bluuebunny/crossref_metadata_2025 huggingface dataset:
A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things
Authors: Khan Zard Ali , Naz Sheneela , khan Rahim , Teo Jason , Ghani Abdullah , Almaiah Mohammed Amin
Published: April 2022

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.
Decision Tree of Materials: A Model of Halal Control Point (HCP) Identification in Small-Scale Bakery to Support Halal Certification
Authors: Sucipto Sucipto , Damayanti Reza Wahyu , Perdani Claudia Gadizza , Kamal Muhammad Arif , Astuti Retno , Hasanah Nur
Published: April 2022

A bakery is a business that bakes flour-based foods, including bread, cookies, cakes, pastries, and pies, and sells them. Some bakeries are also categorized as large scale, medium scale, and small scale. Halal embraces all food category; bakery product can satisfy the challenge and opportunity of halal food segment market. The small-scale bakery will benefit from creating a halal certification to attract new customers. The first stage of submission for halal certification is identifying Halal Control Points (HCP) of materials and production. The material tracing uses a decision tree. The purpose of this study is to identify HCP in materials and production processes and provide alternative improvements. Identification of HCP in material decision trees to determine contains non-HCP (halal) material, HCP material, and haram (forbidden) material. Happy Cake bakery uses 75% non-HCP (halal) materials and 25% HCP (noncertified halal) materials from 80 ingredients. Bakery Canggi Fully has 83.3% halal materials and 16.6% noncertified halal materials from 24 ingredients. Bakery MacCheese has 79% of halal materials and 21% of noncertified halal materials from 43 ingredients. The decision tree makes it very easy to identify the halal status of ingredients. The HCP ingredients need to be replaced with clearly halal ingredients. Substitution of HCP material to halal-certified ingredients may affect production costs, product quality, and profit. Therefore, it is necessary to choose a suitable halal material. Halal certification requires a high commitment of small-scale bakery businesses.
Experimental Investigation on Impact of EGR Configuration on Exhaust Emissions in Optimized PCCI-DI Diesel Engine
Authors: Alemayehu Getachew , Nallamothu Ramesh Babu , Firew Deresse , Gopal Rajendiran
Published: April 2022

The main objective of this work is to analyse the impact of different EGR configurations (no EGR, cold EGR, and hot EGR) on exhaust emissions of PCCI-DI engine. Methanol port injection, dieseline direct injection, advanced injection timing, and different EGR rates were adapted and optimized on the baseline engine. A hybrid algorithm of grey relational analysis with the Taguchi method was implemented for optimization. Results were compared among the PCCI-DI combustion strategy with the baseline using cold EGR, hot EGR, and no EGR configurations. Both cold and hot EGR configurations resulted in lower emission of NOx plus smoke at different loads. At low loads, hot EGR showed promising results of lower HC and CO than the cold EGR with a difference of 18.33% and 33.3%, respectively. NOx and smoke reductions simultaneously and better trade-offs were obtained using cold EGR configuration.
Clinical Statistics of Dysphagia Rehabilitation Provided on Dental Visits to a Partner Hospital
Authors: Ohkubo Mai , Hanzawa Atsushi , Miura Keina , Sugiyama Tetsuya , Ishida Ryo , Fukuda Ken-ichi
Published: April 2022

Maintaining oral hygiene is an important yet often neglected aspect of rehabilitation medicine. Our visiting dental team, which provides dental treatments and swallowing rehabilitation, partnered with a medical hospital that had no dental department and began visiting and treating inpatients at this hospital. This study is aimed at evaluating the effects of dysphagia rehabilitation, and this was jointly conducted by medical and dental hospitals. The survey was conducted between May 2017 and March 2018. We retrospectively examined dysphagia rehabilitation provided to 25 patients (12 men and 13 women) aged 40–92 years (mean age:

77.1
±
12.3

years). The largest number of requests for dental treatment was received from the internal medicine department (13 requests, 52.0%). A total of 39 videofluoroscopic or videoendoscopic examinations of swallowing interventions for dysphagia rehabilitation were conducted. All patients’ oral and swallowing functions were evaluated using the functional oral intake scale (FOIS). At initial assessment, 9, 13, and 0 patients were at FOIS levels 1, 2, and 3 (use of tube feeding), respectively, and 1, 2, and 0 patients were at FOIS levels 4, 5, and 6 (only oral feeding), respectively. At the final assessment, 6, 10, and 4 patients were at FOIS levels 1, 2, and 3, respectively, and 0, 2, and 3 patients were at FOIS levels 4, 5, and 6, respectively. Oral and swallowing functions differed significantly between the first and final visits (

p
=
0.02

). Visits conducted by a team of oral health practitioners to a medical hospital without a dental department appear to have a major impact and will become even more important in the future.
Visualization Research of College Students’ Career Planning Paths Integrating Deep Learning and Big Data
Authors: Guo Jing , Qi Lei
Published: April 2022

As China’s education enters a high-level stage, more and more students graduate from Chinese colleges and universities. In particular, the current employment environment is flexible and multilateral, and there are more and more opportunities to choose from. In view of this situation, this article aims to visualize the career planning (CP) path of college students, so as to help college students adapt to the environment of flexible employment. For deep learning and big data (DLBA) technology, this article proposes the LSTM-Canopy algorithm, which is added to the traditional Canopy algorithm to enhance the self-learning clustering ability of the algorithm. Also, this study applies this algorithm to the visualization system of college students’ CP path, which can effectively improve the analysis and judgment of experts on career. The experiments in this article have proved that the system can meet the normal use of 400–500 users, and the system server has successfully passed 40 load tests, and the running time is also less than 2.5s, which proves the reliability of the system.
COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques
Authors: Kumar Vijay , Zarrad Anis , Gupta Rahul , Cheikhrouhou Omar
Published: April 2022

In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays. The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification. The heading model construction phase utilizes four modified deep learning architectures, namely Modified-VGG16, Modified-VGG19, Modified-ResNet50, and Modified-InceptionV3. An attempt is made to modify these neoteric architectures by incorporating the average pooling and dense layers. The dropout layer is also added to prevent the overfitting problem. Two dense layers with different activation functions are also added. Thereafter, the output of these modified models is applied during the classification phase, when COV-DLS are applied on a COVID-19 chest X-ray image data set. Classification accuracy of 98.61% is achieved by Modified-VGG16, 97.22% by Modified-VGG19, 95.13% by Modified-ResNet50, and 99.31% by Modified-InceptionV3. COV-DLS outperforms existing deep learning models in terms of accuracy and F1-score.
Separating the Impact of Climate Changes and Human Activities on Vegetation Growth Based on the NDVI in China
Authors: Lai Wenli , Wang Mingming , Wei Jun , Zhang Jie , Song Jiayu , Zhou Haiyan , Chou Shuren , Wang Yongping
Published: April 2022

Vegetation growth is affected by both climate changes and human activities. In this study, we investigated the vegetation growth response to climate change (precipitation and temperature) and human activities in nine subregions and for nine vegetation types in China from 1982 to 2015. The normalized difference vegetation index (NDVI) and the RESTREND method based on a multiple linear regression model were employed to this end. An overall increasing trend in the NDVI was observed in recent decades, and the fastest increases were identified in southern China (TrendNDVI = +0.0190) and evergreen broad-leaved forests (TrendNDVI = +0.0152). For >66% of China, vegetation is more sensitive to temperature and less sensitive to precipitation based on the regression coefficients. The water demand for vegetative growth increased significantly from 1999 to 2015 with global warming, especially in parts of the temperate zone. We defined a relative change in the residual trend to quantify the impact of human activities on vegetation.

RESTREND

NDVI

/

NDVI

in two periods (P1, 1982–1998 and P2, 1999–2015) markedly increased, indicating that human activities play a key role in the reversal of land degradation.
An Improved Crop Disease Identification Method Based on Lightweight Convolutional Neural Network
Authors: Wang Tingzhong , Xu Honghao , Hai Yudong , Cui Yutian , Chen Ziyuan
Published: April 2022

Identifying crop disease fast, intelligently and accurately, plays a vital role in agricultural informatization development, while existing methods are almost performed manually, which depends on expert experience, and thus the identifying result is inevitably influenced by personal preferences. To address these issues, an improved crop disease identification method based on convolutional neural network is proposed to process images of crops for identifying diseases. Firstly, the original crop images were cut and normalized, and the irrelevant noises were removed by image data enhancement to improve the generalization ability and recognition accuracy of the training network. Then a neural network with nine convolutional layers is built to work on crop images, the first stage of training loads data samples, and divide the training set and verification set, and then set the learning rate, image intensifier, and optimizer and compile the training convolution model. Finally, it saves the loss and accuracy data during the training process and evaluates the accuracy of the model. In order to improve the training learning rate, Adam optimizer combining momentum algorithm and RMSprop algorithm is used to dynamically adjust the learning rate; the combination of the two algorithms makes the loss function converge to the lowest point faster. Then the feature map after each convolution is obtained by using transferred revolution, and the model is adjusted according to the feature map to further improve the effect of model recognition. Finally, validations were carried out by PlantVillage dataset, which consists of images of about 38 kinds of crops. The experiment result shows that the validation accuracy and the test accuracy are 95.7% and 94.3%, respectively; in addition, the recognition accuracy of apple, corn, grape, and other single classes is about 97%, which proves that the convolutional neural network in this paper has faster training speed and higher accuracy. In addition, the proposed method is less time consuming, which is of great significance to promote the development of smart agriculture and precision agriculture.
Variable Cell Transmission Model for Mixed Traffic Flow with Connected Automated Vehicles and Human-Driven Vehicles
Authors: Jin Yuting , Yao Zhihong , Han Jiazhe , Hu Lu , Jiang Yangsheng
Published: April 2022

The current research on the mixed traffic flow characteristics of human-driven vehicles (HDVs) and connected automated vehicles (CAVs) mainly focuses on the micro-level. To study the characteristics of the mixed traffic flow from the medium and macro level, this paper proposes a variable cell transmission model (VCTM). First, the fundamental diagram is introduced based on the phenomena of hysteresis of traffic flow. Second, the VCTM with different market penetration rates (MPR) of CAVs is proposed based on the classical cell transmission model (CTM). Then, the effectiveness of VCTM is verified by micro-simulation based on the intelligent driver model (IDM). Finally, some congestion indexes are selected to discuss the characteristics of mixed traffic flow based on the VCTM with an expressway simulation. The results show that the traffic capacity and congestion dissipation capacity gradually are increased with the increase of MPR of CAVs. The homogeneous CAVs traffic flow capacity can reach 1.41 times that of the homogeneous HDVs traffic flow, and the congestion dissipation time can be reduced by 25%. The larger MPR is, the greater the improvement effect is. In addition, compared with CTM, VCTM can reflect the delay, queuing, and dissipation of mixed traffic flow more accurately, which is helpful to capture the evolution mechanism of mixed traffic flow in the future.
Online Disease Identification and Diagnosis and Treatment Based on Machine Learning Technology
Authors: Hao Feng , Zheng Kai
Published: April 2022

The article uses machine learning algorithms to extract disease symptom keyword vectors. At the same time, we used deep learning technology to design a disease symptom classification model. We apply this model to an online disease consultation recommendation system. The system integrates machine learning algorithms and knowledge graph technology to help patients conduct online consultations. The system analyses the misclassification data of different departments through high-frequency word analysis. The study found that the accuracy rate of our machine learning algorithm model to identify entities in electronic medical records reached 96.29%. This type of model can effectively screen out the most important pathogenic features.
Equalization Network-Aided SCMA Codec Scheme with Deep Learning
Authors: Jiang Fang , Huang Xing , Jiang Shu-ping , Wang Yi , Xu Yao-hua , Yin Tian-yu
Published: April 2022

With the rapid development of the Internet of Things, sparse code multiple access (SCMA), which aims to promote spectrum efficiency and support massive connectivity in the future beyond fifth- and sixth-generation massive machine-type communication (mMTC) scenarios, has been widely investigated. To improve the bit error rate (BER) performance of the SCMA system in the uplink Rayleigh fading channel, we propose a novel deep learning-based SCMA codec scheme. The proposed scheme consists of an equalization network-aided decoder network and a denoising autoencoder- (DAE-) based encoder network. At the decoder, an equalization network and a multiuser detection network constitute the decoder network. The equalization network, composed of two deep neural network (DNN) units, compensates for the phase shift of the signal through the fading channel, which improves the antifading capability of the system. At the encoder, a complete DAE is constructed, which introduces an extra noise layer at the input of the encoder that yields a robust encoder output representation, improving the antinoise capability of the system. We use an end-to-end training method to train the SCMA codec and optimize the parameters and structural model of the neural network. Simulation results show that our proposed scheme can reduce the detection time and improve the BER performance of the system in the uplink Rayleigh fading channel.
Caerin 1 Peptides, the Potential Jack‐of‐All‐Trades for the Multiple Antibiotic‐Resistant Bacterial Infection Treatment and Cancer Immunotherapy
Authors: Xiao Liyin , Yang Xiaodan , Li Junjie , Zhang Pingping , Tang Shuxian , Cao Dongmin , Chen Shu , Li Hejie , Zhang Wei , Chen Guoqiang , Ni Guoying , Wang Tianfang , Liu Xiaosong
Published: April 2022

Antibiotic resistance‐related bacterial infections and cancers become huge challenges in human health in the 21st century. A number of naturally derived antimicrobial peptides possess multiple functions in host defense, including anti‐infective and anticancer activities. One of which is known as the caerin 1 family peptides. The microbicidal properties of these peptides have been long discussed. The recent studies also established the usage of two members in this family, caerin 1.1 and caerin 1.9, in antimultiple antibiotic‐resistant bacteria species. It is increasingly evident that caerin 1.1 and caerin 1.9 also contain additional activities in the suppression of tumor. In this review, we briefly outline the therapeutic potentials and possible mechanism of action of caerin 1.1 and 1.9 in the treatment of multiple antibiotic‐resistant bacterial infection and cancer immunotherapy.
[Retracted] Urban Road Landscape Design and Digital Twin Simulation Modeling Analysis
Authors: Yin Nan , Cai Minghui
Published: April 2022

The concept of a digital twin (DT) has lately become popular in the domains of urban planning and managed services. Densification and competing use of urban highways will result from an increase in local and foreign workers in the next years. The MFFO (modified fruit fly optimization) method for large‐scale data transfer is presented in this study. The first step is to get data from the internet. A map‐matching technique is also given in this section of preprocessing. Validated data may then be loaded into DT to reproduce processes. The results of DT are included in the landscape architecture and the server of the urban road network. The server’s data may be improved by modernized fruit fly optimization (MFFO) to enhance the transmission control protocol (TCP). Finally, the performances from the urban road design are examined and matched with existing techniques. The results are shown in the MATLAB simulation software. The results reveal that the proposed system increased the accuracy of digitalized urban road data protection for all secret information while also taking lesser time to execute than existing methods.
A Retrospective Clinical Study on Factors Influencing the Failure of NCCL Restorations
Authors: Saengnil Wanchanok , Anuntasainont Munlika , Srimaneekarn Natchalee , Miletic Vesna , Pongprueksa Pong
Published: April 2022

Objectives. This retrospective study evaluated the factors influencing the clinical failure of noncarious cervical lesion (NCCL) restorations. Methods. Patients were routinely treated by undergraduate or postgraduate students and randomly received a clinical recall evaluation. A retrospective study was performed with two experienced calibrated examiners to evaluate NCCL restorations, including the critical parameters of retention, caries, marginal discoloration, and marginal integrity. The factors related to the restoration included gender, age, arch site, tooth position, the presence of occlusal wear facets, caries risk, operator, adhesive strategy, and composite. The clinical failure comparison between the parameters and factors was performed using the binary logistic regression analysis. Results. A total of 460 cervical restorations from 96 patients were evaluated. The adhesive strategy and the presence of occlusal wear facets were the most important factors influencing the parameter failure. Therefore, the highest failure was marginal integrity, in which the gingival marginal integrity failure was 50.7%, and the occlusal marginal integrity failure was 42.4%. Conclusions. The main factors influencing clinical failure for partial loss, marginal discoloration, and marginal integrity were the adhesive strategy and the presence of occlusal wear facets. Therefore, marginal integrity was the most frequent failure parameter.
Drop Height Impacts the Lower Limb Elastic Energy’s Utilization for Male High Jumpers: A Experimental Research from Biomechanics
Authors: Tong Zehao , Chen Wenjia , Xu Hang , Zhai Feng
Published: April 2022

The study’s objective is to clarify the influence of drop height on elastic energy’s utilization of the lower extremity, to indicate the correlations between elastic energy’s utilization and personal best, and to determine the optimal loading height for elastic energy’s utilization for male high jumpers. Ten male athletes who belong to high jump events work out the drop jump at different drop heights (0.3 m, 0.45 m, 0.6 m, and 0.75 m). Two AMTI force platforms were used to capture the dynamics data for the lower extremity. Drop height has obvious influence on utilization ratio for elastic energy (

P
<
0.01

). The utilization ratio of elastic energy has no note correlation with personal best (

r
=
0.149

,

P
>
0.05

). In this study, the optimal loading height for utilization ratio of elastic energy was 0.75 m. The optimal loading height can be determined in terms of the elastic energy utilization ratio for each high jumper to enhance their training effects.
A Survey of Dental Caries Segmentation and Detection Techniques
Authors: Majanga Vincent , Viriri Serestina
Published: April 2022

Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found out that methods were grouped according to the type of dental caries (proximal, enamel), type of X-ray images used (extraoral, intraoral), and segmentation method (threshold-based, cluster-based, boundary-based, and region-based). From the works reviewed, the main focus has been found to be on threshold-based segmentation methods. Most of the reviewed papers have preferred the use of intraoral X-ray images over extraoral X-ray images to perform segmentation on dental images of already isolated parts of the teeth. This paper presents an in-depth analysis of recent research in deep learning for dental caries segmentation and detection. It involves discussing the methods and algorithms used in segmenting and detecting dental caries. It also discusses various existing models used and how they compare with each other in terms of system performance and evaluation. We also discuss the limitations of these methods, as well as future perspectives on how to improve their performance.
A Nanostructured Cu(II) Coordination Polymer Based on Alanine as a Trifunctional Mimic Enzyme and Efficient Composite in the Detection of Sphingobacteria
Authors: Maldonado Noelia , Latorre Ana , Zamora Félix , Somoza Álvaro , Gómez-García Carlos J. , Bastida Agatha , Amo-Ochoa Pilar
Published: April 2022

This research raises the potential use of coordination polymers as new useful materials in two essential research fields, allowing the obtaining of a new multiartificial enzyme with the capacity to inhibit the growth of bacteria resistance. The fine selection of the ligands allows the design of a new 2D coordination polymer (CP), with the formula [Cu2(IBA)2(OH2)4]n·6nH2O, by the combination of Cu (II) as the metal center with a pseudoamino acid (H2IBA = isophthaloyl bis β‐alanine). Quantitative total X‐ray fluorescence (TXRF) analyses show that the obtained CP can gradually release Cu (II) ions. Additionally, this CP can be nanoprocessed and transformed into a metal‐organic gel (MOG) by using different Cu (II) salt concentrations and the application of ultrasounds. Considering its nanometric dimensions, the slow Cu (II) release and its simple processability, its performance as an artificial enzyme, and its antibacterial ability were explored. The results obtained show the first nanocoordination polymer acting as an artificial multienzyme (peroxidase, catalase, and superoxodismutase) exhibiting antibacterial activity in the presence of hydrogen peroxide, with selective behavior for three bacterium strains (S. spiritovirum, A. faecales, and B. cereus). Indeed, this CP shows a more robust inhibition capacity for Sphingobacterium. Going beyond that, as there are no comfortable and practically clinical tests capable of detecting the presence of Sphingobacteria, the compound can be easily embedded to form moldable gelatin that will facilitate the handling and low‐cost commercial kits.
Bacterial Metabolomics: Sample Preparation Methods
Authors: Mohd Kamal Khairunnisa , Mahamad Maifiah Mohd Hafidz , Abdul Rahim Nusaibah , Hashim Yumi Zuhanis Has-Yun , Abdullah Sani Muhamad Shirwan , Azizan Kamalrul Azlan
Published: April 2022

Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important “omics” tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.
Research on the Internationalization Strategy of Higher Education under the Background of Network Education
Authors: Wu Chan
Published: April 2022

The nationalization of higher education is the only way for the further development of colleges and universities in the future, among which strengthening the education and management of college students is one of the most important tasks. At present, China’s economy and science and technology are developing by leaps and bounds, which has led to the rapid development of education to a certain extent. Therefore, many foreign students will choose China for further study. However, we can clearly find that the traditional Chinese education concept of overseas students is inconsistent with some foreign cultures, so it is difficult to carry out teaching smoothly. With the internationalization of higher education and the development of network technology, a large number of high-level brain drain increases. Therefore, China’s colleges and universities must change the educational concept and curriculum system, strengthen the training of teaching and management team, innovate teaching mode, strengthen scientific research cooperation, make good use of network technology, and make correct response measures to achieve success. Based on the problems of internationalization of higher education under the background of network education, this paper analyzes the current situation of internationalization of higher education in China and provides some help on how to deal with this change.
Construction and Evaluation of Sports Rehabilitation Training Model under Intelligent Health Monitoring
Authors: Wang Fubin , Huang Qiong
Published: April 2022

Sports rehabilitation training is a comprehensive discipline that solves and helps motor function recovery through physical exercise, also known as rehabilitation sports training. Sports rehabilitation training can help athletes restore health, treat injuries, prevent recurrence, improve physical fitness, and meet the needs of daily training. Starting from the concept of sports rehabilitation training, this paper analyzes the application of sports rehabilitation training in physical training and summarizes and analyzes the role of sports rehabilitation training. It provides reference for the application of physical rehabilitation training in sports training. Intelligent health monitoring technology has been widely used and developed, and many classic algorithms have emerged to complete the extraction of moving targets. This paper is aimed at studying the application of intelligent health monitoring to the construction of sports rehabilitation training models. It proposes the overall design of the intelligent health monitoring terminal and the common algorithms for moving target monitoring, such as optical flow method, frame difference method, and background difference method. It takes the rehabilitation training of an athlete in X city as an example to establish a model study. The final result of the experiment through sports rehabilitation training under intelligent health monitoring is that 92.6% of the athletes recovered from sports injuries through rehabilitation physical training.
Higher Chronic Endometritis Incidences within Infertile Polycystic Ovary Syndrome Clinical Cases
Authors: Wang Shuang , Zhao Huishan , Li Fenghua , Xu Yanping , Bao Hongchu , Zhao Dongmei
Published: April 2022

Background. Clinical cases of a polycystic ovarian syndrome (PCOS) have prolonged subclinical inflammation. Hysteroscopy has revealed worsened chronic endometritis (CE), particularly endometrial diffuse hyperemia, in PCOS patients. However, the possible relationships between PCOS and CE remain largely unexplored. Methods. This retrospective-based investigation was conducted on 3336 infertile patients. The PCOS group consisted of 508 patients, while the non-POCS group consisted of 2828 individuals with normal ovarian function. Their clinical features and CE prevalence diagnosed with hysteroscopy were compared. The risk factors affecting the incidence of diffuse endometrial hyperemia were analyzed by binary logistic regression. Results. The PCOS cohort and the non-PCOS cohort showed marked variations in age, body mass index (BMI), infertility (primary, secondary), basal hormone level (bFSH, bLH, bT, and PRL), anti-Müllerian hormone (AMH), and CA125

P
<
0.05

. The prevalence of CE in PCOS women was 41.73% (212/508), markedly higher than the 28.50% in the non-PCOS cohort (806/2828). Variations within diffuse endometrial hyperemia prevalence were especially marked

P
<
0.05

. Furthermore, we found that the variables of BMI, bLH, bT, and AMH correlated with diffuse endometrial hyperemia. Conclusions. CE prevalence was elevated in clinical cases of infertility associated with PCOS, and diffuse endometrial hyperemia was prevalent, as indicated by hysteroscopy. Furthermore, increased BMI, bLH, bT, and AMH levels all contribute to the risk of diffuse endometrial hyperemia.
Effect of Early Cognitive Training Combined with Aerobic Exercise on Quality of Life and Cognitive Function Recovery of Patients with Poststroke Cognitive Impairment
Authors: Jiang Haiyu , Li Haihong , Wang Zheng , Xia Xiaomei , Su Qinglun , Ma Jinya
Published: April 2022

Objective. To explore the effect of early cognitive training combined with aerobic exercise on quality of life (QOL) and cognitive function recovery of patients with poststroke cognitive impairment (PSCI). Methods. Ninety PSCI patients treated in our hospital from April 2019 to April 2020 were selected as the subjects and were divided into the experimental group (EG) and control group (CG) according to the admission order, with 45 cases each. Patients in CG received conventional health education combined with rehabilitation training, and those in EG accepted early cognitive training combined with aerobic exercise so as to evaluate the clinical effect of different intervention modes on PSCI patients. Results. Compared with CG after intervention, EG obtained an obviously higher Stroke Specific Quality of Life scale (SS-QOL) score, Montreal Cognitive Assessment (MoCA) score, Barthel Index (MBI) (BI) score and Functional Independence Measure (FIM) score (P < 0.001), and obviously shorter time for completing TMT-A and TMT-B (P < 0.001). Conclusion. Performing early cognitive training combined with aerobic exercise for PSCI patients can effectively improve their QOL and promote the recovery of cognitive function. Compared with conventional health education combined with rehabilitation training, this mode presents a higher application value. Further study will be conducive to establishing a better solution for patients.
Design of an Anti-Windup PID Algorithm for Differential Torque Steering Systems
Authors: Peng Dengzhi , Huang Bin , Huang Hao
Published: April 2022

The EV (electric vehicle) with a wheel hub motor has the advantage of independent driving torque distribution for each wheel, which allows the vehicle performance to be improved. Therefore, a lot of work has been done to investigate the torque allocation algorithm for the mechanical and differential torque integration steering system. To investigate the differential steering process, the 2 DOF (degree of freedom) dual-track reference models with linear and nonlinear tire models are established, and based on the steering process analysis and yaw rate gain calculation, a BP-NN (backpropagation neural network) model is initiated to maintain the accuracy of the calculated yaw rate gain. The limitation of DTSS (differential torque steering system) and the difference of reference models with linear and nonlinear tires are drawn. In addition, an anti-windup variable PID (proportion integration differentiation) controller is designed for the torque distribution. Based on the built 8 DOF model, the vehicle performance indicators are calculated and compared, the gap between the models with linear and nonlinear tires is non-negligible, and a reference model with a nonlinear tire model is recommended for the relevant research. The anti-windup variable PID controller has a better performance than the normal PID controller except for the stability phase plane indicator.
Comparison of Electrochemical Chloride Extraction Models for Reinforced Concrete Structures Based on Multiple Potential Theories
Authors: Xiao-hui Wu , Yan-feng Wang , Jiejing Chen , Xin Cheng , Jin Xia
Published: April 2022

For chloride-contaminated RC structures, steel corrosion is one of the most common reasons causing the deterioration of reinforced concrete (RC) structures. Electrochemical chloride extraction (ECE), which migrates the chloride ions out of the RC structure, is preferred as it is a nondestructive repairing method. In this context, electrokinetic models are often used to investigate ECE processes. Currently, electrokinetic models based on different potential theories, such as the constant potential, electroneutrality, and Gauss’ law, may be used to solve the potential distribution. Although many ECE numerical simulations have been conducted, there has been no systematic comparison of models based on different potential theories. Therefore, in this study, a comprehensive comparative study has been conducted on these models in terms of the potential distribution, current density, quantity of electric charge, and ion concentration. Two commonly used ECE methods, namely, steady voltage and steady current, were numerically simulated and analyzed. The numerical results with actual ECE data were also compared. Although the electrokinetic model based on electroneutrality might overestimate the residual chloride concentration and underestimate current density and hydroxyl concentration, critical analysis suggests that electroneutrality is more appropriate than the constant potential and Gauss’ law for electrokinetic modeling.
Wi-Exercise: An Indoor Human Movement Detection Method Based on Bidirectional LSTM Attention
Authors: Hao ZhanJun , Kang YunZhu , Dang XiaoChao , An Ying
Published: April 2022

Human motion recognition has become a hot topic in the field of human-computer interaction. Due to the traditional method of detecting human movement in indoor environments, there are still problems such as high hardware costs, influenced by the environment and the need for experimenters to be equipped with relevant equipment. With the large-scale deployment of wireless devices and the establishment of wireless network infrastructure, indoor personnel movement recognition systems based on wireless signals are gradually proposed. In this paper, we propose a device-independent motion-detection method Wi-exercise based on temporal recurrent neural networks. The acquired channel state information (CSI) is preprocessed by wavelet transform, and the output CSI matrix is filtered by the principal component analysis (PCA) to extract the feature values. The wavelet function is processed to further remove the noise also to make the action data more distinctive, and then PCA is used to track the entire CSI time series to improve and maintain its dimensional energy. The preprocessed data are subjected to feature extraction to obtain CSI of human motion and establish feature sequences, and then the action model is trained using the bidirectional long short-term memory attention (ABLSTM) mechanism to finally realize human motion recognition. Finally, the robustness of Wi-exercise is tested experimentally, and its performance is compared with that of existing recognition methods. The experimental results show that the average recognition rate of the method is 94.43% in the complex indoor environment, and the method is better than the traditional classifier method for action recognition, with lower and higher recognition accuracy.
Mitochondrial Calcium Uniporter Drives Metastasis and Confers a Targetable Cystine Dependency in Pancreatic Cancer
Authors: Wang Xiuchao , Li Yunzhan , Li Zekun , Lin Shengchen , Wang Hongwei , Sun Jianwei , Lan Chungen , Wu Liangliang , Sun Dongxiao , Huang Chongbiao , Singh Pankaj K. , Hempel Nadine , Trebak Mohamed , DeNicola Gina M. , Hao Jihui , Yang Shengyu
Published: April 2022

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease with few effective treatments. Here we show that the mitochondrial calcium uniporter (MCU) promotes PDAC cell migration, invasion, metastasis, and metabolic stress resistance by activating the Keap1-Nrf2 antioxidant program. The cystine transporter SLC7A11 was identified as a druggable target downstream of the MCU-Nrf2 axis. Paradoxically, despite the increased ability to uptake cystine, MCU-overexpressing PDAC demonstrated characteristics typical of cystine-deprived cells and were hypersensitive to cystine deprivation-induced ferroptosis. Pharmacologic inhibitors of SLC7A11 effectively induced tumor regression and abrogated MCU-driven metastasis in PDAC. In patient-derived organoid models in vitro and patient-derived xenograft models in vivo, MCU-high PDAC demonstrated increased sensitivity to SLC7A11 inhibition compared with MCU-low tumors. These data suggest that MCU is able to promote resistance to metabolic stress and to drive PDAC metastasis in a cystine-dependent manner. MCU-mediated cystine addiction could be exploited as a therapeutic vulnerability to inhibit PDAC tumor growth and to prevent metastasis.

Significance:

Elevated mitochondrial calcium uptake in PDAC promotes metastasis but exposes cystine addiction and ferroptosis sensitivity that could be targeted to improve pancreatic cancer treatment.
IFNα Potentiates Anti–PD-1 Efficacy by Remodeling Glucose Metabolism in the Hepatocellular Carcinoma Microenvironment
Authors: Hu Bo , Yu Mincheng , Ma Xiaolu , Sun Jialei , Liu Chenglong , Wang Chunyan , Wu Suiyi , Fu Peiyao , Yang Zhen , He Yungang , Zhu Yuanyuan , Huang Cheng , Yang Xinrong , Shi Yinghong , Qiu Shuangjian , Sun Huichuan , Zhu Andrew X. , Zhou Jian , Xu Yang , Zhu Di , Fan Jia
Published: April 2022

The overall response rate for anti–PD-1 therapy remains modest in hepatocellular carcinoma (HCC). We found that a combination of IFNα and anti–PD-1–based immunotherapy resulted in enhanced antitumor activity in patients with unresectable HCC. In both immunocompetent orthotopic and spontaneous HCC models, IFNα therapy synergized with anti–PD-1 and the combination treatment led to significant enrichment of cytotoxic CD27+CD8+ T cells. Mechanistically, IFNα suppressed HIF1α signaling by inhibiting FosB transcription in HCC cells, resulting in reduced glucose consumption capacity and consequentially establishing a high-glucose microenvironment that fostered transcription of the T-cell costimulatory molecule Cd27 via mTOR–FOXM1 signaling in infiltrating CD8+ T cells. Together, these data reveal that IFNα reprograms glucose metabolism within the HCC tumor microenvironment, thereby liberating T-cell cytotoxic capacities and potentiating the PD-1 blockade–induced immune response. Our findings suggest that IFNα and anti–PD-1 cotreatment is an effective novel combination strategy for patients with HCC.

Significance:

Our study supports a role of tumor glucose metabolism in IFNα-mediated antitumor immunity in HCC, and tumor-infiltrating CD27+CD8+ T cells may be a promising biomarker for stratifying patients for anti–PD-1 therapy.

See related commentary by Kao et al., p. 1615.

This article is highlighted in the In This Issue feature, p. 1599
The Role of Kynurenines Produced by Indolamine-2,3-Dioxygenase 1 in Sepsis
Authors: Lerch Simon , Schefold Joerg C. , Spinetti Thibaud
Published: April 2022

Background: The enzyme indolamine-2,3-dioxygenase 1 (IDO1) is the rate-limiting enzyme of the kynurenine (KYN) pathway and metabolizes the essential amino acid tryptophan to KYNs. The depletion of tryptophan and the generation of KYNs were shown to be involved in the global downregulation of the immune system during the later stages of sepsis, also referred to as sepsis-associated immunosuppression. Summary: The generation of KYNs by IDO1 leads to a depletion of effector T cells, including increased rate of apoptosis, decreased ability of T-cell proliferation and activation, and the generation of FoxP3+ regulatory T cells. Furthermore, KYN was shown a potent vasorelaxant during inflammation-induced hypotension. Experimental studies in murine sepsis models and in humans show promising data for using the activation of IDO1 both as a prognostic marker and potential drug target in sepsis.
Scalp Micropigmentation: A Clinicopathologic Correlation
Authors: Nguyen Betty , Mervis Joshua S. , Romanelli Paolo , Tosti Antonella
Published: April 2022

Introduction: Scalp micropigmentation is a method of concealing alopecia by depositing permanent pigment in a tattoo-like manner. Pigment is deposited between hair follicles in a stippling pattern that resembles closely cut hair. Case Presentation: On trichoscopy, characteristic findings of scalp micropigmentation include homogenous, grey to black circular dots that are evenly spaced and appear larger than adjacent hair follicles. Findings were correlated with histopathology. Conclusion: Trichoscopy is a useful tool to visualize scalp micropigmentation in place of invasive procedures.
Clinical and Epidemiological Characteristics of Hidradenitis Suppurativa Patients with Paradoxical Psoriasiform Reactions following Treatment with Adalimumab
Authors: Kanelleas Antonios , Efthymiou Ourania , Routsi Eleni , Sgouros Dimitrios , Pappa Georgia , Tsoureli Nikita Evridiki , Bozi Evangelia , Katoulis Alexandros
Published: April 2022

Introduction: Immunomodulation using TNF-α inhibitors (anti-TNF-a), especially adalimumab, is highly effective in the treatment of hidradenitis suppurativa (HS) in cases that are poorly controlled by conventional treatments. However, paradoxical psoriasis represents a peculiar type of psoriasis that may occur de novo or as the worsening of pre-existent psoriatic lesions during treatment with adalimumab. Case Presentation: We reported 4 cases of patients suffering from HS, who developed paradoxical psoriasis after treatment with adalimumab for their HS, analyzing their clinical and epidemiological characteristics. All 4 cases were middle aged, smokers, overweight or obese. Half of the patients were males (50%). All of them were classified as Hurley stage III, with a mean duration of HS of 20 years. Two patients had a family history of psoriasis. All 4 patients had been on at least 5 months of successful treatment with adalimumab before the onset of the lesions. Conclusions: Paradoxical psoriasis emerged in 4 patients who received at least 5-month regimen of adalimumab for long-lasting HS. Although different mechanisms have been hypothesized for such events, the exact underlying pathogenetic pathway remains unclear. Consistent reporting of such rare cases, and on a larger scale, is encouraged in order to enrich the available literature.
Evaluation of Mangalarga Marchador foal development in the first year of life
Authors: Cunha Ligia Amaral , Sodré Ticiane d’El Rei Passos , Pereira Neto Evandro , Souza Vinicius Ricardo Cuña de , Hunka Monica Miranda , Manso Filho Hélio Cordeiro , Coelho Clarisse Simões
Published: April 2022

The purpose of this study was to examine the development pattern of Mangalarga-Marchador foals by determination of muscle development and deposition of adipose tissue associated with the evaluation of blood-based biomarkers. Thus, 72 Mangalarga Marchador foals (ages ranged from 1-12 mo) were randomly selected and divided into 12 age groups (n=6 per group), evaluated monthly. The thickness of the Longissimus dorsi (LD) and rump fat (RF) were determined through an ultrasound performed on each foal’s left side. Such parameters were used to calculate the percentage of body fat (BF), fat mass (FM), and fat-free mass (FFM). Morphometric measurements were taken on the foal’s right side using a tape measure and a hypsometer. Blood samples were collected for determinations of total proteins, albumin, triglycerides, cholesterol, urea, creatinine, and glucose. Morphometric measurements,  the thickness of the LD and RF, weight, BF, FM, and FFM showed differences between age groups (P<0.001). Muscle development and fat deposition in subcutaneous tissue were greater between six and 12 months and average weight gain and FFM were proportionally greater between one and six months. We concluded that morphometric measurements increase steadily and linearly during the first 12 months of life, indicating that it is feasible to use the linear regression formula. Moreover, significant differences between age and biochemical biomarkers may be attributed to nutritional and digestive physiology adaptations during these periods.