bluuebunny/crossref_metadata_2025 huggingface dataset:
First-Principle Study of Ca3Y2Ge3O12 Garnet: Dynamical, Elastic Properties and Stability under Pressure
Authors: Muñoz Alfonso , Rodríguez-Hernández Plácida
Published: December 2022

We present here an ab initio study under the framework of the Density Functional Theory of the Ca3Y2Ge3O12 garnet. Our study focuses on the analysis of the structural, electronic, dynamic, and elastic properties of this material under hydrostatic pressure. We report information regarding the equation of state, the compressibility, and the structural evolution of this compound. The dynamical properties and the evolution under pressure of infrared, silent, and Raman frequencies and their pressure coefficients are also presented. The dependence on the pressure of the elastic constants and the mechanical and elastic properties are analyzed. From our results, we conclude that this garnet becomes mechanically unstable at 45.7 GPa; moreover, we also find evidence of soft phonons at 34.4 GPa, showing the dynamical instability of this compound above this pressure.
Diameter Prediction of Silicon Ingots in the Czochralski Process Based on a Hybrid Deep Learning Model
Authors: Zhao Xiaoguo , Liu Ding , Yan Xiaomei
Published: December 2022

The diameter prediction of silicon ingots in the Czochralski process is a complex problem because the process is highly nonlinear, time-varying, and time-delay. To address this problem, this paper presents a novel hybrid deep learning model, which combines the deep belief network (DBN), support vector regression (SVR), and the ant lion optimizer (ALO). Continuous restricted Boltzmann machines (CRBMs) are used in DBN for working with continuous industrial data. The feature aggregates the outputs from various DBNs through an SVR model. Additionally, the ALO algorithm is used for the parameter’s optimization of SVR. The newly developed model is verified with the actual production data and compared with the back propagation neural network (BPNN) and the SVR model. The simulation results demonstrate the availability and accuracy of the CRBM-DBN-ALO-SVR hybrid deep learning model.
First-Principles Calculations of Structural and Mechanical Properties of Cu–Ni Alloys
Authors: Wei Yun , Niu Ben , Liu Qijun , Liu Zhengtang , Jiang Chenglu
Published: December 2022

Nanostructured Cu–Ni alloys have become the focus of public attention due to their better corrosion resistance and high hardness in experimental measurements. First-principles calculation based on the density functional theory (DFT) has been confirmed as an effective tool and used to illustrate the mechanical properties of these alloys. In this paper, the DFT has been employed to calculate the mechanical properties of Cu–Ni alloys, including bulk modulus, shear modulus, Young’s modulus, anisotropic index, Poisson’s ratio, average velocity, and B/G. We find that the Ni-rich Cu–Ni alloys have relatively higher mechanical parameters, and the Cu-rich alloys have smaller mechanical parameters, which is consistent with previous experiments. This provides an idea for us to design alloys to improve alloy strength.
A Large-Scale Dataset of Conservation and Deep Tillage in Mollisols, Northeast Plain, China
Authors: Jiang Fahui , Huang Shangshu , Wu Yan , Islam Mahbub Ul , Dong Fangjin , Cao Zhen , Chen Guohui , Guo Yuming
Published: December 2022

One of the primary challenges of our time is to feed a growing and more demanding world population with degraded soil environments under more variable and extreme climate conditions. Conservation tillage (CS) and deep tillage (DT) have received strong international support to help address these challenges but are less used in major global food production in China. Hence, we conducted a large-scale literature search of English and Chinese publications to synthesize the current scientific evidence to evaluate the effects of CS and DT on soil protection and yield maintenance in the Northeast China Plain, which has the most fertile black soil (Mollisols) and is the main agricultural production area of China. As a result, we found that CS had higher soil bulk density, strong soil penetration resistance, greater water contents, and lower soil temperature, and was well-suited for dry and wind erosion-sensitive regions i.e., the southwest areas of the Northeast. Conversely, DT had better performance in the middle belt of the Northeast China Plain, which contained a lower soil temperature and humid areas. Finally, we created an original dataset from papers [dataset 1, including soil physio-chemical parameters, such as soil water, bulk density, organic carbon, sand, silt, clay, pH, total and available nitrogen (N), phosphorus (P), and potassium (K), etc., on crop biomass and yield], by collecting data directly from publications, and two predicted datasets (dataset 2 and dataset 3) of crop yield changes by developing random forest models based on our data.
Numerical and Experimental Data of the Implementation of Logic Gates in an Erbium-Doped Fiber Laser (EDFL)
Authors: Afanador Delgado Samuel Mardoqueo , Echenausía Monroy José Luis , Huerta Cuellar Guillermo , García López Juan Hugo , Jaimes Reátegui Rider
Published: December 2022

In this article, the methods for obtaining time series from an erbium-doped fiber laser (EDFL) and its numerical simulation are described. In addition, the nature of the obtained files, the meaning of the changing file names, and the ways of accessing these files are described in detail. The response of the laser emission is controlled by the intensity of a digital signal added to the modulation, which allows for various logical operations. The numerical results are in good agreement with experimental observations. The authors provide all of the time series from an experimental implementation where various logic gates are obtained.
Histopathological Variants of Cutaneous Neurofibroma: A Compendious Review
Authors: Nagrani Neha S. , Bhawan Jag
Published: December 2022

The first description of histopathological variants of neurofibroma dates back to 1994. Over the years, many individual case reports elucidating unusual histologic features in neurofibroma have been added to the literature, some of which have defined criteria, with the others falling under the roof of benign neural neoplasms. These unusual features, which sometimes may lead to pauses in identifying a common benign tumor such as neurofibroma. Awareness of these variants may help dermatopathologists avoid misinterpretation. Thus, this review aims to summarize all novel and unusual histopathological variants of cutaneous neurofibroma reported to date, in addition to any unusual variants that we encountered in our practice.
Myogenous Temporomandibular Disorders: Diagnostic Concepts and Prospective Pilot Study on Extracorporeal Shockwave Therapy
Authors: Li Dion Tik Shun , Li Kar Yan , Leung Yiu Yan
Published: December 2022

The aims of this article are to discuss the current, and potential future directions, in the diagnosis of myogenous temporomandibular disorders (M-TMD), as well as to report a pilot study to investigate the feasibility and clinical outcomes of extracorporeal shockwave therapy (ESWT) in the treatment of M-TMD. Forty-one adult patients presented with M-TMD were recruited into the study and randomized into two groups: Group 1 received ESWT treatment, whereas Group 2 received placebo treatment. The variables investigated were pain, measured by a numerical rating scale (NRS) and mouth opening. Twenty-six patients (Group 1: n = 14, mean age = 45.3 (16.7) years; Group 2: n = 12, mean age = 46.8 (19.7) years) completed 1-year follow up and were included into the final analysis. In both groups, reduction in pain and increase in MO (unassisted maximum, assisted maximum, and pain-free) were seen at post-treatment 1 year. There were more reduction in pain and increase in all MO in Group 1 than Group 2, but statistical significance was not detected. No major complications were encountered in this study. Although significant differences were not seen between groups, this prospective pilot study provided preliminary evidence that ESWT is safe and potentially beneficial in the treatment of M-TMD.
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Post-COVID Syndrome: A Common Neuroimmune Ground?
Authors: Ryabkova Varvara A. , Gavrilova Natalia Y. , Fedotkina Tamara V. , Churilov Leonid P. , Shoenfeld Yehuda
Published: December 2022

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease of unknown etiology, sharing a similar clinical presentation with the increasingly recognized post-COVID syndrome. We performed the first cross-sectional study of ME/CFS in a community population in Russia. Then we described and compared some clinical and pathophysiological characteristics of ME/CFS and post-COVID syndrome as neuroimmune disorders. Of the cohort of 76 individuals who suggested themselves as suffering from ME/CFS, 56 were diagnosed with ME/CFS by clinicians according to ≥1 of the four most commonly used case definitions. Of the cohort of 14 individuals with post-COVID-19 syndrome, 14 met the diagnostic criteria for ME/CFS. The severity of anxiety/depressive symptoms did not correlate with the severity of fatigue either in ME/CFS or in post-COVID ME/CFS. Still, a positive correlation was found between the severity of fatigue and 20 other symptoms of ME/CFS related to the domains of “post-exertional exhaustion”, “immune dysfunction”, “sleep disturbances”, “dysfunction of the autonomic nervous system”, “neurological sensory/motor disorders” and “pain syndromes”. Immunological abnormalities were identified in 12/12 patients with ME/CFS according to the results of laboratory testing. The prevalence of postural orthostatic tachycardia assessed in the active orthostatic test amounted to 37.5% in ME/CFS and 75.0% in post-COVID ME/CFS (the latter was higher than in healthy controls, p = 0.02). There was a more pronounced increase in heart rate starting from the 6th minute of the test in post-COVID ME/CFS compared with the control group. Assessment of the functional characteristics of microcirculation by laser doppler flowmetry revealed obvious and very similar changes in ME/CFS and post-COVID ME/CFS compared to the healthy controls. The identified laser doppler flowmetry pattern corresponded to the hyperemic form of microcirculation disorders usually observed in acute inflammatory response or in case of systemic vasoconstriction failure.
An Improved Spanning Tree-Based Algorithm for Coverage of Large Areas Using Multi-UAV Systems
Authors: Chleboun Jan , Amorim Thulio , Nascimento Ana Maria , Nascimento Tiago P.
Published: December 2022

In this work, we propose an improved artificially weighted spanning tree coverage (IAWSTC) algorithm for distributed coverage path planning of multiple flying robots. The proposed approach is suitable for environment exploration in cluttered regions, where unexpected obstacles can appear. In addition, we present an online re-planner smoothing algorithm with unexpected detected obstacles. To validate our approach, we performed simulations and real robot experiments. The results showed that our proposed approach produces sub-regions with less redundancy than its previous version.
ATSUKF-Based Actuator Health Assessment Method for Quad-Copter Unmanned Aerial Vehicles
Authors: Zhang Zhenxin , Zhang Meng , Li Guoxi , Qin Shilong , Xu Chunxiao
Published: December 2022

The actuator, which generally consists of motors, electrical regulations, and propellers, is the key component of the quadrotor Unmanned Aerial Vehicle. During the operation of the UAV, actuators are prone to degrade performance and even cause serious failure, which affects the service quality and flight safety of Unmanned Aerial Vehicles. Therefore, timely and accurate monitoring and evaluation of the health condition of actuators is of great significance to ensure the mission reliability of UAVs. This paper proposes an Adaptive Two-stage Unscented Kalman Filter-based actuator health assessment method for Quadcopter Unmanned Aerial Vehicles. Firstly, a state space equation is established based on dynamic analysis to characterize the degradation mechanism of the actuator. Then, by modifying the Two-stage Unscented Kalman Filter algorithm, the Adaptive Two-stage Unscented Kalman Filter algorithm is constructed by combining the filter divergence criterion and the covariance matching technique to implement the health assessment of actuators. Finally, experiments are carried out for different degradation scenarios to verify the effectiveness of the proposed method.
Dynamic Robust Spectrum Sensing Based on Goodness-of-Fit Test Using Bilateral Hypotheses
Authors: Men Shaoyang , Chargé Pascal , Fu Zhe
Published: December 2022

Dynamic spectrum detection has attracted increasing interest in drone or drone controller detection problems. Spectrum sensing as a promising solution allows us to provide a dynamic spectrum map within the target frequency band by estimating the occupied sub-bands in a specific period. In this paper, a robust Student’s t-distribution model is built to tackle the scenario with a small number of observed samples. Then, relying on the characteristics of the statistical model, we propose an appropriate goodness-of-fit (GoF) test statistic regarding a small number of samples. Moreover, to obtain a reliable sensing, bilateral hypotheses of the test statistic are both used to make a decision. Numerical simulations show the superiority of the proposed method compared with other schemes, including the unilateral hypothesis-based GoF testing and the conventional energy detection, in a small number of sample cases.
Relative Density-Based Intuitionistic Fuzzy SVM for Class Imbalance Learning
Authors: Fu Cui , Zhou Shuisheng , Zhang Dan , Chen Li
Published: December 2022

The support vector machine (SVM) has been combined with the intuitionistic fuzzy set to suppress the negative impact of noises and outliers in classification. However, it has some inherent defects, resulting in the inaccurate prior distribution estimation for datasets, especially the imbalanced datasets with non-normally distributed data, further reducing the performance of the classification model for imbalance learning. To solve these problems, we propose a novel relative density-based intuitionistic fuzzy support vector machine (RIFSVM) algorithm for imbalanced learning in the presence of noise and outliers. In our proposed algorithm, the relative density, which is estimated by adopting the k-nearest-neighbor distances, is used to calculate the intuitionistic fuzzy numbers. The fuzzy values of the majority class instances are designed by multiplying the score function of the intuitionistic fuzzy number by the imbalance ratio, and the fuzzy values of minority class instances are assigned the intuitionistic fuzzy membership degree. With the help of the strong capture ability of the relative density to prior information and the strong recognition ability of the intuitionistic fuzzy score function to noises and outliers, the proposed RIFSVM not only reduces the influence of class imbalance but also suppresses the impact of noises and outliers, and further improves the classification performance. Experiments on the synthetic and public imbalanced datasets show that our approach has better performance in terms of G-Means, F-Measures, and AUC than the other class imbalance classification algorithms.
Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models
Authors: Abbara Omar , Zevallos Mauricio
Published: December 2022

In this paper, we propose a new method for estimating and forecasting asymmetric stochastic volatility models. The proposal is based on dynamic linear models with Markov switching written as state space models. Then, the likelihood is calculated through Kalman filter outputs and the estimates are obtained by the maximum likelihood method. Monte Carlo experiments are performed to assess the quality of estimation. In addition, a backtesting exercise with the real-life time series illustrates that the proposed method is a quick and accurate alternative for forecasting value-at-risk.
Correspondence between Professional Learning Expectations and Learning Opportunities in Financial Management Textbooks
Authors: Acevedo-Arango David , Villa-Ochoa Jhony Alexander , González-Gómez Difariney
Published: December 2022

This research investigated the correspondence between a sample of Financial Management textbooks and professional learning expectations synthesized in the construct of Expectation of Use. To this end, a conceptual framework developed from research on professional practice was integrated with the theoretical perspective of commognition and the analysis of mathematics education textbooks. A qualitative content analysis was performed on the narrative and the end-of-chapter problems of the textbooks, which identified the experiences they can offer and their relationship with professional practice. It was evidenced that the narrative of the textbooks focuses on promoting the development of concepts, principles, and procedures of financial theory; the financial situations presented in the narrative and in the end-of-chapter problems are artificial and therefore have limited relation with professional practice. It was concluded that, according to the textbooks, the mastery of Financial Management consists of appropriating a broad set of financial concepts that excludes the use of these concepts to address problems that simulate the profession. Restructuring the narrative in Financial Management textbooks and consciously including routines that respond to the professional learning needs of the financial community is recommended.
Introduction to Computational Mathematics Using the Historical Solutions of the “Hundred Fowls” Problem
Authors: García-Moreno Miguel Á. , Yáñez Dionisio F. , Diago Pascual D.
Published: December 2022

Through programming tasks, the skills and abilities used to solve mathematical problems are developed and improved. In this article we present a programming teaching trajectory using the solutions presented throughout the history of the “hundred fowls” problem. The proposed itinerary is graduated, meaning that it can be used for different educational stages.
Educational Robotics to Address Behavioral Problems in Early Childhood
Authors: Barragán-Sánchez Raquel , Romero-Tena Rosalía , García-López Miriam
Published: December 2022

After detecting behavioral problems in an early childhood education classroom and verifying the importance of intervening at early ages, we propose the design of an intervention plan through an educational proposition to modify the problematic behaviors. This proposition is focused on developing standards of conduct through computational thinking using robotics. Firstly, needs were identified; then, an educational proposition was implemented; and, lastly, the results were evaluated. For this study, a mixed methodology (quantitative and qualitative) was used. The instruments selected were observation scale, interview and teacher’s diary. The results show a clear improvement of disruptive behaviors. The technology used contributed to the collaborative resolution of the problem, allowing the students to lead such resolution by stimulating their participation and creativity. Thus, it can be asserted that the development of computational thinking through educational robotics is effective for the improvement of social skills at early ages.
The Pedagogical Use of Gamification in English Vocabulary Training and Learning in Higher Education
Authors: Panmei Benjamin , Waluyo Budi
Published: December 2022

One of the persistent challenges in vocabulary teaching is that EFL students must learn a certain number of words to operate in English, yet class time is limited. While the usage of gamification applications may alleviate some of these issues, research on the usefulness of gamification apps and their potential to assist student vocabulary learning outside of the classroom is currently limited. This study used a quasi-experimental method to investigate the effects of gamified vocabulary learning using an application called Quizizz. It compared the learning outcomes of experimental and control groups, as well as the usefulness of gamification in boosting learner autonomy in vocabulary learning. Multiple independent t-tests revealed non-significant differences between students’ overall vocabulary test results in both groups (t (2, 98) = 1.920, p = 0.06) with a modest effect size (Cohen’s d = 0.3). Significant differences were noticed in the results of vocabulary tests 2 (t (2, 98) = 3.229, p = 0.002) and 4 (t (2, 98) = 3.465, p = 0.001), where the students in the experimental groups achieved higher scores (M = 13.63 and M = 12.60), yet no significant differences were noted in the results of vocabulary tests 1 and 2. Significant changes occurred in students’ perceptions of the application of gamification as a means for facilitating training (t (1, 49) = 2.269, p = 0.03) with a modest effect size (Cohen’s d = 0.2). Nonetheless, their perceptions of the use of gamification for enhancing learner autonomy in vocabulary learning did not significantly change before and after the interventions (t (1, 49) = 1.652, p = 1.05) with a very small effect size (Cohen’s d = 0.2). For pedagogy and research, the findings enhance our understanding that gamification apps can be integrated into vocabulary learning to facilitate vocabulary study and foster the growth of learner autonomy, yet utilizing a non-specifically built app for vocabulary learning may not produce better results than those who do not receive gamification support, even if learning outcomes remain good. More research exploring the impacts of integrating a non-specifically built app into English language teaching and learning is needed.
Factors Impacting Short-Term Load Forecasting of Charging Station to Electric Vehicle
Authors: Elahe Md Fazla , Kabir Md Alamgir , Mahmud S. M. Hasan , Azim Riasat
Published: December 2022

The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research.
A Novel Control Scheme Based on Exact Feedback Linearization Achieving Robust Constant Voltage for Boost Converter
Authors: Csizmadia Miklós , Kuczmann Miklós , Orosz Tamás
Published: December 2022

This paper presents a novel form of feedback linearization control (FBL) of boost-type DC/DC converter: to reach highly accurate output voltage control. Integral action has been inserted into the block diagram of the control scheme. The state-space model of the boost converter is highly nonlinear. Accordingly, the design procedure of the controller is more complex. The paper presents the state-space modeling of the boost converter and details the design procedure of the nonlinear FBL controller step by step. The main goal of this paper is to highlight the importance of the error integrator in the FBL control loop. The proposed method has been tested by a numerical example and compared with an existing and validated two-loop controller. Both the dynamical and steady-state behavior of the examined boost converter performed better than the reference system. The steady-state error of the output voltage is almost eliminated, while the dynamical error decreased to 5% in comparison to the two-loop controller.
Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
Authors: Hadi Muhammad Usman , Murtaza Ghulam
Published: December 2022

The terahertz (THz) frequency bands are being explored as a potential means of enabling an ultra-high transmission capacity in sixth-generation (6G) radio-access networks (RAN) because higher frequencies offer broader bandwidths. When utilized in wireless communications, high-frequency electromagnetic waves impose several physical restrictions. To overcome these difficulties and to expand the service coverage, the radio-over-fibre (RoF)-based distributed antenna system (DAS), in particular, can improve the usability of future mobile networks with advantages such as seamless media conversion between wireless and optical signal, flexible multichannel aggregations, and efficiency. RoF technology’s inherent advantages are that it improves the DAS network’s usability and transmission performance by allowing it to provide both 5G and 6G THz services at the same time over a single optical fibre connection. We experimentally broadcast a single carrier-modulated 6G signal using a 256 quadrature amplitude modulation and a 5G new radio signal across a 10 km single mode fibre optic link. Additionally, the 6G signal is received through a 3 m wireless medium providing, proof of concept for fibre wireless integration. The experimental trials are assessed in terms of error vector magnitude and carrier suppression ratio. The dynamic range of the allowed RF input power for a 6G signal is 10 dB, while the dynamic range for a 5G waveform signal is 18 dB, which meets the 3GPP standardization criteria. Moreover, the bit error rate performance significantly improved as the carrier suppression ratio was increased from 0 to 20 dB.
A Player-Specific Framework for Cricket Highlights Generation Using Deep Convolutional Neural Networks
Authors: Mahum Rabbia , Irtaza Aun , Rehman Saeed Ur , Meraj Talha , Rauf Hafiz Tayyab
Published: December 2022

Automatic ways to generate video summarization is a key technique to manage huge video content nowadays. The aim of video summaries is to provide important information in less time to viewers. There exist some techniques for video summarization in the cricket domain, however, to the best of our knowledge our proposed model is the first one to deal with specific player summaries in cricket videos successfully. In this study, we provide a novel framework and a valuable technique for cricket video summarization and classification. For video summary specific to the player, the proposed technique exploits the fact i.e., presence of Score Caption (SC) in frames. In the first stage, optical character recognition (OCR) is applied to extract text summary from SC to find all frames of the specific player such as the Start Frame (SF) to the Last Frame (LF). In the second stage, various frames of cricket videos are used in the supervised AlexNet classifier for training along with class labels such as positive and negative for binary classification. A pre-trained network is trained for binary classification of those frames which are attained from the first phase exhibiting the performance of a specific player along with some additional scenes. In the third phase, the person identification technique is employed to recognize frames containing the specific player. Then, frames are cropped and SIFT features are extracted from identified person to further cluster these frames using the fuzzy c-means clustering method. The reason behind the third phase is to further optimize the video summaries as the frames attained in the second stage included the partner player’s frame as well. The proposed framework successfully utilizes the cricket videoo dataset. Additionally, the technique is very efficient and useful in broadcasting cricket video highlights of a specific player. The experimental results signify that our proposed method surpasses the previously stated results, improving the overall accuracy of up to 95%.
Experimental Evaluation of Predictive Torque Control of IPMSM under Speed Sensor and Sensorless Extended EMF Method
Authors: Alsofyani Ibrahim Mohd , Mohammed Sadeq Ali Qasem
Published: December 2022

This study investigates the impact of the position information on the torque performance for an interior permanent magnet synchronous motor (IPMSM) driven by finite set–predictive torque control (FS-PTC). Unlike induction machines, IPMSMs are significantly sensitive to the rotor position during the motor operation owing to the permanent magnet of the rotor. Any misalignment or displacement of the rotor frame (d-q) can lead to poor prediction of the motor drive performance because the predictive control requires speed/position information during the prediction and evaluation by the cost function. Hence, the performance of the FS-PTC, which uses a speed encoder and senseless extended electromotive force (EMF) estimation, is evaluated and compared with respect to the speed and load conditions. Based on the investigation, the sensorless FS-PTC using the EMF method has superior torque performance and THD reduction compared to the measured speed-based PTC, particularly under large load torque. The performance evaluation of IPMSM was carried out through experimental results.
Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks
Authors: Zhu Liang , Liu Xiaowei , Jing Zhiyong , Yu Liping , Cai Zengyu , Zhang Jianwei
Published: December 2022

Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection.
Streaming Potential in Gas Phase Discrete Unsaturated Pore
Authors: Zhao Yongpeng , Sun Xiangyang , Nie Zaiping
Published: December 2022

The seismoelectric effect of porous media is the main basis for seismoelectric logging. At present, most of the studies on the seismoelectric effect in unsaturated porous media adopt the model of pores with continuous distribution of gas and liquid. There is a lack of theoretical research on the micro mechanism of the seismoelectric effect of unsaturated porous media with discrete gas phase, and the existing studies do not consider the effect of the electric double layer at the gas–liquid interface on the seismoelectric effect. Based on the capillary model, this work adopted the gas phase discrete model, combined the electric double layer theory and the seepage principle, considered the effect of electric double layer at the pore wall and the gas–liquid interface, and studied the micro principle of the seismoelectric effect of unsaturated porous media. Firstly, we studied the variation of gas–water two-phase flow pattern with saturation in unsaturated pores, then proposed the equivalent principle of series circuits, deduced the effective streaming current and conductance of a pore containing multiple bubbles, and then deduced the streaming potential coupling coefficient in the unsaturated pores. We also studied the effect of pore parameters such as saturation, pore size, bubble spacing, pore fluid viscosity, and salinity on the streaming potential coupling coefficient. The results show that the streaming potential coupling coefficient first increases and then decreases with the decrease in saturation, which is the same as the trend measured in Allègre’s experiment, and provide a theoretical explanation for the non-monotonic change in the coupling coefficient with saturation in unsaturated porous media.
Plane-Wave Generation through General Near-Field In-Band Reflectarray Direct Layout Optimization with Figure of Merit Constraints in mm-Wave Band
Authors: Prado Daniel R.
Published: December 2022

A general near-field (NF) in-band synthesis strategy is presented for the direct layout optimization (DLO) of reflectarrays with application to the generation of plane waves for the mm-wave band. The technique relies on the definition of relevant figures of merit (FoM) and the use of a gradient-based minimization algorithm. To this end, the volume where the NF is computed is divided into a number of disjoint regions where the FoM are defined. These FoM define the performance of the antenna and their direct optimization enables improvement compared to previous approaches described in the literature, while reducing the memory footprint of the algorithm and accelerating computation. The optimization procedure is divided into several stages to facilitate convergence towards a successful outcome. First, a phase-only synthesis is carried out at a single frequency. Then, a reflectarray layout is obtained using a method of moments based on local periodicity, accounting for mutual coupling between elements. Finally, an in-band DLO is performed at a number of frequencies directly optimizing the FoM. The results show that the obtained reflectarray layout complies with the requirements in the frequency range 27 GHz–29 GHz within the 5G new radio n257 band.
Thermal Characteristic Simulation Study of Multicolumn Parallel Zinc Oxide Arresters under Extreme Operating Conditions
Authors: Fan Youping , Zhang Peng , Shang Ben , Wang Dianlang , Li Wen’an , Zhuang Dongjian , Chen Zihan , Zhang Zhaoyi , Wen Wu
Published: December 2022

The arrester plays an important role in the protection of the DC transmission system, and its thermal characteristics under different operating conditions greatly affect its performance. To study the thermal characteristics of multicolumn parallel arresters under extreme operating conditions in a DC system, considering the influence of SF6 fluid, the structural parameters of the ±500 kV Niu Cong DC transmission project were applied for this research. Firstly, a 3D model of the four-column parallel zinc oxide arrester installed on the neutral bus of the ±500 kV Niu Cong DC transmission project was built in ANSYS to analyze its thermal conduction. Then, the electromagnetic transient model of the Niu Cong DC transmission system was established in PSCAD to study the withstood energy of a four-column parallel zinc oxide arrester under 22 typical fault conditions in three operation modes. Based on the extreme operating conditions obtained, simulations of steady-state and transient thermal characteristics were performed considering the influence of SF6 fluid flow on the heat dissipation of the arrester. Finally, the field-test temperature test on the four-column parallel zinc oxide arrester was carried out to validate the effectiveness of the proposed simulation model and calculation method, with simulation data matching well with the field-test data. The results also conclude the thermal characteristics findings to reveal the thermal conduction of multicolumn arresters under extreme operating conditions.
A New Methodology to Manage FPGA Distributed Memory Content via Bitstream for Xilinx ZYNQ Devices
Authors: Gomez-Cornejo Julen , Aranzabal Itxaso , Ropero Iraide López , Mazón Angel Javier , Zuloaga Aitzol
Published: December 2022

This paper proposes a methodology to access data and manage the content of distributed memories in FPGA designs through the configuration bitstream. Thanks to the methods proposed, it is possible to read and write the data content of registers without using the in/out ports of registers in a straightforward fashion. Hence, it offers the possibility of performing several operations, such as, to load, copy or compare the information stored in registers without the necessity of physical interconnections. This work includes two flows that simplify the designing process when using the proposed approach: while the first enables the protection or unprotection of writing on different partial regions through the bitstream, the second permits homogeneous instances of a design implemented in different reconfigurable regions to be obtained without losing efficiency. The approach is based and has been physically validated on the ZYNQ from Xilinx, and when using partially reconfigurable designs, it does not affect the hardware overhead nor the maximum operating frequency of the design.
An Analytical Approach to Calculate Soft Error Rate Induced by Atmospheric Neutrons
Authors: Wrobel Frédéric , Aguiar Ygor , Marques Cleiton , Lerner Giuseppe , García Alía Rubén , Saigné Frédéric , Boch Jérôme
Published: December 2022

In the atmosphere, it is generally understood that neutrons are the main contributor to the soft error rate (SER) in electronic devices. These particles are indeed able to trigger nuclear reactions in the sensitive regions of the devices, leading to secondary ions that may ionize the matter sufficiently to upset a memory cell or induce a transient signal, known as soft errors. For reliability purposes, it is crucial to be able to estimate the SER associated with a given technology, which is typically characterized by its sensitive volume and its threshold linear energy transfer (LET). As an alternative to the usual Monte Carlo methods, in this work we present an analytical model for SER prediction, where we separate the radiation–matter interaction from the geometry considerations (sensitive volume). By doing so, we show that the SER can be expressed as the sum of two contributions that can be calculated for any threshold LET and any sensitive volume size. We compare our proposed approach to existing Monte Carlo simulations in the literature, obtaining a very good agreement despite our approximations, thus validating our approach. As an additional result, we can show that, for future down-sized technologies that may be more sensitive to radiation effects, the contribution of neutrons in the 1–10 MeV energy range to the SER is expected to decrease.
Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification
Authors: da Silva Junior Juarez Antonio , Pacheco Admilson da Penha , Ruiz-Armenteros Antonio Miguel , Henriques Renato Filipe Faria
Published: December 2022

Forest fires are considered one of the major dangers and environmental issues across the world. In the Cerrado biome (Brazilian savannas), forest fires have several consequences, including increased temperature, decreased rainfall, genetic depletion of natural species, and increased risk of respiratory diseases. This study presents a methodology that uses data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor of the Sentinel-3B satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite to analyze the thematic accuracy of burned area maps and their sensitivity under different spectral resolutions in a large area of 32,000 km2 in the Cerrado biome from 2019 to 2021. The methodology used training and the Support Vector Machine (SVM) classifier. To analyze the spectral peculiarities of each orbital platform, the Transformed Divergence (TD) index separability statistic was used. The results showed that for both sensors, the near-infrared (NIR) band has an essential role in the detection of the burned areas, presenting high separability. Overall, it was possible to observe that the spectral mixing problems, registration date, and the spatial resolution of 500 m were the main factors that led to commission errors ranging between 15% and 72% and omission errors between 51% and 86% for both sensors. This study showed the importance of multispectral sensors for monitoring forest fires. It was found, however, that the spectral resolution and burning date may gradually interfere with the detection process.
Mycobiome of Post-Agricultural Soils 20 Years after Application of Organic Substrates and Planting of Pine Seedlings
Authors: Malewski Tadeusz , Borowik Piotr , Olejarski Ireneusz , Berezovska Daria , Dyshko Valentyna , Behnke-Borowczyk Jolanta , Pusz Wojciech , Matic Slavica , Oszako Tomasz
Published: December 2022

A 20-year study of a pine stand on post-agricultural land showed that woody debris in the form of organic matter can be successfully used to restore symbiotic mycorrhizal communities, as is the case with forest soils. Woody substrates restored organic matter in soils altered by long agricultural use and had a positive effect on the composition of mycobiota antagonistic to pathogens, especially to Heterobasidion annosum, the causal agent of the dangerous disease root and stump rot of many forest tree species, including stands of Pinus sylvestris (L.). In a study that started in 2001 in the forest district of Czarne Człuchowskie (northern Poland), the following organic materials were used: wood residues (W), sawdust (S), bark compost (B), and compost applied to the root zone during planting (G). The organic materials were spread in the form of mulch over the entire area during planting. After twenty years, it was found that the substrates used provided suitable growth conditions for mycobiome useful for pines. The addition of organic matter did not change the alpha biodiversity of the soil, but in the long term led to significant changes in the composition of mycobiota (beta biodiversity). The changes in the soil after the addition of organic material naturally accelerated the formation of the forest habitat. A number of fungi evolved that degraded added lignin and cellulose while being antagonists of H. annosum and other pine pathogens. In particular, the well-known hyperpathogens of the genus Trichoderma played an important role by promoting resistance of the soil environment to pathogens. Soil enrichment by bark compost and wood residues increased the relative abundance of Trichoderma more than fourfold.Mycorrhizal fungi became dominant in soil enriched with organic matter. After enriching the soil with bark compost, the relative abundance of Amphinema and Inocybe increased to 5%. The relative abundance of Russula in soil enriched with wood residues and sawdust increased to 9% and 5%, respectively. Mycorrhizal fungi, e.g., of the genus Ąmanita, Rusula, which formed root mycorrhizae, not only increased the root receiving area many times over, but also protected the roots (mechanically and chemically from pathogens). Altogether, the observed positive changes increase the chances that the first generation of pines will survive on the ground.
The Influence of Forest Landscape Spaces on Physical and Mental Restoration and Preferences of Young Adults of Different Genders
Authors: Zhang Zhi , Chen Yanling , Qiao Xinru , Zhang Weikang , Meng Huan , Gao Yu , Zhang Tong
Published: December 2022

Forest landscape spaces have positive effects on human physical and mental health. Meanwhile, gender is an important biological factor in differences in human physical and mental responses when facing stress. Therefore, it is necessary to discuss the gender characteristics and differences of people’s experiences of restoration in forest landscapes. Meanwhile, it is urgent to attend to the issue of young adults’ physical and mental health. This study aimed to clarify the impact of forest landscape exposure on physical and mental restoration and preferences in young adults of different genders and to explore the relationship between them. Six representative forest landscape spaces found in field research in Liaoning were presented to participants through virtual reality (VR) video. Physiological indicators (blood pressure, heart rate, and pulse), mood indicators (simplified profile of mood states), and preference scores of young adults (n = 319) before and after viewing the forest landscape videos were collected. Analysis of differences and Spearman’s rho correlation analysis were used to statistically analyse the data. Our results indicated that overlook landscape space, static water landscape space, and coniferous forest landscape space had differential restorative effects on participants’ physical and mental health. Male and female participants had different preferences regarding the forest landscape spaces. Meanwhile, there were strong correlations between participants’ preferences and restorative effects. Our findings provide preliminary practical basis for forest landscape planning that corresponds to the health needs of tourists of different genders to achieve optimization of health benefits of urban forest resources.
Impact of Sugar Beet Pulp Share on Selected Physical and Mechanical Properties of Particleboards
Authors: Auriga Radosław , Borysiuk Piotr , Latos Maciej , Auriga Alicja , Kwaśny Łukasz , Walkiewicz Joanna
Published: December 2022

The aim of this study was to investigate the applicability of sugar beet pulp (SBP) in particleboard production as a substitute for wood material. Two variants of board density, 650 kg/m3 and 550 kg/m3, containing 0%, 25% and 50% of SPB, were evaluated. During the study the following features were measured: modulus of rupture (MOR), modulus of elasticity (MOE), thickness swelling (TS) and water absorption (WA). The results showed that boards containing up to 25% of SBP meet standards for boards for general-purpose used in dry conditions. Further increases in SBP content deteriorated some of the investigated properties of the boards.
A Modelling System for Dead Wood Assessment in the Forests of Northern Eurasia
Authors: Shvidenko Anatoly , Mukhortova Liudmila , Kapitsa Ekaterina , Kraxner Florian , See Linda , Pyzhev Anton , Gordeev Roman , Fedorov Stanislav , Korotkov Vladimir , Bartalev Sergey , Schepaschenko Dmitry
Published: December 2022

Dead wood, including coarse woody debris, CWD, and fine woody debris, FWD, plays a substantial role in forest ecosystem functioning. However, the amount and dynamics of dead wood in the forests of Northern Eurasia are poorly understood. The aim of this study was to develop a spatially distributed modelling system (limited to the territories of the former Soviet Union) to assess the amount and structure of dead wood by its components (including snags, logs, stumps, and the dry branches of living trees) based on the most comprehensive database of field measurements to date. The system is intended to be used to assess the dead wood volume and the amount of dead wood in carbon units as part of the carbon budget calculation of forests at different scales. It is presented using multi-dimensional regression equations of dead wood expansion factors (DWEF)—the ratio of the dead wood component volume to the growing stock volume of the stands. The system can be also used for the accounting of dead wood stock and its dynamics in national greenhouse gas inventories and UNFCCC reporting. The system’s accuracy is satisfactory for the average level of disturbance regimes but it may require corrections for regions with accelerated disturbance regimes.