar relaxation of protons by their attached car-bons. Here we report a 2H-enhanced, 1H-15N correlation approach that enables atom-specific NMR characterization of much larger RNAs. Purine H8 transverse relaxation rates are reduced ~20-fold with ribose perdeuteration, enabling efficient magnetization transfer via two-bond 1H-15N couplings. We focus on H8-N9 correlation spectra which benefit from favorable N9 chemical shift anisotropy. Chemical shift assignment is enabled by retention of protons at the C1′ position, which allow measurement of two-bond H1′-N9 and through-space H1′-H8 correlations with only a minor effect on H8 relaxation. The approach is demonstrated for the 232 nucleotide HIV-1 Rev response element, where chemical shift assignments, 15N-edited nuclear Overhauser effects, and 1H-15N residual dipolar couplings are readily obtained from sensitive, high-resolution spectra. Heteronuclear correlated NMR methods that have been essential for the study of proteins can now be extended to RNAs of at least 78 kDa. Heteronuclear molecule ; Chemistry ; Residual dipolar coupling ; Relaxation (psychology) ; Nuclear magnetic resonance ; Magnetization transfer ; Proton ; Chemical shift ; Rotational correlation time ; Nuclear magnetic resonance spectroscopy ; Nuclear Overhauser effect ; Anisotropy ; Transverse relaxation-optimized spectroscopy ; Heteronuclear single quantum coherence spectroscopy ; Proton NMR ; Chemical physics ; Fluorine-19 NMR ; Molecule ; Physics ; Stereochemistry ; Physical chemistry ; Magnetic resonance imaging Advanced NMR Techniques and Applications ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Aryan Singh | Pepijn Van de Ven | Ciarán Eising | Patrick Denny
Source: arXiv (Cornell University) (preprint, 2024-07-20)
Field: Materials Science — Materials Chemistry

Subgraph Clustering and Atom Learning for Improved Image Classification In this study, we present the Graph Sub-Graph Network (GSN), a novel hybrid image classification model merging the strengths of Convolutional Neural Networks (CNNs) for feature extraction and Graph Neural Networks (GNNs) for structural modeling. GSN employs k-means clustering to group graph nodes into clusters, facilitating the creation of subgraphs. These subgraphs are then utilized to learn representative `atoms` for dictionary learning, enabling the identification of sparse, class-distinguishable features. This integrated approach is particularly relevant in domains like medical imaging, where discerning subtle feature differences is crucial for accurate classification. To evaluate the performance of our proposed GSN, we conducted experiments on benchmark datasets, including PascalVOC and HAM10000. Our results demonstrate the efficacy of our model in optimizing dictionary configurations across varied classes, which contributes to its effectiveness in medical classification tasks. This performance enhancement is primarily attributed to the integration of CNNs, GNNs, and graph learning techniques, which collectively improve the handling of datasets with limited labeled examples. Specifically, our experiments show that the model achieves a higher accuracy on benchmark datasets such as Pascal VOC and HAM10000 compared to conventional CNN approaches. Cluster analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Image (mathematics) ; Atom (system on chip) Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Phusit Nualpijit | Bumned Soodchomshom
Source: arXiv (Cornell University) (preprint, 2024-02-29)
Field: Materials Science — Materials Chemistry

Triplet fermions in MXenes: The Applications for spintronic-based devices We investigate the electronic properties of MXenes by three bands tight-binding model of \d_{z^2} , \d_{xy} , and \d_{x^2-y^2} orbitals. The three corresponding bands touch each other at high symmetry K point in the case of absence of spin-orbit interaction. The proper parameters can be obtained by Slater-Koster parameters related to chemical bonding, π, σ, and δbonds. The model calculated for these band structures make an agreement with the same trend as discussed in DFT calculation which the hopping parameters may be identified roughly by fermi velocity. Furthermore, the triplet fermion occurs around K point hosting by flat band, leading to super-Klein tunnelling and anti-super-Klein tunnelling for gapped and gapless pseudospin-1 fermion, respectively. These may apply for nanodevices operated by spin polarization which is more stable than that of the conventional two-dimensional materials. MXenes ; Spintronics ; Fermion ; Physics ; Materials science ; Condensed matter physics ; Nanotechnology ; Quantum mechanics ; Ferromagnetism MXene and MAX Phase Materials ; Graphene research and applications ; 2D Materials and Applications
Authors: Avadh K. Shah | Heer Patel | Arjun Kanjarpane | Michael F. Summers | Jan Marchant
Source: ChemRxiv (preprint, 2024-12-11)
Field: Chemistry — Spectroscopy

Relaxation optimized heteronuclear experiments for extending the size limit of RNA nuclear magnetic resonance The application of NMR to large RNAs has been limited by the inability to perform heteronuclear correlation experiments essential for resolving overlapping 1H NMR signals, determining inter-proton distance restraints and inter-helical orientations for structure calculations, and evaluating conformational dynamics. Approaches exploiting 1H-13C correlations that are routinely applied to proteins and small RNAs of ~50 nucleotides or fewer are impractical for larger RNAs due to rapid dipolar relaxation of protons by their attached carbons. Here we report a 2H-enhanced, 1H-15N correlation approach that enables atom-specific NMR characterization of much larger RNAs. Purine H8 transverse relaxation rates are reduced ~20-fold with ribose perdeuteration, enabling efficient magnetization transfer via two-bond 1H-15N couplings. We focus on H8-N9 correlation spectra which benefit from favorable N9 chemical shift anisotropy. Chemical shift assignment is enabled by retention of protons at the C1′ position, which allow measurement of H8-H1′ NOEs and two-bond H1′-N9 correlation strategies with only a minor effect on H8 relaxation. The approach is demonstrated for the 232 nucleotide HIV-1 Rev response element, where chemical shift assignments, 15N-edited nuclear Overhauser effects, and 1H-15N residual dipolar couplings are readily obtained from sensitive, high-resolution spectra. Heteronuclear correlated NMR methods that have been essential for the study of proteins can now be extended to RNAs of at least 78 kDa. Heteronuclear molecule ; Chemistry ; Relaxation (psychology) ; Residual dipolar coupling ; Proton ; Nuclear magnetic resonance ; Chemical shift ; Magnetization transfer ; Nuclear magnetic resonance spectroscopy ; Transverse relaxation-optimized spectroscopy ; Anisotropy ; Rotational correlation time ; Nuclear Overhauser effect ; Chemical physics ; Fluorine-19 NMR ; Molecule ; Physics ; Physical chemistry ; Stereochemistry ; Magnetic resonance imaging Advanced NMR Techniques and Applications ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Fariz Wisda Nugraha | Rifky Ismail | Athanasius Priharyoto Bayuseno | Deni Fajar Fitriyana | Mochammad Ariyanto | Muhammad Hafizh Mubarak | Ilham Fajar Bagaskara
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Biomaterials

Influence of Preheat Temperature Variation on Single Screw Extrusion for Characterizing PLA, PCL, and HA Biocomposite Filaments Fractures and ligament injuries often require bone fixation, commonly through Open Reduction and Internal Fixation (ORIF) with compression plates and screws. Additive manufacturing (AM) techniques are considered sustainable due to their potential to reduce emissions and have a minimal adverse impact on the environment. Currently, there is extensive research into producing biocomposite filaments, though there remains limited exploration of the impact of preheat temperature on filament properties. This research explores the effects of varying preheat temperatures in a single screw extruder to achieve biocomposite filament as candidate for biodegradable bone implants. Biocomposite filaments made from Polylactic Acid (PLA) and Polycaprolactone (PCL) in an 85:15 ratio, with 5% hydroxyapatite (HA) derived from green mussel shell waste. Biocomposite mixture was cut into sub-5mm fragments and extruded at 175°C with preheat temperatures between 159°C and 168°C. Filaments were used to fabricate specimens via a 3D printer, following ASTM D790 standards. Specimens were analyzed using X-Ray Diffractometer, Fourier Transform Infrared, Scanning Electron Microscope, density measurements, three-point bending tests, and biodegradation assessments. Results show that preheat temperature variations significantly affect the filament’s crystallinity index, impacting density and mechanical properties. Higher preheat temperatures result in a lower crystallinity index, decreasing density and potentially increasing degradation rates. Biocomposite ; Extrusion ; Materials science ; Composite material ; Chemical engineering ; Engineering biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Electrospun Nanofibers in Biomedical Applications
Authors: Universidad Autónoma Metropolitana, Av. San Pablo 180, Col. Reynosa-Tamaulipas, 02200, CDMX, México | Santiago Arellano-Mora | Jessica Osorio‐Ramos | Elizabeth Refugio‐García | Eduardo Térres‐Rojas | Laboratorio de Microscopía Electrónica de Ultra Alta Resolución, IMP, Eje Central Lázaro Cárdenas Norte 152, San Bartolo Atepehuacan, 07730, CDMX, México | José G. Miranda‐Hernández | Enrique Rocha‐Rangel | Universidad Politécnica de Victoria, Av. Nuevas Tecnologías 5902, Parque Científico y Tecnológico de Tamaulipas, 87138, Cd. Victoria, Tamaulipas, México
Source: Chemistry & Chemical Technology (article, 2024-03-25)
Field: Materials Science — Ceramics and Composites

Strengthening of Mullite Ceramics with Silver Reinforcements Mullite-based composites reinforced with silver particles were obtained by powder techniques. Composites were sintered after an intense mixing of the precursor powders. It was found that additions of silver have a strong effect on the mechanical properties, since fracture toughness was increased up to 350%. The microstructure of composites presents grains with flakes morphology. Mullite ; Materials science ; Composite material ; Microstructure ; Fracture toughness ; Ceramic ; Mixing (physics) ; Toughness ; Reinforcement Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Advanced materials and composites
Authors: Ivan Morenko | I. Yu. Isaeva | Irina Odinokova | G. Yu. Ostaeva
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Green synthesis of metal nanoparticles and application of nanoadditives in diesel fuel: bibliometric analysis The problem of obtaining metal nanoparticles considering environmental risks in connection with the use of synthesis products in mixtures of new types of diesel fuel has been studied. An analysis of existing research on these topics was carried out. Based on the data sets obtained using the Dimensions and OpenAlex services, the characteristics of the studies were determined. To analyze the problem of green synthesis of nanoparticles, a dataset of 6,062 journal articles published from 2001 to October 2023 was used. The use of nanoparticles as diesel fuel components was investigated using a dataset of 540 journal articles published from 2005 to October 2023. The leading countries and organizations by the number of publications and citations have been identified. Based on the analysis of coauthorship, the issue of international cooperation is considered. The most significant publications were determined by the number of citations. Using methods of visual analysis of data on research keywords, their trends and prospects were determined. In addition, an assessment was made of the development of the problem of using “green” methods for obtaining nanoparticles used as components of diesel fuel. Diesel fuel ; Nanoparticle ; Computer science ; Environmental science ; Process engineering ; Nanotechnology ; Engineering ; Waste management ; Materials science Nanoparticles: synthesis and applications
Authors: Hengxuan Jiang | Wang Xiu | Xingping Zhou
Source: arXiv (Cornell University) (preprint, 2024-09-22)
Field: Chemistry — Spectroscopy

Self-Attention Assistant Classification of Non-Hermitian Phases in Two-Dimensional Lattice Classification of the non-Hermitian phases in high-dimensional lattice becomes challenging due to interplay of the band topology and non-Hermiticity. The significant increase in data dimensions and the number of categories has rendered traditional supervised learning and unsupervised manifold learning failed. Here, we propose the self-attention assistant machine learning for clustering non-Hermitian phases in two-dimensional lattice. By incorporating the self-attention mechanism, the model can effectively capture long-range dependencies and important patterns, resulting in a more compact and information-rich latent space. It can achieve Altland-Zirnbauer classification with Bloch vector dataset and distinguish the phases of eigenstates' localized behavior with the competition between non-Hermitian skin effect and topological localization. Our results provide a general method for characterizing non-Hermitian phases in two-dimensional lattice via machine learning. Hermitian matrix ; Topology (electrical circuits) ; Psychology ; Computer science ; Mathematics ; Pure mathematics ; Combinatorics Molecular spectroscopy and chirality
Authors: A. Tamilmani | K. Muthuramalingam
Source: THE SCIENTIFIC TEMPER (article, 2024-10-16)
Field: Chemistry — Analytical Chemistry

An enhanced support vector machine bbased multiclass classification method for crop prediction Crop type classification is a fundamental task in precision agriculture, enabling informed decision-making for crop management and resource allocation. Support vector machines (SVMs) have emerged as robust and effective tools for multiclass classification tasks. This study explores the application of SVM-based multiclass classification techniques to accurately categorize various crop types based on remote sensing data. The SVM algorithm is employed to create decision boundaries that maximize the margin between different crop classes while minimizing classification errors. To enhance classification performance, various kernel functions such as linear, polynomial, and radial basis functions are evaluated to capture complex relationships within the data. The proposed SVM-based approach is compared with other commonly used classification methods to assess its superiority in terms of accuracy, precision, recall, and F1 score. Support vector machine ; Multiclass classification ; Artificial intelligence ; Computer science ; Machine learning ; Structured support vector machine ; Crop ; Pattern recognition (psychology) ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Advanced Algorithms and Applications