bluuebunny/crossref_metadata_2025 huggingface dataset:
Pandemic Financial Stress in Dental Medicine in Croatia
Authors: Orlic Edi , Spalj Stjepan , Ivancic Jokic Natasa , Bakarcic Danko , Cicvaric Odri , Grzic Renata
Published: December 2022

The aim of this cross-sectional research was to investigate how the COVID-19 pandemic affected the activity of dental medicine in the Republic of Croatia in 2020. It included 136 doctors of dental medicine who completed an online survey regarding their personal and professional information; work in dental offices; and level of fear for their own health, the health of others, and financial existence; and their attitude about vaccination. There was a significantly higher decrease in patient visits in dental offices that do not have a contract with public health insurance (70% vs. 37%; p < 0.001) and in dental offices that have a higher percentage of profit from dental tourism (32% vs. 14%; p < 0.001). Fear of financial existence was significantly higher in the group of dentists who do not have a contract with public health insurance (p = 0.0) and is positively correlated with the percentage of profit from dental tourism (r = 0.299; p < 0.001). Dentists with a higher level of fear that they or their loved ones would get infected due to the nature of their job are more likely to get vaccinated (p ≤ 0.007). The decision to get vaccinated and wearing a disposable coat/apron was related to fear when all other parameters were controlled for (R = 0.44; p = 0.037). In conclusion, the COVID-19 pandemic had a minimal impact on the profession of dental medicine in Croatia but represented a larger financial stress for dentists working in dental offices that do not have a contract with public health insurance and have a higher percentage of income from dental tourism.
Cooperative Path Planning of Multiple Unmanned Surface Vehicles for Search and Coverage Task
Authors: Zhao Zhiyao , Zhu Bin , Zhou Yan , Yao Peng , Yu Jiabin
Published: December 2022

This paper solves the problem of cooperative path planning of multiple unmanned surface vehicles (USVs) for search and coverage tasks in water environments. Firstly, taking the search coverage problem of water surface pollutants as an example, the information concentration map is built to predict the diffusion of water surface pollutants. Secondly, we propose a region division method based on a Voronoi diagram, which divides the region and assigns it to each unmanned surface vehicle (USV). Then, on the basis of the traditional Model Predictive Control (MPC), the future reward index based on the regional centroid is introduced, and the Improved Salp Swarm Algorithm (ISSA) is used to solve MPC. Simulation results show the effectiveness of the proposed method.
Gusts Encountered by Flying Vehicles in Proximity to Buildings
Authors: Mohamed Abdulghani , Marino Matthew , Watkins Simon , Jaworski Justin , Jones Anya
Published: December 2022

There is a growing desire to operate Uncrewed Air Vehicles (UAVs) in urban environments for parcel delivery, and passenger-carrying air taxis for Advanced Air Mobility (AAM). The turbulent flows and gusts around buildings and other urban infrastructure can affect the steadiness and stability of such air vehicles by generating a highly transient relative flow field. Our aim is to review existing gust models, then consider gust encounters in the vicinity of buildings as experienced by flight trajectories over the roof of a nominally cuboid building in a suburban atmospheric boundary layer. Simplified models of fixed- and rotary-wing aircraft are used to illustrate the changes in lift and thrust experienced by flight around the building. The analysis showed that fixed-wing aircraft experienced a substantial increase in angle of attack over a relatively short period of time (<1 s) as they fly through the shear layer at a representative forward velocity, which can be well above typical stall angles. Due to the slow flight speeds required for landing and take-off, significant control authority of rotor systems is required to ensure safe operation due to the high disturbance effects caused by localized gusts from buildings and protruding structures. Currently there appears to be negligible certification or regulation for AAM systems to ensure safe operations when traversing building flow fields under windy conditions and it is hoped that the insights provided in this paper will assist with future certification and regulation.
Broken Bar Fault Detection Using Taylor–Fourier Filters and Statistical Analysis
Authors: Aguayo-Tapia Sarahi , Avalos-Almazan Gerardo , Rangel-Magdaleno Jose de Jesus , Paternina Mario R. A.
Published: December 2022

Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on the motors, such as mechanical and electrical stresses, together with an increase in electricity consumption, causing higher operative costs and losses related to the maintenance times or even the motor replacement if the damage has led to a complete failure. To prevent such situations, diverse signal processing algorithms have been applied to incipient fault detection, using different variables to analyze, such as vibrations, current, or flux. To counteract the broken rotor bar damage, this paper focuses on a motor current signal analysis for early broken bar detection and classification by using the digital Taylor–Fourier transform (DTFT), whose implementation allows fine filtering and amplitude estimation with the final purpose of achieving an incipient fault detection. The detection is based on an analysis of variance followed by a Tukey test of the estimated amplitude. The proposed methodology is implemented in Matlab using the O-splines of the DTFT to reduce the computational load compared with other methods. The analysis is focused on groups of 50-test of current signals corresponding to different damage levels for a motor operating at 50% and 75% of its full load.
Prior Distribution and Entropy in Computer Adaptive Testing Ability Estimation through MAP or EAP
Authors: Suárez-Cansino Joel , López-Morales Virgilio , Morales-Manilla Luis Roberto , Alberto-Rodríguez Adrián , Ramos-Fernández Julio César
Published: December 2022

To derive a latent trait (for instance ability) in a computer adaptive testing (CAT) framework, the obtained results from a model must have a direct relationship to the examinees’ response to a set of items presented. The set of items is previously calibrated to decide which item to present to the examinee in the next evaluation question. Some useful models are more naturally based on conditional probability in order to involve previously obtained hits/misses. In this paper, we integrate an experimental part, obtaining the information related to the examinee’s academic performance, with a theoretical contribution of maximum entropy. Some academic performance index functions are built to support the experimental part and then explain under what conditions one can use constrained prior distributions. Additionally, we highlight that heuristic prior distributions might not properly work in all likely cases, and when to use personalized prior distributions instead. Finally, the inclusion of the performance index functions, arising from current experimental studies and historical records, are integrated into a theoretical part based on entropy maximization and its relationship with a CAT process.
Modeling the Evolution of Biological Neural Networks Based on Caenorhabditis elegans Connectomes across Development
Authors: Zhao Hongfei , Shi Zhiguo , Gong Zhefeng , He Shibo
Published: December 2022

Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic Caenorhabditis elegans hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the C. elegans neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree distributions were neither Poisson distributions nor pure power-law distributions. We have proposed a model of network evolution with different initial attractiveness for in-degrees and out-degrees of nodes and preferential attachment, which reproduces the asymmetry between in-degrees and out-degrees and similar degree distributions via the tuning of the initial attractiveness values. In this study, we present the well-preserved structural properties of C. elegans neuronal networks across development, and provide some insight into understanding the evolutionary processes of biological neural networks through a simple network model.
Enthalpy-Entropy Compensation Effect in Saturated Solutions on an Example of Polynuclear Aromatics According to Thermodynamics at Melting Temperature
Authors: Mianowski Andrzej , Łabojko Grzegorz
Published: December 2022

A thermodynamic the influence of temperature on the logarithm of the considered quantity is expressed by bifunctional functional terms (1/T, lnT). For this purpose, the Apelblat & Manzurola (A&M) equation was used for extended model dissolution analysis of 12 aromatic hydrocarbons in tetralin and decalin vs. temperature for saturated solutions. The A&M equation was found to be thermodynamically compensatory in the sense of Enthalpy-Entropy-Compensation (EEC) while limiting melting temperature Tm=∆mH∆mS. The coefficients for the functional terms A1 vs. A2 are a linear relationship, with a slope called the compensation temperature Tc, as ratio of average enthalpy to average entropy. From this dependence, it has been shown that the approximation of ∆cp=∆mS¯ is justified, also assuming the average entropy. Regarding the term representing the activity coefficients, modifications to the A&M equation were proposed by replacing the intercept and it was shown that the new form correctly determines ∆mH. However, the condition is that the molar fraction of the solute exceeds x > 0.5 moles. It has been shown that the simplest equation referred to van ’t Hoff’s isobar also allows the simultaneous determination of enthalpy and entropy, but these quantities do not always come down to melting temperature.
Inside Out: A Scoping Review on Optimism, Growth Mindsets, and Positive Psychology for Child Well-Being in ECEC
Authors: Campbell Janine Anne , Løkken Ingrid Midteide
Published: December 2022

Early social and emotional development is foundational for children’s health, education, well-being, and later adjustment in society. It is also a cornerstone of positive psychology—the exploration of human flourishing at an individual, community, and societal level. Habitual explanatory styles (e.g., mindsets and optimistic thinking) have an impact on human well-being and development and are often acquired during early childhood. These explanatory styles may be influenced by regular interactions with significant adults outside of the family setting. However, few studies have focused on the relationship between optimism, mindsets, and well-being of children in Early Childhood Education and Care (ECEC) contexts. This scoping review systematically explores and maps out the literature on optimism, growth mindsets, and positive psychology with young children in ECEC. It identifies literature reporting qualitatively or quantitatively on theory or programs and interventions including optimism, growth mindset, and positive psychology in young children, reporting or implying a mechanism of change for the well-being of young children, and published between 1995 and 2021.
A Long-Life Predictive Guidance with Homogeneous Competence Promotion for University Teaching Design
Authors: Aciego Juan Jose , Claros Colome Alicia , Gonzalez-Prieto Ignacio , Gonzalez-Prieto Angel , Duran Mario J.
Published: December 2022

Even though planning the educational action to optimize student performance is a very complex task, teachers typically face this challenging issue with no external assistance. Previous experience is, in most cases, the main driving force in curriculum design. This procedure commonly overlooks the students’ perception and weakly integrates the students’ feedback by using a non-systematic approach. Furthermore, transverse competences are, unfortunately, typically omitted in this procedure. This work suggests the use of a predictive tool that determines the optimal application time of different methodological instruments. The suggested method can be used for an infinite number of scenarios of promoted competences. The results can be regarded as a guide to modify the course structure, but, more importantly, it offers valuable information to understand better what is occurring in the teaching-learning process and detect anomalies in the subject and avoid the students’ exclusion. The predictive scheme simultaneously considers the teacher’s perspective, the student’s feedback, and the previous scores in a systematic manner. Therefore, results provide a broader picture of the educational process. The proposal is assessed in a course of Electrical Machines at the University of Malaga during the academic year 2021–2022.
Building Penetration Losses at 3.5 GHz: Dependence on Polarization and Incidence Angle
Authors: García Sánchez Manuel , Iglesias Carlos , Cuiñas Iñigo , Expósito Isabel
Published: December 2022

We measured and analyzed the building penetration losses for four different kinds of facades as a function of the incidence angle and polarization at 3.5 GHz, which is a frequency of interest for fifth generation (5G) communication systems. Results show that the attenuation may vary up to 8 dB with the incidence angle, which justifies the need for an angular characterization of the penetration losses, going beyond the simpler characterization used for normal incidence. We also found that there is a relevant polarization dependence of this attenuation, as electromagnetic theory predicts for wave transmission across flat discontinuities and even across flat dielectric slabs. Results would be of interest for the network design of future 5G base stations using orthogonal polarizations.
The Rescuer’s Navigation in Metro Stations Based on Inertial Sensors and WiFi
Authors: Wang Qingyong , Qu Weiqiang , Chen Jian , Wang Zhiwei
Published: December 2022

The demand for metro station rescue navigation is increasing. This paper presents an improved particle filter to challenge the navigation problem in metro stations. A particle filter is often used to estimate the position of pedestrians. However, the particle-impoverishment problem is inevitable. To solve this problem, a dingo optimization algorithm (DOA) with global search ability is introduced, and an improved particle filter called a dingo particle filter (DPF) is proposed. Dead reckoning (DR) is taken as the system equation, and WiFi matching results are used as the observation equation. The improved particle filter algorithm introduces a dingo optimization algorithm to improve the diversity of particles and effectively reduce the particle-impoverishment problem. The experimental results show that the average positioning accuracy is 1.1 m and 1.2 m.
Terahertz Hybrid Topological Chip for 10-Gbps Full-Duplex Communications
Authors: Li Haisu , Zhang Yu , Liu Yajing , Atakaramians Shaghik
Published: December 2022

Terahertz photonic chips play an important role in next-generation information systems, such as high-speed inter/intrachip connections. Here we report a hybrid hollow-core terahertz topological planar waveguide where topological silicon pillars (assembled as expanded and compressed hexagonal clusters) are sandwiched between parallel gold plates. Assisted by numerical optimization of topological waveguide unit cells and supercells, we achieve a wide topological bandgap (relative bandwidth of 16.1%) in which two low-loss (below 0.1 dB/mm) topological pseudospin states exist and cover 81.8% of the bandgap. The dual unidirectional channel in a single waveguide path can be used for short-range (below 100 mm) 10-Gbps full-duplex on-chip transmissions under the forward error correction limit (bit error rate less than 10−3). Simulation results reveal that the communication performance of the proposed topological waveguide is largely group velocity dispersion dependent. This work may pave an avenue for high-speed integrated circuit design and applications in both electric and photonic fields.
Applications of Ontology in the Internet of Things: A Systematic Analysis
Authors: Qaswar Fahad , Rahmah M. , Raza Muhammad Ahsan , Noraziah A. , Alkazemi Basem , Fauziah Z. , Hassan Mohd. Khairul Azmi , Sharaf Ahmed
Published: December 2022

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions.
Robust Hierarchical Federated Learning with Anomaly Detection in Cloud-Edge-End Cooperation Networks
Authors: Zhou Yujie , Wang Ruyan , Mo Xingyue , Li Zhidu , Tang Tong
Published: December 2022

Federated learning (FL) enables devices to collaborate on machine learning (ML) model training with distributed data while preserving privacy. However, the traditional FL is inefficient and costly in cloud–edge–end cooperation networks since the adopted classical client-server communication framework fails to consider the real network structure. Moreover, malicious attackers and malfunctioning clients may be implied in all participators to exert adverse impacts as abnormal behaviours on the FL process. To address the above challenges, we leverage cloud–edge–end cooperation to propose a robust hierarchical federated learning (R-HFL) framework to enhance inherent system resistance to abnormal behaviours while improving communication efficiency in practical networks and keeping the advantages of the traditional FL. Specifically, we introduce a hierarchical cloud–edge–end collaboration-based FL framework to reduce communication costs. For the framework, we design a detection mechanism as partial cosine similarity (PCS) to filter adverse clients to improve performance, where the proposed lightweight technique has high computation parallelization. Besides, we theoretically discuss the influence of the proposed PCS on the convergence and stabilization of FL. Finally, the experimental results show that the proposed R-HFL always outperforms baselines in general cases under malicious attacks, which further shows the effectiveness of our scheme.
A Framework for Smart Home System with Voice Control Using NLP Methods
Authors: Iliev Yuliy , Ilieva Galina
Published: December 2022

The proliferation of information technologies and the emergence of ubiquitous computing have quickly transformed electronic devices from isolated islands of data and control into interconnected parts of intelligent systems. These network-based systems have advanced features, including Internet of Things (IoT) sensors and actuators, multiple connectivity options and multimodal user interfaces, and they also enable remote monitoring and management. In order to develop a human machine interface of smart home systems with speech recognition, we propose a new IoT-fog-cloud framework using natural language processing (NLP) methods. The new methodology adds utterance to command transformation to the existing cloud-based speech-to-text and text-to-speech services. This approach is flexible and can be easily adapted for different types of automation systems and consumer electronics as well as to almost every non-tonal language not currently supported by online platforms for intent detection and classification. The proposed framework has been employed in the development of prototypes of voice user interface extension of existing smart security system via new service for speech intent recognition. Tests on the system were carried out and the obtained results show the effectiveness of the new voice communication option. The speech-based interface is reliable; it facilitates customers and improves their experience with smart home devices.
Combining Transformer Embeddings with Linguistic Features for Complex Word Identification
Authors: Ortiz-Zambrano Jenny A. , Espin-Riofrio César , Montejo-Ráez Arturo
Published: December 2022

Identifying which words present in a text may be difficult to understand by common readers is a well-known subtask in text complexity analysis. The advent of deep language models has also established the new state-of-the-art in this task by means of end-to-end semi-supervised (pre-trained) and downstream training of, mainly, transformer-based neural networks. Nevertheless, the usefulness of traditional linguistic features in combination with neural encodings is worth exploring, as the computational cost needed for training and running such networks is becoming more and more relevant with energy-saving constraints. This study explores lexical complexity prediction (LCP) by combining pre-trained and adjusted transformer networks with different types of traditional linguistic features. We apply these features over classical machine learning classifiers. Our best results are obtained by applying Support Vector Machines on an English corpus in an LCP task solved as a regression problem. The results show that linguistic features can be useful in LCP tasks and may improve the performance of deep learning systems.
Entity Factor: A Balanced Method for Table Filling in Joint Entity and Relation Extraction
Authors: Liu Zhifeng , Tao Mingcheng , Zhou Conghua
Published: December 2022

The knowledge graph is an effective tool for improving natural language processing, but manually annotating enormous amounts of knowledge is expensive. Academics have conducted research on entity and relation extraction techniques, among which, the end-to-end table-filling approach is a popular direction for achieving joint entity and relation extraction. However, once the table has been populated in a uniform label space, a large number of null labels are generated within the array, causing label-imbalance problems, which could result in a tendency of the model’s encoder to predict null labels; that is, model generalization performance decreases. In this paper, we propose a method to mitigate non-essential null labels in matrices. This method utilizes a score matrix to calculate the count of non-entities and the percentage of non-essential null labels in the matrix, which is then projected by the power of natural constant to generate an entity-factor matrix. This is then incorporated into the scoring matrix. In the back-propagation process, the gradient of non-essential null-labeled cells in the entity factor layer is affected and shrinks, the amplitude of which is related to the size of the entity factor, thereby reducing the feature learning of the model for a large number of non-essential null labels. Experiments with two publicly available benchmark datasets show that the incorporation of entity factors significantly improved model performance, especially in the relation extraction task, by 1.5% in both cases.
User Engagement Comparison between Advergames and Traditional Advertising Using EEG: Does the User’s Engagement Influence Purchase Intention?
Authors: Castiblanco Jimenez Ivonne Angelica , Gomez Acevedo Juan Sebastian , Olivetti Elena Carlotta , Marcolin Federica , Ulrich Luca , Moos Sandro , Vezzetti Enrico
Published: December 2022

In the context of human–computer interaction (HCI), understanding user engagement (UE) while interacting with a product or service can provide valuable information for enhancing the design process. UE has been a priority research theme within HCI, as it assesses the user experience by studying the individual’s behavioral response to some stimulus. Many studies looking to quantify the UE are available; however, most use self-report methods that rely only on participants’ answers. This study aims to explore a non-traditional method, specifically electroencephalography, to analyze users’ engagement while interacting with an advergame, an interactive form of advertising in video games. We aim to understand if a more interactive type of advertising will enhance the UE and whether, at the same time, it would influence the user’s purchase intention (UPI). To do this, we computed and compared the UE during the interaction with an advergame and a conventional TV commercial while measuring the participants’ brain activity. After the interaction with both types of advertising, the UPI was also evaluated. The findings demonstrate that a more interactive advertisement increased the participants’ UE and that, in most cases, a UE increment positively influenced the UPI. This study shows an example of the potential of physiological feedback applications to explore the users’ perceptions during and after the human–product interaction. The findings show how physiological methods can be used along with traditional ones for enhancing the UE analysis and provide helpful information about the advantages of engagement measurement in HCI applications.
Detection of Hello Flood Attacks Using Fuzzy-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
Authors: Radhika S. , Anitha K. , Kavitha C. , Lai Wen-Cheng , Srividhya S. R.
Published: December 2022

Clustering is one among the most important strategies to improve the lifetime of wireless sensor networks (WSNs). The frequent occurrence of clustering and the subsequent interchange of data overload the sensor nodes and result in wasting power. WSNs are susceptible to attacks because of their resource-constrained nature and large applications in critical military areas. The objective of the threats to the security of wireless sensor networks is to compromise the network by seizing information for misuse. Security features have become a major concern in these types of networks as it is important to protect sensitive data from unauthorized users. This paper aims to present an enriched clustering strategy to minimize the overhead caused by clustering, by formulating an effective cluster update schedule. It also focuses on the attacks that occur during an exchange of initialization messages with neighbors. Clustering of the network is carried out on the basis of the energy of sensor nodes. The nodes that are the heads of the cluster nodes are determined according to the characteristics of energy factors; hence, the role is frequently switched among the nodes of the cluster. To formulate the next cluster update schedule, a fuzzy inference system is employed, and this uses the energy factor of the node, the distance the node is placed from the sink, and the number of member nodes of the cluster. A mechanism is included during an exchange of initialization messages that detects any malicious node pretending to be a neighbor node. The proposed algorithm is evaluated using simulation, and it is found to produce an improved lifetime of 1700 time units. It is shown to conserve the energy of sensor nodes and protect them from unauthorized nodes posing as legitimate neighbors.
Highly Dispersive Optical Solitons in Fiber Bragg Gratings with Quadratic-Cubic Nonlinearity
Authors: Zayed Elsayed M. E. , Alngar Mohamed E. M. , Shohib Reham M. A. , Biswas Anjan , Yıldırım Yakup , Moraru Luminita , Moldovanu Simona , Iticescu Catalina
Published: December 2022

Highly dispersive solitons in fiber Bragg gratings with quadratic-cubic law of nonlinear refractive index are studied in this paper. The G′/G-expansion approach and the enhanced Kudryashov’s scheme have made this retrieval possible. A deluge of solitons, that emerge from the two integration schemes, are presented.
Bidirectional Interleaved DC–DC Converter for Supercapacitor Energy Storage Integration with Reduced Capacitance
Authors: Kroičs Kaspars , Staņa Ģirts
Published: December 2022

This paper analyzes the control method of a multiphase interleaved DC–DC converter for supercapacitor energy storage system integration in a DC bus with reduced input and output filter size. A reduction in filter size is achieved by operating only in modes with duty cycles that correspond to smaller output current ripples. This leads to limited control of the charging and discharging process of the supercapacitor energy storage system. Therefore, a detailed analysis of the optimal charging strategy is provided in this paper for interleaved converters with different numbers of phases. The results show that such control can be used, albeit with some percentage loss in efficiency. Experimental results are presented in this paper to verify the theoretical results.
Towards Adversarial Attacks for Clinical Document Classification
Authors: Fatehi Nina , Alasad Qutaiba , Alawad Mohammed
Published: December 2022

Regardless of revolutionizing improvements in various domains thanks to recent advancements in the field of Deep Learning (DL), recent studies have demonstrated that DL networks are susceptible to adversarial attacks. Such attacks are crucial in sensitive environments to make critical and life-changing decisions, such as health decision-making. Research efforts on using textual adversaries to attack DL for natural language processing (NLP) have received increasing attention in recent years. Among the available textual adversarial studies, Electronic Health Records (EHR) have gained the least attention. This paper investigates the effectiveness of adversarial attacks on clinical document classification and proposes a defense mechanism to develop a robust convolutional neural network (CNN) model and counteract these attacks. Specifically, we apply various black-box attacks based on concatenation and editing adversaries on unstructured clinical text. Then, we propose a defense technique based on feature selection and filtering to improve the robustness of the models. Experimental results show that a small perturbation to the unstructured text in clinical documents causes a significant drop in performance. Performing the proposed defense mechanism under the same adversarial attacks, on the other hand, avoids such a drop in performance. Therefore, it enhances the robustness of the CNN model for clinical document classification.
Efficient Route Planning Using Temporal Reliance of Link Quality for Highway IoV Traffic Environment
Authors: Yaduwanshi Ritesh , Kumar Sushil , Kumar Arvind , Kaiwartya Omprakash , Deepti Deepti , Aljaidi Mohammad , Lloret Jaime
Published: December 2022

Intermittently connected vehicular networks, terrain of the highway, and high mobility of the vehicles are the main critical constraints of highway IoV (Internet of Vehicles) traffic environment. These cause GPS outage problem and the existence of short-lived wireless mobile links that reduce the performance of designed routing approaches. Nevertheless, geographic routing has attracted a lot of attention from researchers as a potential means of accurate and efficient information delivery. Various distance-based routing protocols have been proposed in the literature, with an emphasis on restricting the forwarding area to the next forwarding vehicle. Many of these protocols have issues with significant one-hop link disconnection, long end-to-end delays, and low throughput even at normal vehicle speeds in high-vehicular-density environments due to frequently interrupted wireless links. In this paper, an efficient geocast routing (EGR) approach for highway IoV–traffic environment considering the shadowing fading condition is proposed. In EGR, a geometrical localization for GPS outage problem and a temporal link quality estimation model considering underlying vehicular movement have been proposed. Geocast routing to select a next forwarding vehicle from forward region by utilizing temporal link quality is proposed for four different scenarios. To evaluate the effectiveness and scalability of EGR, a comparative performance evaluation based on simulations has been performed. It is clear from the analysis of the results that EGR performs better than state-of-the-art approaches in highway traffic environment in terms of handling the problem of wireless communication link breakage and throughput, as well as ensuring the faster delivery of the messages.
Integrated 0.35-µm CMOS Control Circuits for High-Performance Voltage Mode DC–DC Boost Converter
Authors: Lee Chan-Soo , Gendensuren Munkhsuld , Dansran Bayarsaikan , Ahn Bierng-Chearl , Choi Seong-Gon
Published: December 2022

The integrated DC–DC converter is appropriate for use in many domains, namely, display, cellular, and portable applications. This paper presents an integrated control circuit for a monolithic voltage mode DC–DC boost converter for display driver applications. The control circuits consist of a transconductance amplifier, a comparator, and an oscillator. The boost converter consists of an inductor, two MOSFET, and an output RC filter. The control circuits are designed for fast transient response and low output ripple. The transconductance amplifier, comparator, and oscillator in the control circuit are designed to operate at a supply voltage of 3.3 V and an operating frequency of 5.5 MHz. The transconductance amplifier consists of an operational amplifier and an RC filter in the feedback path. The RC filter has a pole with a sufficient phase margin for high stability. The control circuits are realized in a 0.35-μm CMOS process together with the DC–DC converter. The fabricated DC–DC converter was evaluated by experiment and simulation. Testing of the proposed control circuits shows that the output transient time can be controlled within 7 μs, and the output voltage is accurately controlled with a ripple ratio of 3%.
New Frontier in Terahertz Technologies for Virus Sensing
Authors: Mancini Tiziana , Marcelli Augusto , Lupi Stefano , D’Arco Annalisa
Published: December 2022

The recent pandemic of SARS-CoV-2 virus has made evident critical issues relating to virus sensing and the need for deployable tools for adequate, rapid, effective viral recognition on a large-scale. Although many conventional molecular and immuno-based techniques are widely used for these purposes, they still have some drawbacks concerning sensitivity, safety, laboriousness, long-term collection and data analysis. Therefore, new rapidly emerging approaches have been introduced such as terahertz (THz)-based technologies. In this contribution, we summarize the emerging THz radiation technology, its solutions and applications for high-sensitivity viral detection.
An Indoor Multi-Environment Sensor System Based on Intelligent Edge Computing
Authors: Kim Myeongseop , Kim Taehyeon , Park Seho , Lee Kyungtaek
Published: December 2022

Monitoring and predicting the environment in an indoor space plays an important role in securing big data and detecting abnormal conditions in the industrial environment and living space. This study proposes an indoor multi-environment sensor system based on intelligent edge computing that collects and predicts environmental data. The system collects data using 14 types of environmental sensors and object detection technology models and implements a model that predicts indoor air quality based on the bi-directional LSTM network. The trained model shows high performance in predicting indoor air quality (IAQ) factors, such as CO2, PM2.5, and total volatile organic compounds (TVOC). The indoor multi-environment sensor system based on intelligent edge computing is available for data collection and environmental prediction in various spaces without restrictions on specific locations. This study proposes an integrated approach with various functions by applying edge computing to indoor environment monitoring. We verify the proposed system through various experiments.
Low-Cost/High-Precision Smart Power Supply for Data Loggers
Authors: Amorim Marcio L. M. , Ginja Gabriel Augusto , Carmo João Paulo , Moreira Melkzedekue Moraes Alcântara , Siqueira Adriano Almeida Goncalves , Afonso Jose A.
Published: December 2022

This paper presents a low-cost/high-precision smart power supply for application on data loggers. The microprocessor unit is the brain of the system and manages the events and was optimized to provide electrical energy to the electronic devices under normal operation and under the presence of disruptive events. The measurements showed that when switching either from battery to AC or from AC to battery, neither caused the shutdown of the power supply nor affected the behavior of the power supply. The power supply was able to charge 80% of the battery on a fast recharge of 1 h and the remaining 20% on a slow recharge of 2 h. The current allocated to the battery did not affect the operation of the power supply. The tests also showed that the power supply was able to transmit relevant information about its operation to external computers through a serial connection. This information includes the voltages at the battery and at the output of the voltage regulators, the voltage level of the AC network, the level of the battery charge and if it was being recharged, the current being drained, the internal temperatures at two locations (one measured on the resistor that limits battery charge and another measured on the output diode of the regulators), and whether the cooling system is being used. The total cost of this smart power supply is less than $150, demonstrating good potential for its popularization.
Wide-Speed Range Sensorless Control of Five-Phase PMSM Drive under Healthy and Open Phase Fault Conditions for Aerospace Applications
Authors: Assoun Ihab , Idkhajine Lahoucine , Nahid-Mobarakeh Babak , Meibody-Tabar Farid , Monmasson Eric , Pacault Nicolas
Published: December 2022

This paper presents a speed sensorless control of a five-phase PMSM in healthy operation and under the Open-Phase Fault on any phase of the machine. The solution is recommended for mission-critical applications requiring high reliability capacities, such as Aerospace applications. An adapted Active Fault Tolerant Control is proposed with the aim of obtaining electromechanical torque as close as possible to that normally developed by a machine working in healthy condition. In instances of a loss of power to one phase of the machine, a reconfiguration of the control law is performed to ensure the continuity of service and to maintain acceptable control performances without requiring a hardware rearrangement of the power architecture. The motor rotation speed and position, required for the Field Oriented Control (FOC) of the stator currents, are estimated using a Back-Electromotive Forces (Back-EMF) observer based on a mathematical model of the motor and implemented in the stator diphase reference frame. Different electrical models that describe the behavior of the five-phase machine in the normal and degraded operations are given. Experimental results on a 1.25 kW synchronous PM machine are shown to confirm the effectiveness of the motor control.
State-of-the-Art Research on Wireless Charging of Electric Vehicles Using Solar Energy
Authors: Kashani Seyed Ali , Soleimani Alireza , Khosravi Ali , Mirsalim Mojtaba
Published: December 2022

Within the past decade, since impediments in nonrenewable fuel sources and the contamination they cause, utilizing green energies, such as those that are sun-oriented, in tandem with electric vehicles, is a developing slant. Coordinating electric vehicle (EV) charging stations with sun-powered boards (PV) reduces the burden of EV charging on the control framework. This paper presents a state-of-the-art literature review on remote control transmission frameworks for charging the batteries of electric vehicles utilizing sun-based boards as a source of power generation. The goal of this research is to advance knowledge in the wireless power transfer (WPT) framework and explore more about solar-powered electric vehicle charging stations. To do this, a variety of solar-powered electric vehicle charging station types are thoroughly studied. Following a study of many framework elements, the types of WPT components are explored in a different section. Within the wireless power transmission framework for solar-powered electric vehicle charging, compensators and various coil structures are also investigated, along with the advantages of each coil over the others. This study also discusses the use of artificial intelligence (AI) in WPT frameworks and highlights the important aspects of developing an AI model.
Prediction of the Market of End-of-Life Photovoltaic Panels in the Context of Common EU Management System
Authors: Czajkowski Adrian , Wajda Agata , Poranek Nikolina , Bhadoria Shubhangi , Remiorz Leszek
Published: December 2022

A significant development of the photovoltaic market in the European Union has been observed recently. This is mainly due to the adopted climate policy and the development of photovoltaic technology, resulting in increased availability for consumers at lower prices. In the long run, increased installed PV capacity is associated with an increased amount of photovoltaic waste generated at the end of life. Since this waste belongs to the group of WEEE (waste electrical and electronic equipment) waste, it is subjected to high recovery levels. Existing installations for the highly efficient recycling of PV panels are just proofs of concept. However, the situation will change in the near future, and it will be necessary to implement a full-scale waste management system dedicated to PV waste. The paper estimates mass streams of photovoltaic waste generated by 2050 in individual EU countries. Consequently, the characteristics of the European market of waste PV panels are considered together with the demand of individual Member States for installations. The estimation enables the fulfillment of the Directive on WEEE recovery rates.
Performance and Total Cost of Ownership of a Fuel Cell Hybrid Mining Truck
Authors: Ahluwalia Rajesh K. , Wang Xiaohua , Papadias Dionissios D. , Star Andrew G.
Published: December 2022

The main objective of this work was to investigate the potential of hydrogen and fuel cells replacing diesel and internal combustion engines in the ultraclass haul trucks deployed in the mining sector. Performance, range, durability, and cost are the main criteria considered for comparing the two fuels and engine options. Fuel cell system (FCS) performance is characterized in terms of heat rejection, efficiency, and fuel consumption for a hybrid platform equivalent to a 3500 hp diesel engine operating on a representative open pit mining duty cycle. A hybrid platform was chosen because the heat rejection, with a constrained radiator frontal area, limits the maximum fuel cell-rated power by about 50% compared to that of the diesel truck. The hybrid powertrain was 81–88% more efficient than the diesel powertrain on the truck duty cycle. A liquid hydrogen storage system is required for an equal range or time between refilling, but the packaging remains a challenge. Fuel cell and battery durability were evaluated for their performance degradation and lifetime. Achieving a fuel cell lifetime comparable to the time between major overhauls for diesel trucks necessitates the oversizing of the membrane-active area, catalyst overloading, and voltage clipping. For an equal lifetime, the battery must be oversized to control its depth of discharge and charge/discharge rates. A total cost of ownership (TCO) analysis considering the initial capital expenditures, as well as the lifetime cost of fuel, operation, and maintenance, indicates that fuel cells and hydrogen can compete with diesel. A breakeven fuel cost for TCO parity is obtained if H2 is available at USD 5.79–6.85/kg vs. diesel at USD 3.25/gal and the FCS-specific cost is USD 323/kWe relative to USD 250/kW for a diesel genset. Volume manufacturing is required for FCS cost reduction. High volume is possible through the standardization, modularity, and proliferation of class 8 long-haul truck systems across different heavy-duty applications.
A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective
Authors: Pamulapati Trinadh , Cavus Muhammed , Odigwe Ishioma , Allahham Adib , Walker Sara , Giaouris Damian
Published: December 2022

The energy sector is undergoing a paradigm shift among all the stages, from generation to the consumer end. The affordable, flexible, secure supply–demand balance due to an increase in renewable energy sources (RESs) penetration, technological advancements in monitoring and control, and the active nature of distribution system components have led to the development of microgrid (MG) energy systems. The intermittency and uncertainty of RES, as well as the controllable nature of MG components such as different types of energy generation sources, energy storage systems, electric vehicles, heating, and cooling systems are required to deploy efficient energy management systems (EMSs). Multi-agent systems (MASs) and model predictive control (MPC) approaches have been widely used in recent studies and have characteristics that address most of the EMS challenges. The advantages of these methods are due to the independent characteristics and nature of MAS, the predictive nature of MPC, and their ability to provide affordable, flexible, and secure MG operation. Therefore, for the first time, this state-of-the-art review presents a classification of the MG control and optimization methods, their objectives, and help in understanding the MG operational and EMS challenges from the perspective of the energy trilemma (flexibility, affordability, and security). The control and optimization architectures achievable with MAS and MPC methods predominantly identified and discussed. Furthermore, future research recommendations in MG-EMS in terms of energy trilemma associated with MAS, MPC methods, stability, resiliency, scalability improvements, and algorithm developments are presented to benefit the research community.