bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinical and Serological Findings of COVID-19 Participants in the Region of Makkah, Saudi Arabia
Authors: Alzahrani Othman R. , Alanazi Abdullah D. , Kareinen Lauri , Hawsawi Yousef M. , Alhadrami Hani A. , Khogeer Asim A. , Alatwi Hanan E. , Alharbi Amnah A. , Sironen Tarja , Vapalahti Olli , Hepojoki Jussi , Zakham Fathiah
Published: July 2022

Makkah in Saudi Arabia hosts the largest annual religious event in the world. Despite the many strict rules enacted, including Hajj cancellation, city lockdowns, and social distancing, the region has the second highest number of new COVID-19 cases in Saudi Arabia. Public health interventions that identify, isolate, and manage new cases could slow the infection rate. While RT-PCR is the current gold standard in SARS-CoV-2 identification, it yields false positive and negative results, which mandates the use of complementary serological tests. Here, we report the utility of serological assays during the acute phase of individuals with moderate and severe clinical manifestations of SARS-CoV-2 (COVID19). Fifty participants with positive RT-PCR results for SARS-CoV-2 were enrolled in this study. Following RT-PCR diagnosis, serum samples from the same participants were analyzed using in-house ELISA (IgM, IgA, and IgG) and microneutralization test (MNT) for the presence of antibodies. Of the 50 individuals analyzed, 43 (86%) showed a neutralizing antibody titer of ≥20. Univariate analysis with neutralizing antibodies as a dependent variable and the degree of disease severity and underlying medical conditions as fixed factors revealed that patients with no previous history of non-communicable diseases and moderate clinical manifestation had the strongest neutralizing antibody response “Mean: 561.11”. Participants with severe symptoms and other underlying disorders, including deceased individuals, demonstrated the lowest neutralizing antibody response. Anti-spike protein antibody responses, as measured by ELISA, showed a statistically significant correlation with neutralizing antibodies. This reinforces the speculation that serological assays complement molecular testing for diagnostics; however, patients’ previous medical history (anamnesis) should be considered in interpreting serological results.
Natural Language Processing in Diagnostic Texts from Nephropathology
Authors: Legnar Maximilian , Daumke Philipp , Hesser Jürgen , Porubsky Stefan , Popovic Zoran , Bindzus Jan Niklas , Siemoneit Joern-Helge Heinrich , Weis Cleo-Aron
Published: July 2022

Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination.
Auditory Display of Fluorescence Image Data in an In Vivo Tumor Model
Authors: Lee Sheen-Woo , Lee Sang Hoon , Cheng Zhen , Yeo Woon Seung
Published: July 2022

Objectives: This research aims to apply an auditory display for tumor imaging using fluorescence data, discuss its feasibility for in vivo tumor evaluation, and check its potential for assisting enhanced cancer perception. Methods: Xenografted mice underwent fluorescence imaging after an injection of cy5.5-glucose. Spectral information from the raw data was parametrized to emphasize the near-infrared fluorescence information, and the resulting parameters were mapped to control a sound synthesis engine in order to provide the auditory display. Drag–click maneuvers using in-house data navigation software-generated sound from regions of interest (ROIs) in vivo. Results: Four different representations of the auditory display were acquired per ROI: (1) audio spectrum, (2) waveform, (3) numerical signal-to-noise ratio (SNR), and (4) sound itself. SNRs were compared for statistical analysis. Compared with the no-tumor area, the tumor area produced sounds with a heterogeneous spectrum and waveform, and featured a higher SNR as well (3.63 ± 8.41 vs. 0.42 ± 0.085, p < 0.05). Sound from the tumor was perceived by the naked ear as high-timbred and unpleasant. Conclusions: By accentuating the specific tumor spectrum, auditory display of fluorescence imaging data can generate sound which helps the listener to detect and discriminate small tumorous conditions in living animals. Despite some practical limitations, it can aid in the translation of fluorescent images by facilitating information transfer to the clinician in in vivo tumor imaging.
Effective Prevention and Management Tools for Metabolic Syndrome Based on Digital Health-Based Lifestyle Interventions Using Healthcare Devices
Authors: Lee Jung-Hun , Lee Kang-Hyun , Kim Hee-Jin , Youk Hyun , Lee Hee-Young
Published: July 2022

Digital health-based lifestyle interventions (e.g., mobile applications, short messaging service, wearable devices, social media, and interactive websites) are widely used to manage metabolic syndrome (MetS). This study aimed to confirm the utility of self-care for prevention or management of MetS. We recruited 106 participants with one or more MetS risk factors from December 2019 to September 2020. Participants were provided five healthcare devices and applications. Characteristics were compared at baseline and follow-up to examine changes in risk factors, engagement, persistence, and physical activity (analyzed through device use frequency and lifestyle interventions performed). Participants with 1–2 MetS risk factors showed statistically significant reductions in waist circumference (WC) and blood pressure (BP). Participants with ≥3 MetS risk factors showed statistically significant reductions in risk factors including weight, body mass index, WC, BP, and fasting blood sugar (FBS). The prevention and improvement groups used more healthcare devices than the other groups. Smartwatch was the most frequently used device (5 times/week), and physical activity logged more than 7000 steps/week. WC, BP, and FBS of the improvement group were reduced by more than 40%. Based on engagement, persistence, and physical activity, digital health-based lifestyle interventions could be helpful for MetS prevention and management.
The Effect of Physical Exercise on Oxidation Capacity and Utero-Placental Circulation in Pregnancies with Gestational Diabetes Mellitus and Uncomplicated Pregnancies, a Pilot Study
Authors: Chatzakis Christos , Sotiriadis Alexandros , Fatouros Ioannis G. , Jamurtas Athanasios Z. , Deli Chariklia K. , Papagianni Maria , Dinas Konstantinos , Mastorakos George
Published: July 2022

Background: Gestational diabetes mellitus (GDM) is associated with aggravated oxidation capacity and utero-placental circulation, while aerobic exercise could improve both. The study aims to assess oxidation capacity and utero-placental circulation before and after a bout of aerobic exercise in GDM and uncomplicated pregnancies.; Methods: In this cross-sectional study, women with GDM (GDMs) and women with uncomplicated pregnancies(controls), underwent 30 min of moderate intensity cycling. Total antioxidant capacity (TAC), catalase activity (CAT), reduced glutathione (GSH), Uterine Arteries (UtA PI) and Umbilical Artery (UmA PI) pulsatility indexes were estimated prior-to, immediately after and one hour after exercise; Results: In each group, 25 pregnant women were included. In both groups, between prior-to and immediately after exercise, TAC and CAT increased, while GSH decreased, (p < 0.001). In GDMs, CAT was lower than controls at any time point (p < 0.05), while in GDMs delta(Δ) CAT (prior-to and immediately after exercise) was lower than controls (p = 0.003). In GDMs, UtA PI centiles decreased between prior-to and either immediately or one hour after exercise, while they did not change in controls. In GDMs, pre-conceptional BMI and weight gain predicted negatively ΔTAC (prior-to to one hour after exercise); Conclusions: Moderate intensity exercise bout improves oxidation capacity in GDM and uncomplicated pregnancies, although at a lesser extent in the former. Exercise leads to decreased UtA arteries resistance in women with GDM.
Regular and Long-Term Effects of a Commercial Diet on Bone Mineral Density
Authors: Parm Ülle , Tamm Anna-Liisa , Aasmäe Triin , Liiv Kaido , Orav Aivar , Jaansoo Ester , Lohu Kaisa , Tamme Irina
Published: July 2022

(1) Background: Although the effects of diets used worldwide, such as the Mediterranean diet, have been repeatedly studied, the effects of diet plans developed by national nutritionists are unknown. Our cross-sectional study aimed to assess the effects of the commercial Fitlap diet plan, widely used among Estonians, on bone mineral density (BMD), while considering other influential factors (physical activity, body composition, and macro- and micro-nutrients). (2) Methods: A total of 68 women participated (followers of Fitlap diet—FDF, n = 34; age-matched controls, n = 34). Body composition, bone mineral density (BMD), including the whole body (WB) and areal BMD from the femoral neck (FN) and lumbar spine (LS), and blood micro-nutrient levels were measured. The menu analysis was based on dietary recalls. (3) Results: The Fitlap diet contains significantly more calcium (p < 0.001) and magnesium (p = 0.007). FDF consume more fiber (coef. 6.49; p < 0.001) and protein (coef. 20.12; p < 0.001), which influences fat-free mass (coef. 3674.8; p = 0.008) and vitamin B12 blood values (coef. 184.98; p < 0.001). The only influencing factor of WB BMD, LS, and FN aBMD was fat-free mass (coef. in all locations 0.000009; and p = 0.015; p = 0.015; p = 0.01, respectively). (4) Conclusions: Fitlap is an example of a commercial diet plan that has no negative effects on bones.
An Adaptive Mapping Method Using Spectral Envelope Approach for DNA Spectral Analysis
Authors: Arruda Milena , da Silva Andresso , de Assis Francisco
Published: July 2022

The digital signal processing approaches were investigated as a preliminary indicator for discriminating between the protein coding and non-coding regions of DNA. This is because a three-base periodicity (TBP) has already been proven to exist in protein-coding regions arising from the length of codons (three nucleic acids). This demonstrates that there is a prominent peak in the energy spectrum of a DNA coding sequence at frequency 13 rad/sample. However, because DNA sequences are symbolic sequences, these should be mapped into one or more signals such that the hidden information is highlighted. We propose, therefore, two new algorithms for computing adaptive mappings and, by using them, finding periodicities. Both such algorithms are based on the spectral envelope approach. This adaptive approach is essentially important since a single mapping for any DNA sequence may ignore its intrinsic properties. Finally, the improved performance of the new methods is verified by using them with synthetic and real DNA sequences as compared to the classical methods, especially the minimum entropy mapping (MEM) spectrum, which is also an adaptive method. We demonstrated that our method is both more accurate and more responsive than all its counterparts. This is especially important in this application since it reduces the risks of a coding sequence being missed.
Three-Way Decision Models Based on Ideal Relations in Multi-Attribute Decision-Making
Authors: Chen Xiaozhi , Zou Ligeng
Published: July 2022

In recent years, research on applications of three-way decision (e.g., TWD) has attracted the attention of many scholars. In this paper, we combine TWD with multi-attribute decision-making (MADM). First, we utilize the essential idea of TOPSIS in MADM theory to propose a pair of new ideal relation models based on TWD, namely, the three-way ideal superiority model and the three-way ideal inferiority model. Second, in order to reduce errors caused by the subjectivity of decision-makers, we develop two new methods to calculate the state sets for the two proposed ideal relation models. Third, we employ aggregate relative loss functions to calculate the thresholds of each object, divide all objects into three different territories and sort all objects. Then, we use a concrete example of building appearance selection to verify the rationality and feasibility of our proposed models. Furthermore, we apply comparative analysis, Spearman’s rank correlation analysis and experiment analysis to illustrate the consistency and superiority of our methods.
A Bregman-Split-Based Compressive Sensing Method for Dynamic Harmonic Estimation
Authors: Chi Aobing , Zeng Chengbi , Guo Yufu , Miao Hong
Published: July 2022

In order to overcome the spectral interference of the conventional Fourier transform in the International Electrotechnical Commission framework, this paper introduces a Bregman-split-based compressive sensing (BSCS) method to estimate the Taylor–Fourier coefficients in a multi-frequency dynamic phasor model. Considering the DDC component estimation, this paper transforms the phasor problem into a compressive sensing model based on the regularity and sparsity of the dynamic harmonic signal distribution. It then derives an optimized hybrid regularization algorithm with the Bregman split method to reconstruct the dynamic phasor estimation. The accuracy of the model was verified by using the cross entropy to measure the distribution differences of values. Composite tests derived from the dynamic phasor test conditions were then used to verify the potentialities of the BSCS method. Simulation results show that the algorithm can alleviate the impact of dynamic signals on phasor estimation and significantly improve the estimation accuracy, which provides a theoretical basis for P-class phasor measurement units (PMUs).
Dense-Frequency Signal-Detection Based on the Primal–Dual Splitting Method
Authors: Zheng Jiaoyu , Liao Zheng , Ma Xiaoyang , Jin Yanlin , Ma Huangqi
Published: July 2022

Aiming to solve the problem of dense-frequency signals in the power system caused by the growing proportion of new energy, this paper proposes a dense-frequency signal-detection method based on the primal–dual splitting method. After establishing the Taylor–Fourier model of the signal, the proposed method uses the sparse property of the coefficient matrix to obtain the convex optimization form of the model. Then, the optimal solution of the estimated phasor is obtained by iterating over the fixed-point equation, finally acquiring the optimal estimation result for the dense signal. When representing the Taylor–Fourier model as a convex optimization form, the introduction of measuring-error entropy makes the solution of the model more rigorous. It can be further verified through simulation experiments that the estimation accuracy of the primal–dual splitting method proposed in this paper for dense signals can meet the M-class PMU accuracy requirements.
Polarization Attack on Continuous-Variable Quantum Key Distribution with a Local Local Oscillator
Authors: Shao Yun , Pan Yan , Wang Heng , Pi Yaodi , Li Yang , Ma Li , Zhang Yichen , Huang Wei , Xu Bingjie
Published: July 2022

The estimation of phase noise of continuous-variable quantum key distribution protocol with a local local oscillator (LLO CVQKD), as a major process in quantifying the secret key rate, is closely relevant to the intensity of the phase reference. However, the transmission of the phase reference through the insecure quantum channel is prone to be exploited by the eavesdropper (Eve) to mount attacks. Here, we introduce a polarization attack scheme against the phase reference. Presently, in a practical LLO CVQKD system, only part of the phase reference pulses are measured to compensate for the polarization drift of the quantum signal pulses in a compensation cycle due to the limited polarization measurement rate, while the other part of the phase reference pulses are not measured. We show that Eve can control the phase noise by manipulating the polarization direction of the unmeasured phase reference to hide her attack on the quantum signal. Simulations show that Eve can obtain partial or total key rates information shared between Alice and Bob as the transmission distance increases. Improving the polarization measurement rate to 100% or monitoring the phase reference intensity in real-time is of great importance to protect the LLO CVQKD from polarization attack.
Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges
Authors: Das Samarendra , Rai Anil , Rai Shesh N.
Published: July 2022

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.
Carbon Stock Assessment in Gypsum-Bearing Soils: The Role of Subsurface Soil Horizons
Authors: Rodríguez-Rastrero Manuel , Ortega-Martos Almudena
Published: July 2022

With the aim of contributing to the knowledge of soil organic carbon stocks in dry areas, this work is based on a quantification of SOC stocks in gypsum-bearing soils whose vertical and spatial heterogeneity greatly limits inferring the total SOC stocks solely from soil surface information. Public databases of soil profiles were key to this quantification, through which it was estimated which amounts of organic carbon can potentially be excluded from calculations associated with soil C cycle models in the absence of information regarding deep soil horizons. These databases include two key factors in the quantification of SOC stocks, which are often excluded: the volume of coarse fragments and the thickness of all sampled soil horizons where SOC concentration was determined. The observed average value of SOC stocks in the studied subsurface horizons reaches 73% of the whole soil. Climate, relief, and land use influence the quantity and heterogeneity of SOC stocks in these soils. Information based on the mere surface of the soil is not relevant to quantify the total SOC; however, the calculation of stocks through soil pits of medium depth (30 cm) has proven to be potentially useful as a complementary approach to these stocks.
Teachers’ Work Engagement, Burnout, and Interest toward ICT Training: School Level Differences
Authors: Cacciamani Stefano , Cesareni Donatella , Fiorilli Caterina , Ligorio Maria Beatrice
Published: July 2022

Teachers’ work engagement is associated with positive outcomes regarding work-related well-being. Conversely, burnout menaces teachers’ work and attitudes toward professional development. As indicated in the literature, burnout can influence teachers’ work engagement. Considering the impact of ICT on school activities, interest toward ICT training can also affect teachers’ work engagement. The present study aims to explore the differences among different school levels concerning work engagement, burnout, and interest toward ICT training. Furthermore, we study the extent to which teachers’ burnout and interest toward ICT training predict work engagement, taking into account the school level. The participants were 358 Italian teachers of primary, middle, and high school. We proposed to fill out the Utrecht Work Engagement Scale, the Copenhagen Burnout Inventory, and three ad hoc items assessing interest toward ICT training among 358 Italian teachers. To compare the school levels, an ANOVA and a Multiple regression analysis for each group corresponding to a different school level has been used. Results showed that: (a) primary school teachers have a higher level of work engagement and interest in ICT training compared to their colleagues at high schools; (b) burnout predicts work engagement in all school levels; (c) interest toward ICT training influences work engagement only in primary and high school. Cultural and contextual dimensions are considered when interpreting the results. Implications for teachers’ enhancing their commitment at work are discussed, as well as limitations of this study and possible further development.
Supporting Inclusive Online Higher Education in Developing Countries: Lessons Learnt from Sri Lanka’s University Closure
Authors: Yang Danlin , Tang Yuen Man , Hayashi Ryotaro , Ra Sungsup , Lim Cher Ping
Published: July 2022

Online higher education teaching and learning has become a new normal in many countries due to the COVID-19 pandemic. Yet, the support for online learning seems inadequate to address students’ diverse online learning needs and may impede the inclusiveness in higher education. Therefore, based on a questionnaire administered to higher education students in Sri Lanka, this paper examines the support or lack of support students have experienced during the university closure that may enable or hinder inclusive online learning. It draws on Self-Determination Theory (SDT) as a theoretical lens to analyse and make sense of these enablers for and barriers to inclusive online higher education. The key findings suggest that students first need autonomy support to access stable and affordable internet and devices, and quality online learning resources. They also need competence support for monitoring and managing their own learning through feedback and scaffolding as they engage in their learning online. Finally, they need relatedness support for reducing their anxiety and having a sense of connectedness by interacting and communicating with teachers and students.
Design of Multimodal Sensor Module for Outdoor Robot Surveillance System
Authors: Uhm Taeyoung , Park Jeongwoo , Lee Jungwoo , Bae Gideok , Ki Geonhui , Choi Youngho
Published: July 2022

Recent studies on surveillance systems have employed various sensors to recognize and understand outdoor environments. In a complex outdoor environment, useful sensor data obtained under all weather conditions, during the night and day, can be utilized for application to robots in a real environment. Autonomous surveillance systems require a sensor system that can acquire various types of sensor data and can be easily mounted on fixed and mobile agents. In this study, we propose a method for modularizing multiple vision and sound sensors into one system, extracting data synchronized with 3D LiDAR sensors, and matching them to obtain data from various outdoor environments. The proposed multimodal sensor module can acquire six types of images: RGB, thermal, night vision, depth, fast RGB, and IR. Using the proposed module with a 3D LiDAR sensor, multimodal sensor data were obtained from fixed and mobile agents and tested for more than four years. To further prove its usefulness, this module was used as a monitoring system for six months to monitor anomalies occurring at a given site. In the future, we expect that the data obtained from multimodal sensor systems can be used for various applications in outdoor environments.
Digital Twin Based Network Latency Prediction in Vehicular Networks
Authors: Fu Yanfang , Guo Dengdeng , Li Qiang , Liu Liangxin , Qu Shaochun , Xiang Wei
Published: July 2022

Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in which digital twin technology is employed to obtain a large quantity of actual time delay data for vehicular networks and to verify autocorrelation. Subsequently, to meet the prediction conditions of the ARMA time series model, two neural networks, i.e., Radial basis function (RBF) and Elman networks, were employed to construct a time delay prediction model. The experimental results show that the average relative error of the RBF is 7.6%, whereas that of the Elman-NN is 14.2%. This indicates that the RBF has a better prediction performance, and a better real-time performance than the Elman-NN.
Outage Performance Estimation of a MISO FSO System with OOK Signaling and Alamouti Type Space-Time Coding
Authors: Khalid Hira , Nistazakis Hector , Muhammad Sajid , Raza Allah
Published: July 2022

In this work, multi input and single output optical wireless communication (OWC) links are analyzed, assuming the channel experiences fading due to atmospheric turbulence. OOK signaling and Alamouti-type space-time coding with intensity modulation and direct detection is assumed for weak to strong turbulence conditions modeled with the gamma–gamma distribution and closed form analytical expressions for their outage probability are derived for the first time, to the best of our knowledge. Finally, the corresponding numerical results are obtained, through the obtained mathematical expressions. Performance of a multi input and single output channel is analyzed for its outage probability as a function of power to noise ratio and normalized threshold. Three different cases for strong, medium, and weak turbulences are investigated.
Big-Data Platform for Performance Monitoring of Telecom-Service-Provider Networks
Authors: Simakovic Milan , Cica Zoran , Drajic Dejan
Published: July 2022

Large telecom-service-provider networks are typically based on complex communications infrastructures comprising millions of network devices. The performance monitoring of such networks is a very demanding and challenging task. A large amount of data is collected and processed during performance monitoring to obtain information that gives insights into the current network performance. Using the obtained information, providers can efficiently detect, locate, and troubleshoot weak spots in the network and improve the overall network performance. Furthermore, the extracted information can be used for planning future network expansions and to support the determination of business-strategy decisions. However, traditional methods for processing and storing data are not applicable because of the enormous amount of collected data. Thus, big-data technologies must be used. In this paper, a big-data platform for the performance monitoring of telecom-service-provider networks is proposed. The proposed platform is capable of collecting, storing, and processing data from millions of devices. Typical challenges and problems in the development and deployment process of the platform, as well as the solutions to overcome them, are presented. The proposed platform is adjusted to HFC (Hybrid Fiber-Coaxial) network and currently operates in the real HFC network, comprising millions of users and devices.
Research on Medical Text Classification Based on Improved Capsule Network
Authors: Zhang Qinghui , Yuan Qihao , Lv Pengtao , Zhang Mengya , Lv Lei
Published: July 2022

In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients’ symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models.
Laser Inter-Satellite Link Visibility and Topology Optimization for Mega Constellation
Authors: Zhu Qinyu , Tao Haicheng , Cao Yanhua , Li Xinhong
Published: July 2022

In this paper, we begin by describing the Starlink constellation’s configuration plan, treating each satellite as a network node, naming and numbering the various nodes, and then classifying the laser interstellar links (LISLs) according to their orbital alignment and whether they are permanently visible. Whereupon, the method for calculating the respective theoretical interstellar distances required for establishing LISLs under two distinct orbital scenarios, co-orbital and hetero-orbital are analyzed, and the optimal phasing factors are proposed by solving an optimization algorithm for the shortest distance. The OneWeb and Starlink constellations, as well as the constellation states of various phasing factors are simulated, respectively. The Starlink constellation with F = 11 obtains the best coverage between 60° north and south latitudes predicated upon the analysis of the constellation N Asset Coverage. Following that, the first phase of the Starlink constellation deployment was modeled in order to analyze and count the number of permanent LISLs in orbit, adjacent to, and nearby. Subsequently, the characteristics of azimuthal, elevation, and range (AER) were subsequently analyzed to ascertain their variation law and to compile the number of permanent and temporary LISLs that could be established at various inter-distance ranges. Finally, predicated on the simulation results, the optimal LISLs connection strategy for the Starlink constellation is evaluated and a static topology for the constellation is constructed.
Approximate Nearest Neighbor Search Using Enhanced Accumulative Quantization
Authors: Ai Liefu , Cheng Hongjun , Wang Xiaoxiao , Chen Chunsheng , Liu Deyang , Zheng Xin , Wang Yuanzhi
Published: July 2022

Approximate nearest neighbor (ANN) search is fundamental for fast content-based image retrieval. While vector quantization is one key to performing an effective ANN search, in order to further improve ANN search accuracy, we propose an enhanced accumulative quantization (E-AQ). Based on our former work, we introduced the idea of the quarter point into accumulative quantization (AQ). Instead of finding the nearest centroid, the quarter vector was used to quantize the vector and was computed for each vector according to its nearest centroid and second nearest centroid. Then, the error produced through codebook training and vector quantization was reduced without increasing the number of centroids in each codebook. To evaluate the accuracy to which vectors were approximated by their quantization outputs, we realized an E-AQ-based exhaustive method for ANN search. Experimental results show that our approach gained up to 0.996 and 0.776 Recall@100 with eight size 256 codebooks on SIFT and GIST datasets, respectively, which is at least 1.6% and 4.9% higher than six other state-of-the-art methods. Moreover, based on the experimental results, E-AQ needs fewer codebooks while still providing the same ANN search accuracy.
Efficient Detailed Routing for FPGA Back-End Flow Using Reinforcement Learning
Authors: Baig Imran , Farooq Umer
Published: July 2022

Over the past few years, the computation capability of field-programmable gate arrays (FPGAs) has increased tremendously. This has led to the increase in the complexity of the designs implemented on FPGAs and to the time taken by the FPGA back-end flow. The FPGA back-end flow comprises of many steps, and routing is one of the most critical steps among them. Routing normally constitutes more than 50% of the total time taken by the back-end flow and an optimization at this step can lead to overall optimization of the back-end flow. In this work, we propose enhancements to the routing step by incorporating a reinforcement learning (RL)-based framework. In the proposed RL-based framework, we use the ϵ-greedy approach and customized reward functions to speed up the routing step while maintaining similar or better quality of results (QoR) as compared to the conventional negotiation-based congestion-driven routing solution. For experimentation, we use two sets of widely deployed, large heterogeneous benchmarks. Our results show that, for the RL-based framework, the ϵ-greedy greedy approach combined with a modified reward function gives better results as compared to purely greedy or exploratory approaches. Moreover, the incorporation of the proposed reward function in the RL-based framework and its comparison with a conventional routing algorithm shows that the proposed enhancement requires less routing time while giving similar or better QoR. On average, a speedup of 35% is recorded for the proposed routing enhancement as compared to negotiation-based congestion-driven routing solutions. Finally, the speedup of the routing step leads to an overall reduction in the execution time of the back-end flow of 25%.
A Coalition Formation Game-Based Multi-User Grouping Approach in the Jamming Environment
Authors: Ding Huihui , Niu Yingtao , Han Chen , Xiang Peng
Published: July 2022

Due to the broadcast nature of wireless communication, it is vulnerable to malicious jamming attacks. Meanwhile, multiple users need to share the limited spectrum resources to enhance the spectrum utilization efficiency because of the scarcity of wireless spectrum resources. Therefore, wireless communication systems need to deal with the malicious jamming attacks and mutual interference among different users. In this paper, a multi-user anti-jamming grouping approach based on a coalition formation game is studied. The proposed approach can maximize users’ transmission rate while avoiding channel jamming through optimization of the coalition formation strategy under the influence of malicious jamming. With the help of the exact potential energy game, we demonstrate that the proposed game model can obtain stable Nash equilibrium solutions. Lastly, the effectiveness of the proposed algorithm is demonstrated through numerical.
Quantitative Research on Generalized Linear Modeling of SEU and Test Programs Based on Small Sample Data
Authors: Chu Fei , Chen Hongzhuan , Yu Chunqing , You Lihua , Wang Liang , Liu Yun
Published: July 2022

Complex integrated circuits (ICs) have complex functions and various working modes, which have many factors affecting the performance of a single event effect. The single event effect performance of complex ICs is highly program-dependent and the single event sensitivity of a typical operating mode is generally used to represent the single event performance of the circuits. Traditional evaluation methods fail to consider the cross effects of multiple factors and the comprehensive effects of each factor on the single event soft error cross section. In order to solve this problem, a new quantitative study method of single event error cross section based on a generalized linear model for different test programs is proposed. The laser test data is divided into two groups: a training set and a validation set. The former is used for model construction and parameter estimation based on five methods, such as the generalized linear model and Ensemble, while the latter is used for quantitative evaluation and validation of a single event soft error cross section of the model. In terms of percentage error, the minimum mean estimation error on the validation set is 13.93%. Therefore, it has a high accuracy to evaluate the single event soft error cross section of circuits under different testing programs based on the generalized linear model, which provides a new idea for the evaluation of a single event effect on complex ICs.
Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning
Authors: Gao Qi , Lyu Na , Miao Jingcheng , Pan Wu
Published: July 2022

Wireless network virtualization is widely used to solve the ossification problem of networks, such as 5G and the Internet of Things. The most crucial method of wireless network virtualization is virtual network embedding, which allows virtual networks to share the substrate network resources. However, in wireless networks, link interference is an inherent problem while mapping virtual networks because of the characteristics of wireless channels. To distribute resources efficiently and address the problem of interference, a dynamic embedding algorithm with deep reinforcement learning is proposed. During the training stage, we take resource use and interference from substrate networks as observations to train the agent, and then the agent generates a resource allocation strategy. Aiming at realizing load balance, we reshape the reward function considering the execution ratio and residual ratio of substrate network resources as well as the cost consumed by current virtual network request. Numerical tests show that our embedding approach increases the acceptance ratio and maintains better robustness. Moreover, the results also illustrate that our algorithm maintains a high acceptance ratio while producing less interference and lower cost.
Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
Authors: Shu Chang , Luo Yinhui , Liu Fang
Published: July 2022

The proliferation of IoT applications has pushed the horizon of edge computing, which provides processing ability at the edge of networks. Task offloading is one of the most important issues in edge computing and has attracted continuous research attention in recent years. With task offloading, end devices can offload the entire task or only subtasks to the edge servers to meet the delay and energy requirements. Most existing offloading schemes are limited by the increasing complexity of task topologies, as considerable time is wasted for local/edge subtasks to wait for their precedent subtasks being executed at the edge/local device. This problem becomes even worse when the dependencies among subtasks become complex and the number of end-users increases. To address this problem, our key methodology is to exploit subtask duplications to reduce the inter-subtask delay and shorten the task completion time. Based on this, we propose a Duplication-based and Energy-aware Task Offloading scheme (DETO), which duplicates critical subtasks that have a large impact on the completion time and thus enhances the parallelism between local and edge computing. In addition, among numerous choices of subtask duplications, DETO evaluates the gain/cost ratio for each possible duplication and chooses the most efficient ones. As a result, the extra resource for duplications is greatly reduced. We also design a distributed DETO algorithm to support multi-user, multi-server edge computing. Extensive evaluation results show that DETO can effectively reduce the task completion time (by 12.22%) and improve the resource utilization (by 15.17%), in particular for multi-user edge computing networks.
Resilience Framework, Methods, and Metrics for the Prioritization of Critical Electrical Grid Customers
Authors: Rosales-Asensio Enrique , Elejalde José-Luis , Pulido-Alonso Antonio , Colmenar-Santos Antonio
Published: July 2022

One of the main objectives of new operating regimes, such as transactional power systems, is to make the power grid more resilient to catastrophes and disturbances, while at the same time making it possible to supply electricity to the largest number of customers. Although this is true, it is well known among power system operators that not all customers are the same. The consequence of this is that any discussion around the impact of transactional power systems on power system resilience should consider the needs of its critical users (such as control centers, fire stations, and hospitals) over other users. In assessing power system resilience, a metric is needed that gives “bonus points” to those systems that, under all circumstances, can continue to provide electricity to their critical users. In order to serve as a parameter in the assessment of power grid resilience, the research presented here discusses the proportion of critical loads existing in critical infrastructures. Once the critical loads are characterized, the next step is the inclusion of these loads in resilience metrics. This paper proposes resiliency metrics in which certain customers (those categorized as critical) are assigned a higher weight than others. One thing to keep in mind is the fact that there is no one-size-fits-all approach for all power systems, and that the assignment of such weights to customers can vary significantly from one operator to another based on their unique systems and the current and expected states of their critical customers.
Research on Diesel Engine Fault Diagnosis Method Based on Stacked Sparse Autoencoder and Support Vector Machine
Authors: Bai Huajun , Zhan Xianbiao , Yan Hao , Wen Liang , Yan Yunbin , Jia Xisheng
Published: July 2022

Due to the relative insufficiencies of conventional time-domain waveform and spectrum analysis in fault diagnosis research, a diesel engine fault diagnosis method based on the Stacked Sparse Autoencoder and the Support Vector Machine is proposed in this study. The method consists of two main steps. The first step is to utilize the Stacked Sparse Autoencoder (SSAE) to reduce the feature dimension of the multi-sensor vibration information; when compared with other dimension reduction methods, this approach can better capture nonlinear features, so as to better cope with dimension reduction. The second step consists of diagnosing faults, implementing the grid search, and K-fold cross-validation to optimize the hyperparameters of the SVM method, which effectively improves the fault classification effect. By conducting a preset failure experiment for the diesel engine, the proposed method achieves an accuracy rate of more than 98%, better engineering application, and promising outcomes.
Adaptive Second-Order Sliding Mode Control of Buck Converters with Multi-Disturbances
Authors: Wang Yanmin , Zhang Weiqi , Yang Yalong , Xue Chen , Yuan Shibo , Zhang Hanqing
Published: July 2022

In this paper, a novel adaptive second-order sliding mode (2-SM) control approach, based on online zero-crossing detection, was proposed to solve the problems of the chattering and fixed control gain for buck converters with multi-disturbances. In modeling, the possible parameter perturbations and external disturbances of the converter system were contained. Instead of the traditional first-order sliding mode (1-SM), the twisting algorithm with 2-SM was adopted for the controller design, which could overcome the chattering problem and realize control continuity. Meanwhile, a novel adaptive mechanism was introduced to replace the conventional fixed control gain by time-varying control gain, the idea of which is to calculate the number of the zero-crossing points of the sliding surface online. As a result, the control magnitude of the improved controller could be reduced to a minimal admissible level, and the steady error of the output voltage could converge to the expected value. Furthermore, the robust stability of the converter system with multi-disturbances wads investigated. Comparative simulations and experiments validated the advantages of this paper as offering better robustness and control performance.
Experimental Study on the Performance of a Novel Unidirectional Explosive Element and an Explosive Logic Network
Authors: Wang Fei , Ma Honghao , Shen Zhaowu
Published: July 2022

Considering the unsafety of the present blasting network used in the blasting mining of coalfield fires, a unidirectional explosive element (named explosive diode) is proposed according to explosive logic element principles. Through theoretical and experimental analysis, the internal structure and mechanism of the unidirectional transmission of the detonation signal were studied. For an explosive diode, the length of the quenching channel was defined to be the key parameter. The explosive diode was implemented in the traditional blasting network, obtaining an explosive logic network. To evaluate the safety and reliability of the explosive diode and explosive logic network, detonation propagation and explosion-proof experiments were conducted in the lab. The optimum length of the quenching channel to obtain unidirectional detonation transmission was established. The results showed that the explosive diode could reliably control the propagation direction of the detonation signal when the length of the quenching channel was between 15 mm and 25 mm. The explosive logic network achieved a reliable detonation propagation and was explosion-proof. In comparison with traditional networks, the explosive logic network showed increased safety and reliability as the number of subnets increased. This is a significant improvement to mining safety and demonstrates great promise for engineering applications.