jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peng Zhao | Zhenyu Li | Yue Li
Source: Journal of Spectroscopy (article, 2019-11-15)
Field: Chemistry — Organic Chemistry

An Incremental Self-Adaptive Wood Species Classification Prototype System The present wood species classification systems can usually process the limited wood species quantity. We propose a novel incremental self-adaptive wood species classification system to solve the above-mentioned issue. The visible/near-infrared (VIS/NIR) spectrometer is used to pick up the spectral curves of wood samples for the subsequent wood species classification. First, when new wood samples of unknown wood species are added, they are classified as an unknown category by our one-class classifier, Support Vector Data Description (SVDD), while the existent wood species are classified as a known category by the SVDD. Second, the wood samples of known species are sent into the BP neural network for subsequent wood species classification. Third, the new wood samples of unknown species are sent into the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm for the unsupervised clustering, and the clustering result is evaluated by the internal and external norms. Last, if one cluster of one unknown species has an adequate amount of wood samples, these wood samples are removed and identified by human experts or other schemes to ensure to get the correct wood species name. Then, these wood samples are considered as a new known species and are sent into the classifiers, SVDD and BP neural network, to train them again. Experiments on 13 wood species prove the effectiveness of our prototype system with an overall classification accuracy of above 95%. Cluster analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Classifier (UML) ; Artificial neural network ; Support vector machine ; Computer science ; Mathematics Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis
Authors: Guangyu Shi | Yizhuo Zhang | Yinong Zhao | Chao Li | Jun Cao
Source: BioResources (article, 2019-10-23)
Field: Chemistry — Analytical Chemistry

Prediction methodology for wood compression strength based on particle-filtered near-infrared spectroscopy The measurement of wood mechanical properties is important for engineering design and applications. This study investigated near-infrared (NIR) spectroscopy coupled with particle filter (PF) and partial least-squares (PLS) methods to predict wood compression strength. Three structural timbers (Acer mono, birch, and toothed oak) were studied. The NIR spectra were collected from 900 to 1700 cm-1 and preprocessed by a standard normal variate transformation combined with Savitzky-Golay filtering. The prediction model coefficient matrix and standard variance were obtained by a PF iterative process, and their ratio was used to select the NIR feature wavelength points. A PLS prediction model based on NIR spectroscopy was established to predict the wood compression strength. Compared with the successive projection algorithm (SPA) and Kalman filtering (KF), the PF-PLS prediction model outperformed the other models in all three wood samples, resulting in a high correlation coefficient (r) of 0.89, 0.92, and 0.90, a low root-mean-square error of prediction (RMSEP) of 6.30, 10.60, and 9.71, and a fast average detection speed of 0.28 s, 0.46 s, and 0.33 s, respectively. The optimal PF selection can effectively reduce the redundant information of the NIR matrix and improve the accuracy and efficiency of the prediction model. Materials science ; Correlation coefficient ; Mean squared error ; Near-infrared spectroscopy ; Coefficient of determination ; Partial least squares regression ; Spectroscopy ; Analytical Chemistry (journal) ; Biological system ; Mathematics ; Algorithm ; Statistics ; Optics ; Chromatography ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Wood Treatment and Properties ; Wood and Agarwood Research
Authors: S.І. Kuchuk-Yatsenko | V.S. Havrysh | P.M. Rudenko | A.O. Nakonechny | S. A. Shevchuk | M.S. Zavertanny
Source: Science and innovation (article, 2019-03-17)
Field: Materials Science — Materials Chemistry

Development of Resistance Butt Welding Technology and Prototype Equipment for Joining Carbide Bar Chain Ripper Teeth of VK-8 Material Problem Statement. The ripper teeth manufacturing from the hard alloys of the tungsten-cobalt group VK8 and steel 40X is an important and urgent task for improving of the bar chain operational reliability. In world practice, the soldering technology is used in an inductor or a gas flame with silver-based solders for manufacturing the teeth. However, this process is characterized by a low productivity and a high cost. Welding ; Bar (unit) ; Carbide ; Materials science ; Chain (unit) ; Metallurgy ; Composite material ; Engineering ; Physics Metal Alloys Wear and Properties ; Engineering Applied Research ; Welding Techniques and Residual Stresses
Authors: Johannes Anastasiadis | Wolfgang Krippner | Fernando Puente León
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Spatially resolved ingredient detection in spice mixes using 3D convolutional neural networks Abstract A method using spectral information to detect substances in mixtures is given. The presented convolutional neural network is using three-dimensional convolutions to process hyperspectral images. Reflectance values can be fed directly into the network and are not preprocessed. Due to the architecture, the neural network performs a spatially invariant operation. Detection performance is demonstrated by a dataset containing spice mixtures. Spice ; Convolutional neural network ; Hyperspectral imaging ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Artificial neural network ; Reflectivity ; Invariant (physics) ; Mathematics ; Electronic engineering ; Engineering ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mohammad Ali Zeeshan | Shouket Ali Adnan | Sobia Faisal
Source: Journal of Applied and Emerging Sciences (article, 2019-07-10)
Field: Materials Science — Polymers and Plastics

Optimization of Process Parameters to Control Yarn Packing Density of Cotton Spun Yarn by Using Multiple Regression Models Ring spinning frame is the most common and widely used technique for the production of yarn. This yarn subsequently utilized for the production of fabrics. Recently the research studies are more emphasis to create the living comfort condition. The yarn package density has a vital role to determine the feel, comfort, bulkiness and dyeing characteristics of the spun yarn. The yarn packaging density is largely influenced by the yarn process parameters. Ring spinning technique is the most common and widely used for the production of yarn. This yarn subsequently utilized for the production of fabrics. Recently the research studies are more emphasis to create the living comfort condition. The yarn package density is a vital role to determine the feel, comfort, bulkiness and dyeing characteristics of the spun yarn. It is largely influenced by the yarn manufacturing techniques and raw material used. The effects of spinning process parameters on packing density will be helpful to predetermine the yarn comfort behaviour prior to the complete the fabric manufacturing process. Mechanical behaviour of the staple yarn depends on the fiber characteristics and the yarn structure i.e. the arrangement of the individual fiber on the cross-section of the yarn. The fiber distribution on the cross section of the yarn and packing density of yarn has been investigated. The study provides how the spinning process parameters affect the internal structure of ring spun yarn. The aim of the study is to improve the package density of the yarn to achieve dynamometric and comfort properties of yarn to attract a wide variety of customers. Yarn Packing Density represents inter-fiber distance and friction, hence controlling the mechanical and comfort properties of the yarn. The main objective of this study is to control the packing density of yarn during the spinning process. In this work the 100 percent cotton used as raw material and spun on a ring spinning machine. The 135 ring yarns samples have been produced from three different types of yarn counts include (Ne 8, 16 and 24) with nine different samples of identical counts were produced. The three different variables within the count sample are TPI and spindle speed and count. Yarn packing density was calculated by using the driven equation by the application of multiple regression models. The final result shows that packing density has directly related to the TPI, Spindle speed and count Yarn ; Spinning ; Dyeing ; Linear density ; Composite material ; Fiber ; Materials science Textile materials and evaluations
Authors: Abbas Al‐Refaie | Rami H. Fouad | Raed Al Athamneh
Source: Modern Applied Science (article, 2019-01-17)
Field: Chemistry — Analytical Chemistry

Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance Thus far, the Taguchi technique is found only efficient in obtaining the combination of optimal factor settings when a single product/process response is considered. In today&amp;rsquo;s dynamic environment, customers are interested in multiple quality responses. This research, therefore, utilizes fuzzy logic and backward-propagation neural networks (BPNNs) to optimize process performance for products of multiple quality responses. In this research, quality characteristics are transformed to signal to noise (S/N) ratios, which are then used as inputs to a fuzzy model to obtain a single common output measure (COM). Next, BPNNs are employed to obtain full-factorial experimental data. Finally, the combination of factor levels that maximizes the average COM value is chosen as the optimal combination. Three case studies are provided for illustration; in all of which the proposed approach provided the largest total anticipated improvement. This indicates that the proposed approach is more efficient than Taguchi-fuzzy, grey-Taguchi, and Taguchi-utility methods. In conclusion, the fuzzy-BPNN approach may greatly assist process/product engineers in optimizing performance with multiple responses in a wide range of business applications. Taguchi methods ; Fuzzy logic ; Computer science ; Artificial neural network ; Process (computing) ; Quality (philosophy) ; Product (mathematics) ; Backpropagation ; Range (aeronautics) ; Data mining ; Machine learning ; Mathematical optimization ; Artificial intelligence ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Optimal Experimental Design Methods
Authors: Yu KURASHIGE | Kazunari FUJIYAMA
Source: Transactions of the JSME (in Japanese) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Development of creep damage AI evaluation system for austenitic stainless steel An artificial intelligence evaluation system using neural network was developed for upgrading the creep damage assessment methodology through image analysis of EBSD(Electron BackScateer Diffraction pattern) maps. KAM(Kernel Average Misorientation) maps were obtained for creep damaged austenitic stainless steel SUS 304HTB and the stratified data were manipulated as the representatives of damage degrees. The system consists of an input layer, intermediate layers and an output layer. As the activation function, ReLU(Rectified Linear Unit) function is used for the intermediate layers and Softmax function is used for the output layer. The evaluation results of the proposed system were compared with the results of the conventional quantitative damage evaluation method. As a result, the estimated damage accuracy of the artificial intelligence evaluation system developed in this research was proved to be improved by about 3.3% compared with the estimated damage accuracy using the conventional evaluation method. Furthermore, the accuracy was improved by about 6.7% after the optimization of the neural network compared with the conventional evaluation method. Moreover, it was proved this system had sufficient robustness through the check tests for the case of extremely missing EBSD image. Thus machine learning utilizing neural network was expected to be a potential method for versatile data analysis applicable to various sort of metallographic study. Creep ; Metallurgy ; Austenite ; Materials science ; Austenitic stainless steel ; Corrosion ; Microstructure Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Fatigue and fracture mechanics
Authors: Nicholas Howie | Samuel Rabey
Source: PAM Review Energy Science & Technology (article, 2019-05-24)
Field: Materials Science — Polymers and Plastics

A Meta-Analysis on the Advancement on the thermodynamic properties of clothing in extreme cold environments When two systems with different energies are in contact, the heat from the higher energy system will move into the lower energy system and the two will reach equilibrium. Humans in extreme cold environments will perish if they do not keep appropriate heat contained within their system and thus it is the object of much historic research to maintain heat within a system for as long as possible. Research and development of cold weather clothing focuses on a range of methods regulating heat flow between clothing layers. Modern research focuses on air gaps between layers of clothing, development of new conventional insulating textiles and contemporary solutions such as the use of Phases Change [1]. The purpose of this paper is to conglomerate all of the current research into one meta-analysis highlighting the gaps in the research and potential areas in need of further study, and to propose a new article of cold weather apparel utilizing the most effective advancements from the papers collected in this study. It was found that each component of cold climate clothing affects an aspect of thermal resistivity. Thickness affects the windchill resistance, the specific heat increases thermal resistance of the fabric, while humidity increases thermal conductivity, air gaps reduce it and the rigidity affects all of these factors. Our findings suggest if the air gaps are above 8 mm, natural convection currents can occur which increase the thermal and moisture transfer between clothing layers. By analysing all of these factors, a new prototype garment was able to be proposed.&#x0D; Keywords: Cold environment; clothing; thermoregulation. Clothing ; Heat transfer ; Cold weather ; Thermal resistance ; Thermal conductivity ; Environmental science ; Architectural engineering ; Airflow ; Thermal ; Mechanical engineering ; Meteorology ; Engineering physics ; Materials science ; Engineering ; Mechanics ; Composite material ; Geography Textile materials and evaluations ; Thermoregulation and physiological responses ; Infrared Thermography in Medicine
Authors: Hugo Krynauw | Jannik Buescher | Josepha Koehne | Loes Verrijt | Georges Limbert | Neil H. Davies | Deon Bezuidenhout | Thomas Franz
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-09-24)
Field: Materials Science — Biomaterials

Tissue ingrowth markedly reduces mechanical anisotropy and stiffness in fibre direction of highly aligned electrospun polyurethane scaffolds Abstract Purpose The lack of long-term patency of synthetic vascular grafts currently available on the market has directed research towards improving the performance of small diameter grafts. Improved radial compliance matching and tissue ingrowth into the graft scaffold are amongst the main goals for an ideal vascular graft. Methods Biostable polyurethane scaffolds were manufactured by electrospinning and implanted in subcutaneous and circulatory positions in the rat for 7, 14 and 28 days. Scaffold morphology, tissue ingrowth, and mechanical properties of the scaffolds were assessed before implantation and after retrieval. Results Tissue ingrowth after 24 days was 96.5 ± 2.3% in the subcutaneous implants and 77.8 ± 5.4% in the circulatory implants. Over the 24 days implantation, the elastic modulus at 12% strain decreased by 59% in direction of the fibre alignment whereas it increased by 1379% transverse to the fibre alignment of the highly aligned scaffold of the subcutaneous implants. The lesser aligned scaffold of the circulatory graft implants exhibited an increase of the elastic modulus at 12% strain by 77% in circumferential direction. Conclusion Based on the observations, it is proposed that the mechanism underlying the softening of the highly aligned scaffold in the predominant fibre direction is associated with scaffold compaction and local displacement of fibres by the newly formed tissue. The stiffening of the scaffold, observed transverse to highly aligned fibres and for more a random fibre distribution, represents the actual mechanical contribution of the tissue that developed in the scaffold. Scaffold ; Materials science ; Biomedical engineering ; Electrospinning ; Composite material ; Stiffness ; Tissue engineering ; Modulus ; Polymer ; Medicine Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Conducting polymers and applications
Authors: Constantinos Salis | Eleni Papakonstantinou | Katerina Pierouli | Athanasios Mitsis | Lia Basdeki | Vasileios Megalooikonomou | Dimitriοs Vlachakis | Marianna Hagidimitriou
Source: EMBnet journal (article, 2019-05-22)
Field: Chemistry — Organic Chemistry

A genomic data mining pipeline for 15 species of the genus Olea In the big data era, conventional bioinformatics seems to fail in managing the full extent of the available genomic information. The current study is focused on olive tree species and the collection and analysis of genetic and genomic data, which are fragmented in various depositories. Extra virgin olive oil is classified as a medical food, due to nutraceutical benefits and its protective properties against cancer, cardiovascular diseases, age-related diseases, neurodegenerative disorders, and many other diseases. Extensive studies have reported the benefits of olive oil on human health. However, available data at the nucleotide sequence level are highly unstructured. Towards this aim, we describe an <i>in-silico</i> approach that combines methods from data mining and machine learning pipelines to ontology classification and semantic annotation. Fusing and analysing all available olive tree data is a step of uttermost importance in classifying and characterising the various cultivars, towards a comprehensive approach under the context of food safety and public health. Context (archaeology) ; Pipeline (software) ; Annotation ; Genomics ; Tree (set theory) ; Big data ; Computer science ; Data science ; Data mining ; Computational biology ; Biology ; Bioinformatics ; Artificial intelligence ; Gene ; Genetics ; Genome ; Mathematics Edible Oils Quality and Analysis ; Identification and Quantification in Food ; Nutritional Studies and Diet
Authors: Amine Hadj Taieb | Slah Msahli | Faouzi Sakli
Source: Journal of Textile Engineering & Fashion Technology (article, 2019-04-04)
Field: Materials Science — Polymers and Plastics

A new approach for optimizing water resistance and air permeability of denim cotton fabric The quality of the denim fabric is a phenomenon that requires successfully meeting several properties at the same time; among these properties are air permeability and water resistance. Very little study has been carried out on multiple properties and parameters at the same time. In this study, we used Response Surface Methodology to optimize levels of thickness and optimum and levels of fabric mass per unit area which simultaneously maximize water resistance and minimize air permeability of denim cotton fabric. Denim ; Air permeability specific surface ; Permeability (electromagnetism) ; Water resistance ; Resistance (ecology) ; Pulp and paper industry ; Environmental science ; Composite material ; Materials science ; Engineering ; Chemistry ; Agronomy ; Biology Textile materials and evaluations
Authors: Denas Andrijauskis | Ieva Keturkaitė
Source: Lithuanian University of Health Sciences (review, 2019-01-01)
Field: Materials Science — Materials Chemistry

Adverse effects of gadolinium-based contrast agents in organ systems: a literature review Background and aim: Gadolinium based contrast agents are widely used materials in magnetic resonance imaging (MRI) diagnostic procedures. Gadolinium based contrast agents (GBCAs) are classified by structure to linear or macrocyclic. It is suspected that the difference of the structure may be responsible for the mechanism of side effects after MRI. The aim of this article is to assess the side effects of MRI contrast agents in various organ systems. Materials and methods: The study was conducted by an electronic database subscription of Lithuanian University of Health Sciences: PubMed (Medline), EMBASE, ClinicalKey, ScienceDirect. We tried to review and summarize the published literature of GBCAs effect in various organ systems. Our research also carried out a forward citation and bibliographic search of identifying studies regarding nephrogenic systemic fibrosis (NSF), gadolinium deposition, gadolinium retention, gadolinium based contrast agents in the brain. Results: There are evidence that linear GBCAs affect the body more than their macrocyclic structure. GBCAs cause uncommon conditions such as deposits in brain and NSF. NSF firstly affects skin and spreads to liver, bones, lungs and other organs. Conclusions: Gadolinium based contrast agents should be under strict regulation just like other pharmaceuticals for their adverse effects and damage of organs structures. Therefore, these agents should be strictly monitored and reviewed case by case before administration to patients to avoid gadolinium accumulation in tissues and possible diseases. Gadolinium ; Contrast (vision) ; Medicine ; Adverse effect ; Intensive care medicine ; Computer science ; Internal medicine ; Chemistry ; Artificial intelligence Lanthanide and Transition Metal Complexes ; Cerebrovascular and Carotid Artery Diseases ; Intracranial Aneurysms: Treatment and Complications
Authors: Andrei Ionuţ Simion | Cristina-Gabriela Grigoraș | Lidia Favier | Lucian Gavrilă
Source: Studia Universitatis Babeș-Bolyai Chemia (article, 2019-06-03)
Field: Chemistry — Analytical Chemistry

MATHEMATICAL MODELLING AND PREDICTION OF CONGO RED ADSORPTION ON CHERRY STONES ACTIVATED CARBON The present paper was aimed to establish mathematical models useful to reduce the time required to discover the appropriate adsorption conditions of Congo Red (an intensively used organic dye) on an activated carbon prepared from cherry stones through calcination. To this purpose, various values of three parameters known as influencing the process, namely dye initial concentration (200 mg/L to 1000 mg/L), pH (2 to 12) and contact time (10 to 180 minutes) between the adsorbent and the adsorbate were variated. The recorded results of the adsorption process were used as data for Response Surface Methodology and Artificial Neural Network and several mathematical equations were generated. The conducted statistical analyses revealed that these equations can accurately express the Congo Red elimination from aqueous solutions. Moreover, the developed procedure is able to predict the process evolution in different conditions than those experimentally tested. Congo red ; Adsorption ; Activated carbon ; Calcination ; Aqueous solution ; Process (computing) ; Chemistry ; Carbon fibers ; Chemical engineering ; Materials science ; Chromatography ; Organic chemistry ; Computer science ; Engineering ; Catalysis ; Composite material ; Composite number Dye analysis and toxicity ; Water Quality Monitoring and Analysis