bluuebunny/crossref_metadata_2025 huggingface dataset:
Building resilience in US military families: why it matters
Authors: Rossiter Alicia Gill , Ling C G
Published: March 2021

Service members and their families have endured significant stressors over the past 19 years in support of the nation’s engagement in the wars in Iraq and Afghanistan. ‘Resilience’ is the term most commonly used to describe the military spouse and military-connected child. However, due to a paucity of research on military families, little is known about the impact of spousal/parental military service on the military family. The ability of the healthcare provider is critical to ensuring the success of the military spouse and military-connected child. Providers can support the physical and psychological health needs of military families through (1) identification of military family members in clinical practice and (2) providing culturally competent care that correlates the unique lifestyle and physical and psychological health exposures associated with spousal/parental military service. Historically, in the United States, there has been a proud legacy of generational military service in families—upwards of 80% of new recruits have a family member who has served in the military. The leading factor associated with retention of the service member on Active Duty or in the Reserve or National Guard is the satisfaction of the at-home spouse. Disenfranchising the military spouse and lack of services and support for military-connected children could create a gap in meeting recruitment goals creating a threat to national security in the United States.
Advance care planning in Dutch primary care: a pre/post-implementation study
Authors: van der Plas Annicka , Glaudemans Jolien , Onwuteaka-Philipsen Bregje
Published: March 2021

Background

Despite known advantages of advance care planning (ACP) and a positive attitude towards ACP by older people living in the community and general practitioners (GPs), such conversations are not yet commonplace in GP practices.

Aim

To implement ACP as part of routine care in general practice and thereby increasing the number of ACP conversations and advance directives; to investigate characteristics of older people with and without an ACP conversation.

Methods

(1) A pre-evaluation and post-evaluation study using questionnaire data from people aged 75 years or older living in the community. (2) A prospective study using data provided by healthcare professionals (people they started an ACP conversation with).

Results

After implementation of ACP, significantly more people had spoken to their GP about hospitalisations, intensive care admission and treatment preferences in certain circumstances, compared with before. Advance directives were drawn up more often. People who had an ACP conversation were older, have had a cerebrovascular accident, had a clear idea about future health problems, had a preference to start ACP before they were ill, already had an ACP conversation at pre-measurement and indicated at pre-measurement that their GP knows their preferences.

Conclusion

Results in number of ACP conversations and advance drectives were modest but positive. ACP was implemented as routine care. GPs select people with whom they have a conversation. This can be an efficient use of time, but there is a risk that certain groups may be underserved (for example, patients with multimorbidity or patients with less health skills).
Plasma from healthy donors protects blood–brain barrier integrity via FGF21 and improves the recovery in a mouse model of cerebral ischaemia
Authors: Mamtilahun Muyassar , Jiang Lu , Song Yaying , Shi Xiaojing , Liu Chang , Jiang Yixu , Deng Lidong , Zheng Haoran , Shen Hui , Li Yongfang , Zhang Zhijun , Wang Yongting , Tang Yaohui , Yang Guo-Yuan
Published: March 2021

Background

Healthy plasma therapy reverses cognitive deficits and promotes neuroplasticity in ageing brain disease. However, whether healthy plasma therapy improve blood–brain barrier integrity after stroke remains unknown.

Methods

Here, we intravenously injected healthy female mouse plasma into adult female ischaemic stroke C57BL/6 mouse induced by 90 min transient middle cerebral artery occlusion for eight consecutive days. Infarct volume, brain atrophy and neurobehavioural tests were examined to assess the outcomes of plasma treatment. Cell apoptosis, blood–brain barrier integrity and fibroblast growth factor 21 knockout mice were used to explore the underlying mechanism.

Results

Plasma injection improved neurobehavioural recovery and decreased infarct volume, brain oedema and atrophy after stroke. Immunostaining showed that the number of transferase dUTP nick end labelling+/NeuN+cells decreased in the plasma-injected group. Meanwhile, plasma injection reduced ZO-1, occluding and claudin-5 tight junction gap formation and IgG extravasation at 3 days after ischaemic stroke. Western blot results showed that the FGF21 expression increased in the plasma-injected mice. However, using FGF21 knockout mouse plasma injecting to the ischaemic wild-type mice diminished the neuroprotective effects.

Conclusions

Our study demonstrated that healthy adult plasma treatment protected the structural and functional integrity of blood–brain barrier, reduced neuronal apoptosis and improved functional recovery via FGF21, opening a new avenue for ischaemic stroke therapy.
Recognition of Tibetan Maximal-length Noun Phrases Based on Syntax Tree
Authors: Long Congjun , Zhou Xuewen , Zhou Maoke
Published: March 2021

Frequently corresponding to syntactic components, the Maximal-length Noun Phrase (MNP) possesses abundant syntactic and semantic information and acts a certain semantic role in sentences. Recognition of MNP plays an important role in Natural Language Processing and lays the foundation for analyzing and understanding sentence structure and semantics. By comparing the essence of different MNPs, this article defines the MNP in the Tibetan language from the perspective of syntax tree. A total of 6,038 sentences are extracted from the syntax tree corpus, the structure type, boundary feature, and frequency of MNPs are analyzed, and the MNPs are recognized by applying the sequence tagging model and the syntactic analysis model. The accuracy, recall, and F1 score of the recognition results of applying sequence tagging model are 87.14%, 84.72%, and 85.92%, respectively. The accuracy, recall, and F1 score of the recognition results of applying syntactic analysis model are 87.66%, 87.63%, and 87.65%, respectively.
Attend and Discriminate
Authors: Abedin Alireza , Ehsanpour Mahsa , Shi Qinfeng , Rezatofighi Hamid , Ranasinghe Damith C.
Published: March 2021

Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis. Although our collective know-how to solve Human Activity Recognition (HAR) problems with wearables has progressed immensely with end-to-end deep learning paradigms, several fundamental opportunities remain overlooked. We rigorously explore these new opportunities to learn enriched and highly discriminating activity representations. We propose: i) learning to exploit the latent relationships between multi-channel sensor modalities and specific activities; ii) investigating the effectiveness of data-agnostic augmentation for multi-modal sensor data streams to regularize deep HAR models; and iii) incorporating a classification loss criterion to encourage minimal intra-class representation differences whilst maximising inter-class differences to achieve more discriminative features. Our contributions achieves new state-of-the-art performance on four diverse activity recognition problem benchmarks with large margins---with up to 6% relative margin improvement. We extensively validate the contributions from our design concepts through extensive experiments, including activity misalignment measures, ablation studies and insights shared through both quantitative and qualitative studies. The code base and trained network parameters are open-sourced on GitHub https://github.com/AdelaideAuto-IDLab/Attend-And-Discriminate to support further research.
S3
Authors: Farrukh Habiba , Yang Tinghan , Xu Hanwen , Yin Yuxuan , Wang He , Celik Z. Berkay
Published: March 2021

With smart devices being an essential part of our everyday lives, unsupervised access to the mobile sensors' data can result in a multitude of side-channel attacks. In this paper, we study potential data leaks from Apple Pencil (2nd generation) supported by the Apple iPad Pro, the latest stylus pen which attaches to the iPad body magnetically for charging. We observe that the Pencil's body affects the magnetic readings sensed by the iPad's magnetometer when a user is using the Pencil. Therefore, we ask: Can we infer what a user is writing on the iPad screen with the Apple Pencil, given access to only the iPad's motion sensors' data? To answer this question, we present Side-channel attack on Stylus pencil through Sensors (S3), a system that identifies what a user is writing from motion sensor readings. We first use the sharp fluctuations in the motion sensors' data to determine when a user is writing on the iPad. We then introduce a high-dimensional particle filter to track the location and orientation of the Pencil during usage. Lastly, to guide particles, we build the Pencil's magnetic map serving as a bridge between the measured magnetic data and the Pencil location and orientation. We evaluate S3 with 10 subjects and demonstrate that we correctly identify 93.9%, 96%, 97.9%, and 93.33% of the letters, numbers, shapes, and words by only having access to the motion sensors' data.
Towards Finding the Optimum Position in the Visual Field for a Head Worn Display Used for Task Guidance with Non-registered Graphics
Authors: Lin Georgianna , Haynes Malcolm , Srinivas Sarthak , Kotipalli Pramod , Starner Thad
Published: March 2021

Where should a HWD be placed in a user's visual field? We present two studies that compare comfort, preference, task efficiency and accuracy for various HWD positions. The first study offsets a 9.2° horizontal field-of-view (FOV) display temporally (toward the ear) from 0° to 30° in 10° steps. 30° proves too uncomfortable while 10° is the most preferred position for a simple button-pushing game, corroborating results from previous single-task reading experiments. The second experiment uses a Magic Leap One to compare 10° x 10° FOV interfaces centered at line-of-sight, temporally offset 15° (center-right), inferiorly offset 15° (bottom-center), and offset in both directions (bottom-right) for an order picking task. The bottom-right position proved worst in terms of accuracy and several subjective metrics when compared to the line-of-sight position.
WiPhone
Authors: Liu Jinyi , Zeng Youwei , Gu Tao , Wang Leye , Zhang Daqing
Published: March 2021

Recent years have witnessed a trend of monitoring human respiration using Channel State Information (CSI) retrieved from commodity WiFi devices. Existing approaches essentially leverage signal propagation in a Line-of-Sight (LoS) setting to achieve good performance. However, in real-life environments, LoS can be easily blocked by furniture, home appliances and walls. This paper presents a novel smartphone-based system named WiPhone, aiming to robustly monitor human respiration in NLoS settings. Since a smartphone is usually carried around by one subject, leveraging directly-reflected CSI signals in LoS becomes infeasible. WiPhone exploits ambient reflected CSI signals in a Non-Line-of-Sight (NLoS) setting to quantify the relationship between CSI signals reflected from the environment and a subject's chest displacement. In this way, WiPhone successfully turns ambient reflected signals which have been previously considered "destructive" into beneficial sensing capability. CSI signals obtained from smartphone are usually very noisy and may scatter over different sub-carriers. We propose a density-based preprocessing method to extract useful CSI amplitude patterns for effective respiration monitoring. We conduct extensive experiments with 8 subjects in a real home environment. WiPhone achieves a respiration rate error of 0.31 bpm (breaths per minute) on average in a range of NLoS settings.
Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions
Authors: Jones Simon L. , Hue William , Kelly Ryan M. , Barnett Rosemarie , Henderson Violet , Sengupta Raj
Published: March 2021

The use of interactive mobile and wearable technologies for understanding and managing health conditions is a growing area of interest for patients, health professionals and researchers. Self-tracking technologies such as smartphone apps and wearable devices for measuring symptoms and behaviours generate a wealth of patient-centric data with the potential to support clinical decision making. However, the utility of self-tracking technologies for providing insight into patients' conditions is impacted by poor adherence with data logging. This paper explores factors associated with adherence in smartphone-based tracking, drawing on two studies of patients living with axial spondyloarthritis (axSpA), a chronic rheumatological condition. In Study 1, 184 axSpA patients used the uMotif health tracking smartphone app for a period of up to 593 days. In Study 2, 108 axSpA patients completed a survey about their experience of using self-tracking technologies. We identify six significant correlates of self-tracking adherence, providing insight into the determinants of tracking behaviour. Specifically, our data provides evidence that adherence correlates with the age of the user, the types of tracking devices that are being used (smartphone OS and physical activity tracker), preferences for types of data to record, the timing of interactions with a self-tracking app, and the reported symptom severity of the user. We discuss how these factors may have implications for those designing, deploying or using mobile and wearable tracking technologies to support monitoring and management of chronic diseases.
Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness
Authors: Buda Teodora Sandra , Khwaja Mohammed , Matic Aleksandar
Published: March 2021

Enabling smartphones to understand our emotional well-being provides the potential to create personalised applications and highly responsive interfaces. However, this is by no means a trivial task - subjectivity in reporting emotions impacts the reliability of ground-truth information whereas smartphones, unlike specialised wearables, have limited sensing capabilities. In this paper, we propose a new approach that advances emotional state prediction by extracting outlier-based features and by mitigating the subjectivity in capturing ground-truth information. We utilised this approach in a distinctive and challenging use case - happiness detection - and we demonstrated prediction performance improvements of up to 13% in AUC and 27% in F-score compared to the traditional modelling approaches. The results indicate that extreme values (i.e. outliers) of sensor readings mirror extreme values in the reported happiness levels. Furthermore, we showed that this approach is more robust in replicating the prediction model in completely new experimental settings.
CrossGR
Authors: Li Xinyi , Chang Liqiong , Song Fangfang , Wang Ju , Chen Xiaojiang , Tang Zhanyong , Wang Zheng
Published: March 2021

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.
Leveraging Collaborative-Filtering for Personalized Behavior Modeling
Authors: Xu Xuhai , Chikersal Prerna , Dutcher Janine M. , Sefidgar Yasaman S. , Seo Woosuk , Tumminia Michael J. , Villalba Daniella K. , Cohen Sheldon , Creswell Kasey G. , Creswell J. David , Doryab Afsaneh , Nurius Paula S. , Riskin Eve , Dey Anind K. , Mankoff Jennifer
Published: March 2021

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of-the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.
Pantomime
Authors: Palipana Sameera , Salami Dariush , Leiva Luis A. , Sigg Stephan
Published: March 2021

We introduce Pantomime, a novel mid-air gesture recognition system exploiting spatio-temporal properties of millimeter-wave radio frequency (RF) signals. Pantomime is positioned in a unique region of the RF landscape: mid-resolution mid-range high-frequency sensing, which makes it ideal for motion gesture interaction. We configure a commercial frequency-modulated continuous-wave radar device to promote spatial information over the temporal resolution by means of sparse 3D point clouds and contribute a deep learning architecture that directly consumes the point cloud, enabling real-time performance with low computational demands. Pantomime achieves 95% accuracy and 99% AUC in a challenging set of 21 gestures articulated by 41 participants in two indoor environments, outperforming four state-of-the-art 3D point cloud recognizers. We further analyze the effect of the environment in 5 different indoor environments, the effect of articulation speed, angle, and the distance of the person up to 5m. We have publicly made available the collected mmWave gesture dataset consisting of nearly 22,000 gesture instances along with our radar sensor configuration, trained models, and source code for reproducibility. We conclude that pantomime is resilient to various input conditions and that it may enable novel applications in industrial, vehicular, and smart home scenarios.
SelfHAR
Authors: Tang Chi Ian , Perez-Pozuelo Ignacio , Spathis Dimitris , Brage Soren , Wareham Nick , Mascolo Cecilia
Published: March 2021

Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition. However, the performance of such models in real-world settings largely depends on the availability of large datasets that captures diverse behaviors. Recently, studies in computer vision and natural language processing have shown that leveraging massive amounts of unlabeled data enables performance on par with state-of-the-art supervised models.

In this work, we present SelfHAR, a semi-supervised model that effectively learns to leverage unlabeled mobile sensing datasets to complement small labeled datasets. Our approach combines teacher-student self-training, which distills the knowledge of unlabeled and labeled datasets while allowing for data augmentation, and multi-task self-supervision, which learns robust signal-level representations by predicting distorted versions of the input.

We evaluated SelfHAR on various HAR datasets and showed state-of-the-art performance over supervised and previous semi-supervised approaches, with up to 12% increase in F1 score using the same number of model parameters at inference. Furthermore, SelfHAR is data-efficient, reaching similar performance using up to 10 times less labeled data compared to supervised approaches. Our work not only achieves state-of-the-art performance in a diverse set of HAR datasets, but also sheds light on how pre-training tasks may affect downstream performance.
ViFin
Authors: Chen Wenqiang , Chen Lin , Ma Meiyi , Parizi Farshid Salemi , Patel Shwetak , Stankovic John
Published: March 2021

Wearable devices, such as smartwatches and head-mounted devices (HMD), demand new input devices for a natural, subtle, and easy-to-use way to input commands and text. In this paper, we propose and investigate ViFin, a new technique for input commands and text entry, which harness finger movement induced vibration to track continuous micro finger-level writing with a commodity smartwatch. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing, works across different users, and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. We quantify our approach's accuracy through real-time system experiments in different arm positions, writing speeds, and smartwatch position displacements. Finally, a real-time writing system and two user studies on real-world tasks are implemented and assessed.
AdaSpring
Authors: Liu Sicong , Guo Bin , Ma Ke , Yu Zhiwen , Du Junzhao
Published: March 2021

There are many deep learning (e.g. DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives. To enable robust and private mobile sensing, DNN tends to be deployed locally on the resource-constrained mobile devices via model compression. The current practice either hand-crafted DNN compression techniques, i.e., for optimizing DNN-relative performance (e.g. parameter size), or on-demand DNN compression methods, i.e., for optimizing hardware-dependent metrics (e.g. latency), cannot be locally online because they require offline retraining to ensure accuracy. Also, none of them have correlated their efforts with runtime adaptive compression to consider the dynamic nature of deployment context of mobile applications. To address those challenges, we present AdaSpring, a context-adaptive and self-evolutionary DNN compression framework. It enables the runtime adaptive DNN compression locally online. Specifically, it presents the ensemble training of a retraining-free and self-evolutionary network to integrate multiple alternative DNN compression configurations (i.e., compressed architectures and weights). It then introduces the runtime search strategy to quickly search for the most suitable compression configurations and evolve the corresponding weights. With evaluation on five tasks across three platforms and a real-world case study, experiment outcomes show that AdaSpring obtains up to 3.1x latency reduction, 4.2x energy efficiency improvement in DNNs, compared to hand-crafted compression techniques, while only incurring ≤ 6.2ms runtime-evolution latency.
Music Personalized Label Clustering and Recommendation Visualization
Authors: Huo Yongkang
Published: March 2021

With the advent of big data, the performance of traditional recommendation algorithms is no longer enough to meet the demand. Most people do not leave too many comments and other data when using the application. In this case, the user data are too scattered and discrete, with obvious data sparsity problems. First, this paper describes the main ideas and methods used in current recommendation systems and summarizes the areas that need attention and consideration. Based on these algorithms and based on the user history data information and music data information that can be found now, the paper aims to build a personalized music recommendation system based on directed tags, which can provide basic music services to users and push them personalized music recommendation lists. Then, the collaborative filtering algorithm based on tags is introduced. Usually this method uses discrete tags, and the user tags and music tags are juxtaposed and leveled with each other, which does not reflect the importance and ranking order relationship of each tag and does not reflect the cognitive sequence of users when they listen to and annotate music. In order to improve this problem and increase the accuracy of recommendations, the user‐tag and music‐tag data are correlated through the tag sequence of tag and music‐tag data are correlated and modeled analytically, and feature directed graphs are constructed.
A Hybrid Mean Value Involving Dedekind Sums and the Generalized Kloosterman Sums
Authors: Pan Xiaowei , Guo Xiaoyan
Published: March 2021

In this paper, we use the mean value theorem of Dirichlet

L

-functions and the properties of Gauss sums and Dedekind sums to study the hybrid mean value problem involving Dedekind sums and the general Kloosterman sums and give an interesting identity for it.
To Determine Pivotal Genes Driven by Methylated DNA in Obstructive Sleep Apnea Hypopnea Syndrome
Authors: Li Yan , Zhang Yajuan
Published: March 2021

Obstructive sleep apnea syndrome (OSAHS) is a widespread respiratory dysfunction that has attracted more and more attention in recent years. Recently, a large number of studies have shown that abnormal DNA methylation epigenetically silences genes necessary for the pathogenesis of human diseases. However, the exact mechanism of abnormal DNA methylation in OSAHS is still elusive. In this study, we downloaded the OSAHS data from the GEO database. Our data for the first time revealed 520 hypermethylated genes and 889 hypomethylated genes in OSAHS. Bioinformatics analysis revealed that these abnormal methylated genes exhibited an association with the regulation of angiogenesis, apoptosis, Wnt, and ERBB2 signaling pathways. PPI network analysis displayed the interactions among these genes and validated several hub genes, such as GPSM2, CCR8, TAS2R20, TAS2R4, and TAS2R5, which were related to regulating liganded Gi-activating GPCR and the transition of mitotic metaphase/anaphase. In conclusion, our study offers a new hint of understanding the molecular mechanisms in OSAHS progression and will provide OSAHS with newly generated innovative biomarkers.
Risk Control of Virtual Enterprise Based on Distributed Decision‐Making Model
Authors: Ouyang Zhaoying
Published: March 2021

Virtual enterprise is a dynamic alliance of businesses, in which multiple members undertake joint research, development, manufacturing, operation, etc. The complexity of the relationship between business members, coupled with many new technologies or methods applied in the alliance operation, leads to more uncertain factors and difficulties in the operation and risk management of the virtual enterprise. The distributed decision‐making model is a fast and effective decision‐making model, in which dispersed intellectual resources and information resources are dynamically integrated through virtual organization forms and operating mechanisms, and the dynamic reorganization, result evaluation, and synthesis of the decision‐making system are realized by certain control rules. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the risk control of virtual enterprise; elaborated the development background, current status, and future challenges of distributed decision‐making model; introduced the related works of decision‐making variable calculation and constraint determination; constructed a risk control model for virtual enterprise based on distributed decision‐making model; analyzed the multiobjective model and interval programming model of risk control; established a risk control approach for virtual enterprise based on distributed decision‐making model; performed the optimal allocation of risk control funds and the selective optimization of backup allies; and finally conducted a case analysis. The study results show that, in view of the diversity of relationships between alliance businesses and the complexity of the distribution of cooperative areas, the distributed decision‐making model can more reasonably describe the relationship between alliance members and accurately control the risks of virtual enterprise from multiple angles, which can effectively manage the risks of virtual enterprise. The study results of this paper provide a reference for further research on the risk control of virtual enterprise based on distributed decision‐making model.
Feature‐Enhanced Occlusion Perception Object Detection for Smart Cities
Authors: Xu Jie , Wang Hanyuan , Xu Mingzhu , Yang Fan , Zhou Yifei , Yang Xiaolong
Published: March 2021

Object detection is used widely in smart cities including safety monitoring, traffic control, and car driving. However, in the smart city scenario, many objects will have occlusion problems. Moreover, most popular object detectors are often sensitive to various real‐world occlusions. This paper proposes a feature‐enhanced occlusion perception object detector by simultaneously detecting occluded objects and fully utilizing spatial information. To generate hard examples with occlusions, a mask generator localizes and masks discriminated regions with weakly supervised methods. To obtain enriched feature representation, we design a multiscale representation fusion module to combine hierarchical feature maps. Moreover, this method exploits contextual information by heaping up representations from different regions in feature maps. The model is trained end‐to‐end learning by minimizing the multitask loss. Our model obtains superior performance compared to previous object detectors, 77.4% mAP and 74.3% mAP on PASCAL VOC 2007 and PASCAL VOC 2012, respectively. It also achieves 24.6% mAP on MS COCO. Experiments demonstrate that the proposed method is useful to improve the effectiveness of object detection, making it highly suitable for smart cities application that need to discover key objects with occlusions.
Improvement of Chronic Rhinosinusitis and Reduction of the Myeloperoxidase-Antineutrophil Cytoplasmic Antibody Titer in a Patient with Eosinophilic Granulomatosis with Polyangiitis by Additional Mepolizumab
Authors: Tamechika Shin-ya , Isogai Shuntaro , Maeda Shinji , Naniwa Taio , Niimi Akio
Published: March 2021

A case of eosinophilic granulomatosis with polyangiitis (EGPA) in which chronic rhinosinusitis (CRS) was improved with a reduction in the myeloperoxidase-antineutrophil cytoplasmic antibody (MPO-ANCA) titer after the addition of mepolizumab is reported. A 55-year-old woman with EGPA receiving prednisolone 5 mg/day developed CRS with increases in the eosinophil count and the MPO-ANCA titer. Although it improved with prednisolone 15 mg/day in addition to mizoribine 150 mg/day, because azathioprine could not be taken orally due to side effects, it relapsed after prednisolone was tapered to 5 mg/day. There was no exacerbation of other vasculitis symptoms such as mononeuropathy multiplex. The patient was treated with additional mepolizumab 300 mg every 4 weeks, which resulted in the improvement of CRS and marked reductions of the eosinophil count and MPO-ANCA titer, and the reduction of prednisolone to 2 mg/day. Furthermore, even after tapering mepolizumab to 200 mg every 4 weeks, her condition remained stable without relapse of EGPA and without increases in the eosinophil count and MPO-ANCA titer. The clinical course of mepolizumab treatment in this patient suggests that the IL5-dependent inflammatory cascade is one of the factors contributing to the increase in MPO-ANCA in EGPA.
Design and Implementation of English Intelligent Communication Platform Based on Similarity Algorithm
Authors: Chai Yujie
Published: March 2021

Intelligent communication processing in English aims to obtain effective information from unstructured text data using various text processing techniques. Text vector representation and text similarity calculation are important fundamental tasks in the whole field of natural language processing. In response to the shortcomings of existing sentence vector representation models and the singularity of text similarity algorithms, improved models and algorithms are proposed based on a thorough study of related domain technologies. This paper presents an in‐depth and comprehensive study of text vectorization representation and text similarity calculation algorithms in the field of natural language processing. The existing text vectorized representation models and text similarity computation algorithms are described, and their shortcomings are summarized to provide a basis for the background and significance of this paper, as well as to provide ideas for improvement directions. It is experimentally verified that the sentence vector model proposed in this paper achieves higher accuracy than the SIF sentence vector model for text classification tasks. In the task of text similarity computation, it achieves better results in three evaluation metrics: accuracy, recall, and F1 value. The algorithm also improves the computational efficiency of the model to a certain extent by removing feature words with low feature contribution. The algorithm first improves the deficiencies of the traditional word‐shift distance algorithm by defining multifeature fusion weights and realizes a text similarity calculation algorithm based on multifeature weighted fusion with better similarity calculation results. Then, a linear weighting model is constructed to further combine the similarity calculation results of the hierarchical pooled IIG‐SIF sentence vectors to realize the multimodel fusion text similarity calculation algorithm.
Influence of Uncertainty of Soil Hydraulic Parameters on Stability of Unsaturated Slopes Based on Bayesian Updating
Authors: Yeh Hsin-Fu , Huang Tsien-Ting , Yang Ya-Sin , Ke Chien-Chung
Published: March 2021

In geotechnical engineering, the soil water retention curve (SWRC) is key to solving problems arising from unsaturated soil, and the methodology used to obtain the SWRC parameters is crucial for investigating rainfall infiltration and slope stability. However, on-site measurements of soil data are expensive and time-consuming, and therefore, there is high uncertainty in the SWRC parameters due to the limited amount of data available. This study explores the impact of uncertainty in SWRC parameters on unsaturated soil slope seepage and stability under rainfall conditions. Bayesian updating was initially used to update the posterior distribution of the SWRC parameters of the model and in situ soil. Subsequently, a Markov Chain Monte Carlo (MCMC) method was used to generate random samples, and the uncertainty of the parameters was analyzed. Additionally, SWRC parametric models with different confidence intervals were created, and a hydraulic coupled model was used to evaluate the influence of the SWRC parameters (with different confidence intervals) on slope seepage and stability under rainfall conditions. The results indicated that the parameters

α

and

n

affecting the air entry value of the soil and the pore size distribution, respectively, increased as the confidence interval percentile increased. The changes in these two parameters increased the effect of rainfall on the pressure head and volumetric water content of the soil. After rainfall infiltrated the slope, the soil volumetric water content and the internal suction stress of the soil increased, resulting in a reduction in the local factor of safety (LFS) and, hence, a decrease in the stability of the slope. These results show that the predictions for the pressure head and volumetric water content were affected by the uncertainty in the SWRC parameters, leading to errors in the slope stability analysis.
Morphological Component Analysis-Based Perceptual Medical Image Fusion Using Convolutional Sparsity-Motivated PCNN
Authors: Tian Chuangeng , Tang Lu , Li Xiao , Liu Kaili , Wang Jian
Published: March 2021

This paper proposes a perceptual medical image fusion framework based on morphological component analysis combining convolutional sparsity and pulse-coupled neural network, which is called MCA-CS-PCNN for short. Source images are first decomposed into cartoon components and texture components by morphological component analysis, and a convolutional sparse representation of cartoon layers and texture layers is produced by prelearned dictionaries. Then, convolutional sparsity is used as a stimulus to motivate the PCNN for dealing with cartoon layers and texture layers. Finally, the medical fused image is computed via combining fused cartoon layers and texture layers. Experimental results verify that the MCA-CS-PCNN model is superior to the state-of-the-art fusion strategy.
Computing FGZ Index of Sum Graphs under Strong Product
Authors: Peng Zhi-Ba , Javed Saira , Javaid Muhammad , Liu Jia-Bao
Published: March 2021

Topological index (TI) is a function that assigns a numeric value to a (molecular) graph that predicts its various physical and structural properties. In this paper, we study the sum graphs (S-sum, R-sum, Q-sum and T-sum) using the subdivision related operations and strong product of graphs which create hexagonal chains isomorphic to many chemical compounds. Mainly, the exact values of first general Zagreb index (FGZI) for four sum graphs are obtained. At the end, FGZI of the two particular families of sum graphs are also computed as applications of the main results. Moreover, the dominating role of the FGZI among these sum graphs is also shown using the numerical values and their graphical presentations.
Strong Far‐Field Vertical Excitation and Building Damage: A Systematic Review and Future Avenues
Authors: Gautam Dipendra , Baruwal Rewati
Published: March 2021

Strong vertical excitation may lead to detrimental consequences on structures and infrastructures. To date, the impacts of strong vertical shaking on structures and infrastructures are considered for near‐field regions only. However, anomalies in terms of recorded evidence and damage occurrence in the central Himalayan earthquakes dragged the attention of the researchers to explore the possibility of strong vertical shaking in far‐field regions as well. Systematic review approach is used to sum up the findings from scholastic works reported to date and juxtaposed the findings with the evidence from central Himalayan earthquakes. It is concluded that the strong vertical shaking in the far‐field is undeniable, at least in the central Himalayas; thus, incorporation of strong far‐field vertical shaking in structural analysis and design is required. This paper reports the evidence reported in the literature for strong vertical shaking and adds evidence from Nepal focusing on strong far‐field vertical excitation.
High Prevalence of Metabolic Syndrome and Its Related Demographic Factors in North of Iran: Results from the PERSIAN Guilan Cohort Study
Authors: Naghipour Mohammadreza , Joukar Farahnaz , Nikbakht Hossein-Ali , Hassanipour Soheil , Asgharnezhad Mehrnaz , Arab-Zozani Morteza , Mansour-Ghanaei Fariborz
Published: March 2021

Introduction. The prevalence of metabolic syndrome has increased in recent decades around the world and is currently reaching epidemic levels as it is a major public health and clinical concern. The aim of this study was to evaluate the prevalence of metabolic syndrome and its related demographic factors in a population-based study. Methods. In this cross-sectional study, the target population consisted of 10520 individuals aged 35–70 years in Phase 1 of the Persian Guilan cohort study (Guilan site/Some’e Sara) that was conducted in 2014–2017. Demographic, anthropometric, blood pressure, and biochemical data were used in this study. The IDF definitions were used to diagnose the metabolic syndrome. Results. The prevalence of the syndrome according to IDF and ATP definition was 42.87% (95% CI: 41.92–41.81) and 40.68% (95% CI: 39.74–41.62), respectively. The prevalence of components for central obesity, high triglyceride, HDL cholesterol, blood glucose, and hypertension components was 75.8%, 43.1%, 40.6%, 39.2% and 37.9%, respectively. All demographic variables were related to the syndrome, and among them age, gender, and residence were identified as independent and strong predictive variables in the regression model. More than 92% of the population had at least one component of the syndrome. Conclusion. The results of the study show a high prevalence of metabolic syndrome risk factors. It is essential to educate healthy lifestyle behaviors and further health education in the high-risk groups identified in this study, especially the elderly, women, and rural residents.
Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization
Authors: Gu Yihong , Liu Yucheng , Lu Congda
Published: March 2021

Brake noise is one of the principal components of vehicle noise and is also one of the most critical measures of vehicle quality. During the braking process, the occurrence of brake noise has a significant relationship with the working conditions of the brake system. In the present study, dynamometer test data and the finite element method (FEM) were used to analyze the direct and indirect effects of variations in the working parameters on the brake noise, and a brake noise reduction method was developed. With this method, Monte Carlo sampling was used to consider variations in the parameters of the brake lining during the braking procedure, and the particle swarm optimization method was used to calculate the optimal parameter combination for the brake lining. A dynamometer test was carried out to validate the effect of optimization on brake noise mitigation.
Stimulation of Codonopsis pilosula Polysaccharide on Bifidobacterium of Human Gut Bacteria In Vitro
Authors: Li Jiankuan , Dong Lina , Liu Yue , Gao Jianping
Published: March 2021

Objective. To evaluate the prebiotic effects of Codonopsis pilosula polysaccharide (CPP) on human gut bacteria in vitro. Methods. Codonopsis Radix was extracted with water at 100°C, and the extract was precipitated by 80% ethanol to obtain CPP. Human fresh fecal samples were collected from three healthy adults and used to ferment CPP. The fermented samples were collected to be analyzed by 16S rRNA sequencing. Results. The results showed that CPP exhibited significantly the stimulation on the growth of genus Bifidobacterium of human gut bacteria (Padj < 0.05). Although CPP also exhibited regulative trends on the genera including Acidaminococcus, Bilophila, Dorea, and Eggerthella, no significant differences were observed (Padj > 0.05), which was likely associated with the limited samples (n = 3). Conclusion. CPP has the potential to stimulate the growth of Bifidobacterium of the human gut bacteria and to be benefit to human health.
Impact of Baseline and On-Treatment Glycemia on Everolimus-Exemestane Efficacy in Patients with Hormone Receptor–Positive Advanced Breast Cancer (EVERMET)
Authors: Vernieri Claudio , Nichetti Federico , Lalli Luca , Moscetti Luca , Giorgi Carlo Alberto , Griguolo Gaia , Marra Antonio , Randon Giovanni , Rea Carmen G. , Ligorio Francesca , Scagnoli Simone , De Angelis Claudia , Molinelli Chiara , Fabbri Agnese , Ferraro Emanuela , Trapani Dario , Milani Andrea , Agostinetto Elisa , Bernocchi Ottavia , Catania Giovanna , Vantaggiato Amelia , Palleschi Michela , Moretti Anna , Basile Debora , Cinausero Marika , Ajazi Arta , Castagnoli Lorenzo , Lo Vullo Salvatore , Gerratana Lorenzo , Puglisi Fabio , La Verde Nicla , Arpino Grazia , Rocca Andrea , Ciccarese Mariangela , Pedersini Rebecca , Fabi Alessandra , Generali Daniele , Losurdo Agnese , Montemurro Filippo , Curigliano Giuseppe , Del Mastro Lucia , Michelotti Andrea , Cortesi Enrico , Guarneri Valentina , Pruneri Giancarlo , Mariani Luigi , de Braud Filippo
Published: March 2021

Purpose:

The mTOR complex C1 (mTORC1) inhibitor everolimus in combination with the aromatase inhibitor exemestane is an effective treatment for patients with hormone receptor—positive (HR+), HER2-negative (HER2−), advanced breast cancer (HR+/HER2− aBC). However, everolimus can cause hyperglycemia and hyperinsulinemia, which could reactivate the PI3K/protein kinase B (AKT)/mTORC1 pathway and induce tumor resistance to everolimus.

Experimental Design:

We conducted a multicenter, retrospective, Italian study to investigate the impact of baseline and on-treatment (i.e., during first 3 months of therapy) blood glucose levels on progression-free survival (PFS) in patients with HR+/HER2− aBC treated with everolimus-exemestane.

Results:

We evaluated 809 patients with HR+/HER2− aBC treated with everolimus-exemestane as any line of therapy for advanced disease. When evaluated as dichotomous variables, baseline and on-treatment glycemia were not significantly associated with PFS. However, when blood glucose concentration was evaluated as a continuous variable, a multivariable model accounting for clinically relevant patient- and tumor-related variables revealed that both baseline and on-treatment glycemia are associated with PFS, and this association is largely attributable to their interaction. In particular, patients who are normoglycemic at baseline and experience on-treatment diabetes have lower PFS compared with patients who are already hyperglycemic at baseline and experience diabetes during everolimus-exemestane therapy (median PFS, 6.34 vs. 10.32 months; HR, 1.76; 95% confidence interval, 1.15–2.69; P = 0.008).

Conclusions:

The impact of on-treatment glycemia on the efficacy of everolimus-exemestane therapy in patients with HR+/HER2− aBC depends on baseline glycemia. This study lays the foundations for investigating novel therapeutic approaches to target the glucose/insulin axis in combination with PI3K/AKT/mTORC1 inhibitors in patients with HR+/HER2− aBC.