bluuebunny/crossref_metadata_2025 huggingface dataset:
Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra
Authors: Hussain Sayyed Shahid , Degang Xu , Shah Pir Masoom , Islam Saif Ul , Alam Mahmood , Khan Izaz Ahmad , Awwad Fuad A. , Ismail Emad A. A.
Published: August 2023

Parkinson’s disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson’s Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network.
Radiological and Functional Pulmonary Evolution in Post-COVID-19 Patients: An Observational Study
Authors: Cavallari Strozze Catharin Virgínia Maria , Zutin Tereza Laís Menegucci , Guiguer Elen Landgraf , Cressoni Araújo Adriano , Fornari Laurindo Lucas , Chagas Eduardo F. Baisi , Gasparotti Zorzetto Cássia Fernanda , Bueno Patrícia C. dos Santos , dos Santos Bueno Manoela , Cervelim Nunes Yandra , Cavallari Strozze Catharin Vitor , Gonzaga Heron Fernando , Barbalho Sandra Maria
Published: August 2023

COVID-19 has generated a scenario for global health with multiple systemic impairments. This retrospective study evaluated the clinical, radiological, and pulmonary functional evolution in 302 post-COVID-19 patients. Regarding post-COVID-19 pulmonary symptoms, dry cough, dyspnea, and chest pain were the most frequent. Of the associated comorbidities, asthma was more frequent (23.5%). Chest tomography (CT) initially showed a mean pulmonary involvement of 69.7%, and evaluation in the subsequent months showed improvement in the evolutionary image. With less than six months post-pathology, there was a commitment of 37.7% from six to twelve months it was 20%, and after 12 months it was 9.9%. As for most of the sample, 50.3% of the patients presented CT normalization less than six months after infection, 23% were normalized between six and twelve months, and 5.2% presented with normalized images after twelve months, with one remaining. A percentage of 17.3% maintained post-COVID-19 pulmonary residual sequelae. Regarding spirometry, less than six months after pathology, 59.3% of the patients presented regular exam results, 12.3% had their function normalized within six to twelve months, and 6.3% had normal exam results twelve months after their post-pathology evaluation. Only 3.6% of the patients still showed some alteration during this period.
Quantum Chaos—Dedicated to Professor Giulio Casati on the Occasion of His 80th Birthday
Authors: Robnik Marko
Published: August 2023

Quantum chaos is the study of phenomena in the quantum domain which correspond to classical chaos [...]
Sustainable Technology Adoption as a Source of Competitive Advantage for Pineapple Production in Ejigbo, Nigeria
Authors: Oladapo Moshood Olatunde , Abualqumboz Moheeb , Ngoe Lawrence M. , Oyetunji Abiodun Kolawole , Amaechi Chiemela Victor , Bello Rasheed , Amaechi Ebube Charles
Published: August 2023

Adopting new technology as a strategic resource can result in a competitive edge in any market. However, a competitive advantage cannot be acquired in the production of horticultural goods without first embracing the practices that are inextricably linked to those goods. This paper investigates the adoption of farm practices in conjunction with technology transferred to farmers. Some research debates on competitive advantages have identified both resources and processes of production as sources of competitive advantage. The emphasis on the resource-based view and dynamic capability view stipulates that firms acquire competitiveness via internal resources and capabilities. However, there has not been much empirical exploration of horticultural production sustainability in this regard despite its sufficiently outstanding contribution to the gross domestic product in developing and developed economies. It specifically discusses how Technology Adoption Practices (TAP) could lead to a competitive advantage in horticulture with particular reference to the production of pineapple fruit in Ejigbo, Nigeria. From the angle of professional practice; the study provides an insight into how farmers strive to suggest solutions to practical challenges faced within the production process. Therefore, it is essential to have practices in place for the adoption of sustainable technology. The outcomes of the study generate two different storylines and demonstrate that attributing factors as well as reinforcing capabilities both boost competitiveness at the farm level and enhance the farmers’ desire for farming pineapples. Pineapple farmers in Ejigbo employ a differentiation approach to gain a competitive advantage in their agro-farming industry. This could lead to an increase in the volume of fresh pineapple products that are exported.
Teacher, Think Twice: About the Importance and Pedagogical Value of Blended Learning Design in VET
Authors: Makovec Radovan Danijela , Radovan Marko
Published: August 2023

The integration of technology into educational institutions is transforming education and fostering innovative approaches to learning. Blended learning, an increasingly popular approach, integrates technology with traditional teaching approaches. Blended learning can overcome the limitations associated with using technology purely as a tool, facilitating its full integration into the educational process. The present study involved 106 students enrolled in a vocational education program (VET) in Slovenia. The students were subjected to a pilot implementation of blended learning, and their experiences were subsequently analyzed and evaluated. The results showed that the use of different teaching approaches led to different outcomes in terms of student workload, learning outcomes, and motivation. The different teaching approaches were found to present different levels of difficulty for students. For example, live sessions and the integration of virtual reality/augmented reality (VR /AR) technology presented greater challenges. Conversely, collaborative group work and online courses focused on developing professional skills were found to be beneficial. The study also highlighted the importance of feedback and instructor support in online learning environments. In general, the results of our study suggest that the adoption of blended learning in education can lead to positive outcomes. However, it is important to emphasize the importance of careful planning and thoughtful consideration of optimal pedagogical strategies.
The Fascists Are Coming! Teacher Education for When Right-Wing Activism Micro-Governs Classroom Practice
Authors: Appelbaum Peter
Published: August 2023

U.S. educational reform is often the harbinger of global demands on mathematics education practices globally. It behooves teacher education to ‘catch up’ on current trends, hopefully, to stave off the worst of the fascist tendencies of contemporary politics of education. Past foci on research-based ‘best practices’ and ‘mathematics for all’, grounded in liberal multiculturalism (confirming expectations from critical mathematics education scholarship), have become the targets of activists and politicians, turning once-exemplary teachers and their students into casualties. The four phases of currere are employed to study this phenomenon and to identify strategies and tactics for teacher education programs. The currere methodology indicates that the content of such programs must reduce time devoted to evidence and research-based practice in order to accommodate techniques and knowledge bases for the recognition of right-wing tactics, clowning, slogan parody, and political organizing. Teacher education must further place mathematics teachers’ embrace of expertise, authority, and neutrality within broader perspectives on the politics of education, organizational infrastructure strategies and tactics, resource curation, and personal safety planning. Teacher educators themselves must prepare responses to threats on their careers, lives, and families, and proactive ‘game plans’ for the development of new program curricula.
Integrating PhET Simulations into Elementary Science Education: A Qualitative Analysis
Authors: Rayan Baraa , Daher Wajeeh , Diab Hussam , Issa Nael
Published: August 2023

This research delved into the integration of PhET simulations in elementary science education, specifically aimed at Grade 3 students. The primary objective was to evaluate how the use of these digital simulations influenced students’ conceiving of scientific concepts, focusing on “States of M1atter and Phase Changes” and “Solubility and Saturation”. Employing a qualitative research approach, the study observed 19 students who worked in pairs and trios as they engaged with PhET simulations to explore assigned science topics and address related questions. The observations centered on tracking students’ interactions with simulations and their progression through different knowledge phases. We used deductive and inductive content analysis to analyze the transcripts of the observation. The findings reveal that in the “Remembering” phase, students demonstrated a tendency to relate personal experiences to simulations, underscoring real-life context’s role in learning. The “Understanding” phase highlighted how PhET simulations facilitated deeper comprehension, with students making insightful observations. Additionally, the “Application” phase showcased the effective translation of simulation-derived knowledge into practical scenarios, bridging theoretical and real-world understanding. Students’ use of high-order thinking skills, at the analysis, evaluation, and creative phases, showed that simulations supported Grade 3 students in their learning processes of scientific concepts. The research underscores the efficacy of integrating PhET simulations into elementary science education, enhancing students’ knowledge by promoting active engagement and problem-solving skills. Integrating simulations into teaching methodologies emerges as a promising avenue to nurture scientific expertise and holistic understanding among elementary school students.
Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies
Authors: Abdulraheem Abdulkabir , Suleiman Jamiu T. , Jung Im Y.
Published: August 2023

Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrated exceptional performance in generating high-quality synthetic images that closely resemble the original imprinted characters required for augmenting the limited datasets. And the evaluation metric, Fréchet inception distance, further validated the outstanding performance of the WGAN-GP and WGANDIV models, establishing them as optimal choices for dataset augmentation to enhance the accuracy and reliability of recognition systems.
Time-Series Machine Learning Techniques for Modeling and Identification of Mechatronic Systems with Friction: A Review and Real Application
Authors: Ayankoso Samuel , Olejnik Paweł
Published: August 2023

Developing accurate dynamic models for various systems is crucial for optimization, control, fault diagnosis, and prognosis. Recent advancements in information technologies and computing platforms enable the acquisition of input–output data from dynamical systems, resulting in a shift from physics-based methods to data-driven techniques in science and engineering. This review examines different data-driven modeling approaches applied to the identification of mechanical and electronic systems. The approaches encompass various neural networks (NNs), like the feedforward neural network (FNN), convolutional neural network (CNN), long short-term memory (LSTM), transformer, and emerging machine learning (ML) techniques, such as the physics-informed neural network (PINN) and sparse identification of nonlinear dynamics (SINDy). The main focus is placed on applying these techniques to real-world problems. A real application is presented to demonstrate the effectiveness of different machine learning techniques, namely, FNN, CNN, LSTM, transformer, SINDy, and PINN, in data-driven modeling and the identification of a geared DC motor. The results show that the considered ML techniques (traditional and state-of-the-art methods) perform well in predicting the behavior of such a classic dynamical system. Furthermore, SINDy and PINN models stand out for their interpretability compared to the other data-driven models examined. Our findings explicitly show the satisfactory predictive performance of six different ML models while also highlighting their pros and cons, such as interpretability and computational complexity, using a real-world case study. The developed models have various applications and potential research areas are discussed.
Research on a High-Precision State-of-Charge Estimation Method Based on Forgetting Factor Recursive Least Squares and Adaptive Extended Kalman Filter Applied to LiFePO4 Battery
Authors: Xia Yihui , Ye Zhihao , Huang Liming , Sun Lucheng , Jiang Yunxiang
Published: August 2023

The state-of-charge (SOC) estimation accuracy is closely associated with the estimation method and the battery parameter identification performance. The battery parameter identification method based on forgetting factor recursive least squares (FFRLS) has the advantages of high parameter identification accuracy and fast dynamic response speed. On this basis, the performance of two SOC estimation methods, the extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) are compared and studied. The results show that AEKF has better steady-state and dynamic SOC estimation performance, but the estimation accuracy and dynamic response performance are still not objective. To further improve the performance of SOC estimation, a joint SOC estimation method based on FFRLS-AEKF is proposed, and the SOC estimation experimental results with FFRLS-AEKF and AEKF are conducted. The experimental results show that the proposed joint SOC estimation method based on FFRLS-AEKF has a better steady-state and dynamic performance of SOC estimation. The maximum absolute error of the proposed algorithm is 4.97%. As the battery working time increases, the SOC estimation accuracy continues to converge to the true value, and the average absolute error is reduced to 2.5%. The proposed method and theoretical analysis are proven to be correct and feasible.
Enhanced Infrared Detection Algorithm for Weak Targets in Complex Backgrounds
Authors: Zou Zhihui , Ma Lianji , Yang Shuai , Li Yingchao , Shi Haodong , Fu Qiang
Published: August 2023

In this article, we design a new lightweight infrared optical system that fully meets airborne settings and greatly reduces the collection of invalid information. This new system targets the technical problems of stray light, strong invalid information, weak texture information of small targets, and low intensity of valid information under a complex background, which lead to difficult identification of small targets. Image enhancement of weak, small targets against complex backgrounds has been the key to improving small-target search and tracking technology. For the complex information that is still collected, an improved two-channel image enhancement processing algorithm is proposed: the A-channel adopts an improved nonlinear diffusion method and improved curvature filtering, and the B-channel adopts bootstrap filtering and a local contrast enhancement algorithm. The weak target is then extracted by the algorithm of weighted superposition. The false alarm rate is effectively weakened, and robustness is improved. As a result of the experimental data analysis, the method can effectively extract the weak targets in complex backgrounds, such as artificial backgrounds, surface vegetation, etc., enlarge the target gray value, and reduce Fa by 56%, compared with other advanced methods, while increasing Pd by 17%. The algorithm proposed in this paper is of great significance and value for weak target identification and tracking, and it has been successfully applied to industrial detection, medical detection, and in the military field.
Microstrip to Slot-Line-Fed Microstrip Patch Antenna with Radiation Pattern Diversity for X-Band Application
Authors: Ghimire Jiwan , Khan Daud , Choi Dong-You
Published: August 2023

This paper proposes a planar wideband microstrip feedline to a slot-line-based patch antenna for high-frequency pattern diversity applications. The antenna consists of two adjacent rectangular patches separated by the ground slots, with a directive patch slot along the edge of the substrate. A compact common feedline-to-slot configuration is used to miniaturize the antenna. The antenna is designed in stages, starting with an in-phase feedline, followed by a slot line structure, with two radiating patches on top and a director on the side. This creates radiation diversity with directive radiation patterns. The antenna was fabricated and analyzed inside a far-field anechoic chamber. The experimental results validate the return loss, gain, and radiation performance. The measured results of the antenna within a frequency range of 8.5 to 11 GHz show good agreement with the simulation. The antenna has a maximum gain of 9.2 dBi and has the potential to be beneficial for beam steering and X-band applications due to its low profile, broad bandwidth, high gain, and good directivity.
Investigation of Trap Density Effect in Gate-All-Around Field Effect Transistors Using the Finite Element Method
Authors: Belkhiria Maissa , Aouaini Fatma , A. Aldaghfag Shatha , Echouchene Fraj , Belmabrouk Hafedh
Published: August 2023

Trap density refers to the density of electronic trap states within dielectric materials that can capture and release charge carriers (electrons or holes) in a semiconductor channel, affecting the transistor’s performance. This study aims to investigate the influence of trap density on the electrothermal behavior of nanowire gate-all-around GAAFET devices. The numerical solution of Poisson’s equations and continuity equations, coupled with the heat conduction model, has been used to predict the temperature inside the GAAFET device. The finite element method has been used to discretize the semiconductor equations. Investigations have been carried out on a number of physical and geometric parameters, such as oxide thickness, nanowire radius, and gate length. Their effects on output characteristics and device temperature have been discussed. A thinner oxide thickness, lower device radius, and longer channel length led to a higher current flow. Results also reveal that high trap densities can have significant impacts on the degradation of electronic devices, particularly in the context of semiconductor devices like transistors.
Research on an Improved Detection Algorithm Based on YOLOv5s for Power Line Self-Exploding Insulators
Authors: Hu Caiping , Min Shiyu , Liu Xinyi , Zhou Xingcai , Zhang Hangchuan
Published: August 2023

In the process of inspecting the self-exploding defects of power line insulators, traditional algorithms suffer from various issues such as long detection time, insufficient accuracy, and difficulties in effective detection under complex environments. To address these problems, we introduce an advanced one-stage object detection algorithm called YOLOv5s, which offers fast training and excellent detection performance. In this paper, we applied the YOLOv5s algorithm to improve the detection precision and classification accuracy of insulator self-explosions. To further enhance the YOLOv5s algorithm, we introduced a BiFPN (Bidirectional Feature Pyramid Network) module for feature fusion. This module improved the feature fusion process by learning the importance weights of different input features, considering their contributions. To tackle the challenge of detecting small objects in the self-exploding insulator dataset, we incorporated an SPD (spatial-to-depth convolution) module that focuses on capturing features in small regions and utilizes one-step convolution layers to avoid losing fine-grained information. To address the issue of high similarity between self-exploding insulator regions and intact insulator regions, we introduced an attention mechanism that concentrates attention on the defective insulator regions to gather more information about insulator defects. Experimental results validate that all three improvement methods significantly enhance detection precision. The final model achieves improvements of 2.0% in precision, 0.9% in recall, and 1.5% in average detection accuracy. Through target detection of the test dataset, insulators with self-explosion cases can be effectively detected.
Stylometric Fake News Detection Based on Natural Language Processing Using Named Entity Recognition: In-Domain and Cross-Domain Analysis
Authors: Tsai Chih-Ming
Published: August 2023

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.
Design Technique of K-Band CMOS Phase Shifter with L-C-L T-Type Low Pass Structure
Authors: Jang Seongjin , Kim Choul-Young , Park Changkun
Published: August 2023

In this study, we designed a 5-bit K-band CMOS switch type phase shifter. In order to minimize phase and gain errors, a design technique for bits constituting the phase shifter was proposed. The proposed design technique has been achieved by adjusting the resonant frequencies of inductance and capacitance in the L-C-L T-type low pass filter structure. Through this, a method of optimizing the phase shifter with the T-type low pass filter structure was presented. The K-band 5-bit phase shifter was designed with a 65 nm CMOS process to verify the feasibility of the proposed design technique. The core size was 0.78 × 0.21 mm2. At the frequency ranges of 22.0 to 23.0 GHz, the insertion loss and RMS phase and gain errors were measured to be 7.44 ± 2.0 dB, 2.6°, and 1.2 dB, respectively.
ROS Implementation of Planning and Robust Control Strategies for Autonomous Vehicles
Authors: Hachem Mohamad , Borrell Ariel M. , Sename Olivier , Atoui Hussam , Morato Marcelo
Published: August 2023

Autonomous vehicles are rapidly emerging as a crucial sector within the automotive industry. Several companies are investing in the development and enhancement of technologies, which presents challenging problems in the context of robotics and control. In particular, this work primarily focuses on the creation of an autonomous vehicle architecture utilizing the robotic operating system (ROS2) framework, accompanied by advanced control algorithms. In order to facilitate the development and implementation of lateral vehicle dynamics controllers, a reduced-size automated car available in GIPSA-Lab is used as an experimental platform. The objective is to design robust controllers capable of achieving optimal tracking and stability. Accordingly, this problem is tackled under different robust control syntheses, considering the H∞ approach: using linear time-invariant (LTI) and linear parameter-varying (LPV) model representations. Several simulation and experimental results are included to demonstrate the efficiency of the controllers.
Design of a Low Power Condenser for Underwater Ships
Authors: Shen Shijun , Liu Chang , Chen Jiaoyuan , Gong Dawei
Published: August 2023

The power unit generates heat during the operation of a ship, and excessive temperature may cause damage to equipment. Therefore, it is necessary to design a cooling system for the multi-working conditions and variable working conditions of a ship to reduce the adverse effects of high temperatures. The traditional pipeline heat exchanger is inefficient, resulting in a serious waste of resources. In this work, under the background of energy conservation and emission reduction, a new type of cooling system is designed. Using a jet condenser with direct mixing of hot and cold fluids, the technology of frequency conversion regulation is used to optimize the energy conservation of the ship’s cooling system. By adjusting the frequency of the circulating water pump and using the PID algorithm to control the flow of cooling water, the temperature of the condensate outlet can be controlled under variable operating conditions, achieving the goal of maintaining the temperature within a certain range. Because of the complex structure and controlled process of the cooling water system, which has the characteristics of non-linearity and hysteresis, PID control has some limitations. Based on this, the BP neural network is used to adjust and tune the parameters of PID control, thus optimizing the control speed and achieving accuracy in the new cooling system.
Simulation and Implementation of Signal Processing for LFM Radar Using DSK 6713
Authors: Mon Yi-Jen
Published: August 2023

This research aims to propose a comprehensive simulation and implementation methodology for LFM (Linear Frequency Modulated) Radar Signal Processing and its application, using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board. The motivation behind this study is to develop control software based on MATLAB R14 and SIMULINK to model various system software tasks, including detection, A/D conversion, Fast Fourier Transform (FFT), modulation, accumulation, decision-making, and target detection. The simulations are categorized into two groups: ideal beat frequency and parameterized beat frequency. We introduce several important terminologies for consideration, including pulse compression, SNR, matched filter, Doppler effect, and more. The use of real-time data exchange (RTDX) will facilitate the generation of input data and enable real-time calculations for outputs, leading to the creation of machine code for the DSP chip. This process aims to ensure data verification calculations and enhance the credibility and performance of the proposed methodology. By conducting thorough simulations, verification, and practical testing, the study demonstrates the satisfactory credibility and performance of the developed method. Using this research, we aim to contribute to the advancement of LFM Radar Signal Processing and enable its efficient implementation using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board.
Modelling Methodologies to Design and Control Renewables and Hydrogen-Based Telecom Towers Power Supply Systems
Authors: Aliberti Paolo , Sorrentino Marco , Califano Marco , Pianese Cesare , Capozucca Luca , Cristiani Laura , Lops Gianpiero , Mancini Roberto
Published: August 2023

Proton exchange membrane fuel cell (PEMFCS) and electrolyser (PEMELS) systems, together with a hydrogen storage tank (HST), are suitable to be integrated with renewable microgrids to cover intermittency and fully exploit the excess of electrical energy. Such an integration perfectly fits telecom tower power supply needs, both in off-grid and grid-connected sites. In this framework, a model-based tool enabling both optimal sizing and proper year-through energy management of both the above applications is proposed. Respectively, the islanded optimisation is performed considering two economic indices, i.e., simple payback (SPB) and levelised cost of energy (LCOE), together with two strategies of hydrogen tank management, charge sustaining and depleting, and also accounting for the impact of grid extension distance. On the other hand, the grid connection is addressed through the dynamic programming method, while downsizing PEMELS and HST sizes to improve techno-economic effectiveness, thanks to grid contribution towards renewables curtailment issues mitigation. For both the above introduced HST management strategies, a reduction of more than 70% of the nominal PEMELS power and 90% of the HST size, which will in turn lead to SPB and LCOE being reduced by 80% and 60% in comparison to the islanded case, respectively, is achieved. Furthermore, the charge depleting strategy, relying on possible hydrogen purchase, interestingly provides an SPB and LCOE of 9% and 7% lower than the charge sustaining one.
Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review
Authors: Chen Julan , Qi Guangheng , Wang Kai
Published: August 2023

Lithium-ion batteries, as a typical energy storage device, have broad application prospects. However, developing lithium-ion batteries with high energy density, high power density, long lifespan, and safety and reliability remains a huge challenge. Machine learning, as an emerging artificial intelligence technology, has successfully solved many problems in academic research on business, financial management, and high-dimensional complex problems. It has great potential for mining and revealing valuable information from experimental and theoretical datasets. Therefore, quantitative “structure function” correlations can be established to predict battery health status. Machine learning also shows significant advantages in strategy optimization such as energy optimization management strategy. For lithium-ion batteries, their performance and safety are closely related to the material structure, battery health, fault analysis, and diagnosis. This article reviews the application of machine learning in lithium-ion battery material research, battery health estimation, fault analysis, and diagnosis, and analyzes its application in aviation batteries in conjunction with the development of green aviation technology. By exploring the practical applications of machine learning algorithms and the advantages and disadvantages of different applications, this article summarizes and prospects the application of machine learning in lithium batteries, which is conducive to further understanding and development in this direction.
An Automatic Apparatus for Simultaneous Measurement of Seebeck Coefficient and Electrical Resistivity
Authors: Xiong Ruifeng , Masoumi Saeed , Pakdel Amir
Published: August 2023

A fully automated experimental system was designed for simultaneous measurement of the Seebeck coefficient and electrical resistivity of thermoelectric materials in bulk form. The system incorporates a straightforward and easily fabricated sample holder along with commercially available electronic instrument components. The sample holder showcases a compact design that utilizes two Peltier module heaters to induce sample heating and generate the required temperature gradient. System automation and control are achieved through the implementation of a LabView program. The Seebeck voltage and resistance of the sample (under specified temperature conditions) are determined using I–V measurements. The Seebeck voltage and resistance of the sample correspond to the intercept and slope of the I–V characteristic diagram in the four-point probe method, respectively. To verify the accuracy and reliability of the developed apparatus, a variety of experiments were performed on N-type and P-type bismuth telluride samples. The measurement results closely matched those obtained from commercial systems, with an overall data difference of less than 10% for both the Seebeck coefficient and resistivity measurements.
Impact of Non-Uniform Irradiance and Temperature Distribution on the Performance of Photovoltaic Generators
Authors: Thomas Petrakis , Ktena Aphrodite , Kosmopoulos Panagiotis , Konstantaras John , Vrachopoulos Michael
Published: August 2023

The use of photovoltaic (PV) panels has increased rapidly in the last few years and as a result has become one of the main sources of renewable energy. In this context, it is important to understand in detail how a PV panel reacts to different environmental conditions and how these affect total performance. An experiment has been designed to investigate the performance of a PV panel under various highly non-uniform temperature and irradiance profiles, generated by artificial lighting. Measurements of irradiance and temperature distribution are related to measured I–V curves and used as input to the five-parameter model. The results show the limitations of the model to emulate the PV response under such extreme conditions and provide useful insights about the effect of the temperature profile on the PV performance.
A Power External Transmission Strategy for Regional Power Grids Considering Internal Flexibility Supply and Demand Balance
Authors: Hu Sile , Zhao Yucan , Guo Xiangwei , Zhang Zhenmin , Cai Wenbin , Cao Linfeng , Yang Jiaqiang
Published: August 2023

This paper provides an optimization strategy for flexible operation at the system level to guide the real-time flexible ramping of the power grid. Firstly, the concept of regional power grid flexibility is clarified, and the ramping factor is proposed as a flexibility metric. On this basis, taking the output priority of each node as the objective function and considering constraints such as line flow, power balance, and system flexibility, a mathematical model for regional power grid transmitting flexible power externally is proposed. Compared with the traditional models, this model focuses more on flexibility rather than economy. A case analysis based on the improved IEEE 30-Bus System verifies the feasibility and effectiveness of the proposed strategy and its advantages over the traditional models.
Devolatilization of Polypropylene Particles in Fluidized Bed
Authors: Vitale Armando , Papa Alessandro Antonio , Iannello Stefano , Ciro Erwin , Hatunoglu Arda , Corradetti Valerio , Rovelli Nicola , Foscolo Pier Ugo , Di Carlo Andrea
Published: August 2023

Gasification of plastic waste is an emerging technology of particular interest to the scientific world given the production of a hydrogen-rich gas from waste material. Devolatilization is a first step thermochemical decomposition process which is crucial in determining the quality of the gas in the whole gasification process. The devolatilization of polypropylene (a key compound of plastic waste) has been investigated experimentally in a bench-scale fluidized bed reactor. Experimental tests were carried out by varying two key parameters of the process—the size of the polypropylene spheres (8–12 mm) and temperature (650–850 °C). Temperature shows the highest influence on the process. Greater molecular cracking results were more pronounced at higher temperatures, increasing the production of light hydrocarbons along with the formation of solid carbon residue and tar. The overall syngas output reduced, while the H2 content increased. Furthermore, a pseudo-first-order kinetic model was developed to describe the devolatilization process (Eapp = 11.8 kJ/mol, A1 = 0.55 s−1, ψ = 0.77).
Investigation of the Impact of Biochar Application on Foaming Slags with Varied Compositions in Electric Arc Furnace-Based Steel Production
Authors: Kieush Lina , Schenk Johannes
Published: August 2023

This paper investigates the influence of biochar, either as an individual component or in combination with high-temperature coke, on the slag foaming behavior. High-temperature coke serves as a reference. Three scenarios were considered to study the slag foaming behavior, each characterized by different slag chemical compositions. The results indicate that biochar can promote steady foaming for specific slags when the basicity (CaO/SiO2) falls within a range of 1.2 to 3.4. Experimental findings also reveal that stable foaming can be achieved when a mixture containing biochar and coke with a ratio of 1:1 is employed, with a minimum slag basicity of 1.0 and FeO content of 25 wt.%. The foaming range obtained using different FeO contents (15 wt.% to 40 wt.%) in the mixture surpasses the range observed with the individual application of coke or biochar. The X-ray diffraction (XRD) analysis showed that unrelated to the carbon source applied, the general pattern was that the phases larnite (Ca2SiO4) or dicalcium silicate were detected for slag foams with high basicity. Monticellite (CaMgSiO4) and magnesium iron oxide (Fe2MgO4) were predominant in slag foam samples, with the highest MgO content. The presence of monticellite and merwinite (Ca3MgSi2O8) occurred in samples with the lowest basicity. Eventually, the application of the mixture of coke and biochar showed the potential to obtain stable foaming across a wide range of slag compositions.
Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration
Authors: Kozlovska Maria , Petkanic Stefan , Vranay Frantisek , Vranay Dominik
Published: August 2023

This paper presents a comprehensive analysis of the potential benefits and feasibility of integrating Building Energy Management Systems (BEMSs) with Building Information Modeling (BIM) in, but not limited to, the construction and building management sectors. By examining advantages, challenges, and real-world case studies, this study offers valuable insights into the impact of BEMS-BIM integration on building operations. The research methodology includes a literature review and bibliometric analysis to understand the subject domain and identify prevalent keywords. Additionally, case studies demonstrate the effectiveness of BEMS-BIM integration in real-world scenarios. This study investigates the possibilities and challenges of BIM to the BEMS methodology for energy-efficient industrial buildings, emphasizing the importance of addressing uncertainties and enhancing software interoperability. This research highlights the potential of BEMS-BIM integration to revolutionize building performance, enhance sustainability, and contribute to a greener and more efficient future for the construction and building management industries.
Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network
Authors: Han Yunlong , Li Conghui , Zheng Linfeng , Lei Gang , Li Li
Published: August 2023

In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an R2 value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications.
Design and Implementation of a Dual-Axis Solar Tracking System
Authors: Shang Huilin , Shen Wei
Published: August 2023

A dual-axis solar tracking system with a novel and simple structure was designed and constructed, as documented in this paper. The photoelectric method was utilized to perform the tracking. The solar radiation values of the designed system and a fixed panel system were theoretically estimated and compared, showing that the proposed system is more efficient in collecting solar energy than a fixed solar panel with a 30° tilted fixed surface facing south. The experimental results verified the validity of the prediction as well as the efficiency of the proposed solar tracking system. In a comparison of the data obtained from the measurements, 24.6% more energy was seen to have been obtained in the dual-axis solar tracking system compared to the fixed system. This study possesses potential value in small- and medium-sized photovoltaic applications.
Review of Methods for Evaluating the Energy Efficiency of Vehicles with Conventional and Alternative Power Plants
Authors: Mateichyk Vasyl , Kostian Nataliia , Smieszek Miroslaw , Gritsuk Igor , Verbovskyi Valerii
Published: August 2023

The evaluation of the energy efficiency of vehicles in operating conditions is used to solve management and control tasks in intelligent transport systems. The modern world fleet is characterized by an increase in the share of vehicles with alternative power plants (hybrid, electric, and hydrogen fuel cells). At the same time, vehicles with conventional power plants (internal combustion engines) remain in operation. A wide range of modern power plants determines the relevance of studying the advantages and limitations of existing methods of evaluating the vehicle energy efficiency, delineating the application scope and highlighting promising directions for their further development. The article systematizes the methods of evaluation and management of the energy efficiency of vehicles with conventional and alternative power plants. Special attention is paid to the assessment of energy consumption per unit of transport work at the stage of vehicle operation, taking into account various operational factors. The concept of a 3D morphological model of the transport system for evaluating the energy efficiency of vehicles is presented. An algorithm for the optimization of the current transport system configuration according to the criterion of an increase in the energy efficiency indicator is given.
A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids
Authors: Mirshekali Hamid , Santos Athila Q. , Shaker Hamid Reza
Published: August 2023

The maintenance of electrical grids is crucial for improving their reliability, performance, and cost-effectiveness. It involves employing various strategies to ensure smooth operation and address potential issues. With the advancement of digital technologies, utilizing time-series prediction has emerged as a valuable approach to enhance maintenance practices in electrical systems. The utilization of various recorded data from electrical grid components plays a crucial role in digitally enabled maintenance. However, the comprehensive exploration of time-series data prediction for maintenance is still lacking. This review paper extensively explores different time series that can be utilized to support maintenance efforts in electrical grids with regard to different maintenance strategies and grid components. The digitization of the electrical grids has enabled the collection of diverse time-series data from various network components. In this context, the paper provides an overview of how these time-series and historical-fault data can be utilized for maintenance purposes in electrical grids. Various maintenance levels and time series used for maintenance purposes in different components of the electrical grid are presented.