bluuebunny/crossref_metadata_2025 huggingface dataset:
Association of Neutrophil-to-Lymphocyte Ratio and Bloodstream Infections with Survival after Curative-Intent Treatment in Elderly Patients with Oral Cavity Squamous Cell Carcinoma
Authors: Huang Chun-Hou , Chou Yu-Fu , Hsieh Tsung-Cheng , Chen Peir-Rong
Published: January 2023

Patients with oral cavity squamous cell carcinoma (OSCC) undergoing curative-intent treatment may become immunocompromised. This study aimed to investigate the association of pretreatment sarcopenia, nutritional status, comorbidities, and blood-based inflammation prognostic biomarkers in bloodstream infection (BSI) with survival status in elderly patients with OSCC. Retrospective data were collected from 235 patients who were newly diagnosed with OSCC, were aged ≥ 65 years, had undergone curative-intent treatment, and were classified into either the BSI group or the no-BSI group within 6 months after surgery and/or adjuvant therapy initiation. Of the 235 elderly patients, 27 presented with BSI episodes. A preoperative high neutrophil-to-lymphocyte ratio (NLR) was a significant independent risk factor for BSI. BSI was not significantly associated with survival status. Ever betel nut chewing, hypoalbuminemia, and advanced tumor stage were associated with shorter overall survival. Moreover, a high NLR was an independent risk factor associated with disease-free survival. A high NLR was associated with BSI and resistance to curative-intent treatment. Pretreatment of NLR could act as an independent prognostic indicator and help inform treatment strategies for older patients with OSCC.
Differences in Cardiac-Pulsatility-Induced Displacement and Geometry Changes between the Cook ZBIS and Gore IBE: Postoperative Comparison Using ECG-Gated CTA Scans
Authors: Simmering Jaimy A. , van Helvert Majorie , van Herwaarden Joost A. , Slump Cornelis H. , Geelkerken Robert H. , Reijnen Michel M. P. J.
Published: January 2023

To what extent the stentgraft design of iliac branch devices (IBDs) relates to dynamic deformation is currently unknown. Therefore, this study aimed to quantify and compare displacement and geometry changes during the cardiac cycle of two common IBDs. This paper presents a two-center trial with patients treated with a Zenith bifurcated iliac side (ZBIS) or Gore iliac branch endoprosthesis (IBE). All patients underwent a retrospective electrocardiogram (ECG)-gated computed tomographic angiography (CTA) during follow-up. Cardiac-pulsatility-induced displacement was quantified for the following locations: (neo) bifurcation of the aorta, IBD flow divider, distal markers of the internal iliac artery (IIA) component and first IIA bifurcation. Geometrical parameters (length, tortuosity index, curvature and torsion) were quantified over centerlines. Displacement was more pronounced for the IBE than the ZBIS, e.g., craniocaudal displacement of 0.91 mm (0.91–1.13 mm) vs. 0.57 mm (0.40–0.75 mm, p = 0.004), respectively. The IBDs demonstrated similar geometrical parameters in the neo-common iliac artery and distal IIA, except for the larger dynamic curvature and torsion of the distal IIA in IBEs. The IBEs showed more dynamic length and curvature change compared to the ZBIS in the stented IIA. The IIA trajectory showed more pronounced deformation during the cardiac cycle after placement of an IBE than a ZBIS, suggesting the IBE is more conformable than the ZBIS.
Anti-Mullerian Hormone-Based Phenotyping Identifies Subgroups of Women with Polycystic Ovary Syndrome with Differing Clinical and Biochemical Characteristics
Authors: Nguyen Minhthao Thi , Krishnan Sridevi , Phatak Sonal V. , Karakas Sidika E.
Published: January 2023

Even though polycystic ovary syndrome (PCOS) was originally defined as “amenorrhea associated with bilateral polycystic ovaries”, women without PCO morphology can be included in this diagnosis. This may contribute to the clinical heterogeneity seen in PCOS. Serum anti-Mullerian hormone (AMH) correlates with the number of ovarian cysts. We investigated whether phenotyping based on serum AMH can distinguish subgroups of PCOS with different clinical and biochemical characteristics. The electronic medical records of 108 women with PCOS (Rotterdam criteria) were reviewed. The serum AMH value correlated inversely (0.03 < p < 0.0001) with age, weight, and BMI values and directly with serum total testosterone (T), free T, and bioavailable T values. When divided into quartiles based on serum AMH values, the women in the highest quartile (AMH: 18.5 ± 9.9 ng/mL; n = 27) had lower BMI (29.4 ± 6.9 vs. 34.0 ± 10.6–36.7 ± 7.2 kg/m2) but higher total T (51.3 ± 27.2 vs. 26.5 ± 10.4–35.1 ± 16.3 ng/dL), free T (7.7 ± 6.0 vs. 4.4 ± 2.3–5.7 ± 3.2 ng/dL), and bioavailable T (22.1 ± 17.0 vs. 12.2 ± 6.6–16.5 ± 8.7 ng/dL) values. The combination of high AMH and high testosterone values may point to the ovaries and reproductive etiology for PCOS in this subgroup. Thus, AMH-based phenotyping may provide a practical and cost-effective tool to explore the heterogeneity in PCOS.
Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning
Authors: Torres Romina , Zurita Christopher , Mellado Diego , Nicolis Orietta , Saavedra Carolina , Tuesta Marcelo , Salinas Matías , Bertini Ayleen , Pedemonte Oneglio , Querales Marvin , Salas Rodrigo
Published: January 2023

Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of 0.03±0.013 and an R2 of 63±19%, where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an R2 up to 92%. The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase.
Role of Patient’s Ethnicity in Seeking Preventive Dental Services at the Community Health Centers of South-Central Texas: A Cross-Sectional Study
Authors: Shelke Girish Suresh , Marwaha Rochisha Singh , Shah Pankil , Challa Suman
Published: January 2023

Background: This study was conducted to determine the impact of a patient’s ethnicity on seeking preventive dental services at the Community Health Centers (CHCs) in South-Central Texas. Methods: Primary electronic health records (EHR) data were collected regarding each patient’s medical and dental history, and comprehensive treatment planning. The researchers retrieved EHR from January 2016 to 2022. Bivariate analysis was completed to test the outcome with the predictor variable and covariates using the appropriate statistical tests. A multiple linear regression model was used to understand the association between the predictor and outcome variable while controlling for confounders. Results: The study findings revealed significantly higher dental visits (2.26 ± 2.88) for Hispanic patients. The results from the multiple regression model indicated that non-Hispanic patients had a smaller chance of visiting CHC for preventive dental services, by eight percent, compared to the Hispanic population (p-value < 0.001) when all other variables were held constant. However, the study results were not significant, as the effect size was too small to conclude the effect of ethnicity on the patients visiting the dental clinic at the CHC for preventive services. Conclusion: The study concluded that there is no difference in the preventive dental services completed by Hispanics and non-Hispanics when all other variables are controlled.
The Impact of Dental Care Programs on Individuals and Their Families: A Scoping Review
Authors: Ghoneim Abdulrahman , D’Souza Violet , Ebnahmady Arezoo , Kaura Parbhakar Kamini , He Helen , Gerbig Madeline , Laporte Audrey , Hancock Howard Rebecca , Gomaa Noha , Quiñonez Carlos , Singhal Sonica
Published: January 2023

Background: Despite significant global improvements in oral health, inequities persist. Targeted dental care programs are perceived as a viable approach to both improving oral health and to address inequities. However, the impacts of dental care programs on individual and family oral health outcomes remain unclear. Objectives: The purpose of this scoping review is to map the evidence on impacts of existing dental programs, specifically on individual and family level outcomes. Methods: We systematically searched four scientific databases, MEDLINE, EMBASE, CINAHL, and Sociological Abstracts for studies published in the English language between December 1999 and November 2021. Search terms were kept broad to capture a range of programs. Four reviewers (AG, VD, AE, and KKP) independently screened the abstracts and reviewed full-text articles and extracted the data. Cohen’s kappa inter-rater reliability score was 0.875, indicating excellent agreement between the reviewers. Data were summarized according to the PRISMA statement. Results: The search yielded 65,887 studies, of which 76 were included in the data synthesis. All but one study assessed various individual-level outcomes (n = 75) and only five investigated family outcomes. The most common program interventions are diagnostic and preventive (n = 35, 46%) care, targeted children (n = 42, 55%), and delivered in school-based settings (n = 28, 37%). The majority of studies (n = 43, 57%) reported a significant improvement in one or more of their reported outcomes; the most assessed outcome was change in dental decay (n = 35). Conclusions: Dental care programs demonstrated effectiveness in addressing individual oral health outcomes. However, evidence to show the impact on family-related outcomes remains limited and requires attention in future research.
Wavelet Entropy: A New Tool for Edge Detection of Potential Field Data
Authors: Dwivedi Divyanshu , Chamoli Ashutosh , Rana Sandip Kumar
Published: January 2023

Subsurface source boundary identification is a major step in the interpretation of potential field anomalies in geophysical exploration. We investigated the behavior of wavelet space entropy over the boundaries of 2D potential field source edges. We tested the robustness of the method for complex source geometries with distinct source parameters of prismatic bodies. We further validated the behavior with two datasets by delineating the edges of (i) the magnetic anomalies due to the popular Bishop model and (ii) the gravity anomalies of the Delhi fold belt region, India. The results showed prominent signatures for the geological boundaries. Our findings indicate sharp changes in the wavelet space entropy values corresponding to the source edges. The effectiveness of wavelet space entropy was compared with the established edge detection techniques. The findings can help with a variety of geophysical source characterization problems.
Trend Feature Consistency Guided Deep Learning Method for Minor Fault Diagnosis
Authors: Jia Pengpeng , Wang Chaoge , Zhou Funa , Hu Xiong
Published: January 2023

Deep learning can be applied in the field of fault diagnosis without an accurate mechanism model. However, the accurate diagnosis of minor faults using deep learning is limited by the training sample size. In the case that only a small number of noise-polluted samples is available, it is crucial to design a new learning mechanism for the training of deep neural networks to make it more powerful in feature representation. The new learning mechanism for deep neural networks model is accomplished by designing a new loss function such that accurate feature representation driven by consistency of trend features and accurate fault classification driven by consistency of fault direction both can be secured. In such a way, a more robust and more reliable fault diagnosis model using deep neural networks can be established to effectively discriminate those faults with equal or similar membership values of fault classifiers, which is unavailable for traditional methods. Validation for gearbox fault diagnosis shows that 100 training samples polluted with strong noise are adequate for the proposed method to successfully train deep neural networks to achieve satisfactory fault diagnosis accuracy, while more than 1500 training samples are required for traditional methods to achieve comparative fault diagnosis accuracy.
Covariant Lyapunov Vectors and Finite-Time Normal Modes for Geophysical Fluid Dynamical Systems
Authors: Frederiksen Jorgen S.
Published: January 2023

Dynamical vectors characterizing instability and applicable as ensemble perturbations for prediction with geophysical fluid dynamical models are analysed. The relationships between covariant Lyapunov vectors (CLVs), orthonormal Lyapunov vectors (OLVs), singular vectors (SVs), Floquet vectors and finite-time normal modes (FTNMs) are examined for periodic and aperiodic systems. In the phase-space of FTNM coefficients, SVs are shown to equate with unit norm FTNMs at critical times. In the long-time limit, when SVs approach OLVs, the Oseledec theorem and the relationships between OLVs and CLVs are used to connect CLVs to FTNMs in this phase-space. The covariant properties of both the CLVs, and the FTNMs, together with their phase-space independence, and the norm independence of global Lyapunov exponents and FTNM growth rates, are used to establish their asymptotic convergence. Conditions on the dynamical systems for the validity of these results, particularly ergodicity, boundedness and non-singular FTNM characteristic matrix and propagator, are documented. The findings are deduced for systems with nondegenerate OLVs, and, as well, with degenerate Lyapunov spectrum as is the rule in the presence of waves such as Rossby waves. Efficient numerical methods for the calculation of leading CLVs are proposed. Norm independent finite-time versions of the Kolmogorov-Sinai entropy production and Kaplan-Yorke dimension are presented.
Determinants of Young People with Secondary Education Being Employed
Authors: Stundziene Alina , Giziene Vilda
Published: January 2023

The aim of this paper is to find out the main factors that determine whether young people with secondary education are employed or not in Lithuania. A survey of young people, aged 18–25, was carried out to gather information about individual characteristics and to find out the reasons why they are not employed. The analysis of the collected data was performed using independent samples tests and the calculation of the contingency coefficient. The research showed that young people start work quite young and are willing to enter the labor market. However, they find it difficult to combine work and study. The regression analysis found five significant variables to explain why young people are employed or not, i.e., their job contract, satisfaction with other work conditions, gender, the opportunity to work remotely, and 40 h worked per week. The probit model showed that temporary and full-time jobs reduce the probability of being employed; meanwhile, the opportunity to work remotely and greater satisfaction with other work conditions increases the probability of employment. The probit model also provided evidence that women are more likely to work than men.
Methodologies for Fostering Critical Thinking Skills from University Students’ Points of View
Authors: Campo Lucía , Galindo-Domínguez Héctor , Bezanilla María-José , Fernández-Nogueira Donna , Poblete Manuel
Published: January 2023

Developing critical thinking skills appears to be a challenge for higher education institutions. However, little is known about the students’ points of view regarding the methodologies they consider to foster the development of this competence. To answer this question, a total of 263 Spanish university students participated in this study and were asked what they understood by critical thinking and the methodologies they considered to be the most effective for developing it. The results indicated that, based on students’ points of view, there were six methodologies that mainly contributed to developing critical thinking: Debates, project-based learning, practices in real contexts, doing research, cooperative learning, and case studies. These results were unanimous regardless of the student’s understanding of critical thinking: Analyzing/organizing, reasoning/arguing, questioning/asking oneself, evaluating, taking a position/taking a decision, compromising/acting. These findings have important theoretical and practical implications for teachers and educational institutions in relation to the development of critical thinking in higher education.
Helping Students Become Proficient Problem Solvers Part II: An Example from Waves
Authors: Maries Alexandru , Singh Chandralekha
Published: January 2023

Helping students become proficient problem solvers is one of the primary goals of physics courses. In Part I of this article, we summarized the vast research on problem solving relevant for physics instruction, and here we discuss a concrete example of problem solving in the context of waves from introductory physics. The goal of this research was to investigate how drawing diagrams affects students’ problem-solving performance. An introductory class was broken up into three recitations which received different instructions related to diagrams on their weekly quizzes: one group was provided a diagram, another was asked to draw one, and the third was the comparison group which was given no instructions about diagrams. We find that students who were provided a diagram performed significantly worse than students in the other two groups. Furthermore, we find that irrespective of the condition, students who drew diagrams as part of the problem-solving process performed better overall, despite primarily using a mathematical approach to solving the problem. Lastly, we conducted think-aloud interviews with students who solved the same problem to further understand their solution approaches as well as how drawing a diagram is useful even in situations where a primarily mathematical approach is used.
Training the Next Generation of Researchers in the Organ-on-Chip Field
Authors: Moruzzi Alessia , Shroff Tanvi , Keller Silke , Loskill Peter , Cipriano Madalena
Published: January 2023

Organ-on-chip (OoC) technology bridges the principles of biology and engineering to create a new generation of in vitro models and involves highly interdisciplinary collaboration across STEM disciplines. Training the next generation of scientists, technicians and policy makers is a challenge that requires a tailored effort. To promote the qualification, usability, uptake and long-term development of OoC technology, we designed a questionnaire to evaluate the key aspects for training, identify the major stakeholders to be trained, their professional level and specific skillset. The 151 respondents unanimously agreed on the need to train the next generation of OoC researchers and that the training should be provided early, in interdisciplinary subjects and throughout the researchers’ career. We identified two key training priorities: (i) training scientists with a biology background in microfabrication and microfluidics principles and (ii) training OoC developers in pharmacology/toxicology. This makes training in OoC a transdisciplinary challenge rather than an interdisciplinary one. The data acquired and analyzed here serves to guide training initiatives for preparing competent and transdisciplinary researchers, capable of assuring the successful development and application of OoC technologies in academic research, pharmaceutical/chemical/cosmetic industries, personalized medicine and clinical trials on chip.
Harmonic Distortion Aspects in Upper Limb Swings during Gait in Parkinson’s Disease
Authors: Pietrosanti Luca , Calado Alexandre , Verrelli Cristiano Maria , Pisani Antonio , Suppa Antonio , Fattapposta Francesco , Zampogna Alessandro , Patera Martina , Rosati Viviana , Giannini Franco , Saggio Giovanni
Published: January 2023

Parkinson’s disease (PD) is responsible for a broad spectrum of signs and symptoms, including relevant motor impairments generally rated by clinical experts. In recent years, motor measurements gathered by technology-based systems have been used more and more to provide objective data. In particular, wearable devices have been adopted to evidence differences in the gait capabilities between PD patients and healthy people. Within this frame, despite the key role that the upper limbs’ swing plays during walking, no studies have been focused on their harmonic content, to which this work is devoted. To this end, we measured, by means of IMU sensors, the walking capabilities of groups of PD patients (both de novo and under-chronic-dopaminergic-treatment patients when in an off-therapy state) and their healthy counterparts. The collected data were FFT transformed, and the frequency content was analyzed. According to the results obtained, PD determines upper limb rigidity objectively evidenced and correlated to lower harmonic contents.
Dispersive Optical Solitons with Differential Group Delay Having Multiplicative White Noise by Itô Calculus
Authors: Zayed Elsayed M. E. , Alngar Mohamed E. M. , Shohib Reham M. A. , Biswas Anjan , Yıldırım Yakup , Moraru Luminita , Moldovanu Simona , Georgescu Puiu Lucian
Published: January 2023

The current paper recovers dispersive optical solitons in birefringent fibers that are modeled by the Schrödinger–Hirota equation with differential group delay and white noise. Itô Calculus conducts the preliminary analysis. The G′/G-expansion approach and the enhanced Kudryashov’s scheme gave way to a wide spectrum of soliton solutions with the white noise component reflected in the phase of the soliton.
Neural Adaptive Impedance Control for Force Tracking in Uncertain Environment
Authors: An Hao , Ye Chao , Yin Zikang , Lin Weiyang
Published: January 2023

Torque-based impedance control, a kind of classical active compliant control, is widely required in human–robot interaction, medical rehabilitation, and other fields. Adaptive impedance control effectively tracks the force when the robot comes in contact with an unknown environment. Conventional adaptive impedance control (AIC) introduces the force tracking error of the last moment to adjust the controller parameters online, which is an indirect method. In this paper, joint friction in the robot system is first identified and compensated for to enable the excellent performance of torque-based impedance control. Second, neural networks are inserted into the torque-based impedance controller, and a neural adaptive impedance control (NAIC) scheme with directly online optimized parameters is proposed. In addition, NAIC can be deployed directly without the need for data collection and training. Simulation studies and real-world experiments with a six link rotary robot manipulator demonstrate the excellent performance of NAIC.
Stacked Siamese Generative Adversarial Nets: A Novel Way to Enlarge Image Dataset
Authors: Liu Shanlin , Han Ren , Yared Rami
Published: January 2023

Deep neural networks often need to be trained with a large number of samples in a dataset. When the training samples in a dataset are not enough, the performance of the model will degrade. The Generative Adversarial Network (GAN) is considered to be effective at generating samples, and thus, at expanding the datasets. Consequently, in this paper, we proposed a novel method, called the Stacked Siamese Generative Adversarial Network (SSGAN), for generating large-scale images with high quality. The SSGAN is made of a Color Mean Segmentation Encoder (CMS-Encoder) and several Siamese Generative Adversarial Networks (SGAN). The CMS-Encoder extracts features from images using a clustering-based method. Therefore, the CMS-Encoder does not need to be trained and its output has a high interpretability of human visuals. The proposed Siamese Generative Adversarial Network (SGAN) controls the category of generated samples while guaranteeing diversity by introducing a supervisor to the WGAN. The SSGAN progressively learns features in the feature pyramid. We compare the Fréchet Inception Distance (FID) of generated samples of the SSGAN with previous works on four datasets. The result shows that our method outperforms the previous works. In addition, we trained the SSGAN on the CelebA dataset, which consists of cropped images with a size of 128 × 128. The good visual effect further proves the outstanding performance of our method in generating large-scale images.
Modeling and Simulation of Si Grating Photodetector Fabricated Using MACE Method for NIR Spectrum
Authors: Surawijaya Akhmadi , Chandra Zefanya , Sulthoni Muhammad Amin , Idris Irman , Adiono Trio
Published: January 2023

In this research, we modeled a silicon-based photodetector for the NIR-IR spectrum using a grating structure fabricated using the metal-assisted chemical etching method. A nanostructure fabricated by using this method is free of defects such as unwanted sidewall metal depositions. The device is simulated using Lumerical finite difference time domain (FDTD) for optical characteristics and Lumerical CHARGE for electrical characteristics. First, we optimized the grating structure duty cycle parameter for maximum optical power absorption using the particle swarm optimization algorithm provided in Lumerical FDTD, and then used the optimized parameter for our simulations. From Lumerical FDTD simulations, we found that the Cr masker metal used in the fabrication process acts as a resonant cavity and a potential candidate for internal photo emission (IPE) effects. By using Lumerical CHARGE, we performed electrical simulation and by adding the IPE calculation we found that at 850 nm wavelength the Si grating photodetector device exhibited 19 mA/W responsivity and detectivity of 2.62 × 106 Jones for −1 volt operating voltage.
Acknowledgment to the Reviewers of Electronics in 2022
Authors:   Electronics Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Analysis of Enrollment Criteria in Secondary Schools Using Machine Learning and Data Mining Approach
Authors: Abideen Zain ul , Mazhar Tehseen , Razzaq Abdul , Haq Inayatul , Ullah Inam , Alasmary Hisham , Mohamed Heba G.
Published: January 2023

Out-of-school children (OSC) surveys are conducted annually throughout Pakistan, and the results show that the literacy rate is increasing gradually, but not at the desired speed. Enrollment campaigns and targets system of enrollment given to the schools required a valuable model to analyze the enrollment criteria better. In existing studies, the research community mainly focused on performance evaluation, dropout ratio, and results, rather than student enrollment. There is a great need to develop a model for analyzing student enrollment in schools. In this proposed work, five years of enrollment data from 100 schools in the province of Punjab (Pakistan) have been taken. The significant features have been extracted from data and analyzed through machine learning algorithms (Multiple Linear Regression, Random Forest, and Decision Tree). These algorithms contribute to the future prediction of school enrollment and classify the school’s target level. Based on these results, a brief analysis of future registrations and target levels has been carried out. Furthermore, the proposed model also facilitates determining the solution of fewer enrollments in school and improving the literacy rate.
Robot Bionic Eye Motion Posture Control System
Authors: Zhang Hongxin , Lee Suan
Published: January 2023

This paper mainly studies the structure and system of robot bionic eye. Given that most robots usually run on battery power, the STM32L053C8T6 with high efficiency and super low power consumption was selected as the main control. By carrying IMU, the bionic eye attitude data can be acquired quickly and accurately, and the measurement data of accelerometer and gyroscope can be fused by the algorithm to obtain stable and accurate bionic eye attitude data. Thus, the precise control of the motor can be realized through the drive control system equipped with PCA9685, which can enhance the motion control precision of robot bionic eye. In the present study, three kinds of IMU sensors, MPU6050, MPU9250, and WT9011G4K, were selected to carry out experiments. Finally, MPU9250 with better power consumption and adaptability is selected. This is the attitude acquisition device of bionic eye. In addition, three different filters, CF, GD, and EKF, were used for data fusion and comparison. The experimental result showed that the dynamic mean errors of CF, GD, and EKF are 0.62°, 0.61°, and 0.43°, respectively, and the static mean errors are 0.1017°, 0.1001°, and 0.0462°, respectively. The result showed that, after the use of EKF, the robot bionic eye system designed in this paper can significantly reduce the attitude angle error and effectively improve the image quality. It ensures accuracy and reduces power consumption and cost, which has lower requirements on hardware and is easier to popularize.
Sensorless Control of Dual Three-Phase Permanent Magnet Synchronous Machines—A Review
Authors: Teymoori Vahid , Kamper Maarten , Wang Rong-Jie , Kennel Ralph
Published: January 2023

This paper presents an overview of various sensorless control methods, with a focus on dual three-phase permanent magnet synchronous machines (DTP-PMSM). Owing to the important role that DTP-PMSMs play in motion-control applications in industry, most academic researchers and industry activists seek to reduce costs and size while increasing the capability and efficiency of motion applications. This has led to an increase in the number of publications about multiphase machines in recent years. The purpose of this article is to review the most important sensorless control techniques, which are divided into two main categories, namely saliency-based control method for low-speed range and model-based control method for high-speed range. Both methods are subdivided into other categories, with a focus on DTP-PMSMs. The methods are compared with each other for the purpose of selecting the most suitable control technique for implementation in applications such as ship propulsion, wind turbines, and aerospace.
Hybrid Renewable Energy Systems for Sustainable Rural Development: Perspectives and Challenges in Energy Systems Modeling
Authors: Natividad Lauren E. , Benalcazar Pablo
Published: January 2023

Hybrid Renewable Energy Systems (HRESs) have proven to be viable solutions for rural electrification. They not only electrify rural locations but also provide environmentally sustainable, secure, and affordable energy if optimized. These systems can best be described as generators of electricity from multiple energy sources that complement each other. Optimized HRESs often generate affordable electricity by minimizing the levelized cost of electricity (LCOE) and carbon emissions. The investigation of social benefit factors within energy poverty is a relatively new discussion in the study of modeling off-grid energy systems. In this perspective article, we examine the importance of computational tools for the energy transition of rural and remote communities. We show that classical and heuristic models possess the capability of optimizing hybrid renewable energy systems considering social parameters including health, education, and income. This is followed by a discussion about the potential changes these computational tools would need to go through to integrate interdisciplinary factors and address societal transformations. The essence of this paper showcases the influx of literature about this topic; additionally, we look beyond the traditional optimization approaches to disclose that new contributions are evolving based on both current and potential needs within society.
Biodiesel Additives Synthesis Using Solid Heteropolyacid Catalysts
Authors: da Silva Marcio Jose , Lopes Neide Paloma Gonçalves , Rodrigues Alana Alves
Published: January 2023

Fossil additives are a primary energy source and their contribution is around 80% in the world. Therefore, bioadditives that reduce their impact are each very important. This article discusses the chemical transformation of glycerol to carbonate, ethers, esters, ketals, and acetals, compounds with high technological applications, especially in the fuel sector as bioadditives. Mainly, heterogeneous catalysts are important in the production of more than 80% of chemicals in the word. The focus is on demonstrating how the Keggin heteropolyacids (HPAs) are efficient catalysts in the reactions of syntheses of glycerol-derived bioadditives, either in homogeneous or heterogeneous phases. Although solid, HPAs have a low surface area and are soluble in polar solvents, hampering their use as heterogeneous catalysts. Alternatively, they have been successfully used supported on solid matrixes with a high surface area. Another option is converting the Keggin HPAs to insoluble salts simply by exchanging their protons with large cations like potassium, cesium, or ammonium-derivatives. Therefore, solid heteropoly salts have reduced the cost and the environmental impact of bioadditive synthesis processes, being an alternative to traditional mineral acids or solid-supported catalysts. This review describes the most recent advances achieved in the processes of synthesis of glycerol-derived bioadditives over solid-supported HPAs or their solid heteropoly salts.
Optimized Operation and Sizing of Solar District Heating Networks with Small Daily Storage
Authors: Delubac Régis , Sadr Mohammad , Sochard Sabine , Serra Sylvain , Reneaume Jean-Michel
Published: January 2023

To continue improving the integration of solar thermal in district heating networks, optimization tools that can study both sizing and operation of heating plants are needed. In this article, the ISORC tool was used to study the sizing and coupled operation of smaller storage and solar fields with other heating sources such as biomass and gas boilers. For this, a k-medoids algorithm was applied to select consecutive characteristic days to size the system based on an optimal operation of consecutive days in the same season. The formulated problem was nonlinear, and the objective function to be minimized was the total cost. Two case studies with different day constructions and distributions were studied with various sensitivity analysis. The formulation and methodology allowed us to study different cases and situations easily and proved the importance of the selection and attribution of typical days. In all cases, the results showed that even with a daily approach, solar thermal covers approximately 20% of the demand, which demonstrates the relevance of considering and developing small daily storage with small solar fields.
Wireless Technologies for Industry 4.0 Applications
Authors: Artetxe Eneko , Barambones Oscar , Calvo Isidro , Fernández-Bustamante Pablo , Martin Imanol , Uralde Jokin
Published: January 2023

Wireless technologies are increasingly used in industrial applications. These technologies reduce cabling, which is costly and troublesome, and introduce several benefits for their application in terms of flexibility to modify the layout of the nodes and scaling of the number of connected devices. They may also introduce new functionalities since they ease the connections to mobile devices or parts. Although they have some drawbacks, they are increasingly accepted in industrial applications, especially for monitoring and supervision tasks. Recently, they are starting to be accepted even for time-critical tasks, for example, in closed-loop control systems involving slow dynamic processes. However, wireless technologies have been evolving very quickly during the last few years, since several relevant technologies are available in the market. For this reason, it may become difficult to select the best alternative. This perspective article intends to guide application designers to choose the most appropriate technology in each case. For this purpose, this article discusses the most relevant wireless technologies in the industry and shows different examples of applications.
Roadmap to Achieving Sustainable Development via Green Hydrogen
Authors: Mneimneh Farah , Ghazzawi Hasan , Abu Hejjeh Mohammad , Manganelli Matteo , Ramakrishna Seeram
Published: January 2023

The conversion to renewable energy can be achieved when cities and communities start to depend on sustainable resources capable of providing for the basic needs of the community, along with a reduction in the daily problems and issues that people face. These issues, such as poverty, hunger, sanitation, and economic difficulties are highlighted in the Sustainable Development Goals (SDGs), which aim to limit and eradicate these problems along with other environmental obstacles including climate change and Greenhouse Gases (GHGs). These SDGs, containing 17 goals target each sector and provide propositions to solve such devastating problems. Hydrogen contributes to the targets of these sustainable developments, since through its implementation in different industries the levels of GHG will drop and thus contribute to the climate change which Earth is facing. Further, through the usage of such resources, many job opportunities will also be developed, thus enhancing the economy, and lifting the status of society. This paper classifies the four different types of hydrogen and outlines the differences between them. The paper then emphasizes the importance of green hydrogen use within the shipping industry, transportation, and infrastructure, along with economic and social development through job opportunities. Furthermore, this paper provides case studies tackling green hydrogen status in the United Kingdom, United States of America, and European Union as well as Africa, United Arab of Emirates, and Asia. Finally, challenges and recommendations concerning the green hydrogen industry are addressed. This paper aims to relate the use of green hydrogen to the direct and indirect goals of SDG.
Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm
Authors: Alghamdi Abdulrahman A. , Ibrahim Abdelhameed , El-Kenawy El-Sayed M. , Abdelhamid Abdelaziz A.
Published: January 2023

Introduction: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) and genetic algorithm (GA) and it is denoted by the GABER optimization algorithm. This algorithm is used to optimize the parameters of the proposed stacked ensemble model to boost the prediction accuracy and to improve the generalization capability. Results: To evaluate the proposed approach, several experiments are conducted to study its effectiveness and superiority compared to other optimization methods and forecasting models. In addition, statistical tests are conducted to assess the significance and difference of the proposed approach. The recorded results proved the proposed approach’s superiority, effectiveness, generalization, and statistical significance when compared to state-of-the-art methods. Conclusions: The proposed approach is capable of predicting both wind speed and solar radiation with better generalization.
Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm
Authors: Peng Xin , Chen Hui , Guan Cong
Published: January 2023

In order to optimize the energy management strategy and solve the problem of the power quality degradation of fuel cell hybrid electric ships, a particle swarm optimization algorithm based energy management strategy is proposed in this paper. Taking a fuel cell ship as the target ship, a system simulation model is built in Matlab/Simulink to verify the proposed energy management strategy. Through simulations and comparisons, the bus voltage curve of the optimized hybrid power system fluctuates more gently, and the voltage sag is smaller. The amplitude of the voltage fluctuation under maneuvering conditions is reduced by 55% compared with that of the original ship. The charging and discharging process of the composite energy storage system is optimized under maneuvering conditions, the power quality of the marine power grid is improved, and the use of the energy management strategy can extend the service life of the battery.
Energy Security in Light of Sustainable Development Goals
Authors: Luty Lidia , Zioło Monika , Knapik Wioletta , Bąk Iwona , Kukuła Karol
Published: January 2023

Energy security affects the functioning of countries politically, economically, and socially. Energy is an important factor in sustainable development efforts. Hence, countries are assessing their energy systems for compatibility with sustainable development goals by creating new concepts for energy development. Combining the concepts of energy security and sustainable energy consumption, an analysis of the differentiation of EU countries in terms of selected indicators indicated in Goal 7 of Agenda 2030 was carried out in dynamic terms. Two groups of indicators were distinguished. One group of indicators was selected to represent the changes in energy demand reported by final consumers, taking into account the use of energy obtained from renewable sources. The second group of indicators represents those relating to the security of supply of raw materials, i.e., energy dependency indicators broken down by major energy commodities and an energy productivity indicator. The analysis uses the coefficient of relative proximity of the facility to the ideal facility proposed in the TOPSIS method. The analyses carried out do not indicate that there is a relationship between the level of sustainable energy consumption and energy productivity or energy import dependency. A statistically significant correlation was observed between energy import dependency by oil and petroleum products and primary energy consumption, and between the share of renewable energy in gross final energy consumption and total energy import dependency.
Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios
Authors: Stabile Pietro , Ballo Federico , Previati Giorgio , Mastinu Giampiero , Gobbi Massimiliano
Published: January 2023

This paper aims to provide a quantitative assessment of the effect of driver action and road traffic conditions in the real implementation of eco-driving strategies. The study specifically refers to an ultra-efficient battery-powered electric vehicle designed for energy-efficiency competitions. The method is based on the definition of digital twins of vehicle and driving scenario. The models are used in a driving simulator to accurately evaluate the power demand. The vehicle digital twin is built in a co-simulation environment between VI-CarRealTime and Simulink. A digital twin of the Brooklands Circuit (UK) is created leveraging the software RoadRunner. After validation with actual telemetry acquisitions, the model is employed offline to find the optimal driving strategy, namely, the optimal input throttle profile, which minimizes the energy consumption over an entire lap. The obtained reference driving strategy is used during real-time driving sessions at the dynamic driving simulator installed at Politecnico di Milano (DriSMi) to include the effects of human driver and road traffic conditions. Results assess that, in a realistic driving scenario, the energy demand could increase more than 20% with respect to the theoretical value. Such a reduction in performance can be mitigated by adopting eco-driving assistance systems.
Analysis of the Cutting Strategy of Five Different Tree Species Targeting Carbon Sequestration
Authors: Liu Ziyan , He Ningjie , Wang Chenyu , Qu Chen
Published: January 2023

Fully utilizing the carbon sequestration potential of forests will help to further mitigate the aggravation of the greenhouse effect. In this paper, five typical tree species in Xiaoxing’anling are used as research objects. Based on the differences in the carbon dioxide sequestration capacity of different tree species at different growth cycles, a mathematical model of annual carbon sequestration benefits is established, the optimal annual cutting rates of five tree species are calculated, and the carbon sequestration capacity after ten years and economic benefits of the forest are predicted. The results showed that proper cutting of mature trees can increase annual carbon sequestration by 32% compared to no cutting. In addition, by comparing different forest management strategies, it was found that reasonable harvesting can bring higher economic benefits. We also confirmed that the increase in environmental temperature is one of the factors leading to the decrease in forest carbon sequestration capacity. The results of this paper can provide a theoretical basis for optimal forest management strategies.