bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Physical Exercise and Rehabilitative Implications in the Process of Nerve Repair in Peripheral Neuropathies: A Systematic Review
Authors: Chiaramonte Rita , Pavone Vito , Testa Gianluca , Pesce Isabella , Scaturro Dalila , Musumeci Giuseppe , Mauro Giulia Letizia , Vecchio Michele
Published: January 2023

Background: The various mechanisms involved in peripheral nerve regeneration, induced by exercise and electrical nerve stimulation, are still unclear. Objective: The aim of this review was to summarize the influence of physical exercise and/or electrical stimulation on peripheral nerve repair and regeneration and the variation of impact of intervention depending on timing, as well as kind and dosage of the intervention. A literature survey was conducted on PubMed, Scopus, and Web of Science, between February 2021 to July 2021, with an update in September 2022. Methodology: The literature search identified 101,386 articles with the keywords: “peripheral nerve” OR “neuropathy” AND “sprouting” OR “neuroapraxia” OR “axonotmesis” OR “neurotmesis” OR “muscle denervation” OR “denervated muscle” AND “rehabilitation” OR “physical activity” OR “physical exercise” OR “activity” OR “electrical stimulation”. A total of 60 publications were included. Eligible studies were focused on evaluating the process of nerve repair (biopsy, electromyographic parameters or biomarker outcomes) after electrical stimulation or physical exercise interventions on humans or animals with peripheral sensory or motor nerve injury. Synthesis: This study shows that the literature, especially regarding preclinical research, is mainly in agreement that an early physical program with active exercise and/or electrical stimulation promotes axonal regenerative responses and prevents maladaptive response. This was evaluated by means of changes in electrophysiological recordings of CMAPs for latency amplitude, and the sciatic functional index (SFI). Furthermore, this type of activity can cause an increase in weight and in muscle fiber diameter. Nevertheless, some detrimental effects of exercising and electrical stimulation too early after nerve repair were recorded. Conclusion: In most preclinical studies, peripheral neuropathy function was associated with improvements after physical exercise and electrical stimulation. For humans, too little research has been conducted on this topic to reach a complete conclusion. This research supports the need for future studies to test the validity of a possible rehabilitation treatment in humans in cases of peripheral neuropathy to help nerve sprouting.
Intraoral Ultrasonography for Periodontal Tissue Exploration: A Review
Authors: Renaud Matthieu , Delpierre Alexis , Becquet Hervé , Mahalli Rachid , Savard Guillaume , Micheneau Pierre , Carayon Delphine , Denis Frederic
Published: January 2023

This systematic review aims to investigate the possibilities of ultrasound imaging in the field of periodontal tissues exploration to visualize periodontal anatomical structures and to assess reliability in clinical evaluation using the PRISMA guidelines. An electronic search through the MEDLINE database was realized to identify studies that have explored ultrasonography in the field of periodontal imaging published from 2000 to March 2022. The search resulted in 245 records; after exclusions, a total of 15 papers were included in the present review. Various publications have shown the possibility of using intraoral ultrasound for a precise exploration of intraoral tissues and to perform measurements of periodontal structures. Studies argue that ultrasounds open the prospect of a complete paradigm shift on the diagnosis and follow-up of periodontal disease. However, there is currently no clinical device dedicated to periodontal ultrasound. This field is still under-studied, and studies are needed to explore the large field of applications from periodontal assessment to treatment reassessment, including surgery. Researchers should focus their efforts to develop special intraoral ultrasound device and explore the possibilities of clinical periodontal applications.
Prone versus Supine FDG PET/CT in the Staging of Breast Cancer
Authors: Nassar Lara , Kassas Mutaz , Abi-Ghanem Alain S. , El-Jebai Malak , Al-Zakleet Safaa , Baassiri Amro S. , Naccoul Rami Abou , Barakat Andrew , Tfayli Arafat , Assi Hazem , Berjawi Ghina , Estrada-Lobato Enrique , Giammarile Francesco , Vinjamuri Sobhan , Haidar Mohamad
Published: January 2023

Supine [18F]Fluorodeoxyglucose (FDG) positron emission technology/computed tomography (PET/CT) is a commonly used modality for the initial staging of breast cancer, and several previous studies have shown superior sensitivity and specificity of prone FDG PET/CT in comparison to its supine counterpart. This retrospective study included 25 females with breast cancer referred for staging. They underwent supine FDG PET/CT followed by prone FDG PET/CT. The outcomes were: number of primary breast lesions, anatomical site of FDG-avid lymph nodes (LNs), and number and type of bone lesions, with SUVmax of all corresponding parameters. Performance was superior in prone acquisition compared to supine acquisition, with the respective results: 29 vs. 22 breast tumor lesions detected, 62 vs. 27 FDG-avid axillary LNs detected, sensitivity of 68% vs. 57%, specificity of 64% vs. 53%. The detection rate of axillary LNs in the prone position was significantly higher (p = 0.001). SUVmax for breast tumor lesions (p = 0.000) and number of detected axillary LNs (p = 0.002) were significantly higher in prone acquisition. Five patients were upstaged after experts read the prone acquisition. Prone FDG PET/CT acquisition is a promising technique in detecting primary breast lesions and metastatic LNs possibly missed in supine acquisition, which may lead to change in patient staging and management.
Current Challenges and Future Perspectives of Diagnosis of Hepatitis B Virus
Authors: Kumar Manoj , Pahuja Sangeeta , Khare Prashant , Kumar Anoop
Published: January 2023

It is estimated that approximately 260 million people worldwide are infected with the hepatitis B virus (HBV), which is one of the leading causes of liver disease and liver cancer throughout the world. Compared with developed countries, low-income and middle-income countries have limited access to resources and advanced technologies that require highly specialized staff for HBV diagnosis. In spite of the heavy burden caused by hepatitis B virus, 90% of people are still undiagnosed. The World Health Organization (WHO) goal of eliminating hepatitis B by 2030 seems very difficult to achieve due to the existing diagnostic infrastructure in low-resource regions. The majority of diagnostic laboratories still use hepatitis B surface antigen (HBsAg)-based tests. WHO’s elimination plan is at risk of derailment due to phases like the window period, immune control, and occult HBV infection (OBI) not being detected by standard tests. Here, in this article, we are focusing on various diagnostic platforms for the better diagnosis of HBV. The aim of the elimination of HBV can only be achieved by detecting all phases of HBV infection, which can be executed by a combined approach of using new marker assays along with advanced pretesting and testing methods.
Expression of the Immunohistochemical Markers CK5, CD117, and EGFR in Molecular Subtypes of Breast Cancer Correlated with Prognosis
Authors: Schulmeyer Carla E. , Fasching Peter A. , Häberle Lothar , Meyer Julia , Schneider Michael , Wachter David , Ruebner Matthias , Pöschke Patrik , Beckmann Matthias W. , Hartmann Arndt , Erber Ramona , Gass Paul
Published: January 2023

Molecular-based subclassifications of breast cancer are important for identifying treatment options and stratifying the prognosis in breast cancer. This study aimed to assess the prognosis relative to disease-free survival (DFS) and overall survival (OS) in patients with triple-negative breast cancer (TNBC) and other subtypes, using a biomarker panel including cytokeratin 5 (CK5), cluster of differentiation 117 (CD117), and epidermal growth factor receptor (EGFR). This cohort–case study included histologically confirmed breast carcinomas as cohort arm. From a total of 894 patients, 572 patients with early breast cancer, sufficient clinical data, and archived tumor tissue were included. Using the immunohistochemical markers CK5, CD117, and EGFR, two subgroups were formed: one with all three biomarkers negative (TBN) and one with at least one of those three biomarkers positive (non-TBN). There were significant differences between the two biomarker subgroups (TBN versus non-TBN) in TNBC for DFS (p = 0.04) and OS (p = 0.02), with higher survival rates (DFS and OS) in the non-TBN subgroup. In this study, we found the non-TBN subgroup of TNBC lesions with at least one positive biomarker of CK5, CD117, and/or EGFR, to be associated with longer DFS and OS.
Next-Generation Sequencing (NGS) and Third-Generation Sequencing (TGS) for the Diagnosis of Thalassemia
Authors: Hassan Syahzuwan , Bahar Rosnah , Johan Muhammad Farid , Mohamed Hashim Ezzeddin Kamil , Abdullah Wan Zaidah , Esa Ezalia , Abdul Hamid Faidatul Syazlin , Zulkafli Zefarina
Published: January 2023

Thalassemia is one of the most heterogeneous diseases, with more than a thousand mutation types recorded worldwide. Molecular diagnosis of thalassemia by conventional PCR-based DNA analysis is time- and resource-consuming owing to the phenotype variability, disease complexity, and molecular diagnostic test limitations. Moreover, genetic counseling must be backed-up by an extensive diagnosis of the thalassemia-causing phenotype and the possible genetic modifiers. Data coming from advanced molecular techniques such as targeted sequencing by next-generation sequencing (NGS) and third-generation sequencing (TGS) are more appropriate and valuable for DNA analysis of thalassemia. While NGS is superior at variant calling to TGS thanks to its lower error rates, the longer reads nature of the TGS permits haplotype-phasing that is superior for variant discovery on the homologous genes and CNV calling. The emergence of many cutting-edge machine learning-based bioinformatics tools has improved the accuracy of variant and CNV calling. Constant improvement of these sequencing and bioinformatics will enable precise thalassemia detections, especially for the CNV and the homologous HBA and HBG genes. In conclusion, laboratory transiting from conventional DNA analysis to NGS or TGS and following the guidelines towards a single assay will contribute to a better diagnostics approach of thalassemia.
First Molecular Characterization of Chronic Hepatitis B Carriers in Timbuktu, Mali
Authors: Lawrence Philip , Chabane Mawlouda , Abrouk Lucie , Thiesson Adrien , Berthé Diakaridia , Diarra Amadou B. , Bengaly Karim , Traoré Brehima , Kassogué Djibril , Durand Geoffroy , Voegele Catherine , Le Calvez-Kelm Florence , Steenkeste Nicolas , Hainaut Pierre , Kouriba Bourema , Gormally Emmanuelle
Published: January 2023

In Mali, hepatocellular carcinoma (HCC) is the third and sixth most common cancer in men and women, respectively. Mali comprises several distinct climato-ecological zones. Most studies to date have been conducted in the sub-Sahelian zone of southern Mali, including the capital city Bamako. In this part of the country, the main risk factors for HCC are chronic hepatitis B virus (HBV) carriage and dietary exposure to aflatoxins, a well-known hepatocarcinogen. Data are scarce for other ecological zones, but our preliminary data from 721 blood donors in the area of Timbuktu, presented in this study, suggest that chronic HBV carriage is also endemic in the northern Saharan zone of Mali. For further study, 29 healthy HBV chronic carrier volunteers were recruited from the blood transfusion center in Timbuktu. Successful viral genotyping in 20 volunteers revealed HBV genotype E in 13 cases and D in 7 cases, suggesting that this geographical and anthropological transition zone may also represent a transition zone between HBV genotypes that dominate sub-Saharan and northern Africa, respectively. Sequencing of circulating cell-free plasma DNA (cfDNA) from donors did not reveal the presence of the TP53 R249S mutation in these donors, a marker of dietary exposure to aflatoxins in sub-Saharan Africa. These results suggest that the geo-epidemiological distribution of the risk factors for HCC is not uniform across Mali, but is dependent upon climatic, socioeconomic and anthropological factors that might have an impact on patterns of chronic liver disease and cancer.
Gastrointestinal Stromal Tumour with Liver Metastasis Presenting as Gastric Cancer
Authors: Xu Yongwei , Zhang Bacui , Wang Jing
Published: January 2023

Gastrointestinal stromal tumour (GIST) is a malignant tumour of the gastrointestinal lobe tissue, which mostly occurs in the gastrointestinal tract. Clinical manifestations can range from being benign to malignant. It mainly occurs in the gastric and small intestine. It may also develop in the colon, oesophagus, and bowel membranes, or outside the gastrointestinal tract and intestines. The pathological diagnosis of GIST depends on morphological measurements and immunohistochemistry. We report an interesting case in which the patient’s gastroscopy indicated gastric malignant tumours, and the results of the contrast-enhanced computed tomography (CT) of the upper abdomen showed malignant stomach tumour accompanied by liver metastasis. After the patient knew about this diagnosis, she wanted to give up treatment. Finally, the gastric biopsy suggested positive CD34, CD117, DOG1, and Ki-67, which supported the diagnosis of GIST. We hope that, through this case, we could improve clinicians’ understanding of GIST and improve its diagnosis and treatment.
Pediatric Philadelphia-Negative Myeloproliferative Neoplasms in the Era of WHO Classification: A Systematic Review
Authors: Al-Mashdali Abdulrahman F. , Aldapt Mahmood B. , Rahhal Alaa , Hailan Yousef M. , Elhakeem Israa , Ali Elrazi A. , Rozi Waail , Yassin Mohamed A.
Published: January 2023

Background: Philadelphia-negative myeloproliferative neoplasms (MPN) are most prevalent in the older population (median age at the diagnosis is above 60 years) and rarely diagnosed in pediatrics. Thus, our knowledge about the clinical presentation, mutational status, and complications of MPNs in pediatrics is limited. Methods: The literature in English (PubMed, SCOPUS, and Google Scholar) was searched for studies, reviews, case series, and case reports of patients with Philadelphia-negative MPNs (including essential thrombocythemia, polycythemia vera, primary myelofibrosis, and profibrotic myelofibrosis) in the pediatrics age group (less than 18 years). Only studies that fulfilled WHO 2008 or 2016 criteria for MPNs were included. We aimed to describe the clinical characteristics, vascular and long-term complications, types of driver mutations, and treatment approaches in pediatric patients with MPNs. Results: We reviewed 33 articles of available published literature from 2008 to 2022 and collected data from a total of 196 patients of the pediatric population. Among the cohort of patients, 139 had essential thrombocythemia (ET), 20 had polycythemia vera (PV), and 37 had primary myelofibrosis (PMF). The median age at the time of diagnosis for each disease varied, with 8.8 years for ET, 10 years for PV, and 3.6 years for MF. There was a slight difference in gender prevalence between both gender groups and all three diseases. The presenting symptoms were not mentioned in more than 50% of studies. We found that JAK2 was the most prevalent among all mutations. Both bleeding and thrombosis were present equally in ET, with 9% of cases complicated by bleeding and 9% complicated by thrombosis. Hemorrhagic events did not occur in patients with PV; thrombosis in children with MF was also not found. The progression into AML occurred in two patients with PV and one with ET. Conclusion: Given the rarity of MPNs in pediatrics and their different characteristics compared with adults, we believe there is a need for unique diagnostic criteria to match the different molecular statuses in pediatrics. Based on our review, the incidence of MPN complications in pediatrics, including thrombotic events, hemorrhage, and leukemic transformation, differs from that in adults.
Pauli’s Electron in Ehrenfest and Bohm Theories, a Comparative Study
Authors: Yahalom Asher
Published: January 2023

Electrons moving at slow speeds much lower than the speed of light are described by a wave function which is a solution of Pauli’s equation. This is a low-velocity limit of the relativistic Dirac equation. Here we compare two approaches, one of which is the more conservative Copenhagen’s interpretation denying a trajectory of the electron but allowing a trajectory to the electron expectation value through the Ehrenfest theorem. The said expectation value is of course calculated using a solution of Pauli’s equation. A less orthodox approach is championed by Bohm, and attributes a velocity field to the electron also derived from the Pauli wave function. It is thus interesting to compare the trajectory followed by the electron according to Bohm and its expectation value according to Ehrenfest. Both similarities and differences will be considered.
Nonlocality in Quantum Mechanics Portrayed as a Human Twins’ Metaphor
Authors: Mizrahi Salomon S.
Published: January 2023

Avoiding the use of mathematical formalism, this essay exposes the quantum mechanics phenomenon of nonlocality in terms of a metaphor involving human twins, focused on their hands’ dexterity attribute.
Counterfactual Supervision-Based Information Bottleneck for Out-of-Distribution Generalization
Authors: Deng Bin , Jia Kui
Published: January 2023

Learning invariant (causal) features for out-of-distribution (OOD) generalization have attracted extensive attention recently, and among the proposals, invariant risk minimization (IRM) is a notable solution. In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems remain. By introducing the information bottleneck (IB) principle into the learning of IRM, the IB-IRM approach has demonstrated its power to solve these challenges. In this paper, we further improve IB-IRM from two aspects. First, we show that the key assumption of support overlap of invariant features used in IB-IRM guarantees OOD generalization, and it is still possible to achieve the optimal solution without this assumption. Second, we illustrate two failure modes where IB-IRM (and IRM) could fail in learning the invariant features, and to address such failures, we propose a Counterfactual Supervision-based Information Bottleneck (CSIB) learning algorithm that recovers the invariant features. By requiring counterfactual inference, CSIB works even when accessing data from a single environment. Empirical experiments on several datasets verify our theoretical results.
Driver Stress Detection Using Ultra-Short-Term HRV Analysis under Real World Driving Conditions
Authors: Liu Kun , Jiao Yubo , Du Congcong , Zhang Xiaoming , Chen Xiaoyu , Xu Fang , Jiang Chaozhe
Published: January 2023

Considering that driving stress is a major contributor to traffic accidents, detecting drivers’ stress levels in time is helpful for ensuring driving safety. This paper attempts to investigate the ability of ultra-short-term (30-s, 1-min, 2-min, and 3-min) HRV analysis for driver stress detection under real driving circumstances. Specifically, the t-test was used to investigate whether there were significant differences in HRV features under different stress levels. Ultra-short-term HRV features were compared with the corresponding short-term (5-min) features during low-stress and high-stress phases by the Spearman rank correlation and Bland–Altman plots analysis. Furthermore, four different machine-learning classifiers, including a support vector machine (SVM), random forests (RFs), K-nearest neighbor (KNN), and Adaboost, were evaluated for stress detection. The results show that the HRV features extracted from ultra-short-term epochs were able to detect binary drivers’ stress levels accurately. In particular, although the capability of HRV features in detecting driver stress also varied between different ultra-short-term epochs, MeanNN, SDNN, NN20, and MeanHR were selected as valid surrogates of short-term features for driver stress detection across the different epochs. For drivers’ stress levels classification, the best performance was achieved with the SVM classifier, with an accuracy of 85.3% using 3-min HRV features. This study makes a contribution to building a robust and effective stress detection system using ultra-short-term HRV features under actual driving environments.
Dynamics of Quantum Correlations in Open Systems
Authors: Isar Aurelian
Published: January 2023

Quantum correlations represent one of the most characteristic traits of quantum mechanics [...]
Cache-Based Privacy Protection Scheme for Continuous Location Query
Authors: Liu Zhenpeng , Miao Dewei , Li Ruilin , Liu Yi , Li Xiaofei
Published: January 2023

Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user’s demand, we use a variable-order Markov model to predict the user’s future query location and generate a k-anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users’ location privacy.
The Experience of Co-Designing a Learning Space with Teachers and Students
Authors: Casanova Diogo , Huet Isabel , Garcia Fabiane
Published: January 2023

This paper presents the findings of an empirical research study investigating the perspectives of students and teachers on learning spaces in higher education and their participation in the design process of such spaces. The study employed a participatory design method, using workshops to involve teachers and students in redesigning a prototype learning space named the ‘Cube’. This approach allowed the researchers to actively engage users in reflective thinking on the learning spaces and their role in learning and to co-create new learning spaces through the use of their experiences and ideas. The findings are organised into 10 design themes, highlighting key considerations for the design of meaningful and effective learning spaces. The study concludes that involving teachers and students in the design process can significantly improve the learning and teaching experiences by fostering an active sense of agency and ownership over the learning spaces.
Acknowledgment to the Reviewers of Electronic Materials in 2022
Authors:   Electronic Materials Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Deep Neural Network with Hilbert–Huang Transform for Smart Fault Detection in Microgrid
Authors: Aqamohammadi Amir Reza , Niknam Taher , Shojaeiyan Sattar , Siano Pierluigi , Dehghani Moslem
Published: January 2023

The fault detection method (FDM) plays a crucial role in controlling and operating microgrids (MGs), because it allows for systems to rapidly isolate and restore faults. Due to the fact that MGs use inverter-interfaced distributed production, conventional FDMs are no longer appropriate because they are dependent on substantial fault currents. This study presents a smart FDM for MGs based on the Hilbert–Huang transform (HHT) and deep neural networks (DNNs). The suggested layout aims to prepare the fast detection of fault kind, phase, and place data to protect MGs and restore services. The HHT pre-processes the branch current measurements obtained from the protective relays to extract the characteristics, and singular value decomposition (SVD) is used to extract some features from intrinsic mode functions (IMFs) that are obtained from HHT to use as input of DNNs. As part of the fault data development, all the information eventually enters the DNNs. Compared with prior studies, this suggested method provides considerably superior fault-type identification accuracy. It is also possible to determine new fault locations. A detailed assessment analysis of this suggested FDM was conducted on IEEE 34-bus and MG systems to demonstrate its effectiveness. The simulations indicated that the proposed method is effective for detecting precision, computing time, and robustness to measurement uncertainties.
Modelling and Simulation of Physical Systems with Dynamically Changing Degrees of Freedom
Authors: Neumayr Andrea , Otter Martin
Published: January 2023

A new approach is introduced to model and simulate equation-based systems where variables can appear and disappear during simulation without re-generation and re-compilation of code when the numbers of equations and states change during events. The method is presented in a generic, mathematical way and can be in principle applied to all types of declarative, equation-based modelling languages, such as Modelica. A concrete implementation is given for the Julia-based experimental modelling language Modia, which is similar to Modelica. However, Modia features far simpler semantics based on hierarchical collections of name/value pairs and has the ability to support domain-specific algorithms, especially for multibody systems with collision handling. The new method is demonstrated with heat-transfer in a rod, separation of stages of a rocket and gripping operations of a robot.
Acceleration of Trading System Back End with FPGAs Using High-Level Synthesis Flow
Authors: Puranik Sunil , Barve Mahesh , Rodi Swapnil , Patrikar Rajendra
Published: January 2023

FPGA technology is widely used in the finance domain. We describe the design of a financial trading system order processing component using FPGAs, implemented with high-level synthesis (HLS) flow. The order processing component is the major contributor to increased delays and low throughput in the current software implementation of trading systems. The objective of FPGA implementation is to reduce the latency of order processing and increase the throughput of trading systems as compared to software implementation. Our design is one of the first attempts to speed up order processing in a trading system using FPGA technology and HLS flow. HLS was used in implementing the design for higher productivity and faster turnaround time. The design shows orders of magnitude of improvement in performance indicating that more complex FPGA systems could be designed using HLS. We obtained more than 2X of an advantage in order processing speed and a reduction in latency with FPGA technology. Moreover, we gained a 4X advantage in terms of productivity using HLS.
Multimodal Mood Consistency and Mood Dependency Neural Network Circuit Based on Memristors
Authors: Wang Yangyang , Sun Junwei , Wang Yanfeng , Liu Peng
Published: January 2023

The factors that affect learning efficiency in different environments have been considered in many studies, but multimode mood-consistent learning has not been considered specifically. In this paper, a neural network circuit based on memristors to determine multimode mood consistency and mood dependency was constructed. The circuit is composed of a voltage control module, an emotion module, and a synaptic neuron module. Through the voltage control module and emotion module, learning materials with different properties are input into the synaptic neurons. The learning efficiency of different learning materials under different emotions was analyzed in detail. A dual-channel mood-consistent learning was realized, and the mood dependency was further considered. The influence of different channels on the learning was studied to provide ideas for the future development of intelligent brain-like neural networks.
Influence of Increasing Renewable Power Penetration on the Long-Term Iberian Electricity Market Prices
Authors: Leal Pedro , Castro Rui , Lopes Fernando
Published: January 2023

In recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease with the increasing generation of renewable power. This is important for both current and future investors, as it can affect profitability. To address these concerns, a long-term analysis is proposed here to examine the influence of the future electricity mix on Iberian electricity prices in 2030. In this study, we employed artificial intelligence forecasting models that incorporated the main electricity price-driven components of MIBEL, providing accurate predictions for the real operation of the market. These can be extrapolated into the future to predict electricity prices in a scenario with high renewable power penetration. The results, obtained considering a framework featuring an increase in the penetration of renewables into MIBEL of up to 80% in 2030, showed that electricity prices are expected to decrease by around 50% in 2030 when compared to 2019, and there will be a new pattern of electricity prices throughout the year due to the uneven distribution of renewable electricity. The study’s findings are relevant for ongoing research on the unique challenges of energy markets with high levels of renewable generation.
The Impact of Awareness for the Consequences from Adopting Electric Scooters—The Crucial Role of Warm Glow and Extrinsic Appeal
Authors: Ho Chien-Wei , Wu Chi-Chuan , Hsieh Min-Tzu
Published: January 2023

With the rapidly development of electricity technology and renewable energy, adopting electric vehicles (EVs) not only can efficiently collaborate with renewable energy, but also can help decrease the negative environmental effects of traditional vehicles. However, even though electric vehicles offer many advantages, most consumers still hesitate to buy or even rent them. While there is a large growing body of studies focusing on topics related to electric public transportation and electric cars, there is still a shortage of them examining electric scooters (ESs). Based on the norm activation model (NAM), green consumption effect, and extrinsic motivation, this study aims to present the crucial antecedence of consumers’ intention to adopt ESs. As a main transportation for most of the people in Taiwan, this research adopts design of experiments and develops two studies to collect data from Taiwan. The results show that warm glow mediates the positive relationship between awareness of consequences and intention to adopt them. Moreover, the interacting effect of awareness of consequences and extrinsic appeal on intention to adopt ESs is mediated by the warm glow. For academia, the results herein fill the gap related to electric transportation. In real-world practice, this study provides valuable suggestions for governments that want to abate any negative impact from traditional scooters.
The Effects of Differential Diffusion on Turbulent Non-Premixed Flames LO2/CH4 under Transcritical Conditions Using Large-Eddy Simulation
Authors: Wang Siyuan , Wang Haiou , Luo Kun , Fan Jianren
Published: January 2023

In this paper, a large-eddy simulation (LES) of turbulent non-premixed LO2/CH4 combustion under transcritical conditions is performed based on the Mascotte test rig from the Office National d’Etudes et de Recherches Ae´rospatiales (ONERA), and the aim is to understand the effects of differential diffusion on the flame behaviors. In the LES, oxygen was injected into the environment above the critical pressure while the temperature was below the critical temperature. The flamelet/progress variable (FPV) approach was used as the combustion model. Two LES cases with different species diffusion coefficient schemes—i.e., non-unity and unity Lewis numbers—for generating the flamelet tables were carried out to explore the effects of differential diffusion on the flame and flow structures. The results of the LES case with non-unity Lewis numbers were in good agreement with the experimental data. It was shown that differential diffusion had evident impacts on the flame structure and flow dynamics. In particular, when unity Lewis numbers were used to evaluate the species diffusion coefficient, the flame length was underestimated and the flame expansion was more significant. Compared to laminar counterflow flames, turbulence in jet flames allows chemical reactions to take place in a wider range of mixture fractions. The density distributions of the two LES cases in the mixture fraction space were very similar, indicating that differential diffusion had no significant effects on the phase transition under transcritical conditions.
Facilitating Investment in Photovoltaic Systems in Iran Considering Time-of-Use Feed-in-Tariff and Carbon Market
Authors: Seyedzahedi Asrin , Bahramara Salah
Published: January 2023

Photovoltaic (PV) systems are the leading solutions for reducing carbon dioxide (CO2) emissions in Iran’s energy system. However, there are some challenges to investing in PV systems in Iran, such as the low energy market price and the high investment cost of PV systems. Although the flat feed-in tariff (FiT) is defined to help purchase energy from the PV systems, it is not attractive to investors. In this paper, a mathematical formulation is developed for the planning problem of the PV systems with battery energy storages (BESs) considering two incentive policies: (1) Designing time-of-use FiT to encourage the PV systems to sell energy to the grid at peak hours (2) Participating in the carbon trading energy market. The insolation in Iran is calculated regarding mathematical formulations which divide Iran into eight zones. The results of the base case show high payback periods for all zones. In the presence of the incentive policies, the payback period decreases considerably from 5.46 yrs. to 3.75 yrs. for the best zone. Also, the net present value increases more than 170 percent in some zones compared to the base case.
A High-Efficiency QR Flyback DC–DC Converter with Reduced Switch Voltage Stress Realized by Applying a Self-Driven Active Snubber (SDAS)
Authors: Yoo Jeong-Sang , Baek Jong-Ok , Ahn Tae-Young
Published: January 2023

In this paper, a QR flyback converter using a self-driven active snubber (SDAS) was proposed to solve the problem of voltage surge in the switch of QR flyback converters. In the proposed converter, the SDAS consisting of a clamping capacitor and an active switch can be configured in parallel with the main switch or transformer to reduce the voltage surge in the switch. To confirm the steady-state characteristics of the QR flyback converter to which the proposed SDAS is applied, equivalent circuits for each state were constructed, and the equations and characteristics for each state were determined. A 60 W class small AC–DC adapter was constructed to confirm the effectiveness of the proposed converter and the control circuit method, and the experimental results were analyzed. The size of the experimental AC–DC adapter was 74×29×23 mm, and it had a high power density of 20 W/in3 or more. The experimental circuit was limited to the high power conversion efficiency of up to 91.56%, and the maximum voltage surge in the switch was approximately 450 V. One of the reasons for such high efficiency is the SDAS circuit, which sufficiently reduces the voltage surge of the QR flyback switch, compared with the RCD clamp circuit, and does not consume power in principle.
Surrogate Model-Based Heat Sink Design for Energy Storage Converters
Authors: Qiao Gege , Cao Wenping , Hu Yawei , Li Jiucheng , Sun Lu , Hu Cungang
Published: January 2023

As forced-air cooling for heat sinks is widely used in the cooling design of electrical and electronic equipment, their thermal performance is of critical importance for maintaining excellent cooling capacity while reducing the size and weight of the heat sink and the equipment as a whole. This paper presents a method based on the combination of computational fluid dynamics (CFD) simulation and surrogate models to optimize heat sinks for high-end energy storage converters. The design takes the thermal resistance and mass of the heat sink as the optimization goals and looks for the best design for the fin height, thickness and spacing, as well as the base thickness. The analytical and numerical results show that the thermal resistance and mass of the heat sink are reduced by the proposed algorithms, as are the temperatures of the heating elements. Test results verify the effectiveness of the optimization method combining CFD simulation with surrogate models.
A Study on the Interdependence in Sustainable Mobility Tools and Home Energy Equipment Choices
Authors: Gu Gaofeng , Pan Xiaofeng
Published: January 2023

Energy consumption in transportation and households are the main two sections of energy consumption in Europe. To cope with the challenges raised by energy security, sustainability, and pollution, great efforts have been made to encourage the transition from fossil fuel vehicles to electric vehicles and car sharing. In terms of households, home renewable energy equipment such as heat pumps and solar panels are promising solutions. To accelerate the diffusion of these promising and environmentally friendly technologies, an abundance of studies has investigated the factors that impact the adoption of them. Most of the existing literature considered the choice behavior of vehicle types and choice behavior of energy equipment to be exogenous to other. Charging electric vehicles at home will lead to an increase in home energy costs. It is important to understand to what extent a household’s decision to purchase new mobility tools or energy equipment is affected by the other. This paper, therefore, attempts to investigate the correlation between the choice of home energy equipment and the choice of new mobility tools. An inverted sequence stated preference experiment was designed and conducted in the Netherlands to examine the interdependence of mobility tools choice and home energy equipment choice. Two logit models were presented to investigate how the choice of mobility tools and energy equipment impact each other. Results indicate that the promoting effect between electric vehicles and solar panels is bidirectional, while the promoting effect between electric vehicles and heat pumps is unidirectional.
Solar and Wind Quantity 24 h—Series Prediction Using PDE-Modular Models Gradually Developed according to Spatial Pattern Similarity
Authors: Zjavka Ladislav
Published: January 2023

The design and implementation of efficient photovoltaic (PV) plants and wind farms require a precise analysis and definition of specifics in the region of interest. Reliable Artificial Intelligence (AI) models can recognize long-term spatial and temporal variability, including anomalies in solar and wind patterns, which are necessary to estimate the generation capacity and configuration parameters of PV panels and wind turbines. The proposed 24 h planning of renewable energy (RE) production involves an initial reassessment of the optimal day data records based on the spatial pattern similarity in the latest hours and their follow-up statistical AI learning. Conventional measurements comprise a larger territory to allow the development of robust models representing unsettled meteorological situations and their significant changes from a comprehensive aspect, which becomes essential in middle-term time horizons. Differential learning is a new unconventionally designed neurocomputing strategy that combines differentiated modules composed of selected binomial network nodes as the output sum. This approach, based on solutions of partial differential equations (PDEs) defined in selected nodes, enables us to comprise high uncertainty in nonlinear chaotic patterns, contingent upon RE local potential, without an undesirable reduction in data dimensionality. The form of back-produced modular compounds in PDE models is directly related to the complexity of large-scale data patterns used in training to avoid problem simplification. The preidentified day-sample series are reassessed secondary to the training applicability, one by one, to better characterize pattern progress. Applicable phase or frequency parameters (e.g., azimuth, temperature, radiation, etc.) are related to the amplitudes at each time to determine and solve particular node PDEs in a complex form of the periodic sine/cosine components. The proposed improvements contribute to better performance of the AI modular concept of PDE models, a cable to represent the dynamics of complex systems. The results are compared with the recent deep learning strategy. Both methods show a high approximation ability in radiation ramping events, often in PV power supply; moreover, differential learning provides more stable wind gust predictions without undesirable alterations in day errors, namely in over-break frontal fluctuations. Their day average percentage approximation of similarity correlation on real data is 87.8 and 88.1% in global radiation day-cycles and 46.7 and 36.3% in wind speed 24 h. series. A parametric C++ executable program with complete spatial metadata records for one month is available for free to enable another comparative evaluation of the conducted experiments.
Novel Active Damping Design Based on a Biquad Filter for an LLCL Grid-Tied Inverter
Authors: Wang Bin , Zhao Qiangsong , Zhang Gong , Zhang Hongwei , Liu Kaiyue , Yue Xuebin
Published: January 2023

LLCL filters for grid-tied inverters have been adopted to get better performance for the harmonics near the switching frequency than commonly used LCL filters. However, the resonant peak caused by a pair of unstable resonance poles of the LLCL filters is introduced and makes the system become unstable. In this paper, a biquad filter composed of a notch filter and a resonator is introduced to restrain the resonant peak. In this method, the resonance point and the notch point of the biquad filter are placed at the appointed frequency, and the resonant peak is transferred to the stable area by phase transformation, so that the system does not cross −180° at the resonant peak. This method makes the system have higher control bandwidth and stronger robustness even in a weak power grid. Meanwhile, a proportional-integral multiresonant repetitive controller is used to restrain low-frequency current harmonics and improve the steady-state and dynamic performance of the control system. Furthermore, based on the active damping of the biquad filter, the stability criterion of the control system under a weak power grid is given. Finally, the accuracy of the analysis and the effectiveness of the method is verified by simulations.
Efficient Energy Use in Manufacturing Systems—Modeling, Assessment, and Management Strategy
Authors: Xia Tangbin , An Xiangxin , Yang Huaqiang , Jiang Yimin , Xu Yuhui , Zheng Meimei , Pan Ershun
Published: January 2023

Currently, studies on the energy efficiency of manufacturing systems usually lack synthetic and systematic techniques. In this paper, a holistic framework is demonstrated in order to achieve more sustainable manufacturing, which covers machine-level, system-level and life-cycle-level energy efficiency techniques. Based on these, the mechanism of how energy consumption is affected by machining processes and system operation is analyzed to achieve a comprehensive decision on energy efficiency optimization. Four main topics are included in this paper: (1) Hierarchical sustainability goals and metrics for energy-efficient manufacturing; (2) Machine-level machining processes optimization for energy efficiency enhancement; (3) System-level innovations for efficient consumption management; (4) Life-cycle level energy flow modeling and energy recycling strategy. An automotive engine manufacturing system is taken as an example to build a concrete understanding of the application of the framework. Moreover, this holistic framework establishes the theoretical basis for promoting the energy efficiency of automotive engine manufacturing systems. Furthermore, the proposed techniques can provide decision-making support for achieving sustainable manufacturing in a wider scope of mechanical manufacturing.
Aerodynamic Optimization of Trailing-Edge-Serrations for a Wind Turbine Blade Using Taguchi Modified Additive Model
Authors: Qaissi Khaoula , Elsayed Omer , Faqir Mustapha , Essadiqi Elhachmi
Published: January 2023

For the rotor, achieving relatively high aerodynamic performance in specific wind conditions is a long-term goal. Inspired by the remarkable flight characteristics of owls, an optimal trailing edge serration design is investigated and proposed for a wind turbine rotor blade. Fluid flow interaction with the proposed serrations is explored for different wind conditions. The result is supported by subsequent validation with three-dimensional numerical tools. The present work employs a statistical-numerical method to predict and optimize the shape of the serrations for maximum aerodynamic improvement. The optimal combination is found using the Taguchi method with three factors: Amplitude, wavelength, and serration thickness. The viability of the solution on an application is assessed using the Weibull distribution of wind in three selected regions. Results show that the presence of serration is capable of improving the annual power generation in all the investigated cities by up to 12%. The rated speed is also shifted from 10 m/s to 8 m/s for most configurations. Additionally, all configurations show similar trends for the instantaneous torque, where an increase is observed in pre-rated speed, whereas a decrease is noticed in the post-rated speed region. A look at the flow field pattern for the optimal design in comparison with the clean blade shows that the modified blade is able to generate more lift in the pre-stall region, while for the post-stall region, early separation and increased wake dominate the flow.
A Development of Optimal Design and Operation Algorithm for Battery-Powered Electric City Tour Bus System
Authors: Ko Young , Ko Young
Published: January 2023

After overcoming COVID-19, the tourism demand around the world is on the rise again. At the same time, the interest in eco-friendliness is growing again, and efforts are being made to build an eco-friendly tourism ecosystem. In this study, assuming that a battery-powered electric city tour bus is adopted instead of an existing internal combustion engine city tour bus, we tried to develop optimal design and operation algorithms for battery-powered electric city tour bus systems. The developed algorithm pursues the maximization of the profit, which is calculated through the ticket price paid by tourists using the city tour bus and the overall cost of the electric city tour bus system. In addition, the decision variables of the algorithm are the daily number and interval of operations of the electric city tour bus, which are related to the tourism demand, the battery capacity of the electric city tour bus, and whether a pantograph-type wireless charger is installed or not at the bus stop. The operations research method is used to develop the design algorithm, and numerical examples are derived as the result of the optimal design to verify the proposed algorithm by referring to the operating situation of the Blue Trolley Line in Oahu, Hawai’i. As a result, it is found that profit maximization can be achieved by changes in the daily number and interval of operations on designated routes.