bluuebunny/crossref_metadata_2025 huggingface dataset:
Retrieval of Arctic Sea Ice Motion from FY-3D/MWRI Brightness Temperature Data
Authors: Chen Haihua , Ni Kun , Liu Jun , Li Lele
Published: August 2023

Sea ice motion (SIM) has significant implications for sea–air interactions, thermohaline circulation, and the development of the Arctic passage. This research proposes an improved SIM retrieval method from Fengyun-3D’s (FY-3D) microwave radiometer imager’s (MWRI) brightness temperature (Tb) data based on the modified classical maximum cross-correlation (MCC) method and the multisource data merging method. This study utilized buoy data to establish the search area range, applied distinct thresholds across various Arctic regions, and merged the buoy data, reanalysis wind data, and SIM retrieved from FY-3D/MWRI Tb data. In 2019, for the final Arctic SIM results retrieved from the MWRI 89 GHz and 36.5 GHz Tb data, the root-mean-square error (RMSE) and the mean average error (MAE) in the east–west direction were 2.07 cm/s and 1.38 cm/s and those in the north–south direction were 1.96 cm/s and 1.15 cm/s, compared to the ice-tethered profiler (ITP) data. Compared with the daily average data of the National Snow and Ice Data Center (NSIDC), the RMSE and MAE of the SIM results obtained in this study were 0.74 cm/s and 0.93 cm/s in the east–west direction, and 0.56 cm/s and 0.72 cm/s in the north–south direction, respectively. The monthly average of the SIM retrieved from the MWRI Tb data in this research also showed a good agreement with the monthly average of the NSIDC SIM product. The comparison showed that the MWRI Tb data could be used to retrieve the Arctic SIM, and the Arctic SIM retrieval method presented in this paper was accurate and general.
Prediction of Site Index and Age Using Time Series of TanDEM-X Phase Heights
Authors: Huuva Ivan , Wallerman Jörgen , Fransson Johan E. S. , Persson Henrik J.
Published: August 2023

Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment.
Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
Authors: Bian Shaojie , Wang Bin , Li Mingze , Kong Xiangqi , Shi Jinning , Chen Yanxi , Fan Wenyi
Published: August 2023

The CO2 fertilization effect (CFE) plays a crucial role in the amelioration of climate change. Many physiological and environmental factors, such as stand age, stand type, and site conditions, may affect the extent of the CFE. However, the relationship between the CFE and these factors remains elusive. In this study, we used the emerging gross primary production (GPP) remote sensing products, with GPP predicted using eddy covariance–light use efficiency models (EC-LUE GPP) based on satellite near-infrared reflectance of vegetation (NIRv GPP) and assessed with a random forest model to explore the CFE trends with stand age in a coniferous forest and a broad-leaved forest in Heilongjiang Province, China. We additionally compared the differences among the CFEs under different site conditions. The CFEs in coniferous forests and broad-leaved forests both showed a rapid increase in stands of 10 to 20 years of age, followed by a decline after reaching a maximum, with the rate of decline reducing with age. Eventually, CFE remained stable in stands near 100 years of age. However, the CFE in coniferous forests exhibited more extended periods of rapid increase and a higher maximum than in broad-leaved forests. Moreover, in this study, we used the site class index (SCI) to grade site conditions. The results demonstrate that the CFE differed significantly under different levels of site conditions, and these differences gradually decreased with age. The site with the highest SCI had fewer environmental restrictions on the CFE, and consequently, the CFE rate of decline was faster. Our results are of significance in understanding the CFE and adapting to future changes in atmospheric CO2 concentration.
MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images
Authors: Zhang Huan , Leng Wei , Han Xiaolin , Sun Weidong
Published: August 2023

The effectiveness of training-based object detection heavily depends on the amount of sample data. But in the field of remote sensing, the amount of sample data is difficult to meet the needs of network training due to the non-cooperative imaging modes and complex imaging conditions. Moreover, the imbalance of the sample data between different categories may lead to the long-tail problem during the training. Given that similar sensors, data acquisition approaches, and data structures could make the targets in different categories possess certain similarities, those categories can be modeled together within a subspace rather than the entire space to leverage the amounts of sample data in different subspaces. To this end, a subspace-dividing strategy and a subspace-based multi-branch network is proposed for object detection in remotely sensed images. Specifically, a combination index is defined to depict this kind of similarity, a generalized category consisting of similar categories is proposed to represent the subspace, and a new subspace-based loss function is devised to address the relationship between targets in one subspace and across different subspaces to integrate the sample data from similar categories within a subspace and to balance the amounts of sample data between different subspaces. Furthermore, a subspace-based multi-branch network is constructed to ensure the subspace-aware regression. Experiments on the DOTA and HRSC2016 datasets demonstrated the superiority of our proposed method.
Earth Observation in the EMMENA Region: Scoping Review of Current Applications and Knowledge Gaps
Authors: Eliades Marinos , Michaelides Silas , Evagorou Evagoras , Fotiou Kyriaki , Fragkos Konstantinos , Leventis Georgios , Theocharidis Christos , Panagiotou Constantinos F. , Mavrovouniotis Michalis , Neophytides Stelios , Papoutsa Christiana , Neocleous Kyriacos , Themistocleous Kyriacos , Anayiotos Andreas , Komodromos George , Schreier Gunter , Kontoes Charalampos , Hadjimitsis Diofantos
Published: August 2023

Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services.
An Interferometric-Synthetic-Aperture-Radar-Based Method for Predicting Long-Term Land Subsidence in Goafs through the Concatenation of Multiple Sources of Short-Term Monitoring Data
Authors: Li Jinyang , Zang Mingdong , Xu Nengxiong , Mei Gang , Yang Sen
Published: August 2023

The land subsidence occurring over a goaf area after coal mining is a protracted process. The accurate prediction of long-term land subsidence over goaf areas relies heavily on the availability of long-term land subsidence monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on interferometric synthetic aperture radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No. 2 mine in Shanxi Province, China. Initially, coal faces with the same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation on each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No. 2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term land subsidence monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.
Assessment and Data Fusion of Satellite-Based Precipitation Estimation Products over Ungauged Areas Based on Triple Collocation without In Situ Observations
Authors: Wu Xiaoqing , Zhu Jialiang , Lai Chengguang
Published: August 2023

Reliable assessment of satellite-based precipitation estimation (SPE) and production of more accurate precipitation data by data fusion is typically challenging in sparsely gauged and ungauged areas. Triple collocation (TC) is a novel assessment approach that does not require gauge observations; it provides a feasible solution for this problem. This study comprehensively validates the TC performance for assessing SPEs and performs data fusion of multiple SPEs using the TC-based merging (TCM) approach. The study area is the Tibetan Plateau (TP), a typical area lacking gauge observations. Three widely used SPEs are used: the integrated multi-satellite retrievals for global precipitation measurement (IMERG) “early run” product (IMERG-E), the precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) dynamic infrared (PDIR), and the Climate Prediction Center (CPC) morphing technique (CMORPH). Validation of the TC assessment approach shows that TC can effectively assess the SPEs’ accuracy, derive the spatial accuracy pattern of the SPEs, and reveal the accuracy ranking of the SPEs. TC can also detect the SPEs’ accuracy patterns, which are difficult to obtain from a traditional approach. The data fusion results of the SPEs show that TCM incorporates the regional advantages of the individual SPEs, providing more accurate precipitation data than the original SPEs, revealing that data fusion is reasonable and reliable in ungauged areas. In general, the TC approach performs well for the assessment and data fusion of SPEs, showing reasonable applicability in the TP and other areas lacking gauge data than other methods because it does not rely on gauge observations.
Surveillance Video Georeference Method Based on Real Scene Model with Geometry Priors
Authors: Zhou Zhongxian , Liu Jianchen , Feng Miaomiao , Cong Yuwei
Published: August 2023

With the comprehensive promotion of digital construction in China, cameras scattered throughout the country are of great significance in obtaining first-hand data. However, their potential role is limited due to the lack of georeference information on current surveillance cameras. Provided surveillance camera images and real scenes are combined and given georeference information, this problem can be solved, allowing cameras to generate significant social benefits. This article proposed an accurate registration method based on misalignment calibration and least squares matching between real scene and surveillance camera images to address this issue. Firstly, it is necessary to convert the navigation coordinate system from which cameras obtain data to the photogrammetric coordinate system and then solve for the misalignment and internal orientation elements of the camera. Then, accurate registration is achieved using the least squares matching on pyramid images. The experiment obtained surrounding image data of two common scenes with lens pitch angles of 45°, 55°, 65°, 75°, and 85° using the surveillance camera and obtained a 3D real scene model of each scene using a low-altitude aircraft. The experiment results show that the proposed method in this paper can achieve the expected goals of accurately matching real scene and surveillance camera images and assigning georeference information. Through extensive data analysis, the success rate and accuracy rate of registration are 98.1% and 97.06%, respectively.
Spectral Segmentation Multi-Scale Feature Extraction Residual Networks for Hyperspectral Image Classification
Authors: Wang Jiamei , Ren Jiansi , Peng Yinbin , Shi Meilin
Published: August 2023

Hyperspectral image (HSI) classification is a vital task in hyperspectral image processing and applications. Convolutional neural networks (CNN) are becoming an effective approach for categorizing hyperspectral remote sensing images as deep learning technology advances. However, traditional CNN usually uses a fixed kernel size, which limits the model’s capacity to acquire new features and affects the classification accuracy. Based on this, we developed a spectral segmentation-based multi-scale spatial feature extraction residual network (MFERN) for hyperspectral image classification. MFERN divides the input data into many non-overlapping sub-bands by spectral bands, extracts features in parallel using the multi-scale spatial feature extraction module MSFE, and adds global branches on top of this to obtain global information of the full spectral band of the image. Finally, the extracted features are fused and sent into the classifier. Our MSFE module has multiple branches with increasing ranges of the receptive field (RF), enabling multi-scale spatial information extraction at both fine- and coarse-grained levels. On the Indian Pines (IP), Salinas (SA), and Pavia University (PU) HSI datasets, we conducted extensive experiments. The experimental results show that our model has the best performance and robustness, and our proposed MFERN significantly outperforms other models in terms of classification accuracy, even with a small amount of training data.
Study on Road Network Vulnerability Considering the Risk of Landslide Geological Disasters in China’s Tibet
Authors: Yao Yunchang , Cheng Liang , Chen Song , Chen Hui , Chen Mingfei , Li Ning , Li Zeming , Dongye Shengkun , Gu Yifan , Yi Junfan
Published: August 2023

Road traffic is occasionally blocked by landslide geological disasters in remote mountainous areas, causing obstruction to economic society and national defense construction. It is vital to conduct landslide geological disaster risk assessment and vulnerability research on the road network. Based on landslide geological disaster risk on the road network, this study analyzed the potential effects of the main environmental elements. Due to the lack of previous research works, this study proposed an effective, rational, and understandable multicriteria heuristic analytical hierarchy process model, fuzzy comprehensive evaluation, and frequency ratio-interactive fuzzy stack analysis for vulnerability assessment of road networks in large and complex networks. Based on the comprehensive use of geographic information technology, the road network vulnerability of Tibet in China was evaluated by introducing slope, topographic relief, normalized difference vegetation index (NDVI), annual mean precipitation, distance from river drainage, glaciers and snow, habitation, seismic center and geological fault zone, and soil erosion intensity. According to the findings of the study, the three-stage framework proposed in this study can provide correct inferences and explanations for the potential phenomena of landslide geological disasters; the geological disaster risk are unevenly distributed in the study area; the distribution of the road network vulnerability in China’s Tibet significantly differs among different cities; the high-vulnerability section presents significant regional characteristics, which overlap with the area with a high risk of landslide geological disasters, and its distribution is mostly located in traffic arteries, link aggregations, and relatively frequent human activity.
Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data
Authors: Wang Jie , Xiong Xuanrui , Chen Gaosheng , Ouyang Ruiqi , Gao Yunli , Alfarraj Osama
Published: August 2023

The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection systems using traffic features have emerged. However, these face difficulties due to varied traffic feature formats, hindering fast and accurate detection model training. To tackle accuracy issues caused by irrelevant features, a new model, LVW-MECO (LVW enhanced with multiple evaluation criteria), is introduced. It uses the LVW (Las Vegas Wrapper) algorithm with multiple evaluation criteria to identify pertinent features from IoT network data, boosting intrusion detection precision. Experimental results confirm its efficacy in addressing IoT security problems. LVW-MECO enhances intrusion detection performance and safeguards IoT data integrity, promoting a more secure IoT environment.
On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures
Authors: Große Sundrup Jonas , Mombaur Katja
Published: August 2023

We investigate the distribution of muscle signatures of human hand gestures under Dynamic Time Warping. For this we present a k-Nearest-Neighbors classifier using Dynamic Time Warping for the distance estimate. To understand the resulting classification performance, we investigate the distribution of the recorded samples and derive a method of assessing the separability of a set of gestures. In addition to this, we present and evaluate two approaches with reduced real-time computational cost with regards to their effectiveness and the mechanics behind them. We further investigate the impact of different parameters with regards to practical usability and background rejection, allowing fine-tuning of the induced classification procedure.
Joint Calibration Method for Robot Measurement Systems
Authors: Wu Lei , Zang Xizhe , Ding Guanwen , Wang Chao , Zhang Xuehe , Liu Yubin , Zhao Jie
Published: August 2023

Robot measurement systems with a binocular planar structured light camera (3D camera) installed on a robot end-effector are often used to measure workpieces’ shapes and positions. However, the measurement accuracy is jointly influenced by the robot kinematics, camera-to-robot installation, and 3D camera measurement errors. Incomplete calibration of these errors can result in inaccurate measurements. This paper proposes a joint calibration method considering these three error types to achieve overall calibration. In this method, error models of the robot kinematics and camera-to-robot installation are formulated using Lie algebra. Then, a pillow error model is proposed for the 3D camera based on its error distribution and measurement principle. These error models are combined to construct a joint model based on homogeneous transformation. Finally, the calibration problem is transformed into a stepwise optimization problem that minimizes the sum of the relative position error between the calibrator and robot, and analytical solutions for the calibration parameters are derived. Simulation and experiment results demonstrate that the joint calibration method effectively improves the measurement accuracy, reducing the mean positioning error from over 2.5228 mm to 0.2629 mm and the mean distance error from over 0.1488 mm to 0.1232 mm.
Adaptive Neural Backstepping Terminal Sliding Mode Control of a DC-DC Buck Converter
Authors: Gong Xiaoyu , Fei Juntao
Published: August 2023

In this paper, an adaptive backstepping terminal sliding mode control (ABTSMC) method based on a double hidden layer recurrent neural network (DHLRNN) is proposed for a DC-DC buck converter. The DHLRNN is utilized to approximate and compensate for the system uncertainty. On the basis of backstepping control, a terminal sliding mode control (TSMC) is introduced to ensure the finite-time convergence of the tracking error. The effectiveness of the composite control method is verified on a converter prototype in different test conditions. The experimental comparison results demonstrate the proposed control method has better steady-state performance and faster transient response.
An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME
Authors: Hussain Iqram , Jany Rafsan , Boyer Richard , Azad AKM , Alyami Salem A. , Park Se Jin , Hasan Md Mehedi , Hossain Md Azam
Published: August 2023

Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitoring the neurological responses during cognitive and motor tasks. Machine learning (ML) represents a promising tool for the recognition of human activities (HAR), and eXplainable artificial intelligence (XAI) can elucidate the role of EEG features in ML-based HAR models. The primary objective of this investigation is to investigate the feasibility of an EEG-based ML model for categorizing everyday activities, such as resting, motor, and cognitive tasks, and interpreting models clinically through XAI techniques to explicate the EEG features that contribute the most to different HAR states. The study involved an examination of 75 healthy individuals with no prior diagnosis of neurological disorders. EEG recordings were obtained during the resting state, as well as two motor control states (walking and working tasks), and a cognition state (reading task). Electrodes were placed in specific regions of the brain, including the frontal, central, temporal, and occipital lobes (Fz, C1, C2, T7, T8, Oz). Several ML models were trained using EEG data for activity recognition and LIME (Local Interpretable Model-Agnostic Explanations) was employed for interpreting clinically the most influential EEG spectral features in HAR models. The classification results of the HAR models, particularly the Random Forest and Gradient Boosting models, demonstrated outstanding performances in distinguishing the analyzed human activities. The ML models exhibited alignment with EEG spectral bands in the recognition of human activity, a finding supported by the XAI explanations. To sum up, incorporating eXplainable Artificial Intelligence (XAI) into Human Activity Recognition (HAR) studies may improve activity monitoring for patient recovery, motor imagery, the healthcare metaverse, and clinical virtual reality settings.
Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks
Authors: AlHaddad Ulaa , Basuhail Abdullah , Khemakhem Maher , Eassa Fathy Elbouraey , Jambi Kamal
Published: August 2023

The Smart Grid aims to enhance the electric grid’s reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementation. However, the reliance of Smart Grid systems on communication networks makes them vulnerable to cyberattacks, posing a significant risk to grid reliability. To mitigate such threats, efficient intrusion detection and prevention systems are essential. This paper proposes a hybrid deep-learning approach to detect distributed denial-of-service attacks on the Smart Grid’s communication infrastructure. Our method combines the convolutional neural network and recurrent gated unit algorithms. Two datasets were employed: The Intrusion Detection System dataset from the Canadian Institute for Cybersecurity and a custom dataset generated using the Omnet++ simulator. We also developed a real-time monitoring Kafka-based dashboard to facilitate attack surveillance and resilience. Experimental and simulation results demonstrate that our proposed approach achieves a high accuracy rate of 99.86%.
Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism
Authors: Ji Naihua , Dong Huiqian , Meng Fanyun , Pang Liping
Published: August 2023

Semantic segmentation and depth estimation are crucial components in the field of autonomous driving for scene understanding. Jointly learning these tasks can lead to a better understanding of scenarios. However, using task-specific networks to extract global features from task-shared networks can be inadequate. To address this issue, we propose a multi-task residual attention network (MTRAN) that consists of a global shared network and two attention networks dedicated to semantic segmentation and depth estimation. The convolutional block attention module is used to highlight the global feature map, and residual connections are added to prevent network degradation problems. To ensure manageable task loss and prevent specific tasks from dominating the training process, we introduce a random-weighted strategy into the impartial multi-task learning method. We conduct experiments to demonstrate the effectiveness of the proposed method.
VIDAR-Based Road-Surface-Pothole-Detection Method
Authors: Xu Yi , Sun Teng , Ding Shaohong , Yu Jinxin , Kong Xiangcun , Ni Juan , Shi Shuyue
Published: August 2023

This paper presents a VIDAR (a Vision-IMU based detection and ranging method)-based approach to road-surface pothole detection. Most potholes on the road surface are caused by the further erosion of cracks in the road surface, and tires, wheels and bearings of vehicles are damaged to some extent as they pass through the potholes. To ensure the safety and stability of vehicle driving, we propose a VIDAR-based pothole-detection method. The method combines vision with IMU to filter, mark and frame potholes on flat pavements using MSER to calculate the width, length and depth of potholes. By comparing it with the classical method and using the confusion matrix to judge the correctness, recall and accuracy of the method proposed in this paper, it is verified that the method proposed in this paper can improve the accuracy of monocular vision in detecting potholes in road surfaces.
Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records
Authors: Ali Aitizaz , Al-rimy Bander Ali Saleh , Tin Ting Tin , Altamimi Saad Nasser , Qasem Sultan Noman , Saeed Faisal
Published: August 2023

Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain’s immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient.
Recovery of Zn and Fe from Steelmaking By-Products by Ar Plasma Smelting
Authors: Cho Seongkyu , Tomas Da Rocha Leonardo , Kim Sung-Wan , Jung Sung-Mo
Published: August 2023

Iron-containing by-products have high recycling value as an iron source due to the high content of Fe. However, some impurities in by-products limit their recycling. In particular, zinc in by-products are repeatedly vaporized and recrystallized inside a blast furnace, which deteriorates the operation. It is necessary to remove zinc from by-products by the zinc removal process such as RHF (rotary hearth furnace). However, due to the low removal ratio of zinc in RHF using coal as reducing agent, it is difficult to achieve carbon neutrality. This research investigated the removal of zinc and the recovery of zinc and iron from zinc-containing by-products by Ar plasma smelting. Two kinds of by-products used in this study contained 0.89 and 3.39 wt% of zinc oxide, respectively. One by-product (BP-A) resulted in a mixture of metallic iron and FeO remaining inside the crucible after Ar plasma smelting. The recovery ratio of iron in the product was evaluated as 94.2%, and the removal ratio of zinc was calculated to be 95.6%. The recovery ratio of zinc collected in the form of dust outside the crucible was calculated as 92.5%. The other by-product (BP-B), produced because of Ar plasma, was mostly FeO, and 82.6% of iron was recovered. In total, 96.4% of zinc from BP-B was removed, and 73.1% of zinc was recovered as dust. By the thermodynamic calculation in terms of FactSage 8.2, the temperature of the sample during plasma smelting was expected to be 2500 °C. The main gases generated during smelting were H2, CO and CO2 which were formed at the initial stage of the process.
The Impact of Enterprise Digital Capability on Employee Sustainable Performance: From the Perspective of Employee Learning
Authors: Fu Feiqiang , Zha Wenhui , Zhou Qiwei
Published: August 2023

Background: Enterprises’ digital transformation is an important issue in the digital era. Exploring how digital transformation can be implemented successfully within enterprises is of considerable theoretical and practical significance. From the perspective of employee learning, this study focuses on employees and aims to establish the theoretical linkage between employees’ perception of enterprise digital capability and their sustainable performance. Methods: A survey using the random sampling technique was adopted to collect data from a large professional data platform. A multi-wave survey featuring 433 full-time Chinese employees was conducted using path analyses to test the hypotheses. Results: The results of the path analyses showed that: (1) employee learning and unlearning mediate the relationship between employees’ perception of an enterprise’s digital capability and their sustainable performance; (2) communication feedback strengthens the positive effects of perceived enterprise digital capability on learning, as well as on unlearning; and (3) the integrated moderated mediation model is valid. Conclusion: This paper proves that during enterprises’ digital transformation, employees’ perception of the enterprise’s digital capability promotes employee sustainable performance via both learning and unlearning. Communication feedback strengthens the above relationships. Therefore, this study contributes to the literature on digital transformation and highlights employee-learning-related organizational management issues, shedding light on the practice of enterprise digital transformation.
Characteristics of Fluctuating Wind Speed Spectra of Moving Vehicles under the Non-Stationary Wind Field
Authors: Hu Peng , Zhang Fei , Han Yan , Yan Naijie
Published: August 2023

To promote energy saving, emission reduction, and sustainable development of high-speed trains, as well as achieve low-carbon operation of these trains. It is necessary to establish a fluctuating wind speed spectra model that can accurately describe the characteristics of the fluctuating wind speed field of the moving vehicle. This will help explore the effects of strong winds on the running resistance, energy consumption, safety, and comfort of trains. In this paper, based on Priestley’s evolutionary power spectral density (EPSD) theory, an efficient method was developed for generating the fluctuating wind speeds at the moving point under the non-stationary wind field. On such basis, the effects of different mean wind speeds, ground clearances, temporal modulation function parameters, and vehicle’s moving speeds on the time-varying correlation function ratio of fluctuating wind speed at fixed and moving points were analyzed. Subsequently, the relationship between the time-varying correlation functions of fluctuating wind speed at the fixed and moving points was established by analyzing the sensitivity of the above parameters, and a theoretical model of fluctuating wind speed spectra of the moving point under the non-stationary wind field was proposed. In addition, the relational expression of fluctuating wind speed spectra of the moving point under stationary and non-stationary wind fields was established, which was further validated using the fluctuating wind speed spectra model at the fixed points with different modulation function forms. The results demonstrated that the direct generation method can avoid n times of POD decomposition and Ns∑j=1nNqjj times of FFT calculation, improve the calculation speed, and save memory. The proposed fluctuating wind speed spectra model at the moving point under the non-stationary wind field is in good agreement with the corresponding target one, indicating the high accuracy of the proposed model. Meanwhile, it is also noted that the fluctuating wind speed spectra at the moving point under the non-stationary wind field can be obtained by modulating the spectra under the stationary wind field using temporal modulation function, which is the same as that of the fluctuating wind speed spectra at fixed points under the non-stationary wind field.
Analysis of Factors Influencing the Severity of Vehicle-to-Vehicle Accidents Considering the Built Environment: An Interpretable Machine Learning Model
Authors: Wang Jianyu , Ji Lanxin , Ma Shuo , Sun Xu , Wang Mingxin
Published: August 2023

Understanding the causes of traffic road accidents is crucial; however, as data collection is conducted by traffic police, accident-related environmental information is not available. To fill this gap, we collect information on the built environment within R = 500 m of the accident site; model the factors influencing accident severity in Shenyang, China, from 2018 to 2020 using the Random Forest algorithm; and use the SHapley Additive exPlanation method to interpret the underlying driving forces. We initially integrate five indicators of the built environment with 18 characteristics, including human and vehicle at-fault characters, infrastructure, time, climate, and land use attributes. Our results show that road type, urban/rural, season, and speed limit in the first 10 factors have a significant positive effect on accident severity; density of commercial-POI in the first 10 factors has a significant negative effect. Factors such as urban/rural and road type, commercial and vehicle type, road type, and season have significant effects on accident severity through an interactive mechanism. These findings provide important information for improving road safety.
Experiences of Underground Mine Backfilling Using Mine Tailings Developed in the Andean Region of Peru: A Green Mining Solution to Reduce Socio-Environmental Impacts
Authors: Cacciuttolo Carlos , Marinovic Alex
Published: August 2023

In Peru, socio-environmental conflicts related to the development of mining-metallurgical processes and the responsible disposal of mine tailings have become central issues for accepting mining projects, especially regarding building relationships of trust with the communities. This condition has prompted the Peruvian mining industry to advance in managing alternatives to the conventional surface disposal of mine tailings. A promising and increasingly popular management strategy for mine tailings in Peru is their disposal inside underground mines. This article presents: site-specific conditions, advantages/disadvantages, and lessons learned from practical experiences of mine tailings disposal in underground mines in Peru. In addition, some techniques are highlighted, such as (i) hydraulic fill, (ii) cemented hydraulic fill, and (iii) cemented paste backfill. Finally, this article concludes that the responsible disposal of mine tailings in underground mines is a green mining solution that reduces negative socio-environmental impacts, limiting the generation of acid rock drainage (ARD) and the leaching of metals due to the decrease in contact with oxygen and rainfall, thus mitigating the contamination of surface and underground waters, reducing the footprint of affectation in the territory, and eliminating the emission of particulate matter in the environment.
Experiential Value, Overall Image, Revisit Intention, and Willingness to Pay a Premium in the Context of Environmentally Certified Hotel Restaurants: The Moderating Effect of Environmental Consciousness
Authors: Ko Ju-Hee , Kang Hee-Seog , Seo Jung-Woon
Published: August 2023

This study applies experiential value and overall image to identify antecedents of customers’ behavioral intention (revisit intention and willingness to pay a premium) toward environmentally certified hotel restaurants (ECHRs). Data from 391 individuals who responded that they had already visited at least one of three selected ECHRs of five-star hotels in South Korea were analyzed. The results show that playfulness had the greatest positive influence on the overall image of ECHRs, followed by service excellence and consumer return on investment. However, aesthetics had a negative effect on the overall image. Moreover, overall image was found to positively influence revisit intention and willingness to pay a premium. Thus, playfulness, service excellence, and consumer return on investment are important for inducing customers’ revisit intention and willingness to pay a premium in ECHRs. In addition, the moderating roles of environmental consciousness on the associations of overall image with revisit intention and with willingness to pay a premium were confirmed. The research design and results contribute to the literature on eco-friendly restaurants in the hospitality industry. Therefore, the theoretical framework verified in this study can be used as a basis for customer experience research on eco-friendly restaurants in hotels.
Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude
Authors: Çetinkaya Ali , Baykan Ömer Kaan , Kırgız Havva
Published: August 2023

With the increasing prevalence and significance of computer programming, a crucial challenge that lies ahead of teachers and parents is to identify students adept at computer programming and direct them to relevant programming fields. As most studies on students’ coding abilities focus on elementary, high school, and university students in developed countries, we aimed to determine the coding abilities of middle school students in Turkey. We first administered a three-part spatial test to 600 secondary school students, of whom 400 completed the survey and the 20-level Classic Maze course on Code.org. We then employed four machine learning (ML) algorithms, namely, support vector machine (SVM), decision tree, k-nearest neighbor, and quadratic discriminant to classify the coding abilities of these students using spatial test and Code.org platform data. SVM yielded the most accurate results and can thus be considered a suitable ML technique to determine the coding abilities of participants. This article promotes quality education and coding skills for workforce development and sustainable industrialization, aligned with the United Nations Sustainable Development Goals.
Modeling Multi-Generation Product Diffusion in the Context of Dual-Brand Competition and Sustainable Improvement
Authors: Tan Bo , Zhu Zhiguo , Jiang Pan , Wang Xiening
Published: August 2023

The diffusion of competition under the coexistence of multi-generation products has become one of the important challenges faced by enterprises in their daily and sustainable operations. At the same time, the competition between different brands has intensified the difficulty and complexity of decision making in the process of multi-generation product operations. Therefore, based on the Norton–Bass model diffusion process, this paper introduces two marketing variables: dynamic price and quality level. Then, this paper builds a multi-generation product diffusion model under dual-brand competition and analyzes the impact of the company’s revenue on launch time to market, pricing, quality, and technical levels. By using the system dynamics (SD) method (from the perspective of strong brand and weak brand enterprises), the competition diffusion model is built and simulated. The simulation indicates the following: (i) When enterprises have the same brand competitiveness, reducing the pricing level cannot obtain more revenue and instead diminishes the overall revenue of the industry. Raising the pricing level can obtain more revenue and also improve the revenue of competitors. (ii) When the competitive strengths of enterprises are different, strong brands tend to maintain stable pricing on the basis of improving the quality level (or slightly raising the price). Weak brands tend to raise the pricing of new products significantly on the basis of improving the quality level. (iii) The launch-time-to-market decision of new products is influenced by the degree of the product quality upgrade. Therefore, the frequency of releasing new products should trade off against the degree of technological upgrading of the product quality. This research provides a theoretical basis and new insights for new product launches and operation decisions of enterprises.
Literature Review on the Indoor Air VOCs Purification Performance of Metal–Organic Frameworks
Authors: Wang Kaiqiao , Nie Jinzhe , Huang Honghao , He Fuqun
Published: August 2023

Controlling the indoor air (volatile organic compound) VOCs concentration plays an important role in creating a healthy and comfortable living environment. Comparing several VOCs purification measures, solid adsorption is found to be a promising air cleaning method for civil buildings, which have diverse VOCs pollutants. For solid adsorption technology, absorbance is the key to its air-cleaning performance. Compared with traditional adsorbent materials, (metal–organic frameworks) MOFs have excellent physical parameters and are promising adsorbent materials. In this paper, the synthesis and adsorption mechanisms underlying several metal–organic frameworks are summarized. The exploration and experiment measurements of VOCs adsorption performance according to the metal–organic frameworks are summarized. The exploration of these materials’ stability during air cleaning is reviewed. Finally, some application examples of metal–organic frameworks for VOCs adsorption are given. This literature review demonstrates that metal–organic frameworks can be promising adsorbents for indoor air cleaning. The green synthesis methods, stability, adsorption performance under low concentration and diverse VOCs conditions, and application methods for metal–organic frameworks should be further researched before their large-scale application.