bluuebunny/crossref_metadata_2025 huggingface dataset:
Reducing Muscle Activity when Playing Tremolo by Using Electrical Muscle Stimulation to Learn Efficient Motor Skills
Authors: Niijima Arinobu , Takeda Toki , Tanaka Kentaro , Aoki Ryosuke , Koike Yukio
Published: September 2021

When beginners play the piano, the activity of the forearm muscles tends to be greater than that of experts because beginners move their fingers with more force than necessary. Reducing forearm muscle activity is important for pianists to prevent fatigue and injury. However, it is difficult for beginners to learn how to do so by themselves. We propose using electrical muscle stimulation (EMS) to teach beginners how to reduce this muscle activity while playing a tremolo: a rapid alternation between two notes. Since experts use wrist rotation efficiently when playing tremolos, we propose an EMS-based support system that applies EMS not to muscles that are relevant to moving the fingers but to the supinator and pronator teres muscles, which are involved in wrist rotation. We conducted a user study with 16 beginners to investigate how the forearm muscle activity on the extensor pollicis longus and digitorum muscles changed when using our EMS-based support system. We divided the participants into two groups: an experimental group who practiced by themselves with EMS and a control group who practiced by themselves without EMS and then practiced with instruction. When practicing by themselves, practicing with EMS was more effective than that without EMS; the activity levels of the extensor pollicis longus and digitorum muscles were significantly lower with EMS, and the participants felt less fatigue when playing tremolos. By comparing the improvement in reducing muscle activity between practicing with EMS and practicing with instruction, there was no significant difference. The results suggest that our EMS-based support system can reduce target muscle activity by applying EMS to other muscles to teach beginners how to move limbs efficiently.
SquiggleMilli
Authors: Regmi Hem , Saadat Moh Sabbir , Sur Sanjib , Nelakuditi Srihari
Published: September 2021

This paper proposes SquiggleMilli, a system that approximates traditional Synthetic Aperture Radar (SAR) imaging on mobile millimeter-wave (mmWave) devices. The system is capable of imaging through obstructions, such as clothing, and under low visibility conditions. Unlike traditional SAR that relies on mechanical controllers or rigid bodies, SquiggleMilli is based on the hand-held, fluidic motion of the mmWave device. It enables mmWave imaging in hand-held settings by re-thinking existing motion compensation, compressed sensing, and voxel segmentation. Since mmWave imaging suffers from poor resolution due to specularity and weak reflectivity, the reconstructed shapes could be imperceptible by machines and humans. To this end, SquiggleMilli designs a machine learning model to recover the high spatial frequencies in the object to reconstruct an accurate 2D shape and predict its 3D features and category. We have customized SquiggleMilli for security applications, but the model is adaptable to other applications with limited training samples. We implement SquiggleMilli on off-the-shelf components and demonstrate its performance improvement over the traditional SAR qualitatively and quantitatively.
A City-Wide Crowdsourcing Delivery System with Reinforcement Learning
Authors: Ding Yi , Guo Baoshen , Zheng Lin , Lu Mingming , Zhang Desheng , Wang Shuai , Son Sang Hyuk , He Tian
Published: September 2021

The revolution of online shopping in recent years demands corresponding evolution in delivery services in urban areas. To cater to this trend, delivery by the crowd has become an alternative to the traditional delivery services thanks to the advances in ubiquitous computing. Notably, some studies use public transportation for crowdsourcing delivery, given its low-cost delivery network with millions of passengers as potential couriers. However, multiple practical impact factors are not considered in existing public-transport-based crowdsourcing delivery studies due to a lack of data and limited ubiquitous computing infrastructures in the past. In this work, we design a crowdsourcing delivery system based on public transport, considering the practical factors of time constraints, multi-hop delivery, and profits. To incorporate the impact factors, we build a reinforcement learning model to learn the optimal order dispatching strategies from massive passenger data and package data. The order dispatching problem is formulated as a sequential decision making problem for the packages routing, i.e., select the next station for the package. A delivery time estimation module is designed to accelerate the training process and provide statistical delivery time guarantee. Three months of real-world public transportation data and one month of package delivery data from an on-demand delivery platform in Shenzhen are used in the evaluation. Compared with existing crowdsourcing delivery algorithms and widely used baselines, we achieve a 40% increase in profit rates and a 29% increase in delivery rates. Comparison with other reinforcement learning algorithms shows that we can improve the profit rate and the delivery rate by 9% and 8% by using time estimation in action filtering. We share the data used in the project to the community for other researchers to validate our results and conduct further research.1 [1].
SyncUp
Authors: Zhou Zhongyi , Xu Anran , Yatani Koji
Published: September 2021

The beauty of synchronized dancing lies in the synchronization of body movements among multiple dancers. While dancers utilize camera recordings for their practice, standard video interfaces do not efficiently support their activities of identifying segments where they are not well synchronized. This thus fails to close a tight loop of an iterative practice process (i.e., capturing a practice, reviewing the video, and practicing again). We present SyncUp, a system that provides multiple interactive visualizations to support the practice of synchronized dancing and liberate users from manual inspection of recorded practice videos. By analyzing videos uploaded by users, SyncUp quantifies two aspects of synchronization in dancing: pose similarity among multiple dancers and temporal alignment of their movements. The system then highlights which body parts and which portions of the dance routine require further practice to achieve better synchronization. The results of our system evaluations show that our pose similarity estimation and temporal alignment predictions were correlated well with human ratings. Participants in our qualitative user evaluation expressed the benefits and its potential use of SyncUp, confirming that it would enable quick iterative practice.
Who Am I?
Authors: Barbosa Natã M. , Wang Gang , Ur Blase , Wang Yang
Published: September 2021

To enable targeted ads, companies profile Internet users, automatically inferring potential interests and demographics. While current profiling centers on users' web browsing data, smartphones and other devices with rich sensing capabilities portend profiling techniques that draw on methods from ubiquitous computing. Unfortunately, even existing profiling and ad-targeting practices remain opaque to users, engendering distrust, resignation, and privacy concerns. We hypothesized that making profiling visible at the time and place it occurs might help users better understand and engage with automatically constructed profiles. To this end, we built a technology probe that surfaces the incremental construction of user profiles from both web browsing and activities in the physical world. The probe explores transparency and control of profile construction in real time. We conducted a two-week field deployment of this probe with 25 participants. We found that increasing the visibility of profiling helped participants anticipate how certain actions can trigger specific ads. Participants' desired engagement with their profile differed in part based on their overall attitudes toward ads. Furthermore, participants expected algorithms would automatically determine when an inference was inaccurate, no longer relevant, or off-limits. Current techniques typically do not do this. Overall, our findings suggest that leveraging opportunistic moments within pervasive computing to engage users with their own inferred profiles can create more trustworthy and positive experiences with targeted ads.
BreathTrack
Authors: Islam Bashima , Rahman Md Mahbubur , Ahmed Tousif , Ahmed Mohsin Yusuf , Hasan Md Mehedi , Nathan Viswam , Vatanparvar Korosh , Nemati Ebrahim , Kuang Jilong , Gao Jun Alex
Published: September 2021

Breathing biomarkers, such as breathing rate, fractional inspiratory time, and inhalation-exhalation ratio, are vital for monitoring the user's health and well-being. Accurate estimation of such biomarkers requires breathing phase detection, i.e., inhalation and exhalation. However, traditional breathing phase monitoring relies on uncomfortable equipment, e.g., chestbands. Smartphone acoustic sensors have shown promising results for passive breathing monitoring during sleep or guided breathing. However, detecting breathing phases using acoustic data can be challenging for various reasons. One of the major obstacles is the complexity of annotating breathing sounds due to inaudible parts in regular breathing and background noises. This paper assesses the potential of using smartphone acoustic sensors for passive unguided breathing phase monitoring in a natural environment. We address the annotation challenges by developing a novel variant of the teacher-student training method for transferring knowledge from an inertial sensor to an acoustic sensor, eliminating the need for manual breathing sound annotation by fusing signal processing with deep learning techniques. We train and evaluate our model on the breathing data collected from 131 subjects, including healthy individuals and respiratory patients. Experimental results show that our model can detect breathing phases with 77.33% accuracy using acoustic sensors. We further present an example use-case of breathing phase-detection by first estimating the biomarkers from the estimated breathing phases and then using these biomarkers for pulmonary patient detection. Using the detected breathing phases, we can estimate fractional inspiratory time with 92.08% accuracy, the inhalation-exhalation ratio with 86.76% accuracy, and the breathing rate with 91.74% accuracy. Moreover, we can distinguish respiratory patients from healthy individuals with up to 76% accuracy. This paper is the first to show the feasibility of detecting regular breathing phases towards passively monitoring respiratory health and well-being using acoustic data captured by a smartphone.
Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing
Authors: Meegahapola Lakmal , Labhart Florian , Phan Thanh-Trung , Gatica-Perez Daniel
Published: September 2021

According to prior work, the type of relationship between a person consuming alcohol and others in the surrounding (friends, family, spouse, etc.), and the number of those people (alone, with one person, with a group) are related to many aspects of alcohol consumption, such as the drinking amount, location, motives, and mood. Even though the social context is recognized as an important aspect that influences the drinking behavior of young adults in alcohol research, relatively little work has been conducted in smartphone sensing research on this topic. In this study, we analyze the weekend nightlife drinking behavior of 241 young adults in a European country, using a dataset consisting of self-reports and passive smartphone sensing data over a period of three months. Using multiple statistical analyses, we show that features from modalities such as accelerometer, location, application usage, bluetooth, and proximity could be informative about different social contexts of drinking. We define and evaluate seven social context inference tasks using smartphone sensing data, obtaining accuracies of the range 75%-86% in four two-class and three three-class inferences. Further, we discuss the possibility of identifying the sex composition of a group of friends using smartphone sensor data with accuracies over 70%. The results are encouraging towards supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully and reducing the need for user self-reports when creating drink logs for self-tracking tools and public health studies.
FaceSense
Authors: Kakaraparthi Vimal , Shao Qijia , Carver Charles J. , Pham Tien , Bui Nam , Nguyen Phuc , Zhou Xia , Vu Tam
Published: September 2021

Face touch is an unconscious human habit. Frequent touching of sensitive/mucosal facial zones (eyes, nose, and mouth) increases health risks by passing pathogens into the body and spreading diseases. Furthermore, accurate monitoring of face touch is critical for behavioral intervention. Existing monitoring systems only capture objects approaching the face, rather than detecting actual touches. As such, these systems are prone to false positives upon hand or object movement in proximity to one's face (e.g., picking up a phone). We present FaceSense, an ear-worn system capable of identifying actual touches and differentiating them between sensitive/mucosal areas from other facial areas. Following a multimodal approach, FaceSense integrates low-resolution thermal images and physiological signals. Thermal sensors sense the thermal infrared signal emitted by an approaching hand, while physiological sensors monitor impedance changes caused by skin deformation during a touch. Processed thermal and physiological signals are fed into a deep learning model (TouchNet) to detect touches and identify the facial zone of the touch. We fabricated prototypes using off-the-shelf hardware and conducted experiments with 14 participants while they perform various daily activities (e.g., drinking, talking). Results show a macro-F1-score of 83.4% for touch detection with leave-one-user-out cross-validation and a macro-F1-score of 90.1% for touch zone identification with a personalized model.
A SAR Target Recognition Method Based on Decision Fusion of Multiple Features and Classifiers
Authors: Lu Zhengwu , Jiang Guosong , Guan Yurong , Wang Qingdong , Wu Jianbo
Published: September 2021

A synthetic aperture radar (SAR) target recognition method combining multiple features and multiple classifiers is proposed. The Zernike moments, kernel principal component analysis (KPCA), and monographic signals are used to describe SAR image features. The three types of features describe SAR target geometric shape features, projection features, and image decomposition features. Their combined use can effectively enhance the description of the target. In the classification stage, the support vector machine (SVM), sparse representation-based classification (SRC), and joint sparse representation (JSR) are used as the classifiers for the three types of features, respectively, and the corresponding decision variables are obtained. For the decision variables of the three types of features, multiple sets of weight vectors are used for weighted fusion to determine the target label of the test sample. In the experiment, based on the MSTAR dataset, experiments are performed under standard operating condition (SOC) and extended operating conditions (EOCs). The experimental results verify the effectiveness, robustness, and adaptability of the proposed method.
Application of EEG Signal Recognition Method Based on Duffing Equation in Psychological Stress Analysis
Authors: Chai Min , Ba Lei
Published: September 2021

Based on the study of the feature extraction algorithm based on the multiple empirical mode decomposition of the Duffing equation, this paper proposes a corresponding improved algorithm, completes the identification and analysis of the psychological pressure dimension space under the audiovisual induction method, and designs two typical psychological types of music and pictures. Based on the stress induction experiment, an audiovisual-induced psychological stress recognition system based on EEG (electroencephalogram) signals was built. Aiming at the problem that the spatial uniform sampling method cannot well reflect the dynamic characteristics of the multivariate EEG signal, based on the Duffing equation, a nonuniform sampling algorithm that adaptively selects the projection direction is proposed. At present, the use of the Duffing equation to detect weak unknown signals is to select a set of fixed parameters. Analysis of these two aspects to determine the parameters of the system is based on the parameter analysis of the Duffing equation oscillator. Due to the sensitivity of the Duffing equation to the initial value, the choice of parameters has a great influence on the detection effect. In response to this situation, the relationship between the parameters and initial values of the Duffing equation is analyzed. From the relationship between the parameters and the initial values, the influence of different parameters on the detection effect is analyzed to verify the superiority of the current equation parameters. First, the multichannel EEG signal is nonuniformly sampled multiempirical modal decomposition, and an effective intrinsic modal function is selected to extract the mental stress EEG characteristics. Experimental results show that the EEG signal recognition algorithm based on the Duffing equation effectively extracts EEG signal features and improves the classification accuracy of mental stress EEG signals.
[Retracted] Evaluation of Teachers’ Educational Technology Ability Based on Fuzzy Clustering Generalized Regression Neural Network
Authors: Zhao Jie , Guan Honghai , Lu Changpeng , Zheng Yushu
Published: September 2021

The improvement of teachers’ educational technology ability is one of the main methods to improve the management efficiency of colleges and universities in China, and the scientific evaluation of teachers’ ability is of great significance. In view of this, this study proposes an evaluation model of teachers’ educational technology ability based on the fuzzy clustering generalized regression neural network. Firstly, the comprehensive evaluation structure system of teachers’ educational technology ability is constructed, and then the prediction method of teachers’ ability based on fuzzy clustering algorithm is analysed. On this basis, the optimization prediction method of fuzzy clustering generalized regression neural network is proposed. Finally, the application effect of fuzzy clustering generalized regression neural network in the evaluation of teachers’ educational technology ability is analysed. The results show that the evaluation system of teachers’ educational technology ability proposed in this study is scientific and reasonable; fuzzy clustering generalized regression neural network model can better accurately predict the ability of teachers’ educational technology and can quickly realize global optimization. According to the fitness analysis results of the fuzzy clustering generalized regression neural network model, the model converges after the 20th iteration and the fitness value remains about 1.45. Therefore, the fuzzy clustering generalized regression neural network has stronger adaptability and has been optimized to a certain extent. The average evaluation accuracy of fuzzy clustering generalized regression neural network model is 98.44%, and the evaluation results of the model are better than other algorithms. It is hoped that this study can provide some reference value for the evaluation of teachers’ educational technology ability in colleges and universities in China.
Analysis of Efficacy, Safety, and Prognostic Factors of mFOLFOX6 Regimen Combined with Cetuximab and Simvastatin in the Treatment of K-RAS Mutant Colorectal Cancer
Authors: Li Guodong , Liu Jiangang
Published: September 2021

Colorectal cancer (CRC) is one of the most common malignant tumors with high morbidity and mortality. The early symptoms are latent, and most patients are in the middle or late stage when they are diagnosed. The best opportunity for surgery has been lost, and surgical resection has failed to achieve good results. In clinical practice, targeted therapy or chemotherapy is usually the main treatment. The mFOLFOX6 regimen is a standardized regimen for the treatment of advanced CRC. The main drugs in this regimen are oxaliplatin and 5-fluorouracil (5-FU). Patients with advanced CRC combined with standard chemotherapy regimens can achieve a higher resection rate of liver metastases in unresectable patients, which can achieve significant survival improvement. Therefore, in this study, oxaliplatin + calcium folinate + 5-Fu + mFOLFOX6 regimen was combined with cetuximab and simvastatin to treat CRC patients, and the clinical efficacy and prognosis were analyzed, as well as the prognostic factors. The results showed that the addition of simvastatin on the basis of conventional mFOLFOX6 regimen combined with cetuximab chemotherapy could effectively improve the efficacy, reduce the total incidence of adverse reactions, improve the overall survival rate, and prolong the overall survival time of patients. Pathological grade and peritoneal metastasis were the factors affecting the mean survival time of CRC patients.
Research on Educational Information Platform Based on Cloud Computing
Authors: Fan Ling , Xia Meiyi , Huang Ping , Hu Jianmin
Published: September 2021

The traditional method only pays attention to hardware construction and ignores the data processing steps, which leads to high redundant resource occupancy rate, untimely resource sharing, and low platform data safety factor. In order to solve this problem, this paper establishes an educational information platform based on cloud computing. The platform gives the overall structure of the education information platform, including the business layer and the support layer. Then the external interface is designed. Based on the MySQL database interface, users are allowed to use custom data formats and storage management modes to ensure flexibility in using data resources and improve the compatibility between smart terminals and virtual machines through the remote desktop terminal architecture. Through the educational data compression and educational resource sharing model, the generation of redundant messages is reduced, thereby realizing the design of the educational information platform. Experimental results show that this method can effectively reduce the occupancy rate of redundant resources, save network bandwidth, and improve the data safety factor of the platform. The resource sharing time is always less than 2.0 s, which verifies the effectiveness of the method
5G-Oriented IoT Big Data Analysis Method System
Authors: Hu Lei , Xia Xianling
Published: September 2021

The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.
Application of Deep Neural Network Model Combined with Factor Analysis in Clinical Nursing Effect Analysis of Blood Glucose Level in Elderly Type 2 Diabetic Patients
Authors: Ding Yi , Zhang DanDan
Published: September 2021

In order to explore the clinical nursing effect of blood glucose level in elderly type 2 diabetic patients, this paper combines the deep neural network model and factor analysis from the perspective of multifactor analysis to construct a multifactor analysis model for the clinical nursing effect of blood glucose levels in elderly type 2 diabetic patients. Moreover, this paper analyzes the effects through experimental methods, collects cases through hospitals, and formulates research methods and related standards based on nursing research needs. In addition, this paper uses statistical methods to perform data processing, uses factor analysis to screen critical factors, and uses deep neural networks to process nursing data. The statistical results of the experimental research show that the deep neural network model combined with factor analysis can play a certain role in the clinical nursing effect. Thus, the blood glucose level analysis in elderly type 2 diabetic patients can provide a reference direction for the clinical care of blood glucose levels in elderly type 2 diabetic patients.
Role and Mechanism of circ_0058063/miR-635 Axis in the Malignant Phenotype of Multiple Myeloma RPMI8226 Cells
Authors: Li Xiaoya , Ding Lingzhi , Gu Geyu , Zheng Changjun , Pan Chenshuai , Zheng Qi , Xiang Ting
Published: September 2021

Objective. This study aims to explore circ_0058063 effect on multiple myeloma cells malignant phenotype and its feasible mechanism. Methods. We selected 47 cases of multiple myeloma tissues and 47 cases of normal bone marrow tissues and then used RT-qPCR method to test circ_0058063 and miR-635 expression in the tissues. Myeloma cells RPMI8226 were transfected with si-circ_0058063, miR-635 mimic, and si-circ_0058063 + anti-miR-635, respectively. Then, we adopt CCK-8 method, flow cytometry method, and Transwell and western blot methods to detect the influences of knockdown of circ_0058063 or miR-635 overexpression on RPMI8226 cell proliferation, apoptosis, migration, and invasion and also Ki-67, Bax, Bcl-2, MMP-2, and MMP-9 protein expression. The dual luciferase reporter gene assay experiment proved that it has regulatory relationship between circ_0058063 and miR-635. Results. circ_0058063 expression of multiple myeloma was higher than that in normal bone marrow tissue (

P
<
0.05

), while miR-635 expression was lower than that in normal bone marrow tissue (

P
<
0.05

). Knockdown of circ_0058063 or overexpression of miR-635 could reduce proliferation capacity, migration, invasion cell quantities, and Ki-67, MMP-2, MMP-9, and Bcl-2 protein expression (

P
<
0.05

), while increasing apoptosis rate together with Bax protein expression (

P
<
0.05

). circ_0058063 targets to negatively regulate miR-635, while knocking down miR-635 reverses the influences of knocking down circ_0058063 on RPMI8226 proliferation, apoptosis, migration, and invasion. Conclusion. circ_0058063 expression increased in multiple myeloma tissues. Knocking down its expression may inhibit myeloma proliferation, migration, and invasion by targeting and upregulating miR-635 and also promote cell apoptosis. As for multiple myeloma treatment, circ_0058063/miR-635 may provide new molecular targets.
The Evaluation and Its Key Influencing Factors Analysis of “Double First-Class” Universities’ Teaching Efficiency in China Based on SBM-DEA and Its Visual Decision Analysis System Development
Authors: Li Hongwei , Pang Sha , Zuo Zhaoying , Tao Min
Published: September 2021

Based on a systematical summary of relevant research on university teaching efficiency, an evaluation index system was built to evaluate teaching efficiency of “Double First-Class” universities in China. The evaluation model to evaluate the teaching efficiency of these universities was built based on Slack-Based Measure model using Superefficiency Data Envelopment Analysis (SBM-DEA). Teaching efficiency of 36 “Double First-Class” universities had been evaluated after their assessment indexes data had been collected. The influencing relations between the teaching efficiency of “Double First-Class” universities and dual prices of each evaluation index had been analysed. It had been proven that teaching efficiency could be greatly improved by increasing the index value with a higher double price. We proceeded to count the frequencies of evaluation indicators with the highest or the second highest dual prices. The indexes with higher total importance were defined as the key factors for the development of teaching efficiency in China’s “Double First-Class” universities. Several countermeasures had been proposed to improve the development of “Double First-Class” universities’ teaching efficiency. Finally, based on the above theoretical research, the evaluation and decision analysis software for teaching efficiency in “Double First-Class” universities in China had been developed by Visual Basic 2015 and MATLAB 2015 with Access database to store the data. It provides a convenient visualization tool to manage the data of the evaluation index system and make the data analysis of the SBM-DEA model.
Development and Application of 3D Bioprinted Scaffolds Supporting Induced Pluripotent Stem Cells
Authors: Lu Dezhi , Liu Yang , Li Wentao , Ma Hongshi , Li Tao , Ma Xiaojun , Mao Yuanqing , Liang Qianqian , Ma Zhenjiang , Wang Jinwu
Published: September 2021

Three‐dimensional (3D) bioprinting is a revolutionary technology that replicates 3D functional living tissue scaffolds in vitro by controlling the layer‐by‐layer deposition of biomaterials and enables highly precise positioning of cells. With the development of this technology, more advanced research on the mechanisms of tissue morphogenesis, clinical drug screening, and organ regeneration may be pursued. Because of their self‐renewal characteristics and multidirectional differentiation potential, induced pluripotent stem cells (iPSCs) have outstanding advantages in stem cell research and applications. In this review, we discuss the advantages of different bioinks containing human iPSCs that are fabricated by using 3D bioprinting. In particular, we focus on the ability of these bioinks to support iPSCs and promote their proliferation and differentiation. In addition, we summarize the applications of 3D bioprinting with iPSC‐containing bioinks and put forward new views on the current research status.
Research on Financial Hedging Decision Based on Exchange Rate Risk in Transnational Supply Chain
Authors: Wang Liang , Xiong Xianyan , Hui Mengmeng
Published: September 2021

This paper considers a three-echelon manufacturer-retailer-supplier supply chain, the purpose of which is to investigate the influence of the bilateral exchange rate risks of import and export and the leading company’s financial hedging on the decision-makers of the supply chain. Firstly, it constructs the profit function and the financial hedging decision-making model of each member in the decentralized supply chain. Secondly, it introduces the incentive mechanism of exchange rate risk hedging in the centralized supply chain. Thirdly, from the perspective of wholesale price agreements and revenue-sharing contracts, it discusses the impact of financial hedging behavior and bilateral exchange rate risks on the decision-making process of each member through mathematical modeling. Finally, it explores the relationships of decision variables through simulation analysis. The results illustrate that (i) for decentralized and centralized decision-making, the manufacturer’s expected profit and profit variance decrease with the increase of the fluctuations of import and export exchange rates under the hedging strategy for exchange rate risks; (ii) compared with the decentralized supply chain, the manufacturer’s expected profit in the centralized supply chain decreases slightly under the revenue-sharing contract; (iii) in the centralized supply chain, if the manufacturer’s risk hedging ratio is high, its profit variance is smaller than that of the decentralized supply chain and the expected profits of the retailer and the supplier will increase significantly; and (iv) for the members of the transnational supply chain, centralized decision-making is better than decentralized decision-making.
Performance of Optical Mobile Communications with User Mobility and Multiple Light Sources
Authors: Dang Jian , Gao Jiajun , Zhang Zaichen , Wu Liang , Zhu Bingcheng , Li Chunguo , Wang Jiangzhou
Published: September 2021

Optical mobile communication (OMC) is a recently proposed optical wireless communication concept aiming to provide very high‐speed data rate optical wireless links for multiple and, in general, distributed mobile users. Previous work analyzed the rate performance of a two‐user OMC system without user mobility. This paper extends the rate analysis to multiple users with mobility. The scenario of employing multiple light sources with possible user grouping is also considered. User mobility and multiple light sources lead to new challenges on the system design which are addressed for broadcast downlink communication in this work. Simulations show that user mobility decreases the rate, and the way of how to utilize multiple light sources has great impact on the performance. In particular, simultaneous power division usage of multiple light sources through user grouping and power allocation brings almost no gain as compared with the case of single light source. On the other hand, time division usage of multiple light sources is capable of compensating for the hardware deficiency and thus increasing the rate greatly. It is found that OMC is not only superior to the conventional scheme with nonadjustable channel gains but also outperforms free space optical scheme at high signal‐to‐noise ratio region.
Natural Products in Therapeutic Management of Multineurodegenerative Disorders by Targeting Autophagy
Authors: Shaikh Sibhghatulla , Ahmad Khurshid , Ahmad Syed Sayeed , Lee Eun Ju , Lim Jeong Ho , Beg Mirza Masroor Ali , Verma Amit K. , Choi Inho
Published: September 2021

Autophagy is an essential cellular process that involves the transport of cytoplasmic content in double‐membraned vesicles to lysosomes for degradation. Neurons do not undergo cytokinesis, and thus, the cell division process cannot reduce levels of unnecessary proteins. The primary cause of neurodegenerative disorders (NDs) is the abnormal deposition of proteins inside neuronal cells, and this could be averted by autophagic degradation. Thus, autophagy is an important consideration when considering means of developing treatments for NDs. Various pharmacological studies have reported that the active components in herbal medicines exhibit therapeutic benefits in NDs, for example, by inhibiting cholinesterase activity and modulating amyloid beta levels, and α‐synuclein metabolism. A variety of bioactive constituents from medicinal plants are viewed as promising autophagy controllers and are revealed to recover the NDs by targeting the autophagic pathway. In the present review, we discuss the role of autophagy in the therapeutic management of several NDs. The molecular process responsible for autophagy and its importance in various NDs and the beneficial effects of medicinal plants in NDs by targeting autophagy are also discussed.
Seismic Resistance Properties of Improved Dry‐Type Beam‐Column Joint: An Experimental Research
Authors: Ma Wei , Yang Fan , Hu Pan , Liang Haiyi , Xu Kai
Published: September 2021

Dry‐type joints are an advanced type of sustainable beam‐column connection mode used in the prefabricated concrete frame structural system. This paper proposed an improvement scheme for high‐strength bolt dry‐type joints and designed a new type of common bolt dry‐type joints. A pseudo test involving low‐cycle repeated loading is conducted to assess the seismic resistance properties of new joints including damage mode, hysteretic curve, skeleton curve, and ductility factor. Numerical simulation is applied to validate the rationality of experimental results. It is found that when the bending capacity of the end block of the beam is consistent with that of the bolt, the deformation of the bolt will no longer increase greatly after a period of large deformation; at this period, the bolt does not fully enter the plastic stage, but at this time, the end block of the beam begins to appear large cracks and enter the plastic deformation and has good energy dissipation performance.
[Retracted] CT and MRI Imaging Characteristics of Gastrointestinal and Mesangial Reactive Nodular Fibrous Pseudotumor Based on Virtual Reality
Authors: Li Shouhong , Guo Xiaohua , Liu Yongjiu , Jiang Moxuan
Published: September 2021

In recent years, with the continued development and development of science and technology and the increasing maturity of medical technology, reactive nodular fibrous pseudotumors remain difficult to diagnose. Therefore, this paper studies CT and MRI imaging properties based on hypothetical gastrointestinal and mesangial reactive erythema nodosum fibrous pseudotumors to improve the diagnosis and treatment of reactive nodular fibrous pseudotumors. In this study, 48 patients with gastrointestinal and intermediate‐reactive erythema nodosum fibrous pseudotumors in the gastrointestinal tract and experimental group were screened and identified by surgical pathology and immunohistochemistry. CT or magnetic resonance imaging was used in all patients. An analysis is performed, and the diagnostic values are compared with the pathological results. Experiments have proved that the postoperative reexamination of reactive nodular fibrous pseudotumors found that single masses are common, tumor sizes are different, and most small nodules have relatively more uniform lesion density, and the density of the plain scan is close to the muscle density, and the delay can be seen after enhancement. When the mass is large, the density is uniform, and low‐density foci of different degrees can be seen in some masses, and this part rarely shows enhancement. The imaging phenomenon and clinical features of reactive nodular fibrous pseudotumors have specific features that distinguish them from invasive tumors such as gastrointestinal stromal tumors and provide an important basis for accurate clinical diagnosis and treatment.
Grading Method of Potted Anthurium Based on RGB-D Features
Authors: Wei Hongyu , Tang Wenqi , Chu Xuan , Mu Yinghui , Ma Zhiyu
Published: September 2021

A grading method of potted Anthurium based on machine vision is proposed. A detection system is designed to acquire color images and depth images of potted Anthurium, and the three-dimensional point-cloud image is reconstructed after registration. According to the testing requirements of potted Anthurium, the minimum enclosing rectangle method is used to measure the width of crowns and spathes. The bubble sequencing method is used to measure the plant height, and the clustering segmentation method is used to calculate the number of spathes. Online automatic grading software for potted Anthurium is developed. Compared with manual measurement, the average measurement accuracies of machine vision for crown width, plant height, spathe width, and spathe number are 98.4%, 98.4%, 98.8%, and 86.7%, respectively. The accuracy rate of grading is 85.86%, which can meet the requirements of automatic grading of potted Anthurium.
A Prediction Method for Postconstruction Settlement of Pile‐Soil Composite Subgrade Based on Fuzzy Comprehensive Evaluation
Authors: Shan Hao , Jiang Guanghui , Chang Yajing , Cheng Junli , Hong Baoning , Wang Shengcheng
Published: September 2021

This paper presents a postconstruction settlement prediction method for pile‐soil composite subgrade based on the multilevel fuzzy comprehensive evaluation principle. In this method, the variation range of postconstruction settlement can be obtained from a simple calculation based on the basic data of actual engineering. Firstly, according to the characteristics of influencing factors in the construction of soft soil subgrade, the evaluation index set and two‐level factor index sets were selected. The grading standards of the evaluation index and factor index were determined according to the allowable value of the standard and the numerical simulation results. Secondly, each factor index was standardized, and the normal distribution function in the form of exponential was used to construct the standard membership function for the first and second factor indexes. Finally, the comprehensive evaluation matrix of postconstruction settlement of composite subgrade was constructed based on the entropy weight method. The variation range of postconstruction settlement was predicted by the principle of maximum membership. The example analysis shows that the predicted results of the prediction method and the field measurement method are in good agreement, indicating that the proposed method can realize the postconstruction settlement prediction of composite subgrade, and the results are more accurate and more instructive.
Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks
Authors: Li Xiaowei , Wang Yuting , Wu Yao , Chen Jun , Zhou Jibiao
Published: September 2021

This study conducts a comprehensive comparative analysis of regression-based multinomial models and artificial neural network models in intercity travel mode choices. The four intercity travel modes of airplane, high-speed rail (HSR), train, and express bus were used for analysis. Passengers’ activity data over the process of intercity travel were collected to develop the models. The standard multinomial logit (MNL) regression and Bayesian multinomial logit (BMNL) regression were compared with the radial basis function (RBF) and multilayer perceptron (MLP). The results show that MLP performs best in terms of predictive accuracy, followed by BMNL and MNL, and RBF is the least accurate. The performances of all models were examined against changes in data balance, and it was found that rebalancing can improve fitting performance while slightly reducing the predictive performance. This comparative study and its parameter estimation shed new light on the comparison of traditional and emerging models in travel behavior studies, and the findings can be used as heuristic guidance for all stakeholders.
Study on Hydrodynamics Model of Total Tailings Filling Slurry Dehydration
Authors: Zhang Aiqing , Li Jinyun , Wang Yiming
Published: September 2021

In order to ensure the dehydration effect of the whole tailings backfill, a dehydration model of the whole tailings backfill was established based on hydrodynamics on the basis of satisfying certain assumptions, and the theoretical solution was carried out. The control variable method is used to study the variation of dewatering capacity with dewatering depth and radius, and the applicability of the dewatering model is verified by experiments. The results show that the dewatering capacity increases with the increase of dewatering radius and decreases with the increase of dewatering depth by a quadratic polynomial. By comparing the experimental values with the theoretical ones, it is found that the variation law of the dewatering capacity with radius is the same and increases with the increase of dewatering depth. The reason is that the content of fine particles in the unclassified tailings is large and the filter hole on the branch pipe is blocked under the action of the hydrodynamic force. Therefore, the dewatering effect of the new root‐like dehydration tube can be guaranteed by using the branch pipe arranged at the full height of main dewatering pipe. The dehydration model is modified by the test results to ensure the applicability.
Network Pharmacology Analysis of the Identification of Phytochemicals and Therapeutic Mechanisms of Paeoniae Radix Alba for the Treatment of Asthma
Authors: Wang Jingwei , Peng Ling , Jin Lu , Fu Huiying , Shou Qiyang
Published: September 2021

Background. Paeoniae Radix Alba (PRA), the root of the plant Paeonia lactiflora Pall., has been suggested to play an important role for the treatment of asthma. A biochemical understanding of the clinical effects of Paeoniae Radix Alba is needed. Here, we explore the phytochemicals and therapeutic mechanisms via a systematic and comprehensive network pharmacology analysis. Methods. Through TCMSP, PubChem, GeneCards database, and SwissTargetPrediction online tools, potential targets of active ingredients from PRA for the treatment of asthma were obtained. Cytoscape 3.7.2 was used to determine the target of active ingredients of PRA. Target protein interaction (PPI) network was constructed through the STRING database. The Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genes (KEGG) pathway enrichment analysis were analyzed through the biological information annotation database (DAVID). Results. Our results indicate that PRA contains 21 candidate active ingredients with the potential to treat asthma. The enrichment analysis of GO and KEGG pathways found that the treatment of asthma by PRA may be related to the process of TNF (tumor necrosis factor) release, which can regulate and inhibit multiple signaling pathways such as ceramide signaling. Conclusions. Our work provides a phytochemical basis and therapeutic mechanisms of PRA for the treatment of asthma, which provides new insights on further research on PRA.