bluuebunny/crossref_metadata_2025 huggingface dataset:
Control and Stability Analysis of the LCL-Type Grid-Connected Converter without Phase-Locked Loop under Weak Grid Conditions
Authors: Xie Lingling , Zeng Sencai , Liu Jinbao , Zhang Zhao , Yao Junyi
Published: October 2022

The stability and dynamic performance of the grid-connected converter is greatly affected by the coupling between the phase-locked loop (PLL) and the current loop control under weak grid conditions. The traditional control strategies use PLL to obtain the frequency and phase of the grid, which ignore the influence of the PLL and cannot adapt to weak grid conditions. To address this problem, the control and stability of the LCL-Type grid connected converter without PLL under weak grid conditions are studied in-depth in this paper. Firstly, the digital controlled model of the LCL-Type grid-connected converter with capacitor-current feedback active damping is established, and the stability of the system is analyzed. Then, a control strategy without PLL is proposed. The proposed strategy decomposes the grid voltage signal into instantaneous active and instantaneous reactive components through the αβ frame, which can realize the independent control of active and reactive current by simple calculation. The obtained results show that the strategy avoids the influence of the PLL on the inner loop current, and has the advantages of strong stability and anti-interference ability under weak grid conditions. Finally, simulation and experiment results are provided to verify the validity of theoretical analysis.
Fast 3D Liver Segmentation Using a Trained Deep Chan-Vese Model
Authors: Akal Orhan , Barbu Adrian
Published: October 2022

This paper introduces an approach for 3D organ segmentation that generalizes in multiple ways the Chan-Vese level set method. Chan-Vese is a segmentation method that simultaneously evolves a level set while fitting locally constant intensity models for the interior and exterior regions. First, its simple length-based regularization is replaced with a learned shape model based on a Fully Convolutional Network (FCN). We show how to train the FCN and introduce data augmentation methods to avoid overfitting. Second, two 3D variants of the method are introduced, one based on a 3D U-Net that makes global shape modifications and one based on a 3D FCN that makes local refinements. These two variants are integrated in a full 3D organ segmentation approach that is capable and efficient in dealing with the large size of the 3D volumes with minimal overfitting. Experiments on liver segmentation on a standard benchmark dataset show that the method obtains 3D segmentation results competitive with the state of the art while being very fast and having a small number of trainable parameters.
Hybrid Random Forest Survival Model to Predict Customer Membership Dropout
Authors: Sobreiro Pedro , Garcia-Alonso José , Martinho Domingos , Berrocal Javier
Published: October 2022

Dropout prediction is a problem that must be addressed in various organizations, as retaining customers is generally more profitable than attracting them. Existing approaches address the problem considering a dependent variable representing dropout or non-dropout, without considering the dynamic perspetive that the dropout risk changes over time. To solve this problem, we explore the use of random survival forests combined with clusters, in order to evaluate whether the prediction performance improves. The model performance was determined using the concordance probability, Brier Score and the error in the prediction considering 5200 customers of a Health Club. Our results show that the prediction performance in the survival models increased substantially in the models using clusters rather than that without clusters, with a statistically significant difference between the models. The model using a hybrid approach improved the accuracy of the survival model, providing support to develop countermeasures considering the period in which dropout is likely to occur.
Cybersecurity Threats, Countermeasures and Mitigation Techniques on the IoT: Future Research Directions
Authors: Altulaihan Esra , Almaiah Mohammed Amin , Aljughaiman Ahmed
Published: October 2022

The Internet of Things (IoT) interconnects physical and virtual objects embedded with sensors, software, and other technologies, which exchange data using the Internet. This technology allows billions of devices and people to communicate, share data, and personalize services to make our lives easier. Despite the multiple benefits offered by IoT, it may also represent a critical issue due its lack of information security. Since the number of IoT devices has been rapidly increasing all over the world, they have become a target for many attackers, who try to steal sensitive information and compromise people’s privacy. As part of the IoT environment, data and services should be protected with features such as confidentiality, accuracy, comprehensiveness, authentication, access control, availability, and privacy. Cybersecurity threats are unique to the Internet of Things, which has unique characteristics and limitations. In consideration of this, a variety of threats and attacks are being launched daily against IoT. Therefore, it is important to identify these types of threats and find solutions to mitigate their risks. Therefore, in this paper, we reviewed and identified the most common threats in the IoT environment, and we classified these threats based on three layers of IoT architecture. In addition, we discussed the most common countermeasures to control the IoT threats and mitigation techniques that can be used to mitigate these threats by reviewing the related publications, as well as analyzing the popular application-layer protocols employed in IoT environments and their security risks and challenges.
A New Sparse Optimal Array Design Based on Extended Nested Model for High-Resolution DOA Estimation
Authors: Wang Shujian , Ren Shiwei , Li Xiangnan , Wang Guiyu , Wang Weijiang
Published: October 2022

In this paper, we propose a new strategy with increased element spacings placed on two-side subarrays and increase the physical sparse sensors array aperture. In order to ensure the continuity of difference co-array, four conditions are proposed. Based on these conditions, a new array structure named TS2-ENA is derived. Compared with other nested-like arrays with a fixed number of elements, our proposed structure possesses a higher degree of freedom, closed-form expression and outstanding performance. Simulation results show the superiority of the new configuration with the spacial smoothing MUSIC algorithm conducted.
Drug Adverse Event Detection Using Text-Based Convolutional Neural Networks (TextCNN) Technique
Authors: Rawat Ashish , Wani Mudasir Ahmad , ElAffendi Mohammed , Imran Ali Shariq , Kastrati Zenun , Daudpota Sher Muhammad
Published: October 2022

With the rapid advancement in healthcare, there has been exponential growth in the healthcare records stored in large databases to help researchers, clinicians, and medical practitioner’s for optimal patient care, research, and trials. Since these studies and records are lengthy and time consuming for clinicians and medical practitioners, there is a demand for new, fast, and intelligent medical information retrieval methods. The present study is a part of the project which aims to design an intelligent medical information retrieval and summarization system. The whole system comprises three main modules, namely adverse drug event classification (ADEC), medical named entity recognition (MNER), and multi-model text summarization (MMTS). In the current study, we are presenting the design of the ADEC module for classification tasks, where basic machine learning (ML) and deep learning (DL) techniques, such as logistic regression (LR), decision tree (DT), and text-based convolutional neural network (TextCNN) are employed. In order to perform the extraction of features from the text data, TF-IDF and Word2Vec models are employed. To achieve the best performance of the overall system for efficient information retrieval and summarization, an ensemble strategy is employed, where predictions of the selected base models are integrated to boost the robustness of one model. The performance results of all the models are recorded as promising. TextCNN, with an accuracy of 89%, performs better than the conventional machine learning approaches, i.e., LR and DT with accuracies of 85% and 77%, respectively. Furthermore, the proposed TextCNN outperforms the existing adverse drug event classification approaches, achieving precision, recall, and an F1 score of 87%, 91%, and 89%, respectively.
A Capacity Optimization Method for a Hybrid Energy Storage Microgrid System Based on an Augmented ε- Constraint Method
Authors: Zhong Xianjing , Sun Xianbo , Wu Yuhan
Published: October 2022

In general, microgrids have a high renewable energy abandonment rate and high grid construction and operation costs. To improve the microgrid renewable energy utilization rate, the economic advantages, and environmental safety of power grid operation, we propose a hybrid energy storage capacity optimization method for a wind–solar–diesel grid-connected microgrid system, based on an augmented ε- constraint method. First, the battery is coupled with a seasonal hydrogen energy storage system to establish a hybrid energy storage model that avoids the shortcomings of traditional microgrid systems, such as a single energy storage mode and a small capacity. Second, by considering the comprehensive cost and carbon emissions of the power grid within the planning period as the objective function, the abandonment rate of renewable energy as the evaluation index, and the electric energy storage and seasonal hydrogen energy storage system operating conditions as the main constraints, the capacity allocation model of the microgrid can be constructed. Finally, an augmented ε- constraint method is implemented to optimize the model above; the entropy–TOPSIS method is used to select the configuration scheme. By comparative analysis, the results show that the optimization method can effectively improve the local absorption rate of wind and solar radiation, and significantly reduce the carbon emissions of microgrids.
Laboratory Research on Hydraulic Losses on SHP Inlet Channel Trash Racks
Authors: Walczak Natalia , Walczak Zbigniew , Tymiński Tomasz
Published: October 2022

There is currently a growing trend towards renewable energy sources, which are characterised by a guaranteed power supply and low failure rate. Hydropower plants (small or large) are an example of such a source. They supply a total of 16% of the world’s electricity. The advantages of a small hydropower plant include the relatively simple construction process and the lack of need for upstream water storage. SHPs are one of the most cost-effective and environmentally friendly energy technologies, which is why they are steadily increasing in popularity. One of the important components of SHPs are the trash racks in the inlet channels. Their main purpose is to catch debris and other elements carried downstream and to prevent these pollutants from reaching the turbine units. They can also protect migrating ichthyofauna such as larger fish. If trash racks are installed in the inlet channel, hydraulic losses are to be expected due to the reduction in the flow cross-section through the racks (bars) themselves and through the accumulation of debris and various types of trash on these racks. Energy losses on the trash racks affect the financial aspect of SHP investments. This paper presents the results of laboratory tests on trash racks for SHPs by taking into account the different shapes of the bars used, their number and spacing, and the angles of the trash racks to estimate the hydraulic losses on the trash racks. The measured values of hydraulic losses Δh on the trash racks varied according to the type of trash racks, the density of the bars in the cross-section, and the angle of the trash racks from the horizontal, reaching the highest values on the trash racks with angle bars (AB). They were almost eight times greater than those recorded on cylindrical-bar (CB) trash racks, although they involved different angles. It was shown that the discrepancy in the magnitude of losses on trash racks can be large, even for the same type of trash racks. It depends significantly on the design (shape and bar spacing) of the trash racks and the way the trash racks are installed. Depending on the inclination angle, the increase in energy losses reached 70% for angle bars, 60% for flat-bar trash racks, and almost 40% for cylindrical bars. The values of energy loss as well as the loss coefficient β varied non-linearly for the different bar types depending on the angle of inclination of the gratings, and the degree of this non-linearity depended on the type of bars and the blockage ratio of the section. The presented research results can be useful both during the design and the operation of an SHP.
A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units
Authors: El-Sehiemy Ragab , Shaheen Abdullah , Ginidi Ahmed , Elhosseini Mostafa
Published: October 2022

Traditionally, the Economic Dispatch Model (EDM) integrating Combined Heat and Power (CHP) units aims to reduce fuel costs by managing power-only, CHP, and heat-only units. Today, reducing pollutant emissions to the environment is of paramount concern. This research presents a novel honey badger optimization algorithm (HBOA) for EDM-integrated CHP units. HBOA is a novel meta-heuristic search strategy inspired by the honey badger’s sophisticated hunting behavior. In HBOA, the dynamic searching activity of the honey badger, which includes digging and honing, is separated into exploration and exploitation phases. In addition, several modern meta-heuristic optimization algorithms are employed, which are the African Vultures Algorithm (AVO), Dwarf Mongoose Optimization Algorithm (DMOA), Coot Optimization Algorithm (COA), and Beluga Whale Optimization Algorithm (BWOA). These algorithms are applied in a comparative manner considering the seven-unit test system. Various loading levels are considered with different power and heat loading. Four cases are investigated for each loading level, which differ based on the objective task and the consideration of power losses. Moreover, considering the pollutant emissions minimization objective, the proposed HBOA achieves reductions, without loss considerations, of 75.32%, 26.053%, and 87.233% for the three loading levels, respectively, compared to the initial case. Moreover, considering minimizing pollutant emissions, the suggested HBOA achieves decreases of 75.32%, 26.053%, and 87.233%, relative to the baseline scenario, for the three loading levels, respectively. Similarly, it performs reductions of 73.841%, 26.155%, and 92.595%, respectively, for the three loading levels compared to the baseline situation when power losses are considered. Consequently, the recommended HBOA surpasses the AVO, DMOA, COA, and BWOA when the purpose is to minimize fuel expenditures. In addition, the proposed HBOA significantly reduces pollutant emissions compared to the baseline scenario.
Scrutinizing the Intermittency of Renewable Energy in a Long-Term Planning Model via Combining Direct Integration and Soft-Linking Methods for Colombia’s Power System
Authors: Younis Ahmed , Benders René , Ramírez Jezabel , de Wolf Merlijn , Faaij André
Published: October 2022

The large-scale integration of variable renewable energy sources into the energy system presents techno–economic challenges. Long–term energy system optimization models fail to adequately capture these challenges because of the low temporal resolution of these tools. This limitation has often been addressed either by direct improvements within the long–term models or by coupling them to higher resolution power system models. In this study, a combined approach is proposed to capitalize on the advantages and overcome the disadvantages of both methods. First, the temporal resolution of an energy model was enhanced by approximating the joint probability of the electricity load and the supply of intermittent sources. Second, the projected electricity mix was simulated by a power model at an hourly resolution. This framework was used to analyze mid–century deep decarbonization trajectories for Colombia, subject to future uncertainties of hydroclimatic variability and the development of the bioeconomy. The direct integration method is found to consistently reduce the overestimation of the feasible penetration of VRES. However, its impact is marginal because of its inability to assess the short–term operation of the power system in detail. When combined with the soft–linking method, the reliable operation of the power system is shown to incur an additional overhead of 12–17% investment in flexible generation capacity, 2–5% of the annual energy system cost, and a 15–27% shortfall in achieving the aspired GHG mitigation target. The results obtained by combining both methods are found to be closer to the global optimum solution than using either of these methods individually.
Design and Fabrication of a Robust Chitosan/Polyvinyl Alcohol-Based Humidity Sensor energized by a Piezoelectric Generator
Authors: Zeeshan  , Soomro Afaque Manzoor , Cho Sungbo
Published: October 2022

Due to their rapid growth in industrial and environmental applications, there is a need to develop self-powered humidity sensor systems with improved sensitivity, a wide detection range, and an eco-friendly nature. In this study, an aqueous solution of chitosan (CS) and polyvinyl alcohol (PVA) was blended to yield a composite film material with enhanced humidity detection properties. Meanwhile, a polyvinylidene difluoride (PVDF)-loaded chitosan composite film was developed and employed as a piezoelectric generator. Moreover, the developed composite materials for both devices (the piezoelectric generator and the humidity sensor) were optimized based on output performance. The piezoelectric generator generates a maximum of 16.2 V when a force of 10 N is applied and works as a power source for the humidity-sensing film. The sensing film swells in response to changes in relative humidity, which affects film resistance. This change in resistance causes a change in voltage through the piezoelectric generator and allows the precise measurement of relative humidity (RH). The fabricated sensor showed a linear response (R2 = 0.981) with a reasonable sensitivity (0.23 V/% RH) in an environment with an RH range of 21–89%. In addition, the device requires no external power, and therefore, it has numerous sensing applications in various fields.
Towards Informed Policy Making: An Analysis of the Impact of COVID-19 on Electricity Purchases in South Africa
Authors: Ritchie Michael Jon , Burger Jacobus Wilhelm , Naidoo Dhasaren , Booysen Marthinus Johannes
Published: October 2022

The COVID-19 pandemic created large disruptions in the energy industry as governments all around the world responded by enforcing lockdowns on 54% of the global population to slow down the spread of the virus. This impacts the energy consumption of the residential sector as policies prevented residents from leaving their homes and accelerated the transition towards remote work environments. The impact of lockdown on South Africa’s energy sector is unique to other countries due to its extreme inequalities across the affluence divide and ongoing blackouts that affect the entire country. A case study is performed in George, a region in South Africa with a distribution of wealth and energy inequality typical for South Africa. In this paper, the following questions are answered: (i) How has the lockdown period influenced the total energy consumption of a household? and (ii) To what extent has the lockdown affected energy consumption of households based on wealth and demographics? The results of this study showed that implementing the highest stage of lockdown resulted in wealthy communities increasing their overall energy consumption by 5 percentage points and poor communities by 2.5 percentage points. The findings of this study support the use of local data and insight for the unique difference in behavioural change across different communities for optimal decision making in future pandemics or natural disasters.
Renovation Results of Finnish Single-Family Renovation Subsidies: Oil Boiler Replacement with Heat Pumps
Authors: Sankelo Paula , Ahmed Kaiser , Mikola Alo , Kurnitski Jarek
Published: October 2022

Finland has approximately 150,000 oil-heated private homes. In 2020, the Finnish government launched subsidies for private homeowner energy renovations. In this study, we examine the impact of two new energy renovation subsidies, the ELY grant and the ARA grant, from an energy efficiency point of view. Data from these subsidies reveal that a typical energy renovation case is a building from the 1970s where the oil boiler is replaced with an air-to-water heat pump. With additional data from the Finnish Energy certificate registry, a reference 1970s house is constructed and modelled in the building simulation programme, IDA ICE 4.8. Combinations of several renovation measures are simulated: air-to-water heat pump, ground-source heat pump, ventilation heat recovery and improved insulation. We found that resorting mainly to air-to-water heat pumps is not the most energy-effective solution. Ground-source heat pumps deliver a more significant reduction in delivered energy, especially with additional measures on insulation and heat recovery. Ground-source heat pumps also demand slightly less power than air-to-water heat pumps. Onsite solar PV generation helps supplement part of the power needed for heat pump solutions. Subsidy policies should emphasize deep renovation, ventilation heat recovery and onsite electricity generation.
Blockchain-Based Hardware-in-the-Loop Simulation of a Decentralized Controller for Local Energy Communities
Authors: Galici Marco , Mureddu Mario , Ghiani Emilio , Pilo Fabrizio
Published: October 2022

The development of local energy communities observed in the last years requires the reorganization of energy consumption and production. In these newly considered energy systems, the commercial and technical decision processes should be decentralized in order to reduce their maintenance costs. This will be allowed by the progressive spreading of IoT systems capable of interacting with distributed energy resources, giving local sources the ability to be optimally coordinated in terms of network and energy management. In this context, this paper presents a decentralized controlling architecture that performs a wide spectrum of power system optimization procedures oriented to the local market management. The controller framework is based on a decentralized genetic algorithm. The manuscript describes the structure of the tool and its validation, considering an automated distributed resource scheduling for local energy markets. The simulation platform permits implementing the blockchain-based trading process and the automated distributed resource scheduling. The effectiveness of the tool proposed is discussed with a hardware-in-the-loop case study.
Design Studies of VSC HVDC Converter According to AC Voltage Tests
Authors: Haliloglu Ali Burhan , Iskender Ires
Published: October 2022

Since high-voltage direct current (HVDC) systems are very expensive and operationally critical, these systems must be tested before they are put into service. Insulation and performance tests are the two main subjects of these tests. AC voltage tests, as part of the insulation tests, should be performed after system installation is complete and before commissioning. However, in this study, the objective was to perform these tests during the prototype phase of VSC HVDC. Unlike other studies, this study attempted to use COMSOL Multiphysics to determine in advance the problems that may occur in the real system. In this regard, the busbars connecting the submodules of the VSC HVDC system were first modeled in 3D, and the tests to be performed were simulated using COMSOL Multiphysics software. During the simulation, the finite element method (FEM) was used to identify critical points that could cause partial discharge. To validate the simulation results, partial discharge tests on a real system were conducted, and the design changes made in response to each test result were explained. After the improvement actions, the targeted partial discharge values were achieved.
A Novel Model for Wind Turbines on Trains
Authors: Hyman Mario , Ali Mohd Hasan
Published: October 2022

Wind turbines that are consistently exposed to the air displaced by moving trains have a high potential for energy generation. Researchers have developed mathematical models to simulate wind energy generation from turbines on moving trains but there are significant gaps in the developed model theory. Most models do not consider the negative effects that additional aerodynamic drag, increased weight, and modified dimensions can have on the train’s operation. To overcome the drawbacks of existing models, this work proposes a novel approach of modeling the wind turbines on trains by considering wind turbine exposure only when the train is decelerating or stationary. There are no models that consider all of these realistic physical effects as a function of time. Real-time analysis and power-system simulations showed that the proposed model could produce over 3 MJ of net energy for favorable train trips. The simulated load profile met the demand of a 1 KW generator connected to onboard electrical components. Some recommendations on possible future research on wind turbines on trains are explained.
Effects of Grain Size and Layer Thickness on the Physical and Mechanical Properties of 3D-Printed Rock Analogs
Authors: Wang Yao , Li Shengjun , Song Rui , Liu Jianjun , Ye Min , Peng Shiqi , Deng Yongjun
Published: October 2022

Due to the complexity of the sedimentary and diagenetic processes, natural rocks generally exhibit strong heterogeneity in mineral composition, physicochemical properties, and pore structure. Currently, 3D printed (3DP) rock analogs fabricated from sandy materials (silica sand) are widely applied to study the petrophysical and geomechanical characteristics of reservoir rocks, which provides an alternative and novel approach for laboratory tests to calibrate the environmental uncertainties, resolve up-scaling issues, and manufacture customized rock specimens with consistent structure and controllable petrophysical properties in a repeatable fashion. In this paper, silica sand with various grain sizes (GS) and Furan resin were used to fabricate rock analogs with different layer thicknesses (LTs) using the binder-jetting 3DP technique. A comprehensive experimental study was conducted on 3DP rock analogs, including helium porosity measurement, micro-CT scanning, SEM, and uniaxial compression. The results indicate that the LT and GS have a great influence on the physical properties, compression strength, and failure behavior of 3DP rock analogs. The porosity decreases (the difference is 7.09%) with the decrease in the LT, while the density and peak strength increase (showing a difference of 0.12 g/cm3 and 5.67 MPa). The specimens printed at the 200 and 300 μm LT mainly experience tensile shear destruction with brittle failure characteristics. The ductility of the 3DP rocks increases with the printing LT. The higher the content of the coarse grain (CG), the larger the density and the lower the porosity of the specimens (showing a difference of 0.16 g/cm3 and 8.8%). The largest peak compression strength with a mean value of 8.53 MPa was recorded in the specimens printed with CG (i.e., 100% CG), and the peak strength experiences a decrease with the increment in the content percentage of the fine grain (FG) (showing a difference of 2.01 MPa). The presented work helps to clarify the controlling factors of the printing process and materials characteristics on the physical and mechanical properties of the 3DP rock analogs, and allows for providing customizable rock analogs with more controllable properties and printing schemes for laboratory tests.
The D2-Law of Droplet Evaporation When Calculating the Droplet Evaporation Process of Liquid Containing Solid State Catalyst Particles
Authors: Ochowiak Marek , Bielecki Zdzisław , Bielecki Michał , Włodarczak Sylwia , Krupińska Andżelika , Matuszak Magdalena , Choiński Dariusz , Lewtak Robert , Pavlenko Ivan
Published: October 2022

The review presents the D2-law of droplet evaporation, which is used to describe the spraying process involving the evaporation of droplets. This law, the subject of numerous publications, can be successfully applied to describe the droplet evaporation process under various conditions, including the calculations of the process of feeding the boiler with a liquid that contains catalyst particles. To date, not a lot of work has been devoted to this issue. The paper is a continuation of previous research concerning the spraying of liquids with a catalyst, which improves the efficiency of the process. The conducted analysis showed that the experimental data from previously published work are very compatible with the data obtained from the D2-law of droplet evaporation. At the standard speed of about 20 m/s of an aerosol flowing through a dust duct, droplets in the stream should be observed up to a distance of 1 m from the outlet of the apparatus supplying the system. Under such flow conditions, a droplet’s lifetime must be above 0.05 s. The dependence between a droplet’s lifetime and its diameter and temperature was determined. The obtained results confirmed that the effective droplet diameter is above 30 µm. Such droplets must be generated and then fed to the boiler for the catalyst to work properly. This law is an engineering approach to the problem, which uses relatively simple model equations in order to determine the evaporation time of a droplet.
Evaluation of Distributed Generation and Electric Vehicles Hosting Capacity in Islanded DC Grids Considering EV Uncertainty
Authors: Zuluaga-Ríos Carlos D. , Villa-Jaramillo Alejandro , Saldarriaga-Zuluaga Sergio D.
Published: October 2022

Current power systems are undergoing an energy transition, where technological elements such as distributed generation and electric vehicles through AC or DC microgrids are important elements to face this transition. This paper presents a methodology for quantifying distributed resource-based generation and the number of electric vehicles that can be connected to isolated DC grids without impacting the safe operation of these networks. The methodology evaluates the maximum capacity of distributed generation considering the uncertainty present in the electric vehicle charging of fleets composed of five types of electric vehicles. Specifically, the uncertainty is associated with the following variables: the home arrival time, home departure time, traveled distance, and battery efficiency. The methodology was applied to a 21-bus DC microgrid and a 33-bus DC network under different test conditions. The results show that higher penetrations of EVs and distributed resource-based generation can be introduced while guaranteeing a secure operation of the DC networks.
Machine Learning-Based Species Classification Methods Using DART-TOF-MS Data for Five Coniferous Wood Species
Authors: Park Geonha , Lee Yun-Gyo , Yoon Ye-Seul , Ahn Ji-Young , Lee Jei-Wan , Jang Young-Pyo
Published: October 2022

Various problems worldwide are caused by illegal production and distribution of timber, such as deception about timber species and origin and illegal logging. Numerous studies on wood tracking are being conducted around the world to demonstrate the legitimacy of timber. Tree species identification is the most basic element of wood tracking research because the quality of wood varies greatly from species to species and is consistent with the botanical origin of commercially distributed wood. Although many recent studies have combined machine learning-based classification methods with various analytical methods to identify tree species, it is unclear which classification model is most effective. The purpose of this work is to examine and compare the performance of three supervised machine learning classification models, support vector machine (SVM), random forest (RF), and artificial neural network (ANN), in identifying five conifer species and propose an optimal model. Using direct analysis in real-time ionization combined with time-of-flight mass spectrometry (DART-TOF-MS), metabolic fingerprints of 250 individual specimens representing five species were collected three times. When the machine learning models were applied to classify the wood species, ANN outperformed SVM and RF. All three models showed 100% prediction accuracy for genus classification. For species classification, the ANN model had the highest prediction accuracy of 98.22%. The RF model had an accuracy of 94.22%, and the SVM had the lowest accuracy of 92.89%. These findings demonstrate the practicality of authenticating wood species by combining DART-TOF-MS with machine learning, and they indicate that ANN is the best model for wood species identification.
Nutrient Resorption and C:N:P Stoichiometry Responses of a Pinus massoniana Plantation to Various Thinning Intensities in Southern China
Authors: Jiang Jun , Lu Yuanchang , Chen Beibei , Ming Angang , Pang Lifeng
Published: October 2022

Understanding the responses of C:N:P stoichiometry and nutrient resorption to thinning is essential to evaluate the effects of management practices on biogeochemical cycling in plantation forest ecosystems. However, nutrient resorption and C:N:P stoichiometry do not always respond in the same way to various thinning intensities, and the underlying mechanisms are not well understood. In this study, we aimed to examine the mechanisms underlying the impacts of thinning on C:N:P stoichiometry in a Pinus massoniana plantation, focusing on interactions among soils, plant tissues (leaves and litter), and soil properties. We conducted four different thinning treatments to determine the effects of thinning on the C:N:P stoichiometric ratios in leaves, litter, and soil in a Pinus massoniana plantation ecosystem. Thinning significantly increased the C, N, and P content of leaves, litter, and soil (p < 0.05). The effects of thinning on C:N:P stoichiometry varied strongly with thinning intensity. Specifically, thinning significantly decreased all C:N:P stoichiometry except leaf N:P and litter C:N (p < 0.05). The N resorption efficiency (NRE) showed no significant change, but thinning significantly decreased the P resorption efficiency (PRE, p < 0.05). This suggests that thinning has inconsistent impacts on N and P cycling in Pinus massoniana plantations. In addition, these different responses suggest that soil physicochemical processes play a crucial role in regulating the effects of thinning. Thinning intensity regulates the biogeochemical cycles of C, N, and P in Pinus massoniana plantation ecosystems by affecting nutrient resorption and soil physicochemical processes. The inconsistent results obtained can be attributed to the complexities of stand environments and the redistribution of site resources following thinning. Therefore, incorporating the effects of thinning intensity into nutrient cycling models may improve predictions related to achieving long-term forest management strategies.
A Comparative Study on the Anatomical Characteristics of Acacia mangium and Acacia hybrid Grown in Vietnam
Authors: Savero Alvin Muhammad , Kim Jong-Ho , Purusatama Byantara Darsan , Prasetia Denni , Park Se-Hwi , Kim Nam-Hun
Published: October 2022

This study aimed to investigate and compare the qualitative and quantitative anatomical characteristics of Acacia mangium and Acacia hybrid in plantation forests in Vietnam. The qualitative and quantitative anatomical characteristics were evaluated according to the International Association of Wood Anatomists list of microscopic features for hardwood identification. In terms of qualitative features, A. mangium had a rough, hard, and fissured bark surface with a golden brown to dark brown color, whereas A. hybrid had a smooth and slightly scaly bark surface with greenish brown to greyish brown color, and yellowish white to pale white spots. Moreover, the heartwood–sapwood color and microscopic features of both species were identical. In terms of quantitative features, A. mangium had higher values in the tangential diameter of vessel lumina, vessel per square millimeter, ray number per millimeter, fiber length, and fiber wall thickness than A. hybrid, whereas A. hybrid had a higher ray height and lumen diameter than A. mangium. Thus, it is suggested that bark morphology and some quantitative anatomical characteristics can be used for identification and evaluation of wood quality between A. mangium and A. hybrid growing in Vietnam.
Vegetation Restoration with Mixed N2-Fixer Tree Species Alleviates Microbial C and N Limitation in Surface Soil Aggregates in South Subtropical Karst Area, China
Authors: Su Xiaoyan , Gao Guannv , Huang Xueman , Wang Yi , Zhang Wen , Yan Jinliu , Shen Weijun , You Yeming
Published: October 2022

Soil extracellular enzyme stoichiometry (EES) is the essential predictor in nutrient status and resource limitation of soil microorganisms, whose metabolism has a vital role in biogeochemical cycling and ecosystem function. However, little is known about how N2-fixer tree species with different planting patterns affect soil nutrient resources in terms of extracellular enzyme activity (EEA) or EES within aggregates in degraded karst ecosystems. In this study, we evaluated soil EEA and EES related to carbon (C), nitrogen (N), and phosphorus (P) cycles across two eight-year-old pure plantations of legume species [Dalbergia odorifera T. Chen (PD) and Acrocarpus fraxinifolius Wight ex Arn. (PA)] and a mixed plantation of the two tree species listed above (MP). Meanwhile, a nearby undisturbed shrubland was used as a control (CK). We concluded that the activities of C-, N-, and P-acquiring enzyme increased to different degrees in the N2-fixer tree species stands (particularly in MP) compared to CK in all aggregates. Compared to CK, MP significantly increased by 39.0%, 54.0%, 39.3%, and 24.8% in total C-acquiring EEA, 41.1%, 60.5%, 47.8%, and 12.5% in total N-acquiring EEA, and 100.4%, 79.7%, 69.2%, and 56.4% in total P-acquiring EEA within >2 mm, 1–2 mm, 0.25–1 mm, and <0.25 mm aggregates, respectively. Furthermore, the logarithmic transformed ratio of C-, N-, and P-acquiring enzyme activities was 1.20:1.08:1, which deviated from the global ratio (1:1:1). Vector analysis of EEA showed that the vector length (VL) within aggregates was significantly lower than that of CK in all stands of N2-fixer species except PD; while in all treatments, vector angle (VA) was <45° for all aggregate sizes, except in MP, where VA reached 45° for <0.25 mm aggregate. These indicated soil microbes were limited by C and N together. However, MP significantly alleviated microbial C and N limitation than CK (p < 0.05). There were obvious positive relationships between enzyme C:N, C:P, and N:P ratios. VL was markedly negatively linked to VA. EES was markedly related to most soil nutrients and microbial biomass stoichiometry ratios. Changes in soil EEA and EES were primarily driven by available phosphorus (AP), microbial biomass carbon (MBC), soil C:N and MBN:MBP ratios. Together, our results demonstrate the influences after introducing N2-fixer tree species (particularly MP) for vegetation recovery on soil microbial nutrient limitation and ecological processes in aggregate level and will contribute to the development of ecological restoration practices and fertility management in degraded karst ecosystems of southwest China.
Cultivation of Inonotus hispidus in Stirred Tank and Wave Bag Bioreactors to Produce the Natural Colorant Hispidin
Authors: Bergmann Pia , Takenberg Meike , Frank Christina , Zschätzsch Marlen , Werner Anett , Berger Ralf G. , Ersoy Franziska
Published: October 2022

Hispidin (6-(3,4-dihydroxystyrl)-4-hydroxy-2-pyrone) production in submerged cultured mycelia of the basidiomycete Inonotus hispidus was doubled in shake flasks through irradiation with white light. The daily addition of 1 mM hydrogen peroxide as a chemical stressor and a repeated supplementation of the shake flask cultures with 2 mM caffeic acid, a biogenetic precursor, further increased the hispidin synthesis. These cultivation conditions were combined and applied to parallel fermentation trials on the 4 L scale using a classical stirred tank bioreactor and a wave bag bioreactor. No significant differences in biomass yield and colorant production were observed. The hispidin concentration in both bioreactors reached 5.5 g·L−1, the highest ever published. Textile dyeing with hispidin was successful, but impeded by its limited light stability in comparison to industrial dyes. However, following the idea of sustainability and the flawless toxicity profile, applications in natural cosmetics, other daily implements, or even therapeutics appear promising.
Bio-Electrochemical Performance of a Ceramic Microbial Fuel Cell Treating Kitchen Waste Leachate: Effect of Organic Loading Rate and Anode Electrode Surface Area
Authors: Gurjar Rishi , Behera Manaswini
Published: October 2022

Performance evaluation of a ceramic microbial fuel cell (CMFC) by varying organic strength, hydraulic retention time (HRT) and anode electrode surface area (AESA) to treat leachate generated from acidogenesis of kitchen waste (KW) was studied by the central composite design of experiment. The increase in organic loading rate (OLR) positively affected power density (PD) while negatively influencing organic removal and coulombic efficiency (CE). This behavior is possible due to substrate inhibition and the coercive effect of low HRT, i.e., substrate washout, biofilm abrasion, and reduced contact period, while at high HRT, the volatile fatty acid (VFA) degradation improved. Since acetic acid is the final product of long-chain VFAs degradation, a pseudo consumption order for VFAs was obtained: butyric > propionic > acetic. The AESA aided organics removal and PD but had a negligible effect on CE. According to ANOVA, the COD removal was linearly modeled, while PD and CE were quadratic. The validation runs (VR) proved efficient as the highest COD removal was for VR2 (83.7 ± 3.6%), while maximum PD and CE values obtained were 0.224 ± 0.02 W/m3 and 2.62 ± 0.33%, respectively, for VR3, supported by the lower anode potential.
Study of the Organization and Implementation of E-Learning in Wartime Inside Ukraine
Authors: Matviichuk Liudmyla , Ferilli Stefano , Hnedko Nataliia
Published: October 2022

The article provides a factual foundation for the possibility of organizing and implementing e-learning in Ukrainian higher educational institutions during the war. The current research topicality is supported by the urgent need for training experience, organization and implementation during wartime because of the fact that both the educational process and the opportunity to obtain an education should not be halted. The study’s goal is to assess the current state of the e-learning organization and implementation, as well as to examine students’ attitude towards the educational process during wartime. Methods such as scientific source analysis, generalization and systematization of the e-learning experience and its practical application were used to achieve the goal. Furthermore, empirical methods such as interviewing and observation were used. Questionnaires have been proposed as important research tools for this purpose. Four structured groups for the use of e-learning have been formed and identified based on the findings. We created an e-learning organization and support model based on them. Furthermore, we identified ten poignant factors as the sources of difficulties for teachers when implementing innovations, with limited resources and a lack of time being among the most significant.
Influence of Gravitational Force on Particle Motion in the Channel Flow Induced by Fluid Injection
Authors: Volkov Konstantin
Published: October 2022

The motion of an individual particle in a circular channel flow induced with fluid injection is considered. Analysis takes into account drag and gravitational forces acting on an individual particle. The change in the radial structure of the flow in a channel with fluid injection on the motion of a particle is studied. A number of simplifying assumptions about the structure of the fluidflow in the channel makes it possible to obtain an analytical solution of the problem for particles. The results of a qualitative analysis of particle trajectories in the channel with fluid injection are compared with numerical simulations. The singular points of the particle trajectory are found in a wide range of characteristic non-dimensional parameters of the problem. The modes of motion of an individual particle in a channel with fluid injection are classified depending on the Stokes and Froude numbers.
Freestream Turbulence Effects on the Aerodynamics of an Oscillating Square Cylinder at the Resonant Frequency
Authors: Chen Yongxin , Djidjeli Kamal , Xie Zheng-Tong
Published: October 2022

Flow past a bluff body in freestream turbulence can substantially change the flow behaviour compared to that in smooth inflow. This paper presents the study of wake flow and aerodynamics of an oscillating square cylinder at the resonant frequency in freestream turbulence, with the integral length not greater than the cylinder side and the turbulence intensity not greater than 10%. Large eddy simulations (LES) in the Cartesian grid using the Immersed Boundary Method (IBM) technique embedded in a FVM solver, together with an efficient synthetic turbulent inflow generator implemented in an in-house parallel FORTRAN code are used for the study. The results are compared with those for smooth inflow, and relevant data published in the literature. The key findings are: the freestream turbulence conditions evidently reduces the local turbulent scales and fluctuations in the shear layer compared to in smooth flow, as small scale freestream turbulence breaks down cylinder-generated larger scale eddies and weakens them; but does not evidently affect the vortex shedding frequency, or the length of the recirculation region behind the cylinder. This suggests negligible change of drag coefficient compared to in smooth inflow. Moreover, this is because the vortex shedding is dominated by the forced oscillation at the resonance frequency, and the turbulence intensity is small.
The Role of Acrolein in Neurodegenerative Diseases and Its Protective Strategy
Authors: Chang Xinxin , Wang Yudan , Zheng Bing , Chen Yi , Xie Jianhua , Song Yiming , Ding Xiaomeng , Hu Xiaoyi , Hu Xiaobo , Yu Qiang
Published: October 2022

Neurodegenerative diseases are characterized by a massive loss of specific neurons, which can be fatal. Acrolein, an omnipresent environmental pollutant, is classified as a priority control contaminant by the EPA. Evidence suggests that acrolein is a highly active unsaturated aldehyde related to many nervous system diseases. Therefore, numerous studies have been conducted to identify the function of acrolein in neurodegenerative diseases, such as ischemic stroke, AD, PD, and MS, and its exact regulatory mechanism. Acrolein is involved in neurodegenerative diseases mainly by elevating oxidative stress, polyamine metabolism, neuronal damage, and plasma ACR-PC levels, and decreasing urinary 3-HPMA and plasma GSH levels. At present, the protective mechanism of acrolein mainly focused on the use of antioxidant compounds. This review aimed to clarify the role of acrolein in the pathogenesis of four neurodegenerative diseases (ischemic stroke, AD, PD and MS), as well as protection strategies, and to propose future trends in the inhibition of acrolein toxicity through optimization of food thermal processing and exploration of natural products.