bluuebunny/crossref_metadata_2025 huggingface dataset:
Harmonic Distortion and Hosting Capacity in Electrical Distribution Systems with High Photovoltaic Penetration: The Impact of Electric Vehicles
Authors: Dávila-Sacoto Miguel , González L. G. , Hernández-Callejo Luis , Duque-Perez Óscar , Zorita-Lamadrid Ángel L. , Alonso-Gómez Víctor , Espinoza J. L.
Published: May 2023

Electric vehicles and the charging stations and their operation require a thorough examination to evaluate the effects on the electrical network. This becomes particularly challenging in the case of high photovoltaic penetration, due to the variability of the solar resource and vehicle connection patterns, which cater to individual user preferences. The current study investigates the impact of harmonics generated by charging stations and electric vehicles on different photovoltaic penetration scenarios within an electrical distribution system. DC and AC charging stations are analyzed. The findings reveal a third harmonic magnitude increase exceeding 300% compared to other cases. Furthermore, this study demonstrates the effects of current and voltage variations on end-users and substation transformers. The impact of harmonics on the hosting capacity of the network is also analyzed, resulting in a 37.5% reduction in the number of vehicles.
Recognition of Children’s Facial Expressions Using Deep Learned Features
Authors: Laraib Unqua , Shaukat Arslan , Khan Rizwan Ahmed , Mustansar Zartasha , Akram Muhammad Usman , Asgher Umer
Published: May 2023

The emotional well-being of a child is crucial for their successful integration into society as a productive individual. While technology has made significant strides in enabling machines to decipher human emotional signals, current research in emotion recognition primarily prioritizes adults, disregarding the fact that children develop emotional awareness at an early stage. This highlights the need to explore how machines can recognize facial expressions in children, although the absence of a standardized database poses a challenge. In this study, we propose a system that employs Convolutional-Neural-Network (CNN)-based models, such as VGG19, VGG16, and Resnet50, as feature extractors, and Support Vector Machine (SVM) and Decision Tree (DT) for classification, to automatically recognize children’s expressions using a video dataset, namely Children’s Spontaneous Facial Expressions (LIRIS-CSE). Our system is evaluated through various experimental setups, including 80–20% split, K-Fold Cross-Validation (K-Fold CV), and leave one out cross-validation (LOOCV), for both image-based and video-based classification. Remarkably, our research achieves a promising classification accuracy of 99% for image-based classification, utilizing features from all three networks with SVM using 80–20% split and K-Fold CV. For video-based classification, we achieve 94% accuracy using features from VGG19 with SVM using LOOCV. These results surpass the performance of the original work, which reported an average image-based classification accuracy of 75% on their LIRIS-CSE dataset. The favorable outcomes obtained from our research can pave the way for the practical application of our proposed emotion recognition methodology in real-world scenarios.
A Network Scheduling Method Based on Segmented Constraints for Convergence of Time-Sensitive Networking and Industrial Wireless Networks
Authors: Wei Min , Liu Chang , Wang Jin , Yang Shujie
Published: May 2023

In industrial applications, it is necessary to select different types of networks according to different communication requirements. To meet this requirement, a converged network of wired and wireless networks is frequently employed. Notably, fulfilling the end-to-end transmission requirements of converged networks is challenging. As a solution, converged-network scheduling methods have proved valuable. In this paper, a network scheduling method for the convergence of industrial wireless networks and time-sensitive networks is proposed. Additionally, the proposed method is tested and verified. The results show that the end-to-end average transmission delay is reduced and the jitter is acceptable.
A Mechatronic Cardiovascular Simulation System for Jugular Venous Echo-Doppler Training
Authors: Proto Antonino , Cavalieri Andrea , Gadda Giacomo , Taibi Angelo , Astolfi Luca , Bonfè Marcello , Bertagnon Alessandro , Nonato Maddalena , Gavanelli Marco , Cristofori Giulia , D’Antona Andrea , Farsoni Saverio
Published: May 2023

Echo-doppler examination of the jugular vessel is a powerful tool for the early diagnosis of cardiovascular disorders that can be further related to central nervous system diseases. Unfortunately, the ultrasound technique is strongly operator-dependent, so the quality of the scan, the accuracy of the measurement, and therefore the rapidity and robustness of the diagnosis reflect the degree of training. The paper presents the development of a mechatronic simulation system for improving the skill of novice physicians in echo-doppler procedures. The patient is simulated by a silicone manikin whose materials are designed to have a realistic ultrasound response. Two tubes allow blood-mimicking fluid to flow inside the manikin, simulating the hemodynamics of the internal jugular vein. The mechatronic system is designed for controlling the flow waveform, to reproduce several clinical cases of interest for diagnosis. The experiments investigate the accuracy of the echo-doppler measurements performed on the proposed system by novice operators using a real ultrasound scanner.
ScrumOntoSPL: Collaborative Method of Agile Product Line Engineering for Software Resource Reuse
Authors: Chen Junhua , Huang Rui , Jiang Yushuang , Pu Chenggen , Huang Xueda , Sun Xia , Liu Yanfei
Published: May 2023

Agile Product Line Engineering (APLE), a relatively new approach combining the two successful methods of Agile Software Development (ASD) and Software Product Lines (SPLs), makes product lines more responsive to ever-changing customer needs or market changes. However, SPLs often fail to keep up with market demand due to high coordination costs, slow development processes, and long release cycles in the case of frequent changes in business requirements; in agile software projects, the lack of a unified specification for describing requirements leads to high coordination costs and inconvenient requirement management. Some studies in the literature have proposed optimized approaches to integrate ASD and SPLs, but they still have not covered all aspects of APLE’s characteristics, and software resource reuse is rarely considered in these approaches during product line development. In view of this, we propose a collaborative framework of agile product line engineering for software resource reuse, namely ScrumOntoSPL. The ScrumOntoSPL approach efficiently merges ASD and SPL based on the agile method Scrum, SPL architecture, and ontology technology. In ScrumOntoSPL, uniform requirement specification is constructed by utilizing ontology, and the Matching Requirement with Component (MRC) process is designed to match product new requirements and software resources stored in a resource pool. In addition, we evaluated the proposed framework and approach with CMMI. In the end, a case study of a software development tool called IMC-Tool based on ScrumOntoSPL for a universal Instrument Microcontroller Chip (IMC) is discussed. The IMC-Tool case illustrates that the ScrumOntoSPL has the advantages of dynamically managing demand changes, enhancing software resource reuse, reducing coordination costs, and reducing time to market.
SimoSet: A 3D Object Detection Dataset Collected from Vehicle Hybrid Solid-State LiDAR
Authors: Sun Xinyu , Jin Lisheng , He Yang , Wang Huanhuan , Huo Zhen , Shi Yewei
Published: May 2023

Three-dimensional (3D) object detection based on point cloud data plays a critical role in the perception system of autonomous driving. However, this task presents a significant challenge in terms of its practical implementation due to the absence of point cloud data from automotive-grade hybrid solid-state LiDAR, as well as the limitations regarding the generalization ability of data-driven deep learning methods. In this paper, we introduce SimoSet, the first vehicle view 3D object detection dataset composed of automotive-grade hybrid solid-state LiDAR data. The dataset was collected from a university campus, contains 52 scenes, each of which are 8 s long, and provides three types of labels for typical traffic participants. We analyze the impact of the installation height and angle of the LiDAR on scanning effect and provide a reference process for the collection, annotation, and format conversion of LiDAR data. Finally, we provide baselines for LiDAR-only 3D object detection.
Robust Deep Learning Models for OFDM-Based Image Communication Systems in Intelligent Transportation Systems (ITS) for Smart Cities
Authors: Islam Nazmul , Shin Seokjoo
Published: May 2023

Internet of Things (IoT) ecosystem in smart cities demands fast, reliable, and efficient image data transmission to enable real-time Computer Vision (CV) applications. To fulfill these demands, an Orthogonal Frequency Division Multiplexing (OFDM)-based communication system has been widely utilized due to its higher spectral efficiency and data rate. When adapting such a system to achieve fast and reliable image transmission over fading channels, noise is introduced in the signal which heavily distorts the recovered image. This noise independently corrupts pixel values, however, certain intrinsic properties of the image, such as spatial information, may remain intact, which can be extracted as multidimensional features (in the convolution layers) and interpreted (in the top layers) by a Deep Learning (DL) model. Therefore, the current study analyzes the robustness of such DL models utilizing various OFDM-based image communication systems for CV applications in an Intelligent Transportation Systems (ITS) environment. Our analysis has shown that the EfficientNetV2-based model achieved a range of 70–90% accuracy across different OFDM-based image communication systems over the Rayleigh Fading channel. In addition, leveraging different data augmentation techniques further improves accuracy up to 18%.
Efficient Intrusion Detection System in the Cloud Using Fusion Feature Selection Approaches and an Ensemble Classifier
Authors: Bakro Mhamad , Kumar Rakesh Ranjan , Alabrah Amerah A. , Ashraf Zubair , Bisoy Sukant K. , Parveen Nikhat , Khawatmi Souheil , Abdelsalam Ahmed
Published: May 2023

The application of cloud computing has increased tremendously in both public and private organizations. However, attacks on cloud computing pose a serious threat to confidentiality and data integrity. Therefore, there is a need for a proper mechanism for detecting cloud intrusions. In this paper, we have proposed a cloud intrusion detection system (IDS) that is focused on boosting the classification accuracy by improving feature selection and weighing the ensemble model with the crow search algorithm (CSA). The feature selection is handled by combining both filter and automated models to obtain improved feature sets. The ensemble classifier is made up of machine and deep learning models such as long short-term memory (LSTM), support vector machine (SVM), XGBoost, and a fast learning network (FLN). The proposed ensemble model’s weights are generated with the CSA to obtain better prediction results. Experiments are executed on the NSL-KDD, Kyoto, and CSE-CIC-IDS-2018 datasets. The simulation shows that the suggested system attained more satisfactory results in terms of accuracy, recall, precision, and F-measure than conventional approaches. The detection rate and false alarm rate (FAR) of different attack types was more efficient for each dataset. The classifiers’ performances were also compared individually to the ensemble model in terms of the false positive rate (FPR) and false negative rate (FNR) to demonstrate the ensemble model’s robustness.
A Hybrid GA/ML-Based End-to-End Automated Methodology for Design Acceleration of Wireless Communications CMOS LNAs
Authors: Sad Christos , Michailidis Anastasios , Noulis Thomas , Siozios Kostas
Published: May 2023

A new methodology for the RF/mmWave analog design process, automation and acceleration, is presented in this work. The proposed framework was implemented so as to accelerate the design cycle of analog/RF circuits by creating a dataset in a fully automated manner and training a combination of machine learning models for the optimal design parameters’ prediction. machine learning polynomial regression was adopted to accelerate the design process, predicting the optimal design parameters’ values while genetic algorithm optimization was exploited for the dataset creation automation. To evaluate the efficiency of the proposed methodology, the framework was implemented for the design of a common source Low-Noise-Amplifier, using a 65 nm CMOS process node. The proposed methodology successfully tackles the design cycle speed-up, automation, and acceleration, utilizing machine learning prediction for the design parameters and genetic algorithm for the dataset creation automation instead of the classical, simulation-based, standard design methodology. The provided experimental results have shown the effectiveness of the proposed hybrid approach, creating very precise RF matching networks for LNA designs and achieving >99.9% wave transmission efficiency while reaching >99% accuracy on the parameters’ prediction task.
REEGAT: RoBERTa Entity Embedding and Graph Attention Networks Enhanced Sentence Representation for Relation Extraction
Authors: Cai Fengze , Hu Qiang , Zhou Renjie , Xiong Neal
Published: May 2023

Relation extraction is one of the most important intelligent information extraction technologies, which can be used to construct and optimize services in intelligent communication systems (ICS). One issue with the existing relation extraction approaches is that they use one-sided sentence embedding as their final prediction vector, which degrades relation extraction performance. The innovative relation extraction model REEGAT (RoBERTa Entity Embedding and Graph Attention networks enhanced sentence representation) that we present in this paper, incorporates the concept of enhanced word embedding from graph neural networks. The model first uses RoBERTa to obtain word embedding and PyTorch embedding to obtain relation embedding. Then, the multi-headed attention mechanism in GAT (graph attention network) is introduced to weight the word embedding and relation embedding to enrich further the meaning conveyed by the word embedding. Finally, the entity embedding component is used to obtain sentence representation by pooling the word embedding from GAT and the entity embedding from named entity recognition. The weighted and pooled word embedding contains more relational information to alleviate the one-sided problem of sentence representation. The experimental findings demonstrate that our model outperforms other standard methods.
Low-Rank and Total Variation Regularization with ℓ0 Data Fidelity Constraint for Image Deblurring under Impulse Noise
Authors: Wang Yuting , Tang Yuchao , Deng Shirong
Published: May 2023

Impulse noise removal is an important problem in the field of image processing. Although many methods exist to remove impulse noise, there is still room for improvement. This paper proposes a new method for removing impulse noise that combines the nuclear norm and the detection ℓ0TV model while considering the low-rank structure commonly found in visual images. The nuclear norm maintains this structure, while the detection ℓ0TV criterion promotes sparsity in the gradient domain, effectively removing impulse noise while preserving edges and other vital features. To solve the non-convex and non-smooth optimization problem, we use a mathematical process with equilibrium constraints (MPEC) to transform it. Subsequently, the proximal alternating direction multiplication algorithm is used to solve the transformed problem. The convergence of the algorithm is proven under mild conditions. Numerical experiments in denoising and deblurring show that for low-rank images, the proposed method outperforms ℓ1TV with detection, ℓ0TV and ℓ0OGSTV.
Optimization Control of Canned Electric Valve Permanent Magnet Synchronous Motor
Authors: Wu Qingsong , Li Wei , Feng Guihong , Zhang Bingyi
Published: May 2023

The traditional canned electric valve consists of an induction motor and a reducer, which need to be matched with the position sensor to achieve precise control of valve position. The position sensor and reducer are not only easily damaged in high-temperature liquids, but also the slip rate of the induction motor is greatly affected by the liquid temperature, which makes it difficult to achieve accurate control. To address the above problems, this paper introduces a new topology of canned electric valve permanent magnet synchronous motor (CEV-PMSM), and a new maximum torque per ampere (MTPA) model is proposed. The new MTPA control equation considering the canned sleeve parameters is derived theoretically. By comparing it with id = 0 control and ideal MTPA control strategy, it is proved that the new MTPA model reflects the electric valve operation characteristics more realistically. In order to achieve sensorless control of the electric valve, and to achieve fast response and high-precision control under external disturbances and parameter uncertainties, the proposed control scheme combines sensorless control and two-degree-of-freedom (2-DOF) control. Consequently, the proposed control scheme can effectively improve the static and dynamic performances of the CEV-PMSM, as well as adjust the tracking and anti-disturbance performances independently. Finally, a 2 kW 100 r/min prototype was manufactured and corresponding experiments were conducted to verify the accuracy of the analysis.
Small Target Detection Algorithm for UAV Aerial Photography Based on Improved YOLOv5s
Authors: Shang Jingcheng , Wang Jinsong , Liu Shenbo , Wang Chen , Zheng Bin
Published: May 2023

At present, UAV aerial photography has a good prospect in agricultural production, disaster response, and other aspects. The application of UAVs can greatly improve work efficiency and decision-making accuracy. However, owing to inherent features such as a wide field of view and large differences in the target scale in UAV aerial photography images, this can lead to existing target detection algorithms missing small targets or causing incorrect detections. To solve these problems, this paper proposes a small target detection algorithm for UAV aerial photography based on improved YOLOv5s. Firstly, a small target detection layer is applied in the algorithm to improve the detection performance of small targets in aerial images. Secondly, the enhanced weighted bidirectional characteristic pyramid Mul-BiFPN is adopted to replace the PANet network to improve the speed and accuracy of target detection. Then, CIoU was replaced by Focal EIoU to accelerate network convergence and improve regression accuracy. Finally, a non-parametric attention mechanism called the M-SimAM module is added to enhance the feature extraction capability. The proposed algorithm was evaluated on the VisDrone-2019 dataset. Compared with the YOLOV5s, the algorithm improved by 7.30%, 4.60%, 5.60%, and 6.10%, respectively, in mAP@50, mAP@0.5:0.95, the accuracy rate (P), and the recall rate (R). The experiments show that the proposed algorithm has greatly improved performance on small targets compared to YOLOv5s.
A Novel Low-Complexity Method for Near-Field Sources Based on an S-IMISC Array Model
Authors: Li Xiaolin , Yang Hongjuan , Han Jiqu , Dong Ningfei
Published: May 2023

Array optimization has recently received significant attention owing to its several advantages, such as larger array aperture and greater degrees of freedom (DOFs). However, current works focus on far-field sources, while array optimization for near-field sources has not been adequately investigated. Therefore, this work develops a new symmetry sparse array model for near-field sources based on the improved maximum inter-element spacing constraint (IMISC). The proposed symmetry IMISC (S-IMISC) array model has all the advantages of traditional sparse array models. Compared with traditional sparse array models, the S-IMISC array model affords more uniform DOFs and is less affected by mutual coupling. Additionally, in order to improve the real-time performance of near-field sources localization, the characteristic equation-based method (CEM) is used to obtain the azimuth information of near-field sources which can avoid eigenvalue decomposition (EVD), and a spectrum peak search and compression scheme is used to obtain the distance information by searching the partial area instead of the whole Fresnel area, thereby significantly reducing computation complexity. Extensive simulations verify the advantages of the proposed algorithm and the S-IMISC array model.
Experimental Study on PIV Measurement and CFD Investigation of the Internal Flow Characteristics in a Reactor Coolant Pump
Authors: Ni Dan , Lu Hongzhong , Huang Shiyuan , Lu Sheng , Zhang Yang
Published: May 2023

The nuclear reactor coolant pump (RCP) is the core piece of equipment of a nuclear power plant (NPP). The energy performance and internal flow characteristics of RCPs are revealed by effective measurement methods, which are helpful to understand the flow mechanism of RCPs. The present work is intended to conduct an integrated study based on the energy performance test and Particle Image Velocimetry (PIV) flow-field non-contact measurement of the RCP. In addition, the prediction results of different turbulence models are compared with experimental results in detail. Through energy performance measurement and numerical calculation analysis, it can be found that various turbulence models have the ability to predict the performance of RCPs in engineering applications. At 0.8~1.2 Φd operating conditions, the maximum error is less than 10% and the minimum error is less than 0.1% by analyzing the energy performance of numerical calculations and experimental results. The PIV results show that the velocity of the discharge nozzle varies greatly from right (outlet of diffuser channel 2) to left (outlet of diffuser channel 12) due to different flow structures. Through the qualitative and quantitative comparison of the internal flow field, it can be concluded that, except for the low flow rate, compared with other computational models, the Realizable k-ε model can better predict the internal flow field of an RCP. The reasons for the experimental error and numerical calculation error are analyzed in detail, and the results can provide a reference for forecasting an RCP internal flow field with a special and complex structure.
Heat Transfer and Reaction Characteristics of Steam Methane Reforming in a Novel Composite Packed Bed Microreactor for Distributed Hydrogen Production
Authors: Wang Jingyu , Liu Zongxin , Ji Changfa , Liu Lang
Published: May 2023

The development of efficient and compact reactors is an urgent need in the field of distributed hydrogen production. Steam reforming of methane is the main method to produce hydrogen. Aiming at the problems of high heat and mass transfer resistance of the existing fixed bed reactors, and the difficulty of replacing the wall-coated catalyst in the microreactors, a composite packed bed was proposed to meet the demand of small-scale hydrogen production. The structure consists of a multi-channel framework with high thermal conductivity, which is filled with Ni/Al2O3 catalyst particles in each channel. A three-dimensional numerical model of the steam methane reforming process in the novel reactor was established using ANSYS FLUENT software. The heat transfer and reaction characteristics in the reactor were studied. Firstly, the advantages of the multi-channel skeleton in enhancing the radial heat transfer performance were verified by comparing it with the traditional randomly packed bed without the channel skeleton. Secondly, the influences of inlet velocity, inlet temperature, and heating wall temperature on the heat transfer and reaction performances in the reactor were studied, and a sensitivity factor was adopted to do the sensitivity analysis. The results show that the methane conversion rate is most sensitive to the wall temperature, while the inlet velocity and inlet temperature have less effect. Finally, the effects of two skeleton materials were studied. The results show that when the wall temperature is higher than 1200 K, there is no significant difference between these two reactors, which indicates that the use of cordierite with a lower price, but also with a lower thermal conductivity can significantly reduce the reactor’s cost. The conclusions can be used as a reference for the design of small-scale hydrogen production reactors.
Catalytic Pyrolysis of Waste Bicycle Tires and Engine Oil to Produce Limonene
Authors: Wang Junzhi , Dong Xinjiang , Zuo Zongliang , Luo Siyi
Published: May 2023

NaOH, dolomite and NiCl2 were used as catalysts to examine their effects on co–pyrolysis with waste bicycle tires (WT) and waste engine oil (WEO). The pyrolysis behaviors with catalysts were investigated by thermogravimetric analysis. The activation energy of the catalytic main reaction stage was derived by the Kissinger–Akahira–Sunose (KAS) method under four different heating rates conditions. The calculations show that all three catalysts can reduce the activation energy of the reaction. Co–pyrolysis of WT and WEO with different catalysts was performed in a self–made lab bench at 600 °C to explore the impact on the distribution of three–phase products. The properties of gas and oil products were characterized by FTIR and Py–GC/MS (Agilent 7890B, Santa Clara, CA, USA). With the mixing of catalysts, activation energy (Eα) decreased by 15–30% in the main reaction process. NaOH and dolomite increased the yield of gas by 7% and 10%. NaOH can significantly improve the yield of CH4. The proportion of limonene in pyrolysis oil increased to 19.65% with 10% NaOH. This article provides a new method for efficiently producing limonene by mixing WT and WEO with NaOH.
Decentralized Control for the Cell Power Balancing of a Cascaded Full-Bridge Multilevel Converter
Authors: Vivert Miguel , Diez Rafael , Cousineau Marc , Bernal Cobaleda Diego , Patino Diego
Published: May 2023

This article presents a decentralized control technique applied to a Cascaded Full-Bridge Multilevel Converter (CFBMC) to balance the amount of power provided by its independent cells connected in series. It is based on the use of elementary modular controllers, associated with each converter cell, communicating with their close neighbors to obtain the appropriate power balancing. A complete theoretical study of the system is provided in terms of modal responses, feedback loop bandwidth and stability criteria and the design method of the correctors is explained as well. Each modular controller can be dynamically removed or added to allow reconfiguration of the number of converter cells during operation for functional safety purposes. This method is illustrated with a five-cell CFBMC, studied both with simulations and experimental tests. The response of the system to load transients and cell voltage disturbances demonstrates the robustness of the proposed control method. Thanks to its modularity, the number of voltage levels of the converter can be easily increased by inserting new cells in series without adding complexity to the control part.
Hybrid Driving Training and Particle Swarm Optimization Algorithm-Based Optimal Control for Performance Improvement of Microgrids
Authors: Zaki Dina A. , Hasanien Hany M. , Alharbi Mohammed , Ullah Zia , Sameh Mariam A.
Published: May 2023

This paper discusses the importance of microgrids in power systems and introduces a new method for enhancing their performance by improving the transient voltage response in the face of disturbances. The method involves using a hybrid optimization approach that combines driving training-based and particle swarm optimization techniques (HDTPS). This hybrid approach is used to fine-tune the system’s cascaded control scheme parameters, based on proportional–integral–accelerator (PIA) and proportional–integral controllers. The optimization problem is formulated using a central composite response surface methodology (CCRSM) to create an objective function. To validate the suggested control methodology, PSCAD/EMTDC software is used to carry out the simulations. The simulations explore various scenarios wherein the microgrid is transformed into an islanded system and is subjected to various types of faults and load changes. A comparison was made between the two proposed optimized controllers. The simulation results demonstrate the effectiveness of using a PIA-optimized controller; it improved the microgrid performance and greatly enhanced the voltage profile. In addition, the two controllers’ gains were optimized using only PSO to ensure that the outcomes of the HDTPS model demonstrated the same results. Finally, a comparison was made between the two optimization techniques (HDTPS and PSO); the results show a better impact when using the HDTPS model for controller optimization.
Single- and Double-Sided Coated Gas Diffusion Layers Used in Polymer Electrolyte Fuel Cells: A Numerical Study
Authors: Okereke Isaac C. , Ismail Mohammed S. , Ingham Derek B. , Hughes Kevin , Ma Lin , Pourkashanian Mohamed
Published: May 2023

A new three-dimensional numerical model of a polymer electrolyte fuel cell (PEFC) with a single straight channel was developed to primarily investigate the important impact of the double-sided microporous layer (MPL) coating on the overall performance of the fuel cell and the distribution of the current and the oxygen concentration within the cathode gas diffusion layers (GDLs). Realistic experimentally estimated interfacial contact resistance values between the gas diffusion layer and each of the bipolar plates and the catalyst layer values were incorporated into the model, and parametric studies were performed. The results showed that the double-sided MPL coating could significantly improve the fuel cell performance by up to 30%. Additionally, it was shown that the neglect of the contact resistance between the MPL and the catalyst layer could overestimate the fuel cell performance by up to 6%. In addition, the results showed that the fuel cell performance and the distribution of the current and oxygen are more sensitive to the porosity of the MPL facing the bipolar plate than the porosity of the MPL facing the catalyst layer. All the above results are presented and critically discussed in detail.
Forest Carbon Density Estimation Using Tree Species Diversity and Stand Spatial Structure Indices
Authors: Li Tao , Wu Xiao-Can , Wu Yi , Li Ming-Yang
Published: May 2023

The forest spatial structure and diversity of tree species, as the important evaluation indicators of forest quality, are key factors affecting forest carbon storage. To analyze the impacts of biodiversity indices and stand spatial structure on forest carbon density, five tree diversity indices were calculated from three aspects of richness, diversity and evenness, and three indices (Reineke’s stand density index, Hegyi’s competition index and Simple mingling degree) were calculated from stand spatial structure. The relationships between these eight indices and forest carbon density were explored using the Structural Equation Model (SEM). Then, these eight indices were used as characteristic variables to predict the aboveground carbon density of trees (abbreviated as forest carbon density) in the sample plots of the National Forest Resources Continuous Inventory (NFCI) in Shaoguan City in 2017. Multiple Linear Regression (MLR) and four typical machine learning models of Random Forest (RF), Tree-based Piecewise Linear Model (M5P), Artificial Neural Network (ANN) and Support Vector Regression (SVR) were used to predict the forest carbon density. The results show that: (1) Based on the analysis results of the structural equation model (SED), the species diversity and forest stand spatial structure have greater impacts on carbon density. (2) The R2 of all the five prediction models is greater than 0.6, among which the random forest model is the highest. (3) Based on the calculation results of optimal model of RF, the mean forest carbon density of Shaoguan city in 2017 was 43.176 tC/ha. The forest carbon density can be accurately estimated based on the species diversity index and stand spatial structure with machine learning algorithms. Therefore, a new method for the prediction of forest carbon density and carbon storage using species diversity indices and stand spatial structure can be explored. By analyzing the impacts of different biodiversity indices and stand spatial structure on forest carbon density, a scientific reference for the making of management measures for increasing forest carbon sinks and reducing emissions can be provided.
Risk Assessments of Plant Leaf and Soil Mercury Pollution in Different Functional Areas of Changchun City
Authors: Pan Jiafang , Chen Ming , Zhang Zhe , Zhang Hongjie , Zong Jing , Wang Zhaojun , Zhang Gang
Published: May 2023

Mercury is a global pollutant that has attracted widespread attention due to its persistence, concealment, and ease of migration. As the main setting for human production and life, urban ecosystems play significant roles in the global mercury cycle. Urban vegetation also plays an important role in regional mercury cycles. In this study, several common tree species in the green vegetation of Changchun were used as research objects to examine the mercury pollution characteristics of leaf litter and the surrounding soil. In this investigation, 100 sampling sites were set up in Changchun City to collect leaf litter and the surrounding topsoil. An RA-915+ Hg analyzer was used to determine the total mercury concentration levels. The results showed that content levels of mercury in plants ranged between 0.0133 and 0.1945 mg/kg, and different species displayed varying levels of mercury accumulation. For example, the content of mercury in Pyrus ussuriensis Maxim. was found to be 0.0755 mg/kg higher than that in the other examined subjects. It was also determined that the plant mercury concentration levels were the highest in the older industrial zones, while the lowest mercury levels were found in the new economic development zones. Furthermore, the plant mercury levels of the roadside areas were higher due to vehicle pollution discharge. Regarding the mercury pollution levels of the surface-layer soil, the soil of old industrial zones’ surface layers had higher levels of mercury pollution than the other tested sites. However, no notable connection was observed between the mercury concentration levels of the vegetation and those of the soil. This study’s results revealed that the mercury pollution of plants in Changchun City is not severe. The mercury levels in the industrial zones were graded as moderate, and those in the other regions were graded as low.
Assessing the Effects of Different Harvesting Practices on the Forestry Sector’s Climate Benefits Potential: A Stand Level Theoretical Study in an Eastern Canadian Boreal Forest
Authors: Moreau Lucas , Thiffault Evelyne , Beauregard Robert
Published: May 2023

The contribution of the forest sector to climate change mitigation needs to rely on optimal strategies that include forest management, wood supply, wood product disposal, and replacement of GHG-intensive materials and energy sources. Our study aimed to assess the impact of alternative forest management practices applied at the stand scale on the capacity of the forest sector to reduce its radiative forcing, using the boreal forests of eastern Canada as a case study. We simulated management of a balsam fir–white birch stand over a sixty-year period and determined the sectorial carbon and radiative forcing budget for a reference scenario (no harvest) and for nineteen clearcut and partial cut alternatives. The results suggest that logging may not significantly reduce carbon emissions compared to a preservation practice and does not yield any climate benefits in terms of radiative forcing. In a context for which the substitution effect of wood products on markets is expected to be limited, the mitigation potential of a scenario is mostly driven by the capacity of the forest ecosystem carbon sink to compensate for the substantial CO2 and CH4 emissions from wood product decay in landfills. The improved assessment of carbon emission temporality, incorporation of ecosystem carbon dynamics, and improved consideration of substitution and the decay of wood products are essential in the development of any forest management strategy. Neglecting these elements can lead to misconceptions and prevent informed mitigation decisions.
Volatile Flavor Improvement and Spoilage Microorganism Inhibition in Low-Salt Fish Sauce (Yulu) by Salt-Tolerant Bacillus subtilis
Authors: Li Chunsheng , Li Laihao , Chen Shengjun , Zhao Yongqiang , Wu Yanyan
Published: May 2023

Use of low-salt fish sauce (Yulu) is limited due to its perishable property and rapid loss of unique flavor. In this study, a salt-tolerant strain, Bacillus subtilis B-2, with high protease production was used as a microbial starter for low-salt Yulu fermentation. A total of 133 volatile compounds were detected through HS-SPME-GC-MS. Most aldehydes, alcohols, ketones, furans, and hydrocarbons reached their maximum at 15 d, while most esters, aromatic compounds, acids, nitrogen compounds, and sulfur compounds peaked at 45 d. The 16S rRNA gene high-throughput sequencing showed that Bacillus remained in high abundance during fermentation, reaching 93.63% at 45 d. The characteristic volatile flavor was obviously improved while the microbial contamination was significantly reduced in low-salt Yulu fermented with B. subtilis, compared with those without a microbial starter. The correlation network map suggested that the significant decrease in Pseudomonas, Achromobacter, Stenotrophomonas, Cyanobium, Rhodococcus, Brucella, Tetragenococcus, and Chloroplast contributed most to the decreasing richness and evenness of the microbial community, while Bacillus was the only genus playing a key role in the inhibition of spoilage microorganisms and improvement of volatile flavor after B. subtilis addition. This study suggests the potential use of salt-tolerant B. subtilis as a special starter for industrial Yulu fermentation in the future.
Dichlororesorcinols Produced by a Rhizospheric Fungi of Panax notoginseng as Potential ERK2 Inhibitors
Authors: Wu Yingying , Zhang Mengyue , Xue Jinyan , Cheng Juan , Xia Mingyu , Zhou Xunyong , Zhang Yixuan
Published: May 2023

Rhizospheric fungi of medicinal plants are important sources for discovering novel and valuable secondary metabolites with potential pharmaceutical applications. In our research, five new dichlororesorcinols (1–5) and five known metabolites (6–10) were separated from the secondary metabolites of Chaetomium sp. SYP-F6997, which was isolated from the rhizospheric soil of Panax notoginseng. The identification of these compounds was confirmed using various spectroscopic techniques including ESI-MS, UV, IR, NMR and ECD analyses. These findings highlight the potential of rhizospheric fungi as a rich source of novel bioactive compounds. In addition, chiral HPLC was used to successfully separate the enantiomers compound 4 and compound 5, and TDDFT-ECD/optical rotation calculations were used to test their absolute configurations. This is the first report of compounds 1–10 from the genus Chaetomium, and the first report of compounds 1–5 and 7 from the family Chaetomiaceae. We proposed plausible biosynthetic pathways for dichlororesorcinols 1–6 based on their analogous carbon skeleton. These findings provide insights into the biosynthesis of these compounds and expand our understanding of the secondary metabolites produced by Chaetomium sp. SYP-F6997. To evaluate their potential as therapeutic agents, we investigated the cytotoxic activity of all the isolated metabolites against cell lines H9, HL-60, K562, THP-1 and CEM using the MTT method. The new compounds 1 and 2 exhibited significant cytotoxic activities against H9 and CEM, with IC50 values lower than 10 µM. To further explore the potential mechanisms of action, we performed molecular docking studies to investigate the interactions between compounds 1 and 2 with the potential target ERK2. Our results demonstrate that the compounds exhibited strong binding abilities and formed H-bond interactions with ERK2, providing support for their potent antitumor activities and promising potential as lead molecules for the development of antitumor therapeutics.
A New Approach for Quantitative Classification of Red Wine Color from the Perspective of Micro and Macro Levels
Authors: Fan Shuyue , Zhang Yu , Li Yunkui
Published: May 2023

The classification of color is important in the management and evaluation of red wine color. Nevertheless, current reports have not given a sound solution. A novel scheme for the quantitative classification of red wine color from the perspective of micro and macro levels was proposed in this work. The overall color performance of 119 representative red wine samples was visualized and classified into 125 micro-classifications according to the partition of the chromaticity and lightness of the wine color. Considering the evolution of red wine color, quantitative macro-classification of the color of the wine samples was explored, and six macro-classifications were obtained. These few classifications determine the extent of aging and aging potential of colors between different red wines well. The performance of this approach was further verified by another set of 56 red wine samples. This efficient and easy-to-implement approach is expected to be helpful in the digital and visual color management, control, evaluation, comparison, and prediction of red wines.
Zinc Tolerance of Special Yeasts and Lactic Acid Bacteria for Use in the Food Industry
Authors: Rossi Samantha , Maares Maria , Kieserling Helena , Rohn Sascha , Schlüter Oliver , Patrignani Francesca , Lanciotti Rosalba , Haase Hajo , Keil Claudia
Published: May 2023

In order to address human zinc deficiency, fortifying staple foods with zinc is a safe and cost-effective solution. To ensure the nutritional properties and quality of a final product, zinc tolerance of the microorganisms involved in the fermentation is necessary. Bread, which is widely consumed, occupies a substantial place in many people’s diets, and is often based on a sourdough making process; thus, it might be an important headlining product. This study investigated the zinc tolerance of yeasts and lactic acid bacteria that are specifically suited to produce sourdough bread made with cricket powder hydrolysate and wheat flour. Amongst the yeasts, Kazachstania servazzii KAZ2 and Kazachstania unispora FM2 were only slightly affected in regard to cell growth and colony-forming ability when cultured in YPD broth spiked with 0.5 or 1 mM ZnSO4, respectively. Yarrowia lipolytica RO25 showed a higher tolerance for up to 2.5 mM zinc (ZnSO4). All the yeast strains were capable of accumulating zinc in the range between 200 and 400 fg/cell. The heterofermentative lactic acid bacterium Fructilactobacillus sanfranciscensis DG1 appeared to have a moderate zinc requirement and was homeostatically balanced, even under a high 20 mM extracellular ZnSO4 load. A better understanding of zinc homeostasis in yeast and lactic acid bacteria for food industry applications may lead to improvements in zinc fortification, which might contribute to diminishing Zn deficiencies, especially in vulnerable population groups.
Enhancement of Bioactive Properties in Momordica charantia by Leuconostoc Fermentation
Authors: Kim Jiwoo , Yu Sungryul , Jeong Yoonhwa , Kim Misook
Published: May 2023

Momordica charantia (MC; commonly known as bitter melon) was fermented with Leuconostoc mesenteroides MKJW, MKSR, and KCTC 3719 (LM), and Leuconosoc citreum KCTC 3526 (LC), and their anti-diabetic, anti-dementia, and antioxidant activities were evaluated. The fermentation was performed for 24 h at 30 °C, and non-fermented MCs (CON1 and CON2) were included for comparison. All fermented MCs produced lactic acids, mannitol, dextran, and oligosaccharides. The highest amount of mannitol (34.76 mg/mL) and lactic acids (10.42 mg/mL) were produced in MKSR-MC, and the highest amount of dextran (22.37%) was produced in MKJW-MC. MKSR-MC showed complete α-glucosidase inhibition (99.91%), but it did not show a significant change in α-amylase inhibition (24.43%) compared to non-fermented MC (20.14%) (p > 0.05). It was also high in acetylcholinesterase inhibition (55.24%) compared to other fermented MCs (31.21–44.46%). Fermentation increased butyrylcholinesterase inhibition, but no significant differences were observed among the groups. Therefore, our results indicated that MKSR-fermented MC might be used as a non-dairy probiotic plant extract to achieve multi-health functional activities such as anti-diabetic, anti-dementia, and antioxidant activities.
Hydrolase Production via Food Waste Fermentation and Its Application to Enhance Anaerobic Digestion of Sewage Sludge
Authors: Zhang Xuedong , Wang Ganghui , Dong Jian , Chen Min , He Yanhua , Liu He , Li Yajie , Liu Hongbo
Published: May 2023

In the present study, a novel strategy for the effective production of hydrolase via fermentation of food waste was developed to improve methane production from anaerobic digestion of waste activated sludge (WAS). Via the pre-fermentation of food waste, hydrolase could be efficiently enriched and then directly used to enhance sludge hydrolysis with no need for extraction and purification of the enzymes. The results of this study indicate that the activities of the predominant hydrolase, mainly including protease and amylase enriched in the pre-fermented food waste, could reach 4861.10 U/g and 3909.14 mg/(mL·min), respectively. The elevated activities of hydrolases evidently enhanced sludge hydrolysis by more than 50% with the addition of 15 g fermented food waste per 200 g sludge. The released organic matter presented much better biodegradability, of which the BOD5/COD (B/C) increased from 0.33 of the raw WAS to 0.41 of the pretreated sludge. Moreover, methane production from sludge digestion was substantially improved and increased from 2140 mL to 7187 mL by adding 30 g fermented food waste into 200 g sludge, about 24.3% of which was contributed by the addition of the enriched hydrolase. The preliminary economic assessments of this present study indicate the net profit of sludge digestion of 7.99 USD/m3 sludge is likely to be harvested via applying this strategy. Furthermore, the results in this present study provide another innovative route to further optimize the conventional co-digestion process of WAS.