jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Erdem Alıç | Mehmet Daş
Source: Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi (article, 2019-11-29)
Field: Materials Science — Polymers and Plastics

GÜNEŞ ENERJİSİ DESTEKLİ KURUTMA SİSTEMİNDE ÜRÜN NEM ORANININ HESAPLAMALI AKIŞKANLAR DİNAMİĞİ ANALİZİ Bu çalışmada hava ısıtmalı güneş kollektörlü bir gıda kurutma sistemi tasarlanmıştır. Tasarlanan kurutma sisteminde elma kurutulmuştur. Kurutulan elma dilimlerinin nem içeriği (MCk), kuruma hızı (DR) ve nem oranı (MR) değerleri hesaplanmıştır. Deneylerde kurutulan elma dilimlerinin dehidrasyonu sayısal analiz programı ile simüle edilmiştir. Kurutma analizi için birebir ölçülerde model oluşturulmuştur. İlk durumda elma diliminin çapı 60mm, kalınlığı 14mm ve yüzey sıcaklığı 4°C dir. Kurutma havasının giriş sıcaklığı 56.4°C ve hava hızı 1.6m/s dir. Analiz sonucunda ele edilen kuruma hızı ve zamanı, nem içeriği ve sıcaklık değişimi verileri mevcut deneysel çalışmalar ile karşılaştırılmıştır. Simülasyon sonucu elde edilen değerler ile deneysel çalışmalar sonucu hesaplanan değerler benzerlik göstermiştir. Physics ; Molecular biology ; Biology Textile materials and evaluations ; Food Drying and Modeling ; Natural Fiber Reinforced Composites
Authors: Amal El Berry
Source: Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering (article, 2019-08-01)
Field: Chemistry — Spectroscopy

Novel Techniques for Saving Energy Consumption in Air Conditioning Systems Electrical energy consumption in air conditioning systems (ACS) is a sizeable portion of global electrical energy consumption. This means that it has a considerable share of global warming, which needs to be reduced. This article reviews the causes of excessive ACS energy consumption as well as modern trends in management and control of ACS energy consumption. Energy demand and consumption are high for air conditioning, especially in humid and hot weather. Opportunities for saving energy in cooling and heating systems depend mainly on reducing loads. Reducing the thermal loads can be achieved through new techniques Brief informational review of some of the most used ACS specifies cooling and heating loads and new techniques to reduce its energy consumption. Performance of ACSs for various types is mostly identified as the ratio of the active or beneficial output to the requisite total input of the equipment. The target to decrease the energy consumption of electrical systems is an essential step to define the components and equipment that are used to build the system and select the types that are considered as an energy saver. After ending this essential step we can concentrate on selecting and improving the overall system performance. Hence, this article provides review information of the most ACSs and specify its new techniques of energy consumption reduction. Air conditioning ; Energy consumption ; Computer science ; Energy (signal processing) ; Consumption (sociology) ; Central air conditioning ; Conditioning ; Automotive engineering ; Electrical engineering ; Engineering ; Mechanical engineering ; Physics Aerogels and thermal insulation ; Building Energy and Comfort Optimization ; Refrigeration and Air Conditioning Technologies
Authors: Ángelo Joseph Soto Vergel | Luís Enrique Mendoza | Byron Medina Delgado
Source: Respuestas (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Analysis of energy and major components in chromatographic signals for the diagnosis of prostate cancer The prostate exam is an early detection tool to prevent prostate cancer and the main diagnostic tools for obtaining signs are generally invasive. This article tries chromatographic signals from the urine of prostate cancer patients and control patients as a non-invasive examination proposal. For this purpose, methodologically, urine samples are taken, digitized in chromatograms, treated with mathematical techniques and classified. The mathematical techniques are time normalization, dead time elimination, baseline correction, noise elimination, and peak alignment. Classification techniques analyze energy, in the domain of time and frequency, and the main components in sedimentation graphs and scores. As a result, the chromatographic signal is characterized and identifies the characteristic curve that represents the signal of prostate cancer patients and control patients. The data structure shows a cluster distribution of 88.88% of the vectors for the control patients. In the case of prostate cancer patients, the distribution of data is in clusters around the area defined by control patients. This characterization demarcates signal classification regions to diagnose possible prostate cancer patients, validating the relationship between the chromatographic signal and cancer. Prostate cancer ; Normalization (sociology) ; Prostate ; SIGNAL (programming language) ; Cancer ; Medicine ; Urine ; Urology ; Computer science ; Internal medicine Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models
Authors: Sharaf Boboev
Source: Transaction of Scientific Papers of the Novosibirsk State Technical University (article, 2019-07-02)
Field: Chemistry — Organic Chemistry

Using the LS–SVM method to study the process of complexation of transition metals with thiourea derivatives in aqueous and aqueous-organic solutions ИСПОЛЬЗОВАНИЕ МЕТОДА LS-SVM ДЛЯ ИЗУЧЕНИЯ ПРОЦЕССА КОМПЛЕКСООБРАЗОВАНИЯ ПЕРЕХОДНЫХ МЕТАЛЛОВ С ПРОИЗВОДНЫМИ ТИОМОЧЕВИНЫ В ВОДНЫХ И ВОДНО Thiourea ; Aqueous solution ; Chemistry ; Process (computing) ; Transition metal ; Inorganic chemistry ; Organic chemistry ; Computer science ; Catalysis Chemical Thermodynamics and Molecular Structure ; Chemical and Physical Properties in Aqueous Solutions ; Thermodynamic properties of mixtures
Authors: Oksana Takhumova | Tatiana Bondarenko | Sergei Shlykov | Ruslan Omarov
Source: Engineering for Rural Development (article, 2019-05-22)
Field: Chemistry — Analytical Chemistry

Studying identification algorithms in food technology Food production specificity requires improvement of systems of automatic regulation of the processes in aggregates, apparatus, and facility. In creating an adaptive process control system for food production, based on the model of a control object, an additional analysis should be done to choose the identification algorithm of a real, fairly representative sample of input data and output measurement results. By now a lot of recurrent identification algorithms are development and in one or another extent investigated. The little part of these algorithms is implemented into software, such as Matlab, Unity Pro, and other similar ones. But the problem is that among all of these algorithms the best one for real conditions of identification is absent. And a practicing engineer, who takes up the implementation of an adaptive control system with an identifier, is faced with the problem of choosing a specific identification algorithm.In this research, by using simulation modelling, over 53 recurrent identification algorithms were analyzed, plus the main modifications of these algorithms, and a total of 47 possible estimation criteria for non-stationary multidimensional dynamic objects. Based on this analysis of the object class in question, several algorithms were recommended to engineering practice. Possibilities of a software package that has the most complete set of parametric identification algorithms today are discussed. For these specific conditions of the identification algorithms comparison into the package by identifying the stationary coefficients into object equation, the most effective were: Aizerman-1, Kachmazh, Nagumo-Noda, Rastrigin, Kalman Filter, Forgetting Factor, Tsypkin. With non-stationary coefficients of the object -Kachmazh, Nagumo-Noda, Kalman Filter. The best result was shown by the Nagumo-Noda identification algorithm. Identification (biology) ; Computer science ; Algorithm ; Biology Spectroscopy and Chemometric Analyses
Authors: Semra Benzer | Recep Benzer
Source: AquaDocs  (United Nations Educational, Scientific and Cultural Organization) (article, 2019-12-31)
Field: Chemistry — Analytical Chemistry

Artificial neural networks model biometric features of marine fish sand smelt. In this study was investigated some biometric properties of the sand smelt with ANN’s, Atherina boyeri Risso, 1810, population in Yamula Dam Lake. Twenty-three morphometric characters of samples were measured. The total length of individuals which were caught between 6.40 and 10.20 cm, and their weight (W) were ranged between 1.50 and 7.31 g. The effect of Artificial Neural Networks was investigated in predicting the statistical of fishery industry. The present study provides the first information on the biometric properties of Atherina boyeri in Yamula Dam Lake by Artificial Neural Networks. Smelt ; Biometrics ; Artificial neural network ; Fishery ; Fish <Actinopterygii> ; Artificial intelligence ; Computer science ; Biology Spectroscopy and Chemometric Analyses
Authors: Elias August | Besmira Sabani | Nurdzane Memeti
Source: Sensors (article, 2019-02-21)
Field: Chemistry — Analytical Chemistry

Using Colour Images for Online Yeast Growth Estimation Automatisation and digitalisation of laboratory processes require adequate online measurement techniques. In this paper, we present affordable and simple means for non-invasive measurement of biomass concentrations during cultivation in shake flasks. Specifically, we investigate the following research questions. Can images of shake flasks and their content acquired with smartphone cameras be used to estimate biomass concentrations? Can machine vision be used to robustly determine the region of interest in the images such that the process can be automated? To answer these questions, 18 experiments were performed and more than 340 measurements taken. The relevant region in the images was selected automatically using K-means clustering. Statistical analysis shows high fidelity of the resulting model predictions of optical density values that were based on the information embedded in colour changes of the automatically selected region in the images. Cluster analysis ; Computer science ; Artificial intelligence ; Computer vision ; Process (computing) ; Shake ; Laboratory flask ; Biomass (ecology) ; High fidelity ; Fidelity ; Pattern recognition (psychology) ; Engineering ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Viral Infectious Diseases and Gene Expression in Insects ; Biosensors and Analytical Detection
Authors: Yohann Couté | Christophe Bruley | Thomas Bürger
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-09-20)
Field: Chemistry — Spectroscopy

Beyond target-decoy competition: stable validation of peptide and protein identifications in mass spectrometry-based discovery proteomics Abstract In bottom-up discovery proteomics, target-decoy competition (TDC) is the most popular method for false discovery rate (FDR) control. Despite unquestionable statistical foundations, this method has drawbacks, including its hitherto unknown intrinsic lack of stability vis-à-vis practical conditions of application. Although some consequences of this instability have already been empirically described, they may have been misinter-preted. This article provides evidence that TDC has become less reliable as the accuracy of modern mass spectrometers improved. We therefore propose to replace TDC by a totally different method to control the FDR at spectrum, peptide and protein levels, while benefiting from the theoretical guarantees of the Benjamini-Hochberg framework. As this method is simpler to use, faster to compute and more stable than TDC, we argue that it is better adapted to the standardization and throughput constraints of current proteomic platforms. False discovery rate ; Decoy ; Proteomics ; Standardization ; Computer science ; Stability (learning theory) ; Mass spectrometry ; Competition (biology) ; Throughput ; Computational biology ; Chemistry ; Biology ; Chromatography ; Machine learning ; Telecommunications Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Kien Liong Wong | Mu Wen Chuan | Wee Khang Chong | Afiq Hamzah | Mohd Shahrizal Rusli | Nurul Ezaila Alias | Cheng Siong Lim | Michael Loong Peng Tan
Source: Indonesian Journal of Electrical Engineering and Informatics (IJEEI) (article, 2019-06-13)
Field: Materials Science — Materials Chemistry

Influence of single vacancy defect at varying length on electronic properties of zigzag graphene nanoribbons Graphene, identified in 2004, is now an established two-dimensional (2D) material due to its outstanding physical and electronic characteristics namely its superior electrical conductivity. Graphene is a zero-gap material that has linear dispersion with electron-hole symmetry. As pristine sheet, it cannot be utilized in digital logic application without the induction of a band gap inside the band structure. In our work, the modeling and simulation of graphene nanoribbons (GNRs) are carried out to determine its electronics properties that are benchmarked with other published simulation data. A 4-Zigzag GNRs (4-ZGNRs) under different length are utilized. A single vacancy defects is introduced at various positions inside the atomic structure. The theoretical model is implemented based on single-neighbour tight binding technique coupled with a non-equilibrium Green’s function formalism. The single vacancy defects are represented by the elimination of tight binding energies in the Hamiltonian matrix. Subsequently, these matrix elements are utilized to compute dispersion relation and density of states (DOS) through Green’s function. It is found that single vacancy defects at different positions in 4-ZGNRs’ atomic structure under varying length has no significant impacts on the sub-band structure but these vacancies impact the DOS that are computed throught Green’s function approach. Zigzag ; Vacancy defect ; Tight binding ; Graphene nanoribbons ; Materials science ; Graphene ; Condensed matter physics ; Band gap ; Electronic structure ; Hamiltonian (control theory) ; Electronic band structure ; Nanotechnology ; Physics ; Geometry ; Mathematics Graphene research and applications ; Quantum and electron transport phenomena ; 2D Materials and Applications
Authors: Kadir Sabancı | Cevat Aydın
Source: Information Security Education Journal (ISEJ) (article, 2019-06-01)
Field: Chemistry — Analytical Chemistry

Determination of Classification Parameters of Barley Seeds Mixed with wheat Seeds by using ANN One of the basic problems that cause loss of yield in wheat is weed seeds that mixed with wheat seeds. In this study, discrimination of barley seed which mixed with wheat seeds has been realized. Classification of wheat and barley seeds has been achieved by using artificial neural network and image processing techniques. In the study, image processing techniques and the use of artificial neural network have been made possible with Matlab software. By using Otsu method, histogram data of seed images that were taken from web camera was obtained. By using histogram data, with multi-layered artificial neural network model, the system was educated and classification was made. Besides, wheat and barley seeds in the picture info where mixed seeds taken from the web camera exist were counted. Mathematics ; Horticulture ; Agronomy ; Biology Spectroscopy and Chemometric Analyses
Authors: Osman Babaarslan | Abdurrahman Telli | Abdulkadir Geylani Gören
Source: Deu Muhendislik Fakultesi Fen ve Muhendislik (article, 2019-05-21)
Field: Materials Science — Polymers and Plastics

FDY ile POY, CDPET ve Mikro POY Polyester Filamentlerin Farklı Tekstüre Şartlarında Birleştirilmesiyle Elde Edilen İpliklerin Özellikleri ve Örme Kumaştaki Görsel Etkileri In recent years, combination of filament yarns in continuous yarn production are used for innovative product development studies. In this study, POY and FDY polyester filaments having different structures and specifications were combined using nip-twisters by false-twist texturing method. Twelve different yarns were obtained by using constant FDY and three different POY materials with the state of FDY in first heater and two different drawing values. Linear density, breaking force, breaking elongation, shrinkage in boiling water and number of nips were measured in these yarns. Furthermore, the visual effects in knitting fabrics produced from texturized yarns were investigated. It was observed that the produced combined yarns have different structural and visual effects compared to single textured yarn. Physics ; Chemistry ; Materials science Textile materials and evaluations ; Fashion and Cultural Textiles ; Dyeing and Modifying Textile Fibers
Authors: Wenan Yuan
Source: Insecta mundi (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

A Multi-Sensor Phenotyping System: Applications on Wheat Height Estimation and Soybean Trait Early Prediction Phenotyping is an essential aspect for plant breeding research since it is the foundation of the plant selection process. Traditional plant phenotyping methods such as measuring and recording plant traits manually can be inefficient, laborious and prone to error. With the help of modern sensing technologies, high-throughput field phenotyping is becoming popular recently due to its ability of sensing various crop traits non-destructively with high efficiency. A multi-sensor phenotyping system equipped with red-green-blue (RGB) cameras, radiometers, ultrasonic sensors, spectrometers, a global positioning system (GPS) receiver, a pyranometer, a temperature and relative humidity probe and a light detection and ranging (LiDAR) was first constructed, and a LabVIEW program was developed for sensor controlling and data acquisition. Two studies were conducted focusing on system performance examination and data exploration respectively. The first study was to compare wheat height measurements from ultrasonic sensor and LiDAR. Canopy heights of 100 wheat plots were estimated five times over the season by the ground phenotyping system, and the results were compared to manual measurements. Overall, LiDAR provided the better estimations with root mean square error (RMSE) of 0.05 m and R2 of 0.97. Ultrasonic sensor did not perform well due to the style of our application. In conclusion LiDAR was recommended as a reliable method for wheat height evaluation. The second study was to explore the possibility of early predicting soybean traits through color and texture features of canopy images. Six thousand three hundred and eighty-three RGB images were captured at V4/V5 growth stage over 5667 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix (GLCM)-based texture features were derived from each image. Another two variables were also introduced to account for the location and timing difference between images. Cubist and Random Forests were used for regression and classification modelling respectively. Yield (RMSE=9.82, R2=0.68), Maturity (RMSE=3.70, R2=0.76) and Seed Size (RMSE=1.63, R2=0.53) were identified as potential soybean traits that might be early-predictable. Advisor: Yufeng Ge Trait ; Estimation ; Agronomy ; Mathematics ; Statistics ; Computer science ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Tamás Orosz | David Pánek | Pavel Karban
Source: Preprints.org (preprint, 2019-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

FEM Based Preliminary Design Optimization in Case of Large Power Transformers Since large power transformers are custom-made, and their design process is a labor-intensive task, their design process is split into different parts. In tendering, the price calculation is based on the preliminary design of the transformer. Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Most of the published algorithms are using a copper filling factor based winding model to calculate the main dimensions of the transformer during this first, preliminary design step. Therefore, these cost optimization methods are not considering the detailed winding layout and the conductor dimensions. However, the knowledge of the exact conductor dimensions is essential to calculate the thermal behaviour of the windings and make a more accurate stray loss calculation. The paper presents a novel, evolutionary algorithm-based transformer optimization method which can determine the optimal conductor shape for the windings during this examined preliminary design stage. The accuracy of the presented FEM method was tested on an existing transformer design. Then the results of the proposed optimization method have been compared with a validated transformer design optimization algorithm. Transformer ; Electromagnetic coil ; Conductor ; Finite element method ; Optimal design ; Computer science ; Engineering design process ; Distribution transformer ; Engineering ; Mathematical optimization ; Electronic engineering ; Mechanical engineering ; Electrical engineering ; Voltage ; Structural engineering ; Mathematics Magnetic Properties and Applications ; Thermal Analysis in Power Transmission ; Electric Motor Design and Analysis
Authors: S. Kavitha
Source: Current Trends in Fashion Technology & Textile Engineering (review, 2019-12-11)
Field: Materials Science — Polymers and Plastics

A Review of Some Significant Research Trends in Thermophysiological Comfort of Fabrics to Suit Varied Areas of Applications and Weather Conditions Fashion Technology & Textile Engineering introduces pioneering technologies, and improved understanding of textile materials, processes, chemistry and systems. It will encourage interdisciplinary research which will share newly developed technology, theory and techniques in the fashion and textile Textile ; Architectural engineering ; Engineering ; Environmental science ; Materials science Textile materials and evaluations ; Dyeing and Modifying Textile Fibers
Authors: Miao Di
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2019-08-12)
Field: Chemistry — Analytical Chemistry

Class-Sensitive Principal Components Analysis Research in a number of fields requires the analysis of complex datasets. Principal Components Analysis (PCA) is a popular exploratory method. However it is driven entirely by variation in the dataset without using any predefined class label information. Linear classifiers make up a family of popular discrimination methods. However, these will face the data piling issue often when the dimension of the dataset gets higher. In this dissertation, we first study the geometric representation of an interesting dataset with strongly auto-regressive errors under the High Dimensional Low Sample Size (HDLSS) setting and understand why the Maximal Data Piling (MDP), proposed by Ahn et al. (2007), is the best in terms of classification compared with several other commonly used linear discrimination methods. Then we introduce the Class-Sensitive Principal Components Analysis (CSPCA), which is a compromise of PCA and MDP, that seeks new direction vectors for better Class-Sensitive visualization. Specifically, this method will be applied to the Thyroid Cancer dataset (see Agrawal et al. (2014)). Additionally, we investigate the asymptotic behavior of the sample and population MDP normal vector and Class-Sensitive Principal Component directions under the HDLSS setting. Moreover, the Multi-class version of CSPCA (MCSP) will be introduced as the last part of this dissertation. Class (philosophy) ; Principal component analysis ; Mathematics ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses