bluuebunny/crossref_metadata_2025 huggingface dataset:
Acoustic-based Upper Facial Action Recognition for Smart Eyewear
Authors: Xie Wentao , Zhang Qian , Zhang Jin
Published: June 2021

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.
DropMonitor
Authors: Lin Yuancan , Xie Lei , Wang Chuyu , Bu Yanling , Lu Sanglu
Published: June 2021

As an important indicator of the infusion monitoring for clinical treatment, the drip rate is expected to be monitored in an accurate and real-time manner. However, state-of-the-art drip rate monitoring schemes either suffer from high maintenance or incur high hardware cost. In this paper, we propose DropMonitor, an RFID-based approach to perform the mm-level sensing for infusion drip rate monitoring. By attaching a pair of batteryless RFID tags on the drip chamber, we can estimate the drip rate by capturing the RF-signals reflected from the vibrating liquid surface caused by the falling droplets. Particularly, we use the sensing tag to perceive the liquid surface vibration in the drip chamber and further derive the drip rate for infusion monitoring. Moreover, to sufficiently mitigate the multi-path interference from the surrounding human activities, we use the reference tag to perceive the multi-path signals from the indoor environment. By computing the difference of RF-signals from tag pairs, we cancel the multi-path interference and extract the drip-rate-related signals. We have implemented a prototype system and evaluated its performance in real applications. The experiment results show that DropMonitor can accurately estimate the infusion drip rate, and the average relative error of drip rate estimation is below 1% for conventional cases. In this way, considering the essential sampling rates of each tag, DropMonitor is able to monitor the drip rate for over a dozen of infusion bottles/bags in parallel with one COTS RFID system.
Enabling Real-time Sign Language Translation on Mobile Platforms with On-board Depth Cameras
Authors: Park HyeonJung , Lee Youngki , Ko JeongGil
Published: June 2021

In this work we present SUGO, a depth video-based system for translating sign language to text using a smartphone's front camera. While exploiting depth-only videos offer benefits such as being less privacy-invasive compared to using RGB videos, it introduces new challenges which include dealing with low video resolutions and the sensors' sensitiveness towards user motion. We overcome these challenges by diversifying our sign language video dataset to be robust to various usage scenarios via data augmentation and design a set of schemes to emphasize human gestures from the input images for effective sign detection. The inference engine of SUGO is based on a 3-dimensional convolutional neural network (3DCNN) to classify a sequence of video frames as a pre-trained word. Furthermore, the overall operations are designed to be light-weight so that sign language translation takes place in real-time using only the resources available on a smartphone, with no help from cloud servers nor external sensing components. Specifically, to train and test SUGO, we collect sign language data from 20 individuals for 50 Korean Sign Language words, summing up to a dataset of ~5,000 sign gestures and collect additional in-the-wild data to evaluate the performance of SUGO in real-world usage scenarios with different lighting conditions and daily activities. Comprehensively, our extensive evaluations show that SUGO can properly classify sign words with an accuracy of up to 91% and also suggest that the system is suitable (in terms of resource usage, latency, and environmental robustness) to enable a fully mobile solution for sign language translation.
EDEN
Authors: Khalfoun Besma , Ben Mokhtar Sonia , Bouchenak Sara , Nitu Vlad
Published: June 2021

Crowd sensing applications have demonstrated their usefulness in many real-life scenarios (e.g., air quality monitoring, traffic and noise monitoring). Preserving the privacy of crowd sensing app users is becoming increasingly important as the collected geo-located data may reveal sensitive information about these users (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. However, each LPPM comes with a given set of configuration parameters. The value of these parameters impacts not only the privacy level but also the utility of the resulting data. Choosing the right LPPM and the right configuration for reaching a satisfactory privacy vs. utility tradeoff is generally a difficult problem mobile app developers have to face. Solving this problem is commonly done by relying on a trusted proxy server to which raw geo-located traces are sent and privacy vs. utility assessment is performed enabling the selection of the best LPPM for each trace. In this paper we present EDEN, the first solution that selects automatically the best LPPM and its corresponding configuration without sending raw geo-located traces outside the user's device. We reach this objective by relying on a federated learning approach. The evaluation of EDEN on five real-world mobility datasets shows that EDEN outperforms state-of-the-art LPPMs reaching a better privacy vs. utility tradeoff.
Towards Early Detection and Burden Estimation of Atrial Fibrillation in an Ambulatory Free-living Environment
Authors: Zhang Hanbin , Zhu Li , Nathan Viswam , Kuang Jilong , Kim Jacob , Gao Jun Alex , Olgin Jeffrey
Published: June 2021

Early detection and accurate burden estimation of atrial fibrillation (AFib) can provide the foundation for effective physician treatment. New approaches to accomplish this have attracted tremendous attention in recent years. In this paper, we develop a novel passive smartwatch-based system to detect AFib episodes and estimate the AFib burden in an ambulatory free-living environment without user engagement. Our system leverages a built-in PPG sensor to collect heart rhythm without user engagement. Then, a data preprocessor module includes time-frequency (TF) analysis to augment features in both the time and frequency domain. Finally, a lightweight multi-view convolutional neural network consisting of 19 layers achieves the AFib detection. To validate our system, we carry out a research study that enrolls 53 participants across three months, where we collect and annotate more than 27,622 hours of data. Our system achieves an average of 91.6% accuracy, 93.0% specificity, and 90.8% sensitivity without dropping any data. Moreover, our system takes 0.51 million parameters and costs 5.18 ms per inference. These results reveal that our proposed system can provide a clinical assessment of AFib in daily living.
Exploration of Person-Independent BCIs for Internal and External Attention-Detection in Augmented Reality
Authors: Vortmann Lisa-Marie , Putze Felix
Published: June 2021

Adding attention-awareness to an Augmented Reality setting by using a Brain-Computer Interface promises many interesting new applications and improved usability. The possibly complicated setup and relatively long training period of EEG-based BCIs however, reduce this positive effect immensely. In this study, we aim at finding solutions for person-independent, training-free BCI integration into AR to classify internally and externally directed attention. We assessed several different classifier settings on a dataset of 14 participants consisting of simultaneously recorded EEG and eye tracking data. For this, we compared the classification accuracies of a linear algorithm, a non-linear algorithm, and a neural net that were trained on a specifically generated feature set, as well as a shallow neural net for raw EEG data. With a real-time system in mind, we also tested different window lengths of the data aiming at the best payoff between short window length and high classification accuracy. Our results showed that the shallow neural net based on 4-second raw EEG data windows was best suited for real-time person-independent classification. The accuracy for the binary classification of internal and external attention periods reached up to 88% accuracy with a model that was trained on a set of selected participants. On average, the person-independent classification rate reached 60%. Overall, the high individual differences could be seen in the results. In the future, further datasets are necessary to compare these results before optimizing a real-time person-independent attention classifier for AR.
MemX
Authors: Chang Yuhu , Zhao Yingying , Dong Mingzhi , Wang Yujiang , Lu Yutian , Lv Qin , Dick Robert P. , Lu Tun , Gu Ning , Shang Li
Published: June 2021

This work presents MemX: a biologically-inspired attention-aware eyewear system developed with the goal of pursuing the long-awaited vision of a personalized visual Memex. MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets. Accurate attentive scene detection and analysis on resource-constrained platforms is challenging because these tasks are computation and energy intensive. We propose a new temporal visual attention network that unifies human visual attention tracking and salient visual content analysis. Attention tracking focuses computation-intensive video analysis on salient regions, while video analysis makes human attention detection and tracking more accurate. Using the YouTube-VIS dataset and 30 participants, we experimentally show that MemX significantly improves the attention tracking accuracy over the eye-tracking-alone method, while maintaining high system energy efficiency. We have also conducted 11 in-field pilot studies across a range of daily usage scenarios, which demonstrate the feasibility and potential benefits of MemX.
ApneaDetector
Authors: Chen Xianda , Xiao Yifei , Tang Yeming , Fernandez-Mendoza Julio , Cao Guohong
Published: June 2021

Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by machine learning techniques for sleep apnea detection. However, there are many technical challenges such as how to extract various special patterns from the noisy and multi-axis sensing data. To address these challenges, we propose signal denoising and data calibration techniques to process the noisy data while preserving the peaks and troughs which reflect the possible apnea events. We identify the characteristics of sleep apnea such as signal spikes which can be captured by smartwatch, and propose methods to extract proper features to train machine learning models for apnea detection. Through extensive experimental evaluations, we demonstrate that our system can detect apnea events with high precision (0.9674), recall (0.9625), and F1-score (0.9649).
IriTrack
Authors: Shen Meng , Wei Yaqian , Liao Zelin , Zhu Liehuang
Published: June 2021

With a growing adoption of face authentication systems in various application scenarios, face Presentation Attack Detection (PAD) has become of great importance to withstand artefacts. Existing methods of face PAD generally focus on designing intelligent classifiers or customized hardware to differentiate between the image or video samples of a real legitimate user and the imitated ones. Although effective, they can be resource-consuming and suffer from performance degradation due to environmental changes.

In this paper, we propose IriTrack, which is a simple and efficient PAD system that takes iris movement as a significant evidence to identify face artefacts. More concretely, users are required to move their eyes along with a randomly generated poly-line, where the resulting trajectories of their irises are used as an evidence for PAD i.e., a presentation attack will be identified if the deviation of one's actual iris trajectory from the given poly-line exceeds a threshold. The threshold is carefully selected to balance the latency and accuracy of PAD. We have implemented a prototype and conducted extensive experiments to evaluate the performance of the proposed system. The results show that IriTrack can defend against artefacts with moderate time and memory overheads.
SonicASL
Authors: Jin Yincheng , Gao Yang , Zhu Yanjun , Wang Wei , Li Jiyang , Choi Seokmin , Li Zhangyu , Chauhan Jagmohan , Dey Anind K. , Jin Zhanpeng
Published: June 2021

We propose SonicASL, a real-time gesture recognition system that can recognize sign language gestures on the fly, leveraging front-facing microphones and speakers added to commodity earphones worn by someone facing the person making the gestures. In a user study (N=8), we evaluate the recognition performance of various sign language gestures at both the word and sentence levels. Given 42 frequently used individual words and 30 meaningful sentences, SonicASL can achieve an accuracy of 93.8% and 90.6% for word-level and sentence-level recognition, respectively. The proposed system is tested in two real-world scenarios: indoor (apartment, office, and corridor) and outdoor (sidewalk) environments with pedestrians walking nearby. The results show that our system can provide users with an effective gesture recognition tool with high reliability against environmental factors such as ambient noises and nearby pedestrians.
Watching Your Phone's Back
Authors: Wang Lei , Zhang Xiang , Jiang Yuanshuang , Zhang Yong , Xu Chenren , Gao Ruiyang , Zhang Daqing
Published: June 2021

Gesture recognition on the back surface of mobile phone, not limited to the touch screen, is an enabling Human-Computer Interaction (HCI) mechanism which enriches the user interaction experiences. However, there are two main limitations in the existing Back-of-Device (BoD) gesture recognition systems. They can only handle coarse-grained gesture recognition such as tap detection and cannot avoid the air-borne propagation suffering from the interference in the air. In this paper, we propose StruGesture, a fine-grained gesture recognition system using the back of mobile phones with ultrasonic signals. The key technique is to use the structure-borne sounds (i.e., sound propagation via structure of the device) to recognize sliding gestures on the back of mobile phones. StruGesture can fully extract the structure-borne component from the hybrid Channel Impulse Response (CIR) based on Peak Selection Algorithm. We develop a deep adversarial learning architecture to learn the gesture-specific representation for robust and effective recognition. Extensive experiments are designed to evaluate the robustness over nine deployment scenarios. The results show that StruGesture outperforms the competitive state-of-the-art classifiers by achieving an average recognition accuracy of 99.5% over 10 gestures.
Unlocking the Beamforming Potential of LoRa for Long-range Multi-target Respiration Sensing
Authors: Zhang Fusang , Chang Zhaoxin , Xiong Jie , Zheng Rong , Ma Junqi , Niu Kai , Jin Beihong , Zhang Daqing
Published: June 2021

Despite extensive research effort in contact-free sensing using RF signals in the last few years, there still exist significant barriers preventing their wide adoptions. One key issue is the inability to sense multiple targets due to the intrinsic nature of relying on reflection signals for sensing: the reflections from multiple targets get mixed at the receiver and it is extremely difficult to separate these signals to sense each individual. This problem becomes even more severe in long-range LoRa sensing because the sensing range is much larger compared to WiFi and acoustic based sensing. In this work, we address the challenging multi-target sensing issue, moving LoRa sensing one big step towards practical adoption. The key idea is to effectively utilize multiple antennas at the LoRa gateway to enable spatial beamforming to support multi-target sensing. While traditional beamforming methods adopted in WiFi and Radar systems rely on accurate channel information or transmitter-receiver synchronization, these requirements can not be satisfied in LoRa systems: the transmitter and receiver are not synchronized and no channel state information can be obtained from the cheap LoRa nodes. Another interesting observation is that while beamforming helps to increase signal strength, the phase/amplitude information which is critical for sensing can get corrupted during the beamforming process, eventually compromising the sensing capability. In this paper, we propose novel signal processing methods to address the issues above to enable long-range multi-target reparation sensing with LoRa. Extensive experiments show that our system can monitor the respiration rates of five human targets simultaneously at an average accuracy of 98.1%.
Characterization of the Electrochemical Behavior of MnSO4 with and without TiOSO4 in H2SO4 Solution
Authors: Bahdad Abdullah Omar O. , Li Yuanchao , Van Nguyen Trung
Published: June 2021

An electrochemical characteristic evaluation of the Ti-Mn electrolyte system in sulfuric acid aqueous solution was conducted in different acid concentrations with and without the mixing of TiOSO4 and MnSO4 electrolyte system for a redox flow battery application. The reaction mechanism of Mn in sulfuric acid electrolyte was studied using the cyclic voltammetry and anodic voltage hold techniques. The overall anodic behavior of the Mn system can be described by the oxidation reaction of Mn2+ to MnO2 where Mn3+ is an intermediate species. As the acid concentration increases, less MnO2 is formed on the working electrode and more Mn3+ is formed in the solution; however, due to the instability of Mn3+ in an aqueous solution, Mn3+ undergoes a hydrolysis reaction to precipitate as MnOOH in the electrolyte solution. The addition of 1 M TiOSO4 to the Mn electrolyte is suggested to lower the H+ concentration which increases the amount of MnOOH in the solution and lowers the concentration of the aqueous Mn3+ species. The MnOOH formed by the Mn3+ hydrolysis reaction is found to be electrochemically active if it is deposited on the working electrode.
Heat Conductivity Based Inner Cavity Pressure Monitoring and Hermeticity Monitoring for Glass Frit Wafer Bonded MEMS Devices
Authors: Knechtel Roy , Dempwolf Sophia , Hering Siegfried
Published: June 2021

Hermetic sealing is important regarding functionality and reliability for MEMS components. Typically this sealing is done at the wafer level using wafer bonding, which simultaneously provides mechanical protective caps. Here, a universally usable MEMS Foundry technology for inertial sensors and the glass frit bonding to seal them are introduced. A test structure for inner cavity pressure measurement, utilizing the pressure dependence of heat transfer in gasses, which can be realized in this MEMS technology, is introduced and described for use in detailed investigations of factors, which influence the inner cavity pressure in the wafer pre-processing and wafer bonding step itself. These investigations ultimately allowed optimized wafer processing, improved process control, and reliability investigation. We show how, based on the inner cavity pressure measurements, the glass frit bond process can be set up, that side effects such as storing times of the glass frit can be understood, how fine tuning of the bond process can be done, and how both process stability and device reliability can be efficiently be proven.
Dysanapsis in men and women with obesity
Authors: Stickford Jonathon L. , Wilhite Daniel P. , Bhammar Dharini M. , Balmain Bryce N. , Babb Tony G.
Published: June 2021

It is unclear whether obesity in combination with sex influences the dysanapsis ratio (DR). These data indicate that DR is unaltered in adults with obesity and is greater in women than in men but similar between sexes when matched for lung volume. We also report a significant difference between predicted and measured static recoil pressure. Thus, we caution against predicting static recoil pressure in the calculation of DR.
Case Studies in Physiology: Temporal variations of the lung parenchyma and vasculature in asymptomatic COVID-19 pneumonia: a multispectral CT assessment
Authors: Nagpal Prashant , Motahari Amin , Gerard Sarah E. , Guo Junfeng , Reinhardt Joseph M. , Comellas Alejandro P. , Hoffman Eric A. , Kaczka David W.
Published: June 2021

We characterized the temporal changes of lung parenchyma and microvascular pathophysiology from COVID-19 infection in an asymptomatic young, healthy nonsmoking male using dual-energy CT. Lung parenchymal mechanics and microvascular disease followed different clinical courses. Heterogeneous perfused blood volume improved on follow-up visits up to 90 days. However, post COVID-19 mechanical heterogeneity of the lung parenchyma increased after apparent improvements in vascular abnormalities, even with normal spirometric indices.
Intraocular pressure and choroidal thickness respond differently to lower body negative pressure during spaceflight
Authors: Greenwald Scott H. , Macias Brandon R. , Lee Stuart M. C. , Marshall-Goebel Karina , Ebert Douglas J. , Liu John H. K. , Ploutz-Snyder Robert J. , Alferova Irina V. , Dulchavsky Scott A. , Hargens Alan R. , Stenger Michael B. , Laurie Steven S.
Published: June 2021

Spaceflight induces a chronic headward fluid shift that is believed to underlie ocular changes observed in astronauts. The present study demonstrates, for the first time, that reversing this headward fluid shift via application of lower body negative pressure (LBNP) during spaceflight may alter the ocular venous system, as evidenced by a decrease in intraocular pressure. This finding indicates that LBNP has the potential to be an effective countermeasure against the headward fluid shift during spaceflight, which may then be beneficial in preventing or reversing associated ocular changes.
Research on the Operating Efficiency of Chinese Listed Pharmaceutical Companies Based on Two-Stage Network DEA and Malmquist
Authors: Lin Tsung-Xian , Wu Zhong-huan , Ji Xiao-xia , Yang Jia-jia
Published: June 2021

The development of pharmaceutical companies, which is an important part of the national economy and industry, is closely related to people’s livelihood issues. With the era of big data, this paper uses the two-stage DEA and Malmquist method to evaluate the efficiency of listed Chinese pharmaceutical companies. From a static and dynamic perspective, it analyses the total factor productivity index, pure efficiency change index, scale efficiency index, and so on. The results show that government subsidies have not had a positive impact on most Chinese pharmaceutical companies, and for films, diseconomies of scale caused by rapid expansion should be avoided.
[Retracted] Research on the Path and Effect Evaluation of Students’ Quality Improvement in Private Colleges
Authors: Qiu Yongjian , Zhu Xinghai , Li Zelong , Lu Jing
Published: June 2021

Entering the 21st century, the development momentum of holistic education in China’s higher education can be described as rapid progress. General Secretary Xi Jinping emphasized at the National Education Conference that efforts should be made to enhance comprehensive quality and educate and guide students to cultivate comprehensive ability and cultivate innovative thinking. The Outline of the 13th Five‐Year Plan clearly states that the training system of academic and applied talents classification, holistic education, and professional education combination shall be implemented. Holistic education is becoming more and more prominent in the cultivation of high‐quality interdisciplinary talents in colleges and universities, and its importance in educational reform has been recognized. However, there are still some problems in holistic education, such as unbalanced development among core modules, separation of theory and practice in the actual teaching process, single classroom mode and low interest of students, and lack of relevant teacher resources. This paper introduces the experience of holistic education curriculum exploration in Sanquan College of Xinxiang Medical University. In the construction of the core modules of the curriculum, the content of the modules is clear, and the cross‐module is emphasized; in the curriculum design, a multidimensional combination of theory and practice is required; the construction of the teaching staff is based on the long‐term perspective, and the classroom assessment is scientific and objective. The research aims to provide a certain reference for the construction of holistic education courses.
Validation of the Acute Physiology and Chronic Health Evaluation (APACHE) II and IV Score in COVID-19 Patients
Authors: Vandenbrande Jeroen , Verbrugge Laurens , Bruckers Liesbeth , Geebelen Laurien , Geerts Ester , Callebaut Ina , Gruyters Ine , Heremans Liesbeth , Dubois Jasperina , Stessel Bjorn
Published: June 2021

Background. Severity scoring systems are inherent to ICU practice for multiple purposes. Acute Physiology and Chronic Health Evaluation (APACHE) scoring systems are designed for ICU mortality prediction. This study aims to validate APACHE IV in COVID-19 patients admitted to the ICU. Methods. All COVID-19 patients admitted to the ICU between March 13, 2020, and October 17, 2020, were retrospectively analyzed. APACHE II and APACHE IV scores as well as SOFA scores were calculated within 24 hours after admission. Discrimination for mortality of all three scoring systems was assessed by receiver operating characteristic curves. Youden index was determined for the scoring system with the best discriminative performance. The Hosmer–Lemeshow goodness-of-fit test was used to assess calibration. All analyses were performed for both the overall population as in a subgroup treated with anti-Xa adjusted dosages of LMWHs. Results. 116 patients were admitted to our ICU during the study period. 13 were excluded for various reasons, leaving 103 patients in the statistical analysis of the overall population. 57 patients were treated with anti-Xa adjusted prophylactic dosages of LMWH and were supplementary analyzed in a subgroup analysis. APACHE IV had the best discriminative power of the three scoring systems, both in the overall population (APACHE IV ROC AUC 0.67 vs. APACHE II ROC AUC 0.63) as in the subgroup (APACHE IV ROC AUC 0.82 vs. APACHE II ROC AUC 0.7). This model exhibits good calibration. Hosmer–Lemeshow

p

values for APACHE IV were 0.9234 for the overall population and 0.8017 for the subgroup. Calibration

p

values of the APACHE II score were 0.1394 and 0.6475 for the overall versus subgroup, respectively. Conclusions. APACHE IV provided the best discrimination and calibration of the considered scoring systems in critically ill COVID-19 patients, both in the overall group and in the subgroup with anti-Xa adjusted LMWH doses. Only in the subgroup analysis, discriminative abilities of APACHE IV were very good. This trial is registered with NCT04713852.
The Relationship between Body Appreciation and Self-Esteem and Associated Factors among Omani University Students: An Online Cross-Sectional Survey
Authors: Khalaf Atika , Al Hashmi Iman , Al Omari Omar
Published: June 2021

Background. Given the rapid pace of globalization and the fact that the Sultanate of Oman is experiencing a significant impact of social media on specifying appearance norms among youth in the country, research into positive body images and self-esteem among young individuals has become a national priority. Whilst body image has been well studied across cultures, both positive body image and the relationship between positive body image and self-esteem among Omani youth have been neglected. The aim of the study was to investigate the relationship between positive body image and self-esteem and associated sociodemographic factors among Omani university students based on gender. Methods. This cross-sectional study used an online survey consisting of the two questionnaires that are Body Appreciation Scale-2 and Rosenberg’s Self-Esteem Scale. A total of 237 students were recruited from Sultan Qaboos University’s different colleges. Results. The results indicated that positive body image has a significant relationship with an individual’s self-esteem (β = 0.122, t = 2.197,

p
=
0.038

), Cumulative Grade Point Average (cGPA) (β = 0.140, t = 2.306,

p
=
0.022

), body mass index (BMI) (β = −0.414, t = −6.930,

p
<
0.001

), monthly household income (β = −0.129, t = 2.467,

p
=
0.029

), and the number of social media accounts (≥2, β = −0.132, t = −2.232,

p
=
0.027

). In addition, an individual’s self-esteem was significantly associated with an individual’s cGPA (β = 0.231, t = 3.592,

p
<
0.001

) and mothers’ educational level (β = −0.130, t = −2.065,

p
=
0.040

) besides body appreciation (β = 0.160, t = 2.491,

p
=
0.013

). Conclusions. The findings of this study shed light on the current status of positive body image among university students of Oman. In light of the new knowledge, we propose health interventions that include strategies such as involvement of family, to maintain and/or promote positive body image perceptions among young individuals and subsequently promote healthy appreciation of the physical appearance and self-esteem.
Reaction-Diffusion on a Spatial Mathematical Model of Cancer Immunotherapy with Effector Cells and IL-2 Compounds’ Interactions
Authors: Suddin Sulasri , Adi-Kusumo Fajar , Aryati Lina , Gunardi
Published: June 2021

Immunotherapy is one of the future treatments applicable in most cases of cancer including malignant cancer. Malignant cancer usually prevents some genes, e.g., p53 and pRb, from controlling the activation of the cell division and the cell apoptosis. In this paper, we consider the interactions among the cancer cell population, the effector cell population that is a part of the immune system, and cytokines that can be used to stimulate the effector cells called the IL-2 compounds. These interactions depend on both time and spatial position of the cells in the tissue. Mathematically, the spatial movement of the cells is represented by the diffusion terms. We provide an analytical study for the constant equilibria of the reaction-diffusion system describing the above interactions, which show the initial behaviour of the tissue, and we conduct numerical simulation that shows the dynamics along the tissue that represent the immunotherapy effects. In this case, we also consider the steady-state conditions of the system that show the long-time behaviour of these interactions.
Prevalence and Determinants of Diarrheal Diseases among Under-Five Children in Horo Guduru Wollega Zone, Oromia Region, Western Ethiopia: A Community-Based Cross-Sectional Study
Authors: Alemayehu Kefalew , Oljira Lemessa , Demena Melake , Birhanu Abdi , Workineh Dasselegn
Published: June 2021

Background. Diarrheal diseases are the leading cause of preventable death, especially among under-five children in developing countries, including Ethiopia. Although efforts have been made to reduce the morbidity and mortality resulting from diarrheal diseases, there is scarce information on the progress of the interventions against the burdens. Therefore, this study aimed to assess the prevalence of diarrhea and its associated factors in under-five children in Horo Guduru Wollega Zone, Oromia Region, Western Ethiopia. Methods. A community-based cross-sectional study was conducted. Of 12,316 households, 620 households that had under-five children were selected by simple random sampling technique from randomly selected kebeles. Before data collection, a pretest of the structured questionnaires was done on nonselected kebeles. Binary logistic regression was used to assess the association of the diarrheal diseases with independent variables. Finally, the odds ratio along with a 95% confidence interval was used to report the significant association between the outcome variable and its associated factors. A

P

value of ≤0.05 was considered statistically significant Results. The prevalence of diarrhea among under-five children was 149 (24%) (95% CI: 20.8, 27.3). Diarrhea was significantly associated with poor knowledge of mothers/caretakers on diarrhea prevention methods (AOR: 2.05, 95% CI (1.14, 3.69), being in the age group of 6–11(AOR = 1.546 (1.68, 3.52), and 12–23 months (AOR = 1.485 (1.84, 2.63)), families with poor wealth index (AOR: 2.41, 95% CI (1.29, 4.51)), children who were not vaccinated against measles (AOR: 4.73, 95% CI (2.43, 9.20)), unsafe child feces disposal (AOR = 3.75; 95% CI (1.91, 7.39)), inappropriate liquid waste disposal (AOR = 3.73 (1.94, 7.42)), and having two or more siblings (AOR: 3.11, 95% CI (1.81, 5.35)). Conclusion and Remarks. The prevalence of diarrhea among under-five children was high. There was a statistically significant association between diarrhea and age of the child (6–11 and 12–23), poor knowledge of mothers/caretakers on diarrhea prevention methods, families with poor wealth index, being unvaccinated against measles, improper liquid waste disposal, unsafe child feces disposal, and having at least two siblings. The findings have a significant policy inference for childhood diarrheal disease prevention programs. Therefore, educating mothers/caregivers on diarrheal disease prevention methods, child spacing, regular hand washing practice after disposing child feces, safely disposing liquid waste, and vaccinating all eligible children against measles should be a priority area of intervention for diarrheal disease prevention. Moreover, since these associated factors are preventable, the government needs to strengthen the health extension workers program implementations to reduce childhood diarrhea.
Reliability Analysis of Special Vehicle Critical System Using Discrete-Time Bayesian Network
Authors: Li Zong-Yuan , Jiang Guang-Jun , Chen Hong-Xia , Li Hai-Bin , Sun Hong-Hua
Published: June 2021

The reliability assessment of special vehicles has become very important. However, due to the special structure of special vehicles, it is difficult to collect a large amount of experimental data. The use of traditional fault tree analysis cannot accurately assess product reliability. In this paper, dynamic fault trees are used to model the critical systems of special vehicles, and discrete Bayesian networks are used to evaluate the reliability of critical systems of special vehicles, which solved the problems of difficulty in accurately describing complex systems in the process of system reliability analysis and difficulty in obtaining accurate data in the process of analysis. Finally, through the combination of expert experience and the evaluation of the calculation results, the rationality of the method used in this paper in the reliability evaluation of special vehicles is verified.
[Retracted] Construction and Simulation of Economic Statistics Measurement Model Based on Time Series Analysis and Forecast
Authors: Xu Lu , Chen Weijie
Published: June 2021

Time series follow the basic principles of mathematical statistics and can provide a set of scientifically based dynamic data processing methods. Using this method, various types of data can be approximated by corresponding mathematical models, and then, the internal structure and complex characteristics of the data can be understood essentially, so as to achieve the purpose of predicting its development trend. This paper mainly studies the combined forecasting model based on the time series model and its application. First, the application prospects and research status of the combined forecasting model, the source of time series analysis, and the status of research development at home and abroad are given, and the purpose and significance of the research topic and the research content are summarized. Then, the paper gives the relevant theories about the ARIMA model and the basic principles of model recognition and explains the method of time series smoothing. Finally, the paper uses the ARIMA model to identify and fit the time series data and then the gray forecast model to fit and predict the time series data. Finally, by assigning reasonable weights and combining these methods, a combined forecasting model is proposed and carried out.
A Review of the Methods for Predicting the Effective In‐Plane Shear Modulus of Cross‐Laminated Timber (CLT)
Authors: Khan Mehsam Tanzim , Chui Ying Hei , Huang Dongsheng
Published: June 2021

Cross‐laminated timber (CLT) is a type of engineered wood product that offers both high in‐plane and out‐of‐plane load‐bearing capacity. It is slowly becoming an alternative material for building high‐rise structures. However, there is no current standard or regulation for determining the shear modulus of CLT under in‐plane loading condition, which is a very important property for its use as structural members. Few methods have been proposed over the last decade to determine the in‐plane shear modulus of CLT. Almost all of the methods proposed until now have their strengths and weaknesses. In this paper, some of the prominent methods for determining the in‐plane shear modulus of CLT are described and analysed. The descriptions along with the critical discussions will facilitate a better understanding and might pave the way to further enhancements of the method(s) to determine the in‐plane shear modulus of CLT.
DNA Methylation in Atherosclerosis: A New Perspective
Authors: Zhang Yan , Mei Jun , Li Jing , Zhang Ying , Zhou Qingbing , Xu Fengqin
Published: June 2021

Atherosclerotic cardiovascular diseases, in which atherosclerosis (AS) is the main pathologic basis, are currently the primary diseases leading to human deaths. Emerging evidence showed that DNA methylation, which could affect the transcription and expression of critical regulatory genes, has key roles in AS. Aberrant DNA methylation including aberrant hypomethylation and hypermethylation plays key roles in endothelial-cell dysfunction, macrophage inflammation, abnormal proliferation of vascular smooth muscle cells, plaque rupture, and thrombosis in AS. Chinese herbal medicines, including single compounds and formulations, showed light on the treatment of AS through regulating the aberrant DNA methylation in AS. Targeting the aberrant DNA methylation may be one of the most important treatment strategies in the cure and prevention of AS. In this review, we focus on the relationship between DNA methylation and AS, as well as the beneficial effects of Chinese herbal medicines on DNA methylation in AS.
Aged Persons Living with HIV and Nutritional Wellness: Analysis of 2013 South Africa-SAGE Well-Being of Older People Study (WOPS) Wave 2
Authors: Oduro Joseph Kojo , Kissah-Korsah Kwaku
Published: June 2021

Introduction. This study sought to examine the nutritional wellness among aged persons living with HIV in Somkhele, South Africa. Methods. Data were extracted from the 2013 South Africa-SAGE Well-Being of Older People Study (WOPS) Wave 2. The study sampled 440 aged persons (50 years and above). The proportion of the aged persons with high nutritional wellness by key covariates was reported with chi-square and

p

-values (

p
<
0.05

). Two-level binary logistic regression models were fitted. Results. Results show that there were more aged women (79.8%) than men (20.2%) and the younger old (50–64) dominated (65.7%) in the ages, among the respondents. A higher percent of the aged persons were widows (39.5%) and had no formal education (46.1%). Six in 10 aged persons were HIV infected (59.5%). Percentage distribution of men with high nutrition was higher (78.7%). Regression results show that having nutritional wellness was low for aged persons who were infected by HIV (OR = 0.74, 95% CI = 0.69, 1.26) when compared to those who were not infected. Regarding ages of respondents, having high nutritional wellness was higher for young old (65–74 years) (OR = 1.21, 95% CI = 0.65, 2.25) compared to younger old (50–64 years). Conclusion. This study suggests that age, education, source of drinking water, household source of income, and financial situation are important for nutritional wellness of aged persons who are HIV infected in South Africa. Moreover, having HIV infection is associated with low nutritional wellness. Being a young old (65–74 years) and receiving government grants and with better financial situation is associated with high nutritional wellness. Stakeholders and agencies who have interest in aged persons affected by HIV must understand the socioeconomic status in relation to their nutritional wellness. The results are of great importance to ageing policies, specifically in health and nutrition.
Shaking Table Study on the Seismic Performance of Geogrid Reinforced Soil Retaining Walls
Authors: Cai Xiaoguang , Li Sihan , Xu Honglu , Jing Liping , Huang Xin , Zhu Chen
Published: June 2021

This study presents experimental results from shaking table tests on a reduced‐scale geogrid reinforced soil retaining wall (RSRW) to investigate the seismic response of the fundamental frequency, acceleration amplification, face displacement, backfill surface settlement, and reinforcement strain under different peak accelerations and durations. The fundamental frequency is in good agreement with the predicted values. The root mean square (RMS) acceleration amplification factors increase nonlinearly with the wall height and decrease with increasing seismic load, which is not regarded as a constant value. The distributions of the peak displacement are consistent with those of the residual displacement. The combination of the sliding and rotation is observed as the predominant mode of displacement, and the rotation mode is dominant. The positions near the face (35 cm) and the ends of the reinforcement (140 cm) demonstrated larger settlement than that of the central position (70 cm and 105 cm). The reinforcement strain increased with increasing peak acceleration and maximum values measured at the central layers. The trends of the potential failure surface are similar to those of the 0.3H bilinear failure surface. The friction coefficient is nonlinearly distributed along with the reinforcements, and the maximum friction coefficient appears at the top layer (F11).
Fuzzy PID Control for Respiratory Systems
Authors: Mehedi Ibrahim M. , Shah Heidir S. M. , Al-Saggaf Ubaid M. , Mansouri Rachid , Bettayeb Maamar
Published: June 2021

This paper presents the implementation of a fuzzy proportional integral derivative (FPID) control design to track the airway pressure during the mechanical ventilation process. A respiratory system is modeled as a combination of a blower-hose-patient system and a single compartmental lung system with nonlinear lung compliance. For comparison purposes, the classical PID controller is also designed and simulated on the same system. According to the proposed control strategy, the ventilator will provide airway flow that maintains the peak pressure below critical levels when there are unknown parameters of the patient’s hose leak and patient breathing effort. Results show that FPID is a better controller in the sense of quicker response, lower overshoot, and smaller tracking error. This provides valuable insight for the application of the proposed controller.
Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator
Authors: So Jaehyun Jason , Park Sungho , Kim Jonghwa , Park Jejin , Yun Ilsoo
Published: June 2021

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.