bluuebunny/crossref_metadata_2025 huggingface dataset:
SPEED OF DIFFUSION, RETHINKING TIME AND FIRMS’ STRATEGY: ANALYSIS OF THE INTERACTIONS TO LEAP ACROSS THE CHASM
Authors: CANTAMESSA MARCO , MONTAGNA FRANCESCA , CASAGRANDE-SERETTI ALESSANDRO
Published: September 2021

Although the literature has recognised short time to market and early entry as relevant factors, they are not enough alone to ensure success. In fact, an early entrant may successfully serve early adopters, but then fail in developing products suitable for those who adopt later (i.e., the early majority), dissipating the first-mover advantages previously gained. This paper argues that the likelihood of being successful in the early majority segment depends also on the rethinking time, defined as the time available to firms serving early adopters for planning and developing products that will be offered to the upcoming early majority segment. The rethinking time is here analytically defined through the Bass model and its relationship with product success is investigated. The paper shows that the market leader in the early majority segment is expected to be the incumbent when rapid diffusion occurs and, conversely, new entrants when rethinking time is longer.
Predicting the Evolution of Pain Relief
Authors: Costa Anderson Bessa Da , Moreira Larissa , Andrade Daniel Ciampi De , Veloso Adriano , Ziviani Nivio
Published: September 2021

Modeling from data usually has two distinct facets: building sound explanatory models or creating powerful predictive models for a system or phenomenon. Most of recent literature does not exploit the relationship between explanation and prediction while learning models from data. Recent algorithms are not taking advantage of the fact that many phenomena are actually defined by diverse sub-populations and local structures, and thus there are many possible predictive models providing contrasting interpretations or competing explanations for the same phenomenon. In this article, we propose to explore a complementary link between explanation and prediction. Our main intuition is that models having their decisions explained by the same factors are likely to perform better predictions for data points within the same local structures. We evaluate our methodology to model the evolution of pain relief in patients suffering from chronic pain under usual guideline-based treatment. The ensembles generated using our framework are compared with all-in-one approaches of robust algorithms to high-dimensional data, such as Random Forests and XGBoost. Chronic pain can be primary or secondary to diseases. Its symptomatology can be classified as nociceptive, nociplastic, or neuropathic, and is generally associated with many different causal structures, challenging typical modeling methodologies. Our data includes 631 patients receiving pain treatment. We considered 338 features providing information about pain sensation, socioeconomic status, and prescribed treatments. Our goal is to predict, using data from the first consultation only, if the patient will be successful in treatment for chronic pain relief. As a result of this work, we were able to build ensembles that are able to consistently improve performance by up to 33% when compared to models trained using all the available features. We also obtained relevant gains in interpretability, with resulting ensembles using only 15% of the total number of features. We show we can effectively generate ensembles from competing explanations, promoting diversity in ensemble learning and leading to significant gains in accuracy by enforcing a stable scenario in which models that are dissimilar in terms of their predictions are also dissimilar in terms of their explanation factors.
The Need for Verbal Robot Explanations and How People Would Like a Robot to Explain Itself
Authors: Han Zhao , Phillips Elizabeth , Yanco Holly A.
Published: September 2021

Although non-verbal cues such as arm movement and eye gaze can convey robot intention, they alone may not provide enough information for a human to fully understand a robot’s behavior. To better understand how to convey robot intention, we conducted an experiment (
N = 366
) investigating the
need for robots to explain
, and the
content
and
properties
of a desired explanation such as
timing
,
engagement importance
,
similarity to human explanations,
and
summarization
. Participants watched a video where the robot was commanded to hand an almost-reachable cup and one of six reactions intended to show the unreachability : doing nothing (No Cue), turning its head to the cup (Look), or turning its head to the cup with the addition of repeated arm movement pointed towards the cup (Look & Point), and each of these with or without a Headshake. The results indicated that participants agreed robot behavior should be explained across all conditions,
in situ
, in a similar manner as what human explain, and provide concise summaries and respond to only a few follow-up questions by participants. Additionally, we replicated the study again with
N = 366
participants after a 15-month span and all major conclusions still held.
Reconfigurable Framework for Resilient Semantic Segmentation for Space Applications
Authors: Sabogal Sebastian , George Alan , Crum Gary
Published: September 2021

Deep learning (DL) presents new opportunities for enabling spacecraft autonomy, onboard analysis, and intelligent applications for space missions. However, DL applications are computationally intensive and often infeasible to deploy on radiation-hardened (rad-hard) processors, which traditionally harness a fraction of the computational capability of their commercial-off-the-shelf counterparts. Commercial FPGAs and system-on-chips present numerous architectural advantages and provide the computation capabilities to enable onboard DL applications; however, these devices are highly susceptible to radiation-induced single-event effects (SEEs) that can degrade the dependability of DL applications. In this article, we propose Reconfigurable ConvNet (RECON), a reconfigurable acceleration framework for dependable, high-performance semantic segmentation for space applications. In RECON, we propose both selective and adaptive approaches to enable efficient SEE mitigation. In our selective approach, control-flow parts are selectively protected by triple-modular redundancy to minimize SEE-induced hangs, and in our adaptive approach, partial reconfiguration is used to adapt the mitigation of dataflow parts in response to a dynamic radiation environment. Combined, both approaches enable RECON to maximize system performability subject to mission availability constraints. We perform fault injection and neutron irradiation to observe the susceptibility of RECON and use dependability modeling to evaluate RECON in various orbital case studies to demonstrate a 1.5–3.0× performability improvement in both performance and energy efficiency compared to static approaches.
LAX-Score
Authors: Jung Woosub , Watson Amanda , Kuehn Scott , Korem Erik , Koltermann Ken , Sun Minglong , Wang Shuangquan , Liu Zhenming , Zhou Gang
Published: September 2021

For the past several decades, machine learning has played an important role in sports science with regard to player performance and result prediction. However, it is still challenging to quantify team-level game performance because there is no strong ground truth. Thus, a team cannot receive feedback in a standardized way. The aim of this study was twofold. First, we designed a metric called LAX-Score to quantify a collegiate lacrosse team's athletic performance. Next, we explored the relationship between our proposed metric and practice sensing features for performance enhancement. To derive the metric, we utilized feature selection and weighted regression. Then, the proposed metric was statistically validated on over 700 games from the last three seasons of NCAA Division I women's lacrosse. We also explored our biometric sensing dataset obtained from a collegiate team's athletes over the course of a season. We then identified the practice features that are most correlated with high-performance games. Our results indicate that LAX-Score provides insight into athletic performance beyond wins and losses. Moreover, though COVID-19 has stalled implementation, the collegiate team studied applied our feature outcomes to their practices, and the initial results look promising with regard to better performance.
DriverSonar
Authors: Jiang Hongbo , Hu Jingyang , Liu Daibo , Xiong Jie , Cai Mingjie
Published: September 2021

Dangerous driving due to drowsiness and distraction is the main cause of traffic accidents, resulting in casualties and economic loss. There is an urgent need to address this problem by accurately detecting dangerous driving behaviors and generating real-time alerts. Inspired by the observation that dangerous driving actions induce unique acoustic features that respond to the signal of an acoustic source, we present the DriverSonar system in this paper. The proposed system detects dangerous driving actions and generates real-time alarms using off-the-shelf smartphones. Compared with the state-of-the-arts, the DriverSonar system does not require dedicated sensors but just uses the built-in speaker and microphone in a smartphone. Specifically, DriverSonar is able to recognize head/hand motions such as nodding, yawning, and abrupt adjustment of the steering wheel. We design, implement and evaluate DriverSonar with extensive experiments. We conduct both simulator-based and and real driving-based experiments (IRB-approved) with 30 volunteers for a period over 12 months. Experiment results show that the proposed system can detect drowsy and distraction related dangerous driving actions at an precision up to 93.2% and a low false acceptance rate of 3.6%.
CellSense
Authors: Fang Zhihan , Yang Yu , Yang Guang , Xian Yikuan , Zhang Fan , Zhang Desheng
Published: September 2021

Data from the cellular network have been proved as one of the most promising way to understand large-scale human mobility for various ubiquitous computing applications due to the high penetration of cellphones and low collection cost. Existing mobility models driven by cellular network data suffer from sparse spatial-temporal observations because user locations are recorded with cellphone activities, e.g., calls, text, or internet access. In this paper, we design a human mobility recovery system called CellSense to take the sparse cellular billing data (CBR) as input and outputs dense continuous records to recover the sensing gap when using cellular networks as sensing systems to sense the human mobility. There is limited work on this kind of recovery systems at large scale because even though it is straightforward to design a recovery system based on regression models, it is very challenging to evaluate these models at large scale due to the lack of the ground truth data. In this paper, we explore a new opportunity based on the upgrade of cellular infrastructures to obtain cellular network signaling data as the ground truth data, which log the interaction between cellphones and cellular towers at signal levels (e.g., attaching, detaching, paging) even without billable activities. Based on the signaling data, we design a system CellSense for human mobility recovery by integrating collective mobility patterns with individual mobility modeling, which achieves the 35.3% improvement over the state-of-the-art models. The key application of our recovery model is to take regular sparse CBR data that a researcher already has, and to recover the missing data due to sensing gaps of CBR data to produce a dense cellular data for them to train a machine learning model for their use cases, e.g., next location prediction.
Fall Detection via Inaudible Acoustic Sensing
Authors: Lian Jie , Yuan Xu , Li Ming , Tzeng Nian-Feng
Published: September 2021

The fall detection system is of critical importance in protecting elders through promptly discovering fall accidents to provide immediate medical assistance, potentially saving elders' lives. This paper aims to develop a novel and lightweight fall detection system by relying solely on a home audio device via inaudible acoustic sensing, to recognize fall occurrences for wide home deployment. In particular, we program the audio device to let its speaker emit 20kHz continuous wave, while utilizing a microphone to record reflected signals for capturing the Doppler shift caused by the fall. Considering interferences from different factors, we first develop a set of solutions for their removal to get clean spectrograms and then apply the power burst curve to locate the time points at which human motions happen. A set of effective features is then extracted from the spectrograms for representing the fall patterns, distinguishable from normal activities. We further apply the Singular Value Decomposition (SVD) and K-mean algorithms to reduce the data feature dimensions and to cluster the data, respectively, before input them to a Hidden Markov Model for training and classification. In the end, our system is implemented and deployed in various environments for evaluation. The experimental results demonstrate that our system can achieve superior performance for detecting fall accidents and is robust to environment changes, i.e., transferable to other environments after training in one environment.
Honeysuckle
Authors: Li Tianshi , Neundorfer Elijah B. , Agarwal Yuvraj , Hong Jason I.
Published: September 2021

In-app privacy notices can help smartphone users make informed privacy decisions. However, they are rarely used in real-world apps, since developers often lack the knowledge, time, and resources to design and implement them well. We present Honeysuckle, a programming tool that helps Android developers build in-app privacy notices using an annotation-based code generation approach facilitated by an IDE plugin, a build system plugin, and a library. We conducted a within-subjects study with 12 Android developers to evaluate Honeysuckle. Each participant was asked to implement privacy notices for two popular open-source apps using the Honeysuckle library as a baseline as well as the annotation-based approach. Our results show that the annotation-based approach helps developers accomplish the task faster with significantly lower cognitive load. Developers preferred the annotation-based approach over the library approach because it was much easier to learn and use and allowed developers to achieve various types of privacy notices using a unified code format, which can enhance code readability and benefit team collaboration.
Audio Keyword Reconstruction from On-Device Motion Sensor Signals via Neural Frequency Unfolding
Authors: Wang Tianshi , Yao Shuochao , Liu Shengzhong , Li Jinyang , Liu Dongxin , Shao Huajie , Wang Ruijie , Abdelzaher Tarek
Published: September 2021

In this paper, we present a novel deep neural network architecture that reconstructs the high-frequency audio of selected spoken human words from low-sampling-rate signals of (ego-)motion sensors, such as accelerometer and gyroscope data, recorded on everyday mobile devices. As the sampling rate of such motion sensors is much lower than the Nyquist rate of ordinary human voice (around 6kHz+), these motion sensor recordings suffer from a significant frequency aliasing effect. In order to recover the original high-frequency audio signal, our neural network introduces a novel layer, called the alias unfolding layer, specialized in expanding the bandwidth of an aliased signal by reversing the frequency folding process in the time-frequency domain. While perfect unfolding is known to be unrealizable, we leverage the sparsity of the original signal to arrive at a sufficiently accurate statistical approximation. Comprehensive experiments show that our neural network significantly outperforms the state of the art in audio reconstruction from motion sensor data, effectively reconstructing a pre-trained set of spoken keywords from low-frequency motion sensor signals (with a sampling rate of 100-400 Hz). The approach demonstrates the potential risk of information leakage from motion sensors in smart mobile devices.
VeriMask
Authors: Long Yan , Curtiss Alexander , Rampazzi Sara , Hester Josiah , Fu Kevin
Published: September 2021

The US CDC has recognized moist-heat as one of the most effective and accessible methods of decontaminating N95 masks for reuse in response to the persistent N95 mask shortages caused by the COVID-19 pandemic. However, it is challenging to reliably deploy this technique in healthcare settings due to a lack of smart technologies capable of ensuring proper decontamination conditions of hundreds of masks simultaneously. To tackle these challenges, we developed an open-source wireless sensor platform---VeriMask1 ---that facilitates per-mask verification of the moist-heat decontamination process. VeriMask is capable of monitoring hundreds of masks simultaneously in commercially available heating systems and provides a novel throughput-maximization functionality to help operators optimize the decontamination settings. We evaluate VeriMask in laboratory and real-scenario clinical settings and find that it effectively detects decontamination failures and operator errors in multiple settings and increases the mask decontamination throughput. Our easy-to-use, low-power, low-cost, scalable platform integrates with existing hospital protocols and equipment, and can be broadly deployed in under-resourced facilities to protect front-line healthcare workers by lowering their risk of infection from reused N95 masks. We also memorialize the design challenges, guidelines, and lessons learned from developing and deploying VeriMask during the COVID-19 Pandemic. Our hope is that by reflecting and reporting on this design experience, technologists and front-line health workers will be better prepared to collaborate for future pandemics, regarding mask decontamination, but also other forms of crisis tech.
HERMAS
Authors: Song Yiwei , Jiang Dongzhe , Liu Yunhuai , Qin Zhou , Tan Chang , Zhang Desheng
Published: September 2021

Efficient representations for spatio-temporal cellular Signaling Data (SD) are essential for many human mobility applications. Traditional representation methods are mainly designed for GPS data with high spatio-temporal continuity, and thus will suffer from poor embedding performance due to the unique Ping Pong Effect in SD. To address this issue, we explore the opportunity offered by a large number of human mobility traces and mine the inherent neighboring tower connection patterns. More specifically, we design HERMAS, a novel representation learning framework for large-scale cellular SD with three steps: (1) extract rich context information in each trajectory, adding neighboring tower information as extra knowledge in each mobility observation; (2) design a sequence encoding model to aggregate the embedding of each observation; (3) obtain the embedding for a trajectory. We evaluate the performance of HERMAS based on two human mobility applications, i.e. trajectory similarity measurement and user profiling. We conduct evaluations based on a 30-day SD dataset with 130,612 users and 2,369,267 moving trajectories. Experimental results show that (1) for the trajectory similarity measurement application, HERMAS improves the Hitting Rate (HR@10) from 15.2% to 39.2%; (2) for the user profiling application, HERMAS improves the F1-score for around 9%. More importantly, HERMAS significantly improves the computation efficiency by over 30x.
Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke
Authors: Yang Rui , Zhang Ying , Xu Miao , Ma Jing
Published: September 2021

This study was to explore the effects of imaging characteristics of magnetic resonance angiography (MRA) based on deep learning on the comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke. In this study, 84 patients with acute stroke who were treated in hospital were selected as the research objects, and they were rolled into a control group (routine care) and an experimental group (comprehensive rehabilitation care). The dense dilated block-convolution neural network (DD-CNN) algorithm under deep learning for cerebrovascular was adopted to assess the effect of comprehensive rehabilitation care on the neurological recovery of patients with acute stroke. The results showed that the Berg scale scores, Fugl-Meyer scores, and Functional Independence Measure (FIM) scores of the experimental group of patients after 6 weeks and 12 weeks of comprehensive rehabilitation nursing were greatly different from those before treatment, showing statistical differences (

P
<
0.05

). Compared with conventional magnetic resonance imaging (MRI) images, MRA images based on CNN algorithm, Dense Net algorithm, and DD-CNN algorithm can more clearly show the patient’s cerebral artery occlusion. The average dice similarity coefficient (DSC) values of CNN algorithm, Dense Net algorithm, and DD-CNN algorithm were determined to be 84.3%, 95.7%, and 97.8%, respectively; the average sensitivity (Sen) values of the three algorithms were 76.1%, 95.4%, and 96.8%, respectively; and the average accuracy (Acc) values were 87.9%, 96.3%, and 97.9%, respectively. Thus, there were statistically obvious differences among the three algorithms in terms of average values of DSC, Sen, and Acc (

P
<
0.05

). The MRA images processed by the DD-CNN algorithm showed that the degree of neurological recovery of the experimental group was observably greater than that of the control group, and the difference was statistically obvious (

P
<
0.05

). In short, the image features of MRA based on the deep learning DD-CNN algorithm showed good application value in studying the effect of comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke, and it was worthy of promotion.
Solving a Nonlinear Fredholm Integral Equation via an Orthogonal Metric
Authors: Gnanaprakasam Arul Joseph , Mani Gunaseelan , Parvaneh Vahid , Aydi Hassen
Published: September 2021

In this paper, we prove fixed point theorems using orthogonal triangular

α

-admissibility on orthogonal complete metric spaces. Some of the well-known outcomes in the literature are generalized and expanded by the obtained results. An instance to help our outcome is being presented.
Malaria Transmission Model with Transmission-Blocking Drugs and a Time Delay
Authors: Wu Huike , Hu Zhixing
Published: September 2021

A class of transmission-blocking drugs (TBDs) that block the transmission of parasites between humans and mosquitoes has recently been shown to be effective in controlling malaria transmission. In this paper, we develop a time-delay differential equation model for malaria using TBDs intervention, in which the human population consists of a treated class and a successfully treated class. In classifying the positive equilibria, the control reproduction number

R

T

was obtained and the forward and backward branching cases were explored. Then, by constructing a Lyapunov function, the disease-free equilibrium is globally asymptotically stable under certain conditions. In addition, when

R

T

>
1

, the model exhibits Hopf bifurcation, the positive equilibrium becomes unstable from stable, and the model exhibits a periodic solution due to the change of time delay. On the other hand, it is concluded that the use of TBDs has a positive effect on disease control when the treatment rate and the efficacy of TBDs meet certain conditions. Finally, numerical simulation was used to observe the effect of treatment rate and the efficacy of TBDs on

R

T

, and it was found that the increase in the efficacy of TBDs had a more pronounced effect on disease control compared to treatment rate.
Effects of Digital Platform Openness on Recognition of Entrepreneurial Opportunities and Integration of Entrepreneurial Resources
Authors: Li Dan , Li Kang
Published: September 2021

Drawing on the theories of open innovation and entrepreneurship, this paper discusses the action mechanism of digital platform openness (DPO) on the recognition of entrepreneurial opportunities (EOR) and the integration of entrepreneurial resources (ERI) and explores the boundary conditions of this mechanism. Several hypotheses were put forward and verified by analysing 302 valid questionnaires. The results show that the breadth and depth of DPO positively affect EOR and ERI; these positive effects are positively moderated by the service level of the digital platform. The research findings provide a reference to digital platform enterprises to decide on platform opening strategy and platform service optimization.
A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM
Authors: Shen Jiang , Wu Jiachao , Xu Man , Gan Dan , An Bang , Liu Fusheng
Published: September 2021

Predicting postoperative survival of lung cancer patients (LCPs) is an important problem of medical decision-making. However, the imbalanced distribution of patient survival in the dataset increases the difficulty of prediction. Although the synthetic minority oversampling technique (SMOTE) can be used to deal with imbalanced data, it cannot identify data noise. On the other hand, many studies use a support vector machine (SVM) combined with resampling technology to deal with imbalanced data. However, most studies require manual setting of SVM parameters, which makes it difficult to obtain the best performance. In this paper, a hybrid improved SMOTE and adaptive SVM method is proposed for imbalance data to predict the postoperative survival of LCPs. The proposed method is divided into two stages: in the first stage, the cross-validated committees filter (CVCF) is used to remove noise samples to improve the performance of SMOTE. In the second stage, we propose an adaptive SVM, which uses fuzzy self-tuning particle swarm optimization (FPSO) to optimize the parameters of SVM. Compared with other advanced algorithms, our proposed method obtains the best performance with 95.11% accuracy, 95.10%

G

-mean, 95.02% F1, and 95.10% area under the curve (AUC) for predicting postoperative survival of LCPs.
Diagnostic Value of CT Angiography Combined with High-Resolution Magnetic Resonance Angiography in Vascular Lesions in Acute Stroke
Authors: Zou Mingyang , Liao Junjie , Zeng Yurong , Guan Qianwen , Lan Bowen
Published: September 2021

Cerebrovascular disease is increasing rapidly because of its high morbidity and high mortality, which is a serious threat to human health. For the early diagnosis and treatment of diseases, the CT vascular noise combined with high-resolution magnetic resonance angiography in acute cerebral apoplexy vascular disease is adopted. 150 patients with ischemic stroke were selected, which were admitted to the Department of Radiology, Huizhou Central People’s Hospital, from January 2020 to December 2020. All patients accepted digital subtraction angiography (DSA), magnetic resonance angiography (MRA), and CT angiography (CTA) examination. Results. There were 76 cases of aneurysm in DSA examination, accounting for 46%; 69 cases with pulsating stenosis, accounting for 50.67%; and 5 cases of moyamoya disease, accounting for 3.33%. The number and proportion of cases of the above diseases in MRA examination were (75, 69, 71; 53.33%, 45.67%, 4%), and those in CTA examination were (71, 76, 3, 47.33%, 50.67%, 2%). Relative to the DSA gold standard, the sensitivity, specificity, and false positive rate of MRA were 81.51%, 95.19%, and 2.1, respectively, and those of CTA were 95.78%, 79.17%, and 11.0, respectively. The number of cases and accuracy of detection of cerebral aneurysms by MRA were (75, 96.57%), and those by CTA were (71, 91.2%), which was not statistically considerable,

P
>
0.05

. For the number of cases and the detection accuracy of cerebrovascular malformations, MRA was (38, 92.68%) and CTA was (37, 90.24%), which was not statistically considerable,

P
>
0.05

. Conclusion. The detection sensitivity and accuracy of MRA were better than those of CTA, while specific CTA was superior to MRA. The differences between the two detections were substantial (

P
<
0.05

), while the sensitivity and false positive rate were not remarkably different (

P
>
0.05

). Therefore, the combination of the two detections was of great significance to the diagnosis and treatment of stroke and other vascular diseases.
Automatic Assessment of Mitral Regurgitation Severity Using the Mask R-CNN Algorithm with Color Doppler Echocardiography Images
Authors: Zhang Qinglu , Liu Yuanqin , Mi Jia , Wang Xing , Liu Xia , Zhao Fenfen , Xie Cuihuan , Cui Peipei , Zhang Qingling , Zhu Xiangming
Published: September 2021

Accurate assessment of mitral regurgitation (MR) severity is critical in clinical diagnosis and treatment. No single echocardiographic method has been recommended for MR quantification thus far. We sought to define the feasibility and accuracy of the mask regions with a convolutional neural network (Mask R-CNN) algorithm in the automatic qualitative evaluation of MR using color Doppler echocardiography images. The authors collected 1132 cases of MR from hospital A and 295 cases of MR from hospital B and divided them into the following four types according to the 2017 American Society of Echocardiography (ASE) guidelines: grade I (mild), grade II (moderate), grade III (moderate), and grade IV (severe). Both grade II and grade III are moderate. After image marking with the LabelMe software, a method using the Mask R-CNN algorithm based on deep learning (DL) was used to evaluate MR severity. We used the data from hospital A to build the artificial intelligence (AI) model and conduct internal verification, and we used the data from hospital B for external verification. According to severity, the accuracy of classification was 0.90, 0.89, and 0.91 for mild, moderate, and severe MR, respectively. The Macro F1 and Micro F1 coefficients were 0.91 and 0.92, respectively. According to grading, the accuracy of classification was 0.90, 0.87, 0.81, and 0.91 for grade I, grade II, grade III, and grade IV, respectively. The Macro F1 and Micro F1 coefficients were 0.89 and 0.89, respectively. Automatic assessment of MR severity is feasible with the Mask R-CNN algorithm and color Doppler electrocardiography images collected in accordance with the 2017 ASE guidelines, and the model demonstrates reasonable performance and provides reliable qualitative results for MR severity.
Optimization Method of Integrated Light‐Screen Array with External Parameters Based on Genetic Algorithm
Authors: Chen Rui , Ji BoWen , Chen Ding , Duan ChenXi
Published: September 2021

Due to the high sensitivity and fast response, the light‐screen array measurement principle is suitable for the dynamic parameter measurement of small and fast targets including projectile. Since the spatial structures of the light‐screen array determine the measurement accuracy, internal parameters such as the angles between the light‐screens are usually calibrated and then directly used in the field. However, the effect of the measuring state is ignored in the test field. This paper takes the integrated light‐screen array sky vertical target as the research object, and two rotation angles are introduced as external parameters to describe the deviation between the calibration state and measuring state of the target, so as to optimize the measurement model. Aiming at the problem that the external parameters cannot be measured directly, an external parameter inversion method of machine learning based on a genetic algorithm is designed under a complex engineering model. The deviation between the projectile hole and the light‐screen array measurement coordinates is used to build an inversion database for the genetic algorithm during the machine learning process. The simulation and the live firing test show that the optimization method and parameter identification algorithm in this paper can optimize the measurement model and improve the measurement accuracy of the light‐screen array principle directly and can also provide a reference for the optimization and parameter identification in other engineering problems.
Deep Learning-Based Assessment of Adverse Cardiovascular Events in Elderly Patients with Coronary Heart Disease after Percutaneous Coronary Intervention Using Intravascular Ultrasound Images
Authors: Wang Shu
Published: September 2021

This study aimed to analyze the risk factors of adverse cardiovascular events (ACVEs) in elderly patients with coronary heart disease (CHD) after percutaneous coronary intervention (PCI) using the intravascular ultrasound (IVUS) images based on the deep learning of convolutional neural networks (CNNs). This study included 90 patients with coronary heart disease as the research object. All the patients were randomly divided into a control group (group C) and an experimental group (group E), and all were treated with PCI. The patients in group C were diagnosed by angiography, and patients in group E underwent IVUS examination under deep learning. The levels of blood lipids and inflammatory factors between the two groups before and after PCI were compared, and the sensitivity, specificity, and positive predictive value (PPV) were recorded. Compared with angiography diagnosis, ultrasound diagnosis based on deep learning algorithm had higher sensitivity (92.3% vs. 81.4%), specificity (90.1% vs. 88.6%), and PPV (94.8% vs. 75.3%) (

P
<
0.05

). Compared with group C, patients in group E had a higher narrowest lesion diameter (2.54 ± 0.18 mm vs. 2.21 ± 0.19 mm) and detection rate of eccentric plaques (80.1% vs. 45.3%) (

P
<
0.05

). High-density lipoprotein cholesterol (HDL-C) after PCI in the two groups was significantly higher than that before surgery, while low-density lipoprotein cholesterol (LDL-C), tumor necrosis factor (TNF), and C-reactive protein (CRP) were significantly lower than those before surgery, and the difference was statistically significant (

P
<
0.05

). In short, the ultrasonic detection method based on deep learning algorithm has high sensitivity, specificity, and accuracy for CHD detection; PCI can improve the patient’s blood lipid level, relieve the patient’s inflammation, and reduce the occurrence of ACVEs in the patient.
Cooperative Midcourse Guidance Law with Communication Delay
Authors: Wu Zihao , Ren Quanbin , Luo Zhiqing , Fang Yangwang , Fu Wenxing
Published: September 2021

The initial conditions of the cooperative terminal guidance law, which are the terminal conditions of the cooperative midcourse guidance, have a greater impact on its cooperation and guidance precision, so it is worthy of investigating the cooperative midcourse guidance. In addition, the problem of communication delay between the network nodes is inevitable and has a greater impact on the cooperative guidance law. To solve the above problems, a novel distributed cooperative midcourse guidance (DCMG) law with communication delay is proposed by combining the cooperative term with a distributed consensus protocol including communication delay under the directed communication topology. Firstly, a DCMG law with communication delay is designed by combining the trajectory shaping guidance with the distributed protocol including communication delay under the directed communication topology; secondly, the consensus of the proposed DCMG law with communication delay under the directed graph is proved; finally, the effectiveness and superiority of the proposed DCMG law are verified by numerical simulations.
Research on the Reasonable Strengthening Time and Stability of Excavation Unloading Surrounding Rock of High-Stress Rock Mass
Authors: Xu Wensong , Xu Wentao , Cheng Yunhai
Published: September 2021

This study is aimed at better understanding the deformation and failure mechanism of surrounding rock during excavation unloading of a high-stress rock mass and determining the reasonable reinforcement time for the surrounding rock. To fulfill this aim, true triaxial tests were carried out on different loading and unloading paths during the unilateral unloading of a high-stress rock mass. The variational condition for minimization of plastic complementary energy is obtained, the optimal reinforcement time is determined, and the range of the plastic zone in the surrounding rock reinforced by anchor mesh-cable-grouting is compared and analyzed. The results are as follows: (1) Based on the Mohr-Coulomb yield criterion and the deformation reinforcement theory of surrounding rock, the stable state with the minimum reinforcement force is obtained. (2) After the true triaxial tests on the unilateral unloading of the third principal stress were carried out under different confining pressures, loading continued to be performed. Compared with rock failure without confining pressure, in the conventional uniaxial compression test, the failure of samples is dominated by composite splitting-shear failure; the unilateral unloading stress-concentration failure is a progressive failure process of splitting into plates followed by cutting into blocks and then the ejection of blocks and pieces. (3) The relationship between the time steps of the surrounding rock stability and the excavation distance is obtained. The supporting time can be divided into four stages: presupport stage, bolt reinforcement stage, anchor cable reinforcement stage, and grouting reinforcement stage. (4) In the range of within 5 m behind the tunneling face, the plastic zone of the surrounding rock with support is reduced by 7 m as compared with that with no support. In the range of over 5 m behind the tunneling face, the plastic zone of the roadway floor with support is reduced by 2.6 m as compared with that without support, and the deformation is reduced by 90%. These results can serve as a reference for controlling the behavior of surrounding rock during excavation unloading of high-stress rock masses.
Leveled Design of Cryptography Algorithms Using Cybernetic Methods for Using in Telemedicine Applications
Authors: Norouzzadeh Gil Molk Ali Mohammad , Aref Mohammad Reza , Ramazani Khorshiddoust Reza
Published: September 2021

The technology world is developing fast with the developments made in the hardware and software areas. Considering that privacy and security of telemedicine applications are among the main necessities of this industry, as a result, there is a need to use lightweight and practical algorithms to be used in applications in the field of telemedicine, while security have the least negative impact. The distinct and contradicting components in the design and implementation of the cryptography algorithm, to achieve various objectives in medicine‐based applications, have made it a complicated system. It is natural that, without identifying the components, indices, and properties of each system component, the hardware and software resources are lost and a proper algorithm cannot be designed. Accordingly, this paper presents a leveled model of cryptography algorithms using the cybernetic method. First, the main objectives and measures in the design of the cryptography algorithms are extracted using the measure reduction methods, and some of the excess and overlapping measures are eliminated. Then, three general classes of the cryptography algorithm design and implementation measures, applications of cryptography algorithms, and cryptography implementation techniques are extracted. Since the complexity of the cryptography algorithm design is relatively high, the cybernetic methodology is used to present a supermodel to make the cryptography algorithm design objective. Such design prevents examining unnecessary details and establishes a bidirectional relationship between the main design and implementation process and the support process. This relationship provides the support requirements of the main process by the support process at each step. Finally, the Q‐analysis tools are used to analyse the proposed method, and the efficiency results are represented.
The Energy Efficiency and the Main Influencing Factors for the Logistics Industry in the Yangtze River Economic Belt in China
Authors: Li Xun , Lin Chuan
Published: September 2021

Logistics is the booster of economic development, and it is imperative to further improve the logistics energy efficiency and adjust its development model. This paper is an attempt to investigate the logistics energy efficiency and main influencing factors of the Yangtze River Economic Belt, which is the most economically intensive region that spans East, Central, and West China. The input-oriented SBM-DEA model is employed to identify factors such as energy input, undesirable output, and service capacity output as well as the logistics energy efficiency of Yangtze River Economic Belt. Energy efficiency is then further decomposed into pure technical efficiency, scale efficiency, and technical efficiency, from the perspective of which provinces and cities are compared. The research results show that the logistics energy efficiency of Yangtze River Economic Belt needs to be further improved and that energy efficiency differs greatly among cities and provinces, indicating that the development is quite unbalanced in different areas. Therefore, the local government should develop development strategies according to the main influencing factors and constraints to the local logistics industry, so as to optimize and upgrade the energy structure of the logistics industry.
A New Criterion for Exponential Stability of a Class of Hopfield Neural Network with Time‐Varying Delay Based on Gronwall’s Inequality
Authors: Guo Faming , Luo Ricai , Qin Xiaolan , Yi Yunfei
Published: September 2021

In this paper, we study the problem of exponential stability for the Hopfield neural network with time‐varying delays. Different from the existing results, we establish new stability criteria by employing the method of variation of constants and Gronwall’s integral inequality. Finally, we give several examples to show the effectiveness and applicability of the obtained criterion.
Development an Inflammation-Related Factor-Based Model for Predicting Organ Failure in Acute Pancreatitis: A Retrospective Cohort Study
Authors: Peng Yunpeng , Zhu Xiaole , Hou Chaoqun , Shi Chenyuan , Huang Dongya , Lu Zipeng , Miao Yi , Li Qiang
Published: September 2021

Several inflammation-related factors (IRFs) have been reported to predict organ failure of acute pancreatitis (AP) in previous clinical studies. However, there are a few shortcomings in these models. The aim of this study was to develop a new prediction model based on IRFs that could accurately identify the risk for organ failure in AP. Methods. 100 patients with their clinical information and IRF data (levels of 10 cytokines, percentages of different immune cells, and data obtained from white blood cell count) were retrospectively enrolled in this study, and 94 patients were finally selected for further analysis. Univariate and multivariate analysis were applied to evaluate the potential risk factors for the organ failure of AP. The area under the ROC curve (AUCs), sensitivity, and specificity of the relevant model were assessed to evaluate the prediction ability of IRFs. A new scoring system to predict the organ failure of AP was created based on the regression coefficient of a multivariate logistic regression model. Results. The incidence of OF in AP patients was nearly 16% (15/94) in our derivation cohort. Univariate analytic data revealed that IL6, IL8, IL10, MCP1, CD3+ CD4+ T lymphocytes, CD19+ B lymphocytes, PCT, APACHE II score, and RANSON score were potential predictors for AP organ failure, and IL6 (

P
=
0.038

), IL8 (

P
=
0.043

), and CD19+B lymphocytes (

P
=
0.045

) were independent predictors according to further multivariate analysis. In addition, a preoperative scoring system (0-11 points) was constructed to predict the organ failure of AP using these three factors. The AUC of the new score system was 0.86. The optimal cut-off value of the new scoring system was 6 points. Conclusions. Our prediction model (based on IL6, IL8, and CD19+ B Lymphocyte) has satisfactory working efficiency to identify AP patients with high risk of organ failure.
Si3N4 Ceramic Ball Surface Defects’ Detection Based on SWT and Nonlinear Enhancement
Authors: Yu Dongling , Zhang Huiling , Zhang Xiaohui , Liao Dahai , Wu Nanxing
Published: September 2021

In order to improve the detection accuracy and efficiency of silicon nitride ceramic ball surface defects, a defect detection algorithm based on SWT and nonlinear enhancement is proposed. In view of the small surface defect area and low contrast of the silicon nitride ceramic ball, a machine vision-based nondestructive inspection system for surface images is constructed. Sobel operation is used to eliminate the nonuniform background, and the silicon nitride ceramic ball surface image is decomposed by SWT. And frequency-domain index low-pass filtering is used to modify the decomposition coefficients, and an adaptive nonlinear model is proposed to enhance defects; finally, the image is reconstructed and segmented by the stationary wavelet inverse transform and the dynamic threshold method, respectively. The enhanced algorithm can effectively identify surface defects and is superior to traditional defect detection algorithms.
Urban Fine Management of Multisource Spatial Data Fusion Based on Smart City Construction
Authors: Long Yuanpeng , Zhang Xuena , Gao Feng
Published: September 2021

With the increase of the types of urban management objects, the intelligent management of the whole city has become a matter of concern in various countries, and it is also one of the indispensable links in urban development. In the construction of cities all over the world, the intelligent and scientific management system has been used innovatively. We provide excellent facilities for transportation development, information exchange, and resource progress. The research on urban fine management based on multisource spatial data fusion is proposed. Aiming at the traffic problems in urban fine management, this paper proposes a deep network architecture based on multisource data fusion. Multisource spatial data fusion technology is used to analyze urban traffic data. Deep network architecture is used to improve the precision management status of a smart city and the accuracy of traffic condition prediction. Then, the convolution neural network technology is explored in the data fusion technology strategy. The research results show that the framework has the ability to deal with heterogeneous data and urban big data and can effectively improve the traffic management state in the construction of a smart city and effectively solve the complexity of urban fine management and processing efficiency in the construction of a smart city.