bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding People's Perceptions of Approaches to Semi-Automated Dietary Monitoring
Authors: Lu Xi , Thomaz Edison , Epstein Daniel A.
Published: September 2022

The respective benefits and drawbacks of manual food journaling and automated dietary monitoring (ADM) suggest the value of semi-automated journaling systems combining the approaches. However, the current understanding of how people anticipate strategies for implementing semi-automated food journaling systems is limited. We therefore conduct a speculative survey study with 600 responses, examining how people anticipate approaches to automatic capture and prompting for details. Participants feel the location and detection capability of ADM sensors influences anticipated physical, social, and privacy burdens. People more positively anticipate prompts which contain information relevant to their journaling goals, help them recall what they ate, and are quick to respond to. Our work suggests a tradeoff between ADM systems' detection performance and anticipated acceptability, with sensors on facial areas having higher performance but lower acceptability than sensors in other areas and more usable prompting methods like those containing specific foods being more challenging to produce than manual reminders. We suggest opportunities to improve higher-acceptability, lower-accuracy ADM sensors, select approaches based on individual and practitioner journaling needs, and better describe capabilities to potential users.
Bootstrapping Human Activity Recognition Systems for Smart Homes from Scratch
Authors: Hiremath Shruthi K. , Nishimura Yasutaka , Chernova Sonia , Plötz Thomas
Published: September 2022

Smart Homes have come a long way: From research laboratories in the early days, through (almost) neglect, to their recent revival in real-world environments enabled through the existence of commodity devices and robust, standardized software frameworks. With such availability, human activity recognition (HAR) in smart homes has become attractive for many real-world applications, especially in the domain of Ambient Assisted Living. Yet, getting started with an activity recognition system in specific smart homes, which are highly specialized spaces inhabited by individuals with idiosyncratic behaviors and habits, is a non-trivial endeavor. We present an approach for bootstrapping HAR systems for individual smart homes from scratch. At the beginning of the life cycle of a smart home, our system passively observes activities and derives rich representations for sensor data-action units-which are then aggregated into activity models through motif learning with minimal supervision. The resulting HAR system is then capable of recognizing relevant, most frequent activities in a smart home. We demonstrate the effectiveness of our bootstrapping procedure through experimental evaluations on CASAS datasets that show the practical value of our approach.
Guard Your Heart Silently
Authors: Cao Yetong , Li Fan , Chen Huijie , liu Xiaochen , Zhang Li , Wang Yu
Published: September 2022

In recent years, particular attention has been devoted to continuous electrocardiogram (ECG) waveform monitoring due to its numerous applications. However, existing solutions require users to be confined to particular locations, rely on dedicated and expensive hardware, or require active user participation. The constrained recording conditions prevent them from being deployed in many practical application scenarios. In view of this, we present VibCardiogram, a continuous and reliable design for estimating ECG waveform shape via ubiquitous wrist-worn wearables; it renders a personal ECG waveform shape estimating system with prolonged recording time accessible to a larger sector of the population. Instead of adding auxiliary sensors to wearables, VibCardiogram leverages the widely integrated motion sensor to characterize cardiac activities, and interpret them to generate an alternative signal that has the same waveform shape as the ECG signal. Specifically, VibCardiogram extracts the cardiogenic body vibrations from noisy sensor data. As the waveform variability and inconstant period hinder the segmentation of cardiac cycles, VibCardiogram extracts features and realizes accurate segmentation via machine learning. To parse cardiac activities from vibration signals, we build a deep-learning pipeline associating the encoder-decoder framework and Generative Adversarial Networks. With dedicated construction and training, it can estimate the ECG waveform accurately. Experiments with 20 participants show that VibCardiogram can achieve an average estimation error of 5.989% for waveform amplitude estimation, which is within the 10% margins regulated by the American National Standards Institute. Moreover, the promising results further confirm that VibCardiogram effectively extracts Heart Rate Variability features and supports downstream applications.
mRisk
Authors: Ullah Md Azim , Chatterjee Soujanya , Fagundes Christopher P. , Lam Cho , Nahum-Shani Inbal , Rehg James M. , Wetter David W. , Kumar Santosh
Published: September 2022

Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via wearable and mobile sensors has created new opportunities to improve the effectiveness of behavioral interventions. A key goal is to find opportune moments for intervention by passively detecting rising risk of an imminent adverse behavior. But, it has been difficult due to substantial noise in the data collected by sensors in the natural environment and a lack of reliable label assignment of low- and high-risk states to the continuous stream of sensor data. In this paper, we propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of an adverse behavior. Next, to circumvent the lack of any confirmed negative labels (i.e., time periods with no high-risk moment), and only a few positive labels (i.e., detected adverse behavior), we propose a new loss function. We use 1,012 days of sensor and self-report data collected from 92 participants in a smoking cessation field study to train deep learning models to produce a continuous risk estimate for the likelihood of an impending smoking lapse. The risk dynamics produced by the model show that risk peaks an average of 44 minutes before a lapse. Simulations on field study data show that using our model can create intervention opportunities for 85% of lapses with 5.5 interventions per day.
IMar
Authors: He Jing , Yang Wei
Published: September 2022

Currently, WiFi-based user continuous action counting and recognition is limited to a single person. Being able to continuously analyze and record the different actions of multiple users in a device-free scene is one of the most challenging job to date. In this paper, we present a new WiFi-based multi-user action recognition system, called IMar, which can achieve decomposition of multi-user action information and retain action features as much as possible, i.e., simultaneously recognize and count the continuous and different actions of multiple people. Our main technical route is to design a Dynamic Propagation Delay Threshold Sanitization (DPDTS) algorithm to retain the path information that only passes through the target user body, in order to reduce the multipath effect and make the data as pure as possible, and then model the amplitude relationship of the multi-person action scene. After acquiring the individual data according to the model and tensor decomposition, we propose a Multiplayer Action Amplitude Decomposition and Completion (MAADC) algorithm to obtain more informative data for individual continuous action. Moreover, the single-person data of subcarrier-level obtained by tensor decomposition is extended to the data of stream-level, which brings great convenience to the single-person action recognition. Experimental results show that IMar can work with up to 6 people. The average recognition accuracy and counting accuracy are 78% and 91% respectively in the experimental group of continuous and natural actions by multiple users, and the average recognition accuracy of the experiments in all cases is 83.7%.
Sensing with Earables
Authors: Röddiger Tobias , Clarke Christopher , Breitling Paula , Schneegans Tim , Zhao Haibin , Gellersen Hans , Beigl Michael
Published: September 2022

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform.
One Ring to Rule Them All
Authors: Bardot Sandra , Rey Bradley , Audette Lucas , Fan Kevin , Huang Da-Yuan , Li Jun , Li Wei , Irani Pourang
Published: September 2022

Smartrings have potential to extend our ubiquitous control through their always available and finger-worn location, as well as their quick and subtle interactions. As such, smartrings have gained popularity in research and in commercial usage; however, they often concentrate on a singular or novel aspect of a smartring's potential. While with any emerging technology the focus on these individual components is important, there is a lack of broader empirical understanding regarding a user's intentions for smartring usage. Thus in this work, we investigate concrete and reported smartring usage scenarios throughout the daily lives of participants. During a two-week in-situ diary study (N = 14), utilizing a mock smartring, we provide an initial understanding of the potential tasks, daily activities, connected devices, and interactions for which augmentation with a smartring was desired. We further highlight patterns of imagined smartring use found by our participants. Finally, we provide and discuss guidelines, grounded through our found knowledge, to inform research and development towards the design of future smartrings.
COCOA
Authors: Deldari Shohreh , Xue Hao , Saeed Aaqib , Smith Daniel V. , Salim Flora D.
Published: September 2022

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labeled data, and has reached comparable or even state-of-the-art results in comparison to supervised counterparts. Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data. CL methods have been mostly developed for applications in computer vision and natural language processing where only a single sensor modality is used. A majority of pervasive computing applications, however, exploit data from a range of different sensor modalities. While existing CL methods are limited to learning from one or two data sources, we propose COCOA (Cross mOdality COntrastive leArning), a self-supervised model that employs a novel objective function to learn quality representations from multisensor data by computing the cross-correlation between different data modalities and minimizing the similarity between irrelevant instances. We evaluate the effectiveness of COCOA against eight recently introduced state-of-the-art self-supervised models, and two supervised baselines across five public datasets. We show that COCOA achieves superior classification performance to all other approaches. Also, COCOA is far more label-efficient than the other baselines including the fully supervised model using only one-tenth of available labeled data.
Predicting Performance Improvement of Human Activity Recognition Model by Additional Data Collection
Authors: Tanigaki Kei , Teoh Tze Chuin , Yoshimura Naoya , Maekawa Takuya , Hara Takahiro
Published: September 2022

The development of a machine-learning-based human activity recognition (HAR) system using body-worn sensors is mainly composed of three phases: data collection, model training, and evaluation. During data collection, the HAR developer collects labeled data from participants wearing inertial sensors. In the model training phase, the developer trains the HAR model on the collected training data. In the evaluation phase, the developer evaluates the trained HAR model on the collected test data. When the HAR model cannot achieve the target recognition accuracy, the developer iterates the above procedures by taking certain measures, including collecting additional training data, until the re-trained model achieves the target accuracy. However, collecting labeled data for HAR requires additional time and incurs high monetary costs. In addition, it is difficult to determine the amount and type of data to collect for achieving the target accuracy while reducing costs. To address this issue, this paper proposes a new method that predicts the performance improvement of the current HAR model, i.e., it determines the level of performance improvement achievable by re-training the HAR model with additional data, before collecting the additional data. Thus, the method enables the HAR developer to establish a strategy for additional data collection by providing advice such as "If labeled data for the Walking and Running activities from two additional participants is collected, the HAR accuracy of the current HAR model for Walking will improve by 20%." To achieve this, a neural network called AIP-Net is proposed to estimate the improvement in performance by analyzing the feature space of the current HAR model using the proposed entropy-based attention mechanism. The performance of AIP-Net was evaluated on eight HAR datasets using leave-one-dataset-out cross-validation.
AmbiEar
Authors: Zhang Jia , Zhou Yinian , Xi Rui , Li Shuai , Guo Junchen , He Yuan
Published: September 2022

Millimeter wave (mmWave) based sensing is a significant technique that enables innovative smart applications, e.g., voice recognition. The existing works in this area require direct sensing of the human's near-throat region and consequently have limited applicability in non-line-of-sight (NLoS) scenarios. This paper proposes AmbiEar, the first mmWave based voice recognition approach applicable in NLoS scenarios. AmbiEar is based on the insight that the human's voice causes correlated vibrations of the surrounding objects, regardless of the human's position and posture. Therefore, AmbiEar regards the surrounding objects as ears that can perceive sound and realizes indirect sensing of the human's voice by sensing the vibration of the surrounding objects. By incorporating the designs like common component extraction, signal superimposition, and encoder-decoder network, AmbiEar tackles the challenges induced by low-SNR and distorted signals. We implement AmbiEar on a commercial mmWave radar and evaluate its performance under different settings. The experimental results show that AmbiEar has a word recognition accuracy of 87.21% in NLoS scenarios and reduces the recognition error by 35.1%, compared to the direct sensing approach.
Synapse
Authors: Jin Xiaofu , Hu Xiaozhu , Wei Xiaoying , Fan Mingming
Published: September 2022

As smartphones are widely adopted, mobile applications (apps) are emerging to provide critical services such as food delivery and telemedicine. While bring convenience to everyday life, this trend may create barriers for older adults who tend to be less tech-savvy than young people. In-person or screen sharing support is helpful but limited by the help-givers' availability. Video tutorials can be useful but require users to switch contexts between watching the tutorial and performing the corresponding actions in the app, which is cumbersome to do on a mobile phone. Although interactive tutorials have been shown to be promising, none was designed for older adults. Furthermore, the trial-and-error approach has been shown to be beneficial for older adults, but they often lack support to use the approach. Inspired by both interactive tutorials and trial-and-error approach, we designed an app-independent mobile service, Synapse, for help-givers to create a multimodal interactive tutorial on a smartphone and for help-receivers (e.g., older adults) to receive interactive guidance with trial-and-error support when they work on the same task. We conducted a user study with 18 older adults who were 60 and over. Our quantitative and qualitative results show that Synapse provided better support than the traditional video approach and enabled participants to feel more confident and motivated. Lastly, we present further design considerations to better support older adults with trial-and-error on smartphones.
Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars
Authors: Cao Dongjiang , Liu Ruofeng , Li Hao , Wang Shuai , Jiang Wenchao , Lu Chris Xiaoxuan
Published: September 2022

Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact tracing during pandemics, etc. This work studies the problem of cross-modal human re-identification (ReID), in response to the regular human movements across camera-allowed regions (e.g., streets) and camera-restricted regions (e.g., offices) deployed with heterogeneous sensors. By leveraging the emerging low-cost RGB-D cameras and mmWave radars, we propose the first-of-its-kind vision-RF system for cross-modal multi-person ReID at the same time. Firstly, to address the fundamental inter-modality discrepancy, we propose a novel signature synthesis algorithm based on the observed specular reflection model of a human body. Secondly, an effective cross-modal deep metric learning model is introduced to deal with interference caused by unsynchronized data across radars and cameras. Through extensive experiments in both indoor and outdoor environments, we demonstrate that our proposed system is able to achieve ~ 92.5% top-1 accuracy and ~ 97.5% top-5 accuracy out of 56 volunteers. We also show that our proposed system is able to robustly reidentify subjects even when multiple subjects are present in the sensors' field of view.
Psychophysiological Arousal in Young Children Who Stutter
Authors: Sharma Harshit , Xiao Yi , Tumanova Victoria , Salekin Asif
Published: September 2022

The presented first-of-its-kind study effectively identifies and visualizes the second-by-second pattern differences in the physiological arousal of preschool-age children who do stutter (CWS) and who do not stutter (CWNS) while speaking perceptually fluently in two challenging conditions: speaking in stressful situations and narration. The first condition may affect children's speech due to high arousal; the latter introduces linguistic, cognitive, and communicative demands on speakers. We collected physiological parameters data from 70 children in the two target conditions. First, we adopt a novel modality-wise multiple-instance-learning (MI-MIL) approach to classify CWS vs. CWNS in different conditions effectively. The evaluation of this classifier addresses four critical research questions that align with state-of-the-art speech science studies' interests. Later, we leverage SHAP classifier interpretations to visualize the salient, fine-grain, and temporal physiological parameters unique to CWS at the population/group-level and personalized-level. While group-level identification of distinct patterns would enhance our understanding of stuttering etiology and development, the personalized-level identification would enable remote, continuous, and real-time assessment of stuttering children's physiological arousal, which may lead to personalized, just-in-time interventions, resulting in an improvement in speech fluency. The presented MI-MIL approach is novel, generalizable to different domains, and real-time executable. Finally, comprehensive evaluations are done on multiple datasets, presented framework, and several baselines that identified notable insights on CWSs' physiological arousal during speech production.
ClenchClick
Authors: Shen Xiyuan , Yan Yukang , Yu Chun , Shi Yuanchun
Published: September 2022

We propose to explore teeth-clenching-based target selection in Augmented Reality (AR), as the subtlety in the interaction can be beneficial to applications occupying the user's hand or that are sensitive to social norms. To support the investigation, we implemented an EMG-based teeth-clenching detection system (ClenchClick), where we adopted customized thresholds for different users. We first explored and compared the potential interaction design leveraging head movements and teeth clenching in combination. We finalized the interaction to take the form of a Point-and-Click manner with clenches as the confirmation mechanism. We evaluated the taskload and performance of ClenchClick by comparing it with two baseline methods in target selection tasks. Results showed that ClenchClick outperformed hand gestures in workload, physical load, accuracy and speed, and outperformed dwell in work load and temporal load. Lastly, through user studies, we demonstrated the advantage of ClenchClick in real-world tasks, including efficient and accurate hands-free target selection, natural and unobtrusive interaction in public, and robust head gesture input.
BodyTrak
Authors: Lim Hyunchul , Li Yaxuan , Dressa Matthew , Hu Fang , Kim Jae Hoon , Zhang Ruidong , Zhang Cheng
Published: September 2022

In this paper, we present BodyTrak, an intelligent sensing technology that can estimate full body poses on a wristband. It only requires one miniature RGB camera to capture the body silhouettes, which are learned by a customized deep learning model to estimate the 3D positions of 14 joints on arms, legs, torso, and head. We conducted a user study with 9 participants in which each participant performed 12 daily activities such as walking, sitting, or exercising, in varying scenarios (wearing different clothes, outdoors/indoors) with a different number of camera settings on the wrist. The results show that our system can infer the full body pose (3D positions of 14 joints) with an average error of 6.9 cm using only one miniature RGB camera (11.5mm x 9.5mm) on the wrist pointing towards the body. Based on the results, we disscuss the possible application, challenges, and limitations to deploy our system in real-world scenarios.
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned, and Perspectives
Authors: Rethmeier Nils , Augenstein Isabelle
Published: September 2022

Modern natural language processing (NLP) methods employ self-supervised pretraining objectives such as masked language modeling to boost the performance of various downstream tasks. These pretraining methods are frequently extended with recurrence, adversarial, or linguistic property masking. Recently, contrastive self-supervised training objectives have enabled successes in image representation pretraining by learning to contrast input-input pairs of augmented images as either similar or dissimilar. In NLP however, a single token augmentation can invert the meaning of a sentence during input-input contrastive learning, which led to input-output contrastive approaches that avoid the issue by instead contrasting over input-label pairs. In this primer, we summarize recent self-supervised and supervised contrastive NLP pretraining methods and describe where they are used to improve language modeling, zero to few-shot learning, pretraining data-efficiency, and specific NLP tasks. We overview key contrastive learning concepts with lessons learned from prior research and structure works by applications. Finally, we point to open challenges and future directions for contrastive NLP to encourage bringing contrastive NLP pretraining closer to recent successes in image representation pretraining.
Open Science in Developmental Science
Authors: Gennetian Lisa A. , Frank Michael C. , Tamis-LeMonda Catherine S.
Published: September 2022

Open science policies have proliferated in the social and behavioral sciences in recent years, including practices around sharing study designs, protocols, and data and preregistering hypotheses. Developmental research has moved more slowly than some other disciplines in adopting open science practices, in part because developmental science is often descriptive and does not always strictly adhere to a confirmatory approach. We assess the state of open science practices in developmental science and offer a broader definition of open science that includes replication, reproducibility, data reuse, and global reach.
Diacetyl inhalation impairs airway epithelial repair in mice infected with influenza A virus
Authors: McGraw Matthew D. , Yee Min , Kim So-Young , Dylag Andrew M. , Lawrence B. Paige , O’Reilly Michael A.
Published: September 2022

Bronchiolitis obliterans (BO) is a debilitating disease of the small airways that can develop following exposure to toxic chemicals as well as respiratory tract infections. BO development is strongly associated with diacetyl (DA) inhalation exposures at occupationally relevant concentrations or severe influenza A viral (IAV) infections. However, it remains unclear whether lower dose exposures or more mild IAV infections can result in similar pathology. In the current work, we combined these two common environmental exposures, DA and IAV, to test whether shorter DA exposures followed by sublethal IAV infection would result in similar airways disease. Adult mice exposed to DA vapors 1 h/day for 5 consecutive days followed by infection with the airway-tropic IAV H3N2 (HKx31) resulted in increased mortality, increased bronchoalveolar lavage (BAL) neutrophil percentage, mixed obstruction and restriction by lung function, and subsequent airway remodeling. Exposure to DA or IAV alone failed to result in significant pathology, whereas mice exposed to DA + IAV showed increased α-smooth muscle actin (αSMA) and epithelial cells coexpressing the basal cell marker keratin 5 (KRT5) with the club cell marker SCGB1A1. To test whether DA exposure impairs epithelial repair after IAV infection, mice were infected first with IAV and then exposed to DA during airway epithelial repair. Mice exposed to IAV + DA developed similar airway remodeling with increased subepithelial αSMA and epithelial cells coexpressing KRT5 and SCGB1A1. Our findings reveal an underappreciated concept that common environmental insults while seemingly harmless by themselves can have catastrophic implications on lung function and long-term respiratory health when combined.
Software Defect Prediction and Analysis Using Enhanced Random Forest (extRF) Technique: A Business Process Management and Improvement Concept in IOT-Based Application Processing Environment
Authors: Alshammari Fahad H.
Published: September 2022

Software defect prediction is a thriving study area in the realm of software engineering and processing in the IOT-based environment. Defect prediction creates a list of defective source code artifacts so that quality assurance companies may successfully assign limited methods for certifying programming things by investing more effort into the bad source code. Defect prediction can assist estimate maintenance times, which can help with quality assurance, dependability, security, and cost reduction. Many predictions in IOT-based processing environment and business process management and enhancement challenges still exist in defect expectation ponders, and there are various noteworthy concerns. In addition, it is difficult to apply these methodologies practically because most of the investigations verified in open-source programming ventures with the goal that present forecast models might not work for other programming items including business programming. Investigating security issues in cross-project deformity expectation is required since if we have more accessible restrictive datasets, the assessment of forecast models will be more stable. In general, every defect is essential regarding quality, reliability, security, and cost-effectiveness. Therefore, an enhanced and improved maintenance schedule is required to acknowledge forecasting techniques. Therefore, in this article, we have evaluated different Semi-Supervised Learning (SSL) techniques, among which Extended Random Forest (extRF) technique is one for defective system prediction. The Extended Random Forest (extRF) technique is the extended form of the Random Forest (RF), which is a supervised learning technique into semi-supervised learning getting the hang of refining every arbitrary tree given an individual-training worldview. An enhancing technique is recommended, and a weighted mixture of irregular trees creates the final forecast results.
Construction of Credit Assessment Model for International Import and Export Trade Based on Fuzzy Hierarchical Analysis
Authors: Liu Yan
Published: September 2022

With the continuous expansion and development of import and export trade, the problem of credit risk faced by foreign trade enterprises has received increasing attention. This thesis focuses on the credit risk problem in import and export trade based on fuzzy hierarchical analysis; international import and export trade credit risk management risk is measured and evaluated to derive the ranking of key risk factors; the risk factors in the guideline layer are ranked from largest to smallest in order of legal risk, market risk, financial risk, and policy risk. The main purpose of this study is to build a fuzzy AHP international import and export trade credit evaluation model, which can provide reliable credit evaluation when enterprises conduct import and export trade and can better help enterprises to complete the trade. The analysis algorithm implemented in this study is a common and effective algorithm used for credit modeling, and more exploration and research on foreign trade credit modeling using existing modeling techniques are needed. The modeling techniques for credit evaluation of international import and export trade should be based on the credit information available to the enterprise to select the most appropriate modeling techniques and propose the most practical evaluation model. This study uses qualitative and quantitative, theoretical, and empirical comprehensive analysis methods based on fuzzy hierarchical analysis, draws on theories and models of credit risk management of 100 well-known enterprises around the world, and combines the actual situation of import and export trade. The credit evaluation model of international import and export trade based on fuzzy hierarchical analysis is constructed with certain operability and can make the credit evaluation of international import and export trade more accurate and reliable.
Coupling Heat Conduction and Radiation by an Isogeometric Boundary Element Method in 2-D Structures
Authors: Jiang Wei , Xu Jiancheng , Kunpeng Li , Ouyang Ye , Yan Jinzhou
Published: September 2022

We propose an efficient isogeometric boundary element method to address the coupling of heat conduction and radiation in homogeneous or inhomogeneous materials. The isogeometric boundary element method is used to construct irregular 2D models, which eliminate errors in model construction. The physical unknowns in the governing equations for heat conduction and radiation are discretized using an interpolation approximation, and the integral equations are finally solved by Newton–Raphson iteration; it is noteworthy that we use the radial integration method to convert the domain integrals to boundary integrals, and we combine the numerical schemes for heat conduction and radiation. The results of the three numerical cases show that the adopted algorithm can improve the computational accuracy and efficiency.
Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm
Authors: Yuan Honglin , Gao Bo
Published: September 2022

Learning about the regional business model is essential for the sustainable development of the regional economy. From the perspective of urban renewable energy, city A is the product of energy development. This paper analyzes the current situation and existing problems of the industrial model of city A through fuzzy k-means clustering algorithm. The results show that although the optimization of industrial structure in city A has achieved some results, the more intuitive problems mainly include low labor productivity of the primary industry, strong resource dependence, insufficient extension of industrial chain, and slow development of technology intensive industries. This paper uses fuzzy k-means clustering algorithm to select the leading industries from the perspective of the current situation of leading industries, urban development pattern, and regional policies in city A. The results show that, as a renewable resource-based city, the leading industries suitable for the current development of city A include manufacturing, power, alkali gas and water production and supply, transportation, warehousing and postal industry, leasing, and business services. The results of fuzzy k-means clustering algorithm are quite excellent, and the accuracy rate is 93.3%. This paper uses the grey dynamic linear programming model to predict the future development of the Urban A business model and combines the selection of key functions to obtain the best business model: deep and efficient technical equipment as a good goal, achieved through regional logistics, transportation, new services, etc., to enhance the output value of the tertiary industry in city A and optimize the internal structure of the secondary industry in city A.
Characteristics of Optic Neuritis in South Korean Children and Adolescents: A Retrospective Multicenter Study
Authors: Park Kyung-Ah , Yang Hee Kyung , Han Jinu , Kim Seong-Joon , Park Sung Eun , Lee Haeng-Jin , Han Sueng-Han , Oh Sei Yeul , Hwang Jeong-Min
Published: September 2022

Purpose. To analyze the clinical characteristics and prognosis of optic neuritis (ON) in pediatric patients aged <19 years in South Korea. Methods. This multicenter retrospective cohort study included 127 pediatric patients (median age: 10.3 (IQR: 7.3–14.2) years; female, 62.2%) who experienced ON for the first time between January 2004 and January 2018, with data obtained from five tertiary university-based hospitals in Korea. When ON was bilateral, the worse eye was selected for analysis. The baseline clinical characteristics and prognoses of patients, as well as the associations between these parameters, were analyzed. Results. The baseline clinical characteristics of the patients were as follows: best-corrected visual acuity (BCVA) < 20/200, 65.9%; pain on eye movement, 47.2%; optic disc swelling, 66.9%; and bilateral involvement, 41.7%. Among 101 patients who were followed up for ≥6 months, 48 (47.5%), 12 (11.9%), 19 (18.8%), 13 (12.9%), and 9 (8.9%) had been diagnosed with isolated ON, recurrent ON, multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and acute disseminated encephalomyelitis (ADEM)-related ON, respectively. At the latest visit, 81.9% and 71.1% had achieved BCVA of ≥20/40 and ≥ 20/25, respectively. Only disc swelling at presentation was associated with poor baseline BCVA (coefficient: 0.31,P=0.004) and greater improvement in BCVA (coefficient: 0.49, P = 0.001P=0.001); there were no significant associations between the baseline factors and final BCVA. Conclusions. This study demonstrated pediatric ON-related clinical characteristics and visual outcomes in South Korea. Within this cohort, in about 40.6% of patients, ON was associated with other demyelinating diseases, namely, MS, NMOSD, and ADEM.
The Treatment of Complementary and Alternative Medicine on Female Infertility Caused by Endometrial Factors
Authors: Lin Jing , Ma Haoyue , Li Hang , Han Jing , Guo Tingting , Qin Zhen , Jia Liyan , Zhang Yuehui
Published: September 2022

Recently, with the development of the social economy, the incidence of infertility has increased year by year. With its complex etiology and diversified syndromes, infertility has become one of the most important diseases that plague the physical and mental health of women of childbearing age worldwide. Endometrial factors as an important part affecting female reproductive capacity, due to which induced repeated abortion and multiple uterine cavity operations occur, can destruct endometrium, failing to provide a normal implantation environment for zygote, thus resulting in infertility. Many patients failed to achieve expected results after receiving conventional treatments such as hormone therapy, assisted reproductive technology (ART), granulocyte colony-stimulating factor (G-CSF) therapy, and cell therapy, then turn to complementary and alternative medicine (CAM) therapies for help. Aiming at clarifying the effectiveness and mechanisms of CAM therapy in the treatment of infertility caused by endometrial factors, our paper systematically searched and studied present related literature on the PubMed, CNKI, and other databases, focusing on the aspects of clinical application and mechanism explorations and highlighting the therapeutic effects of Chinese herbal medicine (CHM), acupuncture, and moxibustion on such diseases. Moreover, this paper also introduces the CAM treatments of traditional Chinese medicine (TCM) retention enema, neuromuscular electrical stimulation (NMES), photobiomodulation therapy, dietary intervention, and other measures for infertility caused by endometrial factors, in order to provide a reference for subsequent basic research and clinical work.
Effect of Endurance Training in COPD Patients Undergoing Pulmonary Rehabilitation: A Meta-Analysis
Authors: Li Yingying , Wu Weiwei , Wang Xiaoqiao , Chen Lili
Published: September 2022

Background. The efficacy of endurance training (ET) on patients with chronic obstructive pulmonary disease (COPD) has been controversial. This study was aimed at meta-analyzing the effect of ET in COPD patients undergoing pulmonary rehabilitation. Methods. The literature retrieval was performed in databases to screen relevant literature. Inclusion criteria were as follows: (1) subjects—COPD patients; (2) inclusion of interventional and control groups; (3) intervention measures—the interventional group received whole-body ET and other lung rehabilitation training, while the control group did not receive intervention or other lung rehabilitation training; (4) outcome indicators which included at least one of the following—6MWD, modified Medical Research Council questionnaire (mMRC), and COPD Assessment Test (CAT); and (5) study type—randomized controlled trials (RCTs). The Cochrane risk-of-bias tool was used to assess the risk of bias. The chi-square test was used to evaluate the magnitude of heterogeneity. Subgroup analysis was used to explore the source of heterogeneity. A funnel plot and Egger’s test were used to evaluate publication bias. Results. The 6MWD in the ET group was significantly higher than that in the control group (

MD
=
47.20

, 95% CI [28.60, 65.79],

P
<
0.00001

). Significant heterogeneity (

P
<
0.00001

,

I

2

=
76
%

) without publication bias (

P
>
0.05

) was noted. Subgroup analysis showed that the 6MWD of the ET group was significantly larger than that of the control group without heterogeneity (

P
=
0.63

,

I

2

=
0
%

;

P
=
0.59

,

I

2

=
0
%

) in both the no training subgroup (

MD
=
79.26

, 95% CI [72.69, 85.82],

P
<
0.00001

) and other rehabilitation training group (

MD
=
23.64

, 95% CI [6.70, 40.57],

P
=
0.006

). The mMRC score (

MD
=
−
0.72

, 95% CI [-1.09, -0.34],

P
=
0.002

) and CAT (

MD
=
−
6.07

, 95% CI [-7.28, -4.87],

P
<
0.00001

) of the ET group were significantly lower than those of the control group. There was no heterogeneity (

P
=
0.32

,

I

2

=
15
%

;

P
=
0.16

,

I

2

=
41
%

). Conclusion. ET can improve patients’ motor function and reduce dyspnea. ET might be incorporated as an important part of lung rehabilitation training.
Force-Induced Nitric Oxide Promotes Osteogenic Activity during Orthodontic Tooth Movement in Mice
Authors: Sun Yuqing , Fu Jingfei , Lin Feiran , Li Shengnan , Du Juan , Liu Yi , Bai Yuxing
Published: September 2022

Objectives. The aim of this study was to investigate the effect of nitric oxide (NO) on orthodontic tooth movement and the regulatory effect on bone formation. Design. A mouse orthodontic tooth movement model was established to measure the level of releasing NO. Besides, orthodontic tooth movement distance and the bone formation in the tension side of the orthodontic tooth were also analyzed. In vitro, human periodontal ligament stem cells (hPDLSCs) were cultured under tensile force stimulation. The production of NO and the expression level of nitric oxide synthase (NOS) were detected after mechanical stimulation. Furthermore, the downstream cellular signaling pathway regulated by NO was also explored. Results. The generation of NO steadily increased throughout the orthodontic tooth movement in mice. Orthodontic tooth movement was decreased in the NOS inhibitor group while it was accelerated in the NO precursor group. Force-induced NO promoted the osteogenic differentiation of human hPDLSCs under tensile force stimulation. And force-induced NO in hPDLSCs regulated the PI3K/Akt/β-catenin signal pathway. Conclusion. NO is involved in the regulation of orthodontic tooth movement and promotes bone formation on the tension side of the orthodontic tooth. The PI3K/Akt/β-catenin pathway is one of the downstream cell signal transduction pathways of NO in the orthodontic process.
Research on Fingerprint Security Based on Improved Yolo Algorithm
Authors: Lu Xuefeng
Published: September 2022

The quantitative identification technology based on the statistical law of fingerprint features has become a new research difficulty and focus, and the automatic detection and classification of fingerprint features are the basis for realizing automatic fingerprint feature statistics. In this paper, a YOLO-based fingerprint feature detection method was proposed. First, a fingerprint feature dataset was established, which contained a total of 4,000 annotated fingerprint images; then, according to the characteristics of small size and dense distribution of fingerprint feature points, the YOLO network structure was improved, the original large target feature detection layer by 32-fold downsampling was deleted, and a new small feature fusion layer was added; the FPN, PAN, and SPP structures were used to achieve local and global feature extraction through multiple-scale fusion methods; finally, the SE channel attention mechanism module was added to effectively enhance the model robustness and detection ability of dense small objects. The experimental results show that compared with the improved FP-YOLO model of the original model, when the detection speed is basically unchanged, the mAP0.5 value is increased from 93.0% to 97.4%, and the weight is reduced by 3/4.
Synthesis, Characterization, and Antimicrobial Activity of New 1,2,3-Thiadiazole and 1,2,3-Selenadiazole Derivatives
Authors: Al-Smadi Mousa L. , Esmadi Fatima , Al-Smadi Mohammad , Alzoubi Karem H. , Alzoubi Osama , Khader Yousef S.
Published: September 2022

1,2,3-Thiadiazole, 1,2,3-selenadiazole, and semicarbazones that are prepared from ketones are promising moieties for lead compound development. New 1,2,3-thiadiazole (2c-4c) and 1,2,3-selenadiazole derivatives (2d–4d) were prepared from the corresponding semicarbazones (2b-4b). The semicarbazones (2b-4b) were prepared from the corresponding ketones (2a-4a). Characterization of the synthesized compounds was performed using infrared spectra (IR), proton nuclear magnetic resonance (1H-NMR) spectra, carbon nuclear magnetic resonance (13C-NMR), ultraviolet spectra, mass spectrometry, and elemental analysis. The antimicrobial activity of the prepared compounds was explored in vitro against various pathogenic microbes. All heterocyclic compounds had positive antimicrobial activity, but these activities varied in the extent of antimicrobial coverage. Compounds (2c) and (2d) had positive activity against Staphylococcus aureus and Escherichia coli but without any antipseudomonal activity. Compound (3c) had the most activity against Candida albicans with potential as a novel antifungal agent along with activity against some Gram-positive and Gram-negative bacteria. Compounds (4c) and (4d) exhibited broad-spectrum coverage in which both compounds demonstrated antimicrobial activity against all microorganisms explored. Interestingly, they both had substantial antipseudomonal activity against local resistant Pseudomonas aeruginosa and reference P. aeruginosa (ATCC 27853). This may suggest the potential for compounds (4c) and (4d) as novel broad-spectrum antibacterial agents with promising antipseudomonal activity. In conclusion, new 1,2,3-thiadiazole (2c-4c) and 1,2,3-selenadiazole (2d-4d) derivatives were identified as potential lead compounds for novel antibacterial agents.
Task Image Setting of 3D Animation Based on Virtual Reality and Artificial Intelligence
Authors: Li Zhiwei
Published: September 2022

In order to make the expression and action of animated virtual characters more realistic, this paper proposes a virtual character expression and action system based on 3D animation. The system hardware module is used to complete the collection and processing of image data and human bone data. Then, the positioning of human skeleton points and the mapping relationship between joint points and moving skeleton points are constructed, and finally the virtual character model is constructed. On this basis, this paper completes the correspondence of feature points through facial feature point mapping and completes the face model alignment by aligning the video face with the 3D animation virtual face. The Laplace coordinate recovery model is used to reconstruct the facial expression action, complete the simulation of 3D animation virtual character expression action and realize the design of the virtual character expression action system based on 3D animation. The experimental results show that in the aspect of expression movement, the system in this paper has a better effect than the real-time motion capture technology system and the 2D animation expression movement system with a 95.40% simulation degree. The fidelity of skin texture processing in this system is 97.60%. Conclusion. The designed system can effectively simulate the facial expression in the character image and integrate it into the three-dimensional animation to make the virtual character more vivid. After rendering and skin texture processing on the Unity3D platform of the system, the authenticity of the virtual character is enhanced.