jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guohang Zhuang | Yue Hu | Tianxing Yan | Jiazhang Gao
Source: Research Square (preprint, 2024-03-25)
Field: Chemistry — Analytical Chemistry

GCAM: Gaussian and causal-attention model of food fine-grained recognition <title>Abstract</title> Currently, most food recognition relies on deep learning for category classification. However, these approaches struggle to effectively distinguish between visually similar food samples, highlighting the pressing need to address fine-grained issues in food recognition. To address these issues, we advocate for a Gaussian and causal-attention model specifically designed for nuanced object recognition. This model involves training to capture Gaussian characteristics in targeted areas, followed by extracting detailed features from the objects, thus improving the target regions’ feature mapping capabilities. To counter data drift caused by skewed data distributions, we implement a counterfactual reasoning strategy. Through counterfactual interventions, the effect of the learned image attention mechanism on network predictions is examined, allowing for the optimization of attention weights in detailed image recognition. A learnable loss strategy is also developed to ensure consistent training across various modules, thereby enhancing the precision of the ultimate recognition task. Our method has been validated on four 1 pertinent datasets, where it demonstrated superior performance. Specifically, the Gaussian and Causal-Attention Model (GCAM) has outperformed existing state-of-the-art methods on the ETH-FOOD101, UECFOOD256, and Vireo-FOOD172 datasets and achieved leading results on the CUB-200 dataset. Gaussian ; Causal model ; Computer science ; Econometrics ; Artificial intelligence ; Mathematics ; Statistics ; Chemistry Spectroscopy and Chemometric Analyses ; Nutritional Studies and Diet ; Traditional Chinese Medicine Studies
Authors: George C.-Y. Chan | Gary M. Hieftje | N. Omenetto | Ove Axner | Arne Bengtson | Nicolas H. Bings | Michael W. Blades | Annemie Bogaerts | Mikhail A. Bolshov | J. A. C. Broekaert | Wing-Tat Chan | José M. Costa‐Fernández | Stanley R. Crouch | Alessandro De Giacomo | Alessandro D’Ulivo | Carsten Engelhard | Heinz Falk | Paul B. Farnsworth | Stefan Florek | Gerardo Gamez | Igor B. Gornushkin | Detlef Günther | David W. Hahn | Wei Hang | Volker Hoffmann | Norbert Jakubowski | Vassili Karanassios | David Koppenaal | R. Kenneth Marcus | Reinhard Noll | John W. Olesik | Vincenzo Palleschi | Ulrich Panne | Jorge Pisonero | Steven J. Ray | Martín Resano | Richard E. Russo | Alexander Scheeline | Benjamin W. Smith | Ralph E. Sturgeon | José Luis Todolí Torró | Elisabetta Tognoni | Frank Vanhaecke | Michael R. Webb | J. D. Winefordner | Lu Yang | Jin Yu | Zhanxia Zhang
Source: Applied Spectroscopy (article, 2024-06-16)
Field: Chemistry — Electrochemistry

Landmark Publications in Analytical Atomic Spectrometry: Fundamentals and Instrumentation Development The almost-two-centuries history of spectrochemical analysis has generated a body of literature so vast that it has become nearly intractable for experts, much less for those wishing to enter the field. Authoritative, focused reviews help to address this problem but become so granular that the overall directions of the field are lost. This broader perspective can be provided partially by general overviews but then the thinking, experimental details, theoretical underpinnings, and instrumental innovations of the original work must be sacrificed. In the present compilation, this dilemma is overcome by assembling the most impactful publications in the area of analytical atomic spectrometry. Each entry was proposed by at least one current expert in the field and supported by a narrative that justifies its inclusion. The entries were then assembled into a coherent sequence and returned to contributors for a round-robin review. A total of 48 scientists participated in this endeavor, contributing a combined list of 1055 individual articles spanning 17 sub-disciplines of spectrochemical analysis into what the current community views as "key" publications. Of these cited articles, 60 received nominations from four or more scientists, establishing them as the most indispensable reading materials. The outcome of this collaborative effort is intended to serve as a valuable resource not only for current practitioners in atomic spectroscopy but also for present and future students who represent coming generations of analytical atomic spectroscopists. Dilemma ; Field (mathematics) ; Computer science ; Instrumentation (computer programming) ; Atomic spectroscopy ; Inclusion (mineral) ; Nanotechnology ; Perspective (graphical) ; Narrative review ; Data science ; Engineering ethics ; Epistemology ; Management science ; Sociology ; Engineering ; Physics ; Materials science ; Psychology ; Artificial intelligence ; Social science ; Philosophy ; Spectroscopy Electrochemical Analysis and Applications ; Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development
Authors: Zhou Jian-xi | Congming Dai | Pengfei Wu | Heli Wei
Source: Remote Sensing (article, 2024-09-28)
Field: Chemistry — Spectroscopy

A Fast Computing Model for the Oxygen A-Band High-Spectral-Resolution Absorption Spectra Based on Artificial Neural Networks A fast and accurate radiative transfer model is the prerequisite in the field of atmospheric remote sensing for limb atmospheric inversion to tackle the drawback of slow calculation speed of traditional atmospheric radiative transfer models. This paper established a fast computing model (ANN-HASFCM) for high-spectral-resolution absorption spectra by using artificial neural networks and PCA (principal component analysis) spectral reconstruction technology. This paper chose the line-by-line radiative transfer model (LBLRTM) as the comparative model and simulated training spectral data in the oxygen A-band (12,900–13,200 cm−1). Subsequently, ANN-HASFCM was applied to the retrieval of the atmospheric density profile with the data of the Global Ozone Monitoring by an Occultation of Stars (GOMOS) instrument. The results show that the relative error between the optical depth spectra calculated by LBLRTM and ANN-HASFCM is within 0.03–0.65%. In the process of using the global-fitting algorithm to invert GOMOS-measured atmospheric samples, the inversion results using Fast-LBLRTM and ANN-HASFCM as forward models are consistent, and the retrieval speed of ANN-HASFCM is more than 200 times faster than that of Fast-LBLRTM (reduced from 226.7 s to 0.834 s). The analysis shows the brilliant application prospects of ANN-HASFCM in limb remote sensing. Artificial neural network ; High resolution ; Spectral line ; Absorption spectroscopy ; Absorption (acoustics) ; Computer science ; Remote sensing ; Materials science ; Artificial intelligence ; Physics ; Geology ; Optics ; Astronomy Spectroscopy and Laser Applications ; Water Quality Monitoring and Analysis ; Atmospheric and Environmental Gas Dynamics
Authors: Rohmah Pari | Dyah Ayu Agustiningrum | Danang Sudarwoko Adi | Imran Arra’d Sofianto | Djarwanto | Raden Gunawan Hadi Rahmanto | Ratih Damayanti | Setiowati | Sung‐Wook Hwang | Citra Oktapiani
Source: AIP conference proceedings (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Determination of three Shorea species by near-infrared spectroscopy and anatomical structures Shorea, well known as Meranti, is one of the most important woods in Indonesia. However, identifying the species is still challenging, especially in the timber trade, to prevent misclassification and document falsification. Thus, a non-destructive method is required as a rapid and handy tool in the field. This study aimed to determine three species of Shorea, namely Shorea parvifolia, S. leprosula and S. bracteolata, originating from Indonesia (Sumatra and Kalimantan). The determination of the woods species was used Near-Infrared Spectroscopy (NIRS), and then validated by their anatomical characteristics. For NIRS, the multivariate analysis, namely Principal Component Analysis (PCA) and a pre-treatment Savitzky-Golay (SG) filter were used To collect the NIR spectral signal at wavenumbers of 8000-4000 cm−1. Further, the data was classified using the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers. The results showed that the accuracy of both algorithms was approximately 67%, which is relatively low. k-NN was able to differentiate S. parvifolia with high accuracy but cannot separate S. leprosula and S. bracteolata. While SVM approaches better separation for S. leprosula and S. bracteolata, even though the matrix score was 50:50. The SVM is probably better at classifying because it does not require complicated data processing like k-NN. The observation of anatomical structures revealed differences between Shorea species. Crystals presented in S. parvifolia and S. leprosula; however, radial canals were observed only in S. leprosula. Crystals were absent in S. bracteolata, but silica bodies were found in rays parenchyma. The observation of microscopic structures can differentiate three species of Meranti wood up to the species level, but this method takes time and requires specific knowledge. Thus, NIRS is feasible as an alternative for rapid wood identification. Shorea robusta ; Spectroscopy ; Infrared spectroscopy ; Infrared ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Optics ; Chemistry ; Botany ; Physics ; Biology ; Environmental chemistry ; Astronomy ; Organic chemistry Spectroscopy and Chemometric Analyses ; Phytochemistry and Biological Activities ; Natural product bioactivities and synthesis
Authors: Wiesław Frącz | Andrzej Pacana | Dominika Siwiec | Grzegorz Janowski | Łukasz Bąk | Paulina Szawara
Source: Materials (article, 2024-06-17)
Field: Materials Science — Biomaterials

Wood Polymer Composite Based on Poly-3-hydroxybutyrate-3-hydroxyvalerate (PHBV) and Wood Flour—The Process Optimization of the Products This study involved the optimization of the molded pieces manufacturing process from a poly-3-hydroxybutyrate-co-3-hydroxyvalerate biocomposite containing 30% wood flour by mass. The amount of wood flour and preliminary processing parameters were determined on the basis of preliminary tests. The aim of the optimization was to find the configuration of important parameters of the injection process to obtain molded pieces of good quality, in terms of aesthetics, dimensions, and mechanical properties. The products tested for quality were dog bone specimens. The biocomposite was produced using a single-screw extruder, whereas molded pieces were made using an injection molding process. The Taguchi method was applied to optimize the injection molding parameters, which determine the products quality. Control factors were selected at three levels. The L27 orthogonal plan was used. For each set of input parameters from this plan, four processing tests were performed. The sample weight, shrinkage, elongation at break, tensile strength, and Young's modulus were selected to assess the quality of the molded parts. As a result of the research, the processing parameters of the tested biocomposite were determined, enabling the production of good-quality molded pieces. No common parameter configuration was found for different optimization criteria. Further research should focus on finding a different range of technological parameters. At the same time, it was found that the range of processing parameters of the produced biocomposite, especially processing temperature, made it possible to use it in the Wood Polymer Composites segment. Wood flour ; Composite number ; Materials science ; Composite material ; Pulp and paper industry ; Polymer science biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites ; Injection Molding Process and Properties
Authors: Aritra Chakraborty | Aaron A. Kohnert | Abigail Hunter | Laurent Capolungo
Source: Journal of Materials Science Materials Theory (article, 2024-02-27)
Field: Materials Science — Materials Chemistry

Role of interfaces on the mechanical response of accumulative roll bonded nanometallic laminates investigated via dislocation dynamics simulations Abstract Unraveling the effects of continuous dislocation interactions with interfaces, particularly at the nanometer length scales, is key to a broader understanding of plasticity, to material design and to material certification. To this end, this work proposes a novel discrete dislocation dynamics-based model for dislocation interface interactions tracking the fate of residual dislocation on interfaces. This new approach is used to predict the impact of dislocation/interface reactions on the overall mechanical behavior of accumulative roll bonded nanometallic laminates. The framework considers the dynamic evolution of the interface concurrent with a large network of dislocations, thus, accounting for the local short and long range effects of the dislocations under the external boundary conditions. Specifically, this study focuses on two-phase Fe/Cu nanometallic laminates, and investigates the role of the underlying elastic and plastic contrast of the Fe and the Cu layers on the composite response of the material. Moreover, the role of initial microstructures, resulting from processing is also investigated. Subsequently, the model is used to examine the effect of layer thickness and interface orientation relationship on the residual stresses of the relaxed microstructure. The associated mechanical response of these laminates are compared when loaded under normal direction compression, as well as shear compression. Finally, this work predicts a dominant effect of the layer thickness, as compared to the interface orientation relationship, on the macroscopic response and on the residual stresses of these nanolaminates, while the local dislocation transmission propensity through the interface is significantly influenced by the corresponding orientation relationship. Materials science ; Dislocation ; Composite material ; Microstructure ; Plasticity ; Work (physics) ; Compression (physics) ; Mechanical engineering Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Robert J. Meier
Source: Molecules (article, 2024-02-18)
Field: Chemistry — Organic Chemistry

Comment on Naef, R.; Acree, W.E., Jr. Calculation of the Three Partition Coefficients logPow, logKoa and logKaw of Organic Molecules at Standard Conditions at Once by Means of a Generally Applicable Group Additivity Method. Preprints 2023, 2023120275 Next to the paper referred to in the title [...]. Partition (number theory) ; Mathematics ; Partition coefficient ; Combinatorics ; Chemistry ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Chemical and Physical Properties in Aqueous Solutions ; Radioactive element chemistry and processing
Authors: Yuan‐Chen Wang | Jianyang Liu | Jianrong Liu | Wenyuan Li | Bin Zhang | Guangping Zhang
Source: Materials (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

Effects of Thermal Exposure Temperature on Room-Temperature Tensile Properties of Ti65 Alloy As a critical material for high-temperature components of aero-engines, the mechanical properties of Ti65 alloy, subjected to high-temperature and long-term thermal exposure, directly affect its service safety. The room-temperature tensile properties of the Ti65 alloy after thermal exposure to temperatures ranging from 450 °C to 650 °C for 100 h were investigated. The results indicate that as the thermal exposure temperature increases, the strength of Ti65 alloy initially increases and then decreases, while ductility exhibits a decreasing trend. The strength of the thermally exposed alloy positively correlates with the size and content of the <i>α</i><sub>2</sub> phase. The ductility of the thermally exposed alloy is comprehensively influenced by the surface oxidation behavior, <i>α</i><sub>2</sub> phase, and silicides. After the prolonged thermal exposure, stress concentration at the crack tips within the oxide layer was enhanced with the increased thickness of the surface TiO<sub>2</sub> oxide layer, leading to premature fracture due to reduced alloy ductility. Furthermore, the <i>α</i><sub>2</sub> phase in the matrix promotes the planar slip of dislocations, while silicides at the <i>α</i>/<i>β</i> phase boundaries hinder dislocation motion, causing dislocation pile-ups. Both behaviors facilitate crack nucleation and deteriorate alloy ductility. Materials science ; Alloy ; Ductility (Earth science) ; Ultimate tensile strength ; Composite material ; Nucleation ; Slip (aerodynamics) ; Oxide ; Dislocation ; Phase (matter) ; Metallurgy ; Creep Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties ; Intermetallics and Advanced Alloy Properties
Authors: Yizhuo Zhang | Guanlong Wu | Shen Shi | Huiling Yu
Source: Information (article, 2024-12-16)
Field: Chemistry — Organic Chemistry

WTSM-SiameseNet: A Wood-Texture-Similarity-Matching Method Based on Siamese Networks In tasks such as wood defect repair and the production of high-end wooden furniture, ensuring the consistency of the texture in repaired or jointed areas is crucial. This paper proposes the WTSM-SiameseNet model for wood-texture-similarity matching and introduces several improvements to address the issues present in traditional methods. First, to address the issue that fixed receptive fields cannot adapt to textures of different sizes, a multi-receptive field fusion feature extraction network was designed. This allows the model to autonomously select the optimal receptive field, enhancing its flexibility and accuracy when handling wood textures at different scales. Secondly, the interdependencies between layers in traditional serial attention mechanisms limit performance. To address this, a concurrent attention mechanism was designed, which reduces interlayer interference by using a dual-stream parallel structure that enhances the ability to capture features. Furthermore, to overcome the issues of existing feature fusion methods that disrupt spatial structure and lack interpretability, this study proposes a feature fusion method based on feature correlation. This approach not only preserves the spatial structure of texture features but also improves the interpretability and stability of the fused features and the model. Finally, by introducing depthwise separable convolutions, the issue of a large number of model parameters is addressed, significantly improving training efficiency while maintaining model performance. Experiments were conducted using a wood texture similarity dataset consisting of 7588 image pairs. The results show that WTSM-SiameseNet achieved an accuracy of 96.67% on the test set, representing a 12.91% improvement in accuracy and a 14.21% improvement in precision compared to the pre-improved SiameseNet. Compared to CS-SiameseNet, accuracy increased by 2.86%, and precision improved by 6.58%. Similarity (geometry) ; Matching (statistics) ; Texture (cosmology) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Computer vision ; Mathematics ; Image (mathematics) ; Statistics Wood and Agarwood Research ; Advanced Image and Video Retrieval Techniques ; Remote Sensing and LiDAR Applications
Authors: Hui Wang | Jianqing Shi | Wanhui Guo | Xiaohui Sun | Shuhui Niu | Li Chen | Shenghong Liu | Lei Ma
Source: Frontiers in Genetics (article, 2024-07-29)
Field: Materials Science — Materials Chemistry

The identification and expression analysis of walnut Acyl-ACP thioesterases Walnuts (<i>Juglans regia</i> L.), renowned for their nutritional potency, are a rich source of unsaturated fatty acids. Their regular intake plays a pivotal role in health maintenance and recuperation from a myriad of ailments. Fatty acyl-acyl carrier protein thioesterases, which orchestrate the hydrolysis of acyl-ACP thioester bonds, thereby yielding fatty acids of varying chain lengths, are instrumental in augmenting plant fatty acid content and modulating the balance between saturated and unsaturated fatty acids. Despite some investigative efforts into the synthesis and metabolic pathways of fatty acids in walnuts, our comprehension of Fat in walnuts remains rudimentary. This research undertook a comprehensive characterization of the JrFat family, predicated on the complete genome sequence of walnuts, leading to the identification of 8 JrFat genes and an exploration of their protein physicochemical properties. Utilizing <i>Arabidopsis</i> and soybean Fat genes as outgroups, JrFat genes can be categorized into 5 distinct subgroups, three of which encompass a pair of homologous gene pairs. These genes have demonstrated remarkable conservation throughout the evolutionary process, with highly analogous conserved base sequences. The promoter region of JrFats genes predominantly harbors light response and plant hormone response regulatory elements, with no discernible disparity in promoter elements among different JrFats. Predictive analyses indicate that JrFats proteins engage extensively with walnut fatty acid synthesis and metabolism-associated proteins. qRT-PCR analysis reveals an initial surge in the expression of JrFats during the development of walnut kernels, which either stabilizes or diminishes following the hard core period. Homologous gene pairs exhibit analogous expression patterns, and the expression trajectory of JrFats aligns with the dynamic accumulation of fatty acids in kernels. The expression of <i>JrFatA2</i> exhibits a strong correlation with the content of Alpha-linolenic acid, while the expression of <i>JrFatB2</i> is inversely correlated with the content of two saturated fatty acids. Collectively, these findings enrich our understanding of fatty acid synthesis and metabolism in walnuts and furnish gene resources for enhancing the content and ratio of fatty acids in walnuts. Identification (biology) ; Biology ; Computational biology ; Genetics ; Biotechnology ; Botany Enzyme Structure and Function ; Plant biochemistry and biosynthesis ; Photosynthetic Processes and Mechanisms
Authors: Hamza Sofiane Meddas | Muftah Zorgani | Majid Heidari | Mousa Javidani | Tom Levasseur | Mohammad Jahazi
Source: Materials (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Enhanced Durability of Wood Cutting Tools through Thermal Cycling This study investigates the impact of multi-step austenitization heat treatment on the in-service life of modified AISI A8 cold work tool steel knives used in wood cutting. The knives were subjected to two treatment methods: single quenching and double tempering (SQDT) and double quenching and double tempering (DQDT). Both treatments were followed by physical vapor deposition (PVD) coating to enhance surface properties. The DQDT treatment resulted in a finer microstructure and more uniform carbide distribution. Field tests on 24 knives over 124 h demonstrated up to 130% improvement in wear resistance for DQDT knives, along with superior edge stability and better PVD coating preservation. DQDT knives exhibited ductile fractures characterized by dimples, contrasting with the brittle fracture and cleavage facets in SQDT knives. Residual stress measurements showed higher compressive stresses in DQDT knives (-280 MPa) compared to SQDT knives (-30 MPa), which increased further after field testing. The enhanced performance of DQDT knives is attributed to their refined microstructure, improved carbide distribution, and higher compressive residual stresses, offering significant potential for improving wood cutting tool efficiency and durability. Durability ; Temperature cycling ; Cycling ; Materials science ; Composite material ; Thermal ; Forensic engineering ; Engineering Metal Alloys Wear and Properties ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies