bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinical Assessment of Chronic Musculoskeletal Pain—A Framework Proposal Based on a Narrative Review of the Literature
Authors: Nogueira Carrer Helen Cristina , Zanca Gisele Garcia , Haik Melina Nevoeiro
Published: December 2022

The assessment of chronic musculoskeletal pain (CMP) is a challenge shared by several health professionals. Fragmented or incomplete assessment can cause deleterious consequences for the patient’s function. The objective of this paper was to propose a framework for clinical assessment of CMP based on the current literature and following the conceptual model of the International Classification of Functioning and Health (ICF). We propose that the ICF rationale may help to guide the processes, acting as a moderator of the clinical assessment, since it changes the perspective used to obtain and interpret findings during anamnesis and physical examination. Additionally, updated specific knowledge about pain, including that of pain domains and mechanisms, along with effective patient–clinician communication may act as a mediator of CMP assessment. We conduct the readers through the steps of the clinical assessment of CMP using both the proposed moderator and mediators and present a clinical example of application. We suggest that the proposed framework may help clinicians to implement a CMP assessment based on the biopsychosocial model using a critical and updated rationale, potentially improving assessment outcomes, i.e., clinical diagnosis.
Iron Deficiency Anemia in Pediatric Gastroesophageal Reflux Disease
Authors: Lupu Vasile Valeriu , Miron Ingrith , Buga Ana Maria Laura , Gavrilovici Cristina , Tarca Elena , Adam Raileanu Anca , Starcea Iuliana Magdalena , Cernomaz Andrei Tudor , Mocanu Adriana , Lupu Ancuta
Published: December 2022

(1) Background: Gastroesophageal reflux disease (GERD) can cause several complications as a result of the acidic pH over various cellular structures, which have been demonstrated and evaluated over time. Anemia can occur due to iron loss from erosions caused by acidic gastric content. In children, anemia has consequences that, in time, can affect their normal development. This study evaluates the presence of anemia as a result of pediatric gastroesophageal reflux disease. (2) Methods: 172 children were diagnosed with gastroesophageal reflux in the gastroenterology department of a regional children’s hospital in northeast Romania by esophageal pH-metry and they were evaluated for presence of anemia. (3) Results: 23 patients with GERD from the studied group also had anemia, showing a moderate correlation (r = −0.35, p = 0.025, 95% confidence interval) and lower levels of serum iron were found in cases with GERD, with statistical significance (F = 8.46, p = 0.012, 95% confidence interval). (4) Conclusions: The results of our study suggest that there is a relationship between anemia or iron deficiency and gastroesophageal reflux due to reflux esophagitis in children, which needs to be further studied in larger groups to assess the repercussions on children’s development.
Role of Endoscopy in Management of Upper Gastrointestinal Cancers
Authors: Liang Jeff , Jiang Yi , Abboud Yazan , Gaddam Srinivas
Published: December 2022

Upper gastrointestinal (GI) malignancy is a leading cause of cancer-related morbidity and mortality. Upper endoscopy has an established role in diagnosing and staging upper GI cancers, screening for pre-malignant lesions, and providing palliation in cases of advanced malignancy. New advances in endoscopic techniques and technology have improved diagnostic accuracy and increased the therapeutic potential of upper endoscopy. We aim to describe the different types of endoscopic technology used in cancer diagnosis, summarize the current guidelines for endoscopic diagnosis and treatment of malignant and pre-malignant lesions, and explore new potential roles for endoscopy in cancer therapy.
Implementation of a Full Digital Workflow by 3D Printing Intraoral Splints Used in Dental Education: An Exploratory Observational Study with Respect to Students’ Experiences
Authors: Kraemer-Fernandez Pablo , Spintzyk Sebastian , Wahl Ebru , Huettig Fabian , Klink Andrea
Published: December 2022

Fully digital workflows gained acceptance in dental practice and thereby are of interest for undergraduate education. An exploratory clinical observation was designed to track the implementation of such a workflow with novice digital users in order to describe its feasibility, time investment, and pitfalls. Methods: Students were invited to provide feedback for their experiences with a training module that consisted of the following: intraoral scanning, computer-aided design (CAD), manual finishing, and insertion of a 3D-printed bite splint for the lower jaw. Results: A total of 82 fourth-year students participated in the module. The average time required to perform an intraoral scan was 17 m 5 s, and all students were able to design a splint with an average time of 2 h 38 m. Students who indicated prior experience with CAD seem to outperform inexperienced students in both CAD task completion and intraoral scanning. The initial fit was reported as clinically acceptable by 68.5% of the participants, while 79% rated the workflow as very good to satisfactory and indicated that the training was helpful for dental practice. Conclusions: The implementation of a digital workflow in undergraduate dental education is feasible and has acceptable clinical results. However, CAD is time-intensive, and the experience can be challenging.
Occlusal Plane Modification in Clear Aligners Treatment: Three Dimensional Retrospective Longitudinal Study
Authors: Ciavarella Domenico , Fanelli Carlotta , Suriano Carmela , Cazzolla Angela Pia , Campobasso Alessandra , Guida Laura , Laurenziello Michele , Illuzzi Gaetano , Tepedino Michele
Published: December 2022

The purpose of the present study was to evaluate: (i) maxillary occlusal plane changes after clear aligners therapy with a 3D measurement technique; and (ii) as a secondary outcome, if such changes were correlated to the patient’s 1axilla-mandibular divergence. 3D maxillary models of 32 patients (7 males and 25 females; mean age 22.3 +/− 3.4 year) treated with clear aligners were analyzed. The angle (α) between a reference palatine plane and a maxillary occlusal plane was measured. Five angular cephalometric measurements (NSL/MP; PP-OP; OP-MP; PP-MP; PFH/AFH%) were performed and related to Δα. The subjects were further divided into three groups according to facial divergence. After aligner treatment, Δα increased in hyperdivergent patients and decreased in hypodivergent patients (p < 0.05). Δα showed a significant positive correlation with NSL/MP (rho = 0.44) and negative correlation with PFH/AFH% (rho = −0.53). Aligners treatment produced a counterlockwise rotation of the maxillary occlusal plane, even if this rotation occurs differently depending on divergence.
Factors Influencing the Effectiveness of Serious Gaming in the Field of Vocational Orientation
Authors: Keller Christopher , Döring Anna K. , Makarova Elena
Published: December 2022

This study investigates the effectiveness of the serious game like2be, which has been developed to support the individual career orientation process of adolescents by broadening their occupational horizon. In this paper, we present results from an intervention study with n = 809 adolescents in Swiss schools at the lower secondary education level. To analyze the extent to which cognitive, affective, and motivational factors are stimulated and what influence they have on expanding knowledge about occupations (measured learning outcome), we applied confirmatory factor analysis, multiple linear regression, and a structural equation model. The results indicate that the stimulation of cognitive processes through serious gaming has a statistically significant impact on learning outcome, although such factors as enjoyment, flow experience, or self-perceived benefits in playing like2be did not significantly impact gain in knowledge about occupations.
Educational Innovation, Open Educational Resources, and Gender in Latin American Universities
Authors: Rodés Paragarino Virginia , Gewerc Adriana
Published: December 2022

This article analyses the professional identities of nine women academics from different universities and Latin American countries in their relationship with open education (OE) and the production of open educational resources (OERs) for teaching, as a significant key to understand the meaning of being an academic today in a social and economic context such as the Latin American one, and the perspectives this contributes to educational innovation. It arises from the need to deepen research on systemic structures of empowerment for open-access creation and publication, where gender is critical, considering the imbalances that are evident in other fields of educational technology, and specifically in the use of OERs. A qualitative methodology based on grounded theory was implemented, together with the biographical method and digital ethnography, with in-depth interviews with nine women academics from different areas of knowledge from Venezuela, Costa Rica, and Uruguay. The results offer an insight into their professional identities and how this is configured in relation to education, open educational resources, and educational innovation in university teaching. Participation in the open education movement provides a space for empowerment and mobilisation, which contributes to a transformative identity, as an emerging habitus, that underpins educational innovation in this field.
Use of Mobile Phones in Classrooms and Digitalisation of Educational Centres in Barcelona
Authors: Pozos-Pérez Katia , Herrera-Urizar Gustavo , Rivera-Vargas Pablo , Alonso-Cano Cristina
Published: December 2022

In the wake of the COVID-19 pandemic, multiple educational contexts experienced a sudden and accelerated digital transformation. However, this is not a new phenomenon. For years, public and private initiatives have been designed and tested in Spain. In this regard, the role and use of cell phones in the classroom has been a key and, at the same time, controversial aspect. In Barcelona (Catalonia), for example, recent educational policies have promoted the pedagogical use of cell phones. Within this framework, this article analyses whether these initiatives to promote the use of mobile phones are effectively transferred and implemented in the classroom. Using qualitative research, based on co-design, case studies and content analysis, we examined the reality of three educational centres in Barcelona. In these three contexts, field observations, interviews with management teams and ICT coordinators, and discussion groups with teachers were conducted. The information generated was grouped into five main categories of analysis. As a result, it was observed that the mobile phone has been losing prominence in the classroom. Schools tend to prohibit the use of cell phones and prefer computers to give priority to the control of technological tools in order to use the Internet safely. Mobile phones, in this sense, are only used at certain times when there is a pedagogical objective, although there is still a need for more pedagogical and digital training for teachers.
Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System Architecture
Authors: Usman Sardar , Mehmood Rashid , Katib Iyad , Albeshri Aiiad
Published: December 2022

Big data has revolutionized science and technology leading to the transformation of our societies. High-performance computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally, HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realization of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high-performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in situ and transit data analysis. This paper provides an extensive review of cutting-edge research on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.
An Accelerated Time-Domain Iterative Physical Optics Method for Analyzing Electrically Large and Complex Targets
Authors: Sun Tai-Ping , Cong Zhou , He Zi , Ding Dazhi
Published: December 2022

A local area coupling-based time-domain iterative physical optics (TD-LIPO) method for the analysis of the scattering from electrically large and complex targets is proposed in this study. A modulated Gaussian-pulse plane wave is taken as the incident wave. Initially, via a ray-tracing mechanism, the current radiation iteration is limited to the local area, and the iteration times are determined by the bounce times. This approach can lower the enormous computation time. In addition, GPU parallel technology is also applied to accelerate the method. Finally, the TD-LIPO method is used to obtain the scattering echo data matrix of the target under different radar observation angles. An inverse fast Fourier transform (IFFT) is performed on the matrix to obtain the ISAR image under a small bandwidth and small angle. Numerical examples and ISAR images show that the accelerated local area coupling technique can significantly improve computational efficiency and verify feasibility in radar imaging.
Mapping Knowledge Area Analysis in E-Learning Systems Based on Cloud Computing
Authors: Dima Adriana , Bugheanu Alexandru-Mihai , Boghian Ruxandra , Madsen Dag Øivind
Published: December 2022

Traditional educational systems and learning have been significantly impacted by the quick growth of information and communication technology. Moreover, the learning process is increasingly important for socioeconomic and business success in our modern society. Therefore, at present, cloud computing is crucially important in connection with e-learning systems. The goal of this research is to conduct a thorough assessment of scientific production in the field of e-learning and cloud technology fields using the bibliometric analysis method’s quantitative approach in order to comprehensively review and analyze the subject. The current study reviews the literature by focusing on specific research areas and revealing certain trends. The research examines 637 articles published between 2007 and 2022 in the Web of Science database (WoS) using the VOSviewer software version 1.6.18 (Leiden University, The Netherlands) and bibliometrix R-package. The goal of this research is to conduct a thorough assessment of scientific production in the field of e-learning and cloud technology fields using the bibliometric analysis method’s quantitative approach to comprehensively review and analyze the subject. Currently, there is no unified approach and extensive bibliometric review that tackle both of these topics cohesively; thus, this research aims to fill this gap. The results shed light on the structure, evolution, main trends, and effect of the research field of e-learning systems based on cloud computing by intensively evaluating and analyzing the scientific output, key contributions to the subject, and possible directions for future research. The most productive country in terms of scientific knowledge and number of citations is China. It is noteworthy that the interest of researchers comes from various regions of the world, while the most prolific authors come from Serbia, Japan, and Romania. The average citation number per document is 6.8, while the most citations were obtained by highly influential article about critical factors influencing learner satisfaction for successful e-learning. Regarding the conceptual structure that assists researchers to understand keyword evolution and trend, four clusters were identified, which reside around the topics “Technology”, “Education”, “Delivery Systems” and “Cloud services”. Given these points, the current study’s implications reveal the significance of e-learning technologies based on cloud computing, along with the direct correlation between these two elements.
Applying Quantum Search Algorithm to Select Energy-Efficient Cluster Heads in Wireless Sensor Networks
Authors: Roy Kripanita , Kim Myung-Kyun
Published: December 2022

Clustering is an effective topology control approach that evenly distributes loads across sensor nodes, enhances network scalability, and increases the lifetime in wireless sensor networks. In this paper, we propose a novel energy-efficient weighted cluster head (CH) selection approach that improves the overall performance of the network and increases energy efficiency. An optimization strategy is proposed that emphasizes adjusting the transmission range with the appropriate node density, which increases energy efficiency for intra- and inter-cluster communications to 86% and 97%, respectively. In addition, the implementation of a quantum search algorithm for choosing the CH is explained. Compared to the classical method such as EECS and HEED, the proposed quantum search algorithm has a quadratic speed-up advantage. The classical search algorithm requires N steps to find a specific element in an array of N elements, but instead of using a classical algorithm, Grover’s quantum search algorithm minimizes the complexity to O (N). In this work, an energy-efficient cluster head selection approach is illustrated through a classical weighted clustering algorithm, and its implementation is also extended through a quantum weighted search algorithm which is demonstrated by the simulation results.
Automatic Modulation Classification Based on CNN and Multiple Kernel Maximum Mean Discrepancy
Authors: Wang Na , Liu Yunxia , Ma Liang , Yang Yang , Wang Hongjun
Published: December 2022

Automatic modulation classification plays a significant role in numerous military and civilian applications. Deep learning methods have attracted increasing attention and achieved remarkable success in recent years. However, few methods can generalize well across changes in varying channel conditions and signal parameters. In this paper, based on an analysis of the challenging domain shift problem, we proposed a method that can simultaneously achieve good classification accuracy on well-annotated source data and unlabeled signals with varying symbol rates and sampling frequencies. Firstly, a convolutional neural network is utilized for feature extraction. Then, a multiple kernel maximum mean discrepancy layer is utilized to bridge the labeled source domain and unlabeled target domain. In addition, a real-world signal dataset consisting of eight digital modulation schemes is constructed to verify the effectiveness of the proposed method. Experimental results demonstrate that it outperforms state-of-the-art methods, achieving higher accuracy on both source and target datasets.
High-Level Sensor Models for the Reinforcement Learning Driving Policy Training
Authors: Turlej Wojciech
Published: December 2022

Performance limitations of automotive sensors and the resulting perception errors are one of the most critical limitations in the design of Advanced Driver Assistance Systems and Autonomous Driving Systems. Ability to efficiently recreate realistic error patterns in a traffic simulation setup not only helps to ensure that such systems operate correctly in presence of perception errors, but also fulfills a key role in the training of Machine-Learning-based algorithms often utilized in them. This paper proposes a set of efficient sensor models for detecting road users and static road features. Applicability of the models is presented on an example of Reinforcement-Learning-based driving policy training. Experimental results demonstrate a significant increase in the policy’s robustness to perception errors, alleviating issues caused by the differences between the virtual traffic environment used in the policy’s training and the realistic conditions.
Identification and Visualization of a Patient’s Medical Record via Mobile Devices without an Internet Connection
Authors: Laso Sergio , Flores-Martin Daniel , Herrera Juan Luis , Galán-Jiménez Jaime , Berrocal Javier
Published: December 2022

Nowadays, people’s medical records are crucial when it comes to providing treatments, discovering pathologies, or keeping track of health status. Advances in technology have allowed these records to be increasingly digitized, to the point that they can be consulted by specialists from anywhere. This also allows people to report their health status, allergies, or treatments. However, knowing a person’s medical history is a delicate and complex process, and accessing these digitized data is not always possible due to problems with network connectivity or physical communication with the person. In this work, we propose a solution for medical staff to obtain a patient’s medical history through facial recognition, and without the need for an internet connection. This proposal is based on the development of an architecture that allows connecting nearby mobile devices of doctors and patients without an internet connection to obtain the desired information using facial recognition as an authentication method. The architecture has been validated with the development of a mobile application that, by focusing on the patient’s face with the camera of the doctor’s mobile device, makes it possible to obtain the patient’s medical information. With this proposal, it is possible to obtain the medical history of a person in dangerous situations or where the connectivity is limited or non-existent, in a simple and fast way.
An Approach for Matrix Multiplication of 32-Bit Fixed Point Numbers by Means of 16-Bit SIMD Instructions on DSP
Authors: Safonov Ilia , Kornilov Anton , Makienko Daria
Published: December 2022

Matrix multiplication is an important operation for many engineering applications. Sometimes new features that include matrix multiplication should be added to existing and even out-of-date embedded platforms. In this paper, an unusual problem is considered: how to implement matrix multiplication of 32-bit signed integers and fixed-point numbers on DSP having SIMD instructions for 16-bit integers only. For examined tasks, matrix size may vary from several tens to two hundred. The proposed mathematical approach for dense rectangular matrix multiplication of 32-bit numbers comprises decomposition of 32-bit matrices to matrices of 16-bit numbers, four matrix multiplications of 16-bit unsigned integers via outer product, and correction of outcome for signed integers and fixed point numbers. Several tricks for performance optimization are analyzed. In addition, ways for block-wise and parallel implementations are described. An implementation of the proposed method by means of 16-bit vector instructions is faster than matrix multiplication using 32-bit scalar instructions and demonstrates performance close to a theoretically achievable limit. The described technique can be generalized for matrix multiplication of n-bit integers and fixed point numbers via handling with matrices of n/2-bit integers. In conclusion, recommendations for practitioners who work on implementation of matrix multiplication for various DSP are presented.
An Integrated Handheld Electronic Nose for Identifying Liquid Volatile Chemicals Using Improved Gradient-Boosting Decision Tree Methods
Authors: Cao Mengli , Hu Xiong
Published: December 2022

The main ingredients of various odorous products are liquid volatile chemicals (LVC). In human society, identifying the type of LVC is the inner logic of many applications, such as exposing counterfeit products, grading food quality, diagnosing interior environments, and so on. The electronic nose (EN) can serve as a cost-effective, time-efficient, and safe solution to LVC identification. In this paper, we present the design and evaluation of an integrated handheld EN, namely SMUENOSEv2, which employs the NVIDIA Jetson Nano module for running the LVC identification method. All components of SMUENOSEv2 are enclosed in a handheld case. This all-in-one structure makes it convenient to use SMUENOSEv2 for quick on-site LVC identification. To evaluate the performance of SMUENOSEv2, two common odorous products, i.e., perfumes and liquors, were used as the samples to be identified. After sampling data preprocessing and feature generation, two improved gradient-boosting decision tree (GBDT) methods were used for feature classification. Extensive experimental results show that SMUENOSEv2 is capable of identifying LVC with considerably high accuracies. With previously trained GBDT models, the time spent for identifying the LVC type is less than 1 s.
The Effects of the COVID-19 Pandemic on the Digital Competence of Educators
Authors: García Boni , Alario-Hoyos Carlos , Pérez-Sanagustín Mar , Morales Miguel , Jerez Oscar
Published: December 2022

The COVID-19 pandemic is having an undeniable impact on all aspects of society [...]
Stochastic Fixed-Time Tracking Control for the Chaotic Multi-Agent-Based Supply Chain Networks with Nonlinear Communication
Authors: Shi Lili , Guo Wanli , Wang Lu , Bekiros Stelios , Alsubaie Hajid , Alotaibi Ahmed , Jahanshahi Hadi
Published: December 2022

The multi-agent-based supply chain network is a dynamic system consisting of multiple subchains connected by information flows, material flows and capital flow, etc. The consensus of multi-agent systems is often applied to the cooperation between subchains and inventory management in supply chain networks. Considering the ubiquitous external disturbances, this paper mainly considers the fixed-time consensus of a stochastic three-echelon multi-agent-based supply chain system. A nonlinear feedback fixed-time control protocol is constructed for ensuring the consensus of the considered supply chain network. Using the stability theory of stochastic differential equations, sufficient conditions for the fixed-time consensus and the upper bound estimation of the settling time are obtained. Finally, the validity of the control protocol and the correctness of the theoretical analysis are revealed by numerical simulation.
Architectural Threats to Security and Privacy: A Challenge for Internet of Things (IoT) Applications
Authors: Khan Yasser , Su’ud Mazliham Bin Mohd , Alam Muhammad Mansoor , Ahmad Sayed Fayaz , Salim Nur Agus , Khan Nasir
Published: December 2022

The internet of things (IoT) is one of the growing platforms of the current era that has encircled a large population into its domain, and life appears to be useless without adopting this technology. A significant amount of data is generated from an immense number of smart devices and their allied applications that are constructively utilized to automate our daily life activities. This big data requires fast processing, storage, and safe passage through secure channels to safeguard it from any malicious attacks. In such a situation, security is considered crucial to protect the technological resources from unauthorized access or any interruption to disrupt the seamless and ubiquitous connectivity of the IoT from the perception layer to cloud computers. Motivated by this, this article demonstrates a general overview about the technology and layered architecture of the IoT followed by critical applications with a particular focus on key features of smart homes, smart agriculture, smart transportation, and smart healthcare. Next, security threats and vulnerabilities included with attacks on each layer of the IoT are explicitly elaborated. The classification of security challenges such as confidentiality, integrity, privacy, availability, authentication, non-repudiation, and key management is thoroughly reviewed. Finally, future research directions for security concerns are identified and presented.
A Novel 4H-SiC Double Trench MOSFET with Built-In MOS Channel Diode for Improved Switching Performance
Authors: Na Jaeyeop , Kim Kwangsoo
Published: December 2022

This study proposed a novel 4H-SiC double trench metal-oxide-semiconductor field-effect-transistor (DTMCD-MOSFET) structure with a built-in MOS channel diode. Further, its characteristics were analyzed using TCAD simulation. The DTMCD-MOSFET comprised active and dummy gates that were divided horizontally; the channel diode operated through the dummy gate and the p-base and N+ source regions at the bottom of the dummy gate. Because the bult-in channel diode was positioned at the bottom, the DTMCD-MOSEFT minimized static deterioration. Despite having a 5.2% higher specific on-resistance (Ron-sp) than a double-trench MOSFET (DT-MOSFET), the DTMCD-MOSFET exhibited a significantly superior body diode and switching properties. In comparison to the DT-MOSFET, its turn-on voltage (VF) and reverse recovery charge (Qrr) were decreased by 27.2 and 30.2%, respectively, and the parasitic gate-drain capacitance (Crss) was improved by 89.4%. Thus, compared with the DT-MOSFET, the total switching energy loss (Etot) was reduced by 41.4%.
A Machine Learning Approach for the Forecasting of Computing Resource Requirements in Integrated Circuit Simulation
Authors: Wu Yue , Chen Hua , Zhou Min , Yu Faxin
Published: December 2022

For the iterative development of the chip, ensuring that the simulation is completed in the shortest time is critical. To meet this demand, the common practice is to reduce simulation time by providing more computing resources. However, this acceleration method has an upper limit. After reaching the upper limit, providing more CPUs can no longer shorten the simulation time, but will instead waste a lot of computing resources. Unfortunately, the recommended values of the existing commercial tools are often higher than this upper limit. To better match this limit, a machine learning optimization algorithm trained with a custom loss function is proposed. Experimental results demonstrate that the proposed algorithm is superior to commercial tools in terms of both accuracy and stability. In addition, the simulations using the resources predicted by the proposed model maintain the same simulation completion time while reducing core hour consumption by approximately 30%.
Improving Modulation Recognition Using Time Series Data Augmentation via a Spatiotemporal Multi-Channel Framework
Authors: Pi Shuang , Zhang Shuanggen , Wang Shumin , Guo Bochi , Yan Wei
Published: December 2022

Automatic modulation recognition technology with deep learning has a broad prospective owing to big data and computing power. However, the accuracy of modulation recognition largely depends on the massive volume of data and the applicability of the model. Here, to eliminate the difficulties of manual feature extraction, a low accuracy, and a small sample dataset, we propose an effective recognition method that combines time series data augmentation with a spatiotemporal multi-channel learning framework. Compared with other advanced network models, the results showed that the method gave a positive index in the order of 93.5% for ten modulation signal types, which was increased by at least 15%. Especially for QAM16 and QAM64 signals, the average recognition accuracy was improved by nearly 50% at SNRs as low as −2 dB, showing a significant recognition performance. The proposed method provides an attractive method for signal modulation recognition in wireless or wired communication fields.
Ambient Assisted Working Solutions for the Ageing Workforce: A Literature Review
Authors: Spoladore Daniele , Trombetta Alberto
Published: December 2022

The increase in older workers in industrialized countries has become evident in the past two decades. The need to support the ageing workforce to effectively perform their tasks has resulted in Ambient Assisted Working (AAW), consisting of developing “smart” systems that can adapt themselves to workers’ needs by exploiting ambient intelligence (AmI) solutions. In AAW, AmI provides flexible workplace adaptations for a wide range of older workers (including persons characterized by chronic conditions and disabilities), while ensuring the ageing workforce’s safety and comfort within the workplace. This work proposes a systematic literature review with the aim of identifying trends among existing AAW solutions specifically designed for older workers. The review adopted the PRISMA methodology, focusing on journal articles and surveying more than 1500 works. The review underlined an absence of articles completely devoted to this research question. Nonetheless, by extending the research question to existing AmI solutions for workers that could potentially be able to support older workers in performing their working activities, it was possible to draw some considerations on the adoption of AmI for the ageing workforce. Among them, the review identified the different types of supporting AmI solutions provided to AAW, which technologies were adopted, and which workplaces were investigated the most. Finally, this work leveraged the findings of the review process to sketch some future research directions for AAW as a discipline.
Analysis and Design of a High-Frequency Isolated Dual-Transformer DC-DC Resonant Converter
Authors: Li Yinan , Wang Rui , Zhong Liping , Mao Limin , Sun Chuan , Li Xiaodong , Hu Song
Published: December 2022

This paper presents the operation, analysis, design, simulation, and experimental results for a proposed Dual-Transformer DC-DC Resonant Converter (DTRC). A three-arm bridge is employed on the input side and an H-type bridge is employed on the output side of the DTRC, and the two bridges are connected with two high-frequency (HF) transformers. By optimizing the ratio k of the two HF transformers, the proposed DTRC has a lower boundary power for losing zero-voltage switching (ZVS). That means the DTRC has a wider ZVS operation range and lower switching loss when compared with a traditional soft-switching pulse width modulation (PWM) resonant converter. The operation principle, power transfer, ZVS characteristics, and design procedures are analyzed in detail. Both simulation and experimental results prove the feasibility and superiority of the proposed Dual-Transformer DC-DC Resonant Converter.
A Monitoring System for Electric Vehicle Charging Stations: A Prototype in the Amazon
Authors: Lobato Elen , Prazeres Lucas , Medeiros Iago , Araújo Felipe , Rosário Denis , Cerqueira Eduardo , Tostes Maria , Bezerra Ubiratan , Fonseca Wellington , Antloga Andréia
Published: December 2022

Among the main problems faced in the context of electric mobility today, the management and monitoring of electric vehicle charging stations, the integration between the diverse types of technologies that make up its architecture, and its low scalability stand out. Therefore, we will present the implementation and complete integration of an electric vehicle charging system in an electric mobility pilot project being executed in the Amazon region in Brazil. Therefore, a literature review of related works will be presented, and its entire implementation will be addressed, from the charging infrastructure, through its back-end system and its Internet of things platform, to its front-end web system for monitoring charging stations. In addition, a complete prototype is created with a real testbed to verify the scalability of the implemented physical system. Based on the testbed evaluations performed, we observe that the implemented system performs well in receiving and sending data from up to 160 electric vehicle charging stations, achieving an average consumption of 26% for CPU and 95% for memory. In addition, it is important to mention that the deployed system supports horizontal scalability, enabling the connection of more charging stations and making it ideal for other integrated systems similar to ours. Based upon the main results obtained with the implemented system, the possibility of carrying out the management and monitoring of charging stations stands out; the integration of different technologies, from the back end and IoT middleware to its front end; a system that supports scalability, enabling the connection of more charging stations; and a reference architecture for charging station management and monitoring systems for the Amazon region.
Reliability Assessment of SiC-Based Depot Charging Infrastructure with Smart and Bidirectional (V2X) Charging Strategies for Electric Buses
Authors: Verbrugge Boud , Rasool Haaris , Hasan Mohammed Mahedi , Chakraborty Sajib , Geury Thomas , El Baghdadi Mohamed , Hegazy Omar
Published: December 2022

Nowadays, the implementation of smart charging concepts and management strategies with vehicle-to-everything (V2X) functionalities, is required to address the increasing number of battery electric buses (BEBs) in cities. However, the introduction of these new functionalities to the charging systems might affect the lifetime of the charging infrastructure. This has not been investigated yet, although it is an important aspect for the BEB operators. Therefore, this paper performs a detailed reliability assessment to study the impact of smart and bidirectional (V2X) charging on the lifetime of SiC-based high-power off-board charging infrastructure used for BEBs in a depot for overnight charging. In this paper, four different charging current profiles, generated by a smart charging algorithm, are considered. In addition, an electro-thermal model of the charging system is developed to accurately estimate the junction temperature of the switching devices when subjected to the applied charging current profiles. The thermal stress is converted into a number of cycles to failures and accumulated damage by means of a rainflow cycle counting algorithm, a lifetime model and Miner’s damage rule. Finally, a Monte Carlo analysis and a Weibull probability function fit are applied to obtain the system reliability. The results have demonstrated that smart charging strategies can improve the lifetime of the charging system by at least a factor of three compared to conventional uncoordinated charging. Moreover, an uncoordinated charging strategy fails to fulfill the lifetime requirements in the parts per million range, while bidirectional charging could even further enhance the lifetime with a factor of one and a half.
Study of Discharge Inception and Propagation in Liquid–Solid Insulation System under DC–LI Superimposed Constraints
Authors: Moufakkir Younes , Zouaghi Ayyoub , Vollaire Christian
Published: December 2022

High-voltage direct current (HVDC) links are starting to become widely implemented thanks to their interesting advantages such as reduced operation losses, the absence of reactive power, which allows energy transport via underground cables over long distances, and improved power control. The latter advantage is very essential for renewable energy resource integration into power grids. However, a thorough understanding of the behavior of insulation systems for HVDC components is critical so as to ensure a more reliable service. Indeed, the existence of the direct current (DC) voltage in HVDC components may induce surface and space charge accumulation that can stress insulation further or even promote discharge inception and propagation. As such, this work focuses on showcasing the effect of surface charge on streamers that develop on the interface of liquid–solid insulation due to the advent of lightning impulse (LI) voltage in the HVDC link. This study was performed using finite-element-based numerical simulations that include a quasi-electrostatic model for surface charge accumulation and an electrohydrodynamic fluid model for streamer initiation and propagation. The geometry used was point–plane configuration where the high voltage is applied to the needle electrode located above the liquid–solid interface. The obtained results suggest that streamer initiation is affected by both the accumulated surface charge density and polarity. For a positive streamer, an accumulation of positive surface charge increases the discharge inception voltage as a result of a weakening in the electric field, while an accumulation of negative surface charge decreases the discharge inception voltage due to an intensification in the electric field. Moreover, streamer travel distance and velocity are also both shown to be affected by surface charge accumulation.
Progress in the Development and Implementation of Residential EV Charging Stations Based on Renewable Energy Sources
Authors: Marinescu Corneliu
Published: December 2022

Charging electric vehicles (EVs) is of great concern both for future vehicle owners and grid operators, with charging at home being the preferred solution by 90% of owners. In addition, the supplied electricity needs to be clean in order to reduce emissions. This paper presents solutions for charging EVs at home using renewable electricity that fulfils such needs. It discusses: (1) the current landscape; (2) the latest hardware developments in the fields of renewable sources and storage; (3) software optimization for home energy management; (4) residential charging station standards and incentives offered by governments; (5) the evolution towards designing smart homes with low energy consumption from the grid; (6) case studies of particular interest.
Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review
Authors: Bilendo Francisco , Meyer Angela , Badihi Hamed , Lu Ningyun , Cambron Philippe , Jiang Bin
Published: December 2022

In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain.
Design and Evaluation of a High Temperature Phase Change Material Carnot Battery
Authors: Jacob Rhys , Liu Ming
Published: December 2022

In the current study, a high temperature thermal storage system with a hybrid of phase change material and graphite as the storage materials is designed and evaluated as to its applicability for use as a utility-scale Carnot battery. The design includes an externally heated liquid sodium tank, which is used as the heat transfer fluid. This is used to charge and discharge the storage system consisting of a graphite storage medium sandwiched by two phase change materials. Finally, electrical generation is by way of a supercritical carbon dioxide Brayton cycle operated at 700 °C. Detailed modelling of these designs was conducted by way of a previously validated numerical model to predict performance metrics. Using the aforementioned designs, a preliminary cost estimate was undertaken to better determine applicability. From these results, it was found that while the graphite system was the most effective at storing energy, it was also the highest cost due to the high cost of graphite. In total, 18 storage tanks containing nearly 17,400 tons of storage material were required to store the 1200 MWht required to run the sCO2 power block for 10 h. Under the study conditions, the cost of a PCM-based Carnot battery was estimated to be $476/kWhe, comparable to other storage technologies. Furthermore, it was found that if the cost of the graphite and/or steel could be reduced, the cost of the system could be reduced to $321/kWhe.
Gas Losses in the Distribution Networks: An Interdisciplinary Analysis
Authors: Eparu Cristian Nicolae , Neacsa Adrian , Stoica Doru Bogdan
Published: December 2022

This work focuses on a very important and current problem in the gas field: gas losses in natural gas distribution networks and their impact on the environment, as well as on the company operating the network. The paper starts with a bibliographic study and aims to identify the sources leading to losses, estimate loss volumes, reduce these losses by replacing high-risk pipeline sections, as well as trace the economic, environmental, and social impact. The calculation methodologies used in various countries in estimating these consumptions are very diverse. Romania uses a very dense methodology that can prompt very broad variations in the values obtained for technological consumption calculations using Order 18/2014, due to the multitude of parameters that must be estimated. To reduce some of the uncertainties in estimating these parameters, a study was proposed and carried out on the ill-fittings in the natural gas distribution systems. The article presents the experimental stand, the analysis of the experimental data, the methodology for calculating gas losses in the natural gas distribution system through leaking equipment, as well as the results obtained and the conclusions. Moreover, an application was made for a dynamic area check of the gas balance. Based on the correlations between the annual values in M&R stations, AMR, the volume for small consumers, technological consumption, linepack, and the equipment and materials used in the network, useful data were obtained in the diagnosis of problem areas. The end of the paper shows an economic calculation regarding the replacement of problematic pipeline sections in natural gas distribution networks. The difference between the volume of investments and the income from loss reduction is very large, but the aspect of protecting the environment and eliminating technological risks intervenes, thus increasing social security and health.