bluuebunny/crossref_metadata_2025 huggingface dataset:
Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization
Authors: Zhao Jiashu , Huang Jimmy Xiangji , Deng Hongbo , Chang Yi , Xia Long
Published: December 2021

In this article, we propose a Latent Dirichlet Allocation– (LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user’s search intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user’s search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user’s search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models.
DAFI
Authors: Li Hang , Chen Xi , Wang Ju , Wu Di , Liu Xue
Published: December 2021

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.
CSMC
Authors: Wang Hai , Guo Baoshen , Wang Shuai , He Tian , Zhang Desheng
Published: December 2021

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.
Are Those Steps Worth Your Privacy?
Authors: Velykoivanenko Lev , Niksirat Kavous Salehzadeh , Zufferey Noé , Humbert Mathias , Huguenin Kévin , Cherubini Mauro
Published: December 2021

Fitness trackers are increasingly popular. The data they collect provides substantial benefits to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker users perceive the utility of the features they provide and the associated privacy-inference risks. We conduct a longitudinal study composed of a four-month period of fitness-tracker use (N = 227), followed by an online survey (N = 227) and interviews (N = 19). We assess the users' knowledge of concrete privacy threats that fitness-tracker users are exposed to (as demonstrated by previous work), possible privacy-preserving actions users can take, and perceptions of utility of the features provided by the fitness trackers. We study the potential for data minimization and the users' mental models of how the fitness tracking ecosystem works. Our findings show that the participants are aware that some types of information might be inferred from the data collected by the fitness trackers. For instance, the participants correctly guessed that sexual activity could be inferred from heart-rate data. However, the participants did not realize that also the non-physiological information could be inferred from the data. Our findings demonstrate a high potential for data minimization, either by processing data locally or by decreasing the temporal granularity of the data sent to the service provider. Furthermore, we identify the participants' lack of understanding and common misconceptions about how the Fitbit ecosystem works.
Ubi-SleepNet
Authors: Zhai Bing , Guan Yu , Catt Michael , Plötz Thomas
Published: December 2021

Sleep is a fundamental physiological process that is essential for sustaining a healthy body and mind. The gold standard for clinical sleep monitoring is polysomnography(PSG), based on which sleep can be categorized into five stages, including wake/rapid eye movement sleep (REM sleep)/Non-REM sleep 1 (N1)/Non-REM sleep 2 (N2)/Non-REM sleep 3 (N3). However, PSG is expensive, burdensome and not suitable for daily use. For long-term sleep monitoring, ubiquitous sensing may be a solution. Most recently, cardiac and movement sensing has become popular in classifying three-stage sleep, since both modalities can be easily acquired from research-grade or consumer-grade devices (e.g., Apple Watch). However, how best to fuse the data for greatest accuracy remains an open question. In this work, we comprehensively studied deep learning (DL)-based advanced fusion techniques consisting of three fusion strategies alongside three fusion methods for three-stage sleep classification based on two publicly available datasets. Experimental results demonstrate important evidences that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep. To accelerate the progression of sleep research in the ubiquitous/wearable computing community, we made this project open source, and the code can be found at: https://github.com/bzhai/Ubi-SleepNet.
DistFL
Authors: Liu Bingyan , Cai Yifeng , Zhang Ziqi , Li Yuanchun , Wang Leye , Li Ding , Guo Yao , Chen Xiangqun
Published: December 2021

Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its superiority, it ignores the non-iid (independently identically distributed) situation, which widely exists in mobile scenarios. Failing to handle non-iid situations could cause problems such as performance decreasing and possible attacks. Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models. However, previous techniques overlook the root causes for the "symptoms": blindly aggregating models with the non-iid distributions. In this paper, we try to fundamentally address the issue by decomposing the overall non-iid situation into several iid clusters and conducting aggregation in each cluster. Specifically, we propose DistFL, a novel framework to achieve automated and accurate Distribution-aware Federated Learning in a cost-efficient way. DistFL achieves clustering via extracting and comparing the distribution knowledge from the uploaded models. With this framework, we are able to generate multiple personalized models with distinctive distributions and assign them to the corresponding clients. Extensive experiments on mobile scenarios with popular model architectures have demonstrated the effectiveness of DistFL.
Towards Device Independent Eavesdropping on Telephone Conversations with Built-in Accelerometer
Authors: Su Weigao , Liu Daibo , Zhang Taiyuan , Jiang Hongbo
Published: December 2021

Motion sensors in modern smartphones have been exploited for audio eavesdropping in loudspeaker mode due to their sensitivity to vibrations. In this paper, we further move one step forward to explore the feasibility of using built-in accelerometer to eavesdrop on the telephone conversation of caller/callee who takes the phone against cheek-ear and design our attack Vibphone. The inspiration behind Vibphone is that the speech-induced vibrations (SIV) can be transmitted through the physical contact of phone-cheek to accelerometer with the traces of voice content. To this end, Vibphone faces three main challenges: i) Accurately detecting SIV signals from miscellaneous disturbance; ii) Combating the impact of device diversity to work with a variety of attack scenarios; and iii) Enhancing feature-agnostic recognition model to generalize to newly issued devices and reduce training overhead. To address these challenges, we first conduct an in-depth investigation on SIV features to figure out the root cause of device diversity impacts and identify a set of critical features that are highly relevant to the voice content retained in SIV signals and independent of specific devices. On top of these pivotal observations, we propose a combo method that is the integration of extracted critical features and deep neural network to recognize speech information from the spectrogram representation of acceleration signals. We implement the attack using commodity smartphones and the results show it is highly effective. Our work brings to light a fundamental design vulnerability in the vast majority of currently deployed smartphones, which may put people's speech privacy at risk during phone calls. We also propose a practical and effective defense solution. We validate that it is feasible to prevent audio eavesdropping by using random variation of sampling rate.
Leakage or Identification
Authors: Ding Dian , Yang Lanqing , Chen Yi-Chao , Xue Guangtao
Published: December 2021

The convenience of laptops brings with it the risk of information leakage, and conventional security systems based on the password or the explicit biometric do little to alleviate this problem. Biometric identification based on anatomical features provides far stronger security; however, a lack of suitable sensors on laptops limits the applicability of this technology. In this paper, we developed a behavior-irrelevant user identification system applicable to laptops with a metal casing. The proposed scheme, referred to as LeakPrint, is based on leakage current, wherein the system uses an earphone to capture current leaking through the body and then transmits the corresponding signal to a server for identification. The user identification is achieved via denoising, dimension reduction, and feature extraction. Compared to other biometric identification methods, the proposed system is less dependent on external hardware and more robust to environmental noise. The experiments in real-world environments demonstrated that LeakPrint can verify user identity with high accuracy (93.6%), while providing effective defense against replay attacks (96.5%) and mimicry attacks (90.9%).
SonicFace
Authors: Gao Yang , Jin Yincheng , Choi Seokmin , Li Jiyang , Pan Junjie , Shu Lin , Zhou Chi , Jin Zhanpeng
Published: December 2021

Accurate recognition of facial expressions and emotional gestures is promising to understand the audience's feedback and engagement on the entertainment content. Existing methods are primarily based on various cameras or wearable sensors, which either raise privacy concerns or demand extra devices. To this aim, we propose a novel ubiquitous sensing system based on the commodity microphone array --- SonicFace, which provides an accessible, unobtrusive, contact-free, and privacy-preserving solution to monitor the user's emotional expressions continuously without playing hearable sound. SonicFace utilizes a pair of speaker and microphone array to recognize various fine-grained facial expressions and emotional hand gestures by emitted ultrasound and received echoes. Based on a set of experimental evaluations, the accuracy of recognizing 6 common facial expressions and 4 emotional gestures can reach around 80%. Besides, the extensive system evaluations with distinct configurations and an extended real-life case study have demonstrated the robustness and generalizability of the proposed SonicFace system.
IMU2Doppler
Authors: Bhalla Sejal , Goel Mayank , Khurana Rushil
Published: December 2021

The proliferation of sensors powered by state-of-the-art machine learning techniques can now infer context, recognize activities and enable interactions. A key component required to build these automated sensing systems is labeled training data. However, the cost of collecting and labeling new data impedes our ability to deploy new sensors to recognize human activities. We tackle this challenge using domain adaptation i.e., using existing labeled data in a different domain to aid the training of a machine learning model for a new sensor. In this paper, we use off-the-shelf smartwatch IMU datasets to train an activity recognition system for mmWave radar sensor with minimally labeled data. We demonstrate that despite the lack of extensive datasets for mmWave radar, we are able to use our domain adaptation approach to build an activity recognition system that classifies between 10 activities with an accuracy of 70% with only 15 seconds of labeled doppler data. We also present results for a range of available labeled data (10 - 30 seconds) and show that our approach outperforms the baseline in every single scenario. We take our approach a step further and show that multiple IMU datasets can be combined together to act as a single source for our domain adaptation approach. Lastly, we discuss the limitations of our work and how it can impact future research directions.
No evidence for motor-recovery-related cortical connectivity changes after stroke using resting-state fMRI
Authors: Branscheidt Meret , Ejaz Naveed , Xu Jing , Widmer Mario , Harran Michelle D. , Cortés Juan Camilo , Kitago Tomoko , Celnik Pablo , Hernandez-Castillo Carlos , Diedrichsen Jörn , Luft Andreas , Krakauer John W.
Published: December 2021

We investigated longitudinal changes in functional connectivity after stroke. Despite substantial motor recovery, we found no differences in functional connectivity patterns between patients and controls, nor any changes over time. Assuming that rs-fMRI is an adequate method to capture connectivity changes between cortical regions after brain injury, these results provide reason to doubt that changes in cortico-cortical connectivity are the relevant mechanism for promoting motor recovery.
Songbird subthalamic neurons project to dopaminergic midbrain and exhibit singing-related activity
Authors: Das Anindita , Goldberg Jesse H.
Published: December 2021

Songbird subthalamic (STN) neurons exhibit singing-related signals and are interconnected with the motor cortical nucleus, auditory pallium, ventral pallidum, and ventral tegmental area, areas important for song generation and learning.
Processing and Compatibility of Corydalis yanhusuo: Phytochemistry, Pharmacology, Pharmacokinetics, and Safety
Authors: Wu Lingyan , Yang Yang , Mao Zhujun , Wu Jianjun , Ren Dan , Zhu Bo , Qin Luping
Published: December 2021

Processed and polyherbal formulations (compatibility) are widely used in traditional Chinese medicine (TCM). However, processing and compatibility may alter the efficacy and safety of herbal medicines, and therefore, evaluating the herbal medicines changes after processing and compatibility is important for their safety. Since Corydalis yanhusuo (Y.H.Chou & Chun C.Hsu) W.T.Wang ex Z.Y.Su & C.Y.Wu (Family: Papaveraceae and Genera: Corydalis), a traditional medicinal plant in China, Japan, Korea, and other Asian countries, has been used for treating a wide range of medical conditions, it is an ideal representative of studying the effects of processing and compatibility on efficacy and toxicity. In this paper, information was obtained by searching electronic databases, classic books, PhD and MSc dissertations, local conference papers, and unpublished materials prior to July 2021. We provide a summary of the phytochemistry, pharmacology, pharmacokinetics, quality control, and safety of C. yanhusuo under various processing or compatibility conditions. Based on our findings, vinegar processing is probably the best C. yanhusuo processing method, which could increase the absorption rate of tetrahydropalmatine (THP) in the heart, liver, spleen, lung, and brain tissues and alleviate mice muscle tremors and liver damage caused by C. yanhusuo. These results indicate that processing and compatibility can reduce toxicity and increase the efficacy of C. yanhusuo. The information provides an expanded understanding of the efficacy and toxicity mechanisms of TCM compounds, which is valuable for industrial production quality control and future drug research.
Optimal Spacing Policy for Vehicle Platoon Control with Road-Friction Coefficient
Authors: Zuo Lei , Meng Duo , Zhang Jinqi
Published: December 2021

This paper investigates the vehicle platoon control problems, in which the road-friction coefficient is taken into consideration. In order to improve the vehicle platoon safety in various road-friction conditions, an optimal spacing policy is proposed for the vehicle platoon. In detail, an intervehicle space optimization framework is developed by using a safety cost function and the gradient decent method. In this way, the optimal intervehicle spacing headway is presented such that the vehicle can be safely driven to the desired platoon under various road-friction conditions. Then, based on the proposed optimal spacing policy, we transform this optimal spacing vehicle platoon control problem into a moving target tracking problem. An adaptive distributed integrated sliding mode (DISM)-based vehicle platoon control scheme is proposed such that the vehicles can effectively follow the presented optimal spacing platoon. Moreover, the stability of the proposed vehicle platoon system is strictly analyzed and numerical simulations are provided to verify the proposed approaches.
Understanding the Role of Humanistic Factors in Trade Network Evolution across the Belt and Road Initiative Countries Using the Exponential Random Graph Model
Authors: Xu Jianjun , Yang Xiaowei , Razzaq Asif
Published: December 2021

Humanistic factors have been playing increasingly significant roles in international trade. Recently, the Belt and Road Initiative (BRI) proposed by China has drawn worldwide attention. This paper examines the roles of humanistic factors in international trade networks across the BRI countries. Firstly, we analyzed the structural characteristics of the import trade network across the 61 BRI countries and subsequently adopted the cross‐sectional exponential random graph model (ERGM) and temporal ERGM to analyze the role of different humanistic factors in the evolution of import trade network from the static and dynamic perspectives, respectively. The results show the following: (I) the network scale of the import trade across the BRI countries has been expanding, the network density of the trade has been increasing gradually, and the “small‐world” characteristics of import network are gradually revealed; (II) all of the factors such as a common (official or spoken) language, a common legal origin, a common religious belief, and ever sibling relationship help the BRI countries establish closer import trade ties; and (III) the differences of trade liberalization and financial liberalization, gross domestic product (GDP), and population in different countries also contribute to the evolution of import trade network among the BRI countries, and the countries with relatively higher GDP and greater population are more active in the import trade network.
The Parkinson’s Disease Progression Neuroimaging Initiative
Authors: Zhu Shiyi , Ju Zizhao , Wu Ping , Liu Fengtao , Ge Jingjie , Zhang Huiwei , Lu Jiaying , Li Ling , Wang Min , Jiang Jiehui , Wang Jian , Zuo Chuantao
Published: December 2021

The Parkinson’s Disease Progressive Neuroimaging Initiative (PDPNI) is a longitudinal observational clinical study. In PDPNI, the clinical and imaging data of patients diagnosed with Parkinsonian syndromes and Idiopathic rapid eye movement sleep behavior disorder (RBD) were longitudinally followed every two years, aiming to identify progression biomarkers of Parkinsonian syndromes through functional imaging modalities including FDG-PET, DAT-PET imaging, ASL MRI, and fMRI, as well as the treatment conditions, clinical symptoms, and clinical assessment results of patients. From February 2012 to March 2019, 224 subjects (including 48 healthy subjects and 176 patients with confirmed PDS) have been enrolled in PDPNI. The detailed clinical information and clinical assessment scores of all subjects were collected by neurologists from Huashan Hospital, Fudan University. All subjects enrolled in PDPNI were scanned with 18F-FDG PET, 11C-CFT PET, and MRI scan sequence. All data were collected in strict accordance with standardized data collection protocols.
Simultaneous Multi-Vessel Very Late Stent Thrombosis in Acute ST-Segment Elevation Myocardial Infarction
Authors: Chen Yuefeng , Amponsah Michael , Nathaniel Cyril
Published: December 2021

Simultaneous multi-vessel very later stent thrombosis (VLST) is a very rare complication of percutaneous coronary intervention (PCI). We present a case of simultaneous multi-vessel VLST as the cause of acute ST-segment elevation myocardial infarction (STEMI). PCI of the culprit vessel was performed at acute presentation. Resolution of in-stent thrombosis in non-culprit vessels was noted on coronary angiography 2 days later. Our case suggests that PCI for culprit lesion in acute setting may be a reasonable option for simultaneous multi-vessel VLST.
Physiological Stimulation of the Synthesis of Preelastic Fibers in the Dermis of a Patient with Fibrosis
Authors: Pereira de Godoy Jose Maria , Pereira de Godoy Lívia Maria , de Fatima Guerreiro Godoy Maria , Neto Dalisio de Santi
Published: December 2021

Objective. The aim of the present study was to report the physiological stimulation of the synthesis of preelastic fibers in the dermis of a patient with fibrosis. Design. A clinical study was conducted involving the analysis of histological changes in preelastic fibers following treatment for stage II primary lymphedema for the clinical reversal of lymphedema and fibrosis. Setting. University Hospital of the São Jose do Rio Preto of School of Medicine in 2020. Participant was a 67-year-old male patient with late-onset primary lymphedema diagnosed 12 years earlier. Intervention is the lymphatic stimulation using the Godoy method adapted to the treatment of fibrosis. Main outcomes and measures are biopsies before and after treatment. Ten randomly selected histological fields were evaluated using the multipoint morphometric method. The values with this method are relative and expressed as percentages. Statistical analysis was performed with the t-test, considering a 95% significance level. Results. A visible, significant difference in the percentage of preelastic fibers was found between the preintervention and postintervention slides, which were confirmed by the microscopic evaluation and quantification (4.95 ± 0.64% and 14.70 ± 1.06%, respectively). Conclusion. The physiological stimulation of the lymphatic system using a specific method resulted in the clinical reduction of fibrosis, the return of the elasticity of the skin, and the stimulation of the synthesis of preelastic fibers.
Effect of Yoganidra on Blood Pressure, Hs-CRP, and Lipid Profile of Hypertensive Subjects: A Pilot Study
Authors: Devraj J. P. , Santosh Kumar B. , Raja Sriswan M. , Jagdish B. , Priya B. S. , Neelu S. B. , Desai Rao Vijayabhaskar , Kumar Manoj , Geddam J. J. Babu , Hemalatha R.
Published: December 2021

Background. Yoganidra is a systematic method of promoting a state of complete physical, mental, and emotional relaxation. It is a safe, inexpensive, and very effective method of management of hypertension when used along with standard pharmacological therapy. This study aims to assess the effect of yoganidra on blood pressure (both systolic blood pressure (SBP) and diastolic blood pressure (DBP)), Hs-CRP, and lipid profile of hypertensive subjects at the time of enrollment (subjects that are hypertensive at the time of enrollment). Methods. Both treated and untreated subjects (n = 74) with hypertension (blood pressure ≥140/90 mmHg) and age between 35 and 70 years were included in this study after obtaining ICMR-NIN-IEC approval and written informed consent from all subjects. Subjects with critical illness and/or psychological disturbances were excluded from this study. The subjects in the experimental group (n = 31) practiced yoganidra for 45 minutes daily for 12 weeks under strict supervision. There was no intervention in the control group (n = 43). Weekly blood pressure was recorded in the experimental group, whereas it was performed at baseline and at endpoint for control groups. Hs-CRP and lipid profile were estimated at baseline and endpoint for both the groups. Results. A significant reduction in mean SBP from 142.9 mm Hg (SD ± 16.46) to 118.68 mm Hg (SD ± 9.21;pvalue 0.0001) and DBP from 89.84 mm Hg (SD ± 10.42) to 77.03 mm Hg (SD ± 6.47:pvalue 0.0001) was observed among the experimental group after 12 weeks of yoganidra practice when compared with the control group. A significant reduction in mean Hs-CRP (2.21 ± 1.49 to 1.06 ± 0.82 mg/L,p < 0.001∗∗∗) was observed among the experimental group. There were no significant differences between triglycerides and total cholesterol levels, whereas LDL-C and HDL-C showed a trend of improvement in the experimental group after intervention. Conclusions. In this pilot study, we observed a significant reduction in blood pressure and Hs-CRP in the yoganidra group compared with the control group. There were no significant side effects observed in the intervention group during the study period.
Removal of the Retained Ventricular Catheter Using the Endoscopic Monopolar Instrument
Authors: Pontes Julia Pereira Muniz , Costa Ferreira-Pinto Pedro Henrique , Simoes Elington Lannes , Cruz Thaina Zanon , Sanchez Jefferson Trivino , Nigri Flavio
Published: December 2021

Background. Ventriculoperitoneal shunt (VPS) remains the main treatment for hydrocephalus. However, VPS revision surgery is very common. Here, we present a case in which the retained ventricular catheter was removed using the endoscopic monopolar instrument. Methods. We report a case of a 28-year-old female who presented with VPS obstruction. She had two previous shunt revision surgeries due to shunt obstruction. Eleven years after the last one, she presented an abdominal pseudocyst that indicated a total system removal. During VPS revision surgery, a retained ventricular catheter was observed. The endoscopic monopolar instrument was introduced into the retained catheter under direct view. Coagulations in a back-and-forth movement were applied to release inner catheter adhesions. After these steps, the catheter was removed, and a new one was placed through the same route. Results. The catheter was removed without complications, confirmed by the postoperative cranial computed tomography. The patient remained asymptomatic. Conclusion. The described technique was effective and avoided ventricular bleeding. Further studies are necessary to validate this method.
Visual Outcome after Vitrectomy with Subretinal tPA Injection to Treat Submacular Hemorrhage Secondary to Age-Related Macular Degeneration or Macroaneurysm
Authors: Ali Said Yasmin , Dewilde Evelien , Stalmans Peter
Published: December 2021

Purpose. To determine the efficacy and safety of 23G transconjunctival sutureless vitrectomy, subretinal injection of tissue plasminogen activator using the EVA Surgical System, and pneumatic displacement with air to treat submacular hemorrhages. Methods. Retrospective analysis of 93 eyes surgically treated for submacular hemorrhage caused by neovascular AMD or retinal macroaneurysms. Main Outcome Measures. Postoperative visual acuity and surgical complications. Results. After surgery, visual acuity improved after 6 weeks but decreased again at the final postoperative visit at 8 months due to progression of the underlying disease. Complications consisted of 2 cases of retinal pigment epithelial tear, 7 vitreous hemorrhages, 4 hyphema, 6 cases of retinal detachment, and 2 subchoroidal hemorrhages during the follow-up period. Conclusions. This study suggests that a surgical approach with 23G vitrectomy, subretinal tPA injection, and pneumatic displacement using air may be an effective procedure for submacular hemorrhage displacement in patients with AMD and retinal macroaneurysms. However, visual outcome is limited by the underlying macular pathology. Larger multicenter randomized controlled studies are warranted to determine the therapeutic effect of this surgical approach.
A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection
Authors: Xiao Taowen , Cai Zijian , Lin Cong , Chen Qiong
Published: December 2021

Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.
Analysis of Flexible Batch Service Queueing System to Constrict Waiting Time of Customers
Authors: C. K. Deena Merit , M Haridass
Published: December 2021

When the required number of customers is available in the general bulk service (GBS) queueing system, the server begins service. Otherwise, the server will remain inactive until the number of consumers in the queue reaches that minimum required number. Customers that have already come must wait throughout this time, regardless of their arrival time. In some circumstances, like specimens awaiting testing in a clinical laboratory or perishable commodities awaiting delivery, it is necessary to finish services before the expiration date. It might only be achievable if consumers’ waiting times are kept under control. As a result, the flexible general bulk service (FGBS) rule is developed in this article to provide flexibility in batching. The effectiveness of FGBS implementation has been demonstrated using two examples: a clinical laboratory and a distribution center. To justify the suggested model, a simulation study and numerical illustration are provided.
Free Vibration of Composite Cylindrical Shells Based on Third-Order Shear Deformation Theory
Authors: Saad AL Nuwairan Muneerah , Javed Saira
Published: December 2021

The focus of this study is to analyse the free vibration of cylindrical shells under third-order shear deformation theory (TSDT). The constitutive equations of the cylindrical shells are obtained using third-order shear deformation theory (TSDT). The surface and traverse displacements are expected to have cubic and quadratic variation. Spline approximation is used to approximate the displacements and transverse rotations. The resulting generalized eigenvalue problem is solved for the frequency parameter to get as many eigenfrequencies as required starting from the least. From the eigenvectors, the spline coefficients are computed from which the mode shapes are constructed. The frequency of cylindrical shells is analysed by varying circumferential node number, length dimension, layer number, and different materials. The authenticity of the present formulation is established by comparing with the available FEM results.
Weighted Gene Coexpression Network Analysis in Mouse Livers following Ischemia-Reperfusion and Extensive Hepatectomy
Authors: Liu Wei , Shi Yongquan , Cheng Tao , Jia Ruixue , Sun Ming-Zhong , Liu Shuqing , Liu Qinlong
Published: December 2021

In mouse models, the recovery of liver volume is mainly mediated by the proliferation of hepatocytes after partial hepatectomy that is commonly accompanied with ischemia-reperfusion. The identification of differently expressed genes in liver following partial hepatectomy benefits the better understanding of the molecular mechanisms during liver regeneration (LR) with appliable clinical significance. Briefly, studying different gene expression patterns in liver tissues collected from the mice group that survived through extensive hepatectomy will be of huge critical importance in LR than those collected from the mice group that survived through appropriate hepatectomy. In this study, we performed the weighted gene coexpression network analysis (WGCNA) to address the central candidate genes and to construct the free-scale gene coexpression networks using the identified dynamic different expressive genes in liver specimens from the mice with 85% hepatectomy (20% for seven-day survial rate) and 50% hepatectomy (100% for seven-day survial rate under ischemia-reperfusion condition compared with the sham group control mice). The WGCNA combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses pinpointed out the apparent distinguished importance of three gene expression modules: the blue module for apoptotic process, the turquoise module for lipid metabolism, and the green module for fatty acid metabolic process in LR following extensive hepatectomy. WGCNA analysis and protein-protein interaction (PPI) network construction highlighted FAM175B, OGT, and PDE3B were the potential three hub genes in the previously mentioned three modules. This work may help to provide new clues to the future fundamental study and treatment strategy for LR following liver injury and hepatectomy.
Higher‐Order Iterative Learning Control with Optimal Control Gains Based on Evolutionary Algorithm for Nonlinear System
Authors: Wei Yun-Shan , Yang Xiaofen , Shang Wenli , Chen Ying-Yu
Published: December 2021

For the nonlinear discrete‐time system, higher‐order iterative learning control (HOILC) with optimal control gains based on evolutionary algorithm (EA) is developed in this paper. Since the updating actions are constituted by the tracking information from several previous iterations, the suitably designed HOILC schemes with appropriate control gains usually achieve fast convergence speed. To optimize the control gains in HOILC approach, EA is introduced. The encoding strategy, population initialization, and fitness function in EA are designed according to the HOILC characteristics. With the global optimization of EA, the optimal control gains of HOILC are selected adaptively so that the number of convergence iteration is reduced in ILC process. It is shown in simulation that the sum absolute error, total square error, and maximum absolute error of tracking in the proposed HOILC based on EA are convergent faster than those in conventional HOILC.
Weather Radar Image Superresolution Using a Nonlocal Residual Network
Authors: Yuan Haoxuan , Zeng Qiangyu , He Jianxin
Published: December 2021

Accurate and high-resolution weather radar images reflecting detailed structure information of radar echo are vital for analysis and forecast of extreme weather. Typically, this is performed by using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated value regardless of the large-scale context feature of weather radar images. Inspired by the striking performance of the convolutional neural network (CNN) applied in feature extraction and nonlocal self-similarity of weather radar images, we proposed a nonlocal residual network (NLRN) on the basis of CNN. The proposed network mainly consists of several nonlocal residual blocks (NLRB), which combine short skip connection (SSC) and nonlocal operation to train the deep network and capture large-scale context information. In addition, long skip connection (LSC) added in the network avoids learning low-frequency information, making the network focus on high-level features. Extensive experiments of ×2 and ×4 super-resolution reconstruction demonstrate that NLRN achieves superior performance in terms of both quantitative evaluation metrics and visual quality, especially for the reconstruction of the edge and detailed information of the weather radar echo.
Congenital Cytomegalovirus Infections Mother-Newborn Pair Study in Southern Ethiopia
Authors: Zenebe Mengistu Hailemariam , Mekonnen Zeleke , Loha Eskindir , Padalko Elizaveta
Published: December 2021

Introduction. Congenital cytomegalovirus (cCMV) is a common cause of neurodevelopmental delays and sensorineural hearing loss of infants, yet the prevalence of cCMV and the associated factors in Ethiopia are not studied. Hence, this study was to assess the prevalence and associated factors of cCMV in Southern Ethiopia. Methodology. A mother-newborn pair cross-sectional study was conducted at Hawassa University Comprehensive and Specialized Hospital, Ethiopia. Newborn’s saliva sample was tested for cCMV using Alethia CMV molecular assay. Mothers’ serum was tested serologically for anti-CMV IgM and IgG by EUROIMMUN ELISA. Pregnant women responded to a questionnaire about their previous and current obstetric history and sociodemographic characteristics. The chi-square (χ2) test and independent-sample t-test were used to determine the associations between infections and possible risk factors; then, potential variables were screened for multivariable analysis. Results. A total of 593 mother-newborn pairs were assessed. CMV was detected in 14 of 593 newborn saliva swabs (2.4%; 95% CI 1.2–3.7). As assessed by CMV IgM-positive results, maternal CMV seropositivity was 8.3% (49/593); thus, the rate of mother-to-child transmission of CMV was 28% (14/49) among CMV IgM-positive women. Congenital CMV infection was significantly associated with maternal exposure through nursery school children in the household, women sharing a feeding cup with children, and any of the detected curable STIs during pregnancy. Birth weight was negatively associated with CMV infection. Maternal age, gravidity, level of education, and sharing of children feeding utensils were not associated with cCMV infection. Conclusion. A high rate of cCMV infection in the absence of awareness demands further in-depth investigation in Ethiopia. Thus, policymakers must take appropriate action through the antenatal care system for prevention strategies and put in place a constant health education and awareness creation of pregnant women about the causes of infection and hygienic measures.
Pretreatment with 6‐Gingerol Ameliorates Sepsis‐Induced Immune Dysfunction by Regulating the Cytokine Balance and Reducing Lymphocyte Apoptosis
Authors: Ju Seong-A , Nguyen Quang-Tam , Nguyen Thu-Ha T. , Suh Jae-Hee , An Won G. , Callaway Zak , Joe Yeonsoo , Chung Hun Taeg , Kim Byung-Sam
Published: December 2021

Sepsis is characterized by an initial net hyperinflammatory response, followed by a period of immunosuppression, termed immunoparalysis. During this immunosuppressive phase, patients may have difficulty eradicating invading pathogens and are susceptible to life‐threatening secondary hospital‐acquired infections. Due to progress in antimicrobial treatment and supportive care, most patients survive early sepsis. Mortality is more frequently attributed to subsequent secondary nosocomial infections and multiorgan system failure. 6‐Gingerol is the major pharmacologically active component of ginger. Although it is known to exhibit a variety of biological activities, including anti‐inflammation and antioxidation, the role of 6‐gingerol in sepsis‐induced immune dysfunction remains elusive. Thus, we investigated whether 6‐gingerol improves septic host response to infections during sepsis. 6‐Gingerol‐treated mice showed significantly lower mortality in polymicrobial sepsis induced by cecal ligation and puncture LPS via enhanced bacterial clearance in the peritoneum, blood, and organs (liver, spleen, and kidney) and inhibited the production of TNF‐α and IL‐6 in TLR2 and/or TLR4‐stimulated macrophages. In addition, we demonstrated that survival improvement of secondary infection following septic insult was associated with an initial response of enhanced neutrophil numbers and function at the infection site, reduced apoptosis of immune cells, and a shift from a T helper cell type 2 (Th2) to a T helper cell type 1 (Th1) cytokine balance in the hypoinflammation phase. Our overall findings suggest that 6‐gingerol potentially restores sepsis‐induced immune dysfunction by shifting the balance of Th1/Th2 and by regulating apoptosis of immune cells.
Exploring the Mechanism of Panax notoginseng Saponins against Alzheimer’s Disease by Network Pharmacology and Experimental Validation
Authors: Jiang Yixuan , Li Shanliang , Xie Xiaoqin , Li Hemei , Huang Panling , Li Bocun , Huo Lini , Zhong Jing , Li Yuqing , Xia Xing
Published: December 2021

Background. Panax notoginseng saponins (PNS) have been used for neurodegenerative disorders such as cerebral ischemia and Alzheimer’s disease (AD). Although increasing evidences show the neuron protective effects of PNS, the vital compounds and their functional targets remain elusive. To explore the potential functional ingredients of PNS for the AD treatment and their molecular mechanisms, an in vitro neuron injured model induced by Aβ was investigated, and the potential mechanism was predicted by network pharmacology approach and validated by molecular biology methods. Methods. Network pharmacology approach was used to reveal the relationship between ingredient-target disease and function-pathway of PNS on the treatment of AD. The active ingredients of PNS were collected from TCMSP, PubChem database, and literature mining in PubMed database. DrugBank and GeneCards database were used to predict potential targets for AD. The STRING database was performed to reveal enrichment of these target proteins, protein-protein interactions, and related pathways. Networks were visualized by utilizing Cytoscape software. The enrichment analysis was performed by the DAVID database. Finally, neuroprotective effect and predictive mechanism of PNS were investigated in an in vitro AD model established by Aβ25–35-treated PC12 cells. Results. An ingredient-target disease and function-pathway network demonstrated that 38 active ingredients were derived from PNS modulated 364 common targets shared by PNS and AD. GO and KEGG analysis, further clustering analysis, showed that mTOR signaling targets were associated with the neuroprotective effects of PNS. In Aβ-treated PC12 cells, PNS treatment improved neuroprotective effect, including mTOR inhibition and autophagy activation. Conclusions. Collectively, the protective effects of PNS on AD-neuron injury are related to the inhibition of mTOR and autophagy activation.
A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges
Authors: Cao Jiting , Zhang Meng , Li Yang
Published: December 2021

With the rapid growth of power market reform and power demand, the power transmission capacity of a power grid is approaching its limit and the secure and stable operation of power systems becomes increasingly important. In particular, in modern power grids, the proportion of dynamic loads with fast recovery characteristics such as air conditioners, refrigerators, and industrial motors is increasing. As well as there is an increasing proportion of different forms of renewable energy in power systems. Therefore, the short-term voltage stability (STVS) of power systems cannot be ignored. This article comprehensively sorts out the STVS problems of power systems from the perspective of data-driven methods and discusses existing challenges.