Miracle-dz/UniFAR-FTUs huggingface dataset:
Drastic improvement of bioethanol recovery using a pervaporation separation technique employing a silicone rubber‐coated silicalite membrane

A coupled fermentation/pervaporation process for reliable production of concentrated ethanol was studied using ethanol permselective silicalite membranes coated with two types of silicone rubber, KE-45 and KE-108, as a hydrophobic material. Ethanol recovery was greatly improved by using a membrane coated with KE-45 silicone rubber. The recovered ethanol concentration in the permeate was 67% (w/w), and the amount of recovered ethanol from the broth was more than 10 times higher than that using a non-coated membrane. Succinic acid and glycerol, by-products created during fermentation, interfered with the pervaporation performance of the coated membrane when used to separate an ethanol/water solution. Copyright © 2003 Society of Chemical Industry
Control of Shell Thickness in Silica-Coating of AgI Nanoparticles

Silica-coating of AgI nanoparticles with a Stöber method was carried out to find out reaction conditions for control of the shell thickness. The AgI nanoparticles were prepared from AgClO4 and KI with the use of 3-mercaptopropyltrimethoxysilane (MPS) as a silane coupling agent and dimethylamine (DMA) catalyst for alkoxide hydrolysis. The silica-coating was performed at 4.5×10-6-4.5×10-5 M MPS, 11-20 M water, 0.002-0.1 M DMA and 0.005-0.04 M tetraethylorthosilicate at AgI concentrations of 0.1-1 mM. Consequently, AgI-silica core-shell particles could be prepared with the use of 4.5×10-5 M MPS, 20 M water, 0.01 M DMA and 1 mM AgI. Silica shell thickness could be varied from 15 to 28 nm with an increase in the TEOS concentration from 0.005 to 0.04 M.
Trajectory Design and Tracking-Based Control of the Passive Compass Biped

The locomotion mechanism of the passive-dynamic compass-type biped robot is modeled by an impulsive hybrid nonlinear system and it is the best model for mimicking the human walking. This work is concerned with the study of the different problems generated by this walk despite its simplicity such as chaos and bifurcations, and to propose a control-based solution in order to have a walk similar to that of the human. For this objective, two control methods relying on the trajectory design are proposed: a method based on the passive dynamic walking and a method based on the tracking of a planned trajectory by a fourth-order Bézier function. Finally, some simulation results are presented to show the control efficiency of the passive walk of the bipedal compass robot.
A Real-time and High-efficiency Surface Defect Detection Method for Metal Sheets Based on Compact CNN

Surface defect detection has received increased attention in the quality control of industrial products. It is an urge need to develop a real-time, high-efficiency defect detection algorithm on automatic detection equipment. The traditional image processing method based on connected domain does not meet the throughput requirement and cost huge efforts. In this paper, A novel detection algorithm based on compact convolutional neural network (CNN) is applied to confirm the defect's existence in the target region of an image. Experimental results show that our proposed compact CNN detection kernel finishes in 7ms per image and it achieves 8.57x speedup when compared to the traditional image processing method. Our compact CNN-based real-time processing pipeline also achieves 96.85% detection accuracy rate, which is more accurate and robust than human detection and traditional image processing algorithms. For the whole image processing pipeline, it achieves 1.79x throughput improvement in terms of image per second and higher efficiency in terms of out-of-pocket cost.
Color Kinetics of Aonla Shreds with Amalgamated Blanching During Drying

Kinetics of color changes of aonla (Emblica officinalis) shreds were investigated using objective colorimetric measurements during drying with different blanching treatments. Kinetic parameters chroma, hue, total color difference, and browning index for the color change were determined using Hunter L, a, b values. The drying process changed all three color parameters (L, a, b), causing a color shift towards the darker region. Parameters L and b decreased and a-value increased during drying. The reaction associated with color change followed zero-order reaction kinetics. The minimum change was observed in the KMS (potassium metabisulphite) blanched shreds.
COVID-19 Detection in X-ray Images using CNN Algorithm

Based on the best published research from Stanford University, the CheXNet algorithm was developed to diagnose and detect pneumonia from chest X-rays. To achieve better performance than experienced radiologists from the same university, simple changes were made to the algorithm to diagnose 14 pathological condition in the chest X-ray with a performance that exceeds all Previously developed deep learning [1]. In this paper, we experimented with applying a convolutional neural networks (CNN) algorithm in a similar way to the mechanism of work in CheXNet algorithm by using a dataset of 550 Chest X-ray images collected from Kaggle website, some of them are infected with Covid-19 virus. We had an acceptable prediction accuracy of 89.7% which is closed to the results of CheXNet algorithm.
Control of the Passive-Dynamic Locomotion of the Compass-Gait Biped Robot

This paper discusses a stabilization method for the passive dynamic walking (PDW) of the compass-gait biped robot using an explicit analytical classical expression of the controlled Poincaré map in order to control its passive gaits. A linearization method is used to cast the problem from the impulsive hybrid nonlinear dynamics into the linear dynamics around a desired one-periodic hybrid limit cycle of the PDW. Thus, using the first-order Taylor approximation, we design an explicit expression of the controlled Poincaré map. In order to control the passive gaits, we develop first the linearized Poincaré map obtained around the period-one fixed point. We adopt a state-feedback control law using the LMI approach to stabilize this fixed point. We present finally some simulation results to reveal the efficiency of the designed control law in the control of the passive-dynamic locomotion of the compass-gait biped robot.
A Novel Depth from Defocus Framework Based on a Thick Lens Camera Model

Reconstruction approaches based on monocular defocus analysis such as Depth from Defocus (DFD) often utilise the thin lens camera model. Despite this widespread adoption, there are inherent limitations associated with it. Coupled with invalid parameterisation commonplace in literature, the overly-simplified image formation it describes leads to inaccurate defocus modelling; especially in macro-scale scenes. As a result, DFD reconstructions based around this model are not geometrically consistent, and are typically restricted to single-view applications. Subsequently, the handful of existing approaches which attempt to include additional viewpoints have had only limited success.In this work, we address these issues by instead utilising a thick lens camera model, and propose a novel calibration procedure to accurately parameterise it. The effectiveness of our model and calibration is demonstrated with a novel DFD reconstruction framework. We achieve highly detailed, geometrically accurate and complete 3D models of real-world scenes from multi-view focal stacks. To our knowledge, this is the first time DFD has been successfully applied to complete scene modelling in this way.
E-commerce Sales Forecast Based on Ensemble Learning

E-commerce, driven by the technological advances of the semiconductor industry, becomes one of the fastest-growing industries and has become the largest sector of the electronics industry in recent years. Compared with the traditional offline business, E-commerce business eliminates geographical limitations by allowing consumers to purchase anywhere. Additionally, E-commerce only requires a web-based platform to start a business, resulting in a lower investment or business capital. Based on more than 75,000,000 transactions of one E-commerce company from January 2013 to June 2019, we analyze the economic impact of the holiday season, the relationship between price and sales, etc. Furthermore, we utilize Artificial Intelligence (AI) techniques to predict the sales of products. Here, assemble learning models, including Catboost, GBDT, LightGBM, and XGBoost, are adopted for regression prediction. The results show that Cat boost performs better than other methods.
Deep Learning-Based Industry Product Defect Detection with Low False Negative Error Tolerance

Many methods for product defect detection have been proposed in the literature. The methods can be roughly divided into two categories, namely conventional statistical methods and machine learning-based ones. Especially for image-based defect detection, deep learning is known as the state-of-the-art. For product defect detection, the main issue is to reduce the false negative error rate (FNER) to almost zero, while keeping a relatively low false positive error rate (FPER). We can reduce the errors by introducing a rejection mechanism, but this approach may reject too many products for manual re-checking. In this study, we found that extremely low FNER can be achieved if we combine several techniques in using deep learning. In this paper, we introduce the techniques briefly, and provide experimental results to show how these techniques affect the performance for defect detection.
Impact of Gate Oxide Traps and In0:53Ga0:47As/BOX traps on the Performance of In0:53Ga0:47As on insulator TFET and its Mitigation

In this paper, we present the impact of gate oxide and back-side interface (In<inf>0.53</inf>Ga<inf>0.47</inf>As/BOX) traps on the performance of In<inf>0.53</inf>Ga<inf>0.47</inf>As on insulator TFET. It is revealed through 2D-TCAD analysis that along with gate oxide traps, traps at back-side interface can significantly alter the performance of In<inf>0.53</inf>Ga<inf>0.47</inf>As on insulator TFET. The presence of In<inf>0.53</inf>Ga<inf>0.47</inf>As/BOX traps alone can increase the OFF current ~ 22 times, and cumulative effect of the presence of traps at gate oxide interface and In<inf>0.53</inf>Ga<inf>0.47</inf>As/BOX interface is an increase in OFF current by ~ 3 order of magnitude, thus deteriorating the ON=OFF current ratio significantly. We also propose the methodologies to mitigate the effect of traps in OFF state. By mitigating the decrease in flat band voltage caused by presence of traps, through p type doping of channel instead of n-type channel and using the graded doping in the source, the OFF current is reduced in trap affected TFET. The application of proposed methodology in trap affected TFET offers > 3 order reduction in OFF current, thus restoring the ON=OFF current of In<inf>0.53</inf>Ga<inf>0.47</inf>As TFET. The proposed methodology provides the ON=OFF of 1.41 × 10<sup>−5</sup>A=_m
Machine Learning Approach for the Detection of Hate Speech in Sinhala Unicode Text

Hate speech published online platforms has become a critical issue in Sri Lanka since this has caused conflicts between different ethnic groups. One of the main barriers to stop this crime is the lack of resources to detect online hate content in Sinhala automatically. Due to the vast amount of content published on online platforms every minute, an automatic method must be implemented in order to solve this issue.As a solution, we suggest a deep learning mechanism that utilizes two convolution neural networks (CNNs) which will first classify a given text corpus as hateful or not. Then, if the text corpus contains hate content text, it will again be classified according to its hate level which can be used by authorities to make decisions. In order to convert the text data into numerical vectors, we have used FastText word embedding in this study.Results indicate an accuracy of 83% and 60% for hate speech classification and hate level classifications, respectively.
Simulation of water diffusion through a simple virtual cardiac cell model: optimization of Monte Carlo parameters and observation by simulated MRI

This paper describes a two fold strategy. First, we create a Monte-Carlo water diffusion simulator able to generate realistic synthetic diffusion data. Second, we observe this synthetical diffusion by simulating a virtual magnetic resonance diffusion imaging device (spin echo sequence). We show how to adequately run Monte-Carlo simulations in a cylindrical core (similar to a cardiac cell shape model) and we optimize the virtual imaging simulated parameters. We compare the virtual diffusion magnetic resonance signal obtained for various values of the parameters to the corresponding analytical models. We give a first analysis of the deviation results when usual clinical parameters are used.
Automatic Fracture Detection in Hand Bone Using MATLAB and Image Processing

Rapidly discovering the possible technological advantages are evolving every day in distinct fields, especially in the clinical circumstances. Be that as it may, in any case, some existing methods are very mainstream, productive and compelling. X-rays enforces methods for analyzing the locations of bone breaks. All things considered in some cases, the size about division inset remains more critical and also it could not stay identified without any problem. In this manner, successful and astute frameworks are ought to be planned. The invented scheme contains dual chief phases. Above all phases, the photograph about the gaps do planned to implement advance special image approach methods, so as to recognize their fields along with architecture and follow onto thegrouping phase, where a back propagation neural system is prepared and afterward tried on preparing the pictures. Tentatively the schemes do check out separate bone gap photographs also the conclusion will display proficiently along with a characterization rate.
MEGC2020 - The Third Facial Micro-Expression Grand Challenge

The recent emergence of automatic facial micro-expression analysis has attracted a lot of attention in the last five years. Compared to the advances made in micro-expression recognition, the task of micro-expression spotting from long videos is tremendously in need of more effective methods. This paper summarises the 3rd Facial Micro-Expression Grand Challenge (MEGC 2020) held in conjunction with the 15th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2020. In this workshop, we propose a new challenge of spotting both macro- and micro-expressions from long videos, to spur the community to develop new techniques for micro-expression spotting and also to extend facial micro-expression analysis to more complex real-world scenarios where micro-expressions are likely to be intertwined among normal expressions. In this paper, we outline the evaluation protocols for the challenge task, and describe the datasets involved. Then, we summarize the methods from the accepted challenge papers, present the comparison and analysis of results, as well as future directions.
Nitrate leaching from grazed grassland lysimeters: effects of fertilizer input, field drainage, age of sward and patterns of weather

SUMMARY Drained and undrained grassland lysimeter plots were established in 1982 on a clay loam of the Hallsworth series at a long-term experimental site in south-west England. The plots were continuously grazed by beef cattle, and received fertilizer at either 200 or 400 kg N ha-1 per annum to the existing permanent sward, or at 400 kg N ha-1 to a new sward, reseeded to perennial ryegrass following cultivation. Drainage water was monitored at V-notch weirs and sampled daily for the analysis of nitrate-N. Seven years of data are presented (five years for the reseeded swards). On the drained plots a large proportion of the rainfall was routed preferentially down large pores to the mole drains, whilst on the undrained plots, drainage was mainly by surface runoff. The average quantities of nitrate N leached per year were 38.5, 133.8 and 55.7 kg ha-1 from the old sward that received 200 and 400 kg N ha-1, and from the reseed that received 400 kg N ha-1 fertilizer, respectively. Ploughing and reseeding resulted in a two-fold reduction in leaching, except during the first winter after ploughing, and twice as much leaching occurred after a hot, dry summer as after a cool, wet one. Nitrate concentrations in drainage from either drained or undrained plots were rather insensitive to rainfall intensity, such that concentration was a good predictor of nitrate load for a given drainage volume. The drainage volume determined the proportion of the leachable N that remained in the soil after the winter drainage period. Initial (peak) concentrations of nitrate N ranged, on average, from 55 mg dm-3 for the drained old sward that received 400 kg N ha-1 fertilizer, to 12 mg dm-3 for the undrained sward at 200 kg N ha-1 fertilizer input. Concentrations of nitrate N in drainage from similar, unfertilized plots rarely exceeded 1 mg dm-3. The results suggest that manipulating the nitrate supply can lessen leaching and that the route of water through soil to the watercourse determines the maximum nitrate concentration for a given load.
KeystoneDepth: History in 3D

This paper introduces KeystoneDepth, the largest and most diverse collection of rectified historical stereo image pairs to date, consisting of tens of thousands of stereographs of people, events, objects, and scenes recorded between 1864 and 1966. Leveraging the Keystone-Mast Collection of stereographs from the California Museum of Photography, we apply multiple processing steps to produce clean stereo image pairs, complete with calibration data, rectification transforms, and disparity maps. We introduce a novel stereo rectification technique based on the unique properties of antique stereo cameras. To better visualize the results on 2D displays, we also introduce a self-supervised deep view synthesis technique trained on historical imagery. Our dataset is available at http://keystonedepth.cs. washington.edu/.
Effects of high tibia osteotomy on knee ligamentous forces during gait: A finite element study

It is generally accepted that the high tibial osteotomy (HTO) improves the stress distribution on the articular cartilage of varus knees in the standing posture. The effects of HTO during dynamic activities, e.g., gait, however, is unclear considering the fact that it also affects the tensions of the knee ligaments and hence the knee configuration under external load and knee flexion. In this study, the HTO was simulated on a detailed 3D model of the knee consisting of femur, tibia, cartilage layer, menisci, and four ligaments. The geometry of the model was acquired from the CT and MRI data of a volunteer and the mechanical properties of the model components were obtained from the literature. In total, 42 simulations were conducted in ABAQUS to observe the effects of the correction angle and the posterior tibial slope on the tensions of the ligaments. Results indicated that the ACL tension is highly affected by both the correction angle and the posterior slope. The ACL force was minimal with the optimal wedge-angle and a small slope. LCL force decreased significantly following HTO, but showed a slight increase for smaller slopes. The PCL force, on the other hand, increased slightly following HTO and with smaller posterior slopes. It was concluded that the most important effect of the HTO and posterior slope on the knee ligaments is that of the ACL. Care should be taken to perform the surgery with optimum correction angle, to pass the mechanical axis through the center of the knee joint, and at the same time keep the posterior slope sufficiently small, beyond 10 degrees.
The Mediating Role of Learning Analytics to Improve Student Academic Performance

Recent indicators of the success of every college graduate still refer to the cumulative academic achievement index. The era of disruption of the digitalization of education has posed different challenges and problems to universities to improve student academic performance. The objective of this study was to determine the extent of influences and relationships of antecedent factors, including soft skills and learning analytics, the intervening variables in improving this performance. This study engaged 203 active students taking courses in the fifth and sixth semesters of universities in Pontianak, West Kalimantan, Indonesia. The research was in the form of surveys. Questionnaires were distributed online through Google Form. SEM-PLS Method was conducted for the analysis. Results show strong influences and relationships between academic, emotional learning and soft skills through learning analytics in improving student academic performance. Furthermore, the negative influences of soft skills have insignificance on this performance. Lastly, a socio-economic factor is irrelevantly linked to learning analytics and student academic performance. All of these findings indicate that academic, personal, socio-economic antecedents are no longer relevant to previous research.
A Machine Learning Approach for Addiction Detection Using Phase Amplitude Coupling of EEG Signals

Currently, the detection of opioid addiction is done by biological tests, but these tests are time-consuming, and their result can be changed by applying some tricks. Using bio-signals such as EEG for detecting opioid abuse can be a good alternative to the current biological tests. In this study, we are aimed to employ EEG signal to detect opioid addiction. The dataset of this study consist of a 19-channel resting-state EEG signal recorded from 22 opioid addicts and 22 healthy normal individuals (without a history of substance abuse). Extracted features of EEG signal include phase-amplitude coupling (PAC) among delta, theta, alpha1, alpha 2, beta1, beta2, and gamma frequency bands. Informative features that can discriminate addicted groups from normal groups are selected by statistical tests and the Minimum Redundancy Maximum Relevance (mRMR) technique. Then selected features are fed to the k-nearest neighbors (KNN) classifier, which is evaluated by Leave-one-out cross-validation. The proposed algorithm classified the addicted and normal group with 93.18% accuracy, 100% sensitivity, and 86.36% specificity. Analyzing the results indicates that delta-beta1 coupling and FZ channel had the most participation in the selected features. The obtained results show that the proposed technique based on EEG signals PAC can be useful in addiction detection.
Electromagnetism as a means for understanding materials mechanics phenomena in magnetic materials

Electromagnetic properties are an interesting means for monitoring a variety of materials' mechanical properties in ferro‐ and paramagnetic materials non‐destructively. Those properties include uni‐ und multi‐axial stress states as well as plasticity and fatigue damage and can be measured at macro‐ as well as at microscopic scales, depending on what measurement equipment will be used. The article describes the general electromagnetic phenomena to be considered as well as the equipment to be used before presenting a variety of different experimental results from which the materials mechanical properties mentioned above can be directly derived being an ideal means for monitoring the health of any magnetic metallic structure.