bluuebunny/crossref_metadata_2025 huggingface dataset:
Nematode Assemblages Development Twenty-One Years after the Introduction of Meadow Soil into Bare Post Mining Spoil Heap
Authors: Benetková Petra , Háněl Ladislav , Frouz Jan
Published: July 2022

The revitalisation of soil fauna in post-mining soils is one of the ways in which we can slow down biodiversity loss. To investigate the effect of a meadow soil transplanted directly into the spoil substrate on the colonisation of a spoil heap, we used nematodes as a tool for an assessment of success in soil fauna recolonisation. Three blocks of meadow topsoil (10 × 3 × 0.4 m) were dug out and transported as intact as possible into a bare substrate of spoil heaps near Sokolov (Czech Republic). The soil samples were taken at the beginning of the experiment (1997) and then, after 19 years (2016), were transported into blocks (B) in their surroundings in a 2 m distance (I) and, finally, 30 m from the transported blocks as a control (C), to compare and assess the complexity of soil food web. Nematode total abundances were highest in B plots and lowest in the nearby I plots at the beginning, whereas later, their abundances were highest in I plots and lowest in the control (C) plots. However, due to the high variability, abundances were statistically insignificant. The trophic composition of I plots became similar to the composition in B plots in the late phase; however, the high occurrence of predators in C plots showed a running succession even after 19 years. Our results together with previous works from the same experiment support the findings that the level of soil development has a larger impact on recolonisation by soil fauna than the migration barrier itself.
A Rare Presentation of Metastatic Renal Cell Carcinoma Masquerading as Vitritis: A Case Report and Review of Literature
Authors: Del Valle Estopinal Maria , Swan Robert T. , Rosenberg Kevin
Published: July 2022

We report a case of a 74-year-old gentleman who presented with floaters and decreased vision in the right eye after cataract surgery. His past medical history was significant for metastatic renal cell carcinoma (mRCC) to bone, lung and abdomen which was presumed stable for the last two years while on the tyrosine kinase inhibitor (TKI), pazopanib. Clinical examination revealed significant vitritis with a distinctive clumping of cells on the pre-retinal surface and posterior hyaloid face. Magnetic resonance imaging of the brain revealed new lesions suspicious for metastases. A diagnostic vitrectomy was performed to determine the nature of the vitritis and clear the visual axis. Cytopathologic evaluation of the vitreous demonstrated clusters of malignant cells that were positive for AE1/AE3 and PAX-8, and negative for the CD20, CD3, RCC, SOX-10 and S-100 immunohistochemical markers. The overall findings favored a metastatic RCC to the vitreous. Choroidal and retinal metastases from mRCC have been previously reported; however, vitreous involvement by mRCC with no evidence of retinal or choroidal mass has not been described. New treatments of mRCC include TKIs which target vascular endothelial growth factor receptors (VEGFRs). Herein, we analyze the factors that could have precipitated this unusual metastasis to the vitreous.
Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation
Authors: Nofallah Shima , Li Beibin , Mokhtari Mojgan , Wu Wenjun , Knezevich Stevan , May Caitlin J. , Chang Oliver H. , Elmore Joann G. , Shapiro Linda G.
Published: July 2022

Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline.
Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models
Authors: Khan Nergis C. , Perera Chandrashan , Dow Eliot R. , Chen Karen M. , Mahajan Vinit B. , Mruthyunjaya Prithvi , Do Diana V. , Leng Theodore , Myung David
Published: July 2022

While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy.
Association of the rs17574 DPP4 Polymorphism with Premature Coronary Artery Disease in Diabetic Patients: Results from the Cohort of the GEA Mexican Study
Authors: Vargas-Alarcón Gilberto , González-Salazar Maria del Carmen , Hernández-Díaz Couder Adrian , Sánchez-Muñoz Fausto , Ramírez-Bello Julian , Rodríguez-Pérez José Manuel , Posadas-Sánchez Rosalinda
Published: July 2022

Previously, it has been reported that hypoalphalipoproteinemia (HA) is associated with rs17574 DDP4 polymorphism. Considering that in diabetic patients, HA is often present and is a risk factor for premature coronary artery disease (pCAD), the study aimed to evaluate the association of this polymorphism with pCAD in diabetic individuals. We genotyped the rs17574 polymorphism in 405 pCAD patients with T2DM, 736 without T2DM, and 852 normoglycemic individuals without pCAD and T2DM as controls. Serum DPP4 concentration was available in 818 controls, 669 pCAD without T2DM, and 339 pCAD with T2DM. The rs17574 polymorphism was associated with lower risk of pCAD (padditive = 0.007; pdominant = 0.003, pheterozygote = 0.003, pcodominant1 = 0.003). In pCAD with T2DM patients, DPP4 levels were lower when compared with controls (p < 0.001). In the whole sample, individuals with the rs17574 GG genotype have the lowest protein levels compared with AG and AA (p = 0.039) carriers. However, when the same analysis was repeated separately in all groups, a significant difference was observed in the pCAD with T2DM patients; carriers of the GG genotype had the lowest protein levels compared with AG and AA (p = 0.037) genotypes. Our results suggest that in diabetic patients, the rs17574G DPP4 allele could be considered as a protective genetic marker for pCAD. DPP4 concentrations were lower in the diabetic pCAD patients, and the rs17574GG carriers had the lowest protein levels.
Practical Echocardiographic Approach of the Regurgitant Mitral Valve Assessment
Authors: Muñoz-Rodríguez Rebeca , Duque-González María Amelia , Igareta-Herraiz Aida Tindaya , Di Silvestre Mauro , Izquierdo-Gómez María Manuela , Baeza-Garzón Flor , Barragán-Acea Antonio , Bosa-Ojeda Francisco , Lacalzada-Almeida Juan
Published: July 2022

Mitral regurgitation is the second-most frequent valvular heart disease in Europe after degenerative aortic stenosis. It is associated with significant morbidity and mortality, and its prevalence is expected to increase with population aging. Echocardiography is the first diagnostic approach to assess its severity, constituting a challenging process in which a multimodality evaluation, integrating quantitative, semiquantitative and qualitative methods, as well as a detailed evaluation of the morphology and function of both left ventricle and atria is the key. In this review, we would like to provide a practical diagnosis approach on the mitral valve regurgitation mechanism, severity quantification, and planning of future therapeutic options.
Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy
Authors: Xia Qingyu , Ding Yuanming , Zhang Ran , Zhang Huiting , Li Sen , Li Xingda
Published: July 2022

This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population’s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.
Low Entropy Future Boundary Conditions
Authors: Schulman Lawrence S.
Published: July 2022

A number of ways to detect future, low-entropy, boundary conditions are considered. The most important of these is the use of slowly-decaying isotopes and the observation (or prediction) of galactic dynamics. There is the expectation that future developments in experimental or observational technique will yield positive results.
Problems of the Grid Size Selection in Differential Box-Counting (DBC) Methods and an Improvement Strategy
Authors: Jiang Wenxuan , Liu Yujun , Wang Ji , Li Rui , Liu Xiao , Zhang Jian
Published: July 2022

The differential box-counting (DBC) method is useful for determining the fractal dimension of grayscale images. It is simple to learn and implement and has been extensively utilized. However, this approach has several problems, such as over- or undercounting the number of boxes due to inappropriate parameter choices, limiting the calculation accuracy. Many studies have been conducted to increase the algorithm’s computational accuracy by improving the calculating parameters of the differential box-counting method. The grid size is a crucial parameter for the DBC method. Generally, there are two typical ways for selecting the grid size in relevant studies: consecutive integer and divisors of image size. However, both methods for grid size selection are problematic. The consecutive integer method cannot partition the image entirely and will result in the undercounting of boxes; the divisors of image size can partition the image completely. However, this method uses fewer grid sizes to compute fractal dimensions and has a relatively huge distance error (DE). To address the shortcomings of the above-mentioned two approaches, this research presents an improved grid size selection strategy. The improved method enhances computational accuracy by computing the discarded image edge areas in the consecutive integer method, allowing the original image information to be used as thoroughly as the divisor strategy. Based on fractional Brownian motion (FBM), Brodatz, and Aerials image sets, the accuracy of the three grid size selection techniques (consecutive integer method, divisors of image size method, and the improved algorithm) to compute the fractal dimension is then compared. The results reveal that, compared to the two prior techniques, the revised algorithm described in this study minimizes the distance error and increases the accuracy of the fractal dimension computation.
Parent–Teacher Interactions during COVID-19: Experiences of U.S. Teachers of Students with Severe Disabilities
Authors: Francis Grace L. , Raines Alexandra R. , Reed Alexandra S. , Kinas Jerome Marci
Published: July 2022

In 2020, COVID-19 disrupted all aspects of society across the globe including healthcare, employment, social interactions, and education. In many parts of the world, abrupt school closures caught teachers off guard, as they were forced to immediately shift their practices from in-person to online instruction with little-to-no preparation. Furthermore, during this time, many parents of school-aged children vacillated between multiple roles associated with their employment, household caregiving activities, and supporting their children at home. These challenges were especially challenging for teachers and parents of students with severe disabilities. The purpose of this study was to explore the experiences of U.S. teachers of students with severe disabilities regarding interacting with parents during the COVID-19 pandemic, including when schools initially closed in March 2020 and then reopened in September of 2020. This manuscript outlines six key themes highlighting parent–teacher interactions: (a) parents directing school decisions, (b) teacher inability to meet parent expectations, (c) parent–teacher communication, (d) parents as teachers, (e) parent exhaustion, and (f) teacher helplessness.
Chinese Calligraphy Generation Based on Involution
Authors: Song Yao , Yang Fang , Li Te
Published: July 2022

The calligraphic works of particular calligraphers often contain only a limited number of characters, rather than the full set of Chinese characters required for typography, which does not meet practical needs. There is therefore a need to develop a complete set of calligraphic characters for calligraphers. Most of the recently popular methods for generating calligraphic characters are based on deep learning, using an end-to-end approach to generate the target image. Deep learning-based methods usually suffer from unsuccessful conversion of stroke structures when the printed font differs significantly from the target font structure. In this paper, we propose an involution-based calligraphic character generation model, which can realize the conversion from printed fonts to target calligraphic fonts. We improve the Pix2Pix model by using a new neural operator, involution, which focuses more on spatial feature processing and can better handle the relationship between strokes than the models using only convolution, so that the generated calligraphic characters have an accurate stroke structure. A self-attentive module and a residual block are also added to increase the depth of the network to improve the feature processing capability of the model. We evaluated our method and some baseline methods using the same dataset, and the experimental results demonstrate that our model is superior in both visual and quantitative evaluation.
Implementation of an Electronically Based Active Power Filter Associated with a Digital Controller for Harmonics Elimination and Power Factor Correction
Authors: Abaza Amlak , El-Sehiemy Ragab A. , Said Mokhtar , Ghoniem Rania M. , Barakat Asmaa F.
Published: July 2022

This paper introduces simulation study and hardware implementation of a shunt active power filter (SAPF). The SAPF is considered an effective method for improving power quality by enhancing the power factor and reducing the total harmonic distortion (THD) for power sources due to nonlinear loads. A digital notch filter is used for extracting the reference current signal. The hysteresis band current controller is used to generate gating switching signals that control the switches of SAPF. The proposed model is constructed using Simulink/MATLAB environment and Proteus VMS, and the performance of the model is tested through a simulation study. The model of SAPF with a digital notch-filter is implemented using the Arduino microcontroller and a printed circuit board (PCB) layout. The model emulating a single-phase SAPF prototype is built and tested in the University of Kafrelshiekh, Faculty of Engineering laboratory. Significant improvements in power quality issues confirm the ability and effectiveness of SAPF, as well as reducing THD to a level less than 5%, which is in proportion to IEEE Std. 519-1992 and IEEE Std. 519-2014. The results emphasize the importance of SAPF to mitigate harmonics problems and enhance power factors.
Dynamic Stiffness Enhancement of the Quadcopter Control System
Authors: Yao Wu-Sung , Lin Chun-Yi
Published: July 2022

Vibrations often result in fatigue breakage in quadcopter flight operations. Reducing the vibration effect is the main important issue for quadcopter flight. Enhanced dynamic stiffness is required in the quadcopter control system for the vibration rejection ability. In this paper, the dynamic stiffness of the quadcopter control system is constructed and is used as an index for the performance of the quadcopter control system to resist an external oscillatory load. To rapidly reduce the vibration effect, a repetitive controller is introduced in the quadcopter control system, where the direct dynamic stiffness and the quadrature dynamic stiffness with variable frequencies are proposed. A theoretical model of the dynamic stiffness of the quadcopter control system is established by analyzing the definition of dynamic stiffness. Simulated and experimental results show that the magnitudes of the direct dynamic stiffness with the proposed repetitive controller are larger than those without the repetitive controller. The quadrature dynamic stiffness with the proposed repetitive controller is relatively smooth compared to that without the proposed repetitive controller, which can be used to verify the stability being improved by the designed repetitive controller. In addition, the magnitudes of the loss factor of the quadcopter control system with the repetitive controller are lower than those without the repetitive controller.
Experimental Demonstration of an Efficient Mach–Zehnder Modulator Bias Control for Quantum Key Distribution Systems
Authors: Park Chang-Hoon , Woo Min-Ki , Park Byung-Kwon , Jeon Seung-Woo , Jung Hojoong , Kim Sangin , Han Sang-Wook
Published: July 2022

A Mach–Zehnder modulator (MZM) is necessary for implementing a decoy-state protocol in a practical quantum key distribution (QKD) system. However, an MZM bias control method optimized for QKD systems has been missing to date. In this study, we propose an MZM bias control method using N (≥2) diagnostic pulses. The proposed method can be efficiently applied to a QKD system without any additional hardware such as light sources or detectors. Furthermore, it does not reduce the key rate significantly because it uses time slots allocated to existing decoy pulses. We conducted an experimental demonstration of the proposed method in a field-deployed 1 × 3 QKD network and a laboratory test. It is shown that our method can maintain the MZM extinction ratio stably over 20 dB (bit error rate ≤1%), even in an actual network environment for a significant period. Consequently, we achieved successful QKD performances.
Hybrid CNN-SVM Inference Accelerator on FPGA Using HLS
Authors: Liu Bing , Zhou Yanzhen , Feng Lei , Fu Hongshuo , Fu Ping
Published: July 2022

Convolution neural networks (CNN), support vector machine (SVM) and hybrid CNN-SVM algorithms are widely applied in many fields, including image processing and fault diagnosis. Although many dedicated FPGA accelerators have been proposed for specific networks, such as CNN or SVM, few of them have focused on CNN-SVM. Furthermore, the existing accelerators do not support CNN-SVM, which limits their application scenarios. In this work, we propose a hybrid CNN-SVM accelerator on FPGA. This accelerator utilizes a novel hardware-reuse architecture and unique computation mapping strategy to implement different calculation modes in CNN-SVM so that it can realize resource-efficient acceleration of the hybrid algorithm. In addition, we propose a universal deployment methodology to automatically select accelerator design parameters according to the target platform and algorithm. The experimental results on ZYNQ-7020 show that our implementation can efficiently map CNN-SVM onto FPGA, and the performance is competitive with other state-of-the-art works.
mmWave Radar Sensors Fusion for Indoor Object Detection and Tracking
Authors: Huang Xu , Tsoi Joseph K. P. , Patel Nitish
Published: July 2022

Indoor object detection and tracking using millimeter-wave (mmWave) radar sensors have received much attention recently due to the emergence of applications of energy assignment, privacy, health, and safety. Increasing the valid field of view of the system and accuracy through multi-sensors is critical to achieving an efficient tracking system. This paper uses two mmWave radar sensors for accurate object detection and tracking: two noise reduction stages to reduce noise and distinguish cluster groups. The presented data fusion method effectively estimates the transformation of the data alignment and synchronizes the result that can allow us to visualize the objects’ information acquired by one radar on another one. An efficient density-based clustering algorithm to provide high clustering accuracy is presented. The Unscented Kalman Filter tracking algorithm with data association tracks multiple objects simultaneously in terms of accuracy and timing. Furthermore, an indoor object tracking system is developed based on our proposed method. Finally, the proposed method is validated by comparing it with our previous system and a commercial system. The experimental results demonstrate that the proposed method’s advantage is of positive significance for handling the effect of occlusions at higher numbers of weak data and for detecting and tracking each object more accurately.
Gain Improvement of a Dual-Band CPW Monopole Antenna for Sub-6 GHz 5G Applications Using AMC Structures
Authors: Abdelghany Mahmoud A. , Fathy Abo Sree Mohamed , Desai Arpan , Ibrahim Ahmed A.
Published: July 2022

A dual-band high gain monopole antenna interfaced with an artificial magnetic conductor (AMC) reflector is introduced in this paper. The antenna is composed of an open-loop shape radiator fed with a coplanar waveguide (CPW) to achieve the desired frequency bands with a total size of 28 mm × 36 mm × 1.6 mm (0.224λ0 × 0.288 λ0 × 0.012 λ0 at 2.4 GHz). A dual-band AMC array structure is integrated on the back of the antenna to enhance the achieved gain. The total size of the integrated model is 79.9 mm × 79.9 mm × 8 mm (0.63λ0 × 0.63 λ0 × 0.064 λ0 at 2.4 GHz). The suggested models are fabricated and tested in terms of the S11, radiation pattern, and peak gain to validate the simulation results. The tested results of the antenna with the AMC array illustrate that the antenna operates at two bands where S11 ≤ −10 dB from 2.37 GHz to 2.5 GHz and from 4.45 GHz to 4.9 GHz. Furthermore, peak gain values of 5 dBi and 7.5 dBi are achieved at both bands, respectively. The suggested model can be used in sub-6 GHz 5G indoor and outdoor applications.
Detection of Web Cross-Site Scripting (XSS) Attacks
Authors: Alsaffar Mohammad , Aljaloud Saud , Mohammed Badiea Abdulkarem , Al-Mekhlafi Zeyad Ghaleb , Almurayziq Tariq S. , Alshammari Gharbi , Alshammari Abdullah
Published: July 2022

Most applications looking for XSS vulnerabilities have a variety of weaknesses related to the nature of constructing internet applications. Existing XSS vulnerability packages solely scan public net resources, which negatively influences the safety of internet resources. Threats may be in non-public sections of internet resources that can only be accessed by approved users. The aim of this work is to improve available internet functions for preventing XSS assaults by creating a programme that detects XSS vulnerabilities by completely mapping internet applications. The innovation of this work lies in its use of environment-friendly algorithms for locating extraordinary XSS vulnerabilities in addition to encompassing pre-approved XSS vulnerability scanning in examined internet functions to generate a complete internet resource map. Using the developed programme to discover XSS vulnerabilities increases the effectiveness of internet utility protection. This programme also simplifies the use of internet applications. Even customers unfamiliar with the fundamentals of internet security can use this programme due to its capability to generate a document with suggestions for rectifying detected XSS vulnerabilities.
Experimental Study of Oil Displacement and Gas Channeling
during CO2 Flooding in Ultra—Low Permeability Oil Reservoir
Authors: Liu Feng , Yue Ping , Wang Qingli , Yu Gaoming , Zhou Jiangtang , Wang Xiukun , Fang Quantang , Li Xinxin
Published: July 2022

Aiming to solve the problems of poor CO2 displacement efficiency and serious gas-channeling and low well-opening rates in ultra-low permeability reservoirs, we carry out CO2 displacement experiments under different permeability reservoirs by using different development methods, water drive to gas drive procedures, and different fracture positions to clarify the effects of physical formation properties, injection methods, and fracture parameters on CO2 displacement efficiency in C8 ultra-low permeability reservoirs. The experimental results show that the recovery degree of CO2 miscible drive increases with an increase in permeability. When the gas–oil ratio is greater than 2000 m3/m3, serious gas channeling can be observed in both the miscible drive and immiscible drive. In addition, when the water drive is altered to be a gas drive, the water cut of 0.45 mD and 0.98 mD cores decreased, and the recovery degree increased by 13.4% and 16.57%, respectively. A long fracture length will deteriorate gas channeling and lower the CO2 oil-displacement efficiency. However, the fracture location is found to have little impact on the recovery of CO2 displacement.
A Modified Geometry-Based MIMO Channel Model for Tunnel Scattering Communication Environments
Authors: Tang Denghong , Xi Xiaoli , Fan Qianying
Published: July 2022

In this study, a modified non-stationary geometry-based scattering model for tunnel vehicle-to-vehicle (V2V) multiple-input multiple-output Ricean fading channels is presented. The proposed channel introduces non-line-of-sight three-dimensional multi-bounced scattering propagation paths under an assumption of equivalent scattering points to depict the tunnel environments, in which the scatterers are randomly distributed on the semi-cylindrical tunnel wall (i.e., cable racks, jetfans, road signs, lighting facilities). Furthermore, the time-varying channel statistics (i.e., the space and frequency correlation functions) resulting from the relative movement between the mobile transmitter and mobile receiver are analyzed. Finally, numerical results show that the space correlations of the proposed model significantly decrease when the multi-bounced scattering rays are taken into account; moreover, when the velocity and the antenna angle γR increase, the correlations decrease therewith, which exhibits good agreement with the existing V2V scattering channel models and measured data in a real tunnel environment, demonstrating the rationality of the underlying channel model.
Quantitative Analysis of China’s Carbon Emissions Trading Policies: Perspectives of Policy Content Validity and Carbon Emissions Reduction Effect
Authors: Luo Juan , Xu Chong , Yang Boyu , Chen Xiaoyu , Wu Yinyin
Published: July 2022

Carbon emissions trading (CET) is now one of China’s key policy tools for achieving the goals of carbon peak and carbon neutrality. To comprehensively explore the consequences of China’s CET policy, the study first evaluated the content validity of CET policies across China’s 31 provinces, autonomous regions, and municipalities falling directly under the central government using policy strengths, tools, and measures from 2011 to 2020. The spatiotemporal drivers of regional carbon efficiency improvements from aspects of actual policy effect, average policy content validity effect, and policy quantity effect were also evaluated via the logarithmic mean Divisia index. This analysis revealed that the policy content validity was increasing in general and was higher in pilot regions. On average, the policy quantity effect was the primary driver of carbon efficiency improvements in both pilot and non-pilot regions, but the actual policy effect failed to promote carbon efficiency in both regions. Beijing’s carbon emissions reduction effect was superior to that of other pilot regions, and where actual policy effect and policy quantity effect were the primary and secondary drivers of local carbon efficiency improvements, respectively. These findings suggest that when formulating CET policies, each region should not only focus on improving policy content validity, but also pay attention to the actual carbon emissions reduction effects produced by policies as well.
A 1D Reduced-Order Model (ROM) for a Novel Latent Thermal Energy Storage System
Authors: Kailkhura Gargi , Mandel Raphael , Shooshtari Amir , Ohadi Michael
Published: July 2022

Phase change material (PCM)-based thermal energy storage (TES) systems are widely used for repeated intermittent heating and cooling applications. However, such systems typically face some challenges due to the low thermal conductivity and expensive encapsulation process of PCMs. The present study overcomes these challenges by proposing a lightweight, low-cost, and low thermal resistance TES system that realizes a fluid-to-PCM additively manufactured metal-polymer composite heat exchanger (HX), based on our previously developed cross-media approach. A robust and simplified, analytical-based, 1D reduced-order model (ROM) was developed to compute the TES system performance, saving computational time compared to modeling the entire TES system using PCM-related transient CFD modeling. The TES model was reduced to a segment-level model comprising a single PCM-wire cylindrical domain based on the tube-bank geometry formed by the metal fin-wires. A detailed study on the geometric behavior of the cylindrical domain and the effect of overlapped areas, where the overlapped areas represent a deviation from 1D assumption on the TES performance, was conducted. An optimum geometric range of wire-spacings and size was identified. The 1D ROM assumes 1D radial conduction inside the PCM and analytically computes latent energy stored in the single PCM-wire cylindrical domain using thermal resistance and energy conservation principles. The latent energy is then time-integrated for the entire TES, making the 1D ROM computationally efficient. The 1D ROM neglects sensible thermal capacity and is thus applicable for the low Stefan number applications in the present study. The performance parameters of the 1D ROM were then validated with a 2D axisymmetric model, typically used in the literature, using commercially available CFD tools. For validation, a parametric study of a wide range of non-dimensionalized parameters, depending on applications ranging from pulsed-power cooling to peak-load shifting for building cooling application, is included in this paper. The 1D ROM appears to correlate well with the 2D axisymmetric model to within 10%, except at some extreme ranges of a few of the non-dimensional parameters, which lead to the condition of axial conduction inside the PCM, deviating from the 1D ROM.
Global Challenges of Current Building-Integrated Solar Water Heating Technologies and Its Prospects: A Comprehensive Review
Authors: Fadzlin Wan Afin , Hasanuzzaman Md. , Rahim Nasrudin Abd , Amin Norridah , Said Zafar
Published: July 2022

Among the renewable energy resources, solar thermal is one of the technologies that significantly contribute to energy supply and reduce global greenhouse gas (GHG) emissions. Solar water heating (SWH) contributes a large proportion of the global solar thermal capacity, with 63% installation for domestic hot water (DHW) systems, 28% for large DHW systems, and the remaining 9% for other applications such as swimming pools heating, solar district heating, and space heating. Still, now, there are many challenges and limitations of those SWH technologies. The present work reviews the current challenges faced in the domestic SWH industry globally. The integration of thermal energy storage (TES) systems for better collector’s radiation absorption and overall performance are also highlighted. Many countries worldwide seem to face similar challenges within the SWH technologies and industry. It is proven that these challenges and limitations can hinder the global capacity of solar thermal utilization.
A Singular Spectrum Analysis and Gaussian Process Regression-Based Prediction Method for Wind Power Frequency Regulation Potential
Authors: Du Xianbo , Yu Jilai
Published: July 2022

The development of primary frequency regulation (FR) technology has prompted wind power to provide support for active power control systems, and it is critical to accurately assess and predict the wind power FR potential. Therefore, a prediction model for wind power virtual inertia and primary FR potential is proposed. Firstly, the primary FR control mode is divided and the mapping relationship of operating wind speed and FR potential is constructed. Secondly, a hybrid prediction method of singular spectrum analysis (SSA) and Gaussian process regression (GPR) is proposed for predicting the speed of wind. Finally, the wind speed sequence is adopted to calculate the FR potential with various regulation modes in future time. The results show the advantages of the proposed method in the prediction accuracy of wind power FR potential and the ability to characterize the uncertainty information of the prediction results. Accurate modeling and prediction of wind power FR potential can significantly promote wind turbines to implement fine control of primary FR and optimal allocation of FR capacity within wind farm and group. Based on the actual operation data, the deterministic prediction and probability prediction of the FR potential of wind farms are conducted in this paper.
Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland
Authors: Kowalska-Styczeń Agnieszka , Owczarek Tomasz , Siwy Janusz , Sojda Adam , Wolny Maciej
Published: July 2022

In this article, we analyze the energy consumption data of business customers registered by trading companies in Poland. We focus on estimating missing data in hourly series, as forecasts of this frequency are needed to determine the volume of electricity orders on the power exchange or the contract market. Our goal is to identify an appropriate method of imputation missing data for this type of data. Trading companies expect a specific solution, so we use a procedure that allows to choose the imputation method, which will consequently improve the accuracy of forecasting energy consumption. Using this procedure, a statistical analysis of the occurrence of missing values is performed. Then, three techniques for generating missing data are selected (missing data are generated in randomly selected series without missing values). The selected imputation methods are tested and the best method is chosen based on MAE and MAPE errors.
Model-Free Predictive Control and Its Applications
Authors: Nauman Muhammad , Shireen Wajiha , Hussain Amir
Published: July 2022

Predictive control offers many advantages such as simple design and a systematic way to handle constraints. Model predictive control (MPC) belongs to predictive control, which uses a model of the system for predictions used in predictive control. A major drawback of MPC is the dependence of its performance on the model of the system. Any discrepancy between the system model and actual plant behavior will greatly affect the performance of the MPC. Recently, model-free approaches have been gaining attention because they are not dependent on the system model parameters. To obtain the advantages of both a model-free approach and predictive control, model-free predictive control (MFPC) is being explored and reported in the literature for different applications such as power electronics and electric drives. This paper presents an overview of model-free predictive control. A comprehensive review of the application of MFPC in power converters, electric drives, power systems, and microgrids is presented in this paper. Moreover, challenges, opportunities, and emerging trends in MFPC are also discussed in this paper.
A Method of Determining Optimal Parameters for the Secondary Energy Source of a Multisource Hydrostatic Drive System in Machines Working in Closed Spaces
Authors: Kędzia Krzysztof
Published: July 2022

What benefits, from the point of view of the energy and ecological performance of a multisource drive system, can be gained for a particular control criterion through the use of appropriate secondary energy source parameters (accumulator volume Vg and initial gas pressure pg) for a given work cycle of a machine working in closed spaces? This paper describes a method of determining energy and ecological efficiency for a selected multisource drive system control criterion and presents potential benefits resulting from a proper choice of secondary energy source parameters for a selected control criterion. Using, as an example, a multisource hydrostatic drive system with a combustion engine as the primary energy source, the energy consumption and the ecological parameters (nitrogen oxides NOx emissions, carbon monoxide CO emissions and opacity DYM) were analysed for selected work cycles and different hydraulic accumulator parameters, i.e., initial gas pressure (pg) and accumulator volume (Vg). The investigations showed that the optimal parameters for the secondary energy source for energy performance criteria are different from those for the ecological criteria. It can be concluded that when a bank of accumulators is used, it will be possible to select a secondary energy source capacity that is optimal for the given work cycle, thereby improving the energy balance or the ecological balance.
Strategies of SAGD Start-Up by Downhole Electrical Heating
Authors: Wu Yongbin , Yang Zhaocheng , Yang Zhi , Wang Chao
Published: July 2022

The SAGD start-up process normally circulates steam in both the injector and producer, which consumes a large amount of steam, requires the high cost of high-temperature-produced liquid treatment, and unavoidably results in preferential communication in heterogeneous reservoirs. In order to achieve uniform preheat and full steam chamber development in SAGD wellpairs, downhole electrical heating to start up SAGD was proposed and investigated in this study using physical and numerical simulation approaches. Two 3-D scaled physical experiments were designed and implemented to investigate the feasibility and heating characteristics of such a method. Numerical simulation was conducted using an SAGD sector model with typical field properties to design the preheating process, optimize key operational parameters, and formulate the soak strategy to determine the SAGD conversion timing. The experimental results indicate that electrical heating outperforms steam circulation in achieving the uniform thermal communication in heterogeneous reservoirs, which is challenged in the conventional steam circulation process. The preheating process and operational parameters of electrical heating were formulated and optimized, which include wellbore pre-flush, wellbore saturation by heat conduction fluid, electrical heating, and replacement of heat conduction fluid periodically. Surveillance of temperature difference along the horizontal section while powering off electrical heating intermittently is optimized to be the SAGD conversion timing determination strategy. Based on the combination results of scaled physical simulation and pilot wellpair numerical simulation, full heat communication and steam chamber development are achieved along the horizontal length by electrical preheating, and the oil recovery factor of the pilot wellpair is enhanced by 14.8%, indicating encouraging potentials in heavy oil and bitumen development.
Enhancement of the Antihypertensive Effect of Fermented Sargassum horneri with Lactiplantibacillus pentosus SN001
Authors: Tamura Momoko , Shimizu Satoshi , Nagai Hiroshi , Yoshinari Orie , Hamada-Sato Naoko
Published: July 2022

Sargassum horneri is a seaweed with antihypertensive properties. However, it is underutilized in some areas, and effective utilization methods are being sought. In this study, we prepared a fermented S. horneri using lactic acid bacteria Lactiplantibacillus pentosus SN001 and investigated its effective utilization by enhancing its antihypertensive effect. The ACE inhibitory activity of S. horneri ranged from 3.6% to a maximum of 63.3% after fermentation. In vivo studies using mice and spontaneously hypertensive rats (SHR) suggested an antihypertensive effect of fermented S. horneri. Purification and NMR analysis of the ACE inhibitory component in fermented S. horneri identified glycerol. Therefore, it is suggested that glycerol is responsible for the strong antihypertensive effect of fermented S. horneri. In conclusion, S. horneri is expected to be used as a dietary ingredient with enhanced antihypertensive effect by fermentation with L. pentosus SN001.
Soybean Oil Bodies as a Milk Fat Substitute Improves Quality, Antioxidant and Digestive Properties of Yogurt
Authors: Dou Nianxu , Sun Rongbo , Su Chengcheng , Ma Yue , Zhang Xuewei , Wu Mengguo , Hou Juncai
Published: July 2022

In this experiment, the effect of replacing milk fat with soybean fat body (25%, 50%, 75%, 100%) on the quality, antioxidant capacity and in vitro digestive characteristics of yogurt was investigated while maintaining the total fat content of the yogurt unchanged. The results showed that increasing the substitution amount of soy fat body for milk fat had little effect on the pH and acidity of yogurt during the storage period, while the physicochemical properties, degree of protein gel network crosslinking, saturated fatty acid content, PV value and TBARS value of the yogurt significantly decreased (p < 0.05). Meanwhile, protein content, solids content, unsaturated fatty acid content, tocopherol content and water holding capacity significantly increased (p < 0.05). Flavor analysis revealed that yogurts with soybean oil bodies were significantly different when compared to those without soybean oil bodies (p < 0.05), and yogurt with 25% substitution had the highest sensory score. After in vitro digestion, the free fatty acid release, antioxidant capacity and protein digestibility of soybean oil body yogurt were significantly higher (p < 0.05). The SDS-PAGE results showed that the protein hydrolysis of the soybean oil body yogurt was faster. Therefore, the use of an appropriate amount of soybean oil bodies to replace milk fat is able to enhance the taste of yogurt and improve the quality of the yogurt.
Research Update on the Impact of Lactic Acid Bacteria on the Substance Metabolism, Flavor, and Quality Characteristics of Fermented Meat Products
Authors: Wang Yi , Han Jun , Wang Daixun , Gao Fang , Zhang Kaiping , Tian Jianjun , Jin Ye
Published: July 2022

This paper reviews the effects of domestic and foreign influences on the substance metabolism pathways and the flavor and flora of LAB in fermented meat products to provide a new theoretical basis for developing new products for the industrial application of lactic acid bacteria (LAB) in fermented meat products. LAB are extensively used among commonly fermented ingredients, such as fermented meat products and yogurt. As fermenting agents, LAB metabolize proteins, lipids, and glycogen in meat products through their enzyme system, which affects the tricarboxylic acid cycle, fatty acid metabolism, amino acid decomposition, and other metabolic processes, and decompose biological macromolecules into small molecules, adding a special flavor with a certain functionality to the final product. Metabolites of LAB in the fermentation process also exert nitrite degradation, as well as antibacterial and antioxidant functions, which improve the physical and chemical qualities of fermented meat products. While fermenting meat products, LAB not only add unique flavor substances to the products, but also improve the safety profile of fermented foods.
Transcriptome Analysis Reveals Potential Mechanisms of L-Serine Production by Escherichia coli Fermentation in Different Carbon–Nitrogen Ratio Medium
Authors: Chen Zheng , Chen Xiaojia , Li Qinyu , Zhou Peng , Zhao Zhijun , Li Baoguo
Published: July 2022

L-serine is an industrially valuable amino acid that is widely used in the food, cosmetics and pharmaceutical industries. In this study, transcriptome sequencing technology was applied to analyze the changes in gene expression levels during the synthesis of L-serine in Escherichia coli fermentation. The optimal carbon–nitrogen ratio for L-serine synthesis in E. coli was determined by setting five carbon–nitrogen ratios for shake flask fermentation. Transcriptome sequencing was performed on E. coli fermented in five carbon–nitrogen ratio medium in which a total of 791 differentially expressed genes (DEGs) were identified in the CZ4_vs_CZ1 group, including 212 upregulated genes and 579 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of these DEGs showed that the effect of an altered carbon–nitrogen ratio on the fermentability of E. coli was mainly focused on metabolic pathways such as GABAergic synapse and the two-component system (TCS) in which the genes playing key roles were mainly gadB, gadA, glsA, glnA, narH and narJ. In summary, these potential key metabolic pathways and key genes were proposed to provide valuable information for improving glucose conversion during E. coli fermentation.