bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis
Authors: Aggarwal Shalini , Peng Weng Kung , Srivastava Sanjeeva
Published: November 2021

Plasmodium vivax malaria is one of the most lethal infectious diseases, with 7 million infections annually. One of the roadblocks to global malaria elimination is the lack of highly sensitive, specific, and accurate diagnostic tools. The absence of diagnostic tools in particular has led to poor differentiation among parasite species, poor prognosis, and delayed treatment. The improvement necessary in diagnostic tools can be broadly grouped into two categories: technologies-driven and omics-driven progress over time. This article discusses the recent advancement in omics-based malaria for identifying the next generation biomarkers for a highly sensitive and specific assay with a rapid and antecedent prognosis of the disease. We summarize the state-of-the-art diagnostic technologies, the key challenges, opportunities, and emerging prospects of multi-omics-based sensors.
Application of the International System for Reporting Serous Fluid Cytopathology with Cytohistological Correlation and Risk of Malignancy Assessment
Authors: Pergaris Alexandros , Stefanou Dimitra , Keramari Panagiota , Sousouris Stylianos , Kavantzas Nikolaos , Gogas Helen , Mikou Panagiota
Published: November 2021

The International System for Reporting Serous Fluid Cytopathology (TIS) classifies serous effusions into five categories: non-diagnostic (ND), negative for malignancy (NFM), atypia of unknown significance (AUS), suspicious for malignancy (SFM) and malignant (MAL). The main objectives of this classification comprise the establishment of a universal code of communication between cytopathologists and clinicians and histopathologists, as well as between different laboratories worldwide, paving the way for the setting of clinical management guidelines based on the risk of malignancy assessment for each diagnostic category. We retrieved the total number of pleural and peritoneal effusion cases of our department for the three-year time period between 2018 and 2020, yielding a total of 528 and 500 cases, respectively. We then proceeded to reclassify each specimen according to TIS guidelines and calculate the risk of malignancy (ROM) for each category by searching each patients’ histology records, medical history and clinical follow-up. For pleural effusions, 3 (0.57%) cases were classified as ND, 430 (81.44%) cases as NFM, 15 (2.84%) as AUS, 15 (2.84%) as SFM and 65 (12.31%) as MAL. ROM amounted to 0%, 5.3%, 33.33%, 93.33% and 100% for each category, respectively. As far as peritoneal effusions are concerned, 6 (1.2%) were categorized as ND with ROM estimated at 16.66%, 347 (69.4%) as NFM (ROM = 9%), 13 (2.6%) as AUS (ROM = 38.46%), 12 (2.4%) as SFM (ROM = 83.33%) and 122 (24.4%) as MAL (ROM = 100%). Our results underline the utility of the current classification, both as a means of communication between doctors of different specialties and as general guidelines for the further clinical management of patients.
History Marginalization Improves Forecasting in Variational Recurrent Neural Networks
Authors: Qiu Chen , Mandt Stephan , Rudolph Maja
Published: November 2021

Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence that their associated inference models are oftentimes too limited and cause the generative model to predict mode-averaged dynamics. Mode-averaging is problematic since many real-world sequences are highly multi-modal, and their averaged dynamics are unphysical (e.g., predicted taxi trajectories might run through buildings on the street map). To better capture multi-modality, we develop variational dynamic mixtures (VDM): a new variational family to infer sequential latent variables. The VDM approximate posterior at each time step is a mixture density network, whose parameters come from propagating multiple samples through a recurrent architecture. This results in an expressive multi-modal posterior approximation. In an empirical study, we show that VDM outperforms competing approaches on highly multi-modal datasets from different domains.
Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
Authors: Papana Angeliki
Published: November 2021

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes
Authors: Galhotra Sainyam , Shanmugam Karthikeyan , Sattigeri Prasanna , Varshney Kush R.
Published: November 2021

The deployment of machine learning (ML) systems in applications with societal impact has motivated the study of fairness for marginalized groups. Often, the protected attribute is absent from the training dataset for legal reasons. However, datasets still contain proxy attributes that capture protected information and can inject unfairness in the ML model. Some deployed systems allow auditors, decision makers, or affected users to report issues or seek recourse by flagging individual samples. In this work, we examine such systems and consider a feedback-based framework where the protected attribute is unavailable and the flagged samples are indirect knowledge. The reported samples are used as guidance to identify the proxy attributes that are causally dependent on the (unknown) protected attribute. We work under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach that performs conditional independence tests on observed data to identify such proxy attributes. We theoretically prove the optimality of our algorithm, bound its complexity, and complement it with an empirical evaluation demonstrating its efficacy on various real-world and synthetic datasets.
Continuous Time Random Walk with Correlated Waiting Times. The Crucial Role of Inter-Trade Times in Volatility Clustering
Authors: Klamut Jarosław , Gubiec Tomasz
Published: November 2021

In many physical, social, and economic phenomena, we observe changes in a studied quantity only in discrete, irregularly distributed points in time. The stochastic process usually applied to describe this kind of variable is the continuous-time random walk (CTRW). Despite the popularity of these types of stochastic processes and strong empirical motivation, models with a long-term memory within the sequence of time intervals between observations are rare in the physics literature. Here, we fill this gap by introducing a new family of CTRWs. The memory is introduced to the model by assuming that many consecutive time intervals can be the same. Surprisingly, in this process we can observe a slowly decaying nonlinear autocorrelation function without a fat-tailed distribution of time intervals. Our model, applied to high-frequency stock market data, can successfully describe the slope of decay of the nonlinear autocorrelation function of stock market returns. We achieve this result without imposing any dependence between consecutive price changes. This proves the crucial role of inter-event times in the volatility clustering phenomenon observed in all stock markets.
Cryptanalysis of a New Chaotic Image Encryption Technique Based on Multiple Discrete Dynamical Maps
Authors: Fan Haiju , Zhang Chenjiu , Lu Heng , Li Ming , Liu Yanfang
Published: November 2021

Recently, a new chaotic image encryption technique was proposed based on multiple discrete dynamic maps. The authors claim that the scheme can provide excellent privacy for traditional digital images. However, in order to minimize the computational cost, the encryption scheme adopts one-round encryption and a traditional permutation–diffusion structure. Through cryptanalysis, there is no strong correlation between the key and the plain image, which leads to the collapse of cryptosystem. Based on this, two methods of chosen-plaintext attacks are proposed in this paper. The two methods require 3 pairs and 258 pairs of plain and cipher images, respectively, to break the original encryption system. The simulation results show the effectiveness of the two schemes.
Quantum Contextual Advantage Depending on Nonzero Prior Probabilities in State Discrimination of Mixed Qubit States
Authors: Shin Jaehee , Ha Donghoon , Kwon Younghun
Published: November 2021

Recently, Schmid and Spekkens studied the quantum contextuality in terms of state discrimination. By dealing with the minimum error discrimination of two quantum states with identical prior probabilities, they reported that quantum contextual advantage exists. Meanwhile, if one notes a striking observation that the selection of prior probability can affect the quantum properties of the system, it is necessary to verify whether the quantum contextual advantage depends on the prior probabilities of the given states. In this paper, we consider the minimum error discrimination of two states with arbitrary prior probabilities, in which both states are pure or mixed. We show that the quantum contextual advantage in state discrimination may depend on the prior probabilities of the given states. In particular, even though the quantum contextual advantage always exists in the state discrimination of two nonorthogonal pure states with nonzero prior probabilities, the quantum contextual advantage depends on prior probabilities in the state discrimination of two mixed states.
Student Collaboration in Action: A Case Study Exploring the Role of Youth Work Pedagogy Transforming Interprofessional Education in Higher Education
Authors: Howell Tim
Published: November 2021

The College of Health, Psychology, and Social Care at the University of Derby has transformed its Interprofessional Education (IPE) offer from a top-down standalone event into a five-year strategy designed and delivered in genuine collaboration with students. Across the higher education sector, IPE has been a struggle, tokenistic at best, with limited buy-in from students. When academic-led it prevents deep learning; however, by utilising an informal education approach students bring their life, programme, and practice learning together to genuinely break down barriers between professional disciplines. This paper will use an autoethnographic case study to explore the challenges and opportunities of genuine collaboration based on youth work principles in the creation of a ‘value-added curriculum’, not aligned to modules or assessments. It found that buy-in from academics and students comes when students are empowered to take the lead. This is based on youth work pedagogical principles of group work, relationships with shrinking professional distance, critical pedagogy, genuine agency, and an emotional connection made between the professionals and service users. It suggests the potential is considerable as youth workers bring their pedagogical practice to a broader range of spaces within and beyond higher education.
The Effect of University Missions on Entrepreneurial Initiative across Multiple Entrepreneurial Ecosystems: Evidence from Europe
Authors: Pita Mariana , Costa Joana , Moreira António Carrizo
Published: November 2021

Entrepreneurial universities are a significant element of entrepreneurial ecosystems and aspire to foster entrepreneurial initiative through their “third mission”. However, while entrepreneurial ecosystems are scrutinized using a contextual approach to detect differences and similarities and how they affect entrepreneurship, little is known about how entrepreneurial universities impact entrepreneurial initiatives in general, considering multiple environments. Drawing on entrepreneurial university and entrepreneurial ecosystem theories, a conceptual framework is proposed that aims to explain the effect of the entrepreneurial university on an entrepreneurial initiative through its three “missions”, using an entrepreneurial ecosystem taxonomy. Based on individual data from the Global Entrepreneurship Monitor, this entrepreneurial initiative analyzed 18 European countries in 2017. The results do not generally support the importance of entrepreneurial universities to entrepreneurial initiative. The relevance of entrepreneurial universities increases in more fragile entrepreneurial ecosystems since individuals need support over multiple dimensions. Conversely, the entrepreneurial universities that are embedded in stronger entrepreneurial ecosystems lose relevance and negatively affect the entrepreneurial initiative. Therefore, the value of entrepreneurial universities is reduced when individuals receive greater support from other dimensions. The variations across both groups suggest that the concept of entrepreneurial universities is not a contemporaneous phenomenon; however, their effect is progressively revealed by the maturity of each university’s mission. This perspective substantially changes the understanding of entrepreneurial universities as a thwartwise strategy, suggesting that the universities’ impact is expanded as their missions gradually evolve. Overall, the study contributes to an understanding of the implications for universities that blindly follow entrepreneurship, neglecting the exogenous environment, namely, the entrepreneurial ecosystem and individual drive.
The Role of Instagram, Facebook, and YouTube Frequency of Use in University Students’ Digital Skills Components
Authors: Perifanou Maria , Tzafilkou Katerina , Economides Anastasios A.
Published: November 2021

The literature provides rich evidence on students’ levels of digital skills as well as on their frequency of Internet and social media use. Several studies have examined the relationships between social media use and academic achievement. However, there are not any studies investigating the relationships between social media use and digital skills of higher education students. To fill this research gap, this study examines the links between the frequency of social media use and students’ digital skills. The survey was conducted with 155 university students in Greece. The instrument on six digital skills components was evaluated in terms of reliability and consistency. The research findings reveal a strong positive association between the use of YouTube and students’ digital skills on content evaluation and protection. Age and educational level differences were noted in several digital skills components, whereas age and gender were related to the frequency of Instagram use. The main conclusion is that the generic use of Facebook and Instagram does not affect students’ digital skills. However, combined literature evidence implies that when social media are used for educational purposes, they might influence students’ perceived levels of digital skills. Implications and limitations are discussed in the study.
Provision of a Free and Appropriate Public Education in an Adult Jail during COVID-19: The Case of Charles H. et al. v. District of Columbia et al.
Authors: Gagnon Joseph Calvin , Benedick Amanda Ross
Published: November 2021

COVID-19 has engendered serious challenges with the provision of special education services for youth and young adults incarcerated in U.S. adult correctional facilities. This article describes the recent lawsuit, Charles H. et al. v. District of Columbia et al., which focused on the lack of a free and appropriate public education (FAPE) at the Inspiring Youth Program (IYP) school in the Washington DC jail during the pandemic. Following a brief review of relevant components of the Individuals with Disabilities Education Act (2006) regulations, we describe the three areas in which there were violations to the provision of IDEA and FAPE, as well as the harm incurred. Specifically, we discuss the lack of: (a) instruction and monitoring of youth academic progress; (b) related services (i.e., behavioral interventions and supports, counseling); and (c) DC public schools Office of the State Superintendent of Education (OSSE) supervision and oversight. Finally, we provide a discussion of the need for compensatory education to remediate harm.
The Use of Computed Tomography as a Teaching Resource for the Teaching of Structural Concrete in the Degree of Civil Engineering
Authors: González Dorys C. , Mínguez Jesús , Vicente Miguel A.
Published: November 2021

This paper shows a teaching experience related to the use of computed tomography in the teaching of concrete for undergraduate students of the civil engineering degree. This experience reveals that computed tomography is a powerful tool to facilitate the understanding of all those aspects related to the microstructure of concrete, thus facilitating comprehension of the correlation between the microstructure and its macroscopic response. In addition, students showed a greater motivation and interest in the subject, which promotes better academic learning. A pilot test was carried out to evaluate the viability of these practices and to analyze the teaching impact of this activity. The results show that students were very interested in the use of new technologies in teaching and, more particularly, in the use of computed tomography. The students satisfactorily received the project. A greater motivation of the students in the subject was also observed, which resulted in better grades when compared with those of previous courses. The results reveal that the average grade of the students rose by around 8%, and a higher percentage of students achieved higher scores when compared to the previous five years.
Compact High-Voltage AC Generator with Pulse Transformer for High-Frequency Irreversible Electroporation (H-FIRE)
Authors: Folprecht Martin , Cervinka Dalibor , Prochazka Petr
Published: November 2021

This paper is focused on a design of a high-voltage (HV) generator, which is proposed for a high-frequency irreversible electroporation (H-FIRE). The generator produces bursts of bipolar symmetrical pulses. Most HV sources used for cell electroporation are based on a controlled discharge of a capacitor into a resistive load. This solution is very simple, but it is associated with a certain risk of an uncontrolled discharge of the capacitor. We present a different type of the generator, where a DC-AC inverter with pulse transformer is used and where the mentioned risk is eliminated. Our generator is able to deliver bursts with variable length from 50 to 150 μs and a gap between bursts can be set from 0.5 to 1.5 s. Pulse frequency can be varied from 65 to 470 kHz and the output voltage is controlled in two ranges from 0 to 1.3 kV or from 0 to 2.5 kV. Results are presented with resistive load and with tissue impedance load.
A Fast Online Classification Method of Solid Wood Floors Based on Stochastic Sampling and Machine Learning
Authors: Zhu Tingting , Ding Kun , Li Zhenye , Zhan Xianxu , Du Rong , Ni Chao
Published: November 2021

Solid wood floors are widely used as an interior decoration material, and the color of solid wood surfaces plays a decisive role in the final decoration effect. Therefore, the color classification of solid wood floors is the final and most important step before laying. However, research on floor classification usually focuses on recognizing complex and diverse features but ignores execution speed, which causes common methods to not meet the requirements of online classification in practical production. In this paper, a new online classification method of solid wood floors was proposed by combining probability theory and machine learning. Firstly, a probability-based feature extraction method (stochastic sampling feature extractor) was developed to obtain rapid key features regardless of the disturbance of wood grain. The stochastic features were determined by a genetic algorithm. Then, an extreme learning machine—as a fast classification neural network—was selected and trained with the selected stochastic features to classify solid wood floors. Several experiments were carried out to evaluate the performance of the proposed method, and the results showed that the proposed method achieved a classification accuracy of 97.78% and less than 1 ms for each solid wood floor. The proposed method has advantages including a high execution speed, great accuracy, and flexible adaptability. Overall, it is suitable for online industry production.
A Quasi-Z-Source-Based Inductive Power Transfer System for Constant Current/Constant Voltage Charging Applications
Authors: Castiglia Vincenzo , Campagna Nicola , Miceli Rosario , Viola Fabio , Blaabjerg Frede
Published: November 2021

This article proposes a quasi-Z-source (qZS)-based Inductive Power Transfer (IPT) system for Electric Vehicles (EVs) charging applications. The IPT systems use the magnetic field to transfer power between two coils wirelessly, achieving improved reliability, safety and less environmental impact. Compared to the conventional IPT system, the proposed qZS-IPT system simultaneously achieves DC/DC regulation and DC/AC conversion through a single-stage conversion, thus lowering the cost and complexity of the system. Moreover, the reliability of the system is improved thanks to the qZS network shoot-though immunity and the reduced number of switches. To ensure the battery efficient charging and long service life, the constant current/constant voltage (CC/CV) method is considered. With the proposed innovative modulation scheme, the qZS can easily change between buck and boost modes, respectively, lowering or increasing the secondary side current. A theoretical analysis is presented for system design. Simulation results based on a 25 kW (200 V/135 A) low duty EV charger are presented to verify the effectiveness of the proposed scheme. Experimental tests are performed on a 150 W scale-down prototype to validate the analysis and demonstrate the effectiveness of the proposed qZS-IPT system for CC/CV chargers.
Artificial Intelligence Models and Techniques Applied to COVID-19: A Review
Authors: Muñoz Lilia , Villarreal Vladimir , Nielsen Mel , Caballero Yen , Sittón-Candanedo Inés , Corchado Juan M.
Published: November 2021

The rapid spread of SARS-CoV-2 and the consequent global COVID-19 pandemic has prompted the public administrations of different countries to establish health procedures and protocols based on information generated through predictive techniques and models, which, in turn, are based on technology such as artificial intelligence (AI) and machine learning (ML). This article presents some AI tools and computational models used to collaborate in the control and detection of COVID-19 cases. In addition, the main features of the Epidempredict project regarding COVID-19 in Panama are presented. This initiative consists of the planning and design of a digital platform, with cloud-based technology, to manage the ingestion, analysis, visualization and exportation of data regarding the evolution of COVID-19 in Panama. The methodology for the design of predictive algorithms is based on a hybrid model that combines the dynamics associated with population data of an SIR model of differential equations and extrapolation with recurrent neural networks. The technological solution developed suggests that adjustments can be made to the rules implemented in the expert processes that are considered. Furthermore, the resulting information is displayed and explored through user-friendly dashboards, contributing to more meaningful decision-making processes.
Tracking and Synchronization with Inversion-Based ILC for a Multi-Actuator-Driven Wafer Inspection Cartridge Transport Robot System
Authors: Oh Dong Jun , Baek Seung Guk , Nam Kyung-Tae , Koo Ja Choon
Published: November 2021

This paper proposes a simple tracking and synchronization control of a dual-drive system using inversion-based iterative learning control (IILC), which reformulates the model at each iteration based on input/output data. By the power of the IILC, this work simplifies the dual-actuator-driven dynamic system control problem that is normally addressed with a MIMO method. This work also shows the potential of the IILC for nonlinear system applications by reformulating the model at each iteration based on the input/output data. An analytical representation of the iteration-varying IILC followed by simulations is provided. A set of physical system testings with a dual-motor gantry and a semiconductor wafer inspection robotic system are carried out to verify the control method.
Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform
Authors: Simakovic Milan , Cica Zoran
Published: November 2021

Modern HFC (Hybrid Fiber–Coaxial) networks comprise millions of users. It is of great importance for HFC network operators to provide high network access availability to their users. This requirement is becoming even more important given the increasing trend of remote working. Therefore, network failures need to be detected and localized as soon as possible. This is not an easy task given that there is a large number of devices in typical HFC networks. However, the large number of devices also enable HFC network operators to collect enormous amounts of data that can be used for various purposes. Thus, there is also a trend of introducing big data technologies in HFC networks to be able to efficiently cope with the huge amounts of data. In this paper, we propose a novel mechanism for efficient failure detection and localization in HFC networks using a big data platform. The proposed mechanism utilizes the already present big data platform and collected data to add one more feature to big data platform—efficient failure detection and localization. The proposed mechanism has been successfully deployed in a real HFC network that serves more than one million users.
Recognition of Rice Sheath Blight Based on a Backpropagation Neural Network
Authors: Lu Yi , Li Zhiyang , Zhao Xiangqiang , Lv Shuaishuai , Wang Xingxing , Wang Kaixuan , Ni Hongjun
Published: November 2021

Rice sheath blight is one of the main diseases in rice production. The traditional detection method, which needs manual recognition, is usually inefficient and slow. In this study, a recognition method for identifying rice sheath blight based on a backpropagation (BP) neural network is posed. Firstly, the sample image is smoothed by median filtering and histogram equalization, and the edge of the lesion is segmented using a Sobel operator, which largely reduces the background information and significantly improves the image quality. Then, the corresponding feature parameters of the image are extracted based on color and texture features. Finally, a BP neural network is built for training and testing with excellent tunability and easy optimization. The results demonstrate that when the number of hidden layer nodes is set to 90, the recognition accuracy of the BP neural network can reach up to 85.8%. Based on the color and texture features of the rice sheath blight image, the recognition algorithm constructed with a BP neural network has high accuracy and can effectively make up for the deficiency of manual recognition.
Dynamic Beam Hopping Time Slots Allocation Based on Genetic Algorithm of Satellite Communication under Time-Varying Rain Attenuation
Authors: Zhang Chen , Yang Jiangtao , Zhang Yong , Liu Ziwei , Zhang Gengxin
Published: November 2021

Beam hopping technology is considered to provide a high level of flexible resource allocation to manage uneven traffic requests in multi-beam high throughput satellite systems. Conventional beam hopping resource allocation methods assume constant rainfall attenuation. Different from conventional methods, by employing genetic algorithm this paper studies dynamic beam hopping time slots allocation under the effect of time-varying rain attenuation. Firstly, a beam hopping system model as well as rain attenuation time series based on Dirac lognormal distribution are provided. On this basis, the dynamic allocation method by employing genetic algorithm is proposed to obtain both quantity and arrangement of time slots allocated for each beam. Simulation results show that, compared with conventional methods, the proposed algorithm can dynamically adjust time slots allocation to meet the non-uniform traffic requirements of each beam under the effect of time-varying rain attenuation and effectively improve system performance.
Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset
Authors: Andreou Andreas , Mavromoustakis Constandinos X. , Mastorakis George , Batalla Jordi Mongay , Pallis Evangelos
Published: November 2021

Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.
Cost-Effective Data Aggregation Method for Smart Grid
Authors: Hsu Hsi-Chou , Zhuang Shi-Ren , Huang Yung-Fa
Published: November 2021

Finding a more efficient use of energy is an important problem that needs attention. Compared with the traditional power grid, a smart grid can monitor users’ electricity situation and electricity consumption instantly. However, it involves many problems of deploying network equipment. Consequently, it is vital to promote smart grids by collecting data from smart meters efficiently and keeping costs low. In this article, we propose a two-stage method of data collection for smart grids. The main contribution of this paper is to lower the number of data aggregation points (DAPs) so that the cost can be reduced. By using the K-means method, an entire smart grid can be divided into many smaller parts. In addition, the needs of transmitting and receiving data in the entire smart grid can be met by installing the least number of DAPs. Finally, the simulations show that the proposed two-stage method of data collection can use fewer DAPs to collect data than other methods which use one-stage methods, so the proposed scheme is more cost-effective.
Geometric Simplifications of Natural Caves in Ray-Tracing-Based Propagation Modelling
Authors: Novak Roman , Hrovat Andrej , Bedford Michael D. , Javornik Tomaž
Published: November 2021

Natural caves show some similarities to human-made tunnels, which have previously been the subject of radio-frequency propagation modelling using deterministic ray-tracing techniques. Since natural caves are non-uniform because of their inherent concavity and irregular limestone formations, detailed 3D models contain a large number of small facets, which can have a detrimental impact on the ray-tracing computational complexity as well as on the modelling accuracy. Here, we analyse the performance of ray tracing in repeatedly simplified 3D descriptions of two caves in the UK, i.e., Kingsdale Master Cave (KMC) Roof Tunnel and Skirwith Cave. The trade-off between the size of the reflection surface and the modelling accuracy is examined. Further, by reducing the number of facets, simulation time can be reduced significantly. Two simplification methods from computer graphics were applied: Vertex Clustering and Quadric Edge Collapse. We compare the ray-tracing results to the experimental measurements and to the channel modelling based on the modal theory. We show Edge Collapse to be better suited for the task than Vertex Clustering, with larger simplifications being possible before the passage becomes entirely blocked. The use of model simplification is predominantly justified by the computational time gains, with the acceptable simplified geometries roughly halving the execution time given the laser scanning resolution of 10 cm.
Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
Authors: Sadique Joarder Jafor , Ullah Shaikh Enayet , Raad Raad , Islam Md. Rabiul , Rahman Md. Mahbubar , Kouzani Abbas Z. , Mahmud M. A. Parvez
Published: November 2021

In this paper, an unmanned aerial vehicle (UAV)-aided multi-antenna configured downlink mmWave cooperative generalized frequency division multiplexing (GFDM) system is proposed. To provide physical layer security (PLS), a 3D controlled Lorenz mapping system is introduced. Furthermore, the combination of T-transformation spreading codes, walsh Hadamard transform, and discrete Fourier transform (DFT) techniques are integrated with a novel linear multi-user multiple-input multiple-output (MU-MIMO) gyre precoding (GP) for multi-user interference reduction. Furthermore, concatenated channel-coding with multi-user beamforming weighting-aided maximum-likelihood and zero forcing (ZF) signal detection schemes for an improved bit error rate (BER) are also used. The system is then simulated with a single base station (BS), eight massive machine-type communications (mMTC) users, and two UAV relay stations (RSs). Numerical results reveal the robustness of the proposed system in terms of PLS and an achievable ergodic rate with signal-to-interference-plus-noise ratio (SINR) under the implementation of T-transformation scheme. By incorporating the 3D mobility model, brownian perturbations of the UAVs are also analyzed. An out-of-band (OOB) reduction of 320 dB with an improved BER of 1×10−4 in 16-QAM for a signal-to-noise ratio, Eb/N0, of 20 dB is achieved.
Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities
Authors: Li Xiaolin , Li Zhiyi , Tian Yahe
Published: November 2021

With the advent of the new media mobile Internet era, the network public opinion in colleges and universities, as an extension of social network public opinion, is also facing a crisis in the prevention, control, and governance system. In this paper, the Fiddler was used to collect the comments and other relevant data of the COVID-19 topic articles on the WeChat Official Accounts of China’s top ten universities in 2020. The BILSTM_LSTM sentiment analysis model was used to analyze the sentiment tendency of the comments, and the LDA topic model was used to mine the topics of the comments with different emotional attributes at different stages of COVID-19. Based on sentiment analysis and text mining, entities and relationships in the theme graph of public opinion events in colleges and universities were identified, and the Neo4j graph database was established to construct the sentimental knowledge graph of the pandemic theme of university public accounts. People’s attitudes in university public opinion are easily influenced by a variety of factors, and the degree of emotional disposition changes over time, with the stage the pandemic is in, and with different commentators; official account opinion topics change with the development of the time stage of the pandemic, and students’ positive and negative comment topics show a diverse trend. By incorporating topic mining into the sentimental knowledge graph, the graph can realize functions such as the emotion retrieval of comments on university public numbers, a source search of security threats in university social networks, and monitoring of comments on public opinion under the theme of the pandemic, which provides new ideas for further exploring the research and governance system of university network public opinion and is conducive to preventing and resolving campus public opinion crises.
A Wavelet-Based Diagnostic Framework for CRD Engine Injection Systems under Emulsified Fuel Conditions
Authors: Akpudo Ugochukwu Ejike , Hur Jang-Wook
Published: November 2021

The impact of the constituent oxides of nitrogen, carbon, sulphur, and other particulate matter which make up the gas emissions from diesel engines has motivated several control techniques for these pollutants. Water-in-diesel emulsions provide a reliable solution, but the wear effects on the fuel injection system (FIS) still pose remarkable concerns. Because pressure signals from the common rail (CR) reflect the dynamics associated with varying emulsion compositions and at varying engine RPMs, an investigative (and diagnostic) study was conducted on a KIA Sorento 2004 four-cylinder line engine at various water-in-diesel emulsion compositions and engine speeds. Alongside visual/microscopic inspections and spectral analyses, the diagnostic framework proposed herein functions on the use of standardized first-order differentials of the CR pressure signals to generate reliable continuous wavelet coefficients (CWCs) which capture discriminative spectral and transient information for accurate diagnosis. The results show that by extracting the CWCs from the first-order CR pressure differentials up to the 512th scale on a Mexican hat wavelet, adequate fault parameters can be extracted for use by a deep neural network (DNN) whose hyperparameters were globally optimized following a grid search. With a test accuracy of 92.3% against other widely-used ML-based diagnostic tools, the proposed DNN-based diagnostics tool was empirically assessed using several performance evaluation metrics.
A Study on the Detecting Cycle Slips and a Repair Algorithm for B1/B3
Authors: Dong Yanchen , Dai Peipei , Wang Sen , Xing Jianping , Xue Yulei , Liu Shijie , Han Shuai , Yang Zhi , Bai Xinchao
Published: November 2021

For the current problem of cycle slips in the observation data of the BDS-2 and BDS-3 (Bei Dou Navigation Satellite System), in this paper, BDS B1I and B3I signals are used as research objects to study the detection of cycle slips, and their repair algorithm. The Geometry-free (GF) and Melbourne–Wübeena (MW) combination algorithm are used for the detection of cycle slips. A new method of arc partition is proposed in this work to detect cycle slips as the boundary to delimit two different observation arcs. In this way, the different values of cycle slips can be divided and marked. Moreover, the gross errors can be removed. Finally, the detection of cycle slips and the analysis of all epochs can be completed and repaired. This work also analyzes the dual-frequency data effect of cycle slips on code multipath observation. The results showed that this method greatly improved the speed of detection of cycle slips.
On-Chip Voltage-Controlled Oscillator Based on a Center-Tapped Switched Inductor Using GaN-on-SiC HEMT Technology
Authors: Kao Hsuan-Ling
Published: November 2021

This study presents a voltage-controlled oscillator (VCO) in a cross-coupled pair configuration using a multi-tapped switched inductor with two switch-loaded transformers in 0.5 µm GaN technology. Two switch-loaded transformers are placed at the inner and outer portions of the multi-tapped inductor. All the switches are turned off to obtain the lowest sub-band. The outer transformer with three pairs of switches is turned on alternately to provide three sub-band modes. A pair of switches at the inner transformer provide a high-frequency band. Two switch-loaded transformers are turned on to provide the highest sub-band. Six modes are selected to provide a wide tuning range. The frequency tuning range (FTR) of the VCO is 27.8% from 3.81 GHz to 8.04 GHz with a varactor voltage from 13 V to 22 V. At a 1 MHz frequency offset from the carrier frequency of 4.27 GHz, the peak phase noise is −119.17 dBc/Hz. At a power supply of 12 V, the output power of the carrier at 4.27 GHz is 20.9 dBm. The figure of merit is −186.93 dB because the VCO exhibits a high output power, low phase noise, and wide FTR. To the best of the author’s knowledge, the FTR in VCOs made of GaN-based high electron mobility transistors is the widest reported thus far.
A Forward-Backward Iterative Procedure for Improving the Resolution of Resonant Microwave Sensors
Authors: Buonanno Giovanni , Brancaccio Adriana , Costanzo Sandra , Solimene Raffaele
Published: November 2021

This paper sets out a method for improving the resolution of resonant microwave sensors. Usually, the frequency response of these devices is associated with a low quality factor, and consequently with a low resolution in terms of tracking capacity of the resonance frequency shift. Furthermore, since only a finite number of samples can be acquired during the measurement process, the “true” resonance frequency may not be included in the set of acquired data. In order to have an accurate estimate of the resonance frequency, high performance systems with very fine frequency sampling are thus required. To limit these drawbacks, an iterative algorithm is presented which aims to refine the response of resonant microwave sensors by means of a suitable post-processing. The algorithm evaluation is first carried out on synthetic data, and then applied on experimental data referring to a practical scenario, which is inherent to return loss measurements performed by a microwave patch antenna immersed in a water-glucose solution with different concentrations.
Ladder-Based Synthesis and Design of Low-Frequency Buffer-Based CMOS Filters
Authors: Jendernalik Waldemar , Jakusz Jacek , Blakiewicz Grzegorz
Published: November 2021

Buffer-based CMOS filters are maximally simplified circuits containing as few transistors as possible. Their applications, among others, include nano to micro watt biomedical sensors that process physiological signals of frequencies from 0.01 Hz to about 3 kHz. The order of a buffer-based filter is not greater than two. Hence, to obtain higher-order filters, a cascade of second-order filters is constructed. In this paper, a more general method for buffer-based filter synthesis is developed and presented. The method uses RLC ladder prototypes to obtain filters of arbitrary orders. In addition, a set of novel circuit solutions with ultra-low voltage and power are proposed. The introduced circuits were synthesized and simulated using 180-nm CMOS technology of X-FAB. One of the designed circuits is a fourth-order, low-pass filter that features: 100-Hz passband, 0.4-V supply voltage, power consumption of less than 5 nW, and dynamic range above 60 dB. Moreover, the total capacitance of the proposed filter (31 pF) is 25% lower compared to the structure synthesized using a conventional cascade method (40 pF).
An FPGA-Based LOCO-ANS Implementation for Lossless and Near-Lossless Image Compression Using High-Level Synthesis
Authors: Alonso Tobías , Sutter Gustavo , López de Vergara Jorge E.
Published: November 2021

In this work, we present and evaluate a hardware architecture for the LOCO-ANS (Low Complexity Lossless Compression with Asymmetric Numeral Systems) lossless and near-lossless image compressor, which is based on JPEG-LS standard. The design is implemented in two FPGA generations, evaluating its performance for different codec configurations. The tests show that the design is capable of up to 40.5 MPixels/s and 124 MPixels/s per lane for Zynq 7020 and UltraScale+ FPGAs, respectively. Compared to the single thread LOCO-ANS software implementation running in a 1.2 GHz Raspberry Pi 3B, each hardware lane achieves 6.5 times higher throughput, even when implemented in an older and cost-optimized chip like the Zynq 7020. Results are also presented for a lossless only version, which achieves a lower footprint and approximately 50% higher performance than the version that supports both lossless and near-lossless. Interestingly, these great results were obtained applying High-Level Synthesis, describing the coder with C++ code, which tends to establish a trade-off between design time and quality of results. These results show that the algorithm is very suitable for hardware implementation. Moreover, the implemented system is faster and achieves higher compression than the best previously available near-lossless JPEG-LS hardware implementation.
Health Promotion through Monetary Incentives: Evaluating the Impact of Different Reinforcement Schedules on Engagement Levels with a mHealth App
Authors: Nuijten Raoul , Van Gorp Pieter , Khanshan Alireza , Le Blanc Pascale , Kemperman Astrid , van den Berg Pauline , Simons Monique
Published: November 2021

Background: Financial rewards can be employed in mHealth apps to effectively promote health behaviors. However, the optimal reinforcement schedule—with a high impact, but relatively low costs—remains unclear. Methods: We evaluated the impact of different reinforcement schedules on engagement levels with a mHealth app in a six-week, three-arm randomized intervention trial, while taking into account personality differences. Participants (i.e., university staff and students, N = 61) were awarded virtual points for performing health-related activities. Their performance was displayed via a dashboard, leaderboard, and newsfeed. Additionally, participants could win financial rewards. These rewards were distributed using a fixed schedule in the first study arm, and a variable schedule in the other arms. Furthermore, payouts were immediate in the first two arms, whereas payouts in the third arm were delayed. Results: All three reinforcement schedules had a similar impact on user engagement, although the variable schedule with immediate payouts was reported to have the lowest cost per participant. Additionally, the impact of financial rewards was affected by personal characteristics. Especially, individuals that were triggered by the rewards had a greater ability to defer gratification. Conclusion: When employing financial rewards in mHealth apps, variable reinforcement schedules with immediate payouts are preferred from the perspective of cost and impact.