bluuebunny/crossref_metadata_2025 huggingface dataset:
CBM Gas Content Prediction Model Based on the Ensemble Tree Algorithm with Bayesian Hyper-Parameter Optimization Method: A Case Study of Zhengzhuang Block, Southern Qinshui Basin, North China
Authors: Yang Chao , Qiu Feng , Xiao Fan , Chen Siyu , Fang Yufeng
Published: February 2023

Gas content is an important parameter for evaluating coalbed methane reservoirs, so it is an important prerequisite for coalbed methane resource evaluation and favorable area optimization to predict the gas content accurately. To improve the accuracy of CBM gas content prediction, the Bayesian hyper-parameter optimization method (BO) is introduced into the random forest algorithm (RF) and gradient boosting decision tree algorithm (GBDT) to establish CBM gas content prediction models using well-logging data in the Zhengzhuang block, south of Qinshui Basin, China. As a result, the GBDT model based on the BO method (BO-GBDT model) and the RF model based on the BO method (BO-RF model) were proposed. The results show that the mean-square-error (MSE) of the BO-RF model and the BO-GBDT model can be reduced by 8.83% and 37.94% on average less than that of the RF and GBDT modes, indicating that the accuracy of the models optimized by the BO method is improved. The prediction effect of the BO-GBDT model is better than that of the BO-RF model, especially in low gas content wells, and the R-squared (RSQ) of the BO-GBDT model and the BO-RF model is 0.82 and 0.66. The accuracy order of different models was BO-GBDT > GBDT > BO-RF > RF. Compared with other models, the gas content curve predicted by the BO-GBDT model has the best fitness with the measured gas content. The rule of gas distribution predicted by all four models is consistent with the measured gas content distribution.
Development of Anomaly Detectors for HVAC Systems Using Machine Learning
Authors: Borda Davide , Bergagio Mattia , Amerio Massimo , Masoero Marco Carlo , Borchiellini Romano , Papurello Davide
Published: February 2023

Faults and anomalous behavior affect the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. This causes performance loss, energy waste, noncompliance with regulations and discomfort among occupants. To prevent damage, automated, fast identification of faults in HVAC systems is needed. Fault Detection and Diagnosis (FDD) techniques are very effective for these purposes. The best FDD methods, in terms of cost effectiveness and data exploitation, are based on process history; i.e., on sensor data from automation systems. In this work, supervised and semi-supervised models were developed. Other than with regard to outdoor temperature and humidity, the input parameters of an HVAC system have few internal variables. Performance of traditional methods (e.g., VAR, Random Forest) is low, so Artificial Neural Networks (ANNs) were selected, since they can capture nonlinear relationships among features and are easily optimized. ANNs can detect simultaneous faults from different classes. ANN metrics are easily evaluated. The ground truth is obtained from process history (supervised case) and from a mix of deterministic methods and clustering (semi-supervised case). The derivation of the ground truth in the semi-supervised case, and extensive comparison with advanced supervised models, set this work apart from previous studies. The Mean Absolute Error (MAE) of the best supervised model was 0.032 over 15 min and 0.034 over 30 min. The Balanced Accuracy Score (BAS) of the best semi-supervised model was 86%.
Non-Thermal Plasma as a Biomass Pretreatment in Biorefining Processes
Authors: Meoli Carmen Maria , Iervolino Giuseppina , Procentese Alessandra
Published: February 2023

Climatic changes and the growing population call for innovative solutions that are able to produce biochemicals by adopting environmentally sustainable procedures. The biorefinery concept meets this requirement. However, one of the main drawbacks of biorefineries is represented by the feedstocks’ pretreatment. Lately, scientific research has focused on non-thermal plasma, which is an innovative and sustainable pretreatment that is able to obtain a high sugar concentration. In the present review, literature related to the use of non-thermal plasma for the production of fermentable sugar have been collected. In particular, its sugar extraction, time, and energy consumption have been compared with those of traditional biomass pretreatments. As reported, on one hand, this emerging technology is characterized by low costs and no waste production; on the other hand, the reactor’s configuration must be optimized to reduce time and energy demand.
Identification and Evaluation of (Non-)Intentionally Added Substances in Post-Consumer Recyclates and Their Toxicological Classification
Authors: Rung Christian , Welle Frank , Gruner Anita , Springer Arielle , Steinmetz Zacharias , Munoz Katherine
Published: February 2023

According to the European circular economy strategy, all plastic packaging placed on the market by 2030 has to be recyclable. However, for recycled plastics in direct contact with food, there are still major safety concerns because (non-)intentionally added substances can potentially migrate from recycled polymers into foodstuffs. Therefore, the European Food Safety Authority (EFSA) has derived very low migration limits (e.g., 0.1 µg/L for recycled polyethylene terephthalate (PET) and 0.06 µg/L for recycled high-density polyethylene (HDPE)) for recycled polymers. Thus, the use of recyclates from post-consumer waste materials in direct food contact is currently only possible for PET. A first step in assessing potential health hazards is, therefore, the identification and toxicological classification of detected substances. Within this study, samples of post-consumer recyclates from different packaging-relevant recycling materials (HDPE, LDPE, PE, PP, PET, and PS) were analyzed. The detected substances were identified and examined with a focus on their abundance, toxicity (Cramer classification), polarity (log P values), chemical diversity, and origin (post-consumer substances vs. virgin base polymer substances). It was demonstrated that polyolefins contain more substances classified as toxic than PET, potentially due to their higher diffusivity. In addition, despite its low diffusivity compared to polyolefins, a high number of substances was found in PS. Further, post-consumer substances were found to be significantly more toxicologically concerning than virgin base polymer substances. Additionally, a correlation between high log P values and a high Cramer classification was found. It was concluded that PET is currently the only polymer that complies with EFSA’s requirements for a circular economy. However, better-structured collection systems and cleaning processes, as well as more analytical methods that enable a highly sensitive detection and identification of substances, might offer the possibility of implementing other polymers into recycling processes in the future.
Semantic Segmentation for Digital Archives of Borobudur Reliefs Based on Soft-Edge Enhanced Deep Learning
Authors: Ji Shenyu , Pan Jiao , Li Liang , Hasegawa Kyoko , Yamaguchi Hiroshi , Thufail Fadjar I. , Brahmantara  , Sarjiati Upik , Tanaka Satoshi
Published: February 2023

Segmentation and visualization of three-dimensional digital cultural heritage are important analytical tools for the intuitive understanding of content. In this paper, we propose a semantic segmentation and visualization framework that automatically classifies carved items (people, buildings, plants, etc.) in cultural heritage reliefs. We also apply our method to the bas-reliefs of Borobudur Temple, a UNESCO World Heritage Site in Indonesia. The difficulty in relief segmentation lies in the fact that the boundaries of each carved item are formed by indistinct soft edges, i.e., edges with low curvature. This unfavorable relief feature leads the conventional methods to fail to extract soft edges, whether they are three-dimensional methods classifying a three-dimensional scanned point cloud or two-dimensional methods classifying pixels in a drawn image. To solve this problem, we propose a deep-learning-based soft edge enhanced network to extract the semantic labels of each carved item from multichannel images that are projected from the three-dimensional point clouds of the reliefs. The soft edges in the reliefs can be clearly extracted using our novel opacity-based edge highlighting method. By mapping the extracted semantic labels into three-dimensional points of the relief data, the proposed method provides comprehensive three-dimensional semantic segmentation results of the Borobudur reliefs.
Deep Ground Filtering of Large-Scale ALS Point Clouds via Iterative Sequential Ground Prediction
Authors: Dai Hengming , Hu Xiangyun , Shu Zhen , Qin Nannan , Zhang Jinming
Published: February 2023

Ground filtering (GF) is a fundamental step for airborne laser scanning (ALS) data processing. The advent of deep learning techniques provides new solutions to this problem. Existing deep-learning-based methods utilize a segmentation or classification framework to extract ground/non-ground points, which suffers from a dilemma in keeping high spatial resolution while acquiring rich contextual information when dealing with large-scale ALS data due to the computing resource limits. To this end, we propose SeqGP, a novel deep-learning-based GF pipeline that explicitly converts the GF task into an iterative sequential ground prediction (SeqGP) problem using points-profiles. The proposed SeqGP utilizes deep reinforcement learning (DRL) to optimize the prediction sequence and retrieve the bare terrain gradually. The 3D sparse convolution is integrated with the SeqGP strategy to generate high-precision classification results with memory efficiency. Extensive experiments on two challenging test sets demonstrate the state-of-the-art filtering performance and universality of the proposed method in dealing with large-scale ALS data.
Weak Signal Processing Method for Moving Target of GNSS-S Radar Based on Amplitude and Phase Self-Correction
Authors: Gao Wenning , Yue Fuzhan , Xia Zhenghuan , Liu Xin , Zhang Chuang , Liu Zongqiang , Jin Shichao , Zhang Yao , Zhao Zhilong , Zhang Tao , Zhang Ying
Published: February 2023

Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar.
Characterizing Coastal Wind Speed and Significant Wave Height Using Satellite Altimetry and Buoy Data
Authors: Mitsopoulos Panagiotis , Peña Malaquias
Published: February 2023

Wind speed and significant wave height are the most relevant metocean variables that support a wide range of engineering and economic activities. Their characterization through remote sensing estimations is required to compensate for the shortage of in situ observations. This study demonstrates the value of satellite altimetry to identify typical spatial patterns of wind speed and significant wave height in the northeastern region of the United States. Data from five altimetry satellite missions were evaluated against the available in situ observations with a 10 km sampling radius and a 30 min time window. An objective analysis of the collective altimeter dataset was performed to create aggregated composite maps of the wind speed and significant wave height. This asynchronous compositing of multi-mission altimeter data is introduced to compile a sufficient sampling of overpasses over the area of interest. The results of this approach allow for quantifying spatial patterns for the wind speed and significant wave height in the summer and winter seasons. The quality of altimeter estimations was assessed regarding the distance from the coast and the topography. It was found that while the altimeter data are highly accurate for the two variables, bias increases near the coast. The average minimum and maximum wind speed values detected in buoy stations less than 40 km from the coast were not matched by the aggregated altimeter time series. The method exposes the spatial and time gaps to be filled using data from future missions. The challenges of the objective analysis near the coast, especially in semi-enclosed areas, and the implications of the altimeter estimations due to the land contamination are explained. The results indicate that the combination of altimetry data from multiple satellite missions provides a significant complementary information resource for nearshore and coastal wind and wave regime estimations.
Geometry Sampling-Based Adaption to DCGAN for 3D Face Generation
Authors: Luo Guoliang , Xiong Guoming , Huang Xiaojun , Zhao Xin , Tong Yang , Chen Qiang , Zhu Zhiliang , Lei Haopeng , Lin Juncong
Published: February 2023

Despite progress in the past decades, 3D shape acquisition techniques are still a threshold for various 3D face-based applications and have therefore attracted extensive research. Moreover, advanced 2D data generation models based on deep networks may not be directly applicable to 3D objects because of the different dimensionality of 2D and 3D data. In this work, we propose two novel sampling methods to represent 3D faces as matrix-like structured data that can better fit deep networks, namely (1) a geometric sampling method for the structured representation of 3D faces based on the intersection of iso-geodesic curves and radial curves, and (2) a depth-like map sampling method using the average depth of grid cells on the front surface. The above sampling methods can bridge the gap between unstructured 3D face models and powerful deep networks for an unsupervised generative 3D face model. In particular, the above approaches can obtain the structured representation of 3D faces, which enables us to adapt the 3D faces to the Deep Convolution Generative Adversarial Network (DCGAN) for 3D face generation to obtain better 3D faces with different expressions. We demonstrated the effectiveness of our generative model by producing a large variety of 3D faces with different expressions using the two novel down-sampling methods mentioned above.
The Estimated Temperature of the Semiconductor Diode Junction on the Basis of the Remote Thermographic Measurement
Authors: Hulewicz Arkadiusz , Dziarski Krzysztof , Krawiecki Zbigniew
Published: February 2023

The value of a semiconductor’s diode temperature determines the correct operation of this element and its useful lifetime. One of the methods for determining the die temperature of a semiconductor diode is through the use of indirect thermographic measurements. The accuracy of the thermographic temperature measurement of the diode case depends on the prevailing conditions. The temperature of the mold body (the black part of the diode case made of epoxy resin) depends on the place of measurement. The temperature of the place above the die is closer to the die temperature than the temperature of mold body fragments above the base plate. In addition, the difficulty of its thermographic temperature measurement increases when the surface whose temperature is being measured is in motion. Then, the temperature measured by thermography may not apply to the warmest point in the case where the die temperature is determined. Information about the difference between temperatures of the different parts of the mold body and the die may be important. For this reason, it was decided to check how much the temperature measurement error of the die diode changes if the temperature of the diode case is not measured at the point that is above the die.
A Nine-Level Inverter with Adjustable Turn-Off Time for Helicopter Transient Electromagnetic Detection
Authors: Peng Fengjiang , Guo Cheng , Chang Zhu , Yan Zilong , Zhao Qing , Huang Xiaoping
Published: February 2023

The current inverter is the core component of the helicopter transient electromagnetic (HTEM) detection system. It should meet the concerns of low loss, high power, and fast turn-OFF time. This article proposes a new circuit topology based on nine-level inverter technology to overcome the drawbacks of typical PWM (pulse width modulation) inverters, such as switching losses and harmonics. This circuit topology overcomes the shortcomings of the traditional single constant voltage clamp circuit in which the turn-OFF time is not adjustable. Using an inverter with the proposed topology is able to avoid the complex PWM control method and switching loss. In this way, the current rising edge and falling edge of this inverter are also improved effectively. The proposed inverter has adjustable turn-ON-time and turn-OFF time, which is significantly different from the conventional single-clamp inverter. Through subsequent experiments, the inverter proved to have the capability of generating trapezoidal current waveforms. Moreover, by modifying the FPGA (Field Programmable Gate Array) control program, three different turn-OFF times are achieved. The nine-level inverter has a peak current of 1.5 A with an adjustable turn-OFF time from 129 μs to 162 μs. Moreover, the switching frequency of the inverter is reduced from 10 kHz to below 100 Hz. The experimental results further demonstrate that it achieves lower switching losses and more flexible transmission. Our work in this article provides an efficient way to improve the performance of HTEM detection systems.
Blockchain-Based Supply Chain Systems, Interoperability Model in a Pharmaceutical Case Study
Authors: Mezquita Yeray , Podgorelec Blaž , Gil-González Ana Belén , Corchado Juan Manuel
Published: February 2023

The main purpose of supply chain systems based on blockchain technology is to take advantage of technology innovations to ensure that a tracked asset’s audit trail is immutable. However, the challenge lies in tracking the asset among different blockchain-based supply chain systems. The model proposed in this paper has been designed to overcome the identified challenges. Specifically, the proposed model enables: (1) the asset to be tracked among different blockchain-based supply-chain systems; (2) the tracked asset’s supply chain to be cryptographically verified; (3) a tracked asset to be defined in a standardized format; and (4) a tracked asset to be described with several different standardized formats. Thus, the model provides a great advantage in terms of interoperability between different blockchain-driven supply chains over other models in the literature, which will need to replicate the information in each blockchain platform they operate with, while giving flexibility to the platforms that make use of it and maintain the scalability of those logistic platforms. This work aims to examine the application of the proposed model from an operational point of view, in a scenario within the pharmaceutical sector.
Convolutional Neural Network Bootstrapped by Dynamic Segmentation and Stigmergy-Based Encoding for Real-Time Human Activity Recognition in Smart Homes
Authors: Najeh Houda , Lohr Christophe , Leduc Benoit
Published: February 2023

Recently, deep learning (DL) approaches have been extensively employed to recognize human activities in smart buildings, which greatly broaden the scope of applications in this field. Convolutional neural networks (CNN), well known for feature extraction and activity classification, have been applied for estimating human activities. However, most CNN-based techniques usually focus on divided sequences associated to activities, since many real-world employments require information about human activities in real time. In this work, an online human activity recognition (HAR) framework on streaming sensor is proposed. The methodology incorporates real-time dynamic segmentation, stigmergy-based encoding, and classification with a CNN2D. Dynamic segmentation decides if two succeeding events belong to the same activity segment or not. Then, because a CNN2D requires a multi-dimensional format in input, stigmergic track encoding is adopted to build encoded features in a multi-dimensional format. It adopts the directed weighted network (DWN) that takes into account the human spatio-temporal tracks with a requirement of overlapping activities. It represents a matrix that describes an activity segment. Once the DWN for each activity segment is determined, a CNN2D with a DWN in input is adopted to classify activities. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database.
Nanosatellite Payload for Research on Seed Germination in a 3D Printed Micropot
Authors: Kawa Bartosz , Śniadek Patrycja , Walczak Rafał , Dziuban Jan
Published: February 2023

In this paper, an autonomous payload proposal for a nanosatellite mission allowing for the cultivation of grains in space was presented. For the first time, a micropot made with 3D printing technology, enabling the parametric determination of plant growth, both on Earth and in the simulated microgravity condition, was presented. A completed system for dosing the nutrient solution and observing the growth of a single grain, where the whole size did not exceed 70 × 50 × 40 mm3, was shown. The cultivation of Lepidium sativum seeds was carried out in the developed system, in terrestrial conditions and simulated microgravity conditions, using the RPM (Random Position Machine) device. The differences in plant growth depending on the environment were observed. It could be seen that the grains grown in simulated microgravity took longer to reach the full development stage of the plant. At the same time, fewer grains reached this stage and only remained at the earlier stages of growth. The conducted research allowed for the presentation of the payload concept for a 3U CubeSat satellite for research into the development of plants in space.
Research Regarding Different Types of Headlights on Selected Passenger Vehicles when Using Sensor-Related Equipment
Authors: Vrabel Jan , Stopka Ondrej , Palo Jozef , Stopkova Maria , Droździel Paweł , Michalsky Martin
Published: February 2023

Statistical surveys show that the majority of traffic accidents occur due to low visibility, highlighting the need to delve into innovative car lighting technologies. A car driver must not only be able to see but also to be seen. The issue of headlight illumination is vital, especially during the dark hours of the night. Therefore, the focus of this article is determining the range of visibility of dipped (low-beam) headlights under specific experimental conditions. We also designed a methodical guideline aimed at identifying the distance at which dipped headlights illuminate the road while a vehicle is in motion. Research conducted on various classes of road confirmed that the Hyundai i40 is best used on higher-class roads, while the Dacia Sandero is better used on lower-class roads due to the shape and spreading out of its light cone. Furthermore, the pros and cons of the distribution of light cones on several classes of road are presented. Sensor-related equipment was also used to investigate light beam afterglow. In particular, an LX-1108 light meter was applied to determine the obstacle illumination intensity, the properties of which enable recording of low lighting values, and a DJI Mavic AIR 2 unmanned aerial vehicle (UAV; drone) was utilized to record the data related to the location of the examined vehicle, as well as light afterglow at night; relevant data evaluation was carried out using Inkscape software.
Multi-Wavelength Biometric Acquisition System Utilizing Finger Vasculature NIR Imaging
Authors: Fiolka Jerzy , Bernacki Krzysztof , Farah Alejandro , Popowicz Adam
Published: February 2023

Personal identification using analysis of the internal and external characteristics of the human finger is currently an intensively developed topic. The work in this field concerns new methods of feature extraction and image analysis, mainly using modern artificial intelligence algorithms. However, the quality of the data and the way in which it is obtained determines equally the effectiveness of identification. In this article, we present a novel device for extracting vision data from the internal as well as external structures of the human finger. We use spatially selective backlight consisting of NIR diodes of three wavelengths. The fast image acquisition allows for insight into the pulse waveform. Thanks to the external illuminator, images of the skin folds of the finger are acquired as well. This rich collection of images is expected to significantly enhance identification capabilities using existing and future classic and AI-based computer vision techniques. Sample data from our device, before and after data processing, have been shared in a publicly available database.
An Application of Analytic Wavelet Transform and Convolutional Neural Network for Radar Intrapulse Modulation Recognition
Authors: Walenczykowska Marta , Kawalec Adam , Krenc Ksawery
Published: February 2023

This article analyses the possibility of using the Analytic Wavelet Transform (AWT) and the Convolutional Neural Network (CNN) for the purpose of recognizing the intrapulse modulation of radar signals. Firstly, the possibilities of using AWT by the algorithms of automatic signal recognition are discussed. Then, the research focuses on the influence of the parameters of the generalized Morse wavelet on the classification accuracy. The paper’s novelty is also related to the use of the generalized Morse wavelet (GMW) as a superfamily of analytical wavelets with a Convolutional Neural Network (CNN) as classifier applied for intrapulse recognition purposes. GWT is used to obtain time–frequency images (TFI), and SqueezeNet was chosen as the CNN classifier. The article takes into account selected types of intrapulse modulation, namely linear frequency modulation (LFM) and the following types of phase-coded waveform (PCW): Frank, Barker, P1, P2, and Px. The authors also consider the possibility of using other time–frequency transformations such as Short-Time Fourier Transform(STFT) or Wigner–Ville Distribution (WVD). Finally, authors present the results of the simulation tests carried out in the Matlab environment, taking into account the signal-to-noise ratio (SNR) in the range from −6 to 0 dB.
A Scalable Cross-Chain Access Control and Identity Authentication Scheme
Authors: Ding Yuhang , Zhang Yanran , Qin Bo , Wang Qin , Yang Zihan , Shi Wenchang
Published: February 2023

Cross-chain is an emerging blockchain technology which builds the bridge across homogeneous and heterogeneous blockchains. However, due to the differentiation of different blockchains and the lack of access control and identity authentication of cross-chain operation subjects, existing cross-chain technologies are struggling to accomplish the identity transformation of cross-chain subjects between different chains, and also pose great challenges in terms of the traceability and supervision of dangerous transactions. To address the above issues, this paper proposes a scalable cross-chain access control and identity authentication scheme, which can authenticate the legitimacy of blockchains in the cross-chain system and ensure that all cross-chain operations are carried out by verified users. Furthermore, it will record all cross-chain operations with the help of Superchain in order to regulate and trace illegal transactions. Our scheme is scalable and, at the same time, has low invasiveness to blockchains in the cross-chain system. We implement the scheme and accordingly conduct the evaluations, which prove its security, efficiency, and scalability.
Digital Twin Model of Electric Drives Empowered by EKF
Authors: Ebadpour Mohsen , Jamshidi Mohammad (Behdad) , Talla Jakub , Hashemi-Dezaki Hamed , Peroutka Zdeněk
Published: February 2023

Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is the integration of cyber–physical systems into machinery to enable connectivity. The digital twin is a promising solution for addressing the challenges of digitally implementing models and smart manufacturing, as it has been successfully applied for many different infrastructures. Using a digital twin for future electric drive applications can help analyze the interaction and effects between the fast-switching inverter and the electric machine, as well as the system’s overall behavior. In this respect, this paper proposes using an Extended Kalman Filter (EKF) digital twin model to accurately estimate the states of a speed sensorless rotor field-oriented controlled induction motor (IM) drive. The accuracy of the state estimation using the EKF depends heavily on the input voltages, which are typically supplied by the inverter. In contrast to previous research that used a low-precision ideal inverter model, this study employs a high-performance EKF observer based on a practical model of the inverter that takes into account the dead-time effects and voltage drops of switching devices. To demonstrate the effectiveness of the EKF digital twinning on the IM drive system, simulations were run using the MATLAB/Simulink software (R2022a), and results are compared with a set of actual data coming from a 4 kW three-phase IM as a physical entity.
Study of Potentially Toxic Metal Adsorption in a Polluted Acid and Alkaline Soil: Influence of Soil Properties and Levels of Metal Concentration
Authors: Golia Evangelia E. , Kantzou Ourania-Despoina , Chartodiplomenou Maria-Anna , Papadimou Sotiria G. , Tsiropoulos Nikolaos G.
Published: February 2023

In the present study, the adsorption of zinc (Zn), lead (Pb), copper (Cu), and cadmium (Cd) was studied in two already polluted urban soil samples with different pH values, an acidic and an alkaline one. The Langmuir and Freundlich adsorption isotherm equations were used to thoroughly study the adsorption of the metallic elements on the solid surface of the soils. Langmuir equation described the adsorption of each metal satisfactorily, with a slight predominance over Freundlich, in both soils, as the R2 value approached almost unity. Even though Zn and Cu were adsorbed on the soil phase, their adsorption was minimal compared to the adsorption of more harmful metals such as Pb and Cd. Using the values of the coefficients obtained from the equations of the mathematical models, we concluded that in alkaline soils, the retention of metals was much greater than in acidic soils. The simultaneous presence of metals during the addition of the single-element solutions of the metals to the already metal-contaminated soils caused competitive adsorption increasing the retention of the more toxic metals on the solid surface of the alkaline soil. Factors affecting soil sorption (such as soil pH and CaCO3 content) were studied to provide theoretical support for understanding the laws and causes of metal sorption in the soils of the survey.
Motivation Profile of Youth Greco-Roman Wrestlers; Differences According to Performance Quality
Authors: Skugor Kreso , Gilic Barbara , Mladenovic Marijana , Stajer Valdemar , Roklicer Roberto , Slacanac Kristijan , Bagaric Domagoj , Karnincic Hrvoje
Published: February 2023

Athletes have to possess high motivation levels to perform each training session and competition at the highest level. Thus, the motivation of the wrestler is essential to reach the highest performance quality. The research included 47 Greco-Roman wrestlers aged 17.71 ± 1.62 years. Variables included anthropometric indices, sports motivation assessed by the revised Sport Motivation Scale (SMS-II), and competitive success (medal winners and non-winners at the National Championship). The Cronbach’s alpha coefficients checked the internal consistency of the SMS-II. Differences between performance quality were determined by Cohen’s d effect sizes, and MANOVA for motivation and anthropometric variables/body build variables. In the total sample, wrestlers had high levels of intrinsic motivation (5.97 ± 0.90), integrated (5.99 ± 0.83), and identified (6.08 ± 0.82) regulation, while they had low amotivation (2.53 ± 0.98) and external regulation (3.26 ± 1.24). Successful wrestlers had significantly higher intrinsic motivation than less successful wrestlers (Cohen’s d = 0.76, moderate effect size). Results evidenced that wrestlers have high self-determined motivation, which is vital for maximal performance and persisting in sports. Future research should investigate wrestlers from other age groups to ultimately determine the sport motivation profile of wrestlers and enable their optimal sports development.
Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach
Authors: Alanazi Fayez
Published: February 2023

Smart mobility systems offers solutions for traffic congestion, transport management, emergency, and road safety. However, the success of smart mobility lies in the availability of intelligent transportation infrastructure. This paper studied smart mobility systems in three Asia-Pacific countries (South Korea, Singapore, and Japan) to highlight the major strategies leading their successful journey to become smart cities for aspiring countries, such as the Kingdom of Saudi Arabia (KSA), to emulate. A robust framework for evaluating smart mobility systems in the three countries and Saudi Arabia was developed based on the indicators derived from the smart mobility ecosystem and three major types of transport services (private, public, and emergency). Sixty indicators of smart mobility systems were identified through a rigorous search of the literature and other secondary sources. Robots, drones, IoT, 5G, hyperloop tunnels, and self-driving technologies formed part of the indicators in those countries. The study reveals that the three Asia-Pacific countries are moving head-to-head in terms of smart mobility development. Saudi Arabia can join these smarter countries through inclusive development, standardization, and policy-driven strategies with clear commitments to public, private, and research collaborations in the development of its smart mobility ecosystem. Moreover, cybersecurity must be taken seriously because most of the smart mobility systems use wireless and IoT technologies, which may be vulnerable to hacking, and thus impact system safety. In addition, the smart mobility system should include data analytics, machine learning, and artificial intelligence in developing and monitoring the evaluation in terms of user experience and future adaptability.
SMEs’ Internationalization: Mapping the Field through Finance, ITC, and Social Ties
Authors: Anwar Muhammad , Li Shaungjie , Al-Omush Ahmed , Al-Nimer Munther
Published: February 2023

Over the last several decades, the internationalization of small and medium enterprises (SMEs) has become an interesting research topic in business and entrepreneurship literature. The massive literature in the field has increased demand for systematic reviews, bibliometric studies and meta-analyses. However, up to date, there has been no systematic literature review, particularly on the role of finance, information technology (IT) and network ties in SMEs’ internationalization. Our research fills the gap and reveals the idiosyncrasies in the literature by conducting a systematic literature review to understand evaluation, scholarly debate, past and current research streams and white spaces for future researchers. We employed 262 documents (1997–2022) and utilized performance analysis and co-occurrence analysis for each area separately and then combined them. The database indicates that research in the particular areas started in 1997, but scholarly interest has been noted since 2006 and onward. We further identified the most influential documents, countries and journals in the fields of finance, IT and network ties and SMEs’ internationalization. Moreover, we conducted a co-occurrence analysis and identified past and current research streams in the fields. Finally, we created an integrated framework to identify white spaces and future research directions. Our insights show that SMEs should use finance, IT and network ties as complementary, rather than exclusive, to boost their internationalization process.
Healthy Community Assessment Model Based on the German DGNB System
Authors: Huang Manshu , Tao Yinying , Qiu Shunian , Chang Yiming
Published: February 2023

As a space for daily life, the community directly affects residents’ lives and has a significant impact on residents’ health. Integrating the concept of health into community construction can promote comprehensive and full-cycle health protection. This study explored the potential contribution of the DGNB system to community health and well-being and collected residents’ perceptions. A community assessment model was established to analyze how the community environment would affect residents’ health. The results show that compared with other community evaluation systems, the DGNB system has a more balanced weight and more comprehensive content, covering many factors that influence physical health, mental health, and social health. Residents pay more attention to personal safety, lifestyle, physical environment, community service, and management, which are related to their well-being and health. The assessment model is helpful to improve the community healthy environment and residents’ life quality.
The COVID-19 Pandemic Response and Its Impact on Post-Corona Health Emergency and Disaster Risk Management in Republic of Korea
Authors: Park Jaeyoung , Min Jeehee , Song Ji-Hun , Park Min Young , Yoo Hyoungseob , Kwon Ohwi , Yang Munyoung , Kim Seoyoung , Lee Jongin , Myong Jun-Pyo
Published: February 2023

The COVID-19 pandemic significantly impacted the Republic of Korea’s Health Emergency and Disaster Risk Management (Health-EDRM). This study aims to examine the Republic of Korea’s response to the COVID-19 pandemic, focusing on Health-EDRM, especially human resources, health services, and logistics. Challenges in the Republic of Korea, including lack of medical workforce, confused risk communication, shortage of hospital beds, and inefficient distribution of medical resources, have been highlighted in this paper in terms of human resources, health service delivery, and logistics, which are components of Health-EDRM. It is essential to address the cooperation between the government and private sectors, the protection of occupational health and safety of medical staff during the pandemic, and strategies and technologies to scale up the health facilities, to respond to a future crisis like the COVID-19 pandemic.
Effects of Climate Change on the Spatial Distribution of the Threatened Species Rhododendron purdomii in Qinling-Daba Mountains of Central China: Implications for Conservation
Authors: Dong Hao , Zhang Ningning , Shen Simin , Zhu Shixin , Fan Saibin , Lu Yang
Published: February 2023

The plant species in the mountainous regions might be relatively more vulnerable to climate change. Understanding the potential effects of climate change on keystone species, such as Rhododendron species in the subalpine and alpine ecosystems, is critically important for montane ecosystems management and conservation. In this study, we used the maximum entropy (MaxEnt) model, 53 distribution records, and 22 environmental variables to predict the potential impacts of climate change on the distribution of the endemic and vulnerable species Rhododendron purdomii in China. The main environmental variables affecting the habitat suitability of R. purdomii were altitude, temperature seasonality, annual precipitation, slope, and isothermality. Our results found suitable distribution areas of R. purdomii concentrated continuously in the Qinling-Daba Mountains of Central China under different climate scenarios, indicating that these areas could potentially be long-term climate refugia for this species. The suitable distribution areas of R. purdomii will expand under the SSP126 (2070s), SSP585 (2050s), and SSP585 (2070s) scenarios, but may be negatively influenced under the SSP126 (2050s) scenario. Moreover, the potential distribution changes of R. purdomii showed the pattern of northward shift and west–east migration in response to climate change, and were mainly limited to the marginal areas of species distribution. Finally, conservation strategies, such as habitat protection and assisted migration, are recommended. Our findings will shed light on biotic responses to climate change in the Qinling-Daba Mountains region and provide guidance for the effective conservation of other endangered tree species.
Impact of New and Old Driving Force Conversion on Air Quality: Empirical Analysis Based on RDD
Authors: Zhu Yan , Zhang Hongfeng , He Xu
Published: February 2023

This paper evaluates the impact of the conversion of new and old driving forces of economic growth on air quality. Based on the policy shock of the establishment of the Shandong pilot zone, this paper took the monthly air quality index (AQI) as a measurement indicator and adopted the Regression Discontinuity Design (RDD) method. It was found that the conversion of new and old driving forces significantly improved air quality, and this effect was still robust for different bandwidths. The examination of individual pollutants revealed that the improvement in air quality (AQI) came mainly from the reduction in the levels of five pollutants in the air: PM2.5, PM10, SO2, CO, and NO2. Moreover, through the convergence analysis, it was found that the air pollution governance effect of the conversion of new and old driving forces had significant β convergence characteristics. The convergence characteristics were still robust even considering control variables and spatial factors, thus indicating that the conversion of new and old driving forces had a synergistic governance effect on air pollution control.
A Goal-Oriented Reflection Strategy-Based Virtual Reality Approach to Promoting Students’ Learning Achievement, Motivation and Reflective Thinking
Authors: Chu Shih-Ting , Hwang Gwo-Jen , Hwang Gwo-Haur
Published: February 2023

Scholars have emphasized the importance of situating learners in authentic learning contexts. Nevertheless, it is sometimes challenging to provide learners with real contexts owing to various reasons, such as safety or economic consideration. The advent of virtual reality (VR) has provided the opportunity to enable learners to experience and interact in authentic contexts. On the other hand, researchers have pinpointed that, during the VR learning process, a student’s attempt or engagement levels play an important role in their knowledge gains. That is, without a clear goal, their learning outcomes could be disappointing. Hence, the present study proposes a goal-oriented reflection strategy-based VR (GRS-VR) model. Moreover, a VR-based learning system is developed based on the model. To examine the effectiveness of the proposed approach, a quasi-experiment was conducted in an English-speaking course at a junior high school. Two classes of ninth graders were recruited in this study: one class was an experimental group adopting the GRS-VR learning approach, while the other was a control group adopting the conventional VR (C-VR) learning approach. The results indicated that the experimental group had significantly better English oral performance, learning motivation and reflective thinking than the C-VR group.
Hesitations and Aspirations of Farmers in Nature-Protected Areas
Authors: Turck Angela , Terlau Wiltrud
Published: February 2023

Pursuant to Sustainable Development Goal (SDG) 15 of the 2030 Agenda for Sustainable Development of the United Nations, one pivotal target is to halt biodiversity loss. This paper’s objective is to analyze why and how German farmers hesitate to implement more than the prescriptive measures with regard to cross compliance and direct payments under the European Common Agricultural Policy (CAP) and what their aspirations are for possible incentives to bring biodiversity into focus. By applying a mixed methods approach, we investigate the experience of individual farmers by means of a qualitative approach followed by a quantitative study. This analysis sheds light on how farmers perceive indirect influencing factors and how these factors play a non-negligible role in farmers´ commitment to biodiversity. Economy, policy and society are intertwined and need to be considered from a multi-faceted perspective. In addition, an in-depth analysis is conducted based on online focus group discussions to determine whether farmers accept financial support, focusing on both action- and success-oriented payments. Our results highlight the importance of paying attention to the heterogeneity of farmers, their locations and, consequently, farmers’ different views on indirect drivers influencing agricultural processes, showing the complexity of the problem. Although farmers’ expectations can be met with financial allocations, other aspects must also be taken into account.
Indices Development for Player’s Performance Evaluation through the Super-SBM Approach in Each Department for All Three Formats of Cricket
Authors: Yin Wei , Ye Zhixiao , Shah Wasi Ul Hassan
Published: February 2023

Player performance evaluations in all three formats of cricket have been a topic of great concern for sports analysts and research experts. This study proposed a comprehensive performance estimation tool that incorporates all the essential inputs–outputs and evaluates a cricketer’s overall performance. This research introduced three different estimation indices for player efficiency in all three formats of cricket for batting, bowling, and fielding. Further, this research employed the DEA Super-SBM model to evaluate the player’s efficiency in batting, bowling, and fielding departments of all three formats. The study estimates the most efficient batsman, bowler, and fielder in cricketing history by using the data of international cricketers (1877–2019). The results indicate that, compared to the traditional parameters, the proposed study indices are more accurate and comprehensive in nature. The most efficient batsman, bowler, and fielder in all three formats are given, respectively: (i) Sir Bradman, Sachin Tendulkar, and Virat Kohli; (ii) Muralitharan, Mitchell Starc, and Umar Gul; and (iii) Saleem Yousuf, Luke Ronchi, and Scott Edwards. For teams, England, Australia, and India were determined to be the most efficient in batting for all three formats; the West Indies, Australia, and Pakistan are the most efficient in bowling; and the Australian (Test & ODIs) and South African teams are efficient in the fielding department.