Records with Subject: Numerical Methods and Statistics
Showing records 1 to 25 of 2127. [First] Page: 1 2 3 4 5 Last
The Nutritional Value of Plant Drink against Bovine Milk—Analysis of the Total Concentrations and the Bio-Accessible Fraction of Elements in Cow Milk and Plant-Based Beverages
Maja Welna, Anna Szymczycha-Madeja, Anna Lesniewicz, Pawel Pohl
June 21, 2024 (v1)
Keywords: bio-accessibility, cow milk, inductively coupled plasma optical emission spectrometry, multi-element analysis, nutritional value, plant-based drink
Four types of non-dairy (plant) drinks—almond, oat, rice, and soy—as well as cow milk with varying fat contents (1.5%, 2.0%, and 3.2%), were examined and compared in terms of the total concentrations of Al, As, B, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Na, Mn, Ni, P, Pb, Sb, Se, Sr, and Zn using inductively coupled optical emission spectrometry (ICP OES). Additionally, in vitro gastrointestinal digestion was used to determine the bio-accessible fraction of selected elements, evaluating the nutritional value and risk assessment involved with the consumption of these beverages. A significant difference in the mineral profile was observed depending on the type of plant drink, with the highest content of elements noted in the soy drink and the lowest in the rice drink. Except for Ca and P, the soy drink appears to be a much better source of essential nutrients, including Cu, Fe, and Mn, than cow’s milk. A similar Ca content in plant beverages can be obtained only by adding calcium salt at the stag... [more]
Rapid and High-Yield Recovery of Sodium Alginate from Undaria pinnatifida via Microwave-Assisted Extraction
Hyeon-Bin Nam, Kang Hyun Lee, Hah Young Yoo, Chulhwan Park, Jong-Min Lim, Ja Hyun Lee
June 21, 2024 (v1)
Keywords: microwave-assisted extraction, Optimization, response surface methodology, sodium alginate
Alginate, a promising biopolymer in the food, biomedical, pharmaceutical, and electronic materials industries, is characterized by its biodegradability, biocompatibility, low toxicity, and gel-forming properties. It is most abundantly found in brown algae. However, conventional dilute acid and alkali extraction methods face limitations in commercialization due to their long processing time, low throughput, and high solvent requirements. In this study, a microwave-assisted extraction (MAE) process for sodium alginate was designed to improve extraction efficiency. The solid/liquid ratio, extraction temperature, and extraction solvent concentration were major variables affecting sodium alginate extraction from Undaria pinnatifida (sea mustard). They were then statistically optimized using response surface methodology. Under optimal conditions (13.27 g/L, 91.86 °C, 2.51% (w/v), and 15 min), the yield was 38.41%, which was 93.43% of the theoretical content of sodium alginate in Undaria pinn... [more]
Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network
Wangwang Bai, Mengxue Jin, Wanwei Li, Juan Zhao, Bin Feng, Tuo Xie, Siyao Li, Hui Li
June 21, 2024 (v1)
Keywords: convolutional neural network, multi-step prediction of wind power, sequence-to-sequence, squirrel search algorithm, variational mode decomposition
Due to the complexity of wind power, traditional prediction models are incapable of fully extracting the hidden features of multidimensional strong fluctuation data, which results in poor multi-step prediction performance. To predict continuous power effectively in the future, an improved wind power multi-step prediction model combining variational mode decomposition (VMD) with sequence-to-sequence (Seq2Seq) is proposed. Firstly, the wind power sequence is smoothed using VMD and the decomposition parameters of VMD are optimized by using the squirrel search algorithm (SSA) to effectively optimize the decomposition effect. Then, the subsequence obtained from decomposition, together with the original wind power data, is reconstructed into multivariate time series features. Finally, a Seq2Seq model is constructed, and convolutional neural networks (CNNs) with bidirectional gate recurrent units (BiGRUs) are used to learn the coupling and timing relationships of the input data and encode the... [more]
Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy
Huixin Tian, Chenning Zhao, Jueping Xie, Kun Li
June 21, 2024 (v1)
Keywords: change detection, change response, concept drift, data-driven, dynamic optimization, performance measures, time series
As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown signi... [more]
Transfer Learning and Interpretable Analysis-Based Quality Assessment of Synthetic Optical Coherence Tomography Images by CGAN Model for Retinal Diseases
Ke Han, Yue Yu, Tao Lu
June 21, 2024 (v1)
Keywords: interpretable analysis, modified CGAN, OCT, retina, transfer learning
This study investigates the effectiveness of using conditional generative adversarial networks (CGAN) to synthesize Optical Coherence Tomography (OCT) images for medical diagnosis. Specifically, the CGAN model is trained to generate images representing various eye conditions, including normal retina, vitreous warts (DRUSEN), choroidal neovascularization (CNV), and diabetic macular edema (DME), creating a dataset of 102,400 synthetic images per condition. The quality of these images is evaluated using two methods. First, 18 transfer-learning neural networks (including AlexNet, VGGNet16, GoogleNet) assess image quality through model-scoring metrics, resulting in an accuracy rate of 97.4% to 99.9% and an F1 Score of 95.3% to 100% across conditions. Second, interpretative analysis techniques (GRAD-CAM, occlusion sensitivity, LIME) compare the decision score distribution of real and synthetic images, further validating the CGAN network’s performance. The results indicate that CGAN-generated... [more]
Model and Parameter Study of Limestone Decomposition Reaction
Rongjia Zhu, Liangyu Fu, Qian Liu, Jiaqiang E, Haoyu Zhou
June 21, 2024 (v1)
Keywords: CO2 concentration, decomposition reaction rate, limestone, numerical calculation
To address the problem that there are many limestone particle decomposition reaction models and it is difficult to accurately select the appropriate one, this paper established two typical one-dimensional unsteady numerical calculation models for single-particle limestone decomposition, coupling the convective heat transfer, thermal conduction, and CO2 mass transfer processes. Two numerical calculation models were solved through the Matlab R2021a platform, and the internal temperature, CO2 concentration distribution, and decomposition reaction rate of the limestone particles during the period from the beginning of temperature rise to the end of decomposition were obtained. Compared with the experimental data, Model 1 has a better agreement with a relative error of less than 10%. The simulation results have shown that the average decomposition reaction rate is 20% higher than the average mass transfer rate. As the particle size increases from 20 mm to 80 mm, the time required for temper... [more]
A Method for Predicting Ground Pressure in Meihuajing Coal Mine Based on Improved BP Neural Network by Immune Algorithm-Particle Swarm Optimization
Xingping Lai, Yuhang Tu, Baoxu Yan, Longquan Wu, Xiaoming Liu
June 21, 2024 (v1)
Keywords: algorithm optimization, BP, ground pressure prediction, IA-PSO-BP
Based on the background of dynamic mining pressure monitoring and pressure prediction research on the No. 232205 working face of the Meihuajing coal mine, this study systematically investigates the predictive model of mining pressure manifestation on the working face of the Meihuajing coal mine by integrating methods such as engineering investigation, theoretical analysis, and mathematical modeling. A mining pressure manifestation prediction method based on IA-PSO-BP is proposed. The IA-PSO optimization algorithm is applied to optimize the hyperparameters of the BP neural network, and the working face mining pressure prediction model based on IA-PSO-BP is established. The mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) are selected as evaluation indicators to compare the prediction performance of the BP model, PSO-BP model, and IA-PSO-BP model. The experimental results of the model show that the convergence speed of the IA-PSO-BP model is about... [more]
Risk Assessment Method for Analyzing Borehole Instability Considering Formation Heterogeneity
Xiangsen Gao, Min Wang, Xian Shi, Cui Li, Mingming Zhang
June 21, 2024 (v1)
Keywords: borehole stability, coefficient of variation, heterogeneity, instability risk, sensitivity analysis
In the study of borehole instability, the majority of input parameters often rely on the average values that are treated as fixed values. However, in practical engineering scenarios, these input parameters are often accompanied by a high degree of uncertainty. To address this limitation, this paper establishes a borehole stability model considering the uncertainty of input parameters, adopts the Monte Carlo method to calculate the borehole stability reliability at different drilling fluid densities, evaluates the sensitivity of borehole instability to a single parameter, and studies the safe drilling fluid density window at different borehole stability reliability values under multi-parameter uncertainties. The results show that the uncertainty of rock cohesion has a great influence on the fracture pressure of the vertical and horizontal wells. The minimum horizontal stress has the greatest influence on the fracture pressure of the vertical and horizontal wells, followed by pore pressu... [more]
A New Comprehensive Indicator for Monitoring Anaerobic Digestion: A Principal Component Analysis Approach
Ru Jia, Young-Chae Song, Zhengkai An, Keugtae Kim, Chae-Young Lee, Byung-Uk Bae
June 21, 2024 (v1)
Keywords: anaerobic digestion, comprehensive indicator, eigenvalue, eigenvector, principal component (PC) score, principal component analysis (PCA)
This paper has proposed a comprehensive indicator based on principal component analysis (PCA) for diagnosing the state of anaerobic digestion. Various state and performance variables were monitored under different operational modes, including start-up, interruption and resumption of substrate supply, and impulse organic loading rates. While these individual variables are useful for estimating the state of anaerobic digestion, they must be interpreted by experts. Coupled indicators combine these variables with the effect of offering more detailed insights, but they are limited in their universal applicability. Time-series eigenvalues reflected the anaerobic digestion process occurring in response to operational changes: Stable states were identified by eigenvalue peaks below 1.0, and they had an average below 0.2. Slightly perturbed states were identified by a consistent decrease in eigenvalue peaks from a value of below 4.0 or by observing isolated peaks below 3.0. Disturbed states wer... [more]
Research on a Pressure Control Method for a Liquid Supply System Based on Online Updating of a Radial Basis Function Neural Network
Yanwei Peng, Ziming Kou, Juan Wu, Jianguo Luo, Hang Liu, Buwen Zhang
June 21, 2024 (v1)
Keywords: long-distance liquid supply, online update, RBF neural network, stable pressure control
In order to solve the problem of frequent pressure fluctuations caused by fluid quantity variation in hydraulic support liquid supply systems and the pressure response lag caused by long-distance pipelines, an online updated radial basis function neural network (RBF neural network) control method was proposed for the long-distance liquid supply system. Based on the analysis of the measured pressure fluctuations of the mining face and the process of the stable pressure liquid supply system, the influencing factors of the stable pressure liquid supply flow demand were obtained. The flow set of the stable pressure liquid supply system was established and fitted in the SimulationX−Simulink co-simulation model and the online correction was carried out by using the characteristics of the repeated action of the hydraulic support. Finally, the online updating RBF neural network regulator was established to realize the pressure regulator control of the pumping station, and the experimental plat... [more]
Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision-Making—Part 2: Framework Application Considering Activity-Based Costing Concepts
Émilie Thibault, Christian Ledoux, Paul Stuart
June 21, 2024 (v1)
Keywords: activity-based costing, framework, operating regimes, optimization analysis, pulp mill
Operating time series data collected and stored in historian must be managed to extract their full potential. Part 1 of this paper proposed a structured way (a sophisticated approach) to process industrial data; this first part explains in detail the data processing framework used as the basis for the costing analysis present in the second part of this series. The framework considers the analysis scope definition, data management steps, and operating regimes detection and identification. The added value of this proposed framework is demonstrated in Part 2 via the use of cost accounting for operational problem-solving (debottlenecking), i.e., its practicality is validated via its application alongside a cost analysis on the brownstock washing department of a kraft pulp mill. The traditional debottlenecking approach assumes a single operating condition considering that operating regimes allow for a much more sophisticated debottlenecking study of the washing department. With the use of o... [more]
Residual Life Prediction of Rolling Bearings Based on a CEEMDAN Algorithm Fused with CNN−Attention-Based Bidirectional LSTM Modeling
Xinggang Zhang, Jianzhong Yang, Ximing Yang
June 21, 2024 (v1)
Keywords: attention mechanism, bidirectional long short-term memory networks, CEEMDAN, convolutional neural networks, remaining service life prediction, rolling bearings
This paper presents a methodology for predicting the remaining usability of rolling bearings. The method combines a fully adaptive ensemble empirical modal decomposition of noise (CEEMDAN), convolutional neural network (CNN), and attention bidirectional long short-term memory network (ABiLSTM). Firstly, a finite number of intrinsic mode functions (IMFs) are obtained from breaking down the initial vibration signals using CEEMDAN. The IMFs are further screened by combining the correlation criterion and the craggy criterion. Then, time-frequency domain features, which are extracted from the screened IMFs, are reconstructed into a feature set. The SPT is recognized through some features, like the root mean square (RMS), variance, and kurtosis. Secondly, the deterioration character of rolling bearings was extracted using CNN and used to train the ABiLSTM network. Based on the output of the ABiLSTM network, it forecasts how long rolling bearings will last during use. Finally, the XJTU-SY rol... [more]
Numerical Prediction of Refrigerant Oil Two-Phase Flow from Scroll Compressor Discharge to the Suction Side via Back Pressure Chamber
Vladimir D. Stevanovic, Milan M. Petrovic, Stojan Cucuz, Sanja Milivojevic, Milica Ilic
June 21, 2024 (v1)
Keywords: back pressure chamber, oil, refrigerant, scroll compressor, two-phase critical flow
Oil lubricates the contact between the orbiting and stationary scroll in the refrigerant scroll compressor, while the sealing between the scrolls is achieved through the refrigerant vapour pressure in the sealed back pressure chamber. The back pressure should be adjusted using the refrigerant oil two-phase flow from the oil separator at the compressor discharge to the back pressure chamber and the refrigerant oil flow from the back pressure chamber to the compressor suction side. Both of the flows are conducted through connecting tubes with corresponding high-pressure and low-pressure nozzles with small diameters. Models for predicting the refrigerant oil critical and subcritical flows through the nozzles were developed and applied in enable the prediction of the back pressure. The models are original, because the slip between the oil and the refrigerant as well as the refrigerant solubility in the oil are taken into account. The critical flow model is validated against the experimenta... [more]
Method of Analyzing Technological Data in Metric Space in the Context of Industry 4.0
Karolina Czerwińska, Andrzej Pacana
June 10, 2024 (v1)
Keywords: 3 × 3 matrix, BOST survey, Industry 4.0, mechanical engineering, process improvement, quality 4.0, statistical analysis
The purpose of this article was to develop a method of analyzing the manufacturing process with variables indicating product competitiveness and technological capabilities in metric space as a cognitive source. The presented method will facilitate the identification of key development factors within the manufacturing processes that have the greatest impact on the adaptation of the manufacturing enterprise to Industry 4.0. The presented method of manufacturing process analysis integrates a number of tools (SMART method, brainstorming, BOST analysis, 3 × 3 metrics) that enable the implementation of statistical analysis. The model developed makes it possible to apply known mathematical methods in areas new to them (adaptation in the manufacturing area), which makes it possible to use scientific information in a new way. The versatility of the method allows it to be used in manufacturing companies to identify critical factors in manufacturing processes. A test of the developed method was c... [more]
Enhancing Additive Restoration of Damaged Polymer Curved Surfaces through Compensated Support Beam Utilization
Dianjin Zhang, Bin Guo
June 7, 2024 (v1)
Keywords: additive repair, compensated method, damaged surfaces, support beam
As additive manufacturing advances, it offers a cost-effective avenue for structurally repairing components. However, a challenge arises in the additive repair of suspended damaged surfaces, primarily due to gravitational forces. This can result in excessive deformation during the repair process, rendering the formation of proper repair impractical and leading to potential failure. In light of this rationale, conventional repair techniques are impractical for extensively damaged surfaces. Thus, this article proposes a novel repair methodology that is tailored to address large-area damage. Moreover, and departing from conventional practices involving the addition and subsequent subtraction of materials for precision machining, the proposed process endeavors to achieve more precise repair outcomes in a single operation. This paper introduces an innovative repair approach employing fused deposition modeling (FDM) to address the complexities associated with the repair of damaged polymer ma... [more]
CrossTx: Cross-Cell-Line Transcriptomic Signature Predictions
Panagiotis Chrysinas, Changyou Chen, Rudiyanto Gunawan
June 7, 2024 (v1)
Keywords: autoencoder, drug repurposing, drug signature, gene expression, principal component analysis
Predicting the cell response to drugs is central to drug discovery, drug repurposing, and personalized medicine. To this end, large datasets of drug signatures have been curated, most notably the Connectivity Map (CMap). A multitude of in silico approaches have also been formulated, but strategies for predicting drug signatures in unseen cells—cell lines not in the reference datasets—are still lacking. In this work, we developed a simple-yet-efficacious computational strategy, called CrossTx, for predicting the drug transcriptomic signatures of an unseen target cell line using drug transcriptome data of reference cell lines and unlabeled transcriptome data of the target cells. Our strategy involves the combination of Predictor and Corrector steps. The Predictor generates cell-line-agnostic drug signatures using the reference dataset, while the Corrector produces target-cell-specific drug signatures by projecting the signatures from the Predictor onto the transcriptomic latent space of... [more]
Investigating Sedimentation Behavior of Montmorillonite Flocs between Flat Plates in a 2D System Using Image Analysis
Md Roknujjaman, Keisuke Yoshida, Muhamad Ezral Bin Ghazali, Jiawei Li, Harumichi Kyotoh, Yasuhisa Adachi, Yohei Asada
June 7, 2024 (v1)
Keywords: formation and collapse, Fourier transformation, montmorillonite flocs, PIV, sedimentation, sedimentation turbulence
The sedimentation of flocs in aquatic environments is a fundamental phenomenon that has not yet been fully elucidated. This study quantitatively examines sedimentation behavior, particularly focusing on sedimentation turbulence, in a two-dimensional system between flat plates, utilizing image analysis. Experiments were conducted in a rectangular container with montmorillonite suspensions coagulated in a sodium chloride solution. The settling motion of flocs was visualized using a green laser from above and captured horizontally with a digital camera. The study employed Particle Image Velocimetry (PIV) to analyze the velocity field in floc sedimentation, using the flocs as tracers to calculate the mean velocity at the sediment−supernatant interface. The results showed that the mean PIV value is affected by rising particles caused by sedimentation turbulence, indicating that PIV analysis of flow fields using flocs as tracers is reliable. The maximum settling velocity was found to increas... [more]
Evaluation of Saturation Interpretation Methods for Ultra-Low Permeability Argillaceous Sandstone Gas Reservoirs: A Case Study of the Huangliu Formation in the Dongfang Area
Bao Wang, Zhonghao Wang, Bo Shen, Di Tang, Yixiong Wu, Bohan Wu, Sen Li, Jinfeng Zhang
June 7, 2024 (v1)
Keywords: argillaceous sandstone gas reservoir, conductivity characteristics, gas saturation, Huangliu formation, principal component analysis, reservoir classification
Ultra-low permeability argillaceous sandstone reservoirs have become a significant focus for exploration and development. Saturation is a crucial parameter in evaluating such reservoirs. Due to the low porosity, low permeability, complex pore structure, and strong heterogeneity in ultra-low permeability argillaceous sandstone reservoirs, traditional evaluation methods are unable to achieve the required level of interpretation accuracy. To improve the accuracy of gas saturation calculations in ultra-low permeability argillaceous sandstone gas reservoirs, the conductivity characteristics of the ultra-low permeability argillaceous sandstone gas reservoirs in the Huangliu Formation in the Dongfang area, China, were analyzed through rock physics experimental data and geological information. The results revealed the clay content in the study area to range from 6% to 33.4%. Influenced by burial depth and temperature, kaolinite and montmorillonite transform into illite and chlorite, and the ca... [more]
Effects of Viscosity Law on High-Temperature Supersonic Turbulent Channel Flow for Chemical Equilibrium
Shuo Zhao, Xiaoping Chen, Yuting Yang, Dengsong Huang
June 7, 2024 (v1)
Keywords: chemical equilibrium, direct numerical simulation, instantaneous structure, turbulence statistics, viscosity law
Direct numerical simulations of temporally evolving high-temperature supersonic turbulent channel flow for chemical equilibrium were conducted with a Mach number of 3.0, a Reynolds number of 4880, and a wall temperature of 1733.2 K to investigate the influence of the viscosity law. The mean and fluctuating viscosity for the mixture rule is higher than that for Sutherland’s law, whereas an opposite trend is observed in the mean temperature, mean pressure, and dissociation degree. The Trettel and Larsson transformed mean velocity, the Reynolds shear stress, the turbulent kinetic energy budget, and the turbulent Prandtl number are insensitive to the viscosity law. The semilocal scaling that take into account local variation of fluid characteristics better collapses the turbulent kinetic energy budget. The modified strong Reynolds analogies provide reasonably good results for the mixture rule, which are better than those for Sutherland’s law. The streamwise and spanwise coherencies for the... [more]
A Fast Reliability Evaluation Strategy for Power Systems under High Proportional Renewable Energy—A Hybrid Data-Driven Method
Jiaxin Zhang, Bo Wang, Hengrui Ma, Yunshuo Li, Meilin Yang, Hongxia Wang, Fuqi Ma
June 7, 2024 (v1)
Keywords: convolutional neural network, explicit analytical expressions, hybrid data-driven strategy, power system, reliability index
With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are ve... [more]
New Method for Logging Evaluation of Total Organic Carbon Content in Shale Reservoirs Based on Time-Domain Convolutional Neural Network
Wangwang Yang, Xuan Hu, Caiguang Liu, Guoqing Zheng, Weilin Yan, Jiandong Zheng, Jianhua Zhu, Longchuan Chen, Wenjuan Wang, Yunshuo Wu
June 7, 2024 (v1)
Keywords: logging evaluation, shale reservoir, time-domain convolutional neural network, total organic carbon content
Total organic carbon (TOC) content is a key indicator for determining the hydrocarbon content of shale. The current model for calculating the TOC content of shale is relatively simplistic, the modeling process is cumbersome, and the parameters involved are influenced by subjective factors, which have certain shortcomings. To address this problem, a time-domain convolutional neural network (TCN) model for predicting total organic carbon content based on logging sequence information was established by starting from logging sequence information, conducting logging parameter sensitivity analysis experiments, prioritizing logging-sensitive parameters as model feature vectors, and constructing a TCN network. Meanwhile, to overcome the problem of an insufficient sample size, a five-fold cross-validation method was used to train the TCN model and obtain the weight matrix with the minimum error, and then a shale reservoir TOC content prediction model based on the TCN model was established. The... [more]
Seeking Optimal Extraction Method for Augmenting Hibiscus sabdariffa Bioactive Compounds and Antioxidant Activity
Athanasia Kourelatou, Theodoros Chatzimitakos, Vassilis Athanasiadis, Konstantina Kotsou, Ioannis Makrygiannis, Eleni Bozinou, Stavros I. Lalas
June 7, 2024 (v1)
Keywords: anthocyanins, antioxidants, Extraction, hibiscus, partial least squares analysis, polyphenols, principal component analysis, pulsed electric field, response surface methodology, ultrasonication
The dried flowers of Hibiscus sabdariffa (HS), available worldwide, have various applications in both non-medicinal and medicinal fields. The growing global interest in the health benefits of HS is linked to its potential prevention or management of non-communicable diseases. The aim of this research was to find the optimal extraction method that ensures the maximum yield of multiple beneficial bioactive components, such as polyphenols, anthocyanins, vitamin C, β-carotene, antioxidant activity, free radical scavenging activity DPPH and ferric reducing antioxidant power (FRAP). To this end, stirring, pulsed electric field, and ultrasound-assisted extraction were evaluated, either alone or in combination. Under optimized extraction conditions, the obtained extract exhibited an elevated total polyphenol content (37.82 mg of gallic acid equivalents/g dry weight (dw)), total anthocyanin content (610.42 μg of cyanidin equivalents/g dw), total carotenoids content (921.84 μg of β-carotene equi... [more]
Optimizing Pneumonia Diagnosis Using RCGAN-CTL: A Strategy for Small or Limited Imaging Datasets
Ke Han, Shuai He, Yue Yu
June 7, 2024 (v1)
Keywords: medical image analysis, pneumonia diagnosis, RCGAN, transfer learning, X-ray
In response to the urgent need for efficient pneumonia diagnosis—a significant health challenge that has been intensified during the COVID-19 era—this study introduces the RCGAN-CTL model. This innovative approach combines a coupled generative adversarial network (GAN) with relativistic and conditional discriminators to optimize performance in contexts with limited data resources. It significantly enhances the efficacy of small or incomplete datasets through the integration of synthetic images generated by an advanced RCGAN. Rigorous evaluations using a wide range of lung X-ray images validate the model’s effectiveness. In binary classification tasks that differentiate between normal and pneumonia cases, RCGAN-CTL demonstrates exceptional accuracy, exceeding 99%, with an area under the curve (AUC) of around 95%. Its capabilities extend to a complex triple classification task, accurately distinguishing between normal, viral pneumonia, and bacterial pneumonia, with precision scores of 89... [more]
Solubility of Methane in Ionic Liquids for Gas Removal Processes Using a Single Multilayer Perceptron Model
Claudio A. Faúndez, Elías N. Fierro, Ariana S. Muñoz
June 7, 2024 (v1)
Keywords: algorithm learning, artificial neural network, Carbon Dioxide, ionic liquids, methane, multilayer perceptron, solubility
In this work, four hundred and forty experimental solubility data points of 14 systems composed of methane and ionic liquids are considered to train a multilayer perceptron model. The main objective is to propose a simple procedure for the prediction of methane solubility in ionic liquids. Eight machine learning algorithms are tested to determine the appropriate model, and architectures composed of one input layer, two hidden layers, and one output layer are analyzed. The input variables of an artificial neural network are the experimental temperature (T) and pressure (P), the critical properties of temperature (Tc) and pressure (Pc), and the acentric (ω) and compressibility (Zc) factors. The findings show that a (4,4,4,1) architecture with the combination of T-P-Tc-Pc variables results in a simple 45-parameter model with an absolute prediction deviation of less than 12%.
Process Analysis and Modelling of Operator Performance in Classical and Digitalized Assembly Workstations
Georgiana Cătălina Neacşu (Dobrişan), Eduard Laurenţiu Niţu, Ana Cornelia Gavriluţă, Georgica Gheorghiţa Vlad, Elena Mădălina Dobre, Marian Gheorghe, Maria Magdalena Stan
June 7, 2024 (v1)
Keywords: assembly workstations, DOJO, Industry 4.0, lean learning factory, regression analysis
Strong competition in the automotive industry has required manufacturers to implement lean production, both with methods and techniques specific to Industry 4.0. At the same time, universities must provide graduates with specific skills for applying these new production methods and techniques. In this context, a lean learning factory was developed in the Pitesti University Center that allows students to learn about, experiment with, and research new lean manufacturing methods and techniques as well as Industry 4.0 in an environment similar to that of enterprises. The research presented in this study aimed to identify the minimum number of repetitions necessary to train operators to perform the same assembly operation while working at two differently organized workstations: one classic and the other including digital techniques. Several indicators were considered in our analysis, such as the number of errors, the number of stops, the effective duration of the work cycle, and the percent... [more]
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