bluuebunny/crossref_metadata_2025 huggingface dataset:
Code-size-aware Scheduling of Synchronous Dataflow Graphs on Multicore Systems
Authors: Ma Mingze , Sakellariou Rizos
Published: March 2021

Synchronous dataflow graphs are widely used to model digital signal processing and multimedia applications. Self-timed execution is an efficient methodology for the analysis and scheduling of synchronous dataflow graphs. In this article, we propose a communication-aware self-timed execution approach to solve the problem of scheduling synchronous dataflow graphs on multicore systems with communication delays. Based on this communication-aware self-timed execution approach, four communication-aware scheduling algorithms are proposed using different allocation rules. Furthermore, a code-size-aware mapping heuristic is proposed and jointly used with a proposed scheduling algorithm to reduce the code size of SDFGs on multicore systems. The proposed scheduling algorithms are experimentally evaluated and found to perform better than existing algorithms in terms of throughput and runtime for several applications. The experiments also show that the proposed code-size-aware mapping approach can achieve significant code size reduction with limited throughput degradation in most cases.
Chimera
Authors: Aras Emekcan , Delbruel Stéphane , Yang Fan , Joosen Wouter , Hughes Danny
Published: March 2021

The Internet of Things (IoT) is being deployed in an ever-growing range of applications, from industrial monitoring to smart buildings to wearable devices. Each of these applications has specific computational requirements arising from their networking, system security, and edge analytics functionality. This diversity in requirements motivates the need for adaptable end-devices, which can be re-configured and re-used throughout their lifetime to handle computation-intensive tasks without sacrificing battery lifetime. To tackle this problem, this article presents Chimera, a low-power platform for research and experimentation with reconfigurable hardware for the IoT end-devices. Chimera achieves flexibility and re-usability through an architecture based on a Flash Field Programmable Gate Array (FPGA) with a reconfigurable software stack that enables over-the-air hardware and software evolution at runtime. This adaptability enables low-cost hardware/software upgrades on the end-devices and an increased ability to handle computationally-intensive tasks. This article describes the design of the Chimera hardware platform and software stack, evaluates it through three application scenarios, and reviews the factors that have thus far prevented FPGAs from being utilized in IoT end-devices.
On Lightweight Privacy-preserving Collaborative Learning for Internet of Things by Independent Random Projections
Authors: Jiang Linshan , Tan Rui , Lou Xin , Lin Guosheng
Published: March 2021

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This article considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this article, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light to moderate data pattern complexities.
Optimal Online Algorithms for File-Bundle Caching and Generalization to Distributed Caching
Authors: Qin Tiancheng , Etesami S. Rasoul
Published: March 2021

We consider a generalization of the standard cache problem called
file-bundle
caching, where different queries (tasks), each containing
l
≥
1
files, sequentially arrive. An online algorithm that does not know the sequence of queries ahead of time must adaptively decide on what files to keep in the cache to incur the minimum number of cache misses. Here a cache miss refers to the case where at least one file in a query is missing among the cache files. In the special case where
l
= 1, this problem reduces to the standard cache problem. We first analyze the performance of the classic least recently used (LRU) algorithm in this setting and show that LRU is a near-optimal online deterministic algorithm for file-bundle caching with regard to competitive ratio. We then extend our results to a generalized (
h,k
)-paging problem in this file-bundle setting, where the performance of the online algorithm with a cache size
k
is compared to an optimal offline benchmark of a smaller cache size
h
<
k
. In this latter case, we provide a randomized
O
(
l
ln
k
/
k-h
)-competitive algorithm for our generalized (
h, k
)-paging problem, which can be viewed as an extension of the classic
marking algorithm
. We complete this result by providing a matching lower bound for the competitive ratio, indicating that the performance of this modified marking algorithm is within a factor of 2 of any randomized online algorithm. Finally, we look at the distributed version of the file-bundle caching problem where there are
m
≥ 1 identical caches in the system. In this case, we show that for
m
=
l
+ 1 caches, there is a deterministic distributed caching algorithm that is (
l
2
+
l
)-competitive and a randomized distributed caching algorithm that is
O
(
l
ln (
2l
+ 1)-competitive when
l
≥ 2. We also provide a general framework to devise other efficient algorithms for the distributed file-bundle caching problem and evaluate the performance of our results through simulations.
Generating Artificial Outliers in the Absence of Genuine Ones — A Survey
Authors: Steinbuss Georg , Böhm Klemens
Published: March 2021

By definition, outliers are rarely observed in reality, making them difficult to detect or analyze. Artificial outliers approximate such genuine outliers and can, for instance, help with the detection of genuine outliers or with benchmarking outlier-detection algorithms. The literature features different approaches to generate artificial outliers. However, systematic comparison of these approaches remains absent. This surveys and compares these approaches. We start by clarifying the terminology in the field, which varies from publication to publication, and we propose a general problem formulation. Our description of the connection of generating outliers to other research fields like experimental design or generative models frames the field of artificial outliers. Along with offering a concise description, we group the approaches by their general concepts and how they make use of genuine instances. An extensive experimental study reveals the differences between the generation approaches when ultimately being used for outlier detection. This survey shows that the existing approaches already cover a wide range of concepts underlying the generation, but also that the field still has potential for further development. Our experimental study does confirm the expectation that the quality of the generation approaches varies widely, for example, in terms of the dataset they are used on. Ultimately, to guide the choice of the generation approach in a specific context, we propose an appropriate general-decision process. In summary, this survey comprises, describes, and connects all relevant work regarding the generation of artificial outliers and may serve as a basis to guide further research in the field.
Microcontroller Fingerprinting Using Partially Erased NOR Flash Memory Cells
Authors: Poudel Prawar , Ray Biswajit , Milenkovic Aleksandar
Published: March 2021

Electronic device fingerprints, unique bit vectors extracted from device's physical properties, are used to differentiate between instances of functionally identical devices. This article introduces a new technique that extracts fingerprints from unique properties of partially erased NOR flash memory cells in modern microcontrollers. NOR flash memories integrated in modern systems-on-a-chip typically hold firmware and read-only data, but they are increasingly in-system-programmable, allowing designers to erase and program them during normal operation. The proposed technique leverages partial erase operations of flash memory segments that bring them into the state that exposes physical properties of the flash memory cells through a digital interface. These properties reflect semiconductor process variations and defects that are unique to each microcontroller or a flash memory segment within a microcontroller. The article explores threshold voltage variation in NOR flash memory cells for generating fingerprints and describes an algorithm for extracting fingerprints. The experimental evaluation utilizing a family of commercial microcontrollers demonstrates that the proposed technique is cost-effective, robust, and resilient to changes in voltage and temperature as well as to aging effects.
Overview of In‐Hospital 3D Printing and Practical Applications in Hand Surgery
Authors: Keller Marco , Guebeli Alissa , Thieringer Florian , Honigmann Philipp
Published: March 2021

Three‐dimensional (3D) printing is spreading in hand surgery. There is an increasing number of practical applications like the training of junior hand surgeons, patient education, preoperative planning, and 3D printing of customized casts, customized surgical guides, implants, and prostheses. Some high‐quality studies highlight the value for surgeons, but there is still a lack of high‐level evidence for improved clinical endpoints and hence actual impact on the patient’s outcome. This article provides an overview over the latest applications of 3D printing in hand surgery and practical experience of implementing them into daily clinical routine.
Factors Associated with Unmet Need for Family Planning among Married Reproductive Age Women in Toke Kutaye District, Oromia, Ethiopia
Authors: G/Meskel Abebe Tadesse , Desta Habtamu Oljira , Bala Elias Teferi
Published: March 2021

Background. It is estimated that more than 142 million married women in developing countries have an unmet need for family planning. This study is aimed at identifying factors associated with the unmet need of family planning among married women of reproductive age in Toke Kutaye district, Ethiopia in 2019. Methods. A community-based cross-sectional study was conducted in Toke Kutaye district from March 1–30, 2019. A systematic random sampling technique was used to select 494 reproductive-age women who were married during data collection. Data were collected using a pretested structured questionnaire. Bivariate and multivariate logistic regression analyses were used to assess factors associated with the unmet need of family planning at 95% CI with a

p

value of ≤ 0.05. Result. The prevalence of unmet need for family planning in the Toke Kutaye district was 23.1% [95% CI (19.2-26.7)], with 15.2% for spacing and 7.9% for limiting. Women’s education [AOR, 3.64, 95% CI: 1.43-9.25], number of living children [AOR, 2.63, 95% CI: 1.37-5.05], husband disapproval of family planning [AOR, 3.68, 95% CI: 2.20-6.16], and discussion with healthcare providers on family planning [AOR, 0.20, 95% CI: 0.13-0.37] were significantly associated with unmet need for family planning. Conclusion. The prevalence of unmet need for family planning was high. Therefore, program managers, partners, and health workers should work to address the gaps in maternal education, the number of living children, partner disapproval of family planning, and discussion on family planning issues through enhancing female education, awareness on family planning, and male involvement in family planning services.
Prussian Blue Analogues of A2[Fe(CN)6] (A: Cu2+, Co2+, and Ni2+) and Their Composition-Dependent Sorption Performances towards Cs+, Sr2+, and Co2+
Authors: Le Lan Ha Thi , Nguyen Son An , Nguyen Trung Dinh , Le Van Cam Thi , Van Cao Hai , Nguyen Ngoc Bao , Le Thao Phuong Thi
Published: March 2021

Investigation in radioactive contaminant removal from aqueous solutions has been considered essential upon unexpected nuclear accidents. In this report, we have successfully prepared Prussian blue analogues (PBAs) with different substituted cations (A2[Fe(CN)6] (A: Cu2+, Co2+, and Ni2+)). The synthesized PBAs were characterized and employed for the removal of Cs+, Sr2+, and Co2+ as sorption models, which are commonly found in radioactive waste. Sorption examinations reveal that Cu2[Fe(CN)6] has the highest sorption capacity towards Cs+, Sr2+, and Co2+ compared with those of Co2[Fe(CN)6] and Ni2[Fe(CN)6]. This is mainly attributed to the cation-exchange ability of substituted metal within the framework of PBAs. The sorption mechanism is qualitatively and quantitatively supported by infrared spectroscopy (IR) and total reflection X-ray fluorescence spectroscopy analysis (TXRF). In addition, it was found that Cs+ is adsorbed most effectively by PBAs due to the size matching between Cs+ ions and the channel windows of PBAs. These findings are important for the design of sorbents with suitable ion-exchange capacity and selectivity toward targeted radioactive wastes.
[Retracted] Optimization of Intelligent English Pronunciation Training System Based on Android Platform
Authors: Cao Qianyu , Hao Hanmei
Published: March 2021

Oral English, as a language tool, is not only an important part of English learning but also an essential part. For nonnative English learners, effective and meaningful voice feedback is very important. At present, most of the traditional recognition and error correction systems for oral English training are still in the theoretical stage. At the same time, the corresponding high‐end experimental prototype also has the disadvantages of large and complex system. In the speech recognition technology, the traditional speech recognition technology is not perfect in recognition ability and recognition accuracy, and it relies too much on the recognition of speech content, which is easily affected by the noise environment. Based on this, this paper will develop and design a spoken English assistant pronunciation training system based on Android smartphone platform. Based on the in‐depth study and analysis of spoken English speech correction algorithm and speech feedback mechanism, this paper proposes a lip motion judgment algorithm based on ultrasonic detection, which is used to assist the traditional speech recognition algorithm in double feedback judgment. In the feedback mechanism of intelligent speech training, a double benchmark scoring mechanism is introduced to comprehensively evaluate the speech of the speech trainer and correct the speaker’s speech in time. The experimental results show that the speech accuracy of the system reaches 85%, which improves the level of oral English trainers to a certain extent.
Determining the Thermal Conductivity of Clay during the Freezing Process by Artificial Neural Network
Authors: Ren Xiuling , You Yanhui , Yu Qihao , Zhang Guike , Yue Pan , Jin Mingyang
Published: March 2021

Thermal conductivity is an important thermal parameter in engineering design in cold regions. By measuring the thermal conductivity of clay using a transient hot‐wire method in the laboratory, the influential factors of the thermal conductivity of soils during the freezing process were analyzed, and a predictive model of thermal conductivity was developed with an artificial neural network (ANN) technology. The results show that the variation of thermal conductivity can be divided into three stages with decreasing temperature, positive temperature stage, transition stage, and negative temperature stage. The thermal conductivity increases sharply in the transition stage. The difference between the thermal conductivity at positive and negative temperature is small when the dry density of the soil specimens is larger than the critical dry density, while the difference is large if the dry density is less than the critical dry density. As the negative temperature decreases, the larger the moisture content of the soil specimens, the larger the increase of thermal conductivity. The effect of initial moisture content on thermal conductivity is more significant than that of dry density and temperature. The change tendency of the thermal conductivity calculated by the established ANN model is basically consistent with that of the laboratory‐measured values, indicating that this model can be able to accurately predict the thermal conductivity of the soil specimens in the freezing process.
Clinical Influencing Factors of Acute Myocardial Infarction Based on Improved Machine Learning
Authors: Du Hongwei , Feng Linxing , Xu Yan , Zhan Enbo , Xu Wei
Published: March 2021

At present, there is no method to predict or monitor patients with AMI, and there is no specific treatment method. In order to improve the analysis of clinical influencing factors of acute myocardial infarction, based on the machine learning algorithm, this paper uses the K-means algorithm to carry out multifactor analysis and constructs a hybrid model combined with the ART2 network. Moreover, this paper simulates and analyzes the model training process and builds a system structure model based on the KNN algorithm. After constructing the model system, this paper studies the clinical influencing factors of acute myocardial infarction and combines mathematical statistics and factor analysis to carry out statistical analysis of test results. The research results show that the system model constructed in this paper has a certain effect in the clinical analysis of acute myocardial infarction.
Distributed Event‐Triggered Output Synchronization of Complex‐Valued Memristive Reaction‐Diffusion Complex Networks with Spatial Sampled‐Data
Authors: Chen Tiane , Cheng Zaihe
Published: March 2021

This study addresses the problem of output quasisynchronization for coupled complex‐valued memristive reaction‐diffusion complex networks via the distributed event‐triggered control scheme. First, by using the separate method, set value mapping, and intermediate value theorem, the complex‐valued memristive reaction‐diffusion complex networks can be transferred into two semi‐uncertain real‐valued reaction‐diffusion complex networks. Second, a distributed output piecewise event‐triggered control (OPETC) scheme with spatial sampled‐data is first proposed including a spatial sampling event‐triggered generator and spatiotemporal sampling state feedback controller. Furthermore, this scheme can effectively save the measurement resources and lower the update rate of controllers in spatial and time domain. Third, the synchronization analysis is considered by utilizing an appropriate Lyapunov function, the Halanay inequality, and the improved Wirtinger inequality. Subsequently, several output event‐triggered quasisynchronization criteria are derived. The relations among event trigger conditions, spatial sampling interval, convergence rate, and control gain are given by rigorous mathematical derivation. Finally, multiple simulations are compared to substantiate the validation of the OPETC scheme.
Subacute Thyroiditis during the COVID-19 Pandemic: Searching for a Clinical Association with SARS-CoV-2
Authors: Trimboli Pierpaolo , Camponovo Chiara , Franscella Sebastiano , Bernasconi Enos , Buetti Niccolò
Published: March 2021

Purpose. To search for a clinical potential link between subacute thyroiditis (SAT) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a series of patients diagnosed with SAT during the COVID-19 pandemic, by retrospective evaluation of (1) clinical symptoms and (2) contact tracing. Methods. SAT  patients diagnosed from March to December 2020 were enrolled. The presence of typical clinical presentation of SARS-CoV-2, diagnostic tests for SARS-CoV-2, and contact with other individuals proven to be positive for SARS-CoV-2 were searched. Results. Ten SAT  cases were included. Fever was recorded in four patients. Cough, dyspnea, and headache were rarely reported. No patient had diagnosis of pneumonia. Two patients had moderate to severe fatigue after SAT. One patient experienced loss of smell and taste and had persistent fatigue over the following five months. No patient had positive SARS-CoV-2 diagnostic tests. At contact-tracing evaluation, only one patient had a contact with people who were diagnosed with SARS-CoV-2. Conclusions. Patients diagnosed with SAT during COVID-19 pandemic rarely experienced SARS-CoV-2-related symptomatology. The contact tracing did not show close contact with SARS-CoV-2 individuals in 9/10 cases.
Predicting Severity and Intrahospital Mortality in COVID‐19: The Place and Role of Oxidative Stress
Authors: Cekerevac Ivan , Turnic Tamara Nikolic , Draginic Nevena , Andjic Marijana , Zivkovic Vladimir , Simovic Stefan , Susa Romana , Novkovic Ljiljana , Mijailovic Zeljko , Andjelkovic Marija , Vukicevic Vladimir , Vulovic Tatjana , Jakovljevic Vladimir
Published: March 2021

SARS‐CoV‐2 virus causes infection which led to a global pandemic in 2020 with the development of severe acute respiratory syndrome. Therefore, this study was aimed at examining its possible role in predicting severity and intrahospital mortality of COVID‐19, alongside with other laboratory and biochemical procedures, clinical signs, symptoms, and comorbidity. This study, approved by the Ethical Committee of Clinical Center Kragujevac, was designed as an observational prospective cross‐sectional clinical study which was conducted on 127 patients with diagnosed respiratory COVID‐19 viral infection from April to August 2020. The primary goals were to determine the predictors of COVID‐19 severity and to determine the predictors of the negative outcome of COVID‐19 infection. All patients were divided into three categories: patients with a mild form, moderate form, and severe form of COVID‐19 infection. All biochemical and laboratory procedures were done on the first day of the hospital admission. Respiratory (p < 0.001) and heart (p = 0.002) rates at admission were significantly higher in patients with a severe form of COVID‐19. From all observed hematological and inflammatory markers, only white blood cell count (9.43 ± 4.62, p = 0.001) and LDH (643.13 ± 313.3, p = 0.002) were significantly higher in the severe COVID‐19 group. We have observed that in the severe form of SARS‐CoV‐2, the levels of superoxide anion radicals were substantially higher than those in two other groups (11.3 ± 5.66, p < 0.001) and the nitric oxide level was significantly lower in patients with the severe disease (2.66 ± 0.45, p < 0.001). Using a linear regression model, TA, anosmia, ageusia, O2−, and the duration at the ICU are estimated as predictors of severity of SARS‐CoV‐2 disease. The presence of dyspnea and a higher heart rate were confirmed as predictors of a negative, fatal outcome. Results from our study show that presence of hypertension, anosmia, and ageusia, as well as the duration of ICU stay, and serum levels of O2− are predictors of COVID‐19 severity, while the presence of dyspnea and an increased heart rate on admission were predictors of COVID‐19 mortality.
Attention Mechanism‐Based CNN‐LSTM Model for Wind Turbine Fault Prediction Using SSN Ontology Annotation
Authors: Xie Yuan , Zhao Jisheng , Qiang Baohua , Mi Luzhong , Tang Chenghua , Li Longge
Published: March 2021

The traditional model for wind turbine fault prediction is not sensitive to the time sequence data and cannot mine the deep connection between the time series data, resulting in poor generalization ability of the model. To solve this problem, this paper proposes an attention mechanism‐based CNN‐LSTM model. The semantic sensor data annotated by SSN ontology is used as input data. Firstly, CNN extracts features to get high‐level feature representation from input data. Then, the latent time sequence connection of features in different time periods is learned by LSTM. Finally, the output of LSTM is input into the attention mechanism module to obtain more fault‐related target information, which improves the efficiency, accuracy, and generalization ability of the model. In addition, in the data preprocessing stage, the random forest algorithm analyzes the feature correlation degree of the data to get the features of high correlation degree with the wind turbine fault, which further improves the efficiency, accuracy, and generalization ability of the model. The model is validated on the icing fault dataset of No. 21 wind turbine and the yaw dataset of No. 4 wind turbine. The experimental results show that the proposed model has better efficiency, accuracy, and generalization ability than RNN, LSTM, and XGBoost.
Ground Response and Mining‐Induced Stress in Longwall Panel of a Kilometer‐Deep Coal Mine
Authors: Wang Zhaohui , Yang Shengli , Xu Guoliang , Wen Zhijie
Published: March 2021

In order to improve ground control of the longwall mining, ground response and mining‐induced stress in the longwall panel of a kilometer‐deep coal mine are investigated in this study. Field measurements on abutment stress, roof displacement, and fracture development indicate that the region influenced by the longwall mining reaches 150 m ahead of the longwall face. Failure scope of the coal seam, where mining‐induced fractures are well developed, ranges from 10 to 13 m inward the face line. Vertical stress concentration coefficient reaches 2.2. Based on the field measurements, a numerical model is moreover developed and utilized to examine the response of the principal stress to the longwall mining. The concentration coefficient, peak point location, and influence scope of the principal stress gradually become stable with an increase in face advancement. Regarding the major principal stress, the concentration coefficient and influence scope are 2.4 and 152 m, respectively, and the peak point locates 13 m inward the face line, which are consistent with the field measurements. With respect to the minor principal stress, the referred coefficient and scope are 1.5 and 172 m, respectively, and its peak point location is 21 m ahead of the face line. The major principal stress in the coal seam rotates from vertical to horizontal direction in the vertical plane parallel with face advance direction. The maximum rotation angle reaches 20°. The minor principal stress first rotates into the referred vertical plane and then it rotates from horizontal to vertical direction at the same speed with the major principal stress in the same plane. Rotation angle of the principal stress in roof strata is greatly enlarged, the rotation trace of which is influenced by the longwall mining and vertical distance above the seam. Based on the relation between rotation trace of the principal stress and face advance direction, the influence of stress rotation on the stability of roof structure is discussed.
A Novel Design of Photovoltaic-Based Charging Station for Battery Vehicles with Dynamic Demand: A Case of Short Runs
Authors: Salameh Mohamed , Khatib Tamer , Alsahili Khaled
Published: March 2021

In this research, a novel design and operation of solar-based charging system for battery vehicle for a 50 km run is proposed. The proposal is aimed at replacing 110 existing diesel vehicles with 39 electric buses. Several operation scenarios for the charging stations are proposed and analyzed. Scenarios include two different battery charging methodologies and one hybrid option between electric buses and diesel vehicles. An energy model of the adapted electric buses is developed first. After that, load demand and needs including number of daily trips, number of passengers per hour, and hourly energy consumption are determined based on the developed model and gathered information. Results show that a 5.7 MWp photovoltaic system is required to power this transportation line with a loss of load probability of 5% and a trip cost per passenger of 2.05 USD. The simple payback period of the system is found to be 10 years, which is 40% of the system’s lifetime. The amount of CO2 mitigated by the proposed system is estimated as 1,629,387 (kg/year). The social impact of the proposed project is found acceptable; whereas, most of the current employees will keep their jobs with higher salaries by about 145% and less working hours by 50%. Moreover, it is expected that the proposed project will significantly increase the reliability, convenience, and sustainability of the transportation process.
Macular Vascular Density Changes following Cataract Surgery in Diabetic Patients: An Optical Coherence Tomography Angiography Study
Authors: Feng Le , Azhati Guliqiwaer , Li Tingting , Liu Fang
Published: March 2021

Purpose. Cataracts and diabetes very commonly coexist. The aim of the present study was to quantify the effect of uncomplicated phacoemulsification on retinal microvasculature in diabetic patients by using optical coherence tomography angiography (OCTA). Methods. A prospective observational study of diabetic and nondiabetic patients at baseline and 1 day, 1 week, 1 month, and 3 months after cataract surgery was performed by using OCTA. We measured the macular thickness (MT), superficial capillary plexus (SCP), deep capillary plexuses (DCP), and foveal avascular zone (FAZ) in the 3 × 3 mm macular images. Results. A total of 32 eyes of 32 type 2 diabetic patients and 40 eyes of 40 nondiabetic patients were assessed. There was a significant increase in MT at 1 month and 3 months after surgery in both groups (all

P
<
0.05

), but there was no significant difference between the two groups (

P
=
0.217

). At 3 months postoperatively, the SCP level in the diabetic group was significantly higher than that at baseline (

P
<
0.05

). The MT and SCP were negatively correlated with logMAR best-corrected visual acuity (BCVA), while the FAZ area and perimeter were positively correlated with logMAR BCVA in the diabetic group. Conclusions. Our study demonstrated that phacoemulsification can increase macular thickness in both diabetic and nondiabetic patients and increase SCP in diabetic patients within 3 months after surgery. Whether these changes persist for a longer period still needs to be investigated.
Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection
Authors: He Mingshu , Wang Xiaojuan , Zhou Junhua , Xi Yuanyuan , Jin Lei , Wang Xinlei
Published: March 2021

With the increase of Internet visits and connections, it is becoming essential and arduous to protect the networks and different devices of the Internet of Things (IoT) from malicious attacks. The intrusion detection systems (IDSs) based on supervised machine learning (ML) methods require a large number of labeled samples. However, the number of abnormal behaviors is far less than that of normal behaviors, let alone that the shots of malicious behavior samples which can be intercepted as training dataset are actually limited. Consequently, it is a key research topic to conduct the anomaly detection for the small number of abnormal behavior samples. This paper proposes an anomaly detection model with a few abnormal samples to solve the problem in few-shot detection based on convolutional neural networks (CNN) and autoencoder (AE). This model mainly consists of the CNN-based supervised pretraining module and the AE-based data reconstruction module. Only a few abnormal samples are utilized to the pretrain module to build the structure of extracting deep features. The data reconstruction module simply chooses the deep features of normal samples as training data. There also exist some effective attention mechanisms in the pretraining module. Through the pretraining of small samples, the accuracy of abnormal detection is improved compared with merely training normal samples with AE. The simulation results prove that this solution can solve the above problems occurring in network behavior anomaly detection. In comparison to the original AE model and other clustering methods, the proposed model advances the detection results in a visible way.
Energy Consumption of Self‐Compacting Concrete during Mixing and Its Impact on the Yield Stress Measured in the Ready‐Mix Concrete Plant
Authors: Arularasi V. , Thamilselvi P. , Avudaiappan Siva , Saavedra Flores Erick I.
Published: March 2021

To find the energy required during the mixing process of self‐compacting concrete in a ready‐mixed concrete plant and correlate the results with the yield stress of concrete. Power consumption required during the mixing of concrete is measured with a wattmeter connected to the mixing unit’s power supply. A coaxial cylinder viscometer is used to measure the yield stress of concrete. The clamp meter measures the power when the impeller rotates inside the coaxial cylinder viscometer, which is filled with concrete. When the impeller rotates in a coaxial cylinder filled with concrete, the power is measured by a clamp meter. Torque is obtained through the power relationship, which is an essential factor in determining the yield stress. The cost of a rheometer is so high that all construction industries, research institutions, and researchers cannot measure rheological parameters. Nowadays, all rheometers are automated; hence, the cost is very high. Tattersall’s approach of power requirement in mixing the concrete and calculating the yield stress reduces the complexity in determining the rheological parameter.
Effect of Whole‐Body Vibration Training on Muscle Activation for Individuals with Knee Osteoarthritis
Authors: Zhang Juan , Wang Rui , Zheng Yili , Xu Jiao , Wu Ya , Wang Xueqiang
Published: March 2021

Whole‐body vibration (WBV) training may improve the strength of lower extremity muscles in patients with knee osteoarthritis (KOA), but the inconsistency in vibration parameters leads to differences in findings. This cross‐sectional study is aimed at observing the effects of different vibration frequencies and knee flexion angles on the activation of lower extremity muscles in patients with KOA. Enrolled participants received WBV training at 0, 30, and 60° knee flexion angles with vibration frequencies of 0, 5, 10, and 20 Hz. Activation rates for vastus medialis, vastus lateralis, rectus femoris, biceps femoris, and semitendinosus in different combinations were collected through surface electromyography. The effects of frequency and angle on muscle activation rate were quantified by repeated measures ANOVA. Individual and synergistic effects of frequency and angle were also analysed. Twenty‐six participants with KOA were included. Muscle activation increased with the vibration frequency in 0–20 Hz range and with knee flexion angle in 0–60° range. WBV training at 20 Hz was the most effective for knee muscle activation, and static squatting at 60° was the most suitable for WBV training. Therefore, WBV training can increase the activation rate of knee flexor and extensor muscles in patients with KOA, and the most efficient combination was 20 Hz vibration frequency and 60° knee flexion. When applying WBV to patients with KOA, individual differences and rehabilitation purposes should be considered in selecting vibration parameters and knee angle to effectively increase neuromuscular activity.
Species Identification and Effects of Aromatic Hydrocarbons on the Fluorescence Spectra of Different Oil Samples in Seawater
Authors: Hou Yongchao , Li Ying , Li Guannan , Xu Mingfei , Jia Yunpeng
Published: March 2021

The identification of oil species and their characteristics in oil spills is particularly important for their efficient disposal. Since aromatic hydrocarbons (AHs) with various characteristics are the major fluorescent components of oils in seawater, they can be used to detect different oil species in seawater. Here, we developed a composition method using the fluorescence spectra of eight AH categories analyzed by gas chromatography-mass spectrometry (GC-MS) to evaluate the contribution of AHs to the total fluorescence of six different oil species. Qualitative and quantitative difference analysis of the experimental and composite fluorescence spectra was performed based on the redshift of the main peak position, the fluorescence intensity distance, and the generalized included angle cosine, while correlation analysis was used to establish the relationship between the different fluorescence spectral parameters and the American Petroleum Institute gravity and viscosity of the oil species. The fluorescence spectra recorded for heavy oil samples indicated a reduction in the fluorescence signal of fluorene series and an increase in the contribution of acenaphthene and pyrene series, indicating that the currently developed composition method by fluorescence spectroscopy combined with GC-MS can be used to distinguish and identify oil species in seawater samples.
Protection Mechanism in Reliability Evaluation Approach to Multistate System with Common Cause Failure
Authors: Qin Jinlei , Li Zheng , Zhu Youchan
Published: March 2021

Except the two types of state, complete failure and perfect functioning, some intermediate states also existed between those two states. This characteristic enables multistate system (MSS) whose components are often of more than two states. Moreover, because of dependence between components, common cause failure (CCF) enhances the failure risk of components during the operating period of MSS. A protection mechanism needs to be adopted because of the importance of certain components. For a MSS with CCF and protection mechanism, its reliability can be evaluated by a proposed approach. Certain components not only can cause failure at one state, but also can cause failure to other components because of the CCF. Under the impact of one type of CCF, those components can constitute a specific group that can overlap with certain components. Universal generating function (UGF) is adopted to incorporate the CCF and protection mechanism of a component. Consequently, reliability indices of a MSS can be evaluated by the proposed approach based on UGF. Examples and comparisons illustrate and validate this method. This method can also help engineers prioritize elements that require protection mechanism under various resource limitations.
A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography
Authors: Ogundokun Roseline Oluwaseun , Abikoye Oluwakemi Christiana
Published: March 2021

Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine. With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead. Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup. Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information. Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue. The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution. The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image. The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works. The number of shifts was added as a new performance metric for the proposed system. The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.
Current Challenges in School Leadership in Estonia and Finland: A Multiple-Case Study among Exemplary Principals
Authors: Tirri Kirsi , Eisenschmidt Eve , Poom-Valickis Katrin , Kuusisto Elina
Published: March 2021

The purpose of this study was to find out what current challenges successful principals in Estonia and Finland identify in developing their schools. The strategies used in dealing with these challenges were also analyzed within the framework of “growth-mindset pedagogy” as an educational approach to school leadership. The principals were interviewed, and the resulting data were analyzed by means of both inductive and deductive content analysis. The similarities among and differences between principals from Estonia and Finland were compared and discussed in the context of high-achievement-oriented but culturally different educational systems. According to the results, the challenges are similar in both countries, relating to the principals’ professional development, as well as developments in the curriculum and the learning community. The Estonian principals identified more challenges related to developments in the learning community than their Finnish peers, although in both countries they tended to use strategies related to growth-mindset pedagogy in dealing with them. Preparation and development programs for principals should pay more attention to their mindset and their views on teaching and learning, which may have a strong impact on the whole school community.
Oral Hygiene Practices and Associated Factors among Patients Visiting Private Dental Clinics at Hawassa City, Southern Ethiopia, 2018
Authors: Beyene Desalegn Humna , Shashamo Bereket Beyene , Digesa Lankamo Ena , Tariku Eshetu Zerihun
Published: March 2021

Introduction. A poor oral hygiene is associated with dental caries, gingivitis, periodontal diseases, bad breath, respiratory and cardiovascular diseases, and chronic kidney diseases. Moreover, a poor oral health has psychosocial impacts that diminish a quality of life and restrict activities in school, at work, and home. African regions carry a major burden of oral health problems. However, very few studies highlighted about oral hygiene practices and there is also paucity of information in Ethiopia. This study was, therefore, designed to identify an oral hygiene practice on patients/clients visiting dental clinics in Hawassa City, Southern Ethiopia. Objective. To assess oral hygiene practices and associated factors among patients/clients visiting private dental clinics, Hawassa City, Southern Ethiopia. Methods. Institution-based cross-sectional study was employed among patients/clients attending private clinics in Hawassa City from January 27 to February 8, 2018. Systematic random sampling technique was used to select 403 study participants. Data were entered into EpiData 3.1, cleaned, and analyzed by SPSS 20. A multivariable logistic regression analysis was performed to assess the association between independent and outcome variables. Crude and adjusted OR with 95% confidence level was estimated, and variables having

P

value ≤0.05 in multivariable analysis were considered as significant. Results. 393 study participants participated making a response rate of 97.52%. A median age of respondents was 27 ± 10.9. About 153 (39.9%) of the study participants had poor oral hygienic practice. Male (AOR: 1.63, 95% CI: (1.053, 2.523)), rural residence (AOR: 3.79, 95% CI: (1.724, 8.317)), and poor knowledge about oral hygiene (AOR: 2.38, 95% CI: (1.402, 4.024)) were independently associated to poor oral hygienic practice. Conclusion. More than one-third of the study participants had poor oral hygienic practice. Providing health information regarding oral hygiene for the patients/clients in the facilities with a special focus from rural areas is recommended.
Location-Price Game in a Dual-Circle Market with Different Demand Levels
Authors: Chen Xiaofeng , Song Qiankun , Rong Luqing , Zhao Zhenjiang
Published: March 2021

This paper researches a location-price game in a dual-circle market system, where two circular markets are interconnected with different demand levels. Based on the Bertrand and Salop models, a double intersecting circle model is established for a dual-circle market system in which two players (firms) develop a spatial game under price competition. By a two-stage (location-then-price) structure and backward induction approach, the existence of price and location equilibrium outcomes is obtained for the location game. Furthermore, by Ferrari method for quartic equation, the location equilibrium is presented by algebraic expression, which directly reflects the relationship between the equilibrium position and the proportion factor of demand levels. Finally, an algorithm is designed to simulate the game process of two players in the dual-circle market and simulation results show that two players almost reach the equilibrium positions obtained by theory, wherever their initial positions are.
The Influence of Information Diffusion on Interbank Risk Contagion
Authors: Li Zhinan , Shen Peilong , Liu Xiaoyuan
Published: March 2021

In this paper, the stylized features of incomplete and asymmetric information in the interbank market leading to banks’ precautionary behaviors are introduced. Based on banks’ stylized behavioral rules, the influencing mechanism of information diffusion on interbank risk contagion is analyzed, and how the existence of information diffusion and banks’ information‐obtaining ability influence the interbank risk contagion is verified through computational simulations. The results show that information diffusion can significantly accelerate the process of and magnify the extent and probability of interbank risk contagion, and improving banks’ information‐obtaining ability could lower the speed and extent of the interbank risk contagion. This paper also finds and explains some special phenomena of rollover risk contagion when information diffusion exists: the saltatory risk contagion, the circular liquidity trap, and the risk discovery of information diffusion.