bluuebunny/crossref_metadata_2025 huggingface dataset:
User-Aware Evaluation for Medium-Resolution Forest-Related Datasets in China: Reliability and Spatial Consistency
Authors: Peng Xueli , He Guojin , Wang Guizhou , Long Tengfei , Zhang Xiaomei , Yin Ranyu
Published: May 2023

Forest cover data are fundamental to sustainable forest management and conservation. Available medium-resolution publicly shared forest-related datasets provide primary information on forest distribution. The evaluation of relevant datasets is of great importance to learn about the differences, characterize the accuracy, and provide a reference for rational use. This study presents an evaluation and analysis of the forest-related datasets in China around 2020, including TreeCover and the forest-related layer (latter referred to as the forest datasets) in WorldCover, Esri land cover, FROM-GLC10, GlobeLand30, and GLC_FCS30. These forest datasets, that are obtained by aggregating forest-related lasses based on the classification schemes, are analyzed from spatial consistency and accuracy comparison. The results illustrate that forest datasets with 10m resolution are generally more precise than those with 30m resolution in China. WorldCover shows the highest accuracy, with producer accuracy and user accuracy of 91.4% and 87.09%, respectively. These datasets exhibit high accuracy but great spatial inconsistency. The more consistent the regions are, the more accurate the accuracy is. High consistency (≥5, i.e., classified into forests by five datasets) areas account for 56.49% of areas of forest classified (AFC), while the area of low consistency (≤2) reach 25.51% of AFC. The analysis delves into the datasets, offering a reliable reference for the usage of these datasets.
BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF
Authors: Qin Zhiwei , Zhang Qin , Huang Guanwen , Tang Longjiang , Wang Jungang , Wang Xiaolei
Published: May 2023

The orbit maneuver detection is crucial in Global Navigation Satellite System (GNSS) precise orbit determination, which is necessary for adjusting data processing strategies. The frequency of orbit maneuvers for the BeiDou Navigation System is significantly higher than that of other navigation systems, especially for geosynchronous orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. We propose a novel real-time and postprocessing method for detecting orbit maneuvers for BeiDou satellites based on the orbit differences between the epoch-updated orbit estimated using square root information (SRIF) and the predicted orbit according to the precise orbit estimated during non-maneuver period, as well as the orbital state difference during maneuver and non-maneuver periods. This method has significant advantages over using observation residuals and it is not affected by observation outliers, thus improving the accuracy and timeliness of orbit maneuver detection. We demonstrated that 32 orbit maneuver events of BeiDou satellites were successfully detected in 2022, of which 1 was for medium Earth orbit (MEO), 7 were for IGSO with an average detected maneuvering time of 7–8 min, and 24 were for GEO satellites with an average detected time of 4–5 min. Moreover, our method can be easily integrated into current real-time filter-based precise orbit determination (POD) processing without any extra task line, which simplifies the overall data processing. The data used in this method can be accessed easily, including GNSS observation data, broadcast ephemeris, and other open-source information files.
Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images
Authors: Shao Zhenfeng , Cheng Tao , Fu Huyan , Li Deren , Huang Xiao
Published: May 2023

Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.
Estimation and Spatiotemporal Evolution Analysis of Actual Evapotranspiration in Turpan and Hami Cities Based on Multi-Source Data
Authors: Wang Lei , Wang Jinjie , Ding Jianli , Li Xiang
Published: May 2023

The accurate inversion of actual evapotranspiration (ETa) at a regional scale is crucial for understanding water circulation, climate change, and drought monitoring. In this study, we produced a 1 km monthly ETa dataset for Turpan and Hami, two typical arid cities in northwest China, using multi-source remote sensing data, reanalysis information, and the ETMonitor model from 1980 to 2021. We analyzed the spatiotemporal variation of ETa using various statistical approaches and discussed the impact of climate and land use and cover changes (LUCC) on ETa. The results show the following: (1) the estimation results correlate well with ETa products on monthly scales (coefficient of determination (R2) > 0.85, root mean square error (RMSE) < 15 mm/month) with high reliability. (2) The ETa values were spatially distributed similarly to precipitation and LUCC, with the multi-year (1980–2021) average of 66.31 mm and a slightly fluctuating downward trend (−0.19 mm/a). (3) During the 42-year period, 63.16% of the study area exhibited an insignificant decrease in ETa, while 86.85% experienced pronounced fluctuations (coefficient of variation (CV) > 0.20), and 78.83% will show an upward trend in the future. (4) ETa was significantly positively correlated with precipitation (94.17%) and insignificantly positively correlated with temperature (55.81%). The impact of human activities showed an insignificant decreasing trend (85.41%). Additionally, the intensity of ETa varied considerably among land types, with the largest for cropland (424.12 mm/a). The results of the study have implications for promoting the rational allocation of regional water resources and improving water use efficiency in arid zones.
Satellite-Derived Bathymetry Mapping on Horseshoe Island, Antarctic Peninsula, with Open-Source Satellite Images: Evaluation of Atmospheric Correction Methods and Empirical Models
Authors: Gülher Emre , Alganci Ugur
Published: May 2023

Satellite-derived bathymetry (SDB) is the process of estimating water depth in shallow coastal and inland waters using satellite imagery. Recent advances in technology and data processing have led to improvements in the accuracy and availability of SDB. The increased availability of free optical satellite sensors, such as Landsat missions and Sentinel 2 satellites, has increased the quantity and frequency of SDB research and mapping efforts. In addition, machine learning (ML)- and deep learning (DL)-based algorithms, which can learn to identify features that are indicative of water depth, such as color or texture variations, have started to be used for extracting bathymetry information from satellite imagery. This study aims to produce an initial optical image-based SBD map of Horseshoe Island’s shallow coasts and to perform a comprehensive and comparative evaluation with Landsat 8 and Sentinel 2 satellite images. Our research considers the performance of empirical SDB models (classical, ML-based, and DL-based) and the effects of the atmospheric correction methods ACOLITE, iCOR, and ATCOR. For all band combinations and depth intervals, the ML-based random forest and XGBoost models delivered the highest performance and best fitting ability by achieving the lowest error with MAEs smaller than 1 m up to 10 m depth and a maximum correlation of R2 around 0.80. These models are followed by the DL-based ANN and CNN models. Nonetheless, the non-linearity of the reflectance–depth connection was significantly reduced by the ML-based models. Furthermore, Landsat 8 showed better performance for 10–20 m depth intervals and in the entire range of (0–20 m), while Sentinel 2 was slightly better up to 10 m depth intervals. Lastly, ACOLITE, iCOR, and ATCOR provided reliable and consistent results for SDB, where ACOLITE provided the highest automation.
Sensing and Control Strategies for a Synergy-Based, Cable-Driven Exosuit via a Modular Test Bench
Authors: Jayakumar Ashwin , Rodríguez Jorge Daniel , Bermejo-García Javier , Agujetas Rafael , Romero-Sánchez Francisco
Published: May 2023

Ageing results in the eventual loss of muscle mass and strength, joint problems, and overall slowing of movements, with a greater risk of suffering falls or other such accidents. The use of gait assistance exoskeletons can help in the active aging of this segment of the population. Given the user specificity of the mechanics and control these devices need, the facility used to test different design parameters is indispensable. This work deals with the modeling and construction of a modular test bench and prototype exosuit to test different mounting and control schemes for a cable-driven exoskeleton or exosuit. The test bench allows the experimental implementation of postural or kinematic synergies to assist multiple joints by using only one actuator and the optimization of the control scheme to better adapt to the characteristics of the specific patient. The design is open to the research community and it is expected to improve the design of cable-driven systems for exosuits.
A Sliced Parabolic Equation Method to Characterize Maritime Radio Propagation
Authors: Wang Yuzhen , Zhou Ting , Xu Tianheng , Hu Honglin
Published: May 2023

For maritime broadband communications, atmospheric ducts can enable beyond line-of-sight communications or cause severe interference. Due to the strong spatial–temporal variability of atmospheric conditions in near-shore areas, atmospheric ducts have inherent spatial heterogeneity and suddenness. This paper aims to evaluate the effect of horizontally inhomogeneous ducts on maritime radio propagation through theoretical analysis and measurement validation. To make better use of meteorological reanalysis data, we design a range-dependent atmospheric duct model. Then, a sliced parabolic equation algorithm is proposed to improve the prediction accuracy of path loss. We derive the corresponding numerical solution and analyze the feasibility of the proposed algorithm under the range-dependent duct conditions. A 3.5 GHz long-distance radio propagation measurement is utilized to verify the algorithm. The spatial distribution characteristics of atmospheric ducts in the measurements are analyzed. Based on actual duct conditions, the simulation results are consistent with the measured path loss. The proposed algorithm outperforms the existing method during the multiple duct periods. We further investigate the influence of different duct horizontal characteristics on the received signal strength.
Image Quality Assessment for Realistic Zoom Photos
Authors: Han Zongxi , Liu Yutao , Xie Rong , Zhai Guangtao
Published: May 2023

New CMOS imaging sensor (CIS) techniques in smartphones have helped user-generated content dominate our lives over traditional DSLRs. However, tiny sensor sizes and fixed focal lengths also lead to more grainy details, especially for zoom photos. Moreover, multi-frame stacking and post-sharpening algorithms would produce zigzag textures and over-sharpened appearances, for which traditional image-quality metrics may over-estimate. To solve this problem, a real-world zoom photo database is first constructed in this paper, which includes 900 tele-photos from 20 different mobile sensors and ISPs. Then we propose a novel no-reference zoom quality metric which incorporates the traditional estimation of sharpness and the concept of image naturalness. More specifically, for the measurement of image sharpness, we are the first to combine the total energy of the predicted gradient image with the entropy of the residual term under the framework of free-energy theory. To further compensate for the influence of over-sharpening effect and other artifacts, a set of model parameters of mean subtracted contrast normalized (MSCN) coefficients are utilized as the natural statistics representatives. Finally, these two measures are combined linearly. Experimental results on the zoom photo database demonstrate that our quality metric can achieve SROCC and PLCC over 0.91, while the performance of single sharpness or naturalness index is around 0.85. Moreover, compared with the best tested general-purpose and sharpness models, our zoom metric outperforms them by 0.072 and 0.064 in SROCC, respectively.
Bi-Directional Free-Space Visible Light Communication Supporting Multiple Moveable Clients Using Light Diffusing Optical Fiber
Authors: Chang Yun-Han , Chow Chi-Wai , Lin Yuan-Zeng , Jian Yin-He , Wang Chih-Chun , Liu Yang , Yeh Chien-Hung
Published: May 2023

In this work, we put forward and demonstrate a bi-direction free-space visible light communication (VLC) system supporting multiple moveable receivers (Rxs) using a light-diffusing optical fiber (LDOF). The downlink (DL) signal is launched from a head-end or central office (CO) far away to the LDOF at the client side via a free-space transmission. When the DL signal is launched to the LDOF, which acts as an optical antenna to re-transmit the DL signal to different moveable Rxs. The uplink (UL) signal is sent via the LDOF towards the CO. In a proof-of-concept demonstration, the LDOF is 100 cm long, and the free space VLC transmission between the CO and the LDOF is 100 cm. 210 Mbit/s DL and 850 Mbit/s UL transmissions meet the pre-forward-error-correction bit error rate (pre-FEC BER = 3.8 × 10−3) threshold.
PeakForce AFM Analysis Enhanced with Model Reduction Techniques
Authors: Chang Xuyang , Hallais Simon , Danas Kostas , Roux Stéphane
Published: May 2023

PeakForce quantitative nanomechanical AFM mode (PF-QNM) is a popular AFM technique designed to measure multiple mechanical features (e.g., adhesion, apparent modulus, etc.) simultaneously at the exact same spatial coordinates with a robust scanning frequency. This paper proposes compressing the initial high-dimensional dataset obtained from the PeakForce AFM mode into a subset of much lower dimensionality by a sequence of proper orthogonal decomposition (POD) reduction and subsequent machine learning on the low-dimensionality data. A substantial reduction in user dependency and subjectivity of the extracted results is obtained. The underlying parameters, or “state variables”, governing the mechanical response can be easily extracted from the latter using various machine learning techniques. Two samples are investigated to illustrate the proposed procedure (i) a polystyrene film with low-density polyethylene nano-pods and (ii) a PDMS film with carbon–iron particles. The heterogeneity of material, as well as the sharp variation in topography, make the segmentation challenging. Nonetheless, the underlying parameters describing the mechanical response naturally offer a compact representation allowing for a more straightforward interpretation of the high-dimensional force–indentation data in terms of the nature (and proportion) of phases, interfaces, or topography. Finally, those techniques come with a low processing time cost and do not require a prior mechanical model.
Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters
Authors: Alyami Lamia , Panda Deepak Kumar , Das Saptarshi
Published: May 2023

The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible–Exposed–Infected–Quarantined–Recovered–Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation.
The Validity of Sensors and Model in the Lane Change Control Process
Authors: Dębowski Andrzej , Faryński Jakub Jan , Żardecki Dariusz Piotr
Published: May 2023

The paper demonstrates the validity of sensors and the model in the algorithm for a lane change controller. The paper presents the systematic derivation of the chosen model from the ground up and the important role played by the sensors used in this system. The whole concept of the system on which the tests were carried out is presented step by step. Simulations were realised in the Matlab and Simulink environments. Preliminary tests were performed to confirm the need for the controller in a closed-loop system. On the other hand, sensitivity (the influence of noise and offset) studies showed the advantages and disadvantages of the developed algorithm. This allowed us to create a research path for future work with the aim of improving the operation of the proposed system.
Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data
Authors: Hölzke Fabian , Borstell Hagen , Golatowski Frank , Haubelt Christian
Published: May 2023

Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the application in real-world industrial contexts. In this paper, we present a method that produces an estimate of the current positioning error for each user stride. To accomplish this, we construct a virtual stride vector from Ultra-Wideband (UWB) position measurements. The virtual vectors are then compared to stride vectors from a foot-mounted Inertial Measurement Unit (IMU). Using these independent measurements, we estimate the current reliability of the UWB measurements. Positioning errors are mitigated through loosely coupled filtering of both vector types. We evaluate our method in three environments, showing that it improves positioning accuracy, especially in challenging conditions with obstructed line of sight and sparse UWB infrastructure. Additionally, we demonstrate the mitigation of simulated spoofing attacks on UWB positioning. Our findings indicate that positioning quality can be judged at runtime by comparing user strides reconstructed from UWB and IMU measurements. Our method is independent of situation- or environment-specific parameter tuning, and as such represents a promising approach for detecting both known and unknown positioning error states.
A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems
Authors: Xu Weiyan , Sun Jack , Cardell-Oliver Rachel , Mian Ajmal , Hong Jin B.
Published: May 2023

Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers’ privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost–benefit perspective while ensuring data privacy.
Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System
Authors: Zhou Haiyang , Zhao Yixin , Liu Yanzhong , Lu Sichao , An Xiang , Liu Qiang
Published: May 2023

Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%~6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates.
Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing
Authors: Xie Gang , Zhou Xincheng , Gao Jinchun
Published: May 2023

In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation results show that our ATTR algorithm can filter out a set of trusted users, eliminate the influence of malicious users, and improve the detection performance of the system.
Grazing Sheep Behaviour Recognition Based on Improved YOLOV5
Authors: Hu Tianci , Yan Ruirui , Jiang Chengxiang , Chand Nividita Varun , Bai Tao , Guo Leifeng , Qi Jingwei
Published: May 2023

Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model’s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model’s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep’s daily behaviour for precision livestock management, promoting modern husbandry development.
Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
Authors: Wei Ningke , Zhao Liang , Chen Dengfeng
Published: May 2023

An enhanced self-assembling network routing algorithm is proposed for the problem of weak connectivity of communication networks caused by factors such as movement or environmental interference in the construction and operation, and the maintenance of construction robot clusters. Firstly, the dynamic forwarding probability is calculated based on the contribution of nodes joining routing paths to network connectivity, and the robust connectivity of the network is achieved by introducing the connectivity feedback mechanism; secondly, the influence of link quality evaluation index Q balanced hop count, residual energy, and load on link stability is used to select appropriate neighbors for nodes as the subsequent hop nodes; finally, the dynamic characteristics of nodes are combined with the topology control technology to eliminate low-quality links and optimize the topology by link maintenance time prediction and to set robot node priority. The simulation results show that the proposed algorithm can guarantee a network connectivity rate above 97% under heavy load, reduce the end-to-end delay, and improve the network survival time, providing a theoretical basis for achieving stable and reliable interconnection between building robot nodes.
Bifunctional Adsorbents Based on Jarosites for Removal of Inorganic Micropollutants from Water
Authors: López-Martínez Arely , Khamkure Sasirot , Gamero-Melo Prócoro
Published: May 2023

This paper presents a novel family of jarosites with the molecular formula MFe3(SO4)2(OH)6·xH2O; M = Na, K, NH4 that have high efficiency in the adsorption of As(V) and Pb(II) dissolved in water. The jarosites have been prepared by conventional heating at temperatures close to 95 °C for 3 h. The synthesis method was improved and optimized to reduce the time and energy consumption. The improved conventional heating method allowed for the synthesis of Na− and K−jarosites with a yield of up to 97.8 wt.% at 105 and 150 °C, respectively, in 3 h. The Na−, K−, and NH4−jarosites were synthesized at 150 °C in 5 min via a microwave-assisted method, which yielded jarosite crystalline agglomerates with more uniform topography, shape, and size than the conventional method. Both methods allowed the selective synthesis of jarosites. Chemical decomposition of jarosites suspended in water occurred at a pH less than 2 and higher than 10 and temperatures up to 150 °C. In the solid state, the jarosites were thermally stable at least to 300 °C. The Na−jarosite presented a maximum adsorption capacity (Qmax) of 65.6 mg g−1 for As(V) and 94 mg g−1 for Pb(II). The jarosites are considered promising bifunctional adsorbents for the remediation of contaminated water due to their improved synthesis method, stability, and high adsorption capacity for ions of different natures.
A Novel Minimal-Cost Power Allocation Strategy for Fuel Cell Hybrid Buses Based on Deep Reinforcement Learning Algorithms
Authors: Li Kunang , Jia Chunchun , Han Xuefeng , He Hongwen
Published: May 2023

Energy management strategy (EMS) is critical for improving the economy of hybrid powertrains and the durability of energy sources. In this paper, a novel EMS based on a twin delayed deep deterministic policy gradient algorithm (TD3) is proposed for a fuel cell hybrid electric bus (FCHEB) to optimize the driving cost of the vehicle. First, a TD3-based energy management strategy is established to embed the limits of battery aging and fuel cell power variation into the strategic framework to fully exploit the economic potential of FCHEB. Second, the TD3-based EMS is compared and analyzed with the deep deterministic policy gradient algorithm (DDPG)-based EMS using real-world collected driving conditions as training data. The results show that the TD3-based EMS has 54.69% higher training efficiency, 36.82% higher learning ability, and 2.45% lower overall vehicle operating cost compared to the DDPG-based EMS, validating the effectiveness of the proposed strategy.
Trends and Challenges of Technology-Enhanced Learning in Geotechnical Engineering Education
Authors: Rehman Zia ur
Published: May 2023

The integration of technology and new tools in engineering education has created opportunities for the advancement of geotechnical engineering education (GEE). Technology-enhanced learning (TEL) has been implemented in GEE by many educators, and its impacts have been studied by different researchers. This review paper presents a comprehensive analysis of the state-of-the-art implementation of TEL in GEE based on published journal articles. This study adopts a systematic approach in examining the literature on TEL in GEE, identifies emerging trends and challenges, presents potential solutions, and outlines research gaps and recommendations. The literature indicates that, over the past couple of decades, the major trends of TEL in GEE have included computer-based simulations and computations, physical and centrifuge modeling, virtual laboratory and field experimentation, internet-based online resources, and virtual and augmented reality. The implementation of TEL in GEE can support students in effectively comprehending complex engineering concepts and augment their preparedness for the demands of professional practice. Furthermore, the integration of TEL with instructional design strategies involving problem-based learning, experiential learning, collaborative learning, and critical thinking cultivation is recognized as a favorable strategy. However, challenges such as a lack of resources and experience, optimization of pedagogies, alignment with course content, and quantification of TEL’s impact on GEE and student learning experiences remain to be addressed by continuing research and development in this field. In addition, an outcome-based education (OBE) theory-inspired quadruple framework is proposed in this study for the efficient implementation of TEL in GEE.
The Relationship between Concrete Strength and Classes of Resistance against Corrosion Induced by Carbonation: A Proposal for the Design of Extremely Durable Structures in Accordance with Eurocode 2
Authors: Gil-Martín Luisa María , Hdz-Gil Luisa , Molero Emilio , Hernández-Montes Enrique
Published: May 2023

The new Eurocode 2 provides valuable information on the required concrete cover to protect reinforcement against corrosion induced by carbonation, for two design service life values of 50 and 100 years. However, to design structures with an even longer service life and assess existing ones, additional tools are necessary. The ‘square root of time’ relationship is a well-established method for estimating the penetration of the carbonation front, making it useful for long-term design and assessment purposes. In this article, we propose a new function that adjusts the evolution of the carbonation front to the Eurocode 2 values. This function is a powerful tool for designing extremely durable structures and assessing existing ones. To demonstrate its effectiveness, we provide two examples of its application.
Exploring Major League Baseball Fans’ Climate Change Risk Perceptions and Adaptation Willingness
Authors: Murfree Jessica R.
Published: May 2023

Major League Baseball (MLB) is particularly vulnerable to climate change due to its season duration, geographic footprint, and largely outdoor nature. Therefore, the purposes of this study were to investigate whether U.S.-based MLB fans’ climate change skepticism and experiential processing influenced their climate change risk perceptions and adaptation willingness, and to determine if those relationships were further influenced by fans’ sport identification with MLB. A cross-sectional survey design tested the study’s purposes using a sample (n = 540) of self-identified MLB fans. Data were analyzed using structural equation modeling on the Mplus 8 statistical package to test the hypothesized model. The results indicated consistencies across low and highly identified MLB fans on their climate change risk perceptions and willingness to adapt, but revealed group differences between the factors influencing fans’ risk perceptions of climate change. The findings provide early empirical evidence to support the United Nations’ (UN) Sport for Climate Action Framework, and managerial implications regarding the nexus of climate change and sport consumer behavior research.
Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption
Authors: Tang Hongtao , Zhou Jiahao , Shao Yiping , Yang Zhixiong
Published: May 2023

A hybrid flow shop scheduling model with missing and re-entrant operations was designed to minimize the maximum completion time and the reduction in energy consumption. The proposed dual-population genetic algorithm was enhanced with a range of improvements, which include the design of a three-layer gene coding method, hierarchical crossover and mutation techniques, and the development of an adaptive operator that considered gene similarity and chromosome fitness values. The optimal and worst individuals were exchanged between the two subpopulations to improve the exploration ability of the algorithm. An orthogonal experiment was performed to obtain the optimal horizontal parameter set of the algorithm. Furthermore, an experiment was conducted to compare the proposed algorithm with a basic genetic algorithm, particle swarm optimization algorithm, and ant colony optimization, which were all performed on the same scale. The experimental results show that the fitness value of the proposed algorithm is above 15% stronger than the other 4 algorithms on a small scale, and was more than 10% stronger than the other 4 algorithms on a medium and large scale. Under the condition close to the actual scale, the results of ten repeated calculations showed that the proposed algorithm had higher robustness.
Grain-Size Analysis of Middle Cretaceous Sandstone Reservoirs, the Wasia Formation, Riyadh Province, Saudi Arabia
Authors: Khalil Rayan
Published: May 2023

Grain-size analysis is a significant descriptive method to determine and evaluate depositional environments and hydrodynamic conditions in addition to classifying sedimentary rocks. In this study, grain-size analysis was conducted using dry-sieving procedures on fourteen representative sandstone samples from the Wasia Formation, a thick water aquifer and a hydrocarbon reservoir. Hydrodynamic conditions and depositional environments were determined using bivariate plots, linear discriminate function (LDF), log probability, and Passega diagram. The results reveal that the lower outcrop section consists of coarse- to medium-grained sandstone with a majority being poorly sorted, while the upper section is made up of medium- to medium-well-sorted fine-grained sandstone units. The sediments have a unimodal distribution of 2∅ (all the lower section) and 3∅ (most of the upper section), while two beds have a bimodal of 2 and 3∅. The lower section has wide range skewness with mainly mesokurtic curves, while the upper section is near-symmetrical to coarse-skewed but mostly leptokurtic. Additionally, log probability plots and the Passega diagram show that the majority of the indicative sediments were transported via one to two saltation levels, while fine-grains were transported via suspension. The results of the LDF method are predominantly indicative of aeolian, marine, and fluvial environments.
An Analysis of Dynamics of Retaining Wall Supported Embankments: Towards More Sustainable Railway Designs
Authors: Feng Guishuai , Luo Qiang , Lyu Pengju , Connolly David P. , Wang Tengfei
Published: May 2023

Retaining walls are structures used to retain earth materials on a slope. Typically, they are designed for static loads, but for highway and railway infrastructures, vehicle-induced dynamic responses are also relevant. Therefore, retaining wall structures are often designed with a factor of safety that is higher than necessary, because it can be challenging to quantify the magnitude of expected dynamic stresses during the design stage. This unnecessary increase in material usage reduces the sustainability of the infrastructures. To improve railway retaining wall sustainability, this paper presents the results from a field monitoring campaign on a heavy-haul rail line with a retaining wall, studying the dynamics induced in response to 30-ton axle load trains running at speeds of between 5 km/h and 100 km/h. The site comprises an earth embankment supported by a gravity retaining wall, with accelerometers on the sleepers, roadbed surface, and retaining wall, velocity sensors on the roadbed, and strain gauges on the rail web to record wheel–rail forces. The vibration intensities collected from various locations are processed to explore the peak particle velocities, maximum transient vibration values, and one-third octave band spectrums. Two transfer functions define the vibration transmission characteristics and attenuation of vibration amplitude along the propagation path. The long-term dynamic stability of the track formation is studied using dynamic shear strain derived from the effective velocity. The peaks of observed contact forces and vibrations are statistically analyzed to assess the impact of train speed on the dynamic behavior of the infrastructure system. Next, a 3D numerical model expresses the maximum stress and displacements on the roadbed surface as a function of train speed. The model evaluates the earth pressures at rest and vehicle-induced additional earth pressures and horizontal wall movement. The investigation provides new insights into the behavior of railway track retaining walls under train loading, and the field data are freely available for other researchers to download. The findings could facilitate the design of more sustainable retaining walls in the future.
A Contingent Valuation-Based Method to Valuate Ecosystem Services for a Proactive Planning and Management of Cork Oak Forests in Sardinia (Italy)
Authors: La Riccia Luigi , Assumma Vanessa , Bottero Marta Carla , Dell’Anna Federico , Voghera Angioletta
Published: May 2023

The paper develops a model through a contingent valuation approach to support public authorities in the exploration and assessment of ecosystem services (ESs) generated by forest and woodlands (FOWLs). This approach is employed to the cork oak forests of the Sardinia region (Italy) due to their ability in the provision and regulation of cultural and recreational values to society. The paper describes the economic valuation of cultural ESs through the contingent valuation method (CVM) with the purpose to explore residents and tourists’ willingness to pay (WTP) preferences towards conservation, valorisation, and the management of Goceano’s cork oak forests in Sardinia. The approach may help retain suitable support for DMs, planners, technicians, and operators for a better understanding of the ESs’ role in policy decisions, leading FOWLs towards a learning process between the environment, human beings, and landscape to promote and develop a proactive landscape and forest planning and management within the region.
Influence of Cellulose on the Anoxic Treatment of Domestic Wastewater in Septic Tanks: Statistical Analysis of the Chemical and Physico-Chemical Parameters
Authors: Djordjevićová Denisa , Carnevale Miino Marco , Raček Jakub , Hlavínek Petr , Chorazy Tomáš , Rajaković-Ognjanović Vladana , Cvijetić Nada
Published: May 2023

Cellulose is a very common polymer in domestic wastewater (WW), representing a not negligible part of the organic substance contained in sewage. To date, many studies have highlighted the feasibility of reusing this compound in several ways (e.g., building sector, wastewater treatment, energy production, etc.) after its separation from domestic WW. However, studies about the impact of the absence of cellulose on the chemical and physico-chemical parameters of a biological process are still lacking. In this work, two pilot-scale plants were used to simulate an anoxic treatment of WW in septic tanks, with and without cellulose (CWW and NCWW, respectively), for three months. The results of the monitoring highlighted that T, pH, and electrical conductivity (EC) remained almost constants, in both cases. The Spearman correlation coefficients (SCC) for turbidity (TUR), total suspended solids (TSS), and color (COL) indicated a higher removal in the case of CWW (65%, 66%, and 56%, respectively). Organic substance and nitrogen forms showed a similar behavior with and without cellulose, but in the case of CWW, N-NH3 was highly negatively correlated with TUR (SCC: −0.54), TSS (−0.49), and COL (−0.39). A biological denitrification process was highlighted in both cases. Despite these differences, when statistically analyzing the trends of the chemical and physico-chemical parameters for CWW and NCWW, a significant difference due to the absence of cellulose was excluded. These results will be useful to the scientific community, as they exclude that the operational parameters of anoxic treatments and the effectiveness on pollutants removal can be affected in the case of preliminary cellulose separation from domestic WW for recovery/reuse purposes.
Analysis of the Influence Mechanism of New Urbanization on High-Quality Economic Development in Northeast China
Authors: Zhang Dongchao , Jiao Fangyi , Zheng Xiyue , Pang Jianing
Published: May 2023

This paper examines the effect of new urbanization on high-quality economic development in Northeast China, focusing on three aspects: urban factor aggregation, internal division of labor, and scale effect. A panel mediated and moderated effects model is used to analyze data from 34 cities between 2000 and 2020. The results show that new urbanization promotes high-quality economic development through the mediating variables of innovation, consumption, and investment, with a strong mediating effect observed for innovation and consumption and a relatively weaker effect for investment. Furthermore, the study finds that public services, ecological environment, and industrial structure upgrading have significant positive moderating effects on innovation and consumption mediation but a negative effect on investment. In promoting new urbanization, Northeast China should prioritize investment in innovative factors, expand domestic demand, upgrade consumption patterns, improve people’s livelihoods, enhance urban investment in social service functions, optimize investment to promote industrial upgrading and improve urban public services, ecological environment, and management efficiency.
Climate Change Impacts on Occupational Health and Safety of Façade Maintenance Workers: A Qualitative Study
Authors: Athauda Ransi Salika , Jayakodi Shashini , Asmone Ashan Senel , Conejos Sheila
Published: May 2023

At present, climate change is considered a considerable future threat due to its possible catastrophic impacts on humans, their properties, and also the environment. Therefore, most people and organizations have paid attention to this area. Thus, special consideration should be given to building operations, as buildings and building operations are capable of being impacted by various negative consequences of climate change. As façade-maintenance workers experience considerable climate change impacts as they perform their work for prolonged hours, at height, on the exterior of buildings, this study focuses on identifying the impacts of climate change on façade-access methods and façade-maintenance workers. Thus, in this study, a qualitative research approach was undertaken with an interview research design. A comprehensive literature review was conducted along with 12 semi-structured expert interviews selected through judgmental sampling. Software-aided thematic content analysis was carried out to analyze the collected data. The findings indicated that climate change could have significant impacts on building operations, as the attention paid to climate change adaptation by building operations is negligible in various countries, especially in developing countries. A significant impact was identified on façade-maintenance workers and façade-access methods caused by climate change, creating various risk factors for their occupational health and safety. Furthermore, the research methods that have been applied in this study are also capable of expanding to address various other probable operations. Accordingly, this research is exploring a new field of study that should be given more consideration by researchers due to its significant importance in scenarios that are experienced worldwide.