bluuebunny/crossref_metadata_2025 huggingface dataset:
An Experimental Study on Measuring Breaking-Wave Bubbles with LiDAR Remote Sensing
Authors: Wang David , Josset Damien , Savelyev Ivan , Anguelova Magdalena , Cayula Stephanie , Abelev Anna
Published: April 2022

Laboratory experiments were conducted to evaluate the feasibility of profiling and characterizing subsurface bubble plumes following a breaking wave event from an above-water Light Detection and Ranging (LiDAR) system. Measurements of LiDAR backscatter profiles of bubble plumes under mechanically generated breaking waves in a wave tank were collected and analyzed. After onset of wave breaking, the LiDAR backscatter increases rapidly by injected bubble plumes of active wave breaking. This intensification reaches a depth of one wave height within one wave period. After active wave breaking, the LiDAR backscatter from dissipated bubble plumes in the upper layer of water column decreases very slowly. The temporal variations of LiDAR backscatter are comparable to the collocated in-water measurements of optical backscatter at 850 nm wavelength and acoustic backscatter at 2000 kHz frequency. The decay rate of LiDAR backscatter of dissipated bubble plumes follows a power-law function consistent with decay rate of void fraction measurements in previous studies. This study demonstrates the viability and potential of using above-water LiDAR remote sensing to characterize subsurface bubble plumes.
Monsoon Effects on Chlorophyll-a, Sea Surface Temperature, and Ekman Dynamics Variability along the Southern Coast of Lesser Sunda Islands and Its Relation to ENSO and IOD Based on Satellite Observations
Authors: Simanjuntak Febryanto , Lin Tang-Huang
Published: April 2022

The Asian–Australian Monsoon (AAM), the El Nino-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) have been known to induce variability in ocean surface characteristics along the southern coast of Lesser Sunda Island (LSI). However, previous studies used low spatial resolution data and little Ekman dynamics analysis. This study aims to investigate the direct influence of AAM winds on ocean surface conditions and to determine how ENSO and IOD affect the ocean surface and depth with higher spatial resolution data. In addition, the variability in Ekman dynamics is also described along with the inconsistent relationship between wind and sea surface temperature (SST) in four different areas. The results indicate that persistent southeasterly winds are likely to induce low SST and chlorophyll-a blooms. Based on the interannual variability, the positive chlorophyll-a (up to 0.5 mg m−3) and negative SST (reaching −1.5 °C) anomalies observed in the El Nino of 2015 coincide with +IOD, which also corroborates positive wind stress and Ekman Mass Transport (EMT) anomalies. In contrast, the La Nina of 2010 coincides with -IOD, and positive SST and negative chlorophyll-a anomalies (more than 1.5 °C and −0.5 mg m−3 respectively) were observed. Furthermore, we found that southern coast of Java and Bali Island have a different generated mechanism that controls SST variability.
Mitigation of Significant Data Noise in F17 SSMIS Observations since October 2017
Authors: Dong Huijie , Zou Xiaolei
Published: April 2022

Special Sensor Microwave Imager Sounder (SSMIS) temperature sounding observations have been made available since the launch of the Defense Meteorological Satellite Program (DMSP), F16, on 18 October 2003. These conical-scanning observations of brightness temperature are ideal for investigating long-term structural changes in tropical cyclones throughout the globe. The SSMIS temperature sounding data started to contain significant across-track high-frequency striping noise spikes at a frequency of 0.14 s−1 starting on 20 October 2017 for F17. A Fast Fourier Transform (FFT) was used to remove the noise in channels 1–7 and 24. The across-track striping noise is most significant for the four channels of the lowest peak weighting functions. The data noise is as large as 15 K for channels 1–3, 2.5 K for channel 4, 0.5 K for channels 5–7, and 0.75 K for channel 24. We found some remaining along-track striping noise around 0.5 K in channels 2–4, which is removed by employing a principal component analysis and an ensemble empirical mode decomposition combined method. An advantage for conical-scanning observations of brightness temperature to directly capture typhoon structures is then illustrated. Although buried under the data noise in the original data, the structural features of typhoon Lekima (2019) could clearly be seen after the noise mitigation. Lekima reached a typhoon intensity on 7 August 2019 and its center was characterized by a warm anomaly of more than 7 K around 200 hPa (channel 5) and a cold center of less than −6 K around 945 hPa (channel 2). This study prepares us for using satellite observations to understand the effect of climate change on tropical cyclone intensity and rain-band structures over the past two decades.
Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation
Authors: Xu Hongfu , Luo Haiyong , Wu Zijian , Wu Fan , Bao Linfeng , Zhao Fang
Published: April 2022

The tightly coupled navigation system is commonly used in UAV products and land vehicles. It adopts the Kalman filter to combine raw satellite observations, including the pseudorange, pseudorange rate and Doppler frequency, with the inertial measurements to achieve high navigational accuracy in GNSS-challenged environments. The accurate estimation of measurement noise covariance can ensure the quick convergence of the Kalman filter and the accuracy of the navigation results. Existing tightly coupled integrated navigation systems employ either constant noise covariance or simple noise covariance updating methods, which cannot accurately reflect the dynamic measurement noises. In this article, we propose an adaptive measurement noise estimation algorithm using a transformer and residual denoising autoencoder (RDAE), which can dynamically estimate the covariance of measurement noise. The residual module is used to solve the gradient degradation problem. The DAE is adopted to learn the essential characteristics from the noisy ephemeris data. By introducing the attention mechanism, the transformer can effectively learn the time and space dependency of long-term ephemeris data, and thus dynamically adjusts the noise covariance with the predicted factors. Extensive experimental results demonstrate that our method can achieve sub-meter positioning accuracy in the outdoor open environment. In a GNSS-degraded environment, our proposed method can still obtain about 3 m positioning accuracy. Another test on a new dataset also confirms that our proposed method has reasonable robustness and adaptability.
Deep Learning for Archaeological Object Detection on LiDAR: New Evaluation Measures and Insights
Authors: Fiorucci Marco , Verschoof-van der Vaart Wouter B. , Soleni Paolo , Le Saux Bertrand , Traviglia Arianna
Published: April 2022

Machine Learning-based workflows are being progressively used for the automatic detection of archaeological objects (intended as below-surface sites) in remote sensing data. Despite promising results in the detection phase, there is still a lack of a standard set of measures to evaluate the performance of object detection methods, since buried archaeological sites often have distinctive shapes that set them aside from other types of objects included in mainstream remote sensing datasets (e.g., Dataset of Object deTection in Aerial images, DOTA). Additionally, archaeological research relies heavily on geospatial information when validating the output of an object detection procedure, a type of information that is not normally considered in regular machine learning validation pipelines. This paper tackles these shortcomings by introducing two novel automatic evaluation measures, namely ‘centroid-based’ and ‘pixel-based’, designed to encode the salient aspects of the archaeologists’ thinking process. To test their usability, an experiment with different object detection deep neural networks was conducted on a LiDAR dataset. The experimental results show that these two automatic measures closely resemble the semi-automatic one currently used by archaeologists and therefore can be adopted as fully automatic evaluation measures in archaeological remote sensing detection. Adoption will facilitate cross-study comparisons and close collaboration between machine learning and archaeological researchers, which in turn will encourage the development of novel human-centred archaeological object detection tools.
Vision-Based Learning from Demonstration System for Robot Arms
Authors: Hwang Pin-Jui , Hsu Chen-Chien , Chou Po-Yung , Wang Wei-Yen , Lin Cheng-Hung
Published: April 2022

Robotic arms have been widely used in various industries and have the advantages of cost savings, high productivity, and efficiency. Although robotic arms are good at increasing efficiency in repetitive tasks, they still need to be re-programmed and optimized when new tasks are to be deployed, resulting in detrimental downtime and high cost. It is therefore the objective of this paper to present a learning from demonstration (LfD) robotic system to provide a more intuitive way for robots to efficiently perform tasks through learning from human demonstration on the basis of two major components: understanding through human demonstration and reproduction by robot arm. To understand human demonstration, we propose a vision-based spatial-temporal action detection method to detect human actions that focuses on meticulous hand movement in real time to establish an action base. An object trajectory inductive method is then proposed to obtain a key path for objects manipulated by the human through multiple demonstrations. In robot reproduction, we integrate the sequence of actions in the action base and the key path derived by the object trajectory inductive method for motion planning to reproduce the task demonstrated by the human user. Because of the capability of learning from demonstration, the robot can reproduce the tasks that the human demonstrated with the help of vision sensors in unseen contexts.
Exploration of an Inflection Point of Ventilation Parameters with Anaerobic Threshold Using Strucchange
Authors: Aida Takenori , Shionoya Akira , Nonaka Hirofumi , Hayami Kouji , Uchiyama Hisashi , Nagamori Masahiro , Ohhashi Satoshi , Kobayashi Mai , Takayama Tsugumi , Kimura Shinji
Published: April 2022

(1) Background: When measuring anaerobic work threshold (AT), the conventional V-slope method includes the subjectivity of the examiner, which cannot be eliminated completely. Therefore, we implemented an engineering method using strucchange to objectively search for the inflection point of AT. (2) Methods: Seventeen subjects (15 men and 2 women) were included in the study. The subjects rode an ergometer and performed a ramp load test for 18 min and 30 s. (3) Results: In VE (Ventilation), 11 out of 12 subjects had the same results with 95% confidence intervals for the AT by the strucchange and respiratory metabolic apparatus. In VCO2 (Carbon dioxide emissions), 9 out of 12 subjects had the same results with 95% confidence intervals for the AT with the strucchange and respiratory metabolic apparatus. In VE, 3 out of 12 subjects showed the same results for respiratory metabolic analysis and the AT by the V-slope method. In VCO2, 3 out of 12 subjects showed the same results for the respiratory metabolic analysis and AT by the V-slope method in VCO2. (4) Conclusions: Strucchange was more objective and significant in identifying the AT than the V-slope method.
Towards Enhancing Traffic Sign Recognition through Sliding Windows
Authors: Atif Muhammad , Zoppi Tommaso , Gharib Mohamad , Bondavalli Andrea
Published: April 2022

Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR.
Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks
Authors: Rossi Matteo , Alessandrelli Giulia , Dombrovschi Andra , Bovio Dario , Salito Caterina , Mainardi Luca , Cerveri Pietro
Published: April 2022

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation.
Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms
Authors: Xie Boqiang , Ding Jianli , Ge Xiangyu , Li Xiaohang , Han Lijing , Wang Zheng
Published: April 2022

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.
Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?
Authors: Choros Krystian A. , Job Andrew T. , Edgar Michael L. , Austin Kevin J. , McAree Peter Ross
Published: April 2022

This work determines whether hyperspectral imaging is suitable for discriminating ore from waste at the point of excavation. A prototype scanning system was developed for this study. This system combined hyperspectral cameras and a three-dimensional LiDAR, mounted on a pan-tilt head, and a positioning system which determined the spatial location of the resultant hyperspectral data cube. This system was used to obtain scans both in the laboratory and at a gold mine in Western Australia. Samples from this mine site were assayed to determine their gold concentration and were scanned using the hyperspectral apparatus in the laboratory to create a library of labelled reference spectra. This library was used as (i) the reference set for spectral angle mapper classification and (ii) a training set for a convolutional neural network classifier. Both classification approaches were found to classify ore and waste on the scanned face with good accuracy when compared to the mine geological model. Greater resolution on the classification of ore grade quality was compromised by the quality and quantity of training data. The work provides evidence that an excavator-mounted hyperspectral system could be used to guide a human or autonomous excavator operator to selectively dig ore and minimise dilution.
Biomechanical Assessment of Post-Stroke Patients’ Upper Limb before and after Rehabilitation Therapy Based on FES and VR
Authors: Montoya Daniela , Barria Patricio , Cifuentes Carlos A. , Aycardi Luis F. , Morís André , Aguilar Rolando , Azorín José M. , Múnera Marcela
Published: April 2022

Stroke is a medical condition characterized by the rapid loss of focal brain function. Post-stroke patients attend rehabilitation training to prevent the degeneration of physical function and improve upper limb movements and functional status after stroke. Promising rehabilitation therapies include functional electrical stimulation (FES), exergaming, and virtual reality (VR). This work presents a biomechanical assessment of 13 post-stroke patients with hemiparesis before and after rehabilitation therapy for two months with these three methods. Patients performed two tests (Maximum Forward Reach and Apley Scratching) where maximum angles, range of motion, angular velocities, and execution times were measured. A Wilcoxon test was performed (p = 0.05) to compare the variables before and after the therapy for paretic and non-paretic limbs. Significant differences were found in range of motion in flexion–extension, adduction–abduction, and internal–external rotation of the shoulder. Increases were found in flexion–extension, 17.98%, and internal–external rotation, 18.12%, after therapy in the Maximum Forward Reach Test. For shoulder adduction–abduction, the increase found was 20.23% in the Apley Scratching Test, supporting the benefits of rehabilitation therapy that combines FES, exergaming, and VR in the literature.
Assessment of the Interrelationships of Soil Nutrient Balances with the Agricultural Soil Emissions and Food Production
Authors: Martinho Vítor João Pereira Domingues , Pereira José L. S. , Gonçalves José Manuel
Published: April 2022

Sustainable and adjusted soil management practices are crucial for soil quality, namely in terms of the nutrient budget. On the other hand, soil characteristics are interlinked with agricultural sustainability and food supply. In other words, soil quality influences agricultural performance and food chains, but it is also impacted by agricultural activities. In this context, this research aims to evaluate the spatial correlations of the soil nutrient balance around the world and analyse how this variable is interrelated with agricultural soil emissions, agricultural output, and food supply. To achieve these goals, data from the FAOSTAT database were considered. This statistical information was analysed with spatial autocorrelation approaches to identify spatial clusters around the world that can be considered as a basis for designing common policies. To perform panel data regressions to identify marginal effects between variables, data were first evaluated using correlation matrices and factor analysis. The results highlight that there is space for common strategies worldwide to preserve soil quality, as in some parts of the world the problems are similar. In these frameworks, the international organizations may have a determinant contribution.
Changes in Corporate Responsibility Management during COVID-19 Crisis and Their Effects on Business Resilience: An Empirical Study of Swiss and German Companies
Authors: Brand Fridolin Simon , Blaese Richard , Weber Giulia , Winistoerfer Herbert
Published: April 2022

The COVID-19 pandemic has affected the activities and performance of companies worldwide. This article examines in what ways corporate responsibility management and corporate responsibility reporting activities have changed during the COVID-19 crisis in Switzerland and Germany, and how corporate responsibility management is related to aspects of business resilience in terms of maintaining expected financial performance during the COVID-19 crisis in Swiss companies. To answer these questions, we conducted a quantitative and qualitative content analysis of company reports of the top 10 Swiss and German companies in terms of market capitalization, as well as a quantitative survey among Swiss managers. Our results show that during the period under consideration, (i) a majority of the companies analyzed in Switzerland and Germany expanded their sustainability reporting, both overall and in all sustainability dimensions; (ii) there is a tendency for corporate responsibility management to move away from upstream and downstream sustainability topics and focus more on in-house sustainability topics; and (iii) there is a correlation between the two corporate responsibility topics of local recruitment and occupational health and expected financial performance as an aspect of maintained business resilience in times of crisis.
Salt Removal through Residue-Filled Cut-Soiler Simulated Preferential Shallow Subsurface Drainage Improves Yield, Quality and Plant Water Relations of Mustard (Brassica juncea L.)
Authors: Neha  , Yadav Gajender , Yadav Rajender Kumar , Kumar Ashwani , Rai Aravind Kumar , Onishi Junya , Omori Keisuke , Sharma Parbodh Chander
Published: April 2022

Soil salinity and the use of saline groundwater are two major constraints in crop production, which covers a ~1.0 billion ha area of arid and semi-arid regions. The improved drainage function of soil can modify the salty growing environment for higher agricultural production. The present study evaluated the effectiveness of cut-soiler-constructed rice residue-filled preferential shallow subsurface drainage (PSSD) to improve the drainage function and its effect on the yield, quality and plant–water relations of mustard over 2019–2021. Cut-soiler-simulated drains were made in a semi-controlled lysimeter (2 × 2 × 3; L*W*H m) as the main plot treatment in a double replicated split–split experiment with two soil types (subplot) and three irrigation water salinities (4, 8 and 12 dS m−1) as the sub-sub-plot treatment. The drainage volume of variable salinity (EC), dependent on the total water input, was substantially higher in the rainy season (April to October), i.e., 16.6, 7.76 and 12.0% during 2018, 2019 and 2020, with 1.7, 0.32 and 0.77 kg salt removal per lysimeter, compared to the post-rainy season. The mustard seed, straw and biological yields were improved by 31.4, 14.41 and 18.08%, respectively, due to a positive effect on plant–water relations. The mustard seeds produced in the cut-soiler-treated plots recorded higher oil, crude fiber and protein contents and a lower erucic acid content. The increase in salt load, by higher-salinity irrigation water, was also efficiently managed by using cut-soiler PSSD. It was found that the saline irrigation water up to 12.0 dS m−1 can be used under such PSSD without any extra salt loading. The present study showed the potential of cut-soiler PSSD in root zone salinity management by improving drainage in salt-affected arid regions.
Rural Tourism Competitiveness and Development Mode, a Case Study from Chinese Township Scale Using Integrated Multi-Source Data
Authors: Jia Ziyu , Jiao Yan , Zhang Wei , Chen Zheng
Published: April 2022

Tourism has been seen and adopted as a vital means for achieving rural economic and social revitalization worldwide without harming sustainable development principles. For China, the evaluation of rural tourism competitiveness at the township level is essential for planning and developing the tourism industry as a basic administration and economic unit, but there is not enough research due to the lack of applicable data and systematic methods. Therefore, this study constructed a town-level rural tourism competitiveness evaluation and development mode classification model based on the modified Michael Porter’s Diamond Model using integrated multi-source data. By taking the 1806 township units in Henan Province, China as examples, we conclude four different modes based on the level of the comprehensive score and industrial internal balance (i.e., balanced development mode with multiple advantages, related and supporting industries driving mode, ecological resource-led mode, and rural landscape experience mode). Policy suggestions for the optimization of the rural tourism industry for Henan are discussed based on the results.
Study on Comprehensive Evaluation of Urban Water Resource Vulnerability
Authors: Xu Wenjie , Zhang Xiaoping , Cui Yujing , Tian Tao , Lin Lijun , Liu Yuyu
Published: April 2022

An evaluation index system of urban water resource vulnerability was constructed to objectively reflect the sustainable development capacity of water resources and provide reference for the construction of sustainable development cities. The improved analytic hierarchy process and entropy weight method were adopted to determine the combined index weights to achieve a unification of subjectivity and objectivity and make the calculation more accurate and reliable. A comprehensive evaluation method was used to build the water resource vulnerability evaluation model. The annual water resource vulnerability index and vulnerability grade were calculated and determined. The results showed that the water resource vulnerability index of Jinan showed a fluctuant trend from 2008 to 2017. The annual vulnerability grade was severe, moderate, or mild vulnerability, and there was a gradual reduction in vulnerability overall, which was consistent with the actual situation. In the future, the water resource vulnerability grade will be reduced if the industrial structure can be further reformed and optimized, and the level of water resource management and the utilization efficiency of water resources can be further improved by strengthening the construction of smart water systems.
Road Safety Policies for Saudi Females: A Fuzzy Logic Analysis
Authors: Lashin Maha M. A. , Saleh Wafaa Salah Eldin Shoukry
Published: April 2022

Perceptions, attitudes and anticipated behavior represent very useful information for decision and policy makers before they implement proposed new policies. Road safety is one of the most critical fields in the built environment. In the Kingdom of Saudi Arabia, women started driving in 2019. There is a huge shortage of studies that investigate users’ attitudes and behavior, particularly women, in Saudi Arabia. It is very timely therefore, to assess their perceptions, attitudes and raise their awareness towards road safety. In this paper, the perceptions and attitudes towards road safety and relevant policies and programs were assessed and compared using two approaches: analytical and a fuzzy logic system. By means of a questionnaire, a total of 17 road safety policies, including engineering, enforcement programs, education programs and legislation programs, were presented to the participants and assessed. These policies were assessed from the viewpoint of Saudi women. The engineering policies included the use of road signs and marking, improving road network alignment, monitoring driver speed and engineering speed reduction measures. The assessment was in terms of the usefulness of the policies to reduce traffic accidents and improve road safety in Riyadh. A total of 375 participants were surveyed. Using a Likert scale response system to report the Saudi female participants’ opinions, two sets of indices were calibrated to evaluate the perceived usefulness and acceptability of the safety policies. A MATLAB simulation of a fuzzy logic system was utilized to assess, investigate and compare travel attitudes and perceptions of road safety. The results indicate overall encouragement for the safety programs and policies by the participants. Most of the support seems to be for the engineering program. The physical presence of traffic police was observed to have scored the highest. This is an interesting result that shows that enforcement policies are still perceived to be the most effective. In this category, the policy of enforcing speed limits showed high support, amongst the other policies. While a fuzzy logic analysis is typically used in hard engineering assessments and investigations, such as mechanical engineering and system design applications, the results obtained in this investigation show that the efficacy and adequacy of the proposed fuzzy logic control system in assessing attitudinal data was sufficiently good to be recommended for future research. The outcomes from both sets of approaches, the analytical and the fuzzy logic, showed comparable results, which also demonstrates the potential benefits of using the fuzzy system in many attitudinal and behavioral studies.
Observed Changes in the Frequency, Intensity, and Spatial Patterns of Nine Natural Hazards in the United States from 2000 to 2019
Authors: Summers J. K. , Lamper A. , McMillion C. , Harwell L. C.
Published: April 2022

There is increasing evidence from across the globe that climate change results in changes in the frequency, location, and impact of natural hazards. Much of this evidence is conceptual, inferential, or simply assumed. To provide objective support to confirm these hypotheses, we constructed county-level time-series datasets (2000–2019) for nine natural hazards for the entire United States. Hazards considered for this study included hurricanes, tropical storms, landslides, wildfires, earthquakes, drought, inland flooding, coastal flooding, and tornadoes. Geospatial analysis techniques were used to calculate the percentage (range: 0–100) of land area in each county exposed to each natural hazard for all the years that hazard data were available. The best available data were acquired from publicly accessible sources. Cumulative distribution functions were calculated for each hazard in five-year intervals to test for statistically significant changes in distribution patterns across the five-year time periods using the Kolmogorov–Smirnov test. There were significant changes in hurricanes, tropical storms, and drought over the two decades; changes in tornadoes, landslides, and wildfires were not significant in terms of frequency, likely due to the site-specific nature of their occurrences. The intensity and spatial distribution and an emerging hot spot and spatial trend analyses and an emerging hot spot and spatial trend analyses were also completed (except for flooding events and earthquakes due to insufficient data). All datasets provide empirical support for earlier inferences concerning the connections between the hazards and climate change. Analyses showed apparent changes in the frequency and intensity of hurricanes, tropical storms, and drought-related to climate change factors. Internal and coastal flooding also demonstrated these connections, although the length of the dataset did not permit significant testing but shows significant hot spots and trending locations. Tornadoes, landslides, and wildfires showed significant hot spots and trending locations, but the specific locational nature of the data did not show significant changes in frequency. Earthquakes showed no significant changes over the time period.
Nature Conservation in Sustainability
Authors: Kostrakiewicz-Gierałt Kinga , Pliszko Artur
Published: April 2022

Nature protection is one of the main pillars of sustainable development, striking a proper balance between anthropocentric and ecocentric justifications [...]
Mechanical Behavior of Concrete Prepared with Waste Marble Powder
Authors: Wang Yumei , Xiao Jianzhuang , Zhang Jintuan , Duan Zhenhua
Published: April 2022

Marble production and processing generates a large amount of marble powder waste that has great potential for cementitious material. This paper investigates the application of waste marble powder with different replacement ratios of cement in concrete and experimentally studies the physical and mechanical properties of this green concrete type. Artificial marble powder and original marble powder are used at different replacement levels. The effect of different kinds of marble powder and its replacement ratio on the mechanical properties of concrete are discussed. The results show that the compressive strength, splitting tensile strength, and flexural strength change significantly when the substitution rate of marble powder exceeds 10%; the strength decreases as the substitution rate increases. The usage of artificial marble powder plays a weakening role on concrete performance due to its resin composition when compared to the performance using original marble powder. The stress–stain curves of the two types of marble powder concrete are compared. For concrete, by using the original marble powder, the variation of strain value is not obvious when the marble powder replacement ratio is less than 20%, but for concrete by using artificial marble powder, the peak and ultimate strain decrease significantly with the replacement ratio of marble powder increase.
Composition Characteristics of an Urban Forest Soil Seed Bank and Its Influence on Vegetation Restoration: A Case Study in Dadu Terrace, Central Taiwan
Authors: Lin Chia-Yen , Liao Min-Chun , Wang Wei , Tzeng Hsy-Yu
Published: April 2022

The contributions of urban forests and green spaces to sustainable development have been confirmed. Meanwhile, cities worldwide have shown that investments in urban forestry can greatly contribute to citizens’ quality of life. This study was conducted in urban forests in the Dadu Terrace of Taichung City, central Taiwan, which were frequently disturbed by fires and had grassland severely invaded by Panicum maximum after the forest degraded. We sampled 46 plots in Dadu Terrace to understand the relationship between the soil seed bank and vegetation as well as to evaluate the feasibility of applying soil seed bank transfers for ecological restoration in Dadu Terrace. The grassland was dominated by Panicum maximum. Forest vegetation was distinguished by cluster analysis into five types, i.e., Ficus microcarpa type, Acacia confusa type, Litsea glutinosa type, Cinnamomum camphora type, and Trema orientalis type. In the aboveground survey, we recorded 141 vascular plants, including 129 seed plants and 12 ferns. There were 40 identified species of naturalized plants. A total of 29,914 seedlings were recorded in the soil seed bank, with an average seed density of 9634 seeds/m2 and a total of 91 species. There were 40 species of naturalized plants, accounting for 90.9% of the total seed reserves. This showed that Dadu Terrace was severely affected by the invasion of naturalized species. The species number and seed reserves of woody plants of the Panicum maximum type were significantly lower than those of forest vegetation. The composition of the soil seed bank was dominated by naturalized plants, indicating that the high frequency of fire reduced the proportion of native species and woody plants in the soil seed bank. Acacia confusa type was the main forest type in Dadu Terrace. Although several woody species and seed reserves were in its soil seed bank, the naturalized proportions were even higher. Trema orientalis type was the secondary forest type in Dadu Terrace; it had the smallest forest area. However, it was the only vegetation type with a greater tree seed abundance than herbs and the lowest proportion of naturalized seed abundance. Trema orientalis type vegetation has a relatively high soil transfer value for ecological restoration but lacks diversity. Our results revealed that the characteristics of the soil seed bank of Dadu Terrace make it challenging to restore the grassland to the forest by natural succession. Therefore, we suggest that artificial restoration is necessary for Dadu Terrace.
Understanding the Effect of Multi-Agent Collaboration on the Performance of Logistics Park Projects: Evidence from China
Authors: Yang Dan , Yin Weili , Liu Sen , Chan Felix T. S.
Published: April 2022

With the rapid development of a new generation of information technology and data systems, there are more and more modern logistics park projects appearing in China. However, in the process of the construction and operation of a logistics park project, there is often a lack of coordination between the multiple parties (usually the government, the development enterprise and the entered enterprises), which leads to a series of problems such as low efficiency and disordered management and so on, finally affecting the project performance. However, few studies have focused on this phenomenon, and prior studies are unclear regarding the impact of multi-agent collaboration on logistics park project performance. Therefore, in this study, we investigate the link between multi-agent collaboration and the logistics park project performance based on survey data from Yunnan Province in China. The empirical analysis was conducted using the partial least squares (PLS)-based structural equation modeling with Smart PLS 2.0. The data analysis results suggest that the three dimensions of multi-agent collaboration (management, mechanism and information collaboration) have a significant positive impact on the performance of a logistics park project. Under different environmental dynamics conditions, different strategies should be adopted by a logistics park project to improve the performance. This study provides a new perspective for understanding the value of multi-agent collaboration in logistics park projects both in theory and practice.
Motives towards e-Shopping Adoption among Pakistani Consumers: An Application of the Technology Acceptance Model and Theory of Reasoned Action
Authors: Saleem Aqeela , Aslam Javed , Kim Yun Bae , Nauman Shazia , Khan Nokhaiz Tariq
Published: April 2022

Digital technologies play a vital role in daily human life and significantly influence human attitudes toward the adoption of new and attractive lifestyles. The internet has been widely accepted in every modern society, and the act of purchasing products or services over the internet, known as online/internet shopping, has revolutionized business. This study was designed using the technology acceptance model and theory of reasoned action. This study identified important factors such as perceived awareness of security, perceived usefulness, personal innovativeness, and perceived ease of use in purchasing, together with the effects of these factors on online purchasing intentions and the mediating role of consumer attitudes toward online purchasing. The results show that the identified factors are positively and significantly related to consumer intentions and attitudes toward online purchasing. This study has the potential to guide online retailers and managers in expanding online purchasing platforms and in increasing their responsiveness to the need to restructure business models according to new technological developments.
Empirical Study on the Impact of COVID-19 on International Student Enrollment for Higher Education in China
Authors: Shijian Zou , Agyemang Andrew Osei
Published: April 2022

International students’ mobility was not spared in terms of the negative impact of COVID-19 on higher education. The majority of prior research on COVID-19 and international higher education has employed a qualitative research design. Few of the studies have employed a quantitative research approach. Our study, therefore, in an attempt to fill in the literature gap, explores the impact of COVID-19 on international students’ enrollment in China using a quantitative approach. Using cross-sectional data for thirty-one provinces, municipalities and autonomous regions on the Chinese mainland, the study utilized Dynamic Ordinary Least Squares for the long-run relationship analysis. Our findings revealed that an inverse and statistically significant relationship exists between total confirmed cases and international students enrolled. Similarly, a negative relationship was found between total deaths and the number of international students enrolled for the 2020/2021 academic year. On the contrary, a positive and statistically significant relationship was found between total recoveries and the number of enrolled international students at higher education. Our findings add up to existing literature on COVID-19 and international higher education.
Toward Sustainable Communities: A Case Study of the Eastern Market in Detroit
Authors: Bergman Zinette , Bergman Manfred Max
Published: April 2022

Community development tends to focus on large-scale, government-funded transformations or on small-scale, grassroot initiatives. In the US, the financial resources, available infrastructure, and broad-based civic support to implement large-scale community transformations are frequently lacking. In contrast, niche interventions, while often locally successful, tend to be unscalable. Accordingly, many community development programs either do not go beyond an ideational stage, or they are unscalable or unsustainable in the long run. In this qualitative case study, we analyze the Eastern Market in Detroit, Michigan, a local institution that contributes considerably and in several ways to the sustainability of multiple communities. Using Content Configuration Analysis (CCA), we conduct a bottom-up exploratory analysis of fieldwork notes, nonparticipant observations, as well as audio, visual, and written materials including policy and strategy documents from the City of Detroit, Wayne County, and the State of Michigan, academic publications, strategy and annual reports, websites, blogs, vlogs, social media outlets, newspapers, podcasts, and interviews along two lines of inquiry: first, to examine how the market contributes to sustainable community development and, second, to explore the systemic underpinnings that facilitate such development. Specifically, we focus on the Eastern Market to identify system-relevant actors, interests, relations, interventions, and outcomes that illustrate an institution which operates well beyond the ideational confines of a conventional farmers market. In the process of exploring the adaptive nature of the Eastern Market within its financial and infrastructural constraints, we also exemplify with this case that a well-established institution, a farmers market, can reinvent itself to serve multiple needs of larger, heterogeneous communities, and that the successful adaptations associated with this reinvention reimagine the community in which it is embedded.
Mathematical Modeling and Analysis of Tumor Chemotherapy
Authors: Song Ge , Liang Guizhen , Tian Tianhai , Zhang Xinan
Published: April 2022

Cancer diseases lead to the second-highest death rate all over the world. For treating tumors, one of the most common schemes is chemotherapy, which can decrease the tumor size and control the progression of cancer diseases. To better understand the mechanisms of chemotherapy, we developed a mathematical model of tumor growth under chemotherapy. This model includes both immune system response and drug therapy. We characterize the symmetrical properties and dynamics of this differential equation model by finding the equilibrium points and exploring the stability and symmetry properties in a range of model parameters. Sensitivity analyses suggest that the chemotherapy drug-induced tumor mortality rate and the drug decay rate contribute significantly to the determination of treatment outcomes. Numerical simulations highlight the importance of CTL activation in tumor chemotherapy.
The Power of Anime: A New Driver of Volunteer Tourism
Authors: Mori Hiroaki
Published: April 2022

In Japan, many academics and practitioners have focused on anime-induced tourism as one of the new alternative forms of tourism in the 21st century. Many fans have visited locations that have appeared in anime as film-induced tourists. Regarding the behavior of anime-induced tourists, many tend to be willing to contribute to the destinations they visit as eco-oriented volunteers, different from purely film-induced tourists. Therefore, anime-induced tourists possess a complex character that entails both an interest in film-induced tourism and volunteer tourism, which may conceptually be opposed to one another. This study reveals anime’s potential as a driver of new volunteer tourism and theoretically contributes to tourism research by redefining anime-induced tourism by relying on the concept of film-induced voluntourism. Using a comparative case study of the behavior of anime-induced volunteer tourists at three destinations, this study found that while anime-induced volunteer tourists have a feeling of gratitude for their host communities with a vacationer’s mindset, they can realize positive outcomes, including economic benefits and problem solutions by engaging in cleaning-up activities at the destinations they visit. In conclusion, this study clarifies that anime tourism is one of the significant alternative forms of tourism that can achieve community development associated with film-induced voluntourists, resolving the negative effects of film-induced tourism and volunteer tourism.