bluuebunny/crossref_metadata_2025 huggingface dataset:
Visual Evaluation of Image Quality of a Low Dose 2D/3D Slot Scanner Imaging System Compared to Two Conventional Digital Radiography X-ray Imaging Systems
Authors: Abdi Ahmed Jibril , Mussmann Bo , Mackenzie Alistair , Gerke Oke , Jørgensen Gitte Maria , Bechsgaard Thor Eriksen , Jensen Janni , Olsen Lone Brunshøj , Andersen Poul Erik
Published: October 2021

The purpose of this study was to assess the image quality of the low dose 2D/3D slot scanner (LDSS) imaging system compared to conventional digital radiography (DR) imaging systems. Visual image quality was assessed using the visual grading analysis (VGA) method. This method is a subjective approach that uses a human observer to evaluate and optimise radiographic images for different imaging technologies. Methods and materials: ten posterior-anterior (PA) and ten lateral (LAT) images of a chest anthropomorphic phantoms and a knee phantom were acquired by an LDSS imaging system and two conventional DR imaging systems. The images were shown in random order to three (chest) radiologists and three experienced (knee) radiographers, who scored the images against a number of criteria. Inter- and intraobserver agreement was assessed using Fleiss’ kappa and weighted kappa. Results: the statistical comparison of the agreement between the observers showed good interobserver agreement, with Fleiss’ kappa coefficients of 0.27–0.63 and 0.23–0.45 for the chest and knee protocols, respectively. Comparison of intraobserver agreement also showed good agreement with weighted kappa coefficients of 0.27–0.63 and 0.23–0.45 for the chest and knee protocols, respectively. The LDSS imaging system achieved significantly higher VGA image quality compared to the DR imaging systems in the AP and LAT chest protocols (p < 0.001). However, the LDSS imaging system achieved lower image quality than one DR system (p ≤ 0.016) and equivalent image quality to the other DR systems (p ≤ 0.27) in the knee protocol. The LDSS imaging system achieved effective dose savings of 33–52% for the chest protocol and 30–35% for the knee protocol compared with DR systems. Conclusions: this work has shown that the LDSS imaging system has the potential to acquire chest and knee images at diagnostic quality and at a lower effective dose than DR systems.
Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier
Authors: Almars Abdulqader M. , Alwateer Majed , Qaraad Mohammed , Amjad Souad , Fathi Hanaa , Kelany Ayda K. , Hussein Nazar K. , Elhosseini Mostafa
Published: October 2021

The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In most of the examined studies, cancer classification (Malignant and benign) accuracy was examined without disclosing biological information related to the classification process. A new approach was proposed to bridge the gap between cancer classification and the interpretation of the biological studies of the genes implicated in cancer. This study aims to develop a new hybrid model for cancer classification (by using feature selection mRMRe as a key step to improve the performance of classification methods and a distributed hyperparameter optimization for gradient boosting ensemble methods). To evaluate the proposed method, NB, RF, and SVM classifiers have been chosen. In terms of the AUC, sensitivity, and specificity, the optimized CatBoost classifier performed better than the optimized XGBoost in cross-validation 5, 6, 8, and 10. With an accuracy of 0.91±0.12, the optimized CatBoost classifier is more accurate than the CatBoost classifier without optimization, which is 0.81± 0.24. By using hybrid algorithms, SVM, RF, and NB automatically become more accurate. Furthermore, in terms of accuracy, SVM and RF (0.97±0.08) achieve equivalent and higher classification accuracy than NB (0.91±0.12). The findings of relevant biomedical studies confirm the findings of the selected genes.
Comparisons of Anti-dsDNA Antibody Detection Methods by Chemiluminescent Immunoassay and Enzyme-Linked Immunosorbent Assay in Systemic Lupus Erythematosus
Authors: Chang Huang-Chen , Wu Yen-Ching , Chen Jun-Peng , Wu Yi-Da , Huang Wen-Nan , Chen Yi-Hsing , Chen Yi-Ming
Published: October 2021

This study aimed to compare the test results of anti-double-stranded DNA (anti-dsDNA) antibodies obtained using chemiluminescent immunoassay (CIA) and enzyme-linked immunosorbent assay (ELISA), and investigate predictors of inconsistent results. This retrospective study included 502 patients who underwent CIA and ELISA to determine their anti-dsDNA antibody values within a year. We compared the diagnostic power for SLE, disease activity, and predictive power for lupus nephritis (LN). A multivariate analysis was performed to determine the predictors of inconsistencies. CIA and ELISA were moderately correlated in terms of their consistency (Cronbach’s α = 0.571), and yielded comparably favorable results in terms of SLE diagnostic power and SLE disease activity. However, if the patient had LN, CIA displayed higher predictive power than ELISA (0.620 vs. 0.555, p = 0.026). Compared with the CIA/ELISA double-positive group, the inconsistent group had lower anti-C1q circulating immune complexes (CIC) antibody values (OR: 0.42, 95% CI: 0.18–0.94, p = 0.036), and lower SLEDAI scores (≥4) (OR: 0.33, 95% CI: 0.14–0.79, p = 0.013). Anti-dsDNA antibody detection with CIA exhibited higher predictability for diagnosing LN than did ELISA. In the event of inconsistencies between anti-dsDNA methods, SLE disease activity and CIC test values should be considered simultaneously.
A Contemporary Evaluation on Posterior Direct Restoration Teaching among Undergraduates in Dental Schools in Malaysia
Authors: Alauddin Muhammad Syafiq , Mohammad Norazlina , Jaafar Azlan , Abdul Fatah Faizah , Ahmad Aimi Amalina
Published: October 2021

There is a current trend to restore posterior teeth with composite resin due to increasing demands on natural tooth colour restoration and increased concern about the safety of amalgam restorations. The objective was to evaluate the current teaching of posterior direct restoration among restorative dental lecturers in Malaysia compared to available international literature. An online questionnaire, which sought information on the teaching of posterior restoration was developed and distributed to 13 dental schools in Malaysia. The response rate for the questionnaire was 53.8%. The most popular posterior restoration teaching methods among the respondents were lecture (95.7%), demonstration (87.0%) and problem-based learning (PBL) (73.9%), while continuous assessment and a practical competency test (82.6%) were the most popular assessment methods. Placing a hard setting calcium hydroxide and GIC base for deep cavity restored by composite restoration was taught in 79.2% of cases. The standard protocols for posterior composite restoration were incremental filling in deep cavity (87.5%), using circumferential metal bands with wooden wedge (91.7%), with a total etch system (95.8%), using a light emitting diode (LED) light curing unit (91.7%), finishing using water cooling (80%) and finishing with a disc (87.5%). Graduates from dental schools in Malaysia received similar theoretical, preclinical and clinical teaching on posterior restoration techniques, although there were variations in the delivery methods, techniques and assessments, pointing to a need for uniformity and consensus.
Validation of the Academic Self-Concept Scale in the Spanish University Context
Authors: Granero-Gallegos Antonio , Baena-Extremera Antonio , Escaravajal Juan Carlos , Baños Raúl
Published: October 2021

The aims of this study were: (i) to provide evidence of reliability and validity regarding the dimensionality of the Spanish version of two correlated subscales measuring Academic Self-Concept (ASC); (ii) to analyze factorial invariance according to sex and the educational level; (iii) to analyze a higher-order model from the two ASC subscales; and (iv) to study the predictive relationship of the ASC to academic engagement. An observational, descriptive, and cross-sectional study was designed in which 681 undergraduate university students (Mage = 24.55; SDage = 5.35) (58% girls) and students of the Master’s in Teacher Education participated. The scale’s psychometric properties were analyzed using different exploratory and confirmatory analyses demonstrating that this instrument with two correlated factors (academic confidence and academic effort) is valid, reliable, and invariant in terms of gender and academic level. Higher-order factors from the two correlated factors showed an excellent goodness-of-fit. A regression model with latent variables was conducted showing a higher and positive prediction of academic self-concept for the two factors of academic engagement (willingness to study and satisfaction with studies). The ASC has shown itself to be a valid and reliable instrument for use with Spanish university students and may be of special interest, both to teachers and to the students themselves, as it allows us to reflect on the importance of developing confidence and effort during the students’ academic lives.
Smartphone Handwritten Circuits Solver Using Augmented Reality and Capsule Deep Networks for Engineering Education
Authors: Alhalabi Marah , Ghazal Mohammed , Haneefa Fasila , Yousaf Jawad , El-Baz Ayman
Published: October 2021

Resolving circuit diagrams is a regular part of learning for school and university students from engineering backgrounds. Simulating circuits is usually done manually by creating circuit diagrams on circuit tools, which is a time-consuming and tedious process. We propose an innovative method of simulating circuits from hand-drawn diagrams using smartphones through an image recognition system. This method allows students to use their smartphones to capture images instead of creating circuit diagrams before simulation. Our contribution lies in building a circuit recognition system using a deep learning capsule networks algorithm. The developed system receives an image captured by a smartphone that undergoes preprocessing, region proposal, classification, and node detection to get a Netlist and exports it to a circuit simulator program for simulation. We aim to improve engineering education using smartphones by (1) achieving higher accuracy using less training data with capsule networks and (2) developing a comprehensive system that captures hand-drawn circuit diagrams and produces circuit simulation results. We use 400 samples per class and report an accuracy of 96% for stratified 5-fold cross-validation. Through testing, we identify the optimum distance for taking circuit images to be 10 to 20 cm. Our proposed model can identify components of different scales and rotations.
Hardware Implementation Study of Particle Tracking Algorithm on FPGAs
Authors: Gabrielli Alessandro , Alfonsi Fabrizio , Annovi Alberto , Camplani Alessandra , Cerri Alessandro
Published: October 2021

In recent years, the technological node used to implement FPGA devices has led to very high performance in terms of computational capacity and in some applications these can be much more efficient than CPUs or other programmable devices. The clock managers and the enormous versatility of communication technology through digital transceivers place FPGAs in a prime position for many applications. For example, from real-time medical image analysis to high energy physics particle trajectory recognition, where computation time can be crucial, the benefits of using frontier FPGA capabilities are even more relevant. This paper shows an example of FPGA hardware implementation, via a firmware design, of a complex analytical algorithm: The Hough transform. This is a mathematical spatial transformation used here to facilitate on-the-fly recognition of the trajectories of ionising particles as they pass through the so-called tracker apparatus within high-energy physics detectors. This is a general study to demonstrate that this technique is not only implementable via software-based systems, but can also be exploited using consumer hardware devices. In this context the latter are known as hardware accelerators. In this article in particular, the Xilinx UltraScale+ FPGA is investigated as it belongs to one of the frontier family devices on the market. These FPGAs make it possible to reach high-speed clock frequencies at the expense of acceptable energy consumption thanks to the 14 nm technological node used by the vendor. These devices feature a huge number of gates, high-bandwidth memories, transceivers and other high-performance electronics in a single chip, enabling the design of large, complex and scalable architectures. In particular the Xilinx Alveo U250 has been investigated. A target frequency of 250 MHz and a total latency of 30 clock periods have been achieved using only the 17 ÷ 53% of LUTs, the 8 ÷ 12% of DSPs, the 1 ÷ 3% of Block Rams and a Flip Flop occupancy range of 9 ÷ 28%.
SEOUL AR: Designing a Mobile AR Tour Application for Seoul Sky Observatory in South Korea
Authors: Shin Soomin , Choi Yongsoon
Published: October 2021

Skyscrapers are symbols of local landmarks, and their prevalence is increasing across the world owing to recent advances in architectural technology. In Korea, the Lotte World Tower, which is now the tallest skyscraper in Seoul, was constructed in 2017. In addition, it has an observatory deck called Seoul Sky, which is currently in operation. This study focuses on the design of Seoul AR, which is a mobile augmented reality (AR) tour application. Visitors can use Seoul AR when visiting the Seoul Sky Observatory, one of the representative landmarks of Seoul, and enjoy a 360° view of the entire landscape of Seoul in the observatory space. With Seoul AR, they can identify tourist attractions in Seoul with simple mission games. Users are also provided with information regarding the specific attraction they are viewing, as well as other information on transportation, popular restaurants, shopping places, etc., in order to increase the level of satisfaction of tourists visiting the Seoul Sky Observatory. The final design is revised through heuristic evaluation, and a study of users’ levels of satisfaction with Seoul AR is conducted through surveys completed by visitors to the Seoul Sky Observatory.
Fine-Grained Implicit Sentiment in Financial News: Uncovering Hidden Bulls and Bears
Authors: Jacobs Gilles , Hoste Véronique
Published: October 2021

The field of sentiment analysis is currently dominated by the detection of attitudes in lexically explicit texts such as user reviews and social media posts. In objective text genres such as economic news, indirect expressions of sentiment are common. Here, a positive or negative attitude toward an entity must be inferred from connotational or real-world knowledge. To capture all expressions of subjectivity, a need exists for fine-grained resources and approaches for implicit sentiment analysis. We present the SENTiVENT corpus of English business news that contains token-level annotations for target spans, polar spans, and implicit polarity (positive, negative, or neutral investor sentiment, respectively). We both directly annotate polar expressions and induce them from existing schema-based event annotations to obtain event-implied implicit sentiment tuples. This results in a large dataset of 12,400 sentiment–target tuples in 288 fully annotated articles. We validate the created resource with an inter-annotator agreement study and a series of coarse- to fine-grained supervised deep-representation-learning experiments. Agreement scores show that our annotations are of substantial quality. The coarse-grained experiments involve classifying the positive, negative, and neutral polarity of known polar expressions and, in clause-based experiments, the detection of positive, negative, neutral, and no-polarity clauses. The gold coarse-grained experiments obtain decent performance (76% accuracy and 63% macro-F1) and clause-based detection shows decreased performance (65% accuracy and 57% macro-F1) with the confusion of neutral and no-polarity. The coarse-grained results demonstrate the feasibility of implicit polarity classification as operationalized in our dataset. In the fine-grained experiments, we apply the grid tagging scheme unified model for  triplet extraction, which obtains state-of-the-art performance on explicit sentiment in user reviews. We observe a drop in performance on our implicit sentiment corpus compared to the explicit benchmark (22% vs. 76% F1). We find that the current models for explicit sentiment are not directly portable to our implicit task: the larger lexical variety within implicit opinion expressions causes lexical data scarcity. We identify common errors and discuss several recommendations for implicit fine-grained sentiment analysis. Data and source code are available.
Integration of Distributed Energy Resources and EV Fast-Charging Infrastructure in High-Speed Railway Systems
Authors: Ahmadi Miad , Jafari Kaleybar Hamed , Brenna Morris , Castelli-Dezza Francesco , Carmeli Maria Stefania
Published: October 2021

Low carbon emission transportation is attracting global attention where electric railway power systems (ERPS) and electric vehicles (EVs) act as a load. Besides the main utility grid, renewable energy sources (RES) including photovoltaic (PV) panels and wind turbines are implemented to supply the loads fully or partially. In this paper, a novel smart DC catenary system is proposed in which renewable sources, storage systems, and DC fast-charging stations are connected to the overhead DC catenary line of the high-speed railway power system. The generated power from renewable sources and consumed power by charging stations are processed by their dedicated DC-DC power electronics converters. Furthermore, a storage system is used as a backup system not only for the case of blackouts but also because of the intermittent nature of renewable energy sources to supply the loads continuously. The paper presents an optimal power control for various parts and a power management system (PMS) that manages the power flow from wind-PV-storage system to EV-ERPS system. The proposed system has been investigated using a real Italian Rome-Florence 3 kV high-speed line as a case study with real data of ERPS load. The EV fast-charging station power demand, wind speed, solar irradiance, and temperature were recorded for 24 h in order to provide us with realistic output data. The simulation results obtained by MATLAB/Simulink are presented to validate the effectiveness of the proposed system.
Joint Diagnosis of RIS and BS for RIS-Aided Millimeter-Wave System
Authors: Ma Siqi , Li Jianguo , Bu Xiangyuan , An Jianping
Published: October 2021

Recently, the reconfigurable intelligent surface (RIS)-aided communication system has emerged as a promising candidate for future millimeter-wave wireless communications. Due to the short wavelength of millimeter wave, the antennas on the base station (BS) and the elements on the RIS can be densely packed. It usually causes the BS and RIS to be blocked by rain, snow, or dust, which will change the channel’s characteristics and decrease the performance of communication system. In order to solve this problem, we propose an iterative compressed sense based algorithm for joint estimating the blockage coefficients of RIS and BS. Then, for the complete blockage scenario, we propose a low complexity algorithm for estimating the blockage coefficients. Our simulation results demonstrate the superior performance of the proposed algorithm to existing ones.
A New Clustering Approach for Automatic Oscillographic Records Segmentation
Authors: Ferreira Vitor Hugo , Pinho André da Costa , de Souza Dickson Silva , Rodrigues Bárbara Siqueira
Published: October 2021

The analysis of waveforms related to transient events is an important task in power system maintenance. Currently, electric power systems are monitored by several event recorders called phasor measurement units (PMUs) which generate a large amount of data. The number of records is so high that it makes human analysis infeasible. An alternative way of solving this problem is to group events in similar classes so that it is no longer necessary to analyze all the events, but only the most representative of each class. Several automatic clustering algorithms have been proposed in the literature. Most of these algorithms use validation indexes to rank the partitioning quality and, consequently, find the optimal number of clusters. However, this issue remains open, as each index has its own performance highly dependent on the data spatial distribution. The main contribution of this paper is the development of a methodology that optimizes the results of any clustering algorithm, regardless of data spatial distribution. The proposal is to evaluate the internal correlation of each cluster to proceed or not in a new partitioning round. In summary, the traditional validation indexes will continue to be used in the cluster’s partition process, but it is the internal correlation measure of each one that will define the stopping splitting criteria. This approach was tested in a real waveforms database using the K-means algorithm with the Silhouette and also the Davies–Bouldin validation indexes. The results were compared with a specific methodology for that database and were shown to be totally consistent.
A Universal Gains Selection Method for Speed Observers of Induction Machine
Authors: Wachowiak Daniel
Published: October 2021

Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not require prior preparation specific to the implemented speed observer, such as linearization.
Sensitivity Analysis of a Two-Phase CFD Simulation of a 1 kN Paraffin-Fueled Hybrid Rocket Motor
Authors: Dequick Benoit , Lefebvre Michel , Hendrick Patrick
Published: October 2021

At Université Libre de Bruxelles (ULB), research was performed on a 1 kN lab-scale Hybrid Rocket Motor (the ULB-HRM). It has a single-port solid paraffin fuel grain and uses liquid N2O as an oxidizer. The first Computational Fluid Dynamics (CFD) model of the motor was developed in 2020 and improved in 2021, using ANSYS Fluent software. It is a 2D axisymmetric, two-phase steady-state Reynolds-Averaged Navier–Stokes (RANS) model, which uses the average fuel and oxidizer mass flow rates as inputs. It includes oxidizer spray droplets and entrained fuel droplets, therefore adding many additional parameters compared to a single-phase model. It must be investigated how they affect the predicted operating conditions. In this article, a sensitivity analysis is performed to determine the model’s robustness. It is demonstrated that the CFD model performs well within the boundaries of its purpose, with average deviations between predicted and experimental values of about 1% for the chamber pressure and 5% for the thrust. From the sensitivity analysis, multiple observations and conclusions are made. An important observation is that oxidizer related parameters have the highest potential impact, introducing deviations of the predicted operating chamber pressure of up to 18%, while this is only about 6% for fuel-related parameters. In general, the baseline CFD model of the ULB-HRM seems quite insensitive and it does not suffer from an excessive or abnormal sensitivity to any of the major parameters. Furthermore, the predicted operating conditions seem to respond in a logical and coherent way to changing input parameters. The model therefore seems sufficiently reliable to be used for future qualitative and quantitative predictions of the performance of the ULB-HRM.
Presentation of the 5Ds in Energy Policy: A Policy Paper to Show How Germany Can Regain Its Role as a Pioneer in Energy Policy
Authors: Wagner Oliver , Götz Thomas
Published: October 2021

The widely recognised Energiewende, (“energy transition”) in Germany has lost its original momentum. We therefore address the question of how the transition process to a new energy system can be reignited. To do so, we developed the “5Ds approach”, which lays the groundwork for a process analysis and the identification of important catalysts and barriers. Focusing on the five major fields required for the energy transition, we analyse the effects of: (1) Decarbonisation: How can efficiency and renewable energies be expanded successfully? (2) Digitalisation: Which digital solutions facilitate this conversion and would be suitable as sustainable business models? (3) Decentralisation: How can potential decentralised energy and efficiency opportunities be developed? (4) Democratisation: How can participation be strengthened in order to foster acceptance (and prevent “yellow vest” protests, etc.)? (5) Diversification of service: Which services can make significant contributions in the context of flexible power generation, demand-side management, storage and grids? Our paper comes to the conclusion that German policy efforts in the “5D” fields have been implemented very differently. Particularly with regard to democratisation, the opportunities for genuine participation among the different social actors must be further strengthened to get the Energiewende back on track. New market models are needed to meet the challenges of the energy transition and to increase the performance of “5D” through economic incentives.
A MILP Model for Revenue Optimization of a Compressed Air Energy Storage Plant with Electrolysis
Authors: Klaas Ann-Kathrin , Beck Hans-Peter
Published: October 2021

Energy storage, both short- and long-term, will play a vital role in the energy system of the future. One storage technology that provides high power and capacity and that can be operated without carbon emissions is compressed air energy storage (CAES). However, it is widely assumed that CAES plants are not economically feasible. In this context, a mixed-integer linear programming (MILP) model of the Huntorf CAES plant was developed for revenue maximization when participating in the day-ahead market and the minute-reserve market in Germany. The plant model included various plant variations (increased power and storage capacity, recuperation) and a water electrolyzer to produce hydrogen to be used in the combustion chamber of the CAES plant. The MILP model was applied to four use cases that represent a market-orientated operation of the plant. The objective was the maximization of revenue with regard to price spreads and operating costs. To simulate forecast uncertainties of the market prices, a rolling horizon approach was implemented. The resulting revenues ranged between EUR 0.5 Mio and EUR 7 Mio per year and suggested that an economically sound operation of the storage plant is possible.
PMSM Torque-Speed-Efficiency Map Evaluation from Parameter Estimation Based on the Stand Still Test
Authors: Candelo-Zuluaga Carlos , Riba Jordi-Roger , Garcia Antoni
Published: October 2021

During the last decades, a wide variety of methods to estimate permanent magnet synchronous motor (PMSM) performance have been developed. These methodologies have several advantages over conventional procedures, saving time and economic costs. This paper presents a new methodology to estimate the PMSM torque-speed-efficiency map based on the blocked rotor test using a single-phase voltage source. The methodology identifies the stator flux linkage depending on the current magnitude and angle while providing a detailed estimation of the iron losses. The torque-speed-efficiency map provides detailed information of the motor efficiency along its operating region, including the nominal conditions and the maximum power envelope. The proposed methodology does not require knowing the geometry of the machine to perform any load test, and it also avoids using expensive measurement devices and a complex experimental setup. Moreover, the proposed method allows the PMSM performance to be reproduced by applying different control strategies, which is useful when testing different drives. The method does not require the application of any optimization algorithm, thus simplifying and speeding up the process to determine the performance. Experimental validation is carried out by comparing motor performances obtained through the proposed method with those obtained by means of a conventional experimental method and against finite element analysis (FEA).
Fundamentals of the Thermal Analysis of Complex Arrangements of Underground Heat Sources
Authors: Brakelmann Heiner , Anders George J. , Zajac Piotr
Published: October 2021

This paper introduces a new analytical method for the 3-dimensional analysis of heat sources installed underground. Such sources include primarily electric power cables and steam pipes in urban areas. For complex arrangements of the heat sources, the heat transfer and cable rating calculations require the application of numerical methods, which call for high level of expertise and are generally difficult to use. The computational algorithm presented in this paper uses a point sources approach and is an extension of the work published by the authors based on line source modeling, with the goal of simplifying the numerical calculations. The proposed approach is applicable for all heat sources, which are directly buried in a uniform or a partially dried out soil. The method is illustrated by several numerical examples. These examples are used for comparison with the existing standard approach.
Clarifying the Effect of Clay Minerals on Methane Adsorption Capacity of Marine Shales in Sichuan Basin, China
Authors: Wang Hongyan , Zhou Shangwen , Zhang Jiehui , Feng Ziqi , Jiao Pengfei , Zhang Leifu , Zhang Qin
Published: October 2021

The effect of clay minerals on the methane adsorption capacity of shales is a basic issue that needs to be clarified and is of great significance for understanding the adsorption characteristics and mechanisms of shale gas. In this study, a variety of experimental methods, including XRD, LTNA, HPMA experiments, were conducted on 82 marine shale samples from the Wufeng–Longmaxi Formation of 10 evaluation wells in the southern Sichuan Basin of China. The controlling factors of adsorption capacities were determined through a correlation analysis with pore characteristics and mineral composition. In terms of mineral composition, organic matter (OM) is the most key methane adsorbent in marine shale, and clay minerals have little effect on methane adsorption. The ultra-low adsorption capacity of illite and chlorite and the hydrophilicity and water absorption ability of clay minerals are the main reasons for their limited effect on gas adsorption in marine shales. From the perspective of the pore structure, the micropore and mesopore specific surface areas (SSAs) control the methane adsorption capacity of marine shales, which are mainly provided by OM. Clay minerals have no relationship with SSAs, regardless of mesopores or micropores. In the competitive adsorption process of OM and clay minerals, OM has an absolute advantage. Clay minerals become carriers for water absorption, due to their interlayer polarity and water wettability. Based on the analysis of a large number of experimental datasets, this study clarified the key problem of whether clay minerals in marine shales control methane adsorption.
Molecular Dynamics Study on Water Flow Behaviour inside Planar Nanochannel Using Different Temperature Control Strategies
Authors: Skarbalius Gediminas , Džiugys Algis , Misiulis Edgaras , Navakas Robertas , Vilkinis Paulius , Šereika Justas , Pedišius Nerijus
Published: October 2021

In the present paper, molecular dynamics simulations were performed to study the influence of two temperature control strategies on water flow behaviour inside planar nanochannel. In the simulations, the flow was induced by the force acting on each water molecule in the channel. Two temperature control strategies were considered: (a) frozen wall simulations, in which the dynamics of confining wall atoms was not solved and the thermostat was applied to the water, and (b) dynamic wall simulations, in which the dynamics of confining wall atoms was solved, and the thermostat was applied to walls while water was simulated in the microcanonical ensemble. The simulation results show that the considered temperature control strategies has no effect on the shape of the water flow profile, and flow behaviour in the channel is well described by the Navier–Stokes equation solution with added slip velocity. Meanwhile, the slip velocity occurring at the boundaries of the channel is linearly dependent on the magnitude of the flow inducing force in both frozen wall and dynamic wall simulations. However, the slip velocity is considerably greater in simulations when the wall dynamics are solved in contrast to the frozen wall simulations.
Frequency Resolved Partial Discharges Based on Spectral Pulse Counting
Authors: Sena Anderson J. C. , de Oliveira Rodrigo M. S. , do Nascimento Júlio A. S.
Published: October 2021

A partial discharge (PD) classification methodology based counting PD pulses in the spectral domain is proposed and presented in this paper. The spectral counting data are processed using the proposed PD Spectral Pulse Counting Mapping technique (PD-SPCM), which leads to a Frequency-Resolved Partial Discharges (FRPD) map. The proposed map is then used for PD detection and classification. In this work, corona and slot FRPDs are presented in frequency bands up to 500 MHz, obtained from laboratory measurements performed using two hydro-generator stator bars. The electromagnetic signals from the PDs were captured using a patch antenna designed for this purpose and a spectral analyzer. The corona and slot PDs were chosen because one can be mistakenly classified as the other because they may present similar Phase Resolved PD (PRPD) maps and may occupy shared spectral bands. Furthermore, corona and slot PDs can occur concurrently. The obtained results show that the corona and slot PDs can be properly identified using the developed methodology, even when they occur simultaneously. This is possible because, as it is experimentally demonstrated, corona and slot PDs have appreciable levels of spectral pulse counting in particular bands of the frequency spectrum.
Numerical Study on the Influence of Vortex Generator Arrangement on Heat Transfer Enhancement of Oil-Cooled Motor
Authors: Zhao Junjie , Zhang Bin , Fu Xiaoli , Yan Shenglin
Published: October 2021

At present, vortex generators have been extensively used in radiators to improve the overall heat transfer performance. However, there is no research on the effect of vortex generators on the ends of motor coils. Meanwhile, the current research mainly concentrates on the attack angle, shape and size, and lacks a detailed study on the transverse and longitudinal distance and arrangement of vortex generators. In this paper, the improved dimensionless number R is used as the key index to evaluate the overall performance of enhanced heat transfer. Firstly, the influence of the attack angle on heat transfer enhancement is discussed through a single pair of rectangular vortex generators, and the results demonstrate that the vortex generator with a 45° attack angle is superior. On this basis, we compare the effects of different longitudinal distances (2 h, 4 h, and 6 h, h meaning the height of vortex generator) on enhanced heat transfer under four distribution modes: Flow-Up (FU), Flow-Down (FU), Flow-Up-Down (FUD), Flow-Down-UP (FDU). Thereafter, the performances of different transverse distances (0.25 h, 0.5 h, and 0.75 h) of the vortex generators are numerically simulated. When comparing the longitudinal distances, FD with a longitudinal distance of 4 h (FD-4 h) performs well when the Reynolds number is less than 4000, and FU with a longitudinal distance of 4 h (FU-4 h) performs better when the Reynolds number is greater than 4000. Similarly, in the comparison of transverse distances, FD-4 h still performs well when the Reynolds number is less than 4000, and FU with a longitudinal distance of 4 h and transverse distance of 0.5 h (FU-4 h–0.5 h) is more prominent when the Reynolds number is greater than 4000.
Plywood Made from Plasma-Treated Veneers: Investigation of Performance Differences between Plasma-Pretreated and Untreated Beech Veneers at Comparable Melamine Resin Load
Authors: Wascher Richard , Avramidis Georg , Viöl Wolfgang
Published: October 2021

In this study, the dimensional stability and mechanical properties of plywood made from untreated and plasma-pretreated beech veneers were compared. The wood veneers used (native and thermally modified) were impregnated with melamine resin in a simple dipping process prior to plywood production. The duration of the impregnation process was adjusted to give the same melamine resin loading for the different veneer types, with the plasma-pretreated veneers requiring only a fraction of the impregnation time compared with non-plasma-pretreated veneers. With comparable melamine loading, testing of the mechanical properties of the plywood for the different specimen collectives showed significant differences in some cases with respect to compressive strength, bending strength and tensile strength (with the associated moduli of elasticity). For example, it was shown that plywood made from plasma-pretreated native beech veneers shows an increase in bending strength of about 8%, and from plasma-pretreated and thermally modified beech veneers, there is an increase of about 10% compared to the reference.
Phenology of Oenocarpus mapora H. Karst in Low-Terrace and High-Terrace Forests of the Madre de Dios Region, Peru
Authors: Best Ivan , Rengifo Helmut , Velarde Ernesto , Loja Juan Francisco , Portugal Alan , Rengifo Piero , Aguilar Luis , Ramos-Escudero Fernando , Muñoz Ana María
Published: October 2021

Oenacarpus mapora H. Karst (O. mapora) is an Amazon palm with high economic and nutraceutical potential, from which the pulp and oil can be extracted. The objective of this study was to evaluate the phenology of O. mapora in low-terrace and high-terrace forests of the Madre de Dios Region, Peru. Two hundred sixteen individuals of O. mapora were monitored between June 2019 and January 2020, evaluating the presence of flower buds, open flowers, immature fruits and ripe fruits. Weighted mean analyses of the phenological pattern and correlation between climatic and phenological variables were carried out. Higher productivity mediated by a greater number of mature green leaves and bunches was observed in terrace forests located at a lower altitude. In both forest subtypes, flower buds and open flowers were continuous with a peak in July and August, respectively, during the dry season. In both habitats, unripe fruits were also continuous with a peak in September, while ripe fruits showed a peak in December and January in low-terrace and high-terrace forests, respectively, during the rainy season. Our findings show that flowering was continuous during the evaluated period, while fruiting increased during the rainy season associated with a greater number of days with precipitation.
Consistent Effects of Canopy vs. Understory Nitrogen Addition on Soil Respiration and Net Ecosystem Production in Moso Bamboo Forests
Authors: Cai Chunju , Yang Zhihan , Liu Liang , Lai Yunsen , Lei Junjie , Fan Shaohui , Tang Xiaolu
Published: October 2021

Nitrogen (N) deposition has been well documented to cause substantial impacts on ecosystem carbon cycling. However, the majority studies of stimulating N deposition by direct N addition to forest floor have neglected some key ecological processes in forest canopy (e.g., N retention and absorption) and might not fully represent realistic atmospheric N deposition and its effects on ecosystem carbon cycling. In this study, we stimulated both canopy and understory N deposition (50 and 100 kg N ha−1 year−1) with a local atmospheric NHx:NOy ratio of 2.08:1, aiming to assess whether canopy and understory N deposition had similar effects on soil respiration (RS) and net ecosystem production (NEP) in Moso bamboo forests. Results showed that RS, soil autotrophic (RA), and heterotrophic respiration (RH) were 2971 ± 597, 1472 ± 579, and 1499 ± 56 g CO2 m−2 year−1 for sites without N deposition (CN0), respectively. Canopy and understory N deposition did not significantly affect RS, RA, and RH, and the effects of canopy and understory N deposition on these soil fluxes were similar. NEP was 1940 ± 826 g CO2 m−2 year−1 for CN0, which was a carbon sink, indicating that Moso bamboo forest the potential to play an important role alleviating global climate change. Meanwhile, the effects of canopy and understory N deposition on NEP were similar. These findings did not support the previous predictions postulating that understory N deposition would overestimate the effects of N deposition on carbon cycling. However, due to the limitation of short duration of N deposition, an increase in the duration of N deposition manipulation is urgent and essential to enhance our understanding of the role of canopy processes in ecosystem carbon fluxes in the future.
The Effects of Combining the Variables in Allometric Biomass Models on Biomass Estimates over Large Forest Areas: A European Beech Case Study
Authors: Osewe Erick O. , Dutcă Ioan
Published: October 2021

Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D2H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D2H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q > 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q < 2.0 (i.e., by –3.9% for Q = 1.50), whereas for Q = 2.03, the differences were minimum (0.1%). The standard errors of mean AGB per hectare were similar for Q = 2.03 (differences up to 0.2%), and the differences increased with the Q-ratio, by up to 10.2% for Q = 5.05. Therefore, we demonstrated for European beech that combining the variables in allometric biomass models when Q ≠ 2.0 resulted in biased estimates of mean AGB per hectare and of uncertainty.
Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data
Authors: Li Yingying , Zhao Zhengyong , Wei Sunwei , Sun Dongxiao , Yang Qi , Ding Xiaogang
Published: October 2021

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.