bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Ecological Spatial Network Topology for Enhanced Carbon Sequestration in the Ecologically Sensitive Middle Reaches of the Yellow River, China
Authors: Wang Fei , Guo Hongqiong , Zhang Qibin , Yu Qiang , Xu Chenglong , Qiu Shi
Published: April 2023

The destruction of vegetation structure and quantity leads to the weakening of the carbon sequestration capacity of the ecosystem. Building an ecological spatial network is a potent method for studying vegetation spatial distribution structures. The relationship between the spatial distribution structure of vegetation networks and carbon sequestration, as approached from the perspective of complex network theory, is understudied. This study uses the minimum resistance model (MCR) and morphological spatial pattern analysis (MSPA) to study the eco-space network and ecological node spatial structure and topological characteristics of vegetation in the ecologically sensitive area of the middle reaches of the Yellow River (ESAMRYR). Based on the Carnegie-Ames-Stanford approach (CASA) model, the vegetation Net Primary Productivity (NPP) of the study area is calculated, and the ecological carbon sequestration function of the ecological node is estimated, and the relationship between the ecological node and the topological indicators is analyzed. The study shows that the forest land carbon storage in the regions situated toward the south and east of the Yellow River ecologically sensitive area is the highest, accounting for twice the proportion of the area, and is very important in terms of increasing carbon storage. Most of the ecological sources in the study area have a higher topological importance than functional importance, and the sources with low coordination are mainly distributed in the southwest and northeast. We construct a topology and function coupling optimization model (TFCO) to explore the coordination between vegetation structure and carbon sequestration function, to determine the network optimization direction, and to propose optimization solutions. Analysis of network robustness and carbon sequestration capacity shows that the sturdiness and carbon sequestration of the enhanced network are significantly improved. This study provides strategies and methods for protecting ecological sensitive areas, optimizing vegetation spatial distribution, and enhancing carbon sequestration capacity.
TRP-Oriented Hyperspectral Remote Sensing Image Classification Using Entropy-Weighted Ensemble Algorithm
Authors: Jia Shuhan , Li Yu , Zhao Quanhua , Wang Changqiang
Published: April 2023

The problem that the randomly generated random projection matrix will lead to unstable classification results is addressed in this paper. To this end, a Tighter Random Projection-oriented entropy-weighted ensemble algorithm is proposed for classifying hyperspectral remote sensing images. In particular, this paper presents a random projection matrix selection strategy based on the separable information of a single class able to project the features of a certain class of objects. The projection result is measured by the degree of separability, thereby obtaining the low-dimensional image with optimal separability of the class. After projecting samples with the same random projection matrix, to calculate the distance matrix, the Minimum Distance classifier is devised, repeating for all classes. Finally, the weight of the distance matrix is considered in ensemble classification by using the information entropy. The proposed algorithm is tested on real hyperspectral remote sensing images. The experiments show an increase in both stability and performance.
Application of Deep Learning for Classification of Intertidal Eelgrass from Drone-Acquired Imagery
Authors: Tallam Krti , Nguyen Nam , Ventura Jonathan , Fricker Andrew , Calhoun Sadie , O’Leary Jennifer , Fitzgibbons Mauriça , Robbins Ian , Walter Ryan K.
Published: April 2023

Shallow estuarine habitats are globally undergoing rapid changes due to climate change and anthropogenic influences, resulting in spatiotemporal shifts in distribution and habitat extent. Yet, scientists and managers do not always have rapidly available data to track habitat changes in real-time. In this study, we apply a novel and a state-of-the-art image segmentation machine learning technique (DeepLab) to two years of high-resolution drone-based imagery of a marine flowering plant species (eelgrass, a temperate seagrass). We apply the model to eelgrass (Zostera marina) meadows in the Morro Bay estuary, California, an estuary that has undergone large eelgrass declines and the subsequent recovery of seagrass meadows in the last decade. The model accurately classified eelgrass across a range of conditions and sizes from meadow-scale to small-scale patches that are less than a meter in size. The model recall, precision, and F1 scores were 0.954, 0.723, and 0.809, respectively, when using human-annotated training data and random assessment points. All our accuracy values were comparable to or demonstrated greater accuracy than other models for similar seagrass systems. This study demonstrates the potential for advanced image segmentation machine learning methods to accurately support the active monitoring and analysis of seagrass dynamics from drone-based images, a framework likely applicable to similar marine ecosystems globally, and one that can provide quantitative and accurate data for long-term management strategies that seek to protect these vital ecosystems.
Detecting Cassava Plants under Different Field Conditions Using UAV-Based RGB Images and Deep Learning Models
Authors: Nnadozie Emmanuel C. , Iloanusi Ogechukwu N. , Ani Ozoemena A. , Yu Kang
Published: April 2023

A significant number of object detection models have been researched for use in plant detection. However, deployment and evaluation of the models for real-time detection as well as for crop counting under varying real field conditions is lacking. In this work, two versions of a state-of-the-art object detection model—YOLOv5n and YOLOv5s—were deployed and evaluated for cassava detection. We compared the performance of the models when trained with different input image resolutions, images of different growth stages, weed interference, and illumination conditions. The models were deployed on an NVIDIA Jetson AGX Orin embedded GPU in order to observe the real-time performance of the models. Results of a use case in a farm field showed that YOLOv5s yielded the best accuracy whereas YOLOv5n had the best inference speed in detecting cassava plants. YOLOv5s allowed for more precise crop counting, compared to the YOLOv5n which mis-detected cassava plants. YOLOv5s performed better under weed interference at the cost of a low speed. The findings of this work may serve to as a reference for making a choice of which model fits an intended real-life plant detection application, taking into consideration the need for a trade-off between of detection speed, detection accuracy, and memory usage.
Forest Fire Mapping Using Multi-Source Remote Sensing Data: A Case Study in Chongqing
Authors: Zhao Yixin , Huang Yajun , Sun Xupeng , Dong Guanyu , Li Yuanqing , Ma Mingguo
Published: April 2023

Forest fires are one of the most severe natural disasters facing global ecosystems, as they have a significant impact on ecological security and social development. As remote sensing technology has developed, burned areas can now be quickly extracted to support fire monitoring and post-disaster recovery. This study focused on monitoring forest fires that occurred in Chongqing, China, in August 2022. The burned area was identified using various satellite images, including Sentinel-2, Landsat8, Environmental Mitigation II A (HJ2A), and Gaofen-6 (GF-6). The burned area was extracted using visual interpretation, differenced Normalized Difference Vegetation Index (dNDVI), and differenced Normalized Burnup Ratio (dNBR). The results showed that: (1) The results of the three monitoring methods were very consistent, with a coefficient of determination R2 > 0.96. (2) A threshold method based on the dNBR-extracted burned area was used to analyze fire severity, with moderate-severity fires making up the majority (58.05%) of the fires. (3) Different topographic factors had some influence on the severity of the forest fires. High elevation, steep slopes and the northwestern aspect had the largest percentage of burned area.
Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques
Authors: López-Cuesta Miguel , Aler-Mur Ricardo , Galván-León Inés María , Rodríguez-Benítez Francisco Javier , Pozo-Vázquez Antonio David
Published: April 2023

Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blended-models-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting.
Inter-Comparison of Landsat-8 and Landsat-9 during On-Orbit Initialization and Verification (OIV) Using Extended Pseudo Invariant Calibration Sites (EPICS): Advanced Methods
Authors: Kaewmanee Morakot , Leigh Larry , Shah Ramita , Gross Garrison
Published: April 2023

Three advanced methodologies were performed during Landsat-9 on orbit and initialization and verification (OIV): Extended Pseudo Invariant Calibration Sites Absolute Calibration Model Double Ratio (ExPAC Double Ratio) and Extended Pseudo Invariant Calibration Sites (EPICS)-based cross-calibration utilizing stable regions in Northern African desert sites (EPICS-NA) and a global scale (EPICS-Global). The development of these three techniques was described using uncertainties analysis. The ExPAC Double Ratio was derived based on the ratio between ExPAC model prediction and satellite measurements for Landsat-8 and Landsat-9. The ExPAC Double Ratio can be performed to determine differences between sensors ranging from visible, red edge, near-infrared, to short-wave infrared wavelengths. The ExPAC Double Ratio and EPICS-based inter-comparison ratio uncertainties were determined using the Monte Carlo Simulation. It was found that the uncertainty levels of 1–2% can be achieved. The EPICS-based cross-calibration results were derived using two targets: EPICS-NA and EPICS-Global, with uncertainties of 1–2.2% for all spectral bands. The inter-comparison results between Landsat-9 and Landsat-8 during the OIV period using the three advanced methods were well within 0.5% for all spectral bands except for the green band, which showed sub 1% agreement.
Passive 3D Imaging Method Based on Photonics Integrated Interference Computational Imaging System
Authors: Ge Ben , Yu Qinghua , Chen Jialiang , Sun Shengli
Published: April 2023

Planetary, lunar, and deep space exploration has become the frontier of remote sensing science, and three-dimensional (3D) positioning imaging technology is an important part of lunar and deep space exploration. This paper presents a novel passive 3D imaging method based on the photonics integrated interference computational imaging system. This method uses a photonics integrated interference imaging system with a complex lens array. The midpoints of the interference baselines formed by these lenses are not completely overlapped. The distance between the optical axis and the two lenses of the interference baseline are not equal. The system is used to obtain the complex coherence factor of the object space at a limited working distance, and the image evaluation optimization algorithm is used to obtain the clear images and 3D information of the targets of interest. The simulation results show that this method is effective for the working scenes with targets located at single or multiple limited working distances. The sharpness evaluation function of the target presents a good unimodality near its actual distance. The experimental results of the interference of broad-spectrum light show that the theoretical basis of this method is feasible.
Slight Mass Loss in Glaciers over the Ulugh Muztagh Mountains during the Period from 2000 to 2020
Authors: Gu Lailei , Che Yanjun , Zhang Mingjun , Chen Lihua , Zhou Yushan , Ma Xinggang
Published: April 2023

Knowledge about changes in the glacier mass balance and climate fluctuation in the East Kunlun Mountains is still incomplete and heterogeneous. To understand the changes in the glacier mass in the Ulugh Muztagh Mountains in the East Kunlun Mountains due to global warming, a time series of satellite stereo-images from the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were derived from 2000 to 2020. Digital elevation models (DEMs) of the glaciers were generated and used to assess the changes in these glacier masses from 2000 to 2020. The results show that the surface elevation of glaciers in the Ulugh Muztagh region changed by −0.17 ± 10.74 m from 2000 to 2020, corresponding to a mass change of −0.14 ± 9.13 m w.e. The glacier mass balance increased by 0.64 ± 9.22 m w.e. in 2000–2011 and then decreased by 0.78 ± 9.04 m w.e. in 2011–2020. The annual mass balance of the glaciers was −0.0072 ± 0.46 m w.e./yr from 2000 to 2020, showing glacial stability. The equilibrium line altitude (ELA) of the glacier was 5514 m a.s.l. from 2000 to 2020. In addition, we also found that the glacier mass losses in the west and north slopes were more significant than those in the east and south slopes. There was a phenomenon of glacier surges in the Yulinchuan glacier from 2007 to 2011. Overall, the glaciers were relatively stable with respect to the total glacier thickness in the Ulugh Muztagh Mountains.
Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging
Authors: Wu Lingxiao , Chen Tianlu , Ciren Nima , Wang Dui , Meng Huimei , Li Ming , Zhao Wei , Luo Jingxuan , Hu Xiaoru , Jia Shengjie , Liao Li , Pan Yubing , Wang Yinan
Published: April 2023

The Qinghai-Tibet Plateau is rich in renewable solar energy resources. Under the background of China’s “dual-carbon” strategy, it is of great significance to develop a global horizontal irradiation (GHI) prediction model suitable for Tibet. In the radiation balance budget process of the Earth-atmosphere system, clouds, aerosols, air molecules, water vapor, ozone, CO2 and other components have a direct influence on the solar radiation flux received at the surface. For the descending solar shortwave radiation flux in Tibet, the attenuation effect of clouds is the key variable of the first order. Previous studies have shown that using Artificial intelligence (AI) models to build GHI prediction models is an advanced and effective research method. However, regional localization optimization of model parameters is required according to radiation characteristics in different regions. This study established a set of AI prediction models suitable for Tibet based on ground-based solar shortwave radiation flux observation and cloud cover observation data of whole sky imaging in the Yangbajing area, with the key parameters sensitively tested and optimized. The results show that using the cloud cover as a model input variable can significantly improve the prediction accuracy, and the RMSE of the prediction accuracy is reduced by more than 20% when the forecast horizon is 1 h compared with a model without the cloud cover input. This conclusion is applicable to a scenario with a forecast horizon of less than 4 h. In addition, when the forecast horizon is 1 h, the RMSE of the random forest and long short-term memory models with a 10-min step decreases by 46.1% and 55.8%, respectively, compared with a 1-h step. These conclusions provide a reference for studying GHI prediction models based on ground-based cloud images and machine learning.
Woody Plant Encroachment in a Seasonal Tropical Savanna: Lessons about Classifiers and Accuracy from UAV Images
Authors: Costa Lucas Silva , Sano Edson Eyji , Ferreira Manuel Eduardo , Munhoz Cássia Beatriz Rodrigues , Costa João Vítor Silva , Rufino Alves Júnior Leomar , de Mello Thiago Roure Bandeira , da Cunha Bustamante Mercedes Maria
Published: April 2023

Woody plant encroachment in grassy ecosystems is a widely reported phenomenon associated with negative impacts on ecosystem functions. Most studies of this phenomenon have been carried out in arid and semi-arid grasslands. Therefore, studies in tropical regions, particularly savannas, which are composed of grassland and woodland mosaics, are needed. Our objective was to evaluate the accuracy of woody encroachment classification in the Brazilian Cerrado, a tropical savanna. We acquired dry and wet season unmanned aerial vehicle (UAV) images using RGB and multispectral cameras that were processed by the support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers. We also compared two validation methods: the orthomosaic and in situ methods. We targeted two native woody species: Baccharis retusa and Trembleya parviflora. Identification of these two species was statistically (p < 0.05) most accurate in the wet season RGB images classified by the RF algorithm, with an overall accuracy (OA) of 92.7%. Relating to validation assessments, the in situ method was more susceptible to underfitting scenarios, especially using an RF classifier. The OA was higher in grassland than in woodland formations. Our results show that woody encroachment classification in a tropical savanna is possible using UAV images and field surveys and is suggested to be conducted during the wet season. It is challenging to classify UAV images in highly diverse ecosystems such as the Cerrado; therefore, whenever possible, researchers should use multiple accuracy assessment methods. In the case of using in situ accuracy assessment, we suggest a minimum of 40 training samples per class and to use multiple classifiers (e.g., RF and DT). Our findings contribute to the generation of tools that optimize time and cost for the monitoring and management of woody encroachment in tropical savannas.
Room Temperature Broadband Bi2Te3/PbS Colloidal Quantum Dots Infrared Photodetectors
Authors: Yu Lijing , Tian Pin , Tang Libin , Zuo Wenbin , Zhong Hefu , Hao Qun , Teng Kar Seng , Zhao Guiqin , Su Runhong , Gong Xiaoxia , Yuan Jun
Published: April 2023

Lead sulfide colloidal quantum dots (PbS CQDs) are promising optoelectronic materials due to their unique properties, such as tunable band gap and strong absorption, which are of immense interest for application in photodetectors and solar cells. However, the tunable band gap of PbS CQDs would only cover visible short-wave infrared; the ability to detect longer wavelengths, such as mid- and long-wave infrared, is limited because they are restricted by the band gap of the bulk material. In this paper, a novel photodetector based on the synergistic effect of PbS CQDs and bismuth telluride (Bi2Te3) was developed for the detection of a mid-wave infrared band at room temperature. The device demonstrated good performance in the visible-near infrared band (i.e., between 660 and 850 nm) with detectivity of 1.6 × 1010 Jones at room temperature. It also exhibited photoelectric response in the mid-wave infrared band (i.e., between 4.6 and 5.1 μm). The facile fabrication process and excellent performance (with a response of up to 5.1 μm) of the hybrid Bi2Te3/PbS CQDS photodetector are highly attractive for many important applications that require high sensitivity and broadband light detection.
Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills
Authors: Lim Chiho , Barragan Juan Antonio , Farrow Jason Michael , Wachs Juan P. , Sundaram Chandru P. , Yu Denny
Published: April 2023

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.
Using Object Detection Technology to Identify Defects in Clothing for Blind People
Authors: Rocha Daniel , Pinto Leandro , Machado José , Soares Filomena , Carvalho Vítor
Published: April 2023

Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing. Therefore, the objective of this paper is to use object detection technology to categorize and detect stains on garments. The defect detection system proposed in this study relies on the You Only Look Once (YOLO) architecture, which is a single-stage object detector that is well-suited for automated inspection tasks. The authors collected a dataset of clothing with defects and used it to train and evaluate the proposed system. The methodology used for the optimization of the defect detection system was based on three main components: (i) increasing the dataset with new defects, illumination conditions, and backgrounds, (ii) introducing data augmentation, and (iii) introducing defect classification. The authors compared and evaluated three different YOLOv5 models. The results of this study demonstrate that the proposed approach is effective and suitable for different challenging defect detection conditions, showing high average precision (AP) values, and paving the way for a mobile application to be accessible for the blind community.
Efficacy of Antivibration Gloves When Used with Electric Hammers of about 10 kg for Chiseling Limestone Rocks
Authors: Alfaro Degan Guido , Antonucci Andrea , Lippiello Dario
Published: April 2023

The ISO Standard 10819:2013 defines the method for evaluating the performances of antivibration (AV) gloves, but when used in real fields, the protection can be dissimilar to that labeled. This paper investigates the transmissibility, at the palm level, of three different types of AV gloves (air, gel, neoprene) and an ordinary leather glove, during the use of four similar electric hammers (average weight of 10 kg, and average impact energy of 18 J), in a limestone quarry plant. As the average triaxial transmissibility for all the hammers, results show very limited benefits in reducing the vibration (6%), with no significative differences among the different gloves. The working leather glove, instead, shows a transmissibility quite equal to the unit. Anyway, results can be different for the same glove when used among the different hammers, providing in some cases 19% of protection. Some differences can be found regarding the transmissibility through the three main axes for the same type of glove: the glove in gel seems to perform better in shear than in compression. The transmissibility in compression is around 20% higher than that provided by the manufacturers of the certified gloves. The usage of specific excitation curves during laboratory tests could help in providing a more accurate estimation of the transmissibility of the gloves when used with a specific tool.
Parametric and General Evaluation of Mathematical Models Used for Critical Diameter Determination in Cyclone Separators
Authors: Mumcu Ali Galip , Avci Atakan
Published: April 2023

Cyclone separators are used in multi-phase flows for phase separation or classification. Pressure drop and separation efficiency are essential parameters for determining the performance of a cyclone separator, and critical diameter is an important parameter related to cyclone separation efficiency. In this study, we investigate the critical diameter prediction ability of various mathematical models; in particular, approximately 400 experimental data available in the literature, reflecting different geometries and operating parameters, are used to investigate the critical diameter prediction ability of the selected models. These data are calculated using ten mathematical models selected among the most widely used models which operate based on different principles, and the associated critical diameter values are determined. These calculated values are then compared with those obtained in the experimental studies. As a result, there are many parameters affecting the performance of cyclone separators. The fact that some of these parameters are not used in the models can lower the prediction accuracy of the model, depending on the context. For this reason, the parameters for which the models give better results are analyzed in detail. According to these results, users can choose a model more appropriately. In addition, an absolute evaluation is provided, demonstrating that the models provide important ideas regarding cyclone performance.
Application of Algorithm for Inventive Problem Solving (ARIZ) for the Heat Dissipation of Energy Storage Supply System for High-Power Locomotive
Authors: Li Dengke , Chen Shiwen , Zhu Yingmou , Qiu Ang , Liao Zhiyuan , Liu Xiaodong , Shen Longjiang , Jian Guiyu
Published: April 2023

With the increasing demand for the energy density of battery system in railway vehicles, the ambient temperature of the battery system is increased. This means that the heat dissipation efficiency and battery service life are reduced, thus reducing the reliability of the battery. Contraposing the problem of the heat dissipation of energy storage batteries, the full deployment of the ARIZ algorithm has been provided for applications of problem-solving processes in this investigation. The advantage of using the ARIZ algorithm is that while the engineering system is kept unchanged or less changed, the effective heat dissipation of the energy storage battery with increasing energy density can be satisfied. During the process of the ARIZ algorithm, a mini problem is first defined. Moreover, the ideal final result and physical contradiction are defined. Following this, expanded material field resources are used, and finally, 22 innovative designs are obtained using the problem-solving tool in TRIZ theory. The final design scheme is provided by combining multiple schemes, and the heat compensator dispersion of a partially improved system in the optimized scheme has been simulated. The results show that the heat compensator dispersion of the air conditioning cooling system for battery modules has reached the design goals in this investigation, in which the maximum temperature of the module’s surface has been reduced to below 35.68 °C, and the maximum temperature difference of the module has reduced to below 5.6 °C.
Evaluation of Emergency Shelter Service Functions and Optimisation Suggestions—Case Study in the Songyuan City Central Area
Authors: Tang Siqi , Wang Jianguo , Xu Yuanhao , Chen Shengbo , Zhang Jiawang , Zhao Wutao , Wang Guojian
Published: April 2023

Reasonable planning and construction of emergency shelters is of great significance in improving the ability of cities to prevent and mitigate disasters and ensuring urban public safety. From the perspective of the needs of the evacuees, this paper constructs an evaluation index system for the service function of emergency evacuation places in four aspects: effectiveness, accessibility, safety and rescue responsiveness. This paper takes the central city of Songyuan as the case study area. We apply the entropy weight–TOPSIS–grey correlation method to evaluate the service functions of emergency shelters in the central city of Songyuan and determine their service function levels. An interactive analysis using the bivariate Moran index is used to determine the current state of supply and demand for places of refuge, in terms of their service functions and population distribution. It also makes recommendations for optimisation, based on the extent to which the service function of the emergency shelter is coordinated with the distribution of the population. The results show that of the 54 emergency shelters in the central city of Songyuan, the low and medium service function levels are divided into 33 and 15, with problems such as unreasonable spatial layout and inadequate emergency supplies and medical resources. The future construction of emergency shelters should focus not only on increasing the number and improving the scale, but also on considering the characteristics of population distribution, optimising the spatial distribution pattern and making full use of existing resources such as parks, squares and schools. The establishment of composite spatial resources for disaster preparedness and the promotion of a government-led model of interconnected shelter and emergency infrastructure can effectively enhance the spatial resilience of cities in response to natural hazards.
Can Data Elements Promote the High-Quality Development of China’s Economy?
Authors: Qi Peipei , Sun Dandan , Xu Can , Li Qiang , Wang Qi
Published: April 2023

In recent years, the digital economy, with data as the key production factor, has developed rapidly and become the driving force of China’s high-quality economic development. The relationship between data factors and high-quality economic development has gradually become a key topic of academic research. Based on the panel data of 30 provincial-level regions in China from 2014–2021, this study first constructs the proposed data factors and economic high-quality development evaluation indexes, respectively, by using the entropy weight method, and empirically analyzes whether data factors can promote economic high-quality development by using regression models. The results demonstrate the following: (1) Data elements play an important driving role in high-quality economic growth, and they have become an important engine for sustainable economic growth. (2) The development level of data elements has a stable growth rate and obvious regional differences, showing a pattern of “strong in the east and weak in the west, strong in the south and weak in the north”. (3) The degree of influence of data elements on different dimensions of high-quality economic development is inconsistent, with a greater influence on coordinated development, green development, and innovation development, while the effect on open development is not obvious. The above findings provide a reference and scientific basis for countries to formulate relevant policies to fully release the vitality of data elements and promote high-quality economic development.
Study on Inhibition Range of Liquefaction of Saturated Sand by Load Using a Shaking Table Test
Authors: Wang Xiaolei , Ren Hai , Liu Run , Liu Libo , Dong Lin , Jiang Yuchen , Liu Zengpei , Li Keke
Published: April 2023

As a novel, renewable, and efficient source of energy, offshore wind power has attracted many scholars across the globe. Studies show that offshore wind power significantly enhances the liquefaction resistance of marine saturated sand foundations exposed to seismic waves as loads. In the present study, a series of shaking table tests were conducted to study the load-induced enhancement of the liquefaction resistance of the sand. To this end, the excess pore pressures of soil mass at different buried depths were monitored in real time and the variations were analyzed. Moreover, a liquefaction constant was proposed and its influencing range was quantified. The obtained results demonstrated that load inhibits sand liquefaction at the near-end area, while it facilitates sand liquefaction at the far-end area. It is found that in soil under load at a buried depth of zero to two times the diameter, the liquefaction resistance increases linearly with the load value. Furthermore, the range of vertical inhibition and the lateral load wall end is 2.55 times and 2.36 times greater than the load diameter, respectively. The present study provides a basis to study the load-induced inhibition range of sand liquefaction, which is of significant importance for the development and optimization of offshore wind farms.
AI-Based Degradation Index from the Microstructure Image and Life Prediction Models Based on Bayesian Inference
Authors: Yu Junsang , Oh Hayoung
Published: April 2023

In this study, we propose a consistent and explainable degradation indexing method and a non-destructive-based degradation and creep-life prediction method from extensive destructive test (creep-rupture) data of a nickel-based superalloy (DA-5161 SX), an extreme-environment material. High-temperature components made of nickel-based superalloys that operate in extreme environments (e.g., gas turbine blades) deteriorate over time and shorten the life of the device. To ensure the safety and efficiency of the equipment, it is important to predict the lifetime of high-temperature parts, and a consistent and explanatory degradation index and a reliable predictive model that can predict the degree of degradation and life without destructive testing of high-temperature parts are needed. As the degradation of nickel-based superalloys progresses, degradation indices reflecting the geometrical characteristics are required that focus on the fact that the shape of the gamma-prime phase becomes longer and larger. A representative value of the degradation index was selected through parameter inference based on a Bayesian method, and the high-dimensional degradation index of previous studies was simplified to only one dimension. The robustness of the degradation index quantification model was verified by confirming that the degradation index obtained from 20% of the test images had the lowest change rate of the degradation index obtained from 80% of the training images at 6.9%. The basis for predicting the life of high-temperature parts without destructive testing was established in the degradation index and life prediction model by connecting environmental conditions and degradation indices/the LMP (Larson–Miller parameter) to represent creep life in regression models. Gaussian process regression (GPR) models based on sampling-based Bayesian inference performed well in terms of both RMSE in the degradation index and the LMP prediction model, demonstrating robust behavior in performance variation. This may be used as a key health factor that indicates the soundness of diagnostic solutions in the future, and it is expected to be a foundational technology for decision-making models for maintenance, repair, and disposal.
Analysis on the Satisfaction of Public Cultural Service by Township Residents: A Qualitative Perspective
Authors: Sun Chuanming , Tan Guoxin , Chai Xingyu , Zhang Haiqing
Published: April 2023

The intense development of urban–rural integration has brought about a constant increase in people’s demand for public culture. In particular, as the equalization of public cultural services is promoted, narrowing the gap between the services offered in urban and rural settings has gradually become the dominant orientation to promote urban–rural integration and enhance the rights and interests of the people. Starting from the principles of the new public service, this research conducted field interviews in Xuling Town, Anhui Province, China. The Zaltman metaphor elicitation technique (ZMET) was used to analyze the interview content, and five inferences about the construction of township public culture were drawn: (1) cultural facilities have a significant impact on township public cultural construction; (2) the role of cultural talents is essential but lack of talent is normal in townships; (3) carrying out cultural activities is the top priority of township public cultural construction; (4) cultural management is an indispensable auxiliary link but the management level is insufficient; (5) and the cultural activities of traditional festivals are particularly important. Moreover, on the basis of these five inferences, the fuzzy-set qualitative comparative analysis (fsQCA) method was used to analyze the survey data and verify the underlying problems around public cultural service effectiveness as well as resource allocation in township areas. Finally, countermeasures are proposed on how to narrow the gap, which are of reference significance for improving the quality of township public cultural services in the context of urban–rural integration and meeting the cultural needs of residents.
The Impact of Environmental Tax and R&D Tax Incentives on Green Innovation
Authors: Zheng Qian , Li Jinye , Duan Xiaole
Published: April 2023

With the increasing severity of environmental threats, the role of governance in environmental protection is particularly important. This paper examines the policy effects of environmental regulation and its implementation and regulatory heterogeneity on green innovation from the perspective of a policy combination formed by demand–pull environmental taxes and supply–push R&D tax incentives, aiming to investigate the targeted effects of green innovation induced by regulatory policies in the tax environments of developing countries. Based on the data on China’s listed manufacturing enterprises from 2013 to 2021, this article uses the DID model to perform a fixed-effect test. The findings show that both environmental taxes and R&D tax incentives can promote the green innovation of enterprises, and their combination has a mutually reinforcing joint effect. Furthermore, an environmental tax can inhibit the strategic innovation problems of R&D tax incentives and improve the quality of green innovation. This paper also reveals that the tax enforcement environment positively affects the induction of green innovation from the perspective of policy implementation and supervision. Finally, the heterogeneity test examines the differences in the effects of policy implementation from the aspects of political association and whether manufacturing firm is a high-tech enterprise. The results of this paper provide a reference for improving the effectiveness of environmental regulatory policy portfolios from the aspects of pre-design optimization and post-implementation and supervision, enriching the evidence for the narrow Porter hypothesis in developing countries.
A Case Study of How Maersk Adopts Cloud-Based Blockchain Integrated with Machine Learning for Sustainable Practices
Authors: Wong Simon , Yeung John Kun-Woon , Lau Yui-Yip , Kawasaki Tomoya
Published: April 2023

In the last six years, there has been a rise in research interest with regard to the applications of blockchain technology in supply chains and how these applications bring benefits to supply chain management. In a broader sense, an essential research focus that has been discussed in the literature is the way in which this emerging blockchain technology in supply chains brings sustainable benefits to a community. The rationale for incorporating cloud technology into a blockchain and integrating the blockchain with machine learning for supply chain applications is to maintain technical sustainability. While previous studies suggested and reported sustainable practices of applying blockchain technology in supply chains, the means with which these practices are brought about by the cloud-based blockchain integrated with machine learning (CBML) have not been thoroughly explored in the literature. The case study presented in this paper aims to fill this gap by exploring technically, environmentally, economically, and socially sustainable practices through the use cases of CBML for supply chain management by the international leading container shipping company Maersk. The use cases by Maersk presented in published documents were collected from the Internet and then analyzed. This document analysis was performed in two ways. The first way was a technical review of the blockchain technology used by Maersk with a consideration of technical sustainability to ensure scalability and big data analytics. The other way was to analyze the applications of the CBML by Maersk to indicate how environmental sustainability, economic sustainability, and social sustainability can be achieved. On the other hand, this paper also highlights the negative technical, environmental, economic, and social sustainability impacts caused by Maersk and discusses implications for future research directions.
Sustainable Application of Automatically Generated Multi-Agent System Model in Urban Renewal
Authors: Liang Zixin , Várady Géza , Zagorácz Márk Balázs
Published: April 2023

As cities expand, many old towns face the threat of being renovated or demolished. In recent years, the drawbacks of extensive urban renewal have become increasingly apparent, and the focus of urban development is gradually shifting from efficiency to quality. This study aims to combine urban renewal with emerging technologies to address the dilemma between efficiency and quality in urban renewal. The study found that algorithm models based on graph theory, topology, and shortest path principles neglect the influence of internal states and visual features on pedestrian activity, resulting in lower simulation accuracy. Although incorporating internal states and visual features into the core of the algorithm further improved the simulation accuracy, the model operates in a 3D environment with lower efficiency. To address the problems of insufficient simulation accuracy and low efficiency, this study proposes a dynamic pedestrian activity model based on a multi-agent system and incorporating visual features. The model simulates pedestrian daily activity paths using pheromones and virtual sensors as the core, and it was found that using Visibility Graph Analysis could accurately divide pheromones in the environment, thus obtaining more accurate simulation results. Subsequently, based on the optimized pedestrian model’s agent activity rules and dynamic pheromone theory, a model for automatically generating road paving in urban renewal projects was developed, and the generated results were verified for their rationality through design practice. This technology can effectively promote urban renewal and the preservation of historic neighborhoods, providing technical support for achieving sustainable urban development.
The Demographic Dividend or the Education Dividend? Evidence from China’s Economic Growth
Authors: Zhou Jian , Deng Jingjing , Li Li , Wang Shuang
Published: April 2023

Developing countries face a significant challenge in sustaining their demographic dividend. However, there are few existing studies that approach this issue from a macroeconomic perspective or through empirical research. This paper aims to systematically analyze the impact of the demographic dividend and education dividend on economic growth. By utilizing China’s empirical evidence and employing the System GMM method, we explore how to improve both dividends. Our main findings can be summarized into three aspects. First, the demographic dividend does not depend on population size alone but also on the size of the labor force. Second, education can improve the demographic dividend and thereby prove the existence of an education dividend. Finally, the effects of the demographic and education dividends vary across regions and stages of development within developing countries. This research enriches the existing literature on education and population age in developing countries.
Influential Variables and Causal Relations Impact on Innovative Performance and Sustainable Growth of SMEs in Aspect of Industry 4.0 and Digital Transformation
Authors: Kim Seoksoo , Ha Taekwan
Published: April 2023

Digital Transformation is essential in the global industry for survival and sustainable growth, and SMEs are mainly required to apply digital technology for sustainable growth. This study aims to verify the causal relationship between the variables significantly affecting Digital Transformation’s sustainable growth and innovative performance and suggest critical variables and strategies in which Digital Transformation’s constituent factors affect sustainable growth. Data were collected from an online survey of 303 CEOs of SMEs. Using Smart PLS, analyzed the factors affecting the sustainable growth of SMEs and verified the causal relationship. We found that applying Digital Transformation in SMEs is necessary because Digital Transformation affects innovation performance and ultimately impacts sustainable growth. By verifying the variables that affect the sustainable growth of 7 industrial sectors, we establish sustainable growth strategies suitable for each industry and provide the variables that affect sustainable growth. The findings imply that DT is essential for the sustainable growth of SMEs and that impact variables appropriate to the industry should be applied. The study results will be a new area of interest for future researchers.
Non-Orthogonal Multiple Access Enabled Mobile Edge Computing in 6G Communications: A Systematic Literature Review
Authors: Ogundokun Roseline Oluwaseun , Awotunde Joseph Bamidele , Imoize Agbotiname Lucky , Li Chun-Ta , Abdulahi AbdulRahman Tosho , Adelodun Abdulwasiu Bolakale , Sur Samarendra Nath , Lee Cheng-Chi
Published: April 2023

Mobile edge computing (MEC) supported by non-orthogonal multiple access (NOMA) has recently gained a lot of interest due to its improved ability to lessen power consumption and MEC offload delay. In recent decades, the need for wireless communications has increased tremendously. Fifth-generation (5G) communications will soon be widely used and offer much more functionality than a fourth generation (4G). Between 2027 and 2030, an innovative wireless communication paradigm is known as the sixth generation (6G) system is projected to be introduced with the full help of artificial intelligence (AI). Advanced system capacity, higher data rate, lower latency, advanced security, and improved quality of service (QoS) than 5G systems are a few of the main challenges to resolve with 5G. The growing need for data rates in the sixth generation (6G) communication networks are being met by extraordinary technologies such as NOMA, Soft Computing (SC), and MEC. Owing to the massive attention to the NOMA-enabled MEC, there has been a significant spike in the number of papers published in this area, while more comprehensive studies and classifications are still needed. Using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines, the investigation reports a systematic literature review (SLR) of NOMA-enabled MEC. This survey also evaluates numerous pieces of literature prudently chosen over a multi-step procedure and meets the selection criteria described in the paper summarizing our review.