bluuebunny/crossref_metadata_2025 huggingface dataset:
Prenatal Diagnosis of Fetal Heart Failure
Authors: Srisupundit Kasemsri , Luewan Suchaya , Tongsong Theera
Published: February 2023

Fetal heart failure (FHF) is a condition of inability of the fetal heart to deliver adequate blood flow for tissue perfusion in various organs, especially the brain, heart, liver and kidneys. FHF is associated with inadequate cardiac output, which is commonly encountered as the final outcome of several disorders and may lead to intrauterine fetal death or severe morbidity. Fetal echocardiography plays an important role in diagnosis of FHF as well as of the underlying causes. The main findings supporting the diagnosis of FHF include various signs of cardiac dysfunction, such as cardiomegaly, poor contractility, low cardiac output, increased central venous pressures, hydropic signs, and the findings of specific underlying disorders. This review will present a summary of the pathophysiology of fetal cardiac failure and practical points in fetal echocardiography for diagnosis of FHF, focusing on essential diagnostic techniques used in daily practice for evaluation of fetal cardiac function, such as myocardial performance index, arterial and systemic venous Doppler waveforms, shortening fraction, and cardiovascular profile score (CVPs), a combination of five echocardiographic markers indicative of fetal cardiovascular health. The common causes of FHF are reviewed and updated in detail, including fetal dysrhythmia, fetal anemia (e.g., alpha-thalassemia, parvovirus B19 infection, and twin anemia-polycythemia sequence), non-anemic volume load (e.g., twin-to-twin transfusion, arteriovenous malformations, and sacrococcygeal teratoma, etc.), increased afterload (intrauterine growth restriction and outflow tract obstruction, such as critical aortic stenosis), intrinsic myocardial disease (cardiomyopathies), congenital heart defects (Ebstein anomaly, hypoplastic heart, pulmonary stenosis with intact interventricular septum, etc.) and external cardiac compression. Understanding the pathophysiology and clinical courses of various etiologies of FHF can help physicians make prenatal diagnoses and serve as a guide for counseling, surveillance and management.
Neoductgenesis in Ductal Carcinoma In Situ Coexists with Morphological Abnormalities Characteristic for More Aggressive Tumor Biology
Authors: Łazarczyk Agnieszka , Streb Joanna , Hałubiec Przemysław , Streb-Smoleń Anna , Jach Robert , Hodorowicz-Zaniewska Diana , Łuczyńska Elżbieta , Szpor Joanna
Published: February 2023

Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer that is generally indolent, however, could advance to invasive carcinoma in more than one-third of cases if left untreated. Thus, there is continuous research to find DCIS characteristics that would enable clinicians to decide if it could be left without intensive treatment. Neoductgenesis (i.e., formation of the new duct of improper morphology) is a promising, but still not sufficiently evaluated indicator of future tumor invasiveness. We gathered data from 96 cases of DCIS (histopathological, clinical, and radiological) to assess the relationship between the neoductgenesis and well-established features of high-risk tumor behavior. Furthermore, our intention was to determine which degree of neoductgenesis should be considered clinically significant. Our major finding was that neoductgenesis is strictly related to other characteristics that indicate the invasive potential of the tumor and, to achieve more accurate prediction, neoductgenesis should be accordingly recognized to less strict criteria. Therefore, we conclude that neoductgenesis is another important revelator of tumor malignancy and that it requires further investigation during prospective controlled trials.
How to Identify Advanced Fibrosis in Adult Patients with Non-Alcoholic Fatty Liver Disease (NAFLD) and Non-Alcoholic Steatohepatitis (NASH) Using Ultrasound Elastography—A Review of the Literature and Proposed Multistep Approach
Authors: Taru Madalina-Gabriela , Neamti Lidia , Taru Vlad , Procopciuc Lucia Maria , Procopet Bogdan , Lupsor-Platon Monica
Published: February 2023

Non-alcoholic fatty liver disease (NAFLD), and its progressive form, non-alcoholic steatohepatitis (NASH), represent, nowadays, real challenges for the healthcare system. Liver fibrosis is the most important prognostic factor for NAFLD, and advanced fibrosis is associated with higher liver-related mortality rates. Therefore, the key issues in NAFLD are the differentiation of NASH from simple steatosis and identification of advanced hepatic fibrosis. We critically reviewed the ultrasound (US) elastography techniques for the quantitative characterization of fibrosis, steatosis, and inflammation in NAFLD and NASH, with a specific focus on how to differentiate advanced fibrosis in adult patients. Vibration-controlled transient elastography (VCTE) is still the most utilized and validated elastography method for liver fibrosis assessment. The recently developed point shear wave elastography (pSWE) and two-dimensional shear wave elastography (2D-SWE) techniques that use multiparametric approaches could bring essential improvements to diagnosis and risk stratification.
Timing of Early Postoperative MRI following Primary Glioblastoma Surgery—A Retrospective Study of Contrast Enhancements in 311 Patients
Authors: Rykkje Alexander Malcolm , Larsen Vibeke Andrée , Skjøth-Rasmussen Jane , Nielsen Michael Bachmann , Carlsen Jonathan Frederik , Hansen Adam Espe
Published: February 2023

An early postoperative MRI is recommended following Glioblastoma surgery. This retrospective, observational study aimed to investigate the timing of an early postoperative MRI among 311 patients. The patterns of the contrast enhancement (thin linear, thick linear, nodular, and diffuse) and time from surgery to the early postoperative MRI were recorded. The primary endpoint was the frequencies of the different contrast enhancements within and beyond the 48-h from surgery. The time dependence of the resection status and the clinical parameters were analysed as well. The frequency of the thin linear contrast enhancements significantly increased from 99/183 (50.8%) within 48-h post-surgery to 56/81 (69.1%) beyond 48-h post-surgery. Similarly, MRI scans with no contrast enhancements significantly declined from 41/183 (22.4%) within 48-h post-surgery to 7/81 (8.6%) beyond 48-h post-surgery. No significant differences were found for the other types of contrast enhancements and the results were robust in relation to the choice of categorisation of the postoperative periods. Both the resection status and the clinical parameters were not statistically different in patients with an MRI performed before and after 48 h. The findings suggest that surgically induced contrast enhancements are less frequent when an early postoperative MRI is performed earlier than 48-h, supporting the recommendation of a 48-h window for an early postoperative MRI.
Retinal Vascular Tortuosity Index Change after Idiopathic Epiretinal Membrane Surgery: Does Internal Limiting Membrane Peeling Affect Retinal Vascular Tortuosity?
Authors: Yanık Özge , Aydın Ellialtıoğlu Pınar , Demirel Sibel , Batıoğlu Figen , Özmert Emin
Published: February 2023

Background: Idiopathic epiretinal membrane (iERM) surgery is one of the most commonly performed vitreoretinal surgeries, and the issue of internal limiting membrane (ILM) peeling in ERM surgery is still controversial. The aims of this study are to evaluate the changes in retinal vascular tortuosity index (RVTI) after pars plana vitrectomy for the iERM using optical coherence tomography angiography (OCTA) and to assess whether ILM peeling has an additional effect on RVTI reduction. Methods: This study included25 eyes of 25 iERM patients who underwent ERM surgery. The ERM was removed without ILM peeling in 10 eyes (40.0%), and the ILM was peeled in addition to the ERM in 15 eyes (60.0%). The existence of the ILM after ERM peeling was checked with second staining in all eyes. Best corrected visual acuity (BCVA) and 6 × 6 mm en-face OCTA images were recorded before surgery and at the first month postoperatively. A skeleton model of the retinal vascular structure was created following Otsu binarization of en-face OCTA images using ImageJ software (1.52U). RVTI was calculated as the ratio of each vessel length to its Euclidean distance on the skeleton model using the Analyze Skeleton plug-in. Results: The mean RVTI declined from 1.220 ± 0.017 to 1.201 ± 0.020 (p = 0.036) in eyes with ILM peeling and from 1.230 ± 0.038 to 1.195 ± 0.024 in eyes without ILM peeling (p = 0.037). There was no difference between the groups in terms of postoperative RVTI (p = 0.494). A statistically significant correlation was found between postoperative RVTI and postoperative BCVA (rho = 0.408, p = 0.043). Conclusions: The RVTI is an indirect indicator of the traction created by the iERM on retinal microvascular structures, and it was effectively reduced after iERM surgery. The postoperative RVTIs were similar in cases who underwent iERM surgery with or without ILM peeling. Therefore, ILM peeling may not have an additive effect on the loosening of microvascular traction and thus may be reserved for recurrent ERM surgeries.
Innovation in Hyperinsulinemia Diagnostics with ANN-L(atin square) Models
Authors: Rankovic Nevena , Rankovic Dragica , Lukic Igor
Published: February 2023

Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi’s orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole.
White Matter Lesions Identified by Magnetic Resonance in Women with Migraine: A Volumetric Analysis and Clinical Correlations
Authors: Silva Natália de Oliveira , Maciel Nicoly Machado , Nather Júlio César , Carvalho Gabriela Ferreira , Pinheiro Carina Ferreira , Bigal Marcelo Eduardo , Santos Antônio Carlos dos , Bevilaqua-Grossi Debora , Dach Fabiola
Published: February 2023

Background: Repeated migraine attacks and aura could independently cause structural changes in the central nervous system. Our research aims to study the correlation of migraine type, attack frequency, and other clinical variables with the presence, volume and localization of white matter lesions (WML), in a controlled study. Methods: Sixty volunteers from a tertiary headache center were selected and divided equally into four groups: episodic migraine without aura (MoA), episodic migraine with aura (MA), chronic migraine (CM) and controls (CG). Voxel-based morphometry techniques were used to analyze WML. Results: There were no differences in WML variables between groups. There was a positive correlation between age and the number and total volume of WMLs, which persisted in the comparison categorized by size and brain lobe. Disease duration was positively correlated with the number and total volume of WML, and when controlled by age, the correlation maintained significance only for the insular lobe. Aura frequency was associated with frontal and temporal lobe WMLs. There was no statistically significant correlation between WML and other clinical variables. Conclusion: Migraine overall is not a risk factor for WML. Aura frequency is, however, associated with temporal WML. Disease duration, in adjusted analyses that account for age, is associated with insular WML.
A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet
Authors: Cifci Mehmet Akif
Published: February 2023

Lung cancer is a leading cause of cancer-related deaths globally. Early detection is crucial for improving patient survival rates. Deep learning (DL) has shown promise in the medical field, but its accuracy must be evaluated, particularly in the context of lung cancer classification. In this study, we conducted uncertainty analysis on various frequently used DL architectures, including Baresnet, to assess the uncertainties in the classification results. This study focuses on the use of deep learning for the classification of lung cancer, which is a critical aspect of improving patient survival rates. The study evaluates the accuracy of various deep learning architectures, including Baresnet, and incorporates uncertainty quantification to assess the level of uncertainty in the classification results. The study presents a novel automatic tumor classification system for lung cancer based on CT images, which achieves a classification accuracy of 97.19% with an uncertainty quantification. The results demonstrate the potential of deep learning in lung cancer classification and highlight the importance of uncertainty quantification in improving the accuracy of classification results. This study’s novelty lies in the incorporation of uncertainty quantification in deep learning for lung cancer classification, which can lead to more reliable and accurate diagnoses in clinical settings.
An Ensemble and Multi-View Clustering Method Based on Kolmogorov Complexity
Authors: Zamora Juan , Sublime Jérémie
Published: February 2023

The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the booming number of multi-view data, but also of clustering algorithms capable of producing a wide variety of representations for the same objects, merging clustering partitions to achieve a single clustering result has become a complex problem with numerous applications. To tackle this problem, we propose a clustering fusion algorithm that takes existing clustering partitions acquired from multiple vector space models, sources, or views, and merges them into a single partition. Our merging method relies on an information theory model based on Kolmogorov complexity that was originally proposed for unsupervised multi-view learning. Our proposed algorithm features a stable merging process and shows competitive results over several real and artificial datasets in comparison with other state-of-the-art methods that have similar goals.
Dollar-Yuan Battle in the World Trade Network
Authors: Coquidé Célestin , Lages José , Shepelyansky Dima L.
Published: February 2023

From the Bretton Woods agreement in 1944 till the present day, the US dollar has been the dominant currency in world trade. However, the rise of the Chinese economy has recently led to the emergence of trade transactions in Chinese yuan. Here, we mathematically analyze how the structure of international trade flows would favor a country to trade whether in US dollar or in Chinese yuan. The trade currency preference of a country is modeled as a binary variable with the properties of a spin in an Ising model. The computation of this trade currency preference is based on the world trade network built from the 2010–2020 UN Comtrade data and is determined by two multiplicative factors: the relative weight of trade volume exchanged by the country with its direct trade partners and the relative weight of its trade partners in global international trade. The performed analysis, based on the convergence of the Ising spin interactions, shows that from 2010 to present a transition took place, and the majority of the world countries would now have a preference to trade in Chinese yuan if one only considers the world trade network structure.
Non-Local Parallel Processing and Database Settlement Using Multiple Teleportation Followed by Grover Post-Selection
Authors: Delgado Francisco , Cardoso-Isidoro Carlos
Published: February 2023

Quantum information applications emerged decades ago, initially introducing a parallel development that mimicked the approach and development of classical computer science. However, in the current decade, novel computer-science concepts were rapidly extended to the fields of quantum processing, computation, and communication. Thus, areas such as artificial intelligence, machine learning, and neural networks have their quantum versions; furthermore, the quantum brain properties of learning, analyzing, and gaining knowledge are discussed. Quantum properties of matter conglomerates have been superficially explored in such terrain; however, the settlement of organized quantum systems able to perform processing can open a new pathway in the aforementioned domains. In fact, quantum processing involves certain requisites as the settlement of copies of input information to perform differentiated processing developed far away or in situ to diversify the information stored there. Both tasks at the end provide a database of outcomes with which to perform either information matching or final global processing with at least a subset of those outcomes. When the number of processing operations and input information copies is large, parallel processing (a natural feature in quantum computation due to the superposition) becomes the most convenient approach to accelerate the database settlement of outcomes, thus affording a time advantage. In the current study, we explored certain quantum features to realize a speed-up model for the entire task of processing based on a common information input to be processed, diversified, and finally summarized to gain knowledge, either in pattern matching or global information availability. By using superposition and non-local properties, the most valuable features of quantum systems, we realized parallel local processing to set a large database of outcomes and subsequently used post-selection to perform an ending global processing or a matching of information incoming from outside. We finally analyzed the details of the entire procedure, including its affordability and performance. The quantum circuit implementation, along with tentative applications, were also discussed. Such a model could be operated between large processing technological systems using communication procedures and also on a moderately controlled quantum matter conglomerate. Certain interesting technical aspects involving the non-local control of processing via entanglement were also analyzed in detail as an associated but notable premise.
An Irreversible and Revocable Template Generation Scheme Based on Chaotic System
Authors: Liu Jinyuan , Wang Yong , Wang Kun , Liu Zhuo
Published: February 2023

Face recognition technology has developed rapidly in recent years, and a large number of applications based on face recognition have emerged. Because the template generated by the face recognition system stores the relevant information of facial biometrics, its security is attracting more and more attention. This paper proposes a secure template generation scheme based on a chaotic system. Firstly, the extracted face feature vector is permuted to eliminate the correlation within the vector. Then, the orthogonal matrix is used to transform the vector, and the state value of the vector is changed, while maintaining the original distance between the vectors. Finally, the cosine value of the included angle between the feature vector and different random vectors are calculated and converted into integers to generate the template. The chaotic system is used to drive the template generation process, which not only enhances the diversity of templates, but also has good revocability. In addition, the generated template is irreversible, and even if the template is leaked, it will not disclose the biometric information of users. Experimental results and theoretical analysis on the RaFD and Aberdeen datasets show that the proposed scheme has good verification performance and high security.
Earthquake Nowcasting: Retrospective Testing in Greece
Authors: Chouliaras Gerasimos , Skordas Efthimios S. , Sarlis Nicholas V.
Published: February 2023

Earthquake nowcasting (EN) is a modern method of estimating seismic risk by evaluating the progress of the earthquake (EQ) cycle in fault systems. EN evaluation is based on a new concept of time, termed ’natural time’. EN employs natural time, and uniquely estimates seismic risk by means of the earthquake potential score (EPS), which has been found to have useful applications both regionally and globally. Amongst these applications, here we focused on Greece since 2019, for the estimation of the EPS for the largest-magnitude events, MW(USGS) ≥ 6, that occurred during our study period: for example, the MW= 6.0 WNW-of-Kissamos EQ on 27 November 2019, the MW= 6.5 off-shore Southern Crete EQ on 2 May 2020, the MW= 7.0 Samos EQ on 30 October 2020, the MW= 6.3 Tyrnavos EQ on 3 March 2021, the MW= 6.0 Arkalohorion Crete EQ on 27 September 2021, and the MW= 6.4 Sitia Crete EQ on 12 October 2021. The results are promising, and reveal that the EPS provides useful information on impending seismicity.
Statistical Topology—Distribution and Density Correlations of Winding Numbers in Chiral Systems
Authors: Guhr Thomas
Published: February 2023

Statistical Topology emerged as topological aspects continue to gain importance in many areas of physics. It is most desirable to study topological invariants and their statistics in schematic models that facilitate the identification of universalities. Here, the statistics of winding numbers and of winding number densities are addressed. An introduction is given for readers with little background knowledge. Results that my collaborators and I obtained in two recent works on proper random matrix models for the chiral unitary and symplectic cases are reviewed, avoiding a technically detailed discussion. There is a special focus on the mapping of topological problems to spectral ones as well as on the first glimpse of universality.
Learnings Networks and Professional Development in Building Energy Management Systems Industry
Authors: Soleymani Ali , van den Brom Paula , Ahmed Samir , Konings Maaike , Sjoer Ellen , Itard Laure , Zeiler Wim , De Laat Maarten , Specht Marcus
Published: February 2023

The energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.
Indoor Localization Method for a Mobile Robot Using LiDAR and a Dual AprilTag
Authors: Huang Yuan-Heng , Lin Chin-Te
Published: February 2023

Global localization is one of the important issues for mobile robots to achieve indoor navigation. Nowadays, most mobile robots rely on light detection and ranging (LiDAR) and adaptive Monte Carlo localization (AMCL) to realize their localization and navigation. However, the reliability and performance of global localization only using LiDAR are restricted due to the monotonous sensing feature. This study proposes a global localization approach to improve mobile robot global localization using LiDAR and a dual AprilTag. Firstly, the spatial coordinate system constructed with two neighboring AprilTags is applied as the reference basis for global localization. Then, the robot pose can be estimated by generating precise initial particle distribution for AMCL based on the relative tag positions. Finally, in pose tracking, the count and distribution of AMCL particles, evaluating the certainty of localization, is continuously monitored to update the real-time position of the robot. The contributions of this study are listed as follows. (1) Compared to the localization method only using LiDAR, the proposed method can locate the robot’s position with a few iterations and less computer power consumption. (2) The failure localization issues due to the many similar indoor features can be solved. (3) The error of the global localization can be limited to an acceptable range compared to the result using a single tag.
Preventing Keratoconus through Eye Rubbing Activity Detection: A Machine Learning Approach
Authors: Nokas George , Kotsilieris Theodore
Published: February 2023

Keratoconus is a non-inflammatory disease of the eyes diagnosed in more than 1/2000 people, making it significantly common. Among others, eye rubbing has been identified as a risk factor for the development of keratoconus. The severity of the disease strongly depends on the frequency and force of eye rubbing. Vast research efforts have focused on diagnosing keratoconus through the application of artificial intelligence techniques over optical coherence tomography images and corneal measurements. However, to the best of the authors’ knowledge, no studies have been conducted which provide an eye rubbing detection and alert mechanism for keratoconus prevention. This study intends to help close this research gap. An inertial measurement unit that is dedicated to collecting hand motion data and machine learning techniques are jointly employed for the early detection of potential problems and complications. Four conventional classification methods (support vector machines, decision trees, random forest, and XGBoost) were evaluated and compared. All methods attain high-quality accuracy results, with SVMs, RF, and XGBoost slightly outperforming DTs. As the results reveal, the performance of all methods is remarkable, allowing the integration of such a solution in wearable devices such as smartwatches to be considered for the early detection of eye rubbing and keratoconus prevention.
Innovative Inertial Response Imitation and Rotor Speed Recovery Control Scheme for a DFIG
Authors: Xue Xiaocen , Huang Jiejie , Sang Shun
Published: February 2023

This paper proposes an innovative inertial response imitation (IRI) and rotor speed recovery (RSR) control scheme of a doubly-fed induction generator (DFIG, Type 3 wind turbine generator) to provide better frequency support response and RSR services for a high wind power penetrated electric power grid. To achieve the first benefit, the coupling relationship between the control coefficient of DFIGs and the frequency deviation was established by using the exponential function so that the control coefficient becomes large with the increasing frequency deviations and sizes of disturbance. After supporting the system frequency, the exponential function was employed to schedule the dynamic control coefficient to alleviate the negative effects of RSR on the instantaneous system frequency. The benefits of the proposed IIR and RSR strategy were investigated in a test system under various scenarios of sizes of disturbance and wind speed conditions. Test results clearly demonstrate that the proposed IIR and RSR strategy is capable of boosting the maximum system frequency excursion and reducing the negative influences on the system frequency during the speed recovery period.
Optimal Charging and Discharging Strategies for Electric Cars in PV-BESS-Based Marina Energy Systems
Authors: Jozwiak Dawid , Pillai Jayakrishnan Radhakrishna , Ponnaganti Pavani , Bak-Jensen Birgitte , Jantzen Jan
Published: February 2023

The emerging concept of integrated community energy systems (ICESs) proves its suitability for improving the operation of local grids—increasing self-consumption from local generation, enhancing the load factor, and reducing energy cost. In Ballen marina—located on the Danish island of Samsø—the battery energy storage system (BESS)’s action can be possibly complemented by the flexibility of boats and electric cars. With the greater involvement of energy consumers, the energy system’s performance may become more efficient—from both technical and economic perspectives. Within this framework, the optimal charging and discharging strategies of the marina’s electric cars were developed and evaluated. The car usage profile was generated, utilising a stochastic approach to resemble daily variations in the driving pattern. The optimal charging strategy was established, subsequently integrating this action with boat flexibility. As a future scenario, the benefits of vehicle-to-grid (V2G) technology implementation were examined, proving significant enhancements of the future marina’s grid—with increased photovoltaic (PV) generation capacity and the number of electric cars. The economic benefits of bidirectional charging were proven, with ample advantages for the marina and the rental company, leading to cost savings of up to 51.7% and minimising the energy export by 21.3%. Therefore, increasing the integration level of Ballen marina’s flexible units—electric cars and boats—was concluded to be an important goal for the coming years.
Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT
Authors: Fu Xiuhua , Peng Rongqun , Yuan Wenhao , Ding Tian , Zhang Zhe , Yu Peng , Kadoch Michel
Published: February 2023

Resource management is a key issue that needs to be addressed in the future smart Internet of Things (IoT). This paper focuses on a Federated Learning (FL)-based resource management mechanism in IoT. It incorporates blockchain technology to guarantee the security of the FL model parameters exchange. We propose an IoT resource management framework incorporating blockchain and federated learning technologies; then, a specific FL-based resource management with a blockchain trust assurance algorithm is given. We use a Support Vector Machine (SVM) classifier to detect malicious nodes in order to avoid the impact on the performance of the FL-based algorithm. Finally, we perform simulation to verify the SVM classification effect and the proposed algorithm performance. The results show that the SVM-based malicious node identification accuracy can be acceptable. Moreover, the proposed algorithm obtains better performance when malicious nodes are excluded from the FL selected participant.
Research on Testing Method for Shielding Effectiveness of Irregular Cavity Based on Field Distribution Characteristics
Authors: Ren Jinjing , Pan Yuhao , Zhou Zhongyuan , Zhang Tao
Published: February 2023

A method of measuring the shielding effectiveness of the irregular cavity is proposed in this paper. An electromagnetic topology model of the irregular cavity is established according to the characteristics of the irregular cavity, and the electromagnetic field distribution characteristics inside the irregular cavity are obtained based on the simulation of the field distribution of the irregular cavity. Combined with the test method of regular cavity shielding effectiveness, the characterization and testing methods of the shielding effectiveness of the irregular cavity are given comprehensively, which compared with the conventional testing method, verifies how effectively the shielding effectiveness of the irregular cavity is tested.
Interoperability Benefits and Challenges in Smart City Services: Blockchain as a Solution
Authors: Biswas Sujit , Yao Zigang , Yan Lin , Alqhatani Abdulmajeed , Bairagi Anupam Kumar , Asiri Fatima , Masud Mehedi
Published: February 2023

The widespread usage of smart devices with various city-centric services speeds up and improves civic life, in contrast to growing privacy and security concerns. Security issues are exacerbated when e-government service providers trade their services within a centralised framework. Due to security concerns, city-centric centralised services are being converted to blockchain-based systems, which is a very time-consuming and challenging process. The interoperability of these blockchain-based systems is also more challenging due to protocol variances, an excessive amount of local transactions that raise scalability and rapidly occupy memory. In this paper, we have proposed a framework for interoperability across various blockchain-based smart city services. It also summarises how independent service providers might continue self-service choices (i.e., local transactions) without overloading the blockchain network and other organisations. A simulated interoperability network is used to show the network’s effectiveness. The experimental outcomes show the scalability and memory optimization of the blockchain network.
A Machine Learning-Based Intrusion Detection System for IoT Electric Vehicle Charging Stations (EVCSs)
Authors: ElKashlan Mohamed , Elsayed Mahmoud Said , Jurcut Anca Delia , Azer Marianne
Published: February 2023

The demand for electric vehicles (EVs) is growing rapidly. This requires an ecosystem that meets the user’s needs while preserving security. The rich data obtained from electric vehicle stations are powered by the Internet of Things (IoT) ecosystem. This is achieved through us of electric vehicle charging station management systems (EVCSMSs). However, the risks associated with cyber-attacks on IoT systems are also increasing at the same pace. To help in finding malicious traffic, intrusion detection systems (IDSs) play a vital role in traditional IT systems. This paper proposes a classifier algorithm for detecting malicious traffic in the IoT environment using machine learning. The proposed system uses a real IoT dataset derived from real IoT traffic. Multiple classifying algorithms are evaluated. Results were obtained on both binary and multiclass traffic models. Using the proposed algorithm in the IoT-based IDS engine that serves electric vehicle charging stations will bring stability and eliminate a substantial number of cyberattacks that may disturb day-to-day life activities.
An Unmanned Aerial Vehicle (UAV) System for Disaster and Crisis Management in Smart Cities
Authors: Alawad Wedad , Halima Nadhir Ben , Aziz Layla
Published: February 2023

Over the course of the last decade, the unmanned aerial vehicle (UAV) research community has received a significant amount of attention. Emergency response operations, such as those that follow a natural disaster, are one of the civil applications that could benefit from the use of UAVs in disaster and crisis management. In the event of a catastrophic event, it would be extremely beneficial for both victims and first responders to have access to a UAV network that is capable of deploying independently and offering communication services. However, when working with complicated situations, one of the most difficult things is coming up with exploratory paths for the networks involved. A crisis and disaster management system using a swarm optimization algorithm (SOA) is proposed to assist in disaster and crisis management. In this system, the UAV search and rescue team follows the strategy called the delay tolerant network, which has the ability to explore. The proposed approach is able to find the global maximum in the search space without ever settling for a suboptimal solution. This work has two primary objectives: the first is to investigate a potential disaster zone, and the second is to direct the UAV to a number of victim groups that were found during the investigation phase. For the purpose of performing a characterization, performance metrics such as delay, throughput, performance rate, and path loss have been analyzed. The results show the superiority of the performance over the existing work.
Modeling of Turbulent Convective Heat-Transfer Characteristics in a Concentric Annular Channel
Authors: Chen Longfei , Zhang Huaibao , Li Liugang , Wang Guangxue
Published: February 2023

Turbulent convective heat-transfer characteristics in a concentric annular channel with both walls heated are theoretically modeled and numerically computed in this article. Generalized algebraic predictive models and equations for heating over a single wall are first reviewed by summarizing the well-known methods in the literature. These methods are then scrutinized according to the most recent investigations such that new viewpoints and corrections are introduced accordingly. Moreover, the application of superposition in temperature is used in the current work instead of the Nusselt number as seen in the literature. The numerical integration method is applied to the generalized equations to obtain the solutions, which are found to be in decent agreement with the direct numerical simulation (DNS) data in the literature. The results in this work also indicate that the wall heat flux density ratio and the annular radius ratio are two key factors that have a great influence on the heat-transfer characteristics of the case with both walls heated.
Analysis of the Influence of the Skin Effect on the Efficiency and Power of the Receiver in the Periodic WPT System
Authors: Stankiewicz Jacek Maciej
Published: February 2023

The article shows an analysis of the influence of the skin effect on the maximum efficiency and maximum power of a receiver in a wireless power transfer system (WPT). For this purpose, the original solution of the WPT system was used, which contained periodically arranged planar coils. The results concern the multi-variant analysis of the WPT system. The geometry of the coils was taken into account, i.e., the size of coils, the number of turns, as well as the distance between the transmitting and receiving coils. The calculations were carried out over the frequency range of 0.1–1 MHz. In order to analyse the influence of the skin effect on the proposed WPT system, two approaches were used: analytical and numerical. The article analyses the appropriate selection of load impedance in order to obtain maximum efficiency or maximum power of the receiver. In this analysis, the influence of the skin effect on each of the two operating procedures was examined. The obtained analytical and numerical results differed by no more than 0.45%, which confirmed the correctness of the proposed WPT model. Based on the results, it was determined that the greatest influence of the skin effect occurred at 1 MHz. Then, the efficiency decreased by no more than 9%, while in the case of the receiver power decreased by an average of 25%. Detailed analysis shows the influence of the skin effect on the system parameters, and can also be an important element in the design of WPT systems.
Does Information and Communication Technology Trade Openness Matter for China’s Energy Transformation and Environmental Quality?
Authors: Wang Yinhui , He Yugang
Published: February 2023

Energy transformation and environmental quality are now fundamental components of China’s economic development plans, which are being reorganized to ensure the dependability of the energy supply and protect environmental quality. Nonetheless, technical inefficiency is one of the most significant obstacles to achieving these overall objectives. Therefore, utilizing yearly data from 2000 to 2021 and the autoregressive distributed lag model, this article examines the implications of information and communication technology trade openness on China’s energy transformation and environmental quality. The findings indicate that information and communication technology trade openness has a favorable impact on environmental quality as a consequence of its negative impact on carbon dioxide emissions. Moreover, the findings indicate that information and communication technology trade openness has a beneficial impact on energy transformation due to its positive impact on renewable energy consumption and negative impact on energy intensity. In conclusion, our findings demonstrate the necessity of eliminating obstacles to information and communication technology trade in China in terms of guaranteeing energy transformation and environmental quality. Therefore, it is optimal for China’s government to progressively reduce trade barriers in order to increase cross-border flows of information and communication technology products.
Determination of Weights for the Integrated Energy System Assessment Index with Electrical Energy Substitution in the Dual Carbon Context
Authors: Zhao Yitao , Lv Xin , Shen Xin , Wang Gang , Li Zhao , Yu Pinqin , Luo Zhao
Published: February 2023

Electrical energy substitution is an important way to achieve the optimization of the energy consumption structure as well as to alleviate environmental problems, and it is also an important source of benefits of the integrated energy system. However, there are few works that study the effects of electrical energy substitution on the construction of the integrated energy system (IES) and electrical energy substitution work without incentives carried out in the IES. To this end, this paper proposed a G1 method with constructing consistency matrix to determine the evaluation index weights for the IES with electrical energy substitution. Specifically, we firstly construct the evaluation index system for the IES including electrical energy substitution indicators, low-carbon indicators, technical indicators and economic indicators as well as their secondary indicators. Then, a tri-level evaluation index system of target-criteria-indicator benefits is established based on pertinent standards and norms, taking the practical operability into account. Finally, a G1-method-constructed consistency judgment matrix is proposed. Compared with the G1 method, it is simple and practical, and the weight calculation results are more in line with the reality, which effectively solves the consistency problem of the judgment matrix. The rationality and feasibility of the proposed weight calculation method are verified by the calculation and analysis of an example.
Rock–Oil–Brine Dominant Mechanisms in Smart Water Flooding
Authors: Maya Gustavo , Carreño Otero Aurora L. , Monares Bueno Fabián L. , Romero Bohórquez Arnold R. , Cortés Farid B. , Franco Camilo A. , Manrique Eduardo
Published: February 2023

Recent research has highlighted wettability alteration as the main consequence of the different mechanisms involved in technologies such as adjusted brine composition water flooding (ABCW) and low-salinity water flooding (LSW). However, studies are still needed to give a phenomenological explanation, and the most influential components of the system (rock–oil–brine) must be clarified. This work focuses on determining the most relevant variables for the smart water effects to occur. Static (contact angles) and dynamic tests (coreflooding) were conducted. For the static tests, aged Berea slices, a specific crude oil (27° API, 10.5 cp at 60 °C), and mono and divalent inorganic salts (Na+, K+, Ca2+, and Mg2+/Cl−) were used in 3 different concentrations of 1000, 3000, and 5000 ppm (ionic strength variation between 0.015 and 0.06) to establish the wettability state by measuring the contact angles of the system. When salts containing chloride were evaluated, a decrease in oil wettability was observed at 5000 ppm. At 3000 and 1000 ppm, tendencies depended on the particular cation. Three brines were selected from the contact angle experiments to be used in coreflooding assays, considering a particular design to identify ion exchange from the rock–oil–brine system. The first assay was carried out in the absence of crude oil as a baseline to determine the ion exchange between the brine and the rock, and a second test considered crude oil to provide insight into ion exchange and its effect on displacement efficiency. Capillary electrophoresis was used in this research as a novel contribution to the systematic study of oil displacement tests, and it has proven to be a powerful tool for understanding the mechanisms involved. The results show that the variations in the concentrations detected in the displacement effluents were the product of the interactions between rock, oil, and brine since the concentrations measured in the absence of oil phase were comparable to those in the injection brine. Significant variations in the effluent ion concentrations were determined for the different brines used, and increases in the pressure differentials were observed for the KCl and CaCl2 brines. These results suggest that the oil–brine ion exchange (salting in/out) represents a relevant mechanism to explain the observed displacement efficiencies and differential pressures. The ionic enrichment of the water phase due to the salting in/out effect needs to be better understood.
A Review of Electric Motors with Soft Magnetic Composite Cores for Electric Drives
Authors: Guo Youguang , Ba Xin , Liu Lin , Lu Haiyan , Lei Gang , Yin Wenliang , Zhu Jianguo
Published: February 2023

Electric motors play a crucial role in modern industrial and domestic applications. With the trend of more and more electric drives, such as electric vehicles (EVs), the requirements for electric motors become higher and higher, e.g., high power density with good thermal dissipation and high reliability in harsh environments. Many efforts have been made to develop high performance electric motors, such as the application of advanced novel electromagnetic materials, modern control algorithms, advanced mathematical modeling, numerical computation, and artificial intelligence based optimization design techniques. Among many advanced magnetic materials, soft magnetic composite (SMC) appears very promising for developing novel electric motors, thanks to its many unique properties, such as magnetic and thermal isotropies, very low eddy current loss, and the prospect of low-cost mass production. This paper aims to present a comprehensive review about the application of SMC for developing various electric motors for electric drives, with emphasis on those with three-dimensional (3D) magnetic flux paths. The major techniques developed for designing the 3D flux SMC motors are also summarized, such as vectorial magnetic property characterization and system-level multi-discipline robust design optimization. Major challenges and possible future work in this area are also discussed.