bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards OCT-Guided Endoscopic Laser Surgery—A Review
Authors: Gunalan Ajay , Mattos Leonardo S.
Published: February 2023

Optical Coherence Tomography (OCT) is an optical imaging technology occupying a unique position in the resolution vs. imaging depth spectrum. It is already well established in the field of ophthalmology, and its application in other fields of medicine is growing. This is motivated by the fact that OCT is a real-time sensing technology with high sensitivity to precancerous lesions in epithelial tissues, which can be exploited to provide valuable information to clinicians. In the prospective case of OCT-guided endoscopic laser surgery, these real-time data will be used to assist surgeons in challenging endoscopic procedures in which high-power lasers are used to eradicate diseases. The combination of OCT and laser is expected to enhance the detection of tumors, the identification of tumor margins, and ensure total disease eradication while avoiding damage to healthy tissue and critical anatomical structures. Therefore, OCT-guided endoscopic laser surgery is an important nascent research area. This paper aims to contribute to this field with a comprehensive review of state-of-the-art technologies that may be exploited as the building blocks for achieving such a system. The paper begins with a review of the principles and technical details of endoscopic OCT, highlighting challenges and proposed solutions. Then, once the state of the art of the base imaging technology is outlined, the new OCT-guided endoscopic laser surgery frontier is reviewed. Finally, the paper concludes with a discussion on the constraints, benefits and open challenges associated with this new type of surgical technology.
Combined 3D Endoanal Ultrasound and Transperineal Ultrasound Improves the Detection of Anal Sphincter Defects
Authors: Carter Dan , Ram Edward , Engel Tal
Published: February 2023

Introduction: Anal sphincter injury, mainly due to obstetric or iatrogenic etiology, is the most common cause of fecal incontinence (FI). Three-dimensional endoanal ultrasound (3D EAUS) is used for assessment of the integrity and the degree of anal muscle injury. However, 3D EAUS accuracy may be hampered by regional acoustic effects, such as intravaginal air. Therefore, our aim was to examine whether a combination of transperineal ultrasound (TPUS) and 3D EAUS would improve the accuracy of detection of anal sphincter injury. Methods: We prospectively performed 3D EAUS followed by TPUS in every patient evaluated for FI in our clinic between January 2020 and January 2021. The diagnosis of anal muscle defects was assessed in each ultrasound technique by two experienced observers that were blinded to each other’s assessments. Interobserver agreement for the results of the 3D EAUS and the TPUS exams was examined. A final diagnosis of anal sphincter defect was based on the results of both ultrasound methods. Discordant results were re-analyzed by the two ultrasonographers for a final consensus on the presence or absence of defects. Results: A total of 108 patients underwent ultrasonographic assessment due to FI (mean age 69 ± 13). Interobserver agreement for the diagnosis of tear on EAUS and TPUS was high (83%) with Cohen’s kappa of 0.62. EAUS confirmed anal muscle defects in 56 patients (52%), while TPUS confirmed them in 62 patients (57%). The final consensus agreed on the diagnosis of 63 (58%) muscular defects and 45 (42%) normal exams. The Cohen’s kappa coefficient of agreement between the results of the 3D EAUS and the final consensus was 0.63. Conclusions: The combination of 3D EAUS and TPUS improved the detection of anal muscular defects. The application of both techniques for the assessment of the anal integrity should be considered in every patient going through ultrasonographic assessment for anal muscular injury.
Tooth Type Enhanced Transformer for Children Caries Diagnosis on Dental Panoramic Radiographs
Authors: Zhou Xiaojie , Yu Guoxia , Yin Qiyue , Yang Jun , Sun Jiangyang , Lv Shengyi , Shi Qing
Published: February 2023

The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely used for caries diagnosis. A tooth type enhanced swin transformer is further proposed by considering the differences among canine, molar and incisor. Modeling the above differences in swin transformer, the proposed method was expected to mine domain knowledge for more accurate caries diagnosis. To test the proposed method, a children panoramic radiograph database was built and labeled with a total of 6028 teeth. Swin transformer shows better diagnosis performance compared with typical CNN methods, which indicates the usefulness of this new technique for children caries diagnosis on panoramic radiographs. Furthermore, the proposed tooth type enhanced swin transformer outperforms the naive swin transformer with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8557, 0.8832, 0.8317, 0.8567 and 0.9223, respectively. This indicates that the transformer model can be further improved with a consideration of domain knowledge instead of a copy of previous transformer models designed for natural images. Finally, we compare the proposed tooth type enhanced swin transformer with two attending doctors. The proposed method shows higher caries diagnosis accuracy for the first and second primary molars, which may assist dentists in caries diagnosis.
Two-Step Approach toward Alignment of Spatiotemporal Wide-Area Unmanned Aerial Vehicle Imageries
Authors: Lee Hyeonseok , Kim Semo , Lim Dohun , Bae Seoung-Hun , Kang Lae-Hyong , Kim Sungchan
Published: February 2023

Recently, analysis and decision-making based on spatiotemporal unmanned aerial vehicle (UAV) high-resolution imagery are gaining significant attention in smart agriculture. Constructing a spatiotemporal dataset requires multiple UAV image mosaics taken at different times. Because the weather or a UAV flight trajectory is subject to change when the images are taken, the mosaics are typically unaligned. This paper proposes a two-step approach, composed of global and local alignments, for spatiotemporal alignment of two wide-area UAV mosaics of high resolution. The first step, global alignment, finds a projection matrix that initially maps keypoints in the source mosaic onto matched counterparts in the target mosaic. The next step, local alignment, refines the result of the global alignment. The proposed method splits input mosaics into patches and applies individual transformations to each patch to enhance the remaining local misalignments at patch level. Such independent local alignments may result in new artifacts at patch boundaries. The proposed method uses a simple yet effective technique to suppress those artifacts without harming the benefit of the local alignment. Extensive experiments validate the proposed method by using several datasets for highland fields and plains in South Korea. Compared with a recent work, the proposed method improves the accuracy of alignment by up to 13.21% over the datasets.
A Data-Driven Preprocessing Framework for Atrial Fibrillation Intracardiac Electrocardiogram Analysis
Authors: Kong Xiangzhen , Ravikumar Vasanth , Mulpuru Siva K. , Roukoz Henri , Tolkacheva Elena G.
Published: February 2023

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3–5 Hz, while the upper bound (BP¯th) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of BP¯th subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the BP¯th  using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different BP¯th on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with BP¯th = 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis.
From Bilinear Regression to Inductive Matrix Completion: A Quasi-Bayesian Analysis
Authors: Mai The Tien
Published: February 2023

In this paper, we study the problem of bilinear regression, a type of statistical modeling that deals with multiple variables and multiple responses. One of the main difficulties that arise in this problem is the presence of missing data in the response matrix, a problem known as inductive matrix completion. To address these issues, we propose a novel approach that combines elements of Bayesian statistics with a quasi-likelihood method. Our proposed method starts by addressing the problem of bilinear regression using a quasi-Bayesian approach. The quasi-likelihood method that we employ in this step allows us to handle the complex relationships between the variables in a more robust way. Next, we adapt our approach to the context of inductive matrix completion. We make use of a low-rankness assumption and leverage the powerful PAC-Bayes bound technique to provide statistical properties for our proposed estimators and for the quasi-posteriors. To compute the estimators, we propose a Langevin Monte Carlo method to obtain approximate solutions to the problem of inductive matrix completion in a computationally efficient manner. To demonstrate the effectiveness of our proposed methods, we conduct a series of numerical studies. These studies allow us to evaluate the performance of our estimators under different conditions and provide a clear illustration of the strengths and limitations of our approach.
Lightweight Deep Neural Network Embedded with Stochastic Variational Inference Loss Function for Fast Detection of Human Postures
Authors: Hsu Feng-Shuo , Su Zi-Jun , Kao Yamin , Tsai Sen-Wei , Lin Ying-Chao , Tu Po-Hsun , Gong Cihun-Siyong Alex , Chen Chien-Chang
Published: February 2023

Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed. This technique was then applied in fast human posture identification. The integer-arithmetic-only algorithm and the feature pyramid network were adopted to reduce the computational complexity in training and to capture features of small objects, respectively. Features of sequential human motion frames (i.e., the centroid coordinates of bounding boxes) were extracted by the self-attention mechanism. With the techniques of Bayesian neural network and stochastic variational inference, human postures could be promptly classified by fast resolving of the Gaussian mixture model for human posture classification. The model took instant centroid features as inputs and indicated possible human postures in the probabilistic maps. Our model had better overall performance than the baseline model ResNet in mean average precision (32.5 vs. 34.6), inference speed (27 vs. 48 milliseconds), and model size (46.2 vs. 227.8 MB). The model could also alert a suspected human falling event about 0.66 s in advance.
Analysis of Tangential Leakage Flow Characteristics of Oil-Free Scroll Expander for a Micro-Scale Compressed Air Energy Storage System
Authors: Sun Jian , Peng Bin , Zhu Bingguo , Li Yaohong
Published: February 2023

Tangential leakage loss is the primary factor that significantly affects the output performance of oil-free scroll expanders. A scroll expander can function under different operating conditions, and the flow of tangential leakage and generation mechanism is different. This study employed computational fluid dynamics to investigate the unsteady flow characteristics of the tangential leakage flow of a scroll expander with air as the working fluid. Consequently, the effects of different radial gap sizes, rotational speeds, inlet pressures, and temperatures on the tangential leakage were discussed. The tangential leakage decreased with increases in the scroll expander rotational speed, inlet pressure, and temperature, and decreased with decrease in radial clearance. With an equal-proportional increase in radial clearance, the flow form of the gas in the first expansion and back-pressure chambers became more complicated; when the radial clearance increased from 0.2 to 0.5 mm, the volumetric efficiency of the scroll expander decreased by approximately 5.0521%. Moreover, because of the large radial clearance, the tangential leakage flow maintained a subsonic flow. Further, the tangential leakage decreased with increase in rotational speed, and when the rotational speed increased from 2000 to 5000 r/min, the volumetric efficiency increased by approximately 8.7565%.
A Complexity Science Account of Humor
Authors: Tschacher Wolfgang , Haken Hermann
Published: February 2023

A common assumption of psychological theories of humor is that experienced funniness results from an incongruity between stimuli provided by a verbal joke or visual pun, followed by a sudden, surprising resolution of incongruity. In the perspective of complexity science, this characteristic incongruity-resolution sequence is modeled by a phase transition, where an initial attractor-like script, suggested by the initial joke information, is suddenly destructed, and in the course of resolution replaced by a less probable novel script. The transition from the initial to the enforced final script was modeled as a succession of two attractors with different minimum potentials, during which free energy becomes available to the joke recipient. Hypotheses derived from the model were tested in an empirical study where participants rated the funniness of visual puns. It was found, consistent with the model, that the extent of incongruity and the abruptness of resolution were associated with reported funniness, and with social factors, such as disparagement (Schadenfreude) added to humor responses. The model suggests explanations as to why bistable puns and phase transitions in conventional problem solving, albeit also based on phase transitions, are generally less funny. We proposed that findings from the model can be transferred to decision processes and mental change dynamics in psychotherapy.
Hellinger Information Matrix and Hellinger Priors
Authors: Shemyakin Arkady
Published: February 2023

Hellinger information as a local characteristic of parametric distribution families was first introduced in 2011. It is related to the much older concept of the Hellinger distance between two points in a parametric set. Under certain regularity conditions, the local behavior of the Hellinger distance is closely connected to Fisher information and the geometry of Riemann manifolds. Nonregular distributions (non-differentiable distribution densities, undefined Fisher information or denisities with support depending on the parameter), including uniform, require using analogues or extensions of Fisher information. Hellinger information may serve to construct information inequalities of the Cramer–Rao type, extending the lower bounds of the Bayes risk to the nonregular case. A construction of non-informative priors based on Hellinger information was also suggested by the author in 2011. Hellinger priors extend the Jeffreys rule to nonregular cases. For many examples, they are identical or close to the reference priors or probability matching priors. Most of the paper was dedicated to the one-dimensional case, but the matrix definition of Hellinger information was also introduced for higher dimensions. Conditions of existence and the nonnegative definite property of Hellinger information matrix were not discussed. Hellinger information for the vector parameter was applied by Yin et al. to problems of optimal experimental design. A special class of parametric problems was considered, requiring the directional definition of Hellinger information, but not a full construction of Hellinger information matrix. In the present paper, a general definition, the existence and nonnegative definite property of Hellinger information matrix is considered for nonregular settings.
Creating Meaningful Learning Opportunities through Incorporating Local Research into Chemistry Classroom Activities
Authors: States Nicole , Stone Elizabeth , Cole Renee
Published: February 2023

Incorporating real-life context through connections to research early in the curriculum can create meaningful learning opportunities that encourage students to engage deeply with classroom content to construct chemistry knowledge. Course-based undergraduate research experiences have been successful at integrating real-life context, but are often only incorporated into upper-level courses. To provide an additional pathway to foster interaction with research, four activities from an introductory chemistry discussion class were created to incorporate authentic research connections. Care was taken to incorporate metacognitive questions designed to help students make connections between their preexisting knowledge and course content. Marzano’s taxonomy was used to analyze the cognitive complexity of tasks, which increased in the revised activities, allowing for more opportunities for knowledge construction. Audio and written work of student groups as they worked through activities was collected. Qualitative analysis of student engagement revealed that control over the content of activities to incorporate opportunities for knowledge construction is not enough to facilitate students consciously engaging in meaningful learning. If instructors wish to promote students integrating chemistry knowledge into their existing framework, course instructors, including graduate teaching assistants, need to be trained on how to properly facilitate classroom experiences to increase the likelihood of success.
Personality, Preferences, Satisfaction, and Achievement in a Biostatistics Course: Traditional versus Flipped Classrooms in Nursing Education
Authors: Yañez Aina M. , Adrover-Roig Daniel , Bennasar-Veny Miquel
Published: February 2023

Background: In higher education, there is a lack of knowledge about how individual factors influence personal preferences for teaching methodology and academic outcomes in biostatistics among nursing students. This study sought to evaluate the associations between personality traits and preferences for the flipped classroom or traditional lessons, satisfaction, and achievement in a biostatistics course in nursing education. Methods: The cross-sectional study included data collection at two time points. During the 2018–2019 academic course, the flipped classroom approach was implemented into a biostatistics course of the nursing degree at the University of the Balearic Islands (Spain). Students responded to an online questionnaire including sociodemographic data, personality traits, locus of control, satisfaction, teaching quality, student involvement, and effort. After the final examination, students’ achievements were collected. Results: A total of 137/161 (85.1%) students were included in the study. Most students preferred a flipped classroom to traditional lectures. Students who scored high in neuroticism preferred the traditional class (p < 0.001). Furthermore, personality was associated with satisfaction: high levels of responsibility and internal locus of control were significantly associated with higher satisfaction (p < 0.05). The percentage of students who did not pass the biostatistics exam was reduced by 50% with the flipped classroom methodology (33% vs. 15%; p < 0.001). Conclusions: A flipped classroom is adequate for the biostatistics course. Teachers should reconsider possible reservations about new teaching methods, which can be related to student personality.
A Novel Multi-Projection Correction Method Based on Binocular Vision
Authors: Wang Sheng , You Zhisheng , Zhang Yuxi
Published: February 2023

In order to improve the accuracy of multi-projection correction fusion, a multi-projection correction method based on binocular vision is proposed. To date, most of the existing methods are based on the single-camera mode, which may lose the depth information of the display wall and may not accurately obtain the details of the geometric structure of the display wall. The proposed method uses the depth information of a binocular camera to build a high-precision 3D display wall model; thus, there is no need to know the specific CAD size of the display wall in advance. Meanwhile, this method can be applied to display walls of any shape. By calibrating the binocular vision camera, the radial and eccentric aberration of the camera can be reduced. The projector projects the encoded structured light stripes, and the high-precision 3D structural information of the projection screen can be reconstructed according to the phase relationship after collecting the deformation stripes on the projection screen with the binocular camera. Thus, the screen-projector sub-pixel-level mapping relationship is established, and a high-precision geometric correction is achieved. In addition, by means of the one-to-one mapping relation between the phase information and the three-dimensional space points, accurate point cloud matching among multiple binocular phase sets could be established, so that such method can be applied to any quantity of projectors. The experimental results based on various special-shaped projection screens show that, comparing to the single-camera-based method, the proposed method improves the geometric correction accuracy of multi-projection stitching by more than 20%. This method also has many advantages, such as strong universality, high measurement accuracy, and rapid measurement speed, which indicate its wide application potential in many different fields.
A Privacy-Preserving Ride Matching Scheme for Ride Sharing Services in a Hot Spot Area
Authors: Li Qingyuan , Wu Hao , Dong Chen
Published: February 2023

Ride sharing is a service that enables users to share trips with others, conserving energy, decreasing emissions and reducing traffic congestion. Selecting a suitable partner for a user based on the their trip data is essential for the service, but it also leads to privacy disclosure, e.g., the user’s location and trajectory. Many privacy-preserving solutions for ride sharing services have been proposed, which are based on cryptographic technology and provide accurate matching services. However, these encryption-based algorithms are very complicated and difficult to calculate. In hot spots, such as stations, airports and sport gymnasiums, a large number of users may apply for a ride sharing service in short space of time, which will place huge pressure on the service provider. Using traditional matching methods increases the matching time and leads to a less favorable user experience. To solve these problems, we model them, aiming to maximize the vehicle’s carrying capacity and propose a lightweight privacy-preserving ride matching scheme for selecting feasible partners during busy periods with a large number of requests. To achieve this, we make use of the homomorphic encryption technique to hide location data and design a scheme to calculate the distances between users in road networks securely and efficiently. We employ a road network embedding technique to calculate the distance between users. Moreover, we use travel time instead of space distance, which makes matching more accurate. Further, with the encrypted itineraries of users, the service provider selects potential ride share partners according to the feasibility of time schedules. We use ciphertext packing to reduce overhead, improving the efficiency of ride matching. Finally, we evaluate our scheme with simulation and demonstrate that our scheme achieves an efficient and accurate matching service. It only takes a few seconds to complete the matching, and the matching accuracy is higher than 85 percent in most cases.
Single-Layer Interconnected Magneto-Electric Dipole Antenna Array for 5G Communication Applications
Authors: Chen Zihao , Zhang Wenxu , Wang Kaixu
Published: February 2023

A high-gain and wideband interconnected magneto-electric (ME) dipole on a single-layer PCB substrate is designed for 5G communication applications. Microstrip lines and coplanar striplines (CPSs) serve as transmission lines to connect the ME dipole elements along the E-plane and the H-plane directions, respectively. Impedance matching and sidelobe-level suppression are the key challenges to design a large-scale interconnected ME dipole antenna array. It is shown that impedance matching can be improved by introducing slots and adjusting the width of microstrip lines. Sidelobe level can be enhanced by properly choosing the length of the microstrip lines. A 5 × 5 interconnected ME dipole array is fabricated on a single layer of the RT/duroid 5880 substrate. The proposed antenna exhibits a measured −10 dB impedance bandwidth of 14.5 GHz (52% at 28 GHz) and a maximal peak realized gain of 20.44 dBi at 27.5 GHz with a 3 dB gain bandwidth of 3.5 GHz (12.5% at 28 GHz). The proposed antenna array is a good candidate for 5G communication applications due to its advantages of simple feeding structure, wide bandwidth, high gain, and low profile.
Two-Stage Cascaded CNN Model for 3D Mitochondria EM Segmentation
Authors: Guo Jing-Ming , Seshathiri Sankarasrinivasan , Liu Jia-Hao , Hsu Wei-Wen
Published: February 2023

Mitochondria are the organelles that generate energy for the cells. Many studies have suggested that mitochondrial dysfunction or impairment may be related to cancer and other neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. Therefore, morphologically detailed alterations in mitochondria and 3D reconstruction of mitochondria are highly demanded research problems in the performance of clinical diagnosis. Nevertheless, manual mitochondria segmentation over 3D electron microscopy volumes is not a trivial task. This study proposes a two-stage cascaded CNN architecture to achieve automated 3D mitochondria segmentation, combining the merits of top-down and bottom-up approaches. For top-down approaches, the segmentation is conducted on objects’ localization so that the delineations of objects’ contours can be more precise. However, the combinations of 2D segmentation from the top-down approaches are inadequate to perform proper 3D segmentation without the information on connectivity among frames. On the other hand, the bottom-up approach finds coherent groups of pixels and takes the information of 3D connectivity into account in segmentation to avoid the drawbacks of the 2D top-down approach. However, many small areas that share similar pixel properties with mitochondria become false positives due to insufficient information on objects’ localization. In the proposed method, the detection of mitochondria is carried out with multi-slice fusion in the first stage, forming the segmentation cues. Subsequently, the second stage is to perform 3D CNN segmentation that learns the pixel properties and the information of 3D connectivity under the supervision of cues from the detection stage. Experimental results show that the proposed structure alleviates the problems in both the top-down and bottom-up approaches, which significantly accomplishes better performance in segmentation and expedites clinical analysis.
The Semantic Segmentation of Standing Tree Images Based on the Yolo V7 Deep Learning Algorithm
Authors: Cao Lianjun , Zheng Xinyu , Fang Luming
Published: February 2023

The existence of humans and the preservation of the natural ecological equilibrium depend greatly on trees. The semantic segmentation of trees is very important. It is crucial to learn how to properly and automatically extract a tree’s elements from photographic images. Problems with traditional tree image segmentation include low accuracy, a sluggish learning rate, and a large amount of manual intervention. This research suggests the use of a well-known network segmentation technique based on deep learning called Yolo v7 to successfully accomplish the accurate segmentation of tree images. Due to class imbalance in the dataset, we use the weighted loss function and apply various types of weights to each class to enhance the segmentation of the trees. Additionally, we use an attention method to efficiently gather feature data while reducing the production of irrelevant feature data. According to the experimental findings, the revised model algorithm’s evaluation index outperforms other widely used semantic segmentation techniques. In addition, the detection speed of the Yolo v7 model is much faster than other algorithms and performs well in tree segmentation in a variety of environments, demonstrating the effectiveness of this method in improving the segmentation performance of the model for trees in complex environments and providing a more effective solution to the tree segmentation issue.
A Comprehensive Review: Study of Artificial Intelligence Optimization Technique Applications in a Hybrid Microgrid at Times of Fault Outbreaks
Authors: Zulu Musawenkosi Lethumcebo Thanduxolo , Carpanen Rudiren Pillay , Tiako Remy
Published: February 2023

The use of fossil-fueled power stations to generate electricity has had a damaging effect over the years, necessitating the need for alternative energy sources. Microgrids consisting of renewable energy source concepts have gained a lot of consideration in recent years as an alternative because they use advances in information and communication technology (ICT) to increase the quality and efficiency of services and distributed energy resources (DERs), which are environmentally friendly. Nevertheless, microgrids are constrained by the outbreaks of faults, which have an impact on their performance and necessitate dynamic energy management and optimization strategies. The application of artificial intelligence (AI) is gaining momentum as a vital key at this point. This study focuses on a comprehensive review of applications of artificial intelligence strategies on hybrid renewable microgrids for optimization, power quality enhancement, and analyses of fault outbreaks in microgrids. The use of techniques such as machine learning (ML), genetic algorithms (GA), artificial neural networks (ANN), fuzzy logic (FL), particle swarm optimization (PSO), heuristic optimization, artificial bee colony (ABC), and others is reviewed for various microgrid strategies such as regression and classification in this study. Applications of AI in microgrids are reviewed together with their benefits, drawbacks, and prospects for the future. The coordination and maximum penetration of renewable energy, solar PV, and wind in a hybrid microgrid under fault outbreaks are furthermore reviewed.
Minimization of Voltage Harmonic Distortion of Synchronous Generators under Non-Linear Loading via Modulated Field Current
Authors: Karakaya Oktay , Balci Murat Erhan , Hocaoglu Mehmet Hakan
Published: February 2023

The synchronous generators (SGs) supplying non-linear loads have harmonically distorted terminal voltages. Hence, these distorted terminal voltages adversely affect the performance parameters of the supplied loads such as the power factor, current distortion, losses, and efficiency. To mitigate the harmonic voltages and currents, passive and active filters are generally employed. However, passive filters cause resonance problems, while active filters can cause high costs. On the other hand, in several recent studies to reduce the SG’s terminal voltage harmonic distortion, which depends on the constructional design under the no-loading condition, the conventional DC excitation current has been modulated with AC harmonic components. These field current modulation methods have high computational complexity, and require extra hardware for their implementation. In the present paper, firstly, for the reduction of the terminal voltage harmonic distortion of the SG under non-linear loading conditions, the validity of the field current modulation technique is investigated. The numerical results show that by using the field current modulation method, under rated loading conditions, the total harmonic distortion of the terminal voltage can be reduced from 18% to 11%. Secondly, to provide a computational efficient and low-cost tool for optimal field current modulation, which minimizes the terminal voltage harmonic distortion, an Artificial Neural Network (ANN)-based model is proposed. Finally, with the integration of ANSYS Maxwell, ANSYS Simplorer, and MATLAB/Simulink software, the implementation of the developed model is demonstrated for the construction of the optimally modulated field current.
Research on the Planning of Electric Vehicle Fast Charging Stations Considering User Selection Preferences
Authors: Chen Julong , Chen Haoyong
Published: February 2023

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.
Computationally Inexpensive CFD Approach for the Combustion of Sewage Sludge Powder, Including the Consideration of Water Content and Limestone Additive Variations
Authors: Ortner Benjamin , Schmidberger Christian , Gerhardter Hannes , Prieler René , Schröttner Hartmuth , Hochenauer Christoph
Published: February 2023

As a result of growing interest in the thermal treatment of sewage sludge with methods such as combustion, gasification or pyrolysis, and also in processes that aim to recover precious components such as phosphorus from this waste, a growing demand has been observed for Computational Fluid Dynamics (CFD) models that provide solutions rapidly and accurately for efficient application in research and development. This study was carried out to develop a computationally inexpensive modelling approach for the combustion of pulverized sewage sludge in entrained flow furnaces. Sewage sludge is a very volatile-rich fuel. Therefore, the Steady Diffusion Flamelet model (SFM), in combination with a validated skeletal reaction mechanism, was applied to consider the pulverized firing of sewage sludge. It was possible to represent the complex composition of volatiles emitted from the sludge particles by releasing surrogate fuels. In addition, the influence of limestone additive (calcination reaction) and varying water content (water–gas shift reaction) was investigated experimentally and modelled via CFD. The simulation results confirm that the surrogate fuel approach is valid and can be used to describe pulverized sewage sludge effectively. The temperature and species concentration results, including the influence of the additive and different levels of water content, were confirmed by experimental data, which is usually hard to obtain due to the tendency of PSS to form agglomerates in entrained flow combustion furnaces. The model yields plausible and experimentally validated results for the combustion of sewage sludge powder across a wide range of operating conditions.
Day-Ahead Scheduling of Multi-Energy Microgrids Based on a Stochastic Multi-Objective Optimization Model
Authors: Seyednouri Seyed Reza , Safari Amin , Farrokhifar Meisam , Ravadanegh Sajad Najafi , Quteishat Anas , Younis Mahmoud
Published: February 2023

Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker with complete information regarding the Pareto front, as well as a clear overview of the various trade-offs that are involved in the problem. The selection of such a representative set is, in and of itself, a multi-objective problem that must take into consideration the number of choices to show the uniformity of the representation and/or the coverage of the representation in order to ensure the quality of the solution. In this study, day-ahead scheduling has been transformed into a multi-objective optimization problem due to the inclusion of objectives, such as the operating cost of multi-energy multi-microgrids (MMGs) and the profit of the Distribution Company (DISCO). The purpose of the proposed system is to determine the best day-ahead operation of a combined heat and power (CHP) unit, gas boiler, energy storage, and demand response program, as well as the transaction of electricity and natural gas (NG). Electricity and gas are traded by MGs with DISCO at prices that are dynamic and fixed, respectively. Through scenario generation and probability density functions, the uncertainties of wind speed, solar irradiation, electrical, and heat demands have been considered. By using mixed-integer linear programming (MILP) for scenario reduction, the high number of generated scenarios has been significantly reduced. The ɛ-constraint approach was used and solved as mixed-integer nonlinear programming (MINLP) to obtain a solution that meets the needs of both of these nonlinear objective functions.
Techno-Economic Green Optimization of Electrical Microgrid Using Swarm Metaheuristics
Authors: Guerraiche Khaled , Dekhici Latifa , Chatelet Eric , Zeblah Abdelkader
Published: February 2023

In electrical power engineering, elements such as reliability analysis, modeling, and optimization for complex systems are of the utmost importance. Although there exist myriad studies regarding reliability optimization with conventional methods, researchers are still seeking to find more efficient and accurate methods to address the issue of the redundancy allocation problem. To that effect, an ideal power energy management approach is put forward for the operation of a hybrid microgrid system with different kinds of productions. In the present study, we suggest three algorithms in order to optimize the series-parallel power energy system: the Firefly (FA), Bat (BA), and Interior Search (ISA) algorithms. Moreover, the reliability estimate of the system is solved with the Ushakov algorithm (UMGF). The components may completely fail, which decreases their performance rate. Furthermore, the optimization results are achieved using objective functions that include the total cost of the system, emission gases (NOX, SO2, and CO2) of the power production from fuel cells, diesel generators, and gas turbines, and take into consideration the dependability indices. Devices used in power subsystems are characterized based on their dependabilities, performances, capital costs, and maintenance costs. Reliability hinges on a functioning system, which naturally entails meeting customer demand; as a result, it is influenced by the accumulated batch curve. This method provides an idea with regards to the economic cost optimization of microgrid systems. Finally, we present the results of numeric simulations.
Impact of Low Reactivity Fuel Type and Energy Substitution on Dual Fuel Combustion at Different Injection Timings
Authors: Narayanan Abhinandhan , Hariharan Deivanayagam , Partridge Kendyl Ryan , Pearson Austin Leo , Srinivasan Kalyan Kumar , Krishnan Sundar Rajan
Published: February 2023

Dual fuel combustion leverages a high-reactivity fuel (HRF) to ignite a premixed low reactivity fuel (LRF)–air mixture to achieve high efficiencies and low engine-out emissions. The difference in the relative amounts of these fuels and in-cylinder fuel reactivity stratification profoundly impacts dual fuel combustion. The effect of increasing LRF energy substitution on dual fuel combustion at various fixed HRF (diesel) quantities was experimentally studied for two different LRFs (natural gas and propane) on a heavy-duty single cylinder engine at a constant intake pressure of 1.5 bar and injection pressure of 500 bar. Further, this effect was studied for three different HRF start of injection (SOI) timings of 310 CAD (50° BTDC), 330 CAD (30° BTDC), and 350 CAD (10° BTDC). For 310 CAD SOI, increasing the LRF substitution at a fixed HRF resulted in higher loads, peak cylinder pressures, and peak apparent heat release rates (AHRR). The onset of low temperature heat release (LTHR) was advanced as the LR fuel flowrate increased at a given pilot quantity for diesel–NG but remained constant for diesel–propane dual fuel combustion at these SOIs due to the impact of propane on the temperature at which the onset of LTHR occurs. The indicated fuel conversion efficiency (IFCE) ranged from 35% at 4 bar IMEPg to 47% at 9 bar IMEPg with NG as the LRF and from 35% at 3 bar IMEPg to 51% at 8 bar IMEPg with propane as the LRF. For 330 CAD SOI, the HC and CO emissions decreased at a higher fixed HRF quantity and an increasing LRF substitution. However, this was accompanied by significantly higher oxides of nitrogen (NOx) emissions for both NG and propane as LRFs. For 350 CAD SOI, increasing the LRF substitution at constant HRF consistently led to a higher second stage AHRR, whereas the first stage AHRR remained relatively unchanged for both NG and propane as LRFs. This was accompanied by higher IFCE for all fixed HRF quantities as LRF substitution was increased. For all SOIs studied, the HC and CO emissions were substantially lower and combustion stability was significantly improved as the LRF substitution (and consequently, the load) was increased. To the best of the authors’ knowledge, the present work is unique in that it involves the first systematic experimental study of the impact of LRF energy substitution at fixed HRF quantities over a range of SOIs, providing comparative results for two different LRFs (NG and propane) on the same engine platform.
Optimization of Liquid−Liquid Mixing in a Novel Mixer Based on Hybrid SVR-DE Model
Authors: Wang Hao , Zhou Peijian , Chen Ting , Mou Jiegang , Cui Jiayi , Zhang Huiming
Published: February 2023

To solve the problem of evenly mixing flocculant and sewage, a new type of two-chamber mechanical pipe mixer was numerically calculated and its working principle was studied by means of the internal flow field. The single factor numerical simulation and analysis of some of the structural parameters in the mixer were carried out to determine the influence of different parameters on the results. Latin hypercube sampling was used to design 100 sets of test tables for the four variables of the branch pipe diameter, sewage flow rate, the installation height of the impeller, and the angle of the deflector. The results were optimized using the SVR-DE algorithm. After optimization, the variation coefficient of export flocculant mixing uniformity was 16.02%, which was increased by 74.94% compared with the initial 63.921%. The power consumption of the impeller was reduced by 8.30%. The concentration curves of the flocculant at different positions of the outlet tube could quickly converge to the target value.
Incorporating ESG Risk in Companies’ Business Models: State of Research and Energy Sector Case Studies
Authors: Zioło Magdalena , Bąk Iwona , Spoz Anna
Published: February 2023

The article aims to systematize the state of knowledge of and research on the inclusion of ESG (Environmental, Social, and Governance) risk in companies’ business models, with a special stress on energy sector companies. Many publications address incorporating ESG, but only some deal with it from the perspective of business models. This paper fills that gap. The methods of incorporating ESG risk into a sustainable business model, identified on the basis of the literature review, were verified based on the examples of three companies from the energy industry. A two-stage review of publications from the WoS and Scopus databases was carried out, considering a more comprehensive (sustainability) and a narrower (ESG risk) range of keywords, and the period from 2000 to 2022. The result showed that SMEs and large enterprises consider ESG risk in their risk management systems (ERMs), while small enterprises and start-ups do not. In Europe, Asia, and Australia, it is common to include ESG risk in an ERM, while it is rare in Latin America. In developing countries, companies in the service sector are more likely to include ESG risk in ERMs than those in the manufacturing sector. These findings may be useful for policymakers who wish to provide support and financial incentives for companies transforming their business models toward sustainability.
Dark Fermentation of Arundo donax: Characterization of the Anaerobic Microbial Consortium
Authors: Toscano Giuseppe , Zuccaro Gaetano , Corsini Anna , Zecchin Sarah , Cavalca Lucia
Published: February 2023

The dark fermentation of lignocellulose hydrolysates is a promising process for the production of hydrogen from renewable sources. Nevertheless, hydrogen yields are often lower than those obtained from other carbohydrate sources due to the presence of microbial growth inhibitors in lignocellulose hydrolysates. In this study, a microbial consortium for the production of hydrogen by dark fermentation has been obtained from a wild methanogenic sludge by means of thermal treatments. The consortium has been initially acclimated to a glucose-based medium and then used as inoculum for the fermentation of Arundo donax hydrolysates. Hydrogen yields obtained from fermentation of A. donax hydrolysates were lower than those obtained from glucose fermentation using the same inoculum (0.30 ± 0.05 versus 1.11 ± 0.06 mol of H2 per mol of glucose equivalents). The hydrogen-producing bacteria belonged mainly to the Enterobacteriaceae family in cultures growing on glucose and to Clostridium in those growing on A. donax hydrolysate. In the latter cultures, Lactobacillus outcompeted Enterobacteriaceae, although Clostridium also increased. Lactobacillus outgrowth could account for the lower yields observed in cultures growing on A. donax hydrolysate.