bluuebunny/crossref_metadata_2025 huggingface dataset:
Observations of Archaeological Proxies through Phenological Analysis over the Megafort of Csanádpalota-Juhász T. tanya in Hungary Using Sentinel-2 Images
Authors: Agapiou Athos , Hegyi Alexandru , Stavilă Andrei
Published: January 2023

This study aims to investigate potential archaeological proxies at a large Bronze Age fortification in Hungary, namely the Csanádpalota–Juhász T. tanya site, using open-access satellite data. Available Sentinel-2 images acquired between April 2017 and September 2022 were used. More than 700 images (727) were initially processed and filtered, accounting at the end of more than 400 (412) available calibrated Level 2A Sentinel images over the case study area. Sentinel-2 images were processed through image analysis. Based on pan-sharpened data, the visibility of crop marks was improved and enhanced by implementing orthogonal equations. Several crop marks, some still unknown, were revealed in this study. In addition, multi-temporal phenological observations were recorded on three archaeological proxies (crop marks) within the case study area, while an additional area was selected for calibration purposes (agricultural field). Phenological observations were performed for at least four complete phenological cycles throughout the study period. Statistical comparisons between the selected archaeological proxies were applied using a range of vegetation indices. The overall results indicated that phenological observations could be used as archaeological proxies for detecting the formation of crop marks.
Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library
Authors: Zhang Xianglin , Xue Jie , Xiao Yi , Shi Zhou , Chen Songchao
Published: January 2023

Soil visible and near-infrared (Vis-NIR, 350–2500 nm) spectroscopy has been proven as an alternative to conventional laboratory analysis due to its advantages being rapid, cost-effective, non-destructive and environmentally friendly. Different variable selection methods have been used to deal with the high redundancy, heavy computation, and model complexity of using full spectra in spectral modelling. However, most previous studies used a linear algorithm in the variable selection, and the application of a non-linear algorithm remains poorly explored. To address the current knowledge gap, based on a regional soil Vis-NIR spectral library (1430 soil samples), we evaluated seven variable selection algorithms together with three predictive algorithms in predicting seven soil properties. Our results showed that Cubist overperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties (R2 > 0.75 for soil organic matter, total nitrogen and pH) when using the full spectra. Most of variable selection can greatly reduce the number of spectral bands and therefore simplified predictive models without losing accuracy. The results also showed that there was no silver bullet for the optimal variable selection algorithm among different predictive algorithms: (1) competitive adaptive reweighted sampling (CARS) always performed best for the PLSR algorithm, followed by forward recursive feature selection (FRFS); (2) recursive feature elimination (RFE) and genetic algorithm (GA) generally had better accuracy than others for the Cubist algorithm; and (3) FRFS had the best model performance for the RF algorithm. In addition, the performance was generally better when the algorithm used in the variable selection matched the predictive algorithm. The outcome of this study provides a valuable reference for predicting soil information using spectroscopic techniques together with variable selection algorithms.
Subseasonal Tidal Variability in the Gulf of Tonkin Observed by Multi-Satellite Altimeters and Tide Gauges
Authors: Pan Haidong , Li Bingtian , Xu Tengfei , Wei Zexun
Published: January 2023

Exploring multi-timescale tidal variability is fundamental and necessary for numerous practical purposes, such as flood protection, marine cultivation, and ocean transport. It is well known that tides show significant seasonal, inter-annual, and 18.61-year nodal variability. Less known and less discussed is the subseasonal tidal variability (i.e., ter-annual, quarter-annual, and penta-annual cycles) in the coastal ocean. In this study, we explore subseasonal tidal modulations in the Gulf of Tonkin via the combination of four tide gauges and 27-year multi-satellite altimeter observations. Both tide gauges and satellite altimeters indicate that tidal subseasonality is significant in the Gulf of Tokin, although the amplitudes of subseasonal variations are much smaller than those of seasonal variations. Compared to spatially limited tide gauges, satellite altimeters successfully derive the basin-scale tidal subseasonality in the Gulf of Tonkin. The largest amplitude of subseasonal tidal constituents originated from the subseasonality of main tidal constituents, and can reach as high as 31.8 mm. It is suggested that subseasonal variations in ocean environments (e.g., sea levels and ocean stratification) induce tidal subseasonality through changing tidal propagation and dissipation. Although powerful, satellite altimeters also have some defects. Due to tidal aliasing related to long-period sampling intervals, some subseasonal tidal constituents are indistinguishable in satellite altimeter records.
Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China
Authors: Zhou Xiaocheng , Hao Youzhuang , Di Liping , Wang Xiaoqin , Chen Chongcheng , Chen Yunzhi , Nagy Gábor , Jancso Tamas
Published: January 2023

Forest canopy height plays an important role in forest resource management and conservation. The accurate estimation of forest canopy height on a large scale is important for forest carbon stock, biodiversity, and the carbon cycle. With the technological development of satellite-based LiDAR, it is possible to determine forest canopy height over a large area. However, the forest canopy height that is acquired by this technology is influenced by topography and climate, and the canopy height that is acquired in complex subtropical mountainous regions has large errors. In this paper, we propose a method for estimating forest canopy height by combining long-time series Landsat images with GEDI satellite-based LiDAR data, with Fujian, China, as the study area. This approach optimizes the quality of GEDI canopy height data in topographically complex areas by combining stand age and tree height, while retaining the advantage of fast and effective forest canopy height measurements with satellite-based LiDAR. In this study, the growth curves of the main forest types in Fujian were first obtained by using a large amount of forest survey data, and the LandTrendr algorithm was used to obtain the forest age distribution in 2020. The obtained forest age was then combined with the growth curves of each forest type in order to determine the tree height distribution. Finally, the obtained average tree heights were merged with the GEDI_V27 canopy height product in order to create a modified forest canopy height model (MGEDI_V27) with a 30 m spatial resolution. The results showed that the estimated forest canopy height had a mean of 15.04 m, with a standard deviation of 4.98 m. In addition, we evaluated the accuracy of the GEDI_V27 and the MGEDI_V27 using the sample dataset. The MGEDI_V27 had a higher accuracy (R2 = 0.67, RMSE = 2.24 m, MAE = 1.85 m) than the GEDI_V27 (R2 = 0.39, RMSE = 3.35 m, MAE = 2.41 m). R2, RMSE, and MAE were improved by 71.79%, 33.13%, and 22.53%, respectively. We also produced a forest age distribution map of Fujian for the year 2020 and a forest disturbance map of Fujian for the past 32 years. The research results can provide decision support for forest ecological protection and management and for carbon sink analysis in Fujian.
Unsupervised SAR Image Change Detection Based on Histogram Fitting Error Minimization and Convolutional Neural Network
Authors: Zhang Kaiyu , Lv Xiaolei , Guo Bin , Chai Huiming
Published: January 2023

Synthetic aperture radar (SAR) image change detection is one of the most important applications in remote sensing. Before performing change detection, the original SAR image is often cropped to extract the region of interest (ROI). However, the size of the ROI often affects the change detection results. Therefore, it is necessary to detect changes using local information. This paper proposes a novel unsupervised change detection framework based on deep learning. The specific method steps are described as follows: First, we use histogram fitting error minimization (HFEM) to perform thresholding for a difference image (DI). Then, the DI is fed into a convolutional neural network (CNN). Therefore, the proposed method is called HFEM-CNN. We test three different CNN architectures called Unet, PSPNet and the designed fully convolutional neural network (FCNN) for the framework. The overall loss function is a weighted average of pixel loss and neighborhood loss. The weight between pixel loss and neighborhood loss is determined by the manually set parameter λ. Compared to other recently proposed methods, HFEM-CNN does not need a fragment removal procedure as post-processing. This paper conducts experiments for water and building change detection on three datasets. The experiments are divided into two parts: whole data experiments and random cropped data experiments. The complete experiments prove that the performance of the method in this paper is close to other methods on complete datasets. The random cropped data experiment is to perform local change detection using patches cropped from the whole datasets. The proposed method is slightly better than traditional methods in the whole data experiments. In experiments with randomly cropped data, the average kappa coefficient of our method on 63 patches is over 3.16% compared to other methods. Experiments also show that the proposed method is suitable for local change detection and robust to randomness and choice of hyperparameters.
The Gravity Wave Activity during Two Recent QBO Disruptions Revealed by U.S. High-Resolution Radiosonde Data
Authors: Li Haiyan , Zhang Jian , Sheng Bosi , Fan Yi , Ji Xuanting , Li Qingxiang
Published: January 2023

The westerly phase of the stratospheric Quasi-Biennial Oscillation (QBO) was unprecedentedly interrupted by an easterly jet at around 22 km during boreal wintertime in 2015/2016 and 2019/2020. Many studies have investigated the role of planetary waves during these disruptions. However, the behavior of gravity waves (GWs) during these disruptions is still unclear. In this paper, we investigated the characteristics of stratospheric GWs during QBO disruptions by analyzing the U.S. high-resolution radiosonde data from 1998 to 2021 from three equatorial stations. The disruptions were separated into three stages: the westerly zonal wind decreasing stage, the easterly zonal wind developing stage, and the westerly zonal wind recovery stage. Notably, the tropical stratospheric GWs’ total energy densities were enhanced during all three stages of both events compared to those in typical years. The low-tropospheric convection, the middle-tropospheric jet, and the low-stratospheric vertical wind shear were statistically associated with the stratospheric GW variations. A quantitative analysis further indicated that the low-tropospheric convection activity, tropospheric jets, and wind shears in the lower stratosphere could well explain the variations in the stratospheric GWs in the westerly zonal wind decreasing and easterly zonal wind developing stages by applying a partial least squares regress analysis.
Quantitative Assessment of Shallow Groundwater Sustainability in North China Plain
Authors: Zhou Hao , Dai Min , Wei Min , Luo Zhicai
Published: January 2023

The depletion of shallow groundwater has seriously affected the sustainable development of water resources in the North China Plain (NCP). Based on 556 well monitoring observations over a period of 13 years, we quantitatively evaluated the shallow groundwater sustainability in the NCP via various indices (e.g., the reliability, resilience, vulnerability, and sustainability indices), and further discussed the contribution of different drivers (including climatic and non-climatic factors). The main conclusions are summarized as follows: (1) the yearly trend of shallow groundwater shows a serious long-term deficit in the Piedmont Plain but is not significant in the East-Central Plain. (2) As for the sustainability of shallow groundwater in the NCP, the reliability is below the medium level (reliability < 0.5) in most areas and the ability of shallow aquifers to restore groundwater is very weak (resilience < 0.2), while the lack of groundwater storage in most shallow aquifers is not serious (vulnerability < 0.4). The final sustainability index (<0.1) shows the poor sustainability of most shallow aquifers in the NCP. (3) The non-climatic factor is the dominant driver of shallow groundwater depletion in the NCP when compared to the climatic factor. This result is helpful to formulate the water management policies for sustainable shallow groundwater storage in the NCP.
MS4D-Net: Multitask-Based Semi-Supervised Semantic Segmentation Framework with Perturbed Dual Mean Teachers for Building Damage Assessment from High-Resolution Remote Sensing Imagery
Authors: He Yongjun , Wang Jinfei , Liao Chunhua , Zhou Xin , Shan Bo
Published: January 2023

In the aftermath of a natural hazard, rapid and accurate building damage assessment from remote sensing imagery is crucial for disaster response and rescue operations. Although recent deep learning-based studies have made considerable improvements in assessing building damage, most state-of-the-art works focus on pixel-based, multi-stage approaches, which are more complicated and suffer from partial damage recognition issues at the building-instance level. In the meantime, it is usually time-consuming to acquire sufficient labeled samples for deep learning applications, making a conventional supervised learning pipeline with vast annotation data unsuitable in time-critical disaster cases. In this study, we present an end-to-end building damage assessment framework integrating multitask semantic segmentation with semi-supervised learning to tackle these issues. Specifically, a multitask-based Siamese network followed by object-based post-processing is first constructed to solve the semantic inconsistency problem by refining damage classification results with building extraction results. Moreover, to alleviate labeled data scarcity, a consistency regularization-based semi-supervised semantic segmentation scheme with iteratively perturbed dual mean teachers is specially designed, which can significantly reinforce the network perturbations to improve model performance while maintaining high training efficiency. Furthermore, a confidence weighting strategy is embedded into the semi-supervised pipeline to focus on convincing samples and reduce the influence of noisy pseudo-labels. The comprehensive experiments on three benchmark datasets suggest that the proposed method is competitive and effective in building damage assessment under the circumstance of insufficient labels, which offers a potential artificial intelligence-based solution to respond to the urgent need for timeliness and accuracy in disaster events.
Experimental Investigation of Microcontroller-Based Acoustic Temperature Transducer Systems
Authors: Al-Rawashdeh Ayman Y. , Younes Tariq M. , Dalabeeh Ali , Al_Issa Huthaifa , Qawaqzeh Mohamed , Miroshnyk Oleksandr , Kondratiev Andrii , Kučera Pavel , Píštěk Václav , Stepenko Serhii
Published: January 2023

Temperature transducers are commonly used to monitor process parameters that are controlled by various types of industrial controllers. The purpose of this study is to design and model a simple microcontroller-based acoustic temperature transducer based on the variations of resonance conditions in a cylindrical resonance tube. The transducer’s operation is based on the generation of an acoustic standing wave in the free resonance mode of generation within a cylindrical resonance tube which is converted into a train of pulses using Schmitt trigger circuit. The frequency of the generated standing wave (i.e., the train of pulses) is measured by the Arduino Uno microcontroller, where a digital pin is used to acquire pulses that are counted using a build-in software function in an Arduino IDE environment. Experimental results are performed for three sizes of diameters to investigate the effect of the diameter of resonance tube on the obtained results. The maximum nonlinearity error according to Full-Scale Deflection (FSD) is about 2.3 percent, and the relative error of the transducer is evaluated using experimental findings and the regression model. The circuit simplicity and design of the suggested transducer, as well as the linearity of its measurements, are notable.
Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings
Authors: Yuvaraj Rajamanickam , Thagavel Prasanth , Thomas John , Fogarty Jack , Ali Farhan
Published: January 2023

Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal. The classification accuracy of five EEG feature sets were investigated, including statistical features, fractal dimension (FD), Hjorth parameters, higher order spectra (HOS), and those derived using wavelet analysis. Performance was evaluated using two classifier methods, support vector machine (SVM) and classification and regression tree (CART), across five independent and publicly available datasets linking EEG to emotional states: MAHNOB-HCI, DEAP, SEED, AMIGOS, and DREAMER. The FD-CART feature-classification method attained the best mean classification accuracy for valence (85.06%) and arousal (84.55%) across the five datasets. The stability of these findings across the five different datasets also indicate that FD features derived from EEG data are reliable for emotion recognition. The results may lead to the possible development of an online feature extraction framework, thereby enabling the development of an EEG-based emotion recognition system in real time.
ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
Authors: Camara Carmen , Peris-Lopez Pedro , Safkhani Masoumeh , Bagheri Nasour
Published: January 2023

In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG records by using wearable devices. This paper moves in that direction and proposes a novel approach for an ECG identification system. For that, we transform the ECG recordings into Gramian Angular Field (GAF) images, a time series encoding technique well-known in other domains but not very common with biosignals. Specifically, the time series is transformed using polar coordinates, and then, the cosine sum of the angles is computed for each pair of points. We present a proof-of-concept identification system built on a tuned VGG19 convolutional neural network using this approach. We confirm our proposal’s feasibility through experimentation using two well-known public datasets: MIT-BIH Normal Sinus Rhythm Database (subjects at a resting state) and ECG-GUDB (individuals under four specific activities). In both scenarios, the identification system reaches an accuracy of 91%, and the False Acceptance Rate (FAR) is eight times higher than the False Rejection Rate (FRR).
Force-Moment Sensor for Prosthesis Structural Load Measurement
Authors: Haque Md Rejwanul , Berkeley Greg , Shen Xiangrong
Published: January 2023

Measurement of prosthesis structural load, as an important way to quantify the interaction of the amputee user with the environment, may serve important purposes in the control of smart lower-limb prosthetic devices. However, the majority of existing force sensors used in protheses are developed based on strain measurement and thus may suffer from multiple issues such as weak signals and signal drifting. To address these limitations, this paper presents a novel Force-Moment Prosthesis Load Sensor (FM-PLS) to measure the axial force and bending moment in the structure of a lower-limb prosthesis. Unlike strain gauge-based force sensors, the FM-PLS is developed based on the magnetic sensing of small (millimeter-scale) deflection of an elastic element, and it may provide stronger signals that are more robust against interferences and drifting since such physical deflection is several orders of magnitude greater than the strain of a typical load-bearing structure. The design of the sensor incorporates uniquely curved supporting surfaces such that the measurement is sensitive to light load but the sensor structure is robust enough to withstand heavy load without damage. To validate the sensor performance, benchtop testing of the FM-PLS and walking experiments of a FM-PLS-embedded robotic lower-limb prosthesis were conducted. Benchtop testing results displayed good linearity and a good match to the numerical simulation results. Results from the prosthesis walking experiments showed that the sensor signals can be used to detect important gaits events such as heel strike and toe-off, facilitating the reliable motion control of lower-limb prostheses.
Halal Healthcare Services: Patients’ Satisfaction and Word of Mouth Lesson from Islamic-Friendly Hospitals
Authors: Rahman Muhammad Khalilur , Zainol Noor Raihani , Nawi Noorshella Che , Patwary Ataul Karim , Zulkifli Wan Farha Wan , Haque Md Mahmudul
Published: January 2023

This study aims to investigate the impact of halal healthcare attributes, intrinsic value, and extrinsic value on satisfaction, and explores how patient satisfaction with halal healthcare services influences word of mouth (WOM) to others. The cross-sectional survey was conducted in two cities with four Islamic-friendly hospitals across Malaysia. This study used purposive and non-probability random sampling methods. Partial least square (PLS) technique was used for data analysis of 309 Muslim patients with a response rate of 61.8%. Findings revealed that hospital’s halal healthcare attributes (β = 0.225, p < 0.01), hospital’s intrinsic value (β = 0.432, p < 0.01), and hospital’s extrinsic value (β = 0.196, p < 0.01) have significant influence on patient satisfaction with halal healthcare service, which in turn reflects the WOM to others (β = 0.692, p < 0.01). The results identified that satisfaction mediates the effect of hospital’s halal attributes, as well as hospital’s intrinsic and extrinsic values on WOM. These findings will contribute to healthcare service providers and academicians for further study to improve a framework for establishing a standard for halal healthcare service for patient satisfaction and WOM to others in Islamic-friendly hospitals.
Air Pollution in Urban Africa: Understanding Attitudes and Economic Valuation in the Case of Dakar, Senegal
Authors: Diallo Sokhna Mbathio , Seck Abdoulaye
Published: January 2023

The degradation of air quality is a real concern in Africa, as pollution levels are consistently above commonly accepted thresholds, and yet, very little is known about individual attitudes and the extent to which improved air quality is valued in the context of rapid urbanization. This research proposes to analyze the willingness to pay for reduced air quality in African urban areas. Using survey data from 427 individuals in Dakar (Senegal) between February and May 2019, a double-bound, dichotomous contingent valuation model is developed. The results first suggest that 70% of individuals are indeed willing to pay an average of CFA Francs 3114.8 (USD 5.6) per month to contribute to air quality improvement, and the corresponding value of a life year gained is estimated at CFA Francs 35,550.2 (USD 80) at least. The results also point to a great deal of heterogeneity in individual valuation schemes, as they vary with the perceptions of life expectancy gains, payment vehicles, and various individual characteristics, and also across months with different levels of temperatures. These findings could constitute important inputs into public strategies aimed at improving air quality in the African context.
Study on Rationality of Public Fitness Service Facilities in Beijing Based on GIS
Authors: Tan Xuefeng , Guo Chenggen , Sun Pu
Published: January 2023

Public fitness service facilities are one of the most important factors affecting national fitness. Analyzing the distribution characteristics and influencing factors of public fitness service facilities is of great significance to optimizing public fitness services. In this paper, using the population concentration index and consistency test, recent proximity index, coupling coordinated development model, buffer analysis, and correlation analysis, combined with ArcGis 10.7 software research, we found that: the site layout of public fitness service facilities in the main urban area of Beijing is relatively reasonable and has good coupling, and each area becomes a cluster trend; In the main urban area of Beijing, the public fitness service facilities and the transportation line are combined better; Public fitness service facilities in the main urban area of Beijing are well combined with schools (kindergartens, primary schools), with schools as the center; The public fitness service facilities in the main urban area of Beijing are well combined with medical sites, and some marginal areas are not covered; Public fitness service facilities in the main urban area in Beijing show uneven distribution when drawing with influential business districts; The layout of public fitness service facilities in the main urban area of Beijing is positively related to the housing price.
Assessment of the Drivers and Effects of International Science and Technology Cooperation in Xinjiang in the Context of the Belt and Road Initiative
Authors: Wang Fei , Dong Zhi , Dong Jichang
Published: January 2023

International science and technology (S&T) cooperation is one of the important ways to ensure the continuous development of a national innovation system. “One Belt One Road” advocates the concepts of common business, common construction, and sharing, and new countries and regions are constantly added. However, different countries/regions have different natural and socio-economic characteristics, so the main controlling factors affecting their international S&T cooperation may not be the same. Therefore, this paper uses a combination of multi-source data and multiple methods to collectively construct an assessment model of the driving effect of international S&T cooperation in Xinjiang. The evaluation results of the entropy-weighted TOPSIS model show that the contributions of S&T level, city nature, and S&T investment to international S&T cooperation in Xinjiang are 22.9%, 22.3%, and 20.4%, respectively. Singapore, Germany, and Russia are the top three countries in terms of the effectiveness of international S&T cooperation with Xinjiang. The results of the STIRPAT model show that the total number of R&D personnel and the number of R&D personnel per 1000 workers are the main factors affecting the driving effect of international S&T cooperation. The former can be regarded as the stabilizer of international S&T cooperation. This paper’s findings can provide theoretical support for the efficient integration of diverse advantageous resources among cooperating subjects.
Optimization of the Well Start-Up Procedure and Operating Parameters for ESP Gas Well Dewatering
Authors: Wang Xiaolei , Feng Xuezhang , Liu Jinbo , Hong Jiangling , Yao Jinsong , Liu Honglei , Liu Zelin , Han Guoqing
Published: January 2023

The Electrical Submersible Pump (ESP) systems were deployed in two gas wells for the dewatering of the gas reservoir. However, problems, such as the failure to start up the ESP, and changes in reservoir parameters occurred during the production. For the first problem, the well start-up operation records indicate that the ESP’s gas locking happened. To avoid this, an optimization method of the well start-up procedure for the ESP well with a check valve was correspondingly proposed, which can solve the problem without any workovers. Secondly, based on the working characteristics of the ESP and the nodal analysis method, a set of optimization methods for the operating parameters of ESPs were introduced to achieve the inflow and outflow balance. For one well, the original ESP system was planned to be installed after hydraulic fracturing. Traditionally, the ESP operating parameters were designed based on the production rate. However, in this case, the production rate and the ESP operating frequency were designed simultaneously to maximize the pump efficiency.
Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas
Authors: Hassan Muhammed A. , Salem Hindawi , Bailek Nadjem , Kisi Ozgur
Published: January 2023

The transportation sector is one of the primary sources of air pollutants in megacities. Strict regulations of newly added vehicles to the local market require precise prediction models of their fuel consumption (FC) and emission rates (ERs). Simple empirical and complex analytical models are widely used in the literature, but they are limited due to their low prediction accuracy and high computational costs. The public literature shows a significant lack of machine learning applications related to onboard vehicular emissions under real-world driving conditions due to the immense costs of required measurements, especially in developing countries. This work introduces random forest (RF) ensemble models, for the urban areas of Greater Cairo, a metropolitan city in Egypt, based on large datasets of precise measurements using 87 representative passenger cars and 10 typical driving routes. Five RF models are developed for predicting FC, as well as CO2, CO, NOx, and hydrocarbon (HC) ERs. The results demonstrate the reliability of RF models in predicting the first four variables, with up to 97% of the data variance being explained. Only the HC model is found less reliable due to the diversity of considered vehicle models. The relative influences of different model inputs are demonstrated. The FC is the most influential input (relative importance of >23%) for CO2, CO, and NOx predictions, followed by the engine speed and the vehicle category. Finally, it is demonstrated that the prediction accuracy of all models can be further improved by up to 97.8% by limiting the training dataset to a single-vehicle category.
Accessibility of Elderly Care Facilities Based on Social Stratification: A Case Study in Tianjin, China
Authors: Liu Bangyu , Qiu Ning , Zhang Tianjie
Published: January 2023

With the increasingly prominent phenomenon of social stratification in urban development, it is of practical significance to study the accessibility of elderly care facilities for different social groups. The study improves the mathematical model of the two-step floating catchment area method (2SFCA) as regards three aspects: the accurate demand of elderly residents, the comprehensive supply capacity of elderly care facilities, and the precision of travel costs. Taking Tianjin as an example, the study measures the accessibility of elderly care facilities from the perspective of social stratification. The results show that: (1) The improved model is more practical in evaluating the accessibility of elderly care facilities. (2) The spatial distribution of social groups in Tianjin presents a concentric structure and the social stratification in the periphery area is more obvious. (3) The accessibility scores of elderly care facilities are higher in the city center, lower in the periphery area, higher in the south, and lower in the north. (4) High- and middle-income groups have better accessibility, while the elite and low-income groups have worse accessibility.
Development and Application of EST-SSR Markers Related to Lead Stress Responses in Kenaf Based on Transcriptome Sequencing Data
Authors: An Xia , Luo Xiahong , Li Wenlue , Liu Tingting , Zou Lina
Published: January 2023

Kenaf is an important bast fiber crop. In order to diversify the available kenaf simple sequence repeat (SSR) molecular markers and generate markers potentially useful for kenaf breeding, we developed expression sequence tag simple sequence repeat (EST-SSR) molecular markers based on lead-stressed kenaf transcriptome sequencing data and spliced unigene sequences. Additionally, the distribution of the SSRs in the transcriptome and the potential functions of the SSR-containing genes were determined. Moreover, SSR markers in the differentially expressed genes (DEGs) of a protein–protein interaction (PPI) network were analyzed to screen for polymorphic markers, which were used to examine the genetic diversity and population structure of kenaf germplasm resources. The genetic diversity and population structure of 138 kenaf germplasm materials revealed that 22 EST-SSR markers could be used to distinguish the kenaf germplasms. The 22 EST-SSR markers enrich the kenaf molecular markers database and provide an important tool for future genetic improvement of kenaf resistance to lead stress.
Effective Load Frequency Control of Power System with Two-Degree Freedom Tilt-Integral-Derivative Based on Whale Optimization Algorithm
Authors: Sahu Preeti Ranjan , Simhadri Kumaraswamy , Mohanty Banaja , Hota Prakash Kumar , Abdelaziz Almoataz Y. , Albalawi Fahad , Ghoneim Sherif S. M. , Elsisi Mahmoud
Published: January 2023

Nowadays, the operation and control of power systems are a big challenge. An essential part of the power system (PS) control is load frequency control (LFC). Different secondary controllers are implemented for the frequency control problem. Hence, cascaded two-degree freedom and a tilt-integral-derivative controller having a filter (2DOFTIDF) are intended in this paper and implemented for load frequency control. In order to determine the efficiency of the 2DOFTIDF controller, a well-known non-reheat thermal system with/without a governor dead band is considered. A new whale optimization algorithm (WOA) is used to enhance the suggested controller parameters. The predominance of the presented method is exhibited by comparing the consequences with different heuristic techniques tuned to controllers published recently. Further, the simulation results for two test cases indicate that system enactments are enhanced by introducing the suggested controller and are also best suited in the presence of system nonlinearity. Finally, random load fluctuation along with noise and changing the system parameters are also used to determine the reliability of the suggested controller. Compared to the WOA-tuned TIDF controller, the settling time of ΔF1, ΔF2, and ΔPTie is improved by 45.45%, 56.77%, and 20.26%, respectively, with the WOA-tuned 2DOFTIDF controller and by 40%, 48.27%, and 20%, respectively, with the DE-tuned TIDF controller. Experimental validation using the hardware-in-the-loop real-time simulation based on OPAL-RT has been carried out to confirm the viability of the proposed approach.
Mechanical Properties and Damage Characteristics of Coal-Based Solid Waste Paste Filling Materials with Different Moisture Content
Authors: Wang Meng , He Xiang , Yang Ke
Published: January 2023

It has been proven that it is a feasible treatment method to prepare paste filling material from coal-based solid waste to fill underground goaf. Based on the complexity of the goaf environment, especially the influence of humidity on paste filling materials, this paper prepared paste filling materials with a mass concentration of 80% by using coal gangue and fly ash, and carried out a uniaxial compression test of coal-based solid waste paste filling materials under four different water-bearing states. The experimental results show that: (1) The binary primary equation fits well the variation trend of paste filling strength with water content. With the increase of moisture content, the compressive strength of paste filling material gradually decreases, and the higher the moisture content, the more obvious the influence on the strength of paste filling. (2) The damage evolution equation and constitutive equation of paste filling materials with different moisture content were established. With the increase of strain, the influence of moisture content on the damage of paste filling material decreases gradually, and the moisture content can promote the damage development of paste filling material to a certain extent. (3) The influence mechanism of moisture content on coal based-solid waste paste filling material is discussed from the three aspects of physical effect, structural effect and chemical effect, which provides a direction for further research on the influence mechanism of moisture content on filling strength. The research on the failure mechanism of coal-based solid waste paste filling and the safety production of the mine can be used as a theoretical support.
Market Developments on Chinese International Air Passenger Markets in Light of COVID-19 Policy Measures
Authors: Graham Anne , Warnock-Smith David , O’Connell John F. , Efthymiou Marina , Zheng Xingwu
Published: January 2023

The world’s governments imposed a plethora of restrictions and quarantine rules to prevent the rapid spread of COVID-19. China was chosen for this study as it was the first market to be impacted. The overall aim of this paper was to analyse international air travel to and from China since the start of COVID-19 and to assess the impact of policy initiatives on seat capacity during this time. The key findings are that implementation of the so called Five one policy in March 2020 was associated with an almost immediate reduction in seat capacity on China to the rest of the world, partially suppressing the more typical impact of underlying GDP and air fares on capacity. It was further found that Chinese international gateways, as airports with substantial proportions of international and connecting traffic, remain the most distressed. Long haul international traffic and revenues from European and North American destinations all experienced unprecedented and sharp reductions. Traffic and revenues from other Asian markets was even more sporadic. Alarmingly, the study extracted that revenues from premium classes were deteriorating much faster than economy class, which is of imminent concern for long-haul carriers reliant on premium traffic coming into the pandemic.
The Role of Critical Thinking in Predicting and Improving Academic Performance
Authors: Rivas Silvia F. , Saiz Carlos , Almeida Leandro S.
Published: January 2023

Today’s world demands new ways of dealing with problems and different ways of preparing for them. Some studies argue that these new demands also require new skills. Critical thinking (CT) involves a set of skills that are entirely relevant to today’s adaptive needs. In this study, we explore the extent to which CT serves to both account for and improve academic performance. To do this, we measured the CT skills of a number of undergraduate students, along with their university admission grades and average course grades. We successfully established the structural validity of the general construct of CT, along with a strong relationship between CT measurements at two different timepoints (at the beginning and end of studying a critical thinking subject area) and the admission grade and average grade variables. These data lead us to reflect on using CT level as a suitable assessment of academic performance. We also consider the limits of our findings and their implications.
A Sustainable Digital Ecosystem: Digital Servitization Transformation and Digital Infrastructure Support
Authors: Chen Yan , Wang Zijin , Ortiz Jaime
Published: January 2023

While the notion of digitalization and sustainability has become prominent in current research, more can be done to bridge these two concepts and explain the interaction between them. Plenty of literature has focused on the impact of digital technology applications and business model innovations on environmental performance but has not considered the counterforce of environmental performance on digitalization. We investigated this question from the perspective of digital ecosystem architects to explore more organic relationships. By analyzing data from 1083 listed firms from 2014 to 2019, we found various effective paths for architects to participate in the digital ecosystem and determined that improved environmental performance has led to more efficient convergence. Digital servitization adopted by private architects contributes to financial performance, whereas the addition of digital infrastructure enables public architects to play a greater role. This is reflected in the discovery that firms with “high” environmental performance can improve their financial performance far more significantly with the help of digital servitization compared to other firms. However, digital infrastructure development can benefit all firms almost indiscriminately. We encourage firms and governments to work together to strengthen digital infrastructure, build digital ecosystems, and focus on environmental performance while transitioning to digital servitization.
Site Characterization and Liquefaction Hazard Assessment for the Erenler Settlement Area (Sakarya Province, Turkey) Based on Integrated SPT-Vs Data
Authors: Silahtar Ali , Karaaslan Hasan , Kocaman Kadir
Published: January 2023

The focus of this study is to examine the soil properties and liquefaction potential of the Erenler center district in a critical tectonic zone that can produce large earthquakes, such as the North Anatolian Fault Zone (NAFZ). In this sense, 40 surface wave measurements and 52 geotechnical drillings were employed. Accordingly, the liquefaction potential index (LPI), liquefaction severity index (LSI), Ishihara boundary (IB) curve, and Ishihara-inspired index (LPIISH) liquefaction approaches from geotechnical and geophysical methods were used as integrated. All liquefaction analyses were examined for two scenarios, Mw: 7.4 1999 Izmit (amax: 0.41 g) and Mw: 7.0 1967 (amax: 0.28 g) Mudurnu. According to the analyses, almost all of the study area showed liquefaction risk in the Izmit scenario. In the Mudurnu scenario, liquefaction risk distribution decreased parallel to acceleration. The LPI, LPIISH, and IB liquefaction risk results for both scenarios support each other. On the other hand, it was determined that the probability of liquefaction was lower in the LSI evaluation. The spatial distribution of the liquefaction potential of the Vs-based and SPT-based LPI assessments had a similar pattern. These results show that the LPI approach, originally SPT-based, can also be calculated based on Vs. In addition, according to Vs30-based (average shear wave velocity at 30 m depth) soil classification criteria, low-velocity character E and D soil groups dominate the Quaternary alluvial basin. This indicates that deformation-induced failures may occur even in areas with a low probability of liquefaction in similar strong ground motions. In addition, the evaluation of liquefaction with many approaches in two different bases within the scope of the study constitutes a novelty for the study area and liquefaction analyses. While performing diversified liquefaction analyses and approaches will contribute to obtaining more reliable soil liquefaction results, more case studies are needed to elucidate these comparisons.
Hybrid Tripping Characteristic-Based Protection Coordination Scheme for Photovoltaic Power Systems
Authors: Alasali Feras , Saidi Abdelaziz Salah , El-Naily Naser , Smadi Mahmoud A. , Holderbaum William
Published: January 2023

Due to the high penetration of renewable energy sources into the electrical power network, overcurrent relays coordination with highly sensitive and selective protection systems are now two of the most important power protection concerns. In this research, an optimal coordination strategy utilising a new hybrid tripping scheme based on current–voltage characteristics has been devised for overcurrent relays in a power network coupled to a photovoltaic system. This research develops and proves a new optimal coordination scheme based on two optimisation methods, the vibrating particles system and particle swarm optimisation algorithms, in consideration of the impact of renewable sources on fault characteristics. The new optimal coordination approach aims to improve the sensitivity and dependability of the protection system by reducing the tripping time of the overcurrent relays by employing a new hybrid tripping scheme. A specific case study, Conseil International des Grands Réseaux Electriques (CIGRE) distribution network connected to two photovoltaic systems is constructed and presented utilising Industrial software (namely ETAP), and the outcomes of the proposed optimal coordination scheme are compared with standard and recent characteristics from the literature. The hybrid tripping scheme and optimisation techniques are evaluated using different fault and power network model scenarios. The results show that the optimal hybrid tripping scheme provided successfully decreases the overall operating time of the overcurrent relays and increases the sensitivity of the relay during all fault scenarios. The reduction in overall time for the proposed hybrid tripping scheme was 35% compared to the literature for the scenario of a power grid with and without photovoltaic systems.
Impact of Green Development Mechanism Innovation on Total-Factor Environmental Efficiency: A Quasi-Natural Experiment Based on National Pilot Cities
Authors: Zhang Linbo , Xiang Wenjing , Shi Dongsheng , Liang Tian , Xiong Xi , Wu Shuyao , Zhang Wentao , Yang Duogui
Published: January 2023

The implementation of green development has become an important choice for countries seeking the harmonious development of the economy and the environment. The National Ecological Civilization Pilot Zone is an innovative institutional mechanism for exploring green development in China. This study utilizes the National Ecological Civilization Pilot Zone policy as a quasi-natural experiment. Adopting data from 290 prefecture-level and above cities in China during 2014–2019 as the research object, this study matches the propensity score and improved differences-in-differences to assess the impact of green development mechanism innovation on regional total-factor environmental efficiency. The results show that this innovation had a significant impact on the improvement of total-factor environmental efficiency. Compared with non-pilot cities, the implementation of pilot zone areas contributed 16.78% to the growth of total-factor environmental efficiency in the experimental group cities. In addition, further analysis shows that mechanism innovation is more effective in areas with high pollution and high resource consumption. This study enriches the research on evaluation of the impact of innovation in green development mechanisms and provides a reference for further promoting pilot national ecological civilization zones.