Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis and Characterization of Cationic and Anionic Cyclodextrin Oligomers and Their Use in Layer-by-Layer Film Formation

E-mail: sungyun@cnu.ac. krReceived Apri 2, 2013, Accepted April 9, 2013Ionically modified β-cyclodextrins, which have excellent water-solubility, have been interested in purificationtechnology as well as drug carrier system. The present study summarizes the synthesis and characterization ofcationic and anionic β-cyclodextrin (β-CyD) products using by polycondensation. The oligo (β-CyD)s aresynthesized from β-CyD, epichlorohydrin (EP) and choline chloride (CC; for cationic polymer) or chloroaceticacid (CAA; for anionic polymer) through one step polycondenstaion process. Unlike the previous studies, wesuccessfully purified the ionic β-CyD condensation products from the β-CyD reaction mixtures and accom-plished a great level of structural analysis. The detailed structural analysis of these ionic β-CyD compounds isdone by
Evaluation and Optimization of Steam and Lye Peeling Processes of Sweet Potato (Ipomea batatas) using Response Surface Methodology (RSM)

Abstract Sweet potato of uniform sizes and shape were subjected to steam peeling (temperature 80–100°C and peeling time 5–15 min) and lye peeling (sodium hydroxide concentration 8–12%, temperature 80–100°C and peeling time 3–5 min). Response surface methodology was used to analyze and optimize the process parameters in each case. For steam peeling, the peel loss and unpeeled surface area were 6.85 and 28.16%, respectively, while optimum peeling conditions where temperature and peeling time of 100°C and 5 min, respectively, for minimum peel loss and unpeeled surface area. For lye peeling, however, the peel loss and unpeeled surface area were 13.19 and 13.35%, respectively, and optimum peeling conditions were lye concentration, temperature and peeling time of 8.89%, 100°C and 4.75 min, respectively, for minimum peel loss and unpeeled surface area. The models developed in each case (R-squared of 0.9225 and 0.8428; 0.5929 and 0.8346) were adequate in producing responses as a function of the independent variables. From the optimized values and results obtained, lye peeling was more suitable for sweet potato.
Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

As COVID-19 spread worldwide, many major grain-producing countries have adopted measures to restrict their grain exports; food security has aroused great concern from various parties. How to improve grain production has become one of the most important issues facing all countries. However, crop diseases are a difficult problem for many farmers so it is important to master the severity of crop diseases timely and accurately to help staff take further intervention measures to minimize plants being further infected. In this paper, a restructured residual dense network was proposed for tomato leaf disease identification; this hybrid deep learning model combines the advantages of deep residual networks and dense networks, which can reduce the number of training process parameters to improve calculation accuracy as well as enhance the flow of information and gradients. The original RDN model was first used in image super resolution, so we need to restructure the network architecture for classification tasks through adjusted input image features and hyper parameters. Experimental results show that this model can achieve a top-1 average identification accuracy of 95% on the Tomato test dataset in AI Challenger 2018 datasets, which verifies its satisfactory performance. The restructured residual dense network model can obtain significant improvements over most of the state-of-the-art models in crop leaf identification, as well as requiring less computation to achieve high performance.
Esterification of glycerol over a solid acid biochar catalyst derived from waste biomass

Karanja seed shells were subjected to pyrolysis in an inert atmosphere at different temperatures to prepare a biochar. The biochar was characterized by X-ray diffraction, FT-infrared spectroscopy, laser Raman spectroscopy, thermogravimetric analysis, CHNS-elemental analysis, BET surface area analysis and for the temperature programmed desorption of ammonia. These biochar carbon catalysts were used as catalysts without any functionalization/treatment for the esterification of glycerol with acetic acid. Carbonization at 400 °C led to the formation of biochar with a greater number of strong acidic sites. Amorphous carbon obtained by high temperature carbonization was composed of aromatic carbon sheets oriented in a considerably random fashion. The biochar obtained at 400 °C exhibited the highest glycerol esterification activity. The catalytic activity of the biochar was explained based on its properties derived from the different characterization methods. The biochar catalyst can be reused with consistent activity.
A Sun-Tracking CMOS Image Sensor With Black-Sun Readout Scheme

This article presents a black-sun readout scheme for CMOS image sensors (CISs) that utilizes the black-sun effect caused by a strong illumination condition. Based on the analysis of the black-sun phenomenon in CISs, the proposed CIS extracts the black-sun indication and its strength images while providing normal images without any degradation. By effectively utilizing the black-sun readout capability, a prototype CIS with the proposed readout scheme was identified as a useful solution to track the brightest source of light. In addition, the proposed black-sun readout scheme is reversible to the conventional readout scheme such that it can be used for various purposes in several electronic devices using CISs. The proposed readout scheme was verified in the typical CIS structure, and it shows the feasibility of utilizing it in sun tracking sensor applications for improving energy conversion efficiency. The prototype chip was implemented in a 0.11-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1P4M CIS process with a 2.9-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> pitch.
Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment

Photoplethysmography (PPG) is a noninvasive way to monitor various aspects of the circulatory system, and is becoming more and more widespread in biomedical processing. Recently, deep learning methods for analyzing PPG have also become prevalent, achieving state of the art results on heart rate estimation, atrial fibrillation detection, and motion artifact identification. Consequently, a need for interpretable deep learning has arisen within the field of biomedical signal processing. In this paper, we pioneer novel explanatory metrics which leverage domain-expert knowledge to validate a deep learning model. We visualize model attention over a whole testset using saliency methods and compare it to human expert annotations. Congruence, our first metric, measures the proportion of model attention within expert-annotated regions. Our second metric, Annotation Classification, measures how much of the expert annotations our deep learning model pays attention to. Finally, we apply our metrics to compare between a signal based model and an image based model for PPG signal quality classification. Both models are deep convolutional networks based on the ResNet architectures. We show that our signal-based one dimensional model acts in a more explainable manner than our image based model; on average 50.78% of the one dimensional model’s attention are within expert annotations, whereas 36.03% of the two dimensional model’s attention are within expert annotations. Similarly, when thresholding the one dimensional model attention, one can more accurately predict if each pixel of the PPG is annotated as artifactual by an expert. Through this testcase, we demonstrate how our metrics can provide a quantitative and dataset-wide analysis of how explainable the model is.
A Real-Time CAN-CAN Gateway with Tight Latency Analysis and Targeted Priority Assignment

There is a demand in the automotive industry to connect two CAN-based subsystems. The commercial CAN-CAN gateway supports basic message forwarding with no real-time behavior. To address this issue, a new gateway architecture is described, on which we present a novel worst-case latency analysis. Specifically, we bound the arrival of the messages at the gateway, which is then used by the Pointer Reachability Exploration (PRE) to derive the interfering message jobs. Our analysis computes a safe gateway latency tighter than the conventional one applied in CAN. Furthermore, we propose a Targeted Priority Assignment (TPA) algorithm that targets at the priorities assigned at the CAN bus and runs a reordering at the gateway to enhance the schedulability. TPA performs better than DMPO (Deadline Monotonic Priority Ordering), while OPA (Audsley’s Optimal Priority Assignment) cannot be applied in this context. Evaluation over real-life and scalable CAN message sets is conducted. The reported analysis and priority assignment algorithm are developed for dynamic use to improve the acceptance ratio and can also be deployed statically to provide timing guarantees. This work can be easily extended to support multiple CAN subsystems.
A Feature Fusion Approach for Multiple Signal Classification

This study proposes a feature fusion classifier (FFC) for multiple signal classification (MUSIC) by integrating different feature extraction methods and various machine learning algorithms. The functional principal component analysis (FPCA), common spatial pattern (CSP), discrete wavelet transform (DWT), and autoregressive (AR) model are considered to extract features from different domains. Using the features collected from the above methods, we propose to fit a logistic regression model with a model selection criterion to prevent overfitting and obtain relevant features at first. The relevant features are employed to learn different classifiers via different machine learning algorithms such as support vector machine, random forest, naive Bayes, extreme gradient boosting, and linear discriminant analysis. In addition, the technique of stacked generalization is also employed to integrate the classification results of these machine learning classifiers. Finally, the classifier with the smallest value of cross validation is selected to be the FFC. In our empirical study, three different MUSIC datasets are adopted to investigate the performance of the proposed method. Except for comparing the classification results of the FFC to the above machine learning classifiers, a fusion method that existed in the literature is also considered for comparison. The numerical results reveal that the FFC is capable of selecting useful features automatically and has robust and satisfactory performances for the 3 datasets.
Structure refinement of a decagonal quasicrsytal of Al72Ni20Co8 by convergent-beam electron diffraction

Abstract Structure refinement of a water quenched alloy of Al72Ni20Co8, which is known as a highly ordered decagonal quasicrystal, has been carried out by quantitative comparison of experimental and simulated intensities of convergent-beam electron diffraction (CBED) patterns. In the present study, higher-order Laue zone reflections, whose intensities are senstively changed with atom positions, have been used for the refinement of atom position parameters in the quasiperiodic directions. The refined parameters have been compared with the results obtained by X-ray diffraction.
Emotion Recognition and Discrimination of Facial Expressions using Convolutional Neural Networks.

Emotion recognition from facial expressions is an interesting topic in research field that opens a lucrative door for a tons of applications. Still now the topic remains much challenging since people usually differ reasonably in the way how their expressions are showed. Machine learning approaches are widely used in many state-of-the-art facial emotion recognition techniques. In this paper, an approach for recognizing emotions is proposed that goes ahead with Deep learning concepts giving a priority to Convolutional Neural Networks using the framework of Keras and TensorFlow backend. After the classification phase the task focuses on surveillance using anger and fear emotions. Because according to the Appraisal theory for emotions; fear is due to the primary appraisal that is related to safety of individual, while anger is due to secondary appraisal, which is related to coping with the unsafe conditions. It is revealed that the emotions fear and anger might also be derived from the same neurotransmitter and both the emotions have an effect of threat perceptions. This surveillance task is fulfilled by notifying the user about anger and fear emotions when they pass a higher probability range. For this task a large and a publicly available dataset FER2013 is used which is also a much challenging dataset and the results observed indicate a considerable performance of 64.56.
The Implication of Accessing Hospital's Social Media and Other Digital Communication Channels During COVID-19 Pandemic

The use of social media and other online platforms have increased during the COVID-19 pandemic. Hospital is one of the institutions managing social media and other digital communication media during the corona pandemic. However, number of accesses to social media and other digital communication channels by the public has not been identified. Therefore, the study aims to describe access to the hospitals' social media and its impact. Two hundred sixty-two respondents were involved in this study, selected using a consecutive sampling method. Data collection was using an online questionnaire to obtain the social media's access information. Various variables are used in this research including demographic characteristics, social media ownership, frequency of access to social media and other digital communication channels based on personal perspectives. Results of this study showed that most respondents visited the official website, Instagram, and webinars managed by hospitals. Access to social media and other platforms give benefits for both, community and hospitals.
Hyperspectral Image Classification With Mixed Link Networks

Convolutional neural networks (CNNs) have improved the accuracy of hyperspectral image (HSI) classification significantly. However, CNN models usually generate a large number of feature maps, which lead to high redundancy and cannot guarantee to effectively extract discriminative features for well characterizing the complex structures of HSIs. In this article, two novel mixed link networks (MLNets) are proposed to enhance the representational ability of CNNs for HSI classification. Specifically, the proposed mixed link architectures integrate the feature reusage property of the residual network and the capability of effective new feature exploration of the densely convolutional network, extracting more discriminative features from HSIs. Compared with the dual path architecture, the proposed mixed link architectures can further improve the information flow throughout the network. Experimental results on three hyperspectral benchmark datasets demonstrate that our MLNets achieve competitive results compared with other state-of-the-art HSI classification approaches.
The Implementation of CNN on Website-based Rice Plant Disease Detection

Rice is the staple food of Indonesian society. As Indonesia's population continues to grow, this implies that the need for rice consumption will also increase in the future. Therefore, it is necessary to have a strategy to maintain and increase rice harvest production in Indonesia. Rice farmers need to get maximum support to maintain the quality of the yield and rice produced. Unfortunately, in order to harvest rice at the right time with good quality, farmers often face various obstacles that can cause crop failure. Harvest failure in rice can be caused by various factors e.g. the disease that infects rice plants. To reduce crop failure caused by rice plant diseases, this research proposes a website-based system with the aim of detecting rice plant diseases to optimize agricultural sector. This system was developed by applying the Deep Learning method. The method of image processing was implemented using a Convolutional Neural Network with the GoogLeNet architecture which is then integrated into a website-based application. The results showed an increase in accuracy in the increasing number of epochs for CNN training models. This application is expected to be able to assist rice farmers in analyzing diseases in rice plants that are planted, so that prevention and handling can be carried out in accordance with the aim of minimizing losses from crop failure.
Enhanced pH Sensitivity of AlGaN/GaN Ion-Sensitive Field-Effect Transistor by Recess Process and Ammonium Hydroxide Treatment

AlGaN/gallium nitride (GaN) ion-sensitive field-effect transistors (ISFETs) were fabricated as pH sensors. The sensitivity of the AlGaN/GaN ISFETs was evolved with gate recess process and ammonium hydroxide (NH<sub>4</sub>OH) treatment. By performing the gate recess process, the threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula>) of the ISFET increased from −3.33 to −0.31 V and the maximum conductance (<inline-formula> <tex-math notation="LaTeX">${G}_{\text {M}}$ </tex-math></inline-formula>) of the ISFET increased from 0.8 to 2 mS, with the current sensitivity of the pH sensor improving from 52.25 to <inline-formula> <tex-math notation="LaTeX">$78.86~\mu \text{A}$ </tex-math></inline-formula>/pH. Further, after performing the ammonium hydroxide treatment, the <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> of the ISFET increased from −0.33 to −0.14 V, with the current sensitivity of the pH sensor improving from 78.86 to <inline-formula> <tex-math notation="LaTeX">$84.39~\mu \text{A}$ </tex-math></inline-formula>/pH. To characterize the surface conditions the X-ray photoelectron spectroscopy (XPS) was deployed. The results indicated that many nitrogen vacancies (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {N}}$ </tex-math></inline-formula>) were introduced during the recess process, leading to a negative <inline-formula> <tex-math notation="LaTeX">${V}_{\text {T}}$ </tex-math></inline-formula> shift and a smaller potential sensitivity (<inline-formula> <tex-math notation="LaTeX">${S}_{V}$ </tex-math></inline-formula>), which can be improved by ammonium hydroxide treatment.
Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

Acoustic seabed classification (ASC) is a fast and large-scale seabed sediment survey method. In particular, combining it with an automated classifier can theoretically achieve fast automatic seabed sediment classification. However, owing to the cost of sampling, a lack of labeled data for sediment classification based on seabed acoustic images impedes the training and deployment of classifiers. Herein, we use shallow-water, side-scan sonar images collected from the Pearl River Estuary combined with deep learning to study sediment classification and optimization methods for a small dataset of seabed acoustic images. In this paper, we applied different and deeper convolutional neural networks (CNNs) and used grayscale CIFAR-10 for pretraining to achieve large-span parameter migration and improve model performance. The best result in the experiment is a 3.459% error rate achieved by ResNet after fine tuning, verifying the improvement brought by our fine tuning strategy and the deeper models used in such tasks. The results of data enhancement based on generative adversarial networks (GANs) indicated that this method can improve the accuracy of sediment classification; however, the effects of GANs are limited and they are computationally expensive. Overall, our findings resolve, to an extent, the dilemma of using small datasets of seabed acoustic images for sediment classification and provide a framework for future studies on sediment classification, which has a certain significance in helping people better understand the seabed.
Study on the Roles of Charge Trapping and Fixed Charge on Subthreshold Characteristics of FeFETs

Charge trapping (CT) and fixed charge (FC) are considered in the model of transient negative capacitance (TNC). The roles of CT and FC on subthreshold characteristics of ferroelectric FET (FeFET) are investigated by experiment and simulation. The simulation results show that both CT and FC can modulate the subthreshold characteristics of FeFET by shifting the operation point dynamically and statically, respectively. In addition, ferroelectric hysteresis can be reduced by the compensation due to CT and minor loop formation due to FC in a certain ${V}_{g}$ sweep range. As a result, sub-60 mV/decade subthreshold swing (SS) with nearly hysteresis-free operation observed in experiments can be qualitatively reproduced by our simulation framework of TNC if both CT and FC are well-considered.
Skill Assessment of an Epidural Anesthesia Using the PeriSIM Simulator

Currently, the training in the lumbar epidural anesthesia procedure, a very common procedure, is mainly performed on patients under the supervision of experts. The reason for this situation is that the simulators that exist on the market are not realistic enough to efficiently learn this particularly haptic-sense-based gesture. To avoid this ethical issue and provide an efficient, repeatable, and affordable way of safely learning this gesture, the Ampère laboratory designed PeriSIM: a haptic training computer-based simulator. In this article, the trainees’ skill assessment is tackled. This work presents various machine learning approaches to the evaluation of trainees and introduces a complete skill assessment algorithm to use in conjunction with the PeriSIM simulator. This work also aims to determine the important variables of an lumbar epidural anesthesia procedure for objective assessment purpose.
Electromagnetic Design and Mechanical Behavior Analysis of an 850 MHz All REBCO Nuclear Magnetic Resonance Magnet

A 20 T/ϕ32 mm all REBa2Cu3O7−δ (REBCO, RE = rare earth) REBCO superconducting magnet for 850 MHz nuclear magnetic resonance spectroscopy is being designed. The high-temperature superconductors, REBCO-coated conductors with both high tensile stress and high critical current density under high magnetic field would allow superconducting magnets to be compact and lighter than their low-temperature superconductor counterparts. The 20-T REBCO superconducting magnet consists of a stack of 28 double-pancake coils with the so-called no-insulation methods. Considering the perpendicular magnetic field to the c-axis, five different width tapes, i.e., 4.1, 5.1, 6.1, 7.1, and 8.1 mm were placed on the midplane to both end positions in an axial direction. To improve the magnetic field homogeneity, the inside notch on the 4.1-mm tapes was adopted. The peak-to-peak magnetic field homogeneity over a 5-mm diameter spherical volume is better than 0.8 ppm. In this article, the design of the REBCO coils, electromagnetic field analysis, and stress analysis of the superconducting coils were presented.