bluuebunny/crossref_metadata_2025 huggingface dataset:
A Deep Learning System for Automated Quality Evaluation of Optic Disc Photographs in Neuro-Ophthalmic Disorders
Authors: Chan Ebenezer , Tang Zhiqun , Najjar Raymond P. , Narayanaswamy Arun , Sathianvichitr Kanchalika , Newman Nancy J. , Biousse Valérie , Milea Dan ,   for the BONSAI Group
Published: January 2023

The quality of ocular fundus photographs can affect the accuracy of the morphologic assessment of the optic nerve head (ONH), either by humans or by deep learning systems (DLS). In order to automatically identify ONH photographs of optimal quality, we have developed, trained, and tested a DLS, using an international, multicentre, multi-ethnic dataset of 5015 ocular fundus photographs from 31 centres in 20 countries participating to the Brain and Optic Nerve Study with Artificial Intelligence (BONSAI). The reference standard in image quality was established by three experts who independently classified photographs as of “good”, “borderline”, or “poor” quality. The DLS was trained on 4208 fundus photographs and tested on an independent external dataset of 807 photographs, using a multi-class model, evaluated with a one-vs-rest classification strategy. In the external-testing dataset, the DLS could identify with excellent performance “good” quality photographs (AUC = 0.93 (95% CI, 0.91–0.95), accuracy = 91.4% (95% CI, 90.0–92.9%), sensitivity = 93.8% (95% CI, 92.5–95.2%), specificity = 75.9% (95% CI, 69.7–82.1%) and “poor” quality photographs (AUC = 1.00 (95% CI, 0.99–1.00), accuracy = 99.1% (95% CI, 98.6–99.6%), sensitivity = 81.5% (95% CI, 70.6–93.8%), specificity = 99.7% (95% CI, 99.6–100.0%). “Borderline” quality images were also accurately classified (AUC = 0.90 (95% CI, 0.88–0.93), accuracy = 90.6% (95% CI, 89.1–92.2%), sensitivity = 65.4% (95% CI, 56.6–72.9%), specificity = 93.4% (95% CI, 92.1–94.8%). The overall accuracy to distinguish among the three classes was 90.6% (95% CI, 89.1–92.1%), suggesting that this DLS could select optimal quality fundus photographs in patients with neuro-ophthalmic and neurological disorders affecting the ONH.
A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network
Authors: Ullah Naeem , Khan Javed , El-Sappagh Shaker , El-Rashidy Nora , Khan Mohammad
Published: January 2023

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.
CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer’s Disease
Authors: Abbas Qaisar , Hussain Ayyaz , Baig Abdul Rauf
Published: January 2023

Mental deterioration or Alzheimer’s (ALZ) disease is progressive and causes both physical and mental dependency. There is a need for a computer-aided diagnosis (CAD) system that can help doctors make an immediate decision. (1) Background: Currently, CAD systems are developed based on hand-crafted features, machine learning (ML), and deep learning (DL) techniques. Those CAD systems frequently require domain-expert knowledge and massive datasets to extract deep features or model training, which causes problems with class imbalance and overfitting. Additionally, there are still manual approaches used by radiologists due to the lack of dataset availability and to train the model with cost-effective computation. Existing works rely on performance improvement by neglecting the problems of the limited dataset, high computational complexity, and unavailability of lightweight and efficient feature descriptors. (2) Methods: To address these issues, a new approach, CAD-ALZ, is developed by extracting deep features through a ConvMixer layer with a blockwise fine-tuning strategy on a very small original dataset. At first, we apply the data augmentation method to images to increase the size of datasets. In this study, a blockwise fine-tuning strategy is employed on the ConvMixer model to detect robust features. Afterwards, a random forest (RF) is used to classify ALZ disease stages. (3) Results: The proposed CAD-ALZ model obtained significant results by using six evaluation metrics such as the F1-score, Kappa, accuracy, precision, sensitivity, and specificity. The CAD-ALZ model performed with a sensitivity of 99.69% and an F1-score of 99.61%. (4) Conclusions: The suggested CAD-ALZ approach is a potential technique for clinical use and computational efficiency compared to state-of-the-art approaches. The CAD-ALZ model code is freely available on GitHub for the scientific community.
Vision-Transformer-Based Transfer Learning for Mammogram Classification
Authors: Ayana Gelan , Dese Kokeb , Dereje Yisak , Kebede Yonas , Barki Hika , Amdissa Dechassa , Husen Nahimiya , Mulugeta Fikadu , Habtamu Bontu , Choe Se-Woon
Published: January 2023

Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer.
Prescribed Performance Back-Stepping Tracking Control for a Class of High-Order Nonlinear Systems via a Disturbance Observer
Authors: Tang Xinrui , Jiang Haijun
Published: January 2023

Due to the widespread presence of disturbances in practical engineering and widespread applications of high-order systems, this paper first pays attention to a class of high-order strict-feedback nonlinear systems subject to bounded disturbance and investigates the prescribed performance tracking control and anti-disturbance control problems. A novel composite control protocol using the technique of a disturbance observer—prescribed performance control—is designed using the back-stepping method. The disturbance observer is introduced for estimating and compensating for unknown disturbances in each step, and the prescribed performance specifications guarantee both transient and steady-state performance of the tracking error to improve the control performance and result in better disturbance rejection. Moreover, the technique of adding a power integrator is modified to tackle controller design problems for the high-order systems. The Lyapunov function method is utilized for rigorous stability analysis. It is revealed that while the control performance completely remains in the prescribed bound, all states in the closed-loop system are input-to-state stable, and the tracking error and the disturbances estimating error asymptotically converge to zero simultaneously. Then, the feasibility and effectiveness of the proposed control protocol are verified by a simulation result.
Partial Autocorrelation Diagnostics for Count Time Series
Authors: Weiß Christian H. , Aleksandrov Boris , Faymonville Maxime , Jentsch Carsten
Published: January 2023

In a time series context, the study of the partial autocorrelation function (PACF) is helpful for model identification. Especially in the case of autoregressive (AR) models, it is widely used for order selection. During the last decades, the use of AR-type count processes, i.e., which also fulfil the Yule–Walker equations and thus provide the same PACF characterization as AR models, increased a lot. This motivates the use of the PACF test also for such count processes. By computing the sample PACF based on the raw data or the Pearson residuals, respectively, findings are usually evaluated based on well-known asymptotic results. However, the conditions for these asymptotics are generally not fulfilled for AR-type count processes, which deteriorates the performance of the PACF test in such cases. Thus, we present different implementations of the PACF test for AR-type count processes, which rely on several bootstrap schemes for count times series. We compare them in simulations with the asymptotic results, and we illustrate them with an application to a real-world data example.
Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching
Authors: Cong Yizhi , Wang Yan , Hou Wenju , Pang Wei
Published: January 2023

Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. However, it is difficult to obtain sufficient correct matching points in low-textured or repetitively-textured regions, resulting in insufficient matching points in the overlapping region, and this further leads to the warping model being estimated erroneously. In this paper, we propose a novel and flexible approach by increasing feature correspondences and optimizing hybrid terms. It can obtain sufficient correct feature correspondences in the overlapping region with low-textured or repetitively-textured areas to eliminate misalignment. When a weak texture and large parallax coexist in the overlapping region, the alignment and distortion often restrict each other and are difficult to balance. Accurate alignment is often accompanied by projection distortion and perspective distortion. Regarding this, we propose hybrid terms optimization warp, which combines global similarity transformations on the basis of initial global homography and estimates the optimal warping by adjusting various term parameters. By doing this, we can mitigate projection distortion and perspective distortion, while effectively balancing alignment and distortion. The experimental results demonstrate that the proposed method outperforms the state-of-the-art in accurate alignment on images with low-textured areas in the overlapping region, and the stitching results have less perspective and projection distortion.
Development of Digital Teaching Competence: Pilot Experience and Validation through Expert Judgment
Authors: Gutiérrez-Castillo Juan Jesús , Palacios-Rodríguez Antonio , Martín-Párraga Lorena , Serrano-Hidalgo Manuel
Published: January 2023

The development of digital teaching competence is one of the fundamental requirements of what is known as the “knowledge society”. With the aim of evaluating, from an expert point of view, the design of a training itinerary oriented to the training development of non-university teachers under the t-MOOC architecture for the improvement of digital teaching competence (DTC), the following quantitative research is presented. For this purpose, a quantitative validation design was established using the expert judgement technique. To measure the level of the expert coefficient, the expert competence (K) index was calculated for a random sample of 292 subjects participating in the study: teachers belonging to preschool and primary schools in the Andalusian community. The responses of those experts who scored ≥0.8 on the external competence index were then selected. The results demonstrate the validity of the tool produced (T-MOOC) as well as the uniformity of the criteria of the experts participating in the evaluation. Consequently, the necessary structuring of personalised training plans supported by reference models is discussed.
Low-Fidelity Prototyping with Design Thinking in Higher Education Management in Japan: Impact on the Utility and Usability of a Student Exchange Program Brochure
Authors: Katsumata Shah Mahdi , Jactat Bruno , Yasui Toshiyuki , Ismailov Murod
Published: January 2023

A project using design thinking (DT) was conducted among internal stakeholders of a large state Japanese university to design a user-centric brochure promoting study abroad programs at francophone partner universities. The low-fidelity prototype and the final product created with DT were tested by asking potential student-users to compare it with a standard brochure through two sets of surveys. Analysis of the quantitative and qualitative data revealed that low-fidelity prototyping was effective to enhance both the utility and usability of the final product. We also show how DT helped expose cognitive biases among designers.
Research on Spectrum Prediction Technology Based on B-LTF
Authors: Wang Xue , Chen Qian , Yu Xiaoyang
Published: January 2023

With the rapid development of global communication technology, the problem of scarce spectrum resources has become increasingly prominent. In order to alleviate the problem of frequency use, rationally use limited spectrum resources and improve frequency utilization, spectrum prediction technology has emerged. Through the effective prediction of spectrum usage, the number of subsequent spectrum sensing processes can be slowed down, and the accuracy of spectrum decisions can be increased to improve the response speed of the whole cognitive radio technology. The rise of deep learning has brought changes to traditional spectrum predicting algorithms. This paper proposes a spectrum predicting method called Back Propagation-Long short-term memory Time Forecasting (B-LTF) by using Back Propagation-Long Short-term Memory (BP-LSTM) network model. According to the historical spectrum data, the future spectrum trend and the channel state of the future time node are predicted. The purpose of our research is to achieve dynamic spectrum access by improving the accuracy of spectrum prediction and better assisting cognitive radio technology. By comparing with BP, LSTM and Gate Recurrent Unit (GRU) network models, we clarify that the improved model of recurrent time network can deal with time series more effectively. The simulation results show that the proposed model has better prediction performance, and the change in time series length has a significant impact on the prediction accuracy of the deep learning model.
An Analysis on the VSC-HVDC Contribution for the Static Voltage Stability Margin and Effective Short Circuit Ratio Enhancement in Hybrid Multi-Infeed HVDC Systems
Authors: Oliveira Diego , Leal Gustavo C. Borges , Herrera Danilo , Galván-Díez Eduardo , Carrasco Juan M. , Aredes Mauricio
Published: January 2023

Over the years, high voltage transmission of large energy blocks over long distances has widely developed from the Line Commutated Converter (LCC) technology. However, the continuous expansion of the AC network and the increase in renewable energy penetration leads to the weakening of Electric Power Systems (EPS), causing operational problems for the LCC-HVDC. This paper evaluates the degree of contribution of the VSC-HVDC on the LCC-HVDC dynamic performance when the infeed is in a hybrid multi-infeed HVDC system. Through a steady-state mathematical framework, the new concept of Hybrid Power Voltage Interaction Factor (hPVIF) is proposed to assess the transient stability improvement of LCC. Additionally, this article introduces two key contributions from hPVIF: the complementary Improved Commutation Failure Immunity Index (iCFII), as a measure of the effective short circuit enhancement, as well as the apparent line length, which emulates the line lengths adjusting the power dispatch in the VSC-HVDC. PSCAD/EMTDCTM time-domain dynamic simulations are performed to assess the indexes, and the Matlab® software will be used to assist in mathematical operations.
Influence of Forecast Climate Changes on Water Needs of Jerusalem Artichoke Grown in the Kuyavia Region in Poland
Authors: Rolbiecki Stanisław , Rolbiecki Roman , Kuśmierek-Tomaszewska Renata , Żarski Jacek , Jagosz Barbara , Kasperska-Wołowicz Wiesława , Sadan Hicran , Łangowski Ariel
Published: January 2023

Most scenarios of climate change in Poland predict an increase in air temperature in the coming years. However, no significant increase in precipitation is forecast. Therefore, an increase in the water needs of plants should be expected, which requires the development of irrigation systems. To precisely determine the schedule of crop irrigation, it is necessary to investigate the water needs of plants and to estimate the prospects of changes in the future. This research aimed to estimate the water needs of Jerusalem artichoke in the period 2021–2050 in the Kuyavia region located in central Poland, where the need for supplementary irrigation is the highest. Based on the calculations, it was found that, in the growing season (21 May–30 September), an increase in the water needs of Jerusalem artichoke, of 26 mm, i.e., 9%, should be expected. The highest increase of 10 mm (i.e., by 16%) is expected in August. The results of our studies are utilitarian in character and can be used for the preparation of a strategy for the development of irrigation systems for Jerusalem artichoke cultivation in central Poland.
Research on Improving Denoising Performance of ROI Computer Vision Method for Transmission Tower Displacement Identification
Authors: Zhang Kai , Liu Jiahao , Li Yuxue , Sun Chao , Zhang Laiyi
Published: January 2023

The health monitoring technology of transmission towers based on vibration data had become a research hotspot. At present, vibration data mainly relied on sensors installed on the tower, which was time-consuming and laborious. Nevertheless, the ROI computer vision method could achieve long-distance, multi-point, and non-contact monitoring, which offers a new possibility for the structure-safety identification of power transmission towers. However, transmission towers are generally located in the field environment, and the background is complicated, resulting in the ROI key point method for vibration data acquisition encountering various types of noise. Thus, the key point in practice was clearing the noise and reducing the impact of noise on identification accuracy. The subpixel corner method was used to detect a minor error with the research object of pixel sets. The dilation + erosion method could reduce image noise. Under white noise with a variance of 0.05, the dilation + erosion could reduce average error (Emae) and mean square error (Emse) by 27% and 23% and increase percentages of data with absolute error less than 5 mm and 10 mm in the total number of data (σ5 and σ10) by 8% and 4.3%, respectively, which was compared to median filter + sharpen. The histogram equalization method was used to balance background lighting conditions and reduce identification errors from non-uniform illumination. Emae and Emse were reduced by 92% and 99%, and σ5 and σ10 were increased by 5 and 3 times, respectively, and the identification time was cut by 62% with the histogram equalization method. Under white noise with a variance of 0.15 or lower, the three methods combined increased the numerical stability of Emae, Emse, σ5, and σ10, which indicated that the combination of the three methods could improve the anti-noise performance, robustness, and identification accuracy of the ROI computer vision method for transmission tower displacement identification.
Development of a Background-Oriented Schlieren (BOS) System for Thermal Characterization of Flow Induced by Plasma Actuators
Authors: Moreira Miguel , Rodrigues Frederico , Cândido Sílvio , Santos Guilherme , Páscoa José
Published: January 2023

Cold climate regions have great potential for wind power generation. The available wind energy in these regions is about 10% higher than in other regions due to higher wind speeds and increased air density. However, these regions usually have favorable icing conditions that lead to ice accumulation on the wind turbine blades, which in turn increases the weight of the blades and disrupts local airflow, resulting in a reduction in wind turbine performance. Considering this problem, plasma actuators have been proposed as devices for simultaneous flow control and deicing. These devices transfer momentum to the local airflow, improving the aerodynamic performances of the turbine blades while producing significant thermal effects that can be used to prevent ice formation. Considering the potential application of plasma actuators for simultaneous flow control and deicing, it is very important to investigate the thermal effects induced by these devices. However, due to the significant electromagnetic interference generated by the operation of these devices, there is a lack of experimental techniques that can be used to analyze them. In the current work, a background-oriented Schlieren system was developed and is presented as a new experimental technique for the thermal characterization of the plasma-induced flow. For the first time, the induced flow temperatures are characterized for plasma actuators with different dielectric materials and different dielectric thicknesses. The results demonstrate that, due to the plasma discharge, the temperature of the plasma-induced flow increases with the increase of the applied voltage and may achieve temperatures five times higher than the room temperature, which proves the potential of plasma actuators for deicing applications. The results are presented and discussed with respect to the potential application of plasma actuators for simultaneous flow control and deicing of wind turbine blades.
Catalytic Upgrading of Plastic Waste of Electric and Electronic Equipment (WEEE) Pyrolysis Vapors over Si–Al Ash Pellets in a Two-Stage Reactor
Authors: de Freitas Costa Augusto , Ferreira Caio , da Paz Simone , Santos Marcelo , Moreira Luiz , Mendonça Neyson , da Costa Assunção Fernanda , de Freitas Ana , Costa Roseane , de Sousa Brandão Isaque , da Costa Carlos , da Mota Sílvio , de Castro Douglas , Duvoisin Sergio , Borges Luiz , Machado Nélio , Bernar Lucas
Published: January 2023

This study investigated thermal cracking and catalytic upgrading of waste from electric and electronic equipment (WEEE) plastics on a semi-batch reactor coupled to a heated catalyst fixed bed (2-stage vapor cracking). The catalyst used is a Si–Al ash obtained from commercial activated carbon pellets treated with concentrated NaOH solution and calcination. The purpose of the study was to characterize the waste stream through its thermogravimetry analysis and pyrolysis products, study the effect of temperature (350–500 °C) and catalyst quantity (0.0–7.5 %.wt) on yields of reaction products, physical chemical properties, and chemical composition of bio-oil in order to understand and evaluate production of fuels and chemical feedstock by recycling of WEEE plastic through catalytic upgrading. Time-fractioned samples were taken in determined reaction times (15, 30, 45, and 60 min) to study the evolution of cracking reactions during experiment runs through changes to chemical composition (GC/MS). A comparison with other previous work is also presented to show similarities between different feedstocks using the same thermal unit. The results indicate composition of brominated acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC), and high impact polystyrene (HIPS) for the WEEE plastic. The temperature of 350 °C produced better results when considering acid value but presented lower bio-oil yields (38%) and high gas yields (42%). Catalytic upgrading experiments revealed the increased presence of polycyclic aromatic hydrocarbons (PAH) with an increase in viscosity of bio-oil, increase in char yield (from 11% to 24%), and decrease in gas yields (15% to 5%). Chemical composition showed presence of aromatic hydrocarbons such as styrene, methyl-styrene, and diphenyl-propane and nitrogenated compounds such as benzene-butane-nitrile, phenolic compounds, PAHs, and brominated compounds. WEEE plastic pyrolysis is a challenging subject due to contaminant presence and varying composition, and chemical composition evaluation according to reaction time provides interesting insights into the evolution of semi-batch pyrolysis/catalytic upgrading experiments. Standardization and reproducibility of the tool should be conducted to continue the evaluation of pyrolysis and catalytic upgrading of a wide range of feedstocks.
Constructing a Database of Reference Hydrothermal Sources for a Zero-Energy Building Certification Rating in South Korea and Analyzing the Renewable Energy Self-Sufficiency Rate Achieved by Water-Source Heat Pumps
Authors: Kim Yeweon , Yu Ki-Hyung
Published: January 2023

This study aims to institutionalize an evaluation methodology to assess water-source heat pumps (WSHPs) when designing a zero-energy building. Thus, regions where zero-energy buildings were designed were subdivided into 66 sub-regions, thereby standardizing the temperatures on the source side of WSHPs using river water and pipeline water. Based on these data, ground-source and water-source heat pump system-based simulation (new and renewable energy self-sufficiency rate compared to building energy consumption) values were derived for cases whose condition (region or heat source) was different among the buildings certified as zero-energy buildings. The application of the standard meteorological data and reference hydrothermal data to the ECO2 program and outcome evaluation led to the following findings: in all cases (reference: Seoul), ground-source heat pumps (GSHPs) showed a higher self-sufficiency rate than WSHPs (ground source > pipeline water > river water). The self-sufficiency rate of GSHPs was 11–33% higher than that of WSHPs. In a regional comparison among the cold (Jeongseon), central (Seoul), and southern (Jeju Island) regions, WSHPs exhibited higher energy self-sufficiency rates than GSHPs under the conditions of higher water temperatures in winter and lower water temperatures in summer, as in the southern region.
Enhanced Methane Production from Pretreatment of Waste Activated Sludge by Economically Feasible Biocatalysts
Authors: Kim Tae-Hoon , Song Dayeong , Lee Jung-Sup , Yun Yeo-Myeong
Published: January 2023

Crude hydrolytic extracellular enzymes (CHEEs) generated by a mixed culture of microorganisms during fermentation have a high potential as economically feasible biocatalysts for the hydrolysis of complex organic wastes. This study investigates the feasibility of CHEEs as substitutes for commercial enzymes based on a series of anaerobic batch tests for CH4 production fed by pretreated waste activated sludge (WAS). The results showed that cellulase presented the highest CH4 yield of 99.1 mL·CH4/g·COD of WAS among the samples pretreated with single commercial enzymes, with a yield 34% higher than that of the control sample. A higher diversity of commercial enzymes used in the pretreatment led to higher CH4 production from WAS. The sample pretreated with a mixture of four commercial enzymes (amylase + protease + cellulase + lipase, APCL) presented a CH4 yield of 216.0 mL·CH4/g·COD of WAS. The WAS prepared with CHEEs resulted in a CH4 yield of 211.9 mL·CH4/g·COD of WAS, which is comparable to the performance of the sample pretreated with APCL. The results of the batch tests using pretreated WAS for different APCL concentrations showed that the CH4 yield of WAS pretreated with CHEEs was comparable to the CH4 yield of 0.34 g·APCL/g·COD of WAS.
Improvement of the Electrical Performance of Outdoor Porcelain Insulators by Utilization of a Novel Nano-TiO2 Coating for Application in Railway Electrification Systems
Authors: Muangpratoom Pichai , Khonchaiyaphum Issaraporn , Vittayakorn Wanwilai
Published: January 2023

The present study aimed to develop the electrical performance of outdoor insulators using a nano-TiO2 coating for railway electrification systems. The prototype design of porcelain insulators with normal coatings and using a nano-TiO2 coating is based on IEC 60815-1. The first test was performed to measure the low-frequency flashover AC voltage under both dry and wet conditions. In addition, the other test was conducted to measure the lightning impulse critical-flashover voltage at positive and negative polarity under dry-normal and wet-contaminated conditions. X-ray diffraction (X-RD) and Scanning electron microscopy (SEM) were used to examine the micro surface and show that the nano-TiO2 coating was adhered to the surface of the outdoor porcelain insulator and exists in an amorphous state. Additionally, it was observed and discovered that scattered nano-TiO2 strengthens the glassy matrix and creates a sturdy barrier that causes flashover voltage to be reduced under conditions of high dielectric strength. Nanostructured ceramic formulations outperform ordinary porcelain in terms of breakdown voltage strength, particularly for the insulators’ low-frequency flashover performances under dry and wet test conditions. However, a significant change in the lightning impulse critical-flashover voltage characteristics is observed and is not much better when adding the nano-TiO2 coating to the porcelain insulators.
Effects of the Aspect Ratio and Cross-Sectional Area of Rectangular Tubes on Packing Characteristics of Mono-Sized Pebble Beds
Authors: Gong Baoping , Cheng Hao , Yan Juemin , Wang Long , Feng Yongjin , Wang Xiaoyu
Published: January 2023

The packing characteristics of a pebble bed are essential to understand the heat- and mass-transfer processes occurring within a granular system. Therefore, the packing characteristics of rectangular prismatic pebble beds randomly packed with mono-sized pebbles are analyzed. In terms of the average and local packing fraction distribution, coordination number, and radial distribution function, the effects of the rectangular tube aspect ratio and cross-sectional area on the packing properties of pebble beds are explored in depth. The findings indicate that the packing structures of the rectangular pebble bed exhibit noticeable fixed-wall effects. The average packing fraction and coordination number gradually decline as the rectangular tube aspect ratio rises. Close to the fixed wall, a noticeable wall effect can be seen in the distribution of axial and local packing fractions and the pebble center distribution. The wall effect has an increasing effect on the axial and local packing fraction distributions in rectangular tubes with increasing aspect ratios. Additionally, the average packing fraction and the average coordination number also increase as the cross-sectional area increases, indicating a gradual weakening of the wall effect as the cross-sectional area increases. Furthermore, the cross-sectional area and aspect ratio of the rectangular tubes affect the RDF values of the rectangular pebble beds but have no impact on the RDF features. The findings reported in this paper will be helpful for designing and optimizing pebble beds in the breeding blanket of fusion reactors.
The Contribution of Petroleum Charging Episodes to Different Strike-Slip Fault Zones in the Shunbei Area, the Tarim Basin, NW China
Authors: Wu Wei , Chen Honghan , Su Ao , Wang Yuwei , Zhu Zhihui , He Jianguo , Zhao Niubin
Published: January 2023

In recent years, newly discovered ultra-deep fault-karst reservoirs in the Shunbei area of the Tarim Basin have greatly increased the prospectivity of the basin and become a hotspot for further hydrocarbon exploration. In this study, the diagenetic sequences of the Lower-Middle Ordovician reservoir were established through observation of thin sections under transmitted light, reflected light, and cathode luminescence. The hydrocarbon charge history in the Shunbei area was reconstructed and associated with a series of diagenetic events using fluid inclusion microthermometric measurements combined with one-dimensional basin modeling. The results show that the Shunbei area has experienced three hydrocarbon charging episodes in total, in the late Caledonian, late Hercynian, and late Himalayan. Finally, the relationship between measured QF-535 factors and the API gravity of crude oil was used to quantify the level of contribution of each of the petroleum charging episodes to the No. 1, No. 5, and No. 7 fault zone reservoirs. The contribution of early hydrocarbon charging episodes gradually increases from west to east in the Shunbei area. This increase was driven by different tectonic events throughout geological history and the subsequent evolution of paleo-structural patterns. This study has a significant impact on marine carbonate reservoir evaluation in strike-slip fault zones in China.
Synergic Effects of Bed Materials and Catalytic Filter Candle for the Conversion of Tar during Biomass Steam Gasification
Authors: Papa Alessandro Antonio , Savuto Elisa , Di Carlo Andrea , Tacconi Alessandra , Rapagnà Sergio
Published: January 2023

This work concerns the activities of the European project BLAZE that aims to integrate a pilot-scale gasifier unit with a Solid Oxide Fuel Cell (SOFC). The objective is to identify the optimal operating conditions for a gasifier and hot gas cleaning and conditioning unit to produce H2-rich syngas with contaminants levels within the limits for the safe operation of the SOFC (750 mg/Nm3 and 75 mg/Nm3 for toluene and naphthalene, respectively). Experimental tests were carried out on a bench-scale gasification plant with a catalytic filter candle placed in the freeboard, to study the influence of temperature (1032 up to 1137 K), bed materials (olivine or olivine/dolomite 80/20%), and a nickel-based catalyst. The tests with a ceramic filter candle filled with catalyst and the mixture of olivine and dolomite in the bed gave the best results in terms of gas composition and gas yield, but the tar content was still higher than the limits for the SOFC. To increase the residence time of the gas in the catalytic bed a new metallic filter candle was tested. This candle, with almost the same external volume, allowed doubling the amount of catalyst used. Under these conditions, the content of toluene and naphthalene was reduced below 150 and 50 mg/Nm3, respectively.
A Facile Approach for the Encapsulation of Perovskite Solar Cells
Authors: Xu Yibo , Xia Rui , Gao Jifan , Wang Shubo , Zhu Jun , Xiong Weicheng , Yuan Ningyi , Ding Jianning
Published: January 2023

Effectively encapsulating perovskite solar cells (PSCs) to enhance the external reliability is the key towards commercialization. We herein propose a facile encapsulation method by introducing conductive ribbons and a polyethylene terephthalate (PET) backsheet on both sides of PSC. Via applying thermoplastic polyolefin (TPO) encapsulant, we implemented PSCs with fine encapsulation, enabling considerable durability in the ambient atmosphere and even with water immersion, demonstrating almost no degradation in the device output, which is ascribed to the low water vapor transmission rate as well as the high chemical stability of TPO. The operation reliability of the encapsulated cell is also significantly increased, maintaining 80% of the initial efficiency after 770 hours’ light illumination in an ambient atmosphere. This novel encapsulation route provides a feasible idea for the commercial application of PSCs in the future.
Comparison of District Heating Supply Options for Different CHP Configurations
Authors: Ruseljuk Pavel , Dedov Andrei , Hlebnikov Aleksandr , Lepiksaar Kertu , Volkova Anna
Published: January 2023

The article discusses the evaluation of potential heat production options for a large-scale district heating system in Narva (Estonia). Heat is currently generated at the Balti Power Plant’s CHP unit using local oil shale mixed with biomass. The CHP unit consists of two circulating fluidised bed boilers and a reheat steam turbine. According to the development strategy, the district heating system is expected to achieve carbon neutrality in the future. Various options and parameter variations should be analysed. The following scenarios were compared: (1) baseline scenario featuring an existing CHP extraction steam turbine; (2) alternative Scenario I featuring a CHP backpressure steam turbine; and (3) alternative Scenario II featuring a CHP gas turbine. To evaluate the above scenarios, a comprehensive energy/exergy analysis was performed, and economic indicators were calculated. The primary energy consumed, as well as the heat and electricity generated, were all taken into account. Based on this analysis, a scenario was selected using multiple-criteria decision-making that will improve energy efficiency and reliability of the system.
Multi-Objective Optimal Long-Term Operation of Cascade Hydropower for Multi-Market Portfolio and Energy Stored at End of Year
Authors: Yu Haojianxiong , Shen Jianjian , Cheng Chuntian , Lu Jia , Cai Huaxiang
Published: January 2023

Taking into account both market benefits and power grid demand is one of the main challenges for cascade hydropower stations trading on electricity markets and secluding operation plan. This study develops a multi-objective optimal operation model for the long-term operation of cascade hydropower in different markets. Two objectives were formulated for economics benefits and carryover energy storage. One was to maximize the market utility value based on portfolio theory, for which conditional value at risk (CVaR) was applied to measure the risk of multi-markets. Another was the maximization of energy storage at the end of a year. The model was solved efficiently through a multi-objective particle swarm optimization (MOPSO). Under the framework of the MOPSO, the chaotic mutation search mechanism and elite individual retention mechanism were introduced to diversify and accelerate the non-inferior solution sets. Lastly, a dynamic updating of archives was established to collect the non-inferior solution. The proposed model was implemented on the hydropower plants on the Lancang River, which traded on the Yunnan Electricity Market (YEM). The results demonstrated non-inferior solution sets in wet, normal and dry years. A comparison in solution sets revealed an imbalanced mutual restriction between objectives, such that a 2.65 billion CNY increase in market utility costs a 13.96 billion kWh decrease in energy storage. In addition, the non-inferior solution provided various schemes for actual demands based on other evaluating indexes such as the minimum output, power generation and spillage.
A Review of Ecosystem Service Trade-Offs/Synergies: Enlightenment for the Optimization of Forest Ecosystem Functions in Karst Desertification Control
Authors: Deng Xuehua , Xiong Kangning , Yu Yanghua , Zhang Shihao , Kong Lingwei , Zhang Yu
Published: January 2023

Ecosystem services provide regulation, provisioning, support, and cultural benefits for human survival, but it needs to be clarified how the trade-off/synergy relationships can be used to optimize function. Based on the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, we collected 254 articles on the ecosystem trade-offs/synergies and functional optimization. Through a systematic review of the literature, this paper summarized the research progress and landmark achievements from three aspects: trade-offs/synergies, functional optimization, and evaluation methods. The results indicated the following: (1) In terms of the number of articles published, there were no reports before 2005; from 2006 to 2022, the annual number of published papers increased from 1 to 72, showing an overall growth trend year by year. This mainly includes three stages: initial (1970–2005), slow development (2005–2014), and rapid development (2014–2022). (2) In terms of research areas, focus was placed mainly on Asia, North America, and Europe, accounting for 40.47%, 25.55%, and 15.07% of all regions, respectively. (3) In the future, it is necessary to focus on scientific issues such as the improvement of forest ecosystem functions, the trade-off/synergy relationships between services, the scale of spatiotemporal research, and the driving factors and evaluation methods for the management of rocky karst desertification. The aim is to provide a theoretical basis to optimize the forest ecosystem service functions.
Physicochemical and Rheological Properties of Stirred Yoghurt during Storage Induced from High-Intensity Thermosonicated Goat and Cow Milk
Authors: Ragab Eman Saad , Zhang Shuwen , Korma Sameh A. , Buniowska-Olejnik Magdalena , Nasser Sahar Abd Allah , Esatbeyoglu Tuba , Lv Jiaping , Nassar Khaled Sobhy
Published: January 2023

The effect of high-intensity thermosonication (HIT) pretreatment (20 kHz frequency, output power 4000 W and 25% amplitude for 5, 10 and 15 min) on the physicochemical and rheological properties of stirred yoghurt made from goat milk was studied. Various parameters of the milk were evaluated, such as the particle size, pH and soluble calcium and phosphorus, while other parameters of the stirred yoghurt were evaluated during storage (up to 18 days), such as the rheological measurements, syneresis, pH values, titratable acidity, color, and sensory properties. The microstructure had more interconnected chains than the stirred yoghurt made from homogenized milk on the first day of the storage period. Moreover, the HIT process reduced the diameter of the fat globules in the goat milk, making them smaller than those of homogenized milk. This pretreatment could be used successfully in the production of stirred yoghurt to improve major quality parameters such as delayed syneresis, increased viscosity and enhanced sensory properties during storage.
Analysis of Metabolic Differences in the Water Extract of Shenheling Fermented by Lactobacillus fermentum Based on Nontargeted Metabolomics
Authors: Yan Xiantao , Liu Min , Guo Congcong , Lian Xinyue , Shen Yun , Liu Yang , Qian Yi , Zhang Longfei , Wang Wenqiong , Chen Dawei , Qian Jianya , Gu Ruixia
Published: January 2023

Objective: To explore the characteristics of metabolites in Shenheling (SHL) fermented by Lactobacillus fermentum. Methods: In this study, ultrahigh-performance liquid chromatography-quadrupole electrostatic field orbit trap mass spectrometry (UHPLC-QE-MS) was used to qualitatively, quantitatively, and differentially analyze the metabolites of SHL before and after fermentation. Results: A total of 102 significant differential metabolites in nine categories were analyzed before and after fermentation. It mainly includes 29 terpenoids, 17 alkaloids, 14 organic acids and derivatives, 10 flavonoids, 9 phenylpropanoids, 6 phenols, 3 aromaticity, and 3 amino acid derivatives. Further screening found that the content of most active substances, such as alkaloids, organic acids, and flavonoids, increased significantly. These metabolites play an important role in improving the taste and efficacy of SHL. After fermentation, the contents of differential metabolites, such as panaquinquecol 2, ginsenoside Rh3, ginsenoside Rg3, dehydronuciferin, nicotinic acid, 5-hydroxytryptophan, azelaic acid, dihydrokaempferol, and chrysin, were increased, which increased the effects of antioxidation, anti-obesity, hypoglycemic, antibacterial, and improved immunity compared with those before fermentation. KEGG pathway analysis identified 10 metabolic pathways. Isoquinoline alkaloid biosynthesis, vitamin B6 metabolism, beta-alanine metabolism, nicotinate, and nicotinamide metabolism, purine metabolism, pantothenate and CoA biosynthesis, glyoxylate and dicarboxylate metabolism, tyrosine metabolism, citrate cycle (TCA cycle), phenylpropanoid biosynthesis, etc. Conclusions: Fermentation significantly changed the metabolites in SHL and played an important role in improving its taste, aroma quality, antioxidant, anti-obesity, and other health care functional components.