bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Multi-Growth Periods UAV Images on Classifying Karst Wetland Vegetation Communities Using Object-Based Optimization Stacking Algorithm
Authors: Zhang Ya , Fu Bolin , Sun Xidong , Yao Hang , Zhang Shurong , Wu Yan , Kuang Hongyuan , Deng Tengfang
Published: August 2023

Combining machine learning algorithms with multi-temporal remote sensing data for fine classification of wetland vegetation has received wide attention from researchers. However, wetland vegetation has different physiological characteristics and phenological information in different growth periods, so it is worth exploring how to use different growth period characteristics to achieve fine classification of vegetation communities. To resolve these issues, we developed an ensemble learning model by stacking Random Forest (RF), CatBoost, and XGBoost algorithms for karst wetland vegetation community mapping and evaluated its classification performance using three growth periods of UAV images. We constructed six classification scenarios to quantitatively evaluate the effects of combining multi-growth periods UAV images on identifying vegetation communities in the Huixian Karst Wetland of International Importance. Finally, we clarified the influence and contribution of different feature bands on vegetation communities’ classification from local and global perspectives based on the SHAP (Shapley Additive explanations) method. The results indicated that (1) the overall accuracies of the four algorithms ranged from 82.03% to 93.37%, and the classification performance was Stacking > CatBoost > RF > XGBoost in order. (2) The Stacking algorithm significantly improved the classification results of vegetation communities, especially Huakolasa, Reed-Imperate, Linden-Camphora, and Cephalanthus tetrandrus-Paliurus ramosissimus. Stacking had better classification performance and generalization ability than the other three machine learning algorithms. (3) Our study confirmed that the combination of spring, summer, and autumn growth periods of UAV images produced the highest classification accuracy (OA, 93.37%). In three growth periods, summer-based UAVs achieved the highest classification accuracy (OA, 85.94%), followed by spring (OA, 85.32%) and autumn (OA, 84.47%) growth period images. (4) The interpretation of black-box stacking model outputs found that vegetation indexes and texture features provided more significant contributions to classifying karst wetland vegetation communities than the original spectral bands, geometry features, and position features. The vegetation indexes (COM and NGBDI) and texture features (Homogeneity and Standard Deviation) were very sensitive when distinguishing Bermudagrass, Bamboo, and Linden-Camphora. These research findings provide a scientific basis for the protection, restoration, and sustainable development of karst wetlands.
Exploring the Spatial Relationship between the Ecological Topological Network and Carbon Sequestration Capacity of Coastal Urban Ecosystems: A Case Study of Yancheng City, China
Authors: Zhu Nanyan , Ai Jingwen , Zeng Zhen , Zhou Chunhua
Published: August 2023

Improving the carbon sequestration capacity (CSC) of an ecosystem by optimizing urban ecological networks is one of the effective ways to achieve the goal of “carbon neutrality” in the world. The contradiction between the irreplaceable ecological function and economic development of Yancheng City is prominent. Therefore, taking Yancheng City as an example, this paper adopted the morphological spatial pattern analysis–minimum cumulative resistance (MSPA-MCR) model to establish the ecological network of Yancheng City in 2020 and combined it with complex network theory to evaluate its ecological base, network quality, and CSC. The results show that the ecological network of Yancheng City has obvious characteristics of coastal cities. There is a significant positive correlation between CSC and the clustering efficiency of ecological sources, and improving the clustering efficiency of vegetation and water ecological nodes is conducive to enhancing the CSC of ecological networks. In terms of functional restoration of ecological networks, four types of 13 ecological stepping stones and 12 ecological corridors have been designed to strengthen the connectivity and balance of the network, and the improvement of network robustness before and after optimization verifies that the optimization scheme is reasonable and effective. This study improved the optimization method of ecological networks in Yancheng City based on enhancing the CSC of ecological nodes, which provided a theoretical framework and practical reference for the realization of global strategic goals of carbon neutrality.
Self-Adaptive Filtering for Ultra-Large-Scale Airborne LiDAR Data in Urban Environments Based on Object Primitive Global Energy Minimization
Authors: Hui Zhenyang , Li Zhuoxuan , Li Dajun , Xu Yanan , Wang Yuqian
Published: August 2023

Filtering from airborne LiDAR datasets in urban area is one important process during the building of digital and smart cities. However, the existing filters encounter poor filtering performance and heavy computational burden when processing large-scale and complicated urban environments. To tackle this issue, a self-adaptive filtering method based on object primitive global energy minimization is proposed in this paper. In this paper, mode points were first acquired for generating the mode graph. The mode points were the cluster centers of the LiDAR data obtained in a mean shift algorithm. The graph constructed with mode points was named “mode graph” in this paper. By defining the energy function based on the mode graph, the filtering process is transformed to iterative global energy minimization. In each iteration, the graph cuts technique was adopted to achieve global energy minimization. Meanwhile, the probability of each point belonging to the ground was updated, which would lead to a new refined ground surface using the points whose probabilities were greater than 0.5. This process was iterated until two successive fitted ground surfaces were determined to be close enough. Four urban samples with different urban environments were adopted for verifying the effectiveness of the filter developed in this paper. Experimental results indicate that the developed filter obtained the best filtering performance. Both the total error and the Kappa coefficient are superior to those of the other three classical filtering methods.
Localization of Mobile Robots Based on Depth Camera
Authors: Yin Zuoliang , Wen Huaizhi , Nie Wei , Zhou Mu
Published: August 2023

In scenarios of indoor localization of mobile robots, Global Positioning System (GPS) signals are prone to loss due to interference from urban building environments and cannot meet the needs of robot localization. On the other hand, traditional indoor localization methods based on wireless signals such as Bluetooth and WiFi often require the deployment of multiple devices in advance, and these methods can only obtain distance information and are unable to obtain the attitude of the positioning target in space. This paper proposes a method for the indoor localization of mobile robots based on a depth camera. Firstly, we extracted ORB feature points from images captured by a depth camera and performed homogenization processing. Then, we performed feature matching between adjacent two frames of images, and the mismatched points are eliminated to improve the accuracy of feature matching. Finally, we used the Iterative Closest Point (ICP) algorithm to estimate the camera’s pose, thus achieving the localization of mobile robots in indoor environments. In addition, an experimental evaluation was conducted on the TUM dataset of the Technical University of Munich to validate the feasibility of the proposed depth-camera-based indoor localization system for mobile robots. The experimental results show that the average localization accuracy of our algorithm on three datasets is 0.027 m, which can meet the needs of indoor localization for mobile robots.
Enhancing Sea Surface Height Retrieval with Triple Features Using Support Vector Regression
Authors: Hu Yuan , Tian Aodong , Liu Wei , Wickert Jens
Published: August 2023

In Global Navigation Satellite System Reflectometry (GNSS-R), SNR spectrum analysis is widely used for surface altimetry inversion because of its low cost and easy operation. However, this method is somewhat limited in environmental situations with large tidal variations in sea level. In this paper, we implemented a machine learning approach to retrieve sea level height using three feature parameters of frequency, amplitude, and phase extracted by GNSS-R as inputs for the support vector regression (SVR) model, achieving better robustness in environments with large tidal variations. In this experiment, two stations, SC02 and BRST, were selected for research comparison, in which the sea surface fluctuation at the SC02 station was smaller at around 3 m while the sea surface fluctuation at the BRST station was larger at around 7 m. Global Navigation Satellite System (GNSS) observations were selected for 6 months for use to perform the assessment. The SC02 station improved 25.64% and 24.05% in the accuracy of RMSE (14.5 cm) and MAE (12.0 cm), respectively, using the SVR model compared to the conventional method (CM). In the environment with large sea level tidal fluctuations, the BRST station improved accuracy by 17.32% and 15.81% using the SVR model compared to the CM for RMSE (25.3 cm) and MAE (21.3 cm), respectively. It is shown that the SVR model is robust for sea level height retrieval with large tidal variations and that these three feature parameters, including frequency, amplitude, and phase extracted by GNSS-R, are crucial for optimizing sea surface height retrieval.
Fault Classification for Cooling System of Hydraulic Machinery Using AI
Authors: Khan Haseeb Ahmed , Bhatti Uzair , Kamal Khurram , Alkahtani Mohammed , Abidi Mustufa Haider , Mathavan Senthan
Published: August 2023

Hydraulic systems are used in all kinds of industries. Mills, manufacturing, robotics, and Ports require the use of Hydraulic Equipment. Many industries prefer to use hydraulic systems due to their numerous advantages over electrical and mechanical systems. Hence, the growth in demand for hydraulic systems has been increasing over time. Due to its vast variety of applications, the faults in hydraulic systems can cause a breakdown. Using Artificial-Intelligence (AI)-based approaches, faults can be classified and predicted to avoid downtime and ensure sustainable operations. This research work proposes a novel approach for the classification of the cooling behavior of a hydraulic test rig. Three fault conditions for the cooling system of the hydraulic test rig were used. The spectrograms were generated using the time series data for three fault conditions. The CNN variant, the Residual Network, was used for the classification of the fault conditions. Various features were extracted from the data including the F-score, precision, accuracy, and recall using a Confusion Matrix. The data contained 43,680 attributes and 2205 instances. After testing, validating, and training, the model accuracy of the ResNet-18 architecture was found to be close to 95%.
Decentralized Sensor Fault-Tolerant Control of DC Microgrids Using the Attracting Ellipsoid Method
Authors: Soliman Hisham M. , Bayoumi Ehab H. E. , El-Sheikhi Farag A. , De Santis Michele
Published: August 2023

System stability deterioration in microgrids commonly occurs due to unpredictable faults and equipment malfunctions. Recently, robust control techniques have been used in microgrid systems to address these difficulties. In this paper, for DC-islanded microgrids that have sensors faults, a new passive fault-tolerant control strategy is developed. The suggested approach can be used to maintain system stability in the presence of flaws, such as faulty actuators and sensors, as well as component failures. The suggested control is effective when the fault is never recognized (or when the fault is not being precisely known, and some ambiguity in the fault may be interpreted as uncertainty in the system’s dynamics following the fault). The design is built around a derived sufficient condition in the context of linear matrix inequalities (LMIs) and the attractive ellipsoid technique. The ellipsoidal stabilization idea is to bring the state trajectories into a small region including the origin (an ellipsoid with minimum volume) and the trajectories will not leave the ellipsoid for the future time. Finally, computational studies on a DC microgrid system are carried out to assess the effectiveness of the proposed fault-tolerant control approach. When compared with previous studies, the simulation results demonstrate that the proposed control technique can significantly enhance the reliability and efficiency of DC microgrid systems.
Passive Beamforming Design of IRS-Assisted MIMO Systems Based on Deep Learning
Authors: Zhang Hui , Jia Qiming , Li Meikun , Wang Jingjing , Song Yuxin
Published: August 2023

In the intelligent reflecting surface (IRS)-assisted MIMO systems, optimizing the passive beamforming of the IRS to maximize spectral efficiency is crucial. However, due to the unit-modulus constraint of the IRS, the design of an optimal passive beamforming solution becomes a challenging task. The feature input of existing schemes often neglects to exploit channel state information (CSI), and all input data are treated equally in the network, which cannot effectively pay attention to the key information and features in the input. Also, these schemes usually have high complexity and computational cost. To address these issues, an effective three-channel data input structure is utilized, and an attention mechanism-assisted unsupervised learning scheme is proposed on this basis, which can better exploit CSI. It can also better exploit CSI by increasing the weight of key information in the input data to enhance the expression and generalization ability of the network. The simulation results show that compared with the existing schemes, the proposed scheme can effectively improve the spectrum efficiency, reduce the computational complexity, and converge quickly.
Gait Stride Length Estimation Using Embedded Machine Learning
Authors: Verbiest Joeri R. , Bonnechère Bruno , Saeys Wim , Van de Walle Patricia , Truijen Steven , Meyns Pieter
Published: August 2023

Introduction. Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for spatiotemporal gait parameters. Such solutions are wearable devices that comprise an inertial measurement unit (IMU) sensor and a microcontroller (MCU). However, these existing wearable devices are resource-constrained. They contain a processing unit with limited processing and memory capabilities which limit the use of machine learning to estimate spatiotemporal gait parameters directly on the device. The solution for this limitation is embedded machine learning or tiny machine learning (tinyML). This study aims to create a machine-learning model for gait stride length estimation deployable on a microcontroller. Materials and Method. Starting from a dataset consisting of 4467 gait strides from 15 healthy people, measured by IMU sensor, and using state-of-the-art machine learning frameworks and machine learning operations (MLOps) tools, a multilayer 1D convolutional float32 and int8 model for gait stride length estimation was developed. Results. The developed float32 model demonstrated a mean accuracy and precision of 0.23 ± 4.3 cm, and the int8 model demonstrated a mean accuracy and precision of 0.07 ± 4.3 cm. The memory usage for the float32 model was 284.5 kB flash and 31.9 kB RAM. The int8 model memory usage was 91.6 kB flash and 13.6 kB RAM. Both models were able to be deployed on a Cortex-M4F 64 MHz microcontroller with 1 MB flash memory and 256 kB RAM. Conclusions. This study shows that estimating gait stride length directly on a microcontroller is feasible and demonstrates the potential of embedded machine learning, or tinyML, in designing wearable sensor devices for gait analysis.
Investigation of the Relationship between Body Parameters and mAs Using Non-Contact Two-Dimensional Thickness Measurement in Chest Digital Radiography
Authors: Lin Jia-Ru , Cheng I-Hao , Liang Yu-Syuan , Li Jyun-Jie , Tsai Jen-Ming , Wang Min-Tsung , Lin Te-Pao , Huang Su-Lan , Chou Ming-Chung
Published: August 2023

The current study aimed to investigate the relationship between body parameters and the current–time product (mAs) in chest digital radiography using a non-contact infrared thickness-measurement sensor. An anthropomorphic chest phantom was first used to understand variations in mAs over multiple positionings during chest radiography when using the automatic exposure control (AEC) technique. In a human study, 929 consecutive male subjects who underwent regular chest examinations were enrolled, and their height (H), weight (W), and body mass index (BMI) were recorded. In addition, their chest thickness (T) was measured at exhalation using a non-contact infrared sensor, and chest radiography was then performed using the AEC technique. Finally, the relationship between four body parameters (T, BMI, T*BMI, and W/H) and mAs was investigated by fitting the body parameters to mAs using three curve models. The phantom study showed that the maximum mAs was 1.76 times higher than the lowest mAs during multiple positionings in chest radiography. In the human study, all chest radiographs passed the routine quality control procedure and had an exposure index between 100 and 212. In curve fitting, the comparisons showed that W/H had a closer relationship with mAs than the other body parameters, while the first-order power model with W/H fitted to mAs performed the best and had an R-square of 0.9971. We concluded that the relationship between W/H and mAs in the first-order power model may be helpful in predicting the optimal mAs and reducing the radiation dose for chest radiography when using the AEC technique.
Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
Authors: Musha Ahmmad , Hasnat Rehnuma , Mamun Abdullah Al , Ping Em Poh , Ghosh Tonmoy
Published: August 2023

Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
Overview of Image Datasets for Deep Learning Applications in Diagnostics of Power Infrastructure
Authors: Ruszczak Bogdan , Michalski Paweł , Tomaszewski Michał
Published: August 2023

The power sector is one of the most important engineering sectors, with a lot of equipment that needs to be appropriately maintained, often spread over large areas. With the recent advances in deep learning techniques, many applications can be developed that could be used to automate the power line inspection process, replacing previously manual activities. However, in addition to these novel algorithms, this approach requires specialized datasets, collections that have been properly curated and labeled with the help of experts in the field. When it comes to visual inspection processes, these data are mainly images of various types. This paper consists of two main parts. The first one presents information about datasets used in machine learning, especially deep learning. The need to create domain datasets is justified using the example of the collection of data on power infrastructure objects, and the selected repositories of different collections are compared. In addition, selected collections of digital image data are characterized in more detail. The latter part of the review also discusses the use of an original dataset containing 2630 high-resolution labeled images of power line insulators and comments on the potential applications of this collection.
Graphene-Based Strain Sensing of Cementitious Composites with Natural and Recycled Sands
Authors: Bibi Uzma , Bahrami Alireza , Shabbir Faisal , Imran Muhammad , Nasir Muhammad Ali , Ahmad Afaq
Published: August 2023

Structural health monitoring is crucial for ensuring the safety and reliability of civil infrastructures. Traditional monitoring methods involve installing sensors across large regions, which can be costly and ineffective due to the sensors damage and poor compliance with structural members. This study involves systematically varying the graphene nanoplatelets (GNPs) concentration and analyzing the strength performance and piezoresistive behavior of the resulting composites. Two different composites having natural and recycled sands with varying percentages of GNPs as 2%, 4%, 6%, and 8% were prepared. Dispersion of GNPs was performed in superplasticizer and then ultrasonication was employed by using an ultrasonicator. The four-probe method was utilized to establish the piezoresistive behavior. The results revealed that the compressive strength of mortar cubes with natural sand was increased up to a GNP content of 6%, beyond which it started to decline. In contrast, specimens with recycled sand showed a continuous decrease in the compressive strength. Furthermore, the electrical resistance stability was observed at 4% for both natural and recycled sands specimens, exhibiting linearity between the frictional change in the resistivity and compressive strain values. It can be concluded from this study that the use of self-sensing sustainable cementitious composites could pave their way in civil infrastructures.
Study on Treatment Performance of Desulfurization Wastewater by Zero-Valent Iron Fenton-like Process
Authors: Liu Ziguo , Zhou Wei , Liu Xianli , Yang Xuefen , Yang Wei , Zheng Han
Published: August 2023

In this study, the zero-valent iron Fenton reagent (ZVI Fenton-like) system was combined with the chemical precipitation method for the deep treatment of desulfurization wastewater from coal-fired power plants, and the chemical oxygen demand (COD) was used as the evaluation criterion for organic matter in the desulfurization wastewater. The effects of reaction time, H2O2 dosage, zero-valent iron dosage, pH, and reaction temperature were also investigated. The results showed that the COD concentration of the effluent was the lowest when the running time of the ZVI Fenton-like reagent system was 1 h, the dosage of H2O2 was 33.3 mg·L−1, the dosage of iron was 0.075 g·L−1, the pH was 4.5~6.5, the reaction temperature was 35 °C, the COD concentration of the wastewater was the lowest and its operating conditions were the best, and the internal reaction mechanism was finally deduced. In summary, the zero-valent iron Fenton reagent system provides a new idea for the treatment of desulfurization wastewater from coal-fired power plants.
MALDI-TOF MS: A Promising Analytical Approach to Cancer Diagnostics and Monitoring
Authors: Sousa Patrícia , Silva Laurentina , Luís Catarina , Câmara José S. , Perestrelo Rosa
Published: August 2023

Cancer remains the second most common cause of death after cardiovascular diseases, accounting for nearly 10 million deaths in 2020. Although the incidence of cancer increases considerably with age, the cancer burden can also be reduced and have a high chance of cure through early detection, appropriate treatment, and care of patients. The development of high-throughput analytical approaches, like matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), contributes to identifying a pool of proteins/peptides as putative biomarkers for the early detection, diagnosis, and tumor progression. The purpose of the current review is to present an updated outline of recent proteome/peptidome research to establish putative cancer biomarkers using MALDI-TOF MS and highlight the applicability of statistical analysis in the oncology field. The pros and cons of MALDI-TOF MS application on cancer diagnostics and monitoring will be discussed, as well as compared with tandem mass spectrometry (MS/MS)-based proteomics (e.g., liquid chromatography–tandem mass spectrometry). In addition, pre-analytical (e.g., sample quality control) and analytical (e.g., sample pre-treatment, instrumental analytical conditions) properties that influence the robustness of MALDI-TOF MS data will be also discussed.
A Driver Behavior Monitoring System for Sustainable Traffic and Road Construction
Authors: Sappl Hannes , Kubjatko Tibor
Published: August 2023

The perception and behavior of human drivers is of great importance for both sustainable road construction and sustainable coexistence with self-driving vehicles. To record the (hazard) perception and responsiveness of drivers, as many parameters as possible of the recognition and reaction process must be measured and recorded. It must be known what the drivers see or perceive and how they react to it. In modern vehicles, all pedal positions and driving data are transmitted to the CAN bus and can be assigned to the associated sources using reverse engineering methods. What drivers see or recognize can be recorded from the driver’s perspective using (eye-tracking) videos or photos. To evaluate the vehicle and image data, systems are required that can, among other things, record both channels and link them synchronously. To fulfill this purpose, two different systems are developed and compared here, both of which can be used in real traffic.
Short-Term Electricity Load Forecasting Using a New Intelligence-Based Application
Authors: Khan Salahuddin
Published: August 2023

Electrical load forecasting plays a crucial role in planning and operating power plants for utility factories, as well as for policymakers seeking to devise reliable and efficient energy infrastructure. Load forecasting can be categorized into three types: long-term, mid-term, and short-term. Various models, including artificial intelligence and conventional and mixed models, can be used for short-term load forecasting. Electricity load forecasting is particularly important in countries with restructured electricity markets. The accuracy of short-term load forecasting is crucial for the efficient management of electric systems. Precise forecasting offers advantages for future projects and economic activities of power system operators. In this study, a novel integrated model for short-term load forecasting has been developed, which combines the wavelet transform decomposition (WTD) model, a radial basis function network, and the Thermal Exchange Optimization (TEO) algorithm. The performance of this model was evaluated in two diverse deregulated power markets: the Pennsylvania-New Jersey-Maryland electricity market and the Spanish electricity market. The obtained results are compared with various acceptable standard forecasting models.
A Strategic Analysis of Photovoltaic Energy Projects: The Case Study of Spain
Authors: Segura Eva , Belmonte Lidia M. , Morales Rafael , Somolinos José A.
Published: August 2023

The Spanish photovoltaic sector could be a serious opportunity for the recovery and economic growth of the country, by serving as a support platform for the National Integrated Energy and Climate Plan (NIECP) 2021–2030, whose objective is to determine the lines of action required for the appropriate and efficient use of clean energy in order to benefit the economy, employment, health, and the environment. Bearing in mind the notable increase in the economic competitiveness of photovoltaic energy in Spain when compared to traditional and other renewable energy sources, it is necessary to carry out a strategic analysis of the macro-environment, using a PESTEL (Political, Economical, Social, Technological, Environmental and Legal) analysis so as to identify the most relevant external aspects that are vital for the performance of photovoltaic industries/markets and to facilitate decision making when developing short-, medium-, and long-term strategies, and the micro-environment, using Porter’s five forces (threat of new entrants, threat from substitution products and/or services, the bargain power of suppliers, the bargaining power of buyers and competitive rivalry) framework, to determine and examine the main factors that define the structure and level of competition that exists within the Spanish industry/market. The results obtained have been combined in a SWOT (Strengths, Weaknesses, Opportunities, and Threats) chart, which facilitates an understanding of the current strengths, weaknesses, opportunities, and threats as regards the photovoltaic sector in Spain.
Can Rural Human Capital Improve Agricultural Ecological Efficiency? Empirical Evidence from China
Authors: Hu Yankang , Yu Hongchao , Yang Xinglong
Published: August 2023

Agricultural ecological efficiency (AEE) is an important index to measure the coordinated development of agricultural production, resources, and the environment. This study tries to explore the impact of rural human capital (RHC) on AEE, hoping to provide a reference for promoting the green development of agriculture. Data sets (2006–2021) used in this study were gathered from Official Statistics Panel data in 30 provinces in China. Firstly, the super-efficient slacks-based measure model (SBM) with non-expected output is employed to effectively measure AEE. Secondly, the entropy method is used to measure RHC in three dimensions: education, health, and migration. Finally, this study discusses the impact of RHC on AEE using the panel Tobit model and further verifies the moderating effect of Internet popularization on the effect of RHC on AEE using the moderating effect model. The results show that RHC has a significant positive impact on the promotion of AEE, and this positive impact has obvious regional heterogeneity. Specifically, RHC has a greater promoting role on AEE in the eastern and northeastern regions, while it has a smaller promoting role on AEE in the central and western regions. In addition, Internet penetration plays a positive moderating role in the mechanism of the effect of RHC on the AEE. This study may serve as a reference for improving AEE by providing theoretical guidance and policy suggestions for promoting agricultural green development.
Fused Filament Fabrication Three-Dimensional Printing: Assessing the Influence of Geometric Complexity and Process Parameters on Energy and the Environment
Authors: Mecheter Asma , Tarlochan Faris
Published: August 2023

Fused filament fabrication (FFF) 3D printing has been recently adopted in various industries and production processes. Three-dimensional printing (3DP) has gained significant popularity and is being adopted in schools, universities, and fabrication labs, as well as in science, technology, engineering, and mathematics (STEM) education curricula. The aim of this study is to evaluate the energy consumption and environmental impacts of multiple parts with different complexity levels based on various process parameters through FFF printing. This paper focuses on three material filaments: polylactic acid (PLA), tough PLA (T-PLA), and acrylonitrile butadiene styrene (ABS). The influence of geometric complexity, layer height, density, infill pattern, speed, and temperature on energy and the environment will be analyzed through a life-cycle assessment approach. Moreover, this study provides a set of guidelines for 3DP users in education for the energy-efficient and sustainable use of 3D printers. Our results reveal that for the proposed geometries, an energy increase of 8% is recorded for PLA when transitioning from the simple geometry to the very complex one. However, for ABS and T-PLA, no change in energy values due to geometric change is observed. Layer height is found to be the most influential parameter on energy consumption, with an increase of 59%, 54%, and 61% for PLA, ABS, and T-PLA, respectively. Printing temperature, on the other hand, is found to be the least influential parameter on energy and the environment. Furthermore, PLA is found to be the most environmentally friendly material, followed by ABS and T-PLA in terms of climate change, human toxicity, and cumulative energy demand impact categories. However, for the ozone depletion category, ABS contributes the most to environmental damage compared to T-PLA. The results suggest that PLA can be used for visual and prototype models, whereas ABS and T-PLA serve as good candidates for complex end-use applications and functional parts. The presented guidelines will assist 3DP users in the adequate and optimal use of 3DP technology in order to achieve resource efficiency, energy savings, and environmental sustainability.
The Impact of the Digital Economy on Industrial Eco-Efficiency in the Yangtze River Delta (YRD) Urban Agglomeration
Authors: Zhang Wenjing , Sun Bin , Li Zaijun , Sarwar Suleman
Published: August 2023

Enhancing industrial eco-efficiency (IEE) represents an inherent imperative in the pursuit of green, low-carbon, and high-quality development. The burgeoning digital economy (DE) contributes to the digitization and intelligent integration and transformation of production and consumption, which contributes to enhancing economic efficiency and resource utilization efficiency while reducing carbon emissions and the environmental pollution associated with industrial production and providing important support for the ecological transformation of industries. In this context, using data from the YRD urban agglomeration and employing the Tobit model and the spatio-temporal correlation model, this study analyzes the impact of the digital economy on industrial eco-efficiency. The findings are as follows: (1) The correlation over time between IEE and the DE followed an inverted V-shaped trend, while the relationship between the DE and pure technical efficiency (PTE) exhibited a fluctuating W-shaped pattern. The spatial correlation revealed Zhejiang province as the primary concentration of positive correlation between the DE and both IEE and PTE. (2) The development of the DE had a significant positive impact on IEE, not only directly but also indirectly through promoting green technological innovation and advancing industrial structure. (3) The analysis of regional heterogeneity showed that the development of the DE in core cities played a catalytic role in improving IEE, whereas the impact of the DE on IEE was not significant in outer cities. This research not only offers new views on how to develop industry in more environmentally friendly ways, but it also sheds light on the real effects of the digital economy on high-quality urban development.
Prediction of Water Carbon Fluxes and Emission Causes in Rice Paddies Using Two Tree-Based Ensemble Algorithms
Authors: Gu Xinqin , Yao Li , Wu Lifeng
Published: August 2023

Quantification of water carbon fluxes in rice paddies and analysis of their causes are essential for agricultural water management and carbon budgets. In this regard, two tree-based machine learning models, which are extreme gradient boosting (XGBoost) and random forest (RF), were constructed to predict evapotranspiration (ET), net ecosystem carbon exchange (NEE), and methane flux (FCH4) in seven rice paddy sites. During the training process, the k-fold cross-validation algorithm by splitting the available data into multiple subsets or folds to avoid overfitting, and the XGBoost model was used to assess the importance of input factors. When predicting ET, the XGBoost model outperformed the RF model at all sites. Solar radiation was the most important input to ET predictions. Except for the KR-CRK site, the prediction for NEE was that the XGBoost models also performed better in the other six sites, and the root mean square error decreased by 0.90–11.21% compared to the RF models. Among all sites (except for the absence of net radiation (NETRAD) data at the JP-Mse site), NETRAD and normalized difference vegetation index (NDVI) performed well for predicting NEE. Air temperature, soil water content (SWC), and longwave radiation were particularly important at individual sites. Similarly, the XGBoost model was more capable of predicting FCH4 than the RF model, except for the IT-Cas site. FCH4 sensitivity to input factors varied from site to site. SWC, ecosystem respiration, NDVI, and soil temperature were important for FCH4 prediction. It is proposed to use the XGBoost model to model water carbon fluxes in rice paddies.
Controlled-Release Blended Fertilizer Combined with Urea Reduces Nitrogen Losses by Runoff and Improves Nitrogen Use Efficiency and Yield of Wet Direct-Seeded Rice in Central China
Authors: Wu Qixia , Qiao Yue , Zhou Qianshun , Chen Jinping , Wang Guangshuai
Published: August 2023

Controlled-release fertilizer is one of the best fertilizer management strategies for improving the yield and nitrogen use efficiency of transplanted seedling rice. Wet direct-seeded rice has gradually replaced transplanted seedling rice since it saves labor. In addition, it is conducive to mechanization promotion. However, the effects of controlled-release fertilizers on wet direct-seeded rice remain largely unknown. A two-year field experiment aimed to compare the effects of controlled-release blended fertilizer at two rates (basal N to tiller N ratio = 7:3 (CRBF+U), CRBF alone), urea at two rates (basal–tiller ratio of 4:6 (U40), 6:4 (U60)) and a control (no N fertilizer) on the ammonia volatilization (AV) loss, nitrogen runoff loss, accumulation, transport, utilization and yield of rice. The nitrogen runoff loss in wet direct-seeded rice paddy fields was concentrated from sowing to the three-leaf and one-leaflet stage, and the loss rat was lowest after CRBF+U (11.41–12.94%). AV loss rate was lowest after CRBF (3.41%), followed by CRBF+U (3.55–3.89%). CRBF+U increased nitrogen accumulation by extending the duration of rapid nitrogen growth and accelerating maximum nitrogen growth. CRBF+U also increased the nitrogen transport rate of stems, sheaths and leaves from full heading to maturity, and intensified the increase in nitrogen in panicles, increasing the harvest index, agronomy utilization rate and apparent utilization rate of nitrogen. Finally, the grain number per panicle, seed-setting rate and actual yield of rice were significantly improved. In conclusion, CRBF+U can reduce nitrogen runoff loss and AV loss and can improve the yield and nitrogen use efficiency of wet direct-seeded rice.
Research on the Items of Importance and Satisfaction for Employability in the Korean Information Communication Technology Sector
Authors: Ahn Jongchang , Jeong Soonki , Lee Donghan
Published: August 2023

This study discusses the employability of the workforce in relation to information communication technology. There is a difference in understanding in terms of items of importance/satisfaction during/after employment processes when considering organizations related to the information technology workforce. An empirical analysis of Korean employability based on literature from Asia was conducted. It indicated that differences in the perception of the 12 items comprising importance and satisfaction related to employability could potentially lead to job mismatch. We acquired an adequate sample of 296 respondents, consisting of various organizational employees. The sample was balanced in terms of gender and large or small/medium organizations. After establishing the rank of preferences among items, a difference analysis between/among groups through demographic variables was conducted. As a result, the need for additional disciplines or courses, the arrangement of preferred rank, and methods for enhancing skills from university or education/practice institutes for “interpersonal or team-working skills” and to “communicate effectively” with a higher rank among the general skills of importance/satisfaction were derived. Therefore, technology did not replace human personalities, communication skills, and human attributes such as human cooperation. This study can help address labor shortages and support sustainable employment in organizations, even if an empirical test is rare compared to descriptive statistical analyses.
The Sustainability of Small Industries Thriving across Generation in Rural Areas
Authors: Charina Anne , Kurnia Ganjar , Mulyana Asep
Published: August 2023

This study investigates the sustainability level of small industries that have persevered for up to three generations in rural areas of Indonesia, addressing the significant issue of high failure rates among small industries. Utilizing the Triple Bottom Line framework, this study examined the economic, social, and environmental performance of four small industries that have thrived for multiple generations in Indonesia. Data collection involved semi-structured interviews, focus group discussions (FGDs), and secondary company data. Thematic analysis principles using N-Vivo 12 software were applied to analyze qualitative data, while the RAP 2016 software version R was used for assessing the sustainability index and status. The research findings revealed that the sustainability status of small industries that survived for up to three generations in rural areas was predominantly unsustainable. Economic performance exhibited variability, some falling into the less sustainable category, while social and environmental performances were deemed moderately sustainable. The practical implications of the findings are as follows: the level of competition, government policy, and market access are the most sensitive factors that should be considered to improve economic performance. Meanwhile, small industries must maintain and enhance their social and environmental performance to ensure stability. In addition, the theoretical implication of this finding suggests that the concept of “sustainability” cannot solely be represented by the company’s longevity. Long-lasting small industries may not necessarily be economically, socially, and environmentally sustainable.
Sustainable Health Education Simulator Using Open-Source Technology
Authors: Oyarzún-Diaz Patricia , Orellana-Cortés Ana , Segura Hugo , Vidal-Silva Cristian , Sánchez-Ortiz Aurora , Serrano-Malebrán Jorge
Published: August 2023

The information society has transformed human life. Technology is almost everywhere, including health and education. For example, years ago, speech and language therapy major students required a long time and high-cost equipment to develop healthcare competencies regarding the auditory and vestibular systems. The high cost of the equipment permitted its practical use only in classes, hindering students’ autonomy in developing those competencies. That situation was a real issue, even more in pandemic times where online education was essential. This article describes SAEF, an open-source software simulator for autonomously developing procedural audiology therapy competencies, user acceptance of this tool, and the validity of experiments and results. A great feature of SAEF is the immediate feedback and performance results delivered. A great achievement is the obtained results of students for its validity and its acceptability among speech and language therapy educators. Those results invite authors to continue developing free-access simulator software solutions in other health education contexts. SAEF was developed using open-source technology to facilitate its accessibility, classification, and sustainability.
Prediction of Compressive Strength and Elastic Modulus for Recycled Aggregate Concrete Based on AutoGluon
Authors: Lin Chenxi , Sun Yidan , Jiao Wenxiu , Zheng Jiajie , Li Zhijuan , Zhang Shujun
Published: August 2023

While the civil construction industry brings great convenience to life, the large amount of waste concrete also poses a significant problem of construction waste disposal. As one of the effective ways to utilize waste concrete, recycled aggregate concrete (RAC) can improve the environment while reducing the consumption of construction materials. This study aims to use AutoGluon (AG), an automated machine learning platform, to predict both the compressive strength and elastic modulus of RAC. Then the performance of AG is compared with traditional empirical formulas and multiple linear regression models. The determination coefficient (R2) is chosen as one of the evaluation standards for predicting values. The results demonstrate that the WeightedEnsemble model of AG performed best in predicting both the compressive strength and elastic modulus, which provides a new method for the rapid and accurate prediction of the properties of RAC in engineering construction.
Unveiling the Connection among ESG, Earnings Management, and Financial Distress: Insights from an Emerging Market
Authors: Almubarak Wadhaah Ibrahim , Chebbi Kaouther , Ammer Mohammed Abdullah
Published: August 2023

Earnings management continues to be a critical ethical concern faced by companies. The management that conducts earnings manipulation may adopt environmental, social, and governance (ESG) activities to safeguard themselves from stakeholders. Engagement in ESG is sometimes viewed as a type of managerial misconduct and as a means to cover up manipulative practices. Thus, the key aim of our study is to investigate the association between ESG disclosure and earnings management levels in the context of listed companies in Saudi Arabia. We also investigate the influence of financial distress on the above association. Data were obtained from 304 company-year observations for the years 2014–2021. The results showed that ESG disclosure had a positive and statistically significant effect on earnings management. In addition, financial distress significantly and positively enhanced this effect. This shows that financially distressed companies tend to disclose more ESG practices and engage in earnings management. Moreover, through the division of the three ESG components—environmental, social, and governance—the impacts of both environmental and social factors on earnings management were found to be positive and robust, while the governance score was negative. The results obtained using diverse regression techniques and further tests were robust. This study makes several contributions to the ESG and earnings management literature. It also minimizes the literature gap by focusing on the influences of financial distress on the ESG–earnings management relationship. The study findings have implications for several stakeholder groups, including regulators, decision makers, investors, and auditors. In particular, it warns policymakers that some practices focused on ESG enhancements may be a tool for preventing other questionable practices.