bluuebunny/crossref_metadata_2025 huggingface dataset:
A Density-Functional Theory Study of Hole and Defect-Bound Exciton Polarons in Lithium Niobate
Authors: Schmidt Falko , Kozub Agnieszka L. , Gerstmann Uwe , Schmidt Wolf Gero , Schindlmayr Arno
Published: November 2022

Hole polarons and defect-bound exciton polarons in lithium niobate are investigated by means of density-functional theory, where the localization of the holes is achieved by applying the +U approach to the oxygen 2p orbitals. We find three principal configurations of hole polarons: (i) self-trapped holes localized at displaced regular oxygen atoms and (ii) two other configurations bound to a lithium vacancy either at a threefold coordinated oxygen atom above or at a two-fold coordinated oxygen atom below the defect. The latter is the most stable and is in excellent quantitative agreement with measured g factors from electron paramagnetic resonance. Due to the absence of mid-gap states, none of these hole polarons can explain the broad optical absorption centered between 2.5 and 2.8 eV that is observed in transient absorption spectroscopy, but such states appear if a free electron polaron is trapped at the same lithium vacancy as the bound hole polaron, resulting in an exciton polaron. The dielectric function calculated by solving the Bethe–Salpeter equation indeed yields an optical peak at 2.6 eV in agreement with the two-photon experiments. The coexistence of hole and exciton polarons, which are simultaneously created in optical excitations, thus satisfactorily explains the reported experimental data.
Ground Truth Dataset: Objectionable Web Content
Authors: Altarturi Hamza H. M. , Anuar Nor Badrul
Published: November 2022

Cyber parental control aims to filter objectionable web content and prevent children from being exposed to harmful content. Succeeding in detecting and blocking objectionable content depends heavily on the accuracy of the topic model. A reliable ground truth dataset is essential for building effective cyber parental control models and validation of new detection methods. The ground truth is the measurement for labeling objectionable and unobjectionable websites of the cyber parental control dataset. The lack of publicly accessible datasets with a reliable ground truth has prevented a fair and coherent comparison of different methods proposed in the field of cyber parental control. This paper presents a ground truth dataset that contains 8000 labelled websites with 4000 objectionable websites and 4000 unobjectionable websites. These websites consist of more than 2 million web pages. Creating a ground truth objectionable web content dataset involved a few phases, including data collection, extraction, and labeling. Finally, the presence of bias, using kappa coefficient measurement, is addressed. The ground truth dataset is available publicly in the Mendeley repository.
The Bitter Side of Sugar Consumption: A Mitochondrial Perspective on Diabetes Development
Authors: Diniz Mariana S. , Tocantins Carolina , Grilo Luís F. , Pereira Susana P.
Published: November 2022

Type 2 diabetes (T2D) has increased worldwide at an alarming rate. Metabolic syndrome (MetS) is a major risk factor for T2D development. One of the main reasons for the abrupt rise in MetS incidence, besides a sedentary lifestyle, is the westernized diet consumption, with high content of industrialized foods, rich in added dietary sugars (DS), mainly sucrose and fructose. It has been suggested that a higher intake of DS could impair metabolic function, inducing MetS, and predisposing to T2D. However, it remains poorly explored how excessive DS intake modulates mitochondrial function, a key player in metabolism. This review explores the relationship between increased consumption of DS and mitochondrial dysfunction associated with T2D development, pointing to a contribution of the diet-induced accumulation of advanced glycation end-products (AGEs), with brief insights on the impact of maternal high-sugar diet and AGEs consumption during gestation on offspring increased risk of developing T2D later in life, contributing to perpetuate T2D propagation.
GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification
Authors: Khan Muhammad Attique , Sahar Naveera , Khan Wazir Zada , Alhaisoni Majed , Tariq Usman , Zayyan Muhammad H. , Kim Ye Jin , Chang Byoungchol
Published: November 2022

In the last few years, artificial intelligence has shown a lot of promise in the medical domain for the diagnosis and classification of human infections. Several computerized techniques based on artificial intelligence (AI) have been introduced in the literature for gastrointestinal (GIT) diseases such as ulcer, bleeding, polyp, and a few others. Manual diagnosis of these infections is time consuming, expensive, and always requires an expert. As a result, computerized methods that can assist doctors as a second opinion in clinics are widely required. The key challenges of a computerized technique are accurate infected region segmentation because each infected region has a change of shape and location. Moreover, the inaccurate segmentation affects the accurate feature extraction that later impacts the classification accuracy. In this paper, we proposed an automated framework for GIT disease segmentation and classification based on deep saliency maps and Bayesian optimal deep learning feature selection. The proposed framework is made up of a few key steps, from preprocessing to classification. Original images are improved in the preprocessing step by employing a proposed contrast enhancement technique. In the following step, we proposed a deep saliency map for segmenting infected regions. The segmented regions are then used to train a pre-trained fine-tuned model called MobileNet-V2 using transfer learning. The fine-tuned model’s hyperparameters were initialized using Bayesian optimization (BO). The average pooling layer is then used to extract features. However, several redundant features are discovered during the analysis phase and must be removed. As a result, we proposed a hybrid whale optimization algorithm for selecting the best features. Finally, the selected features are classified using an extreme learning machine classifier. The experiment was carried out on three datasets: Kvasir 1, Kvasir 2, and CUI Wah. The proposed framework achieved accuracy of 98.20, 98.02, and 99.61% on these three datasets, respectively. When compared to other methods, the proposed framework shows an improvement in accuracy.
A Case of Early-Stage Gallbladder Cancer, Positive for ALDH1A1, Which Arose from Adenomyomatosis of the Gallbladder
Authors: Iwasa Yuhei , Iwata Keisuke , Okuno Mitsuru , Sugiyama Akihiko , Nishigaki Yoichi , Ohashi Yosuke , Tanaka Takuji , Iwashita Takuji , Shimizu Masahito , Tomita Eiichi
Published: November 2022

Adenomyomatosis (ADM) of the gallbladder is a condition characterized by the proliferation of Rokitansky–Aschoff sinus (RAS), in which the epithelium of the gallbladder extends into the muscular layer, causing a thickening of the gallbladder wall. Although ADM is generally considered not to be a precancerous lesion of gallbladder cancer, there are some reports of cases of gallbladder cancer from ADM. Therefore, the relationship between ADM and gallbladder cancer remains controversial. We herein report a case of early-stage gallbladder cancer, BilIN3 (high grade), arising from ADM that was positive for ALDH1A1, an important marker of stem cells and cancer stem cells.
Myofascial Pain Syndrome in Women with Primary Dysmenorrhea: A Case-Control Study
Authors: Serrano-Imedio Ana , Calvo-Lobo Cesar , Casañas-Martin Coraima , Garrido-Marin Alejandro , Pecos-Martin Daniel
Published: November 2022

There is limited information on myofascial trigger points (MTrPs) and specific symptoms of chronic pelvic pain and, more specifically, dysmenorrhea. The objective of this study was to determine whether patients suffering from primary dysmenorrhea present alterations in mechanosensitivity and pain patterns, and greater presence of MTrPs in the abdominal and pelvic floor muscles. A case-control study was carried out with a total sample of 84 participants distributed based on primary dysmenorrhea and contraceptive treatment. The sample was divided into four groups each comprising 21 women. Data on pain, quality of life, and productivity and work absenteeism were collected; three assessments were made in different phases of the menstrual cycle, to report data on pressure pain threshold, MTrP presence, and referred pain areas. One-way ANOVA tests showed statistically significant differences (p < 0.01) between the groups, for the Physical Health domain and the total score of the SF-12 questionnaire, and for all the domains of the McGill questionnaire; but no significant differences were found in the data from the WPAI-GH questionnaire. Statistically significant data (p < 0.01) were found for mechanosensitivity in the abdominal area and limbs, but not for the lumbar assessment, within the group, with very few significant intergroup differences. The frequency of active MTrPs is higher in the groups of women with primary dysmenorrhea and during the menstrual phase, with the prevalence of myofascial trigger points of the iliococcygeus muscle being especially high in all examination groups (>50%) and higher than 70% in women with primary dysmenorrhea, in the menstrual phase, and the internal obturator muscle (100%) in the menstrual phase. Referred pain areas of the pelvic floor muscles increase in women with primary dysmenorrhea.
Clinical and Imaging Predictors of Disease Progression in Type B Aortic Intramural Hematomas and Penetrating Aortic Ulcers: A Systematic Review
Authors: Colacchio Elda Chiara , Squizzato Francesco , Piazza Michele , Menegolo Mirko , Grego Franco , Antonello Michele
Published: November 2022

Background: This work aims to review recent literature on penetrating aortic ulcers (PAUs) and intramural hematomas (IMHs), in order to identify clinical and imaging factors connected to aortic-related adverse events (AAE). Methods: We performed a systematic review according to the Preferred Reporting Items for Systematic review and Metanalyses (PRISMA) guidelines. An electronic search was conducted on Medline and Embase databases. We included articles reporting on PAUs and/or IMHs localized in the descending thoracic and/or abdominal aorta and analyzing clinical and/or radiological markers of AAE. Results: Of 964 records identified through database searching, 17 were incorporated in the present review, including 193 and 1298 patients with type B PAUs and IMHs, respectively. The 30-days aortic-related mortality (ARM) was 4.3% and 3.9% for PAUs and IMHs. A total of 21% of patients with IMHs underwent intervention during the follow-up period, and 32% experienced an AAE. PAU markers of AAE were minimum depth (ranging from 9.5 to 15 mm) and diameter (≥12.5 mm). Maximum aortic diameter (MAD) cut-off values ranging from 38 to 44.75 mm were related to AAE for IMHs, together with ulcer-like projection (ULP) of the aortic wall. Conclusions: Despite data heterogeneity in the literature, this PAU- and IMH-focused review has highlighted the imaging and clinical markers of disease progression, thus identifying patients that could benefit from an early intervention in order to reduce the AAE rate.
Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques
Authors: Lodato Ivano , Iyer Aditya Varna , To Isaac Zachary , Lai Zhong-Yuan , Chan Helen Shuk-Ying , Leung Winnie Suk-Wai , Tang Tommy Hing-Cheung , Cheung Victor Kai-Lam , Wu Tak-Chiu , Ng George Wing-Yiu
Published: November 2022

We conducted a statistical study and developed a machine learning model to triage COVID-19 patients affected during the height of the COVID-19 pandemic in Hong Kong based on their medical records and test results (features) collected during their hospitalization. The correlation between the values of these features is studied against discharge status and disease severity as a preliminary step to identify those features with a more pronounced effect on the patient outcome. Once identified, they constitute the inputs of four machine learning models, Decision Tree, Random Forest, Gradient and RUSBoosting, which predict both the Mortality and Severity associated with the disease. We test the accuracy of the models when the number of input features is varied, demonstrating their stability; i.e., the models are already highly predictive when run over a core set of (6) features. We show that Random Forest and Gradient Boosting classifiers are highly accurate in predicting patients’ Mortality (average accuracy ∼99%) as well as categorize patients (average accuracy ∼91%) into four distinct risk classes (Severity of COVID-19 infection). Our methodical and broad approach combines statistical insights with various machine learning models, which paves the way forward in the AI-assisted triage and prognosis of COVID-19 cases, which is potentially generalizable to other seasonal flus.
T1 Mapping MOLLI 5(3)3 Acquisition Scheme Yields High Accuracy in 1.5 T Cardiac Magnetic Resonance
Authors: Krumm Patrick , Martirosian Petros , Brendel Alexander , Kübler Jens M. , Brendel Jan M. , Gassenmaier Sebastian , Estler Arne , Gawaz Meinrad , Nikolaou Konstantin , Greulich Simon
Published: November 2022

Objectives: To systematically compare two modified Look-Locker inversion recovery (MOLLI) T1 mapping sequences and their impact on (1) myocardial T1 values native, (2) post-contrast and (3) extracellular volume (ECV). Methods: 200 patients were prospectively included for 1.5 T CMR for work-up of ischemic or non-ischemic cardiomyopathies. To determine native and post-contrast T1 for ECV calculation, two different T1 mapping MOLLI acquisition schemes, 5(3)3 (designed for native scans with long T1) and 4(1)3(1)2 (designed for post-contrast scans with short T1), were acquired in identical mid-ventricular short-axis slices. Both schemes were acquired in native and post-contrast scans. Results: Datasets from 163 patients were evaluated (age 55 ± 17 years; 38% female). Myocardial T1 native for 5(3)3 was 1017 ± 42 ms vs. 956 ± 40 ms for 4(1)3(1)2, with mean intraindividual difference −61 ms (p < 0.0001). Post-contrast myocardial T1 in patients was similar for both acquisition schemes, with 494 ± 48 ms for 5(3)3 and 490 ± 45 ms for 4(1)3(1)2 and mean intraindividual difference −4 ms. Myocardial ECV for 5(3)3 was 27.6 ± 4% vs. 27 ± 4% for 4(1)3(1)2, with mean difference −0.6 percentage points (p < 0.0001). Conclusions: The T1 MOLLI 5(3)3 acquisition scheme provides a reliable estimation of myocardial T1 for the clinically relevant range of long and short T1 values native and post-contrast. In contrast, the T1 MOLLI 4(1)3(1)2 acquisition scheme may only be used for post-contrast scans according to its designed purpose.
Assessment of Occlusal Vertical Dimension Change in Mechanical and Virtual Articulation: A Pilot Study
Authors: Lin Yu-Chun , Scialabba Rebecca , Lee Jason D. , Sun Jie , Lee Sang J.
Published: November 2022

The aim of this clinical study was to investigate the change in occlusal vertical dimension (OVD) with dental casts mounted on a mechanical articulator using an average axis facebow and on a virtual articulator mounted using the Bonwill triangle and the Balkwill angle and compare these groups with OVD change observed clinically in patients. Casts were obtained from each patient (n = 14) and mounted on a semi-adjustable articulator in the facebow preservation group (FPG) and on a virtual articulator using average anatomic values in the average mounting group (AMG). Customized mandibular anterior splints were virtually designed at an OVD increased by 3, 6, and 9 mm. Digital buccal scans were performed with the anterior devices in the participants’ mouths in the intraoral group (IOG), AMG, and FPG at the different OVD increases accordingly. While no statistically significant differences (p > 0.05) were observed in the posterior interocclusal measurements with the incisal guide pin raised by 3 mm and 6 mm among all groups, a 9 mm increase resulted in a significant difference between AMG and IOG. The interocclusal posterior-to-anterior opening ratio observed clinically was 1:1.575. Increases in OVD up to 6 mm on dental casts mounted using average anatomic values performed similarly to the actual intraoral changes.
High Performance Convertible Coleopter Drones
Authors: Barrett-Gonzalez Ronald
Published: November 2022

This paper opens with an historical overview of efforts to develop micro-, mini-, and organic aerial vehicles (MAVs and OAVs) in the 1990’s. Although conceived during WWII, coleopters would not see serial production for 60 years. The paper continues with programmatic aspects of hovering coleopter development of the 1990’s and describes the technical motivations behind in-flight conversion from hover-mode to missile-mode flight and the record-setting XQ-138 family of convertible coleopters. As the first commercially successful family of such aircraft, the XQ-138 was taken from initial concept through configuration design, detailed design, patenting, prototyping, proof-of-concept, production, flight testing, qualification, and eventually high rate production, all with private funding. The paper lists basic engineering drivers, covers fundamental sizing methods, presents weight fraction data, and describes flight test procedures, locations, conditions, and results. High-speed flight test data show the stock aircraft achieving speeds in excess of 164 mph (263 kph) with endurances in excess of an hour at that speed with a special dash-optimized version reaching 288 mph (463 kph) for a few minutes. Videos from flight testing and live-fire exercises are shown at Redstone Arsenal, Eglin Air Force Base, and Fort Benning test ranges under extreme conditions. The paper concludes with an assessment of civil and military variants for a variety of military missions and commercial uses.
The Use of the DRASTIC-LU/LC Model for Assessing Groundwater Vulnerability to Nitrate Contamination in Morogoro Municipality, Tanzania
Authors: Mkumbo Neema J. , Mussa Kassim R. , Mariki Eliapenda E. , Mjemah Ibrahimu C.
Published: November 2022

Groundwater is a useful source of water for various uses in different places. The major challenge in the use of this resource is how to manage and protect it from contamination. The current study was conducted in Morogoro Municipality to identify vulnerable groundwater areas by using DRASTIC-LU/LC model. The study applied eight input parameters, i.e., depth to water table, net recharge, aquifer media, soil media, topography, impact of vadose zone, hydraulic conductivity and land use/land cover patterns, which were overlaid in GIS to generate groundwater vulnerable map. The model used rating (R = 1–10) and weighting (W = 1–5) techniques to assess the effect of each parameter on groundwater contamination. The DRASTIC-LU/LC Vulnerability Index map was classified into low- (area = 29.2 km2), moderate- (area = 120.4 km2) and high-vulnerability zones (area = 124.4 km2). Nitrate analysis was conducted using the cadmium reduction method (DR 890) to assess the validity of the model and it was observed that 55%, 15% and 50% of the samples with unacceptable (>50 mg/L), high (29–50 mg/L) and moderate (14–28 mg/L) nitrate concentrations, respectively, fall into the high-vulnerability zone. Furthermore, 45%, 70% and 50% of the samples with unacceptable, high and moderate nitrate concentrations, respectively, fall into the moderate-vulnerability zone. In the low-vulnerability zone, only 15% of samples were found with a high nitrate concentration.
Determinants of Tourism Demand in Spain: A European Perspective from 2000–2020
Authors: Borrego-Domínguez Susana , Isla-Castillo Fernando , Rodríguez-Fernández Mercedes
Published: November 2022

This empirical study evaluates European tourism demand in Spain from 2000 to 2020. To test the hypotheses, we have modelled tourism demand, which is measured in terms of travellers arriving in Spain. An Error Correction Model adapted to a panel structure has been utilised to work within a time series context and differentiate up to 14 European countries of origin. The findings denote that over the short and the long term, gross domestic product (GDP) and the number of beds positively relate to tourism demand. Still, the stock market indices are not significant in both terms. The price index, trade flows, and length of stay differ in the short and the long term. Results of this study call the attention of policy makers and the private sector to encourage an increase in the supply of available beds to ensure post-pandemic sustainability.
Impact of Pharmaceutical R&D Activity on Financial Flexibility and Bargaining Power
Authors: Tömöri Gergő , Bács Zoltán , Felföldi János , Orbán Ildikó
Published: November 2022

The specificities of pharmaceutical companies’ activities also have an impact on their ability to improve profitability compared to other sectors. Examples of such specificities include patent rights on the medicines produced, which for a longer period of time prevent the entry of competing players, allowing sunk costs to be offset to some extent by advantages from using higher margins. The primary purpose of this study is to examine whether the bargaining power of R&D-engaged companies significantly affects the financial margin of their operations to a different extent than other pharmaceutical companies. This study examined panel data focused on companies with the highest turnover, while we filtered out differences in the effect due to different macroeconomic and development environments. Our findings are that while the gross profit was significantly influenced by the type of activity within the industry, this statement for the cash cycle is no longer justified. Our conclusion was that the difference between the engaged and non-engaged in R&D companies expressed mostly in the profitability ratios, besides that, countries which had different development policies and accounting systems also took impact on financial margins, although the relative GDP advantages disappeared when looking at intra-industry, cross-country movements.
Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information
Authors: Zhou Biao , Wang Xiuwei , Zhou Junhao , Jing Changqiang
Published: November 2022

In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information.
A Vision-Based Bio-Inspired Reinforcement Learning Algorithms for Manipulator Obstacle Avoidance
Authors: Singh Abhilasha , Shakeel Mohamed , Kalaichelvi V. , Karthikeyan R.
Published: November 2022

Path planning for robotic manipulators has proven to be a challenging issue in industrial applications. Despite providing precise waypoints, the traditional path planning algorithm requires a predefined map and is ineffective in complex, unknown environments. Reinforcement learning techniques can be used in cases where there is a no environmental map. For vision-based path planning and obstacle avoidance in assembly line operations, this study introduces various Reinforcement Learning (RL) algorithms based on discrete state-action space, such as Q-Learning, Deep Q Network (DQN), State-Action-Reward- State-Action (SARSA), and Double Deep Q Network (DDQN). By positioning the camera in an eye-to-hand position, this work used color-based segmentation to identify the locations of obstacles, start, and goal points. The homogeneous transformation technique was used to further convert the pixel values into robot coordinates. Furthermore, by adjusting the number of episodes, steps per episode, learning rate, and discount factor, a performance study of several RL algorithms was carried out. To further tune the training hyperparameters, genetic algorithms (GA) and particle swarm optimization (PSO) were employed. The length of the path travelled, the average reward, the average number of steps, and the time required to reach the objective point were all measured and compared for each of the test cases. Finally, the suggested methodology was evaluated using a live camera that recorded the robot workspace in real-time. The ideal path was then drawn using a TAL BRABO 5 DOF manipulator. It was concluded that waypoints obtained via Double DQN showed an improved performance and were able to avoid the obstacles and reach the goal point smoothly and efficiently.
Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network
Authors: Khaleghi Nastaran , Rezaii Tohid , Beheshti Soosan , Meshgini Saeed , Sheykhivand Sobhan , Danishvar Sebelan
Published: November 2022

Reaching out the function of the brain in perceiving input data from the outside world is one of the great targets of neuroscience. Neural decoding helps us to model the connection between brain activities and the visual stimulation. The reconstruction of images from brain activity can be achieved through this modelling. Recent studies have shown that brain activity is impressed by visual saliency, the important parts of an image stimuli. In this paper, a deep model is proposed to reconstruct the image stimuli from electroencephalogram (EEG) recordings via visual saliency. To this end, the proposed geometric deep network-based generative adversarial network (GDN-GAN) is trained to map the EEG signals to the visual saliency maps corresponding to each image. The first part of the proposed GDN-GAN consists of Chebyshev graph convolutional layers. The input of the GDN part of the proposed network is the functional connectivity-based graph representation of the EEG channels. The output of the GDN is imposed to the GAN part of the proposed network to reconstruct the image saliency. The proposed GDN-GAN is trained using the Google Colaboratory Pro platform. The saliency metrics validate the viability and efficiency of the proposed saliency reconstruction network. The weights of the trained network are used as initial weights to reconstruct the grayscale image stimuli. The proposed network realizes the image reconstruction from EEG signals.
Multitopic Coherence Extraction for Global Entity Linking
Authors: Zhang Chao , Li Zhao , Wu Shiwei , Chen Tong , Zhao Xiuhao
Published: November 2022

Entity linking is a process of linking mentions in a document with entities in a knowledge base. Collective entity disambiguation refers to mapping of multiple mentions in a document with their corresponding entities in a knowledge base. Most previous research has been based on the assumption that all mentions in the same document represent the same topic. However, mentions usually correspond to different topics. In this article, we proposes a new global model to explore the extraction of multitopic coherence in the same document. Herein, we present mention association graphs and candidate entity association graphs to obtain multitopic coherence features of the same document using graph neural networks (GNNs). In particular, we propose a variant GNN for our model and a particular graph readout function. We conducted extensive experiments on several datasets to demonstrate the effectiveness to the proposed model.
Optimal Solution for Frequency and Voltage Control of an Islanded Microgrid Using Square Root Gray Wolf Optimization
Authors: Almani Aftab Ahmed , Han XueShan , Umer Farhana , ul Hassan Rizwan , Nawaz Aamir , Shah Aamer Abbas , Mustafa Ehtasham
Published: November 2022

Voltage and frequency deviation in the islanded operation of a microgrid (MG), due to the uncertainty and lack of inertia in the selection of optimal proportional integral (PI) controller gain, is a challenging task. Although various optimization algorithms have been proposed to achieve this task, most of them require a large number of iterations and are time intensive, making them inefficient for real-time applications. Gray wolf optimization (GWO), a new meta-heuristic algorithm, addresses these issues and has many advantages, including simplicity due to fewer control parameters, flexibility, and globalism. This paper proposes a simple and efficient modified algorithm, called square root gray wolf optimization (SRGWO) algorithm, to realize superior hunting performance. SRGWO is verified using twenty-three benchmark test functions. The algorithm is applied for optimal voltage and frequency regulation of a photovoltaic-based microgrid system operating in the islanded mode during distributed generation insertion and load change conditions. The voltage and frequency gain parameters of the PI controller are optimized. A comparison of the simulation results of the SRGWO algorithm with those of the original gray wolf algorithm (GWO), particle swarm optimization (PSO), augmented gray wolf optimization (AGWO), enhanced gray wolf optimization (EGWO), and gravitational search algorithm (GSA) reveal that the proposed SRGWO algorithm significantly improves system performance while maintaining its simplicity and easy implementation. Furthermore, the SRGWO algorithm obtains the minimum fitness function value in fewer iterations than other algorithms. Moreover, it improves the power quality of the system with regard to minimum total harmonic distortion.
Repetition with Learning Approaches in Massive Machine Type Communications
Authors: Chen Li-Sheng , Ho Chih-Hsiang , Chen Cheng-Chang , Liang Yu-Shan , Kuo Sy-Yen
Published: November 2022

In the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rate, and increasing the energy consumption. An insufficient number of repetitions prevents the successful deciphering of the data by the receivers, leading to a high bit error rate. The present study developed adaptive repetition approaches with the k-nearest neighbor (KNN) and support vector machine (SVM) to substantially increase network transmission efficacy for the enhanced machine type communication (eMTC) system in the 5G mMTC scenario. The simulation results showed that the proposed repetition with the learning approach effectively improved the probability of successful transmission, the resource utilization, the average number of repetitions, and the average energy consumption. It is therefore more suitable for the eMTC system in the mMTC scenario than the common lookup table.
The MIMO Radar Transmit Beampattern Matching Design with Sidelobe Suppression
Authors: Zhang Xiaojun , He Zishu
Published: November 2022

In this paper, a novel method termed the cosine approach is proposed to address the sidelobe suppression problem in MIMO radar transmit beampattern matching design. In contrast to the traditional optimization algorithms that try to find the optimum solutions from feasible regions, the proposed method, starting from outside the feasible regions, aims to obtain a satisfactory solution from a series of optimal transmit beampatterns. We first standardized the sidelobe suppression problem in MIMO radar transmit beampattern matching design and put forward four criteria to guide the micro-adjustment to the desired beampattern. Then, the cosine method was proposed to adjust the desired beampattern as well as increase the main-to-sidelobe ratio (MSLR) of the transmit beampattern. Finally, several numerical examples were chosen to test the effectiveness and advantages of the proposed method.
A New Optimal Thermal-Based Adaptive Frequency Control for Bidirectional DC–DC Converter with Full-Range ZVS
Authors: Zhang Lulin , Cheng Shu , Xu Jingtao , Xiang Chaoqun , Yu Tianjian
Published: November 2022

A new, optimal thermal-based adaptive frequency control (OTC) for a bidirectional DC–DC converter with full-range zero-voltage switching (ZVS) is presented in this paper. The proposed OTC achieves ZVS for a bidirectional DC–DC converter without any zero-crossing detection (ZCD) circuit or current sensor. In order to ensure a ZVS over a wide voltage and load range, the optimal switching frequency was adjusted by detecting and tracking the lowest junction temperature of the semiconductor device. Because the proposed OTC does not need a ZCD circuit, the complexity of the circuit and the susceptibility of the sampling noise are reduced. Additionally, compared with the expensive current sensor, the proposed method of temperature detection by the NTC thermistor decreases the cost. In addition, the proposed OTC does not need accurate circuit parameters and voltage or current sampling results, so the dependence on the parameters is reduced. Moreover, the temperature of the switch is directly monitored in the proposed OTC, which can effectively protect the device. A prototype with 16–32 V input and 48 V output was built, and the experiment results prove the effectiveness and feasibility of it.
Investigation of the Effect of Rod Worth Uncertainty on the Reactivity Measurements of CEFR Start-Up Tests via McCARD Monte Carlo Calculations
Authors: Lee Min Jae , Won Jong-Hyuck , Choe Jiwon , Lim Jae-Yong
Published: November 2022

In the reactivity measurements of the China Experimental Fast Reactor (CEFR) start-up tests, five independent control rods were moved to compensate for the reactivity insertion. Since the recorded control rod positions were converted to reactivity via S-curves (rod worth curves), any uncertainty in the S-curves can propagate to all reactivity measurements. In this work, we rigorously derived the uncertainty of the reactivity in terms of the statistical uncertainty of the S-curves with Monte Carlo (MC) simulations. Additionally, the average error of the control rod worth from the MC calculation was estimated from experiments and embedded into the uncertainty formulation. The formulation shows that the error of the reactivity is highly correlated to the changes in the control rod position during the reactivity measurements. McCARD MC simulations were then conducted for the CEFR start-up tests, and the calculated reactivity and uncertainty were compared with the measurements. The main error factor of each reactivity calculation was figured out by quantifying the uncertainty components. With the uncertainty formulation, the calculation results showed a better agreement compared with the measurements, as the relative errors were observed mostly within 2σ of the uncertainty.
Analytical Estimation of Power Losses in a Dual Active Bridge Converter Controlled with a Single-Phase Shift Switching Scheme
Authors: Turzyński Marek , Bachman Serafin , Jasiński Marek , Piasecki Szymon , Ryłko Marek , Chiu Huang-Jen , Kuo Shih-Hao , Chang Yu-Chen
Published: November 2022

Micro-grid solutions around the world rely on the operation of DC/DC power conversion systems. The most commonly used solution for these topologies is the use of a dual active bridge (DAB) converter. Increasing the efficiency and reliability of this system contributes to the improvement in the stability of the entire microgrid. This paper discussed an analytical method of energy efficiency and power loss estimation in a single phase dual active bridge (DAB) converter controlled with a single-phase shift (SPS) modulation scheme for microgrid system stability. The presented approach uses conduction and commutation losses of semiconductors and high frequency transformer. All parameters required for the calculation may be obtained from the manufacturers’ datasheets or can be based on a simple measurement. The approach was validated by the comparison of the estimated energy efficiency characteristics with the measured ones for a prototype of a 5 kW single phase DAB converter equipped with silicon carbide metal-oxide semiconductor field-effect transistors (SiC MOSFET).
Creep Phenomenon in a Multiple-Input Single-Output Control System of a Piezoelectric Bimorph Actuator
Authors: Grzybek Dariusz , Sioma Andrzej
Published: November 2022

This article presents a comparison of the course of a creep phenomenon in the control system of a bimorph actuator, in which control voltages were applied to both piezoelectric layers, with the course of the creep phenomenon in the control system of a unimorph actuator, in which a control voltage was applied to only one piezoelectric layer. The bimorph actuator was built from two layers of piezoelectric composite, macro fiber composite was applied, and a carrier layer made of epoxy laminate was used for production of printed circuit boards. A comparative analysis was carried out on the basis of 22 laboratory experiments in which the vision system was used to measure a displacement change of six points of the bimorph actuator structure. Based on the results of laboratory experiments, it was noted that the duration of a transient part is approximately the same in a system with a control voltage applied to one MFC patch as in a system with control voltages applied to two MFC patches. In the system with control voltages applied to two MFC patches, the position change due to the creep process is more than two times bigger in comparison to the system with the control voltage applied to one MFC patch.
Automated Classification of Pipeline Defects from Ultrasonic Phased Array Total Focusing Method Imaging
Authors: Wang Haibin , Fan Zhichao , Chen Xuedong , Cheng Jingwei , Chen Wei , Wang Zhe , Bu Yangguang
Published: November 2022

The defects in the welds of energy pipelines have significantly influenced their safe operation. The inefficient and inaccurate detection of the defects may give rise to catastrophic accidents. Ultrasonic phased array inspection is an important means of ensuring pipeline safety. The total focusing method (TFM), using ultrasonic phased arrays, has become widely used in recent years in non-destructive evaluation (NDE). However, manual defect recognition of TFM images is seen to lack accuracy and robustness, arising from deficiency of practical experience. In this paper, the automated classification of different defects from TFM images is studied with a view to facilitate inspection efficacy. By experimentally implementing the TFM approach on a bespoke specimen, the images corresponding to crack-like defects and pore-like defects were employed to investigate the effectiveness of four different machine learning models (known as Support Vector Machine, CART Decision tree, K Nearest Neighbors, Naive Bayes) containing data augmentation, feature extraction and defect classification. The results suggested that the accuracy of defect classification using the HOG-Poly-SVM algorithm was 93%, which outperformed the results from other algorithms. The advantage of the HOG-Poly-SVM algorithm used in defect classification of ultrasonic phased array TFM data is discussed by conducting ten-fold cross validation and other evaluation metrics. In this paper, in order to improve the efficiency of detecting pipeline defects in the future, and for testing test blocks simulating buried pipelines containing defects, we proposed, for the first time, that ultrasonic phased-array TFM imaging results in small object detection images, and found that the SVM algorithm was applicable to ultrasonic phased array TFM imaging, providing a research method and ideas for the use of artificial intelligence in industrial non-destructive testing.
Biogas Plant Operation: Digestate as the Valuable Product
Authors: Czekała Wojciech , Jasiński Tomasz , Grzelak Mieczysław , Witaszek Kamil , Dach Jacek
Published: November 2022

Fertilization is an essential element in plant cultivation. Supplying the right amounts of nutrients allows plants to grow and develop. Due to the rising price of mineral fertilizers, other fertilizers and soil conditioners are growing in importance. One of these is the digestate produced in agricultural biogas plants. Due to its properties, the digestate can be used directly as a fertilizer. In this case, the effects of application can both change the soil environment and directly affect plant growth. Physical, biological, and thermal transformations can also produce products based on the digestate or its fractions, which can be successfully used for fertilizer purposes. Among other things, this paper discusses the production and use of composts, biocarbon, and/or fertilizer granules from the solid fraction of the digestate. Numerous scientific studies, including the authors’ own research in this article, indicate that digestate can be successfully used as fertilizer, both without processing and with selected methods of treatment. However, further research is needed—especially on the diversity of raw materials used for biogas production and their effects on the composition and performance of the digestate. In addition, research should continue on the processing of digestate into specific products, depending on the needs of soils and plants.
Permeability and Mechanical Response of Granite after Thermal and CO2 Bearing Fluid Hydro-Chemical Stimulation
Authors: Tong Rong-Chen , Liu He-Juan , Song Yu-Jia , Xie Li-Huan , Ban Sheng-Nan
Published: November 2022

The large scale extraction of geothermal energy can reduce CO2 emissions. For hot dry rocks, the key to successful utilization depends on the efficiency of reservoir reconstruction. The chemical and thermal stimulation methods are always used in geothermal reservoir reconstruction except in hydraulic fracturing with high fluid injection pressure, which is believed to reduce the seismic hazard by applying before the high-pressure hydraulic fracturing stimulation. However, at the laboratory scale, there are still very limited experimental studies illustrating the combined effects of chemical and thermal stimulation on the permeability and mechanical properties of granite, which is regarded as the main type of hot dry rock. In this paper, comparative stimulation experiments were carried out, including thermal/cold stimulation, CO2 bearing solution hydro-chemical stimulation, combined thermal and CO2 bearing fluid stimulation. By means of nuclear magnetic resonance analysis, permeability test and triaxial compression test, the changes of the micro-structure, permeability and mechanical properties of granite under various stimulation conditions were analyzed. The experimental results show that, compared with the single thermal stimulation and CO2 bearing fluid hydro-chemical stimulation, the superposition effect of thermal and CO2 bearing fluid hydro-chemical stimulation can increase the number of micro-fractures in granite more effectively, thus increasing the permeability, while the elastic modulus and compressive strength decrease. Moreover, the cooling mode on the granite also has a certain influence on the stimulation effect. After water-cooling on the heated granite (300 °C), combined with the CO2 bearing fluid stimulation (240 °C, 20 MPa), the permeability of granite is the highest, increasing by 17 times that of the initial state, and the porosity also increases by 144.4%, while the elastic modulus and compressive strength decrease by 14.3% and 18.4%, respectively. This implies that the deterioration of mechanical properties due to the micro-fractures increased by the thermal and chemical stimulation can enhance the fluid conductivity and heat extraction of granite. The methods in this paper can provide a reference for the combined application of thermal and chemical stimulation technology in artificial reservoir reconstruction of hot dry rocks.
Energy Performance Assessment of a Novel Solar Poly-Generation System Using Various ORC Working Fluids in Residential Buildings
Authors: Almehmadi Fahad Awjah , Elattar H. F. , Fouda A. , Alqaed Saeed , Mustafa Jawed , Alharthi Mathkar A. , Refaey H. A.
Published: November 2022

Poly-generation systems are an exciting new technology that provide an alternative to separating existing energy production methods in buildings. A poly-generation system enables the efficient simultaneous production of heating, cooling, fresh water, and electricity, resulting in many technological, economic, energy recovery, and environmental advantages. This study numerically investigates three proposed novel solar-driven poly-generation systems (BS, IS-I, and IS-II) integrated with organic Rankine cycle (ORC), humidification-dehumidification desalination system (HDH), and desiccant cooling system (DCS) with different heat recovery system arrangements. The suggested systems supply residential structures with energy, space conditioning, domestic heating, and fresh water. The effects of system operating circumstances on productivity and performance characteristics and several organic working fluid types (n-octane, R245fa, R113, isopentane, and toluene) on optimum system performance have been investigated. The results show that (i) the average enhancement percentage of TGOR using integrated poly-generation systems over the separated ones is 68.5%, 68.5%, and 95.5% for BS, IS-I, and IS-II systems, respectively; (ii) when comparing the three systems, the IS-I system outperforms the other systems (BS & IS-II); and (iii) the maximum values of W•net, m•fresh, Q•cooling, and Q•heating, obtained for different proposed systems using n-octane are 102 kW (all systems), 214.7 kg/h (IS-II), 29.94 kW (IS-II), and 225.6 kW (IS-I); (iv) R113 has the highest TGOR of 0.6924 (IS-I) compared to other organic fluids. (v) The improvements in Wnet•, mfresh•, Qcooling• and Qheating• with using toluene instead of R113 at tf1 = 40 °C are 177.5%, 105.8%, 389.25%, and 79%, respectively.