bluuebunny/crossref_metadata_2025 huggingface dataset:
Statewide Implementation of Salt Stockpile Inventory Using LiDAR Measurements: Case Study
Authors: Mahlberg Justin Anthony , Malackowski Haydn , Joseph Mina , Koshan Yerassyl , Manish Raja , DeLoach Zach , Habib Ayman , Bullock Darcy M.
Published: January 2024

The state of Indiana maintains approximately 120 salt storage facilities strategically distributed across the state for winter operations. In April 2023, those facilities contained approximately 217,000 tons of salt with an estimated value of USD 21 million. Accurate inventories at each facility during the winter season are important for scheduling re-supply so the facilities do not run out of salt. Inventories are also important at the end of the season for restocking to provide balanced inventories. This paper describes the implementation of a portable pole-mounted LiDAR system to measure salt stockpile inventory at 120 salt storage facilities in Indiana. Using two INDOT staff members, the end-of-season inventory took 9 working days, with volumetric inventories provided within 24 h of data collection. To provide an independent evaluation of the methodologies, the Hovermap ST backpack was used at selected facilities to provide control volumes. This system has a range of 100 m and an accuracy of ±3 cm, which reduces the occlusion to less than 8%. The pre-season facility capacity ranged from 0% to 100%, with an average of 66% full across all facilities. The post-season facility percentage ranged from 3% to 100%, with an average of 70% full. In addition, permanent roof-mounted LiDAR systems were deployed at two facilities to evaluate the effectiveness of monitoring salt stockpile inventories during winter operation activities. Plans are now underway to install fixed LiDAR systems at 15 additional facilities for the 2023–2024 winter season.
Mapping and Analyzing the Spatiotemporal Patterns and Drivers of Multiple Ecosystem Services: A Case Study in the Yangtze and Yellow River Basins
Authors: Yu Yuanhe , Xiao Zhouxuan , Bruzzone Lorenzo , Deng Huan
Published: January 2024

The Yangtze River Basin (YZRB) and the Yellow River Basin (YRB), which are crucial for ecology and economy in China, face growing challenges to ecosystem service (ES) functions due to global population growth, urbanization, and climate change. This study assessed the spatiotemporal dynamics of ESs in the YZRB and the YRB between 2001 and 2021, comprehensively encompassing essential aspects such as water yield (WY), carbon sequestration (CS), soil conservation (SC), and habitat quality (HQ) while also analyzing the trade-offs and synergies among these ESs at the grid cells. The GeoDetector was employed to ascertain individual or interactive effects of natural and anthropogenic factors on these ESs and their trade-offs/synergies. The results showed that (1) from 2001 to 2021, the four ESs exhibited significant spatial disparities in the distribution within two basins, with the overall trend of ESs mainly increasing. YZRB consistently exhibited substantially higher ES values than the YRB. (2) Complex trade-offs and synergies among these ESs were apparent in both basins, characterized by distinct spatial heterogeneity. The spatial relationships of WY–CS, WY–SC, CS–SC, and CS–HQ were mainly synergistic. (3) Precipitation, potential evapotranspiration, elevation, land use and land cover (LULC), and slope influenced ESs in both basins. Notably, interactive factors, particularly the interactions involving LULC and other factors, demonstrated more robust explanatory power for ESs and their trade-offs/synergies than individual drivers. These findings significantly affect the refined ecosystem management and sustainable development decision-making in large rivers or regions.
Multiscale Spatiotemporal Variations of GNSS-Derived Precipitable Water Vapor over Yunnan
Authors: Wang Minghua , Lv Zhuochen , Wu Weiwei , Li Du , Zhang Rui , Sun Chengzhi
Published: January 2024

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations’ heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.
A Novel Approach for Instantaneous Waterline Extraction for Tidal Flats
Authors: Yang Hua , Chen Ming , Xi Xiaotao , Wang Yingxi
Published: January 2024

For many remote sensing applications, the instantaneous waterline on the image is critical boundary information to separate land and water and for other purposes. Accurate waterline extraction from satellite images is a desirable feature in such applications. Due to the complex topography of low tidal flats and their indistinct spatial and spectral characteristics on satellite imagery, the waterline extraction for tidal flats (especially at low tides) from remote sensing images has always been a technically challenging problem. We developed a novel method to extract waterline from satellite images, assuming that the waterline’s elevation is level. This paper explores the utilization of bathymetry during waterline extraction and presents a novel approach to tackle the waterline extraction issue, especially for low tidal flats, using remote sensing images at mid/high tide, when most of the tidal flat area is filled with seawater. Repeated optical satellite images are easily accessible in the current days; the proposed approach first generates the bathymetry map using the mid/high-tide satellite image, and then the initial waterline is extracted using traditional methods from the low-tide satellite image; the isobath (depth contour lines of bathymetry), which corresponds to the initial waterline is robustly estimated, and finally an area-based optimization algorithm is proposed and applied to both isobath and initial waterline to obtain the final optimized waterline. A series of experiments using Sentinel-2 multispectral images are conducted on Jibei Island of Penghu Archipelago and Chongming Island to demonstrate this proposed strategy. The results from the proposed approach are compared with the Normalized Difference Water Index (NDWI) and Support Vector Machine (SVM) methods. The results indicate that more accurate waterlines can be extracted using the proposed approach, and it is very suitable for waterline extraction for tidal flats, especially at low tides.
Unsupervised Joint Contrastive Learning for Aerial Person Re-Identification and Remote Sensing Image Classification
Authors: Zhang Guoqing , Li Jiqiang , Ye Zhonglin
Published: January 2024

Unsupervised person re-identification (Re-ID) aims to match the query image of a person with images in the gallery without the use of supervision labels. Most existing methods usually generate pseudo-labels through clustering algorithms for contrastive learning, which inevitably results in noisy labels assigned to samples. In addition, methods that only apply contrastive learning at the clustering level fail to fully consider instance-level relationships between instances. Motivated by this, we propose a joint contrastive learning (JCL) framework for unsupervised person Re-ID. Our proposed method involves creating two memory banks to store features of cluster centroids and instances and applies cluster and instance-level contrastive learning, respectively, to jointly optimize the neural networks. The cluster-level contrastive loss is used to promote feature compactness within the same cluster and reinforce identity similarity. The instance-level contrastive loss is used to distinguish easily confused samples. In addition, we use a WaveBlock attention module (WAM), which can continuously wave feature map blocks and introduce attention mechanisms to produce more robust feature representations of a person without considerable information loss. Furthermore, we enhance the quality of our clustering by leveraging camera label information to eliminate clusters containing single camera captures. Extensive experimental results on two widely used person Re-ID datasets verify the effectiveness of our JCL method. Meanwhile, we also used two remote sensing datasets to demonstrate the generalizability of our method.
Intelligent Recognition of Coastal Outfall Drainage Based on Sentinel-2/MSI Imagery
Authors: Li Hongzhe , He Xianqiang , Bai Yan , Gong Fang , Li Teng , Wang Difeng
Published: January 2024

In this study, we developed an innovative and self-supervised pretraining approach using Sentinel-2/MSI satellite imagery specifically designed for the intelligent identification of drainage at sea discharge outlets. By integrating the geographical information from remote sensing images into our proposed methodology, we surpassed the classification accuracy of conventional models, such as MoCo (momentum contrast) and BYOL (bootstrap your own latent). Using Sentinel-2/MSI remote sensing imagery, we developed our model through an unsupervised dataset comprising 25,600 images. The model was further refined using a supervised dataset composed of 1100 images. After supervised fine-tuning, the resulting framework yielded an adept model that was capable of classifying outfall drainage with an accuracy rate of 90.54%, facilitating extensive outfall monitoring. A series of ablation experiments affirmed the effectiveness of our enhancement of the training framework, showing a 10.81% improvement in accuracy compared to traditional models. Furthermore, the authenticity of the learned features was further validated using visualization techniques. This study contributes an efficient approach to large-scale monitoring of coastal outfalls, with implications for augmenting environmental protection measures and reducing manual inspection efforts.
Inter-Calibration of Passive Microwave Satellite Brightness Temperature Observations between FY-3D/MWRI and GCOM-W1/AMSR2
Authors: Xu Zuomin , Sun Ruijing , Wu Shuang , Shao Jiali , Chen Jie
Published: January 2024

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets.
The Impact of Satellite Soil Moisture Data Assimilation on the Hydrological Modeling of SWAT in a Highly Disturbed Catchment
Authors: Liu Yongwei , Cui Wei , Ling Zhe , Fan Xingwang , Dong Jianzhi , Luan Chengmei , Wang Rong , Wang Wen , Liu Yuanbo
Published: January 2024

The potential of satellite soil moisture (SM) in improving hydrological modeling has been addressed in synthetic experiments, but it is less explored in real data cases. Here, we investigate the added value of Soil Moisture and Passive (SMAP) and Advanced Scatterometer (ASCAT) SM data to distributed hydrological modeling with the soil and water assessment tool (SWAT) in a highly human disturbed catchment (126, 486 km2) featuring a network of SM and streamflow observations. The investigation is based on the ensemble Kalman filter (EnKF) considering SM errors from satellite data using the triple collocation. The assimilation of SMAP and ASCAT SM improved the surface (0–10 cm) and rootzone (10–30 cm) SM at >70% and > 50% stations of the basin, respectively. However, the assimilation effects on distributed streamflow simulation of the basin are un-significant and not robust. SM assimilation improved the simulated streamflow at two upstream stations, while it deteriorated the streamflow at the remaining stations. This can be largely attributed to the poor vertical soil water coupling of SWAT, suboptimal model parameters, satellite SM data quality, humid climate, and human disturbance to rainfall-runoff processes. This study offers strong evidence of integrating satellite SM into hydrological modeling in improving SM estimation and provides implications for achieving the added value of remotely sensed SM in streamflow improvement.
Learning SAR-Optical Cross Modal Features for Land Cover Classification
Authors: Quan Yujun , Zhang Rongrong , Li Jian , Ji Song , Guo Hengliang , Yu Anzhu
Published: January 2024

Synthetic aperture radar (SAR) and optical images provide highly complementary ground information. The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data remains a challenge for current research due to significant disparities in imaging mechanisms from diverse sources. Our goal was to bridge the significant gaps between optical and SAR images by developing a dual-input model that utilizes image-level fusion. To improve most existing state-of-the-art image fusion methods, which often assign equal weights to multiple modalities, we employed the principal component analysis (PCA) transform approach. Subsequently, we performed feature-level fusion on shallow feature maps, which retain rich geometric information. We also incorporated a channel attention module to highlight channels rich in features and suppress irrelevant information. This step is crucial due to the substantial similarity between SAR and optical images in shallow layers such as geometric features. In summary, we propose a generic multimodal fusion strategy that can be attached to most encoding–decoding structures for feature classification tasks, designed with two inputs. One input is the optical image, and the other is the three-band fusion data obtained by combining the PCA component of the optical image with the SAR. Our feature-level fusion method effectively integrates multimodal data. The efficiency of our approach was validated using various public datasets, and the results showed significant improvements when applied to several land cover classification models.
Fast Magnetization Vector Inversion Method with Undulating Observation Surface in Spherical Coordinate for Revealing Lunar Weak Magnetic Anomaly Feature
Authors: Ma Guoqing , Meng Lingwei , Li Lili
Published: January 2024

The three-dimensional magnetic vector structure (magnetization intensity and direction) of the planet can be effectively used to analyze the characteristics of its formation and operation. However, the quick acquisition of a large region of the magnetic vector structure of the planet with bigger observation surfaces undulation is hard and indispensable. We firstly proposed a fast magnetization vector inversion method for the inversion of a magnetic anomaly with the undulating observation surfaces in the spherical coordinate system, which first transforms the data to a plane when the data are distributed on a surface. Then, it uses a block-Toeplitz-Toeplitz-block (BTTB)-FFT to achieve fast inversion with the constraint that the magnetization intensities of the grids between the transformed observation surfaces and the terrain are zero. In addition, Gramian constraint term is used to reduce the ambiguity of the magnetic vector inversion. The theoretical model tests show that the proposed method can effectively improve the computational efficiency by 23 times in the 60 × 60 × 10 grid division compared to the conventional inversion method, and the accuracy of the two computation methods is comparable. The root-mean-square error of the magnetization intensity is only 0.017, and the angle error is within 1°. The magnetization vector structure shows that the largest crater diameter does not exceed 340 km in the Mare Australe region, the amplitude of the magnetic anomaly is much higher than the current meteorite impact simulation results, and the depth of the magnetic source is less than 10 km, which cannot be explained by the impact simulation experiments. In addition, the magnetization directions of adjacent sources differ by 122° (or 238°), and the high-frequency dynamics of the Moon as well as the short-lived dynamics may be responsible for this phenomenon. The magnetization directions of the three adjacent sources in the Mare Crisium region are close to each other and differ in depth with different cooling times, making it difficult to record the transient fields produced by meteorite impacts. In addition to the above characteristics, the magnetization direction of the magnetic sources in both regions is uniformly distributed without reflecting the dispersion of the magnetization direction of the meteorite impact magnetic field. Therefore, it can be inferred that the magnetic anomalies in these two regions are related to the generator hypothesis.
Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles
Authors: Wang Hai , Tao Qing , Zhang Xiaodong
Published: January 2024

Human–machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects’ dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.
Spectral Investigations of Fluorescence Tracers in Automotive and Aviation Fuels under Cryogenic Conditions
Authors: Koegl Matthias , Vogler Jonas , Zigan Lars
Published: January 2024

This study investigated spectral laser-induced fluorescence signals of dyes in fuels for automotive and aerospace applications under low temperatures and cryogenic conditions down to 183 K. For this purpose, a fluorescence chamber was developed based on cooling with liquid nitrogen. The design enabled a minimal inner chamber temperature of 153 K. Furthermore, the applicability of two-color LIF for liquid thermometry was evaluated under these conditions. The temperature determination was based on the temperature-sensitive fluorescence intensity ratio of the special dyes doped into the fuels determined in suitable spectral regions, which represented common bandpass filters. For this purpose, the fluorescence signals of the dye doped into the gasoline and jet fuel surrogate isooctane were tested as well as blends of isooctane and the ethanol biofuels E20 (comprising 80 vol.% isooctane and 20 vol.% ethanol), E40, and E100. Additionally, a realistic multi-component fuel Jet A-1 mixed with a suitable fluorescence dye was investigated. E100 was doped with Eosin-Y, and the remaining fuels were doped with Nile red. Temperature-dependent spectral LIF intensities were recorded in the range of 183 K–293 K, which simulate extreme environments for aerospace and automotive applications. Frozen fuel–dye mixtures cause significant extinction effects and prevent sufficient signal detection at low and cryogenic temperatures, defining the detection limit. A temperature decrease led to a spectral shift in the emission peaks of E100 doped with Eosin-Y toward shorter wavelengths, while the spectra of mixtures doped with Nile red were shifted toward longer wavelengths. The suggested bandpass filters produced the temperature-sensitive intensity ratio (the average over the temperature interval) of the dyes with the largest sensitivity for Jet A-1 (5.2%/K), followed by E100 (4.95%/K), E40 (4.07%/K), E20 (3.23%/K), and isooctane (3.07%/K), even at cryogenic temperatures.
Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach
Authors: Abbas Mohammed , Chafouk Houcine , Ardjoun Sid Ahmed El Mehdi
Published: January 2024

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.
Investigating the Effect of Social Media on Dependency and Communication Practices in Emirati Society
Authors: Youssef Enaam , Medhat Mervat , Alserkal Maryam
Published: January 2024

In the evolving landscape of information dissemination, the importance of social media has become crucial. This is especially apparent in the aftermath of the COVID-19 pandemic, where we observed social media integration into different parts of daily life, yielding myriad impacts. The present study investigates the effects of social media on the communication dependency of Emirati individuals who engage with these platforms, further leading to communication with friends, family, and professional connections in the post-pandemic era. Based on the media dependency theory, this research gathered data from 385 respondents that were further analyzed by using Partial Least Square-Structural Equation modeling (PLS-SEM). Results showed that Emirati users widely rely on social media for communication and interactivity. It is further found that social media use is significantly linked to communication with friends and families among the study respondents. Finally, the use of social media for professional communication also remained significantly related, indicating social media as a potential source of communication among Emiratis in the post-pandemic era. Thus, the broader agreement remained towards the role of social media as an agent to sustain socialization even after the disease outbreak. It is concluded that as we progress, both individuals and organizations must adopt the potential benefits of these platforms while also effectively managing the challenges they bring. Improving digital literacy and adaptability will be crucial for effectively navigating this growing communication environment.
Effects of Parental Workplace Discrimination on Sickness Presenteeism
Authors: Gerich Joachim , Beham-Rabanser Martina
Published: January 2024

This paper analyzes the association between experienced and observed parental workplace discrimination and sickness presenteeism. Following stress theoretical approaches and reactance theory, we expected that both experienced and observed parental discrimination of others at the workplace would lead to a reactance behavior and could increase sickness presenteeism, especially in those individuals who deny arguments of justification. Based on survey data from employees aged between 20 and 45 years (n = 347), we confirmed experienced discrimination as a double risk factor that goes along with increased sickness, as well as an increased sickness presence propensity. Although observed discrimination against others was unrelated to sickness, it was similarly associated with increased presenteeism. For respondents with their own children, the association between experienced discrimination and presenteeism was amplified in those who disagree with economic justifications of discrimination. The relationship between presenteeism and observed discrimination in childless respondents was amplified in those who appraise discrimination as unfair. In accordance with a stress theoretical approach, we confirm negative health effects of parental discrimination. In accordance with reactance theory, it is concluded that discrimination encourages workers’ presenteeism in the sense of a self-endangering behavior to counter inappropriate stereotypes held against them.
Repeated Solid Digestate Amendment Increases Denitrifying Enzyme Activity in an Acid Clayey Soil
Authors: Badagliacca Giuseppe , Lo Presti Emilio , Gelsomino Antonio , Monti Michele
Published: January 2024

The use of organic fertilizers to replace chemically synthesized fertilizers has assumed an important role in managing plant nutrition and soil fertility. The various organic matrices currently available as organic byproducts and digestates are relatively abundant and have shown promising effects in terms of plant-available nutrients. However, like mineral fertilizers, organic fertilizers must be carefully managed to avoid negative effects on the environment, especially when they are repeatedly applied over time. The aim of the present study was to assess the effect of the single (DIG) and repeated application (DIGP) of solid anaerobic digestates compared to an unamended control (CTR) on the denitrifying enzymatic activity (DEA), which is responsible for nitrous oxide emissions into the atmosphere, and some related soil properties, such as total soluble nitrogen (TSN), nitrate (NO3−-N), extractable carbon (Cextr), microbial biomass carbon (MBC), and basal respiration (Rbas), for a period of ~3 months after application. The application of solid anaerobic digestates progressively boosts N and C concentrations in the soil, with the degree of enhancement directly correlated with the frequency of application over the sampling period. Depending on the textural properties of soils, there was a notable rise in denitrification enzyme activity (DEA), particularly during the DIGP treatment, suggesting that clay soils are highly susceptible to denitrification under suitable conditions. The results of this study recommend the careful management of soils subjected to repeated digestate amendment to prevent the occurrence of conditions conducive to denitrification and the promotion of N2O emissions.
Influence of Snowy and Icy Weather on Vehicle Sideslip and Rollover: A Simulation Approach
Authors: Wang Qingzhou , Zhao Yaxuan , Li Lujia , Kong Liying , Si Wenjing
Published: January 2024

Many northern hemisphere countries have experienced exceptionally heavy snow, blizzards, and cold snaps in recent years, causing considerable public concern about the high crash rate and safety issues in road traffic. This study used the CarSim dynamics simulation to recreate several vehicle driving scenarios in snow and ice conditions. To explore the influence of speed, curve radius, and road adhesion coefficient on vehicle sideslip and rollover, four lateral stability evaluation indicators, namely lateral offset, lateral acceleration, yaw rate, and roll angle, are chosen. Unfavorable combinations of these factors result in vehicle deviation from their intended trajectory and dramatically increase the likelihood of sideslip and rollover incidents. In particular, road adhesion coefficients ranging from 0.10 to 0.20 lead first to sideslip, while coefficients of 0.21 to 0.35 lead straight to rollover. Additionally, in the initial segment of the curve, cars are more susceptible to lateral instability. Curve radius has the greatest influence on sideslip when the three influencing factors are combined, while speed is the key component affecting rollover incidents. Smaller curve radii and higher speeds are major factors in such incidents. The results are helpful for proper road alignment parameter selection and dynamic speed-limit measures. This can provide a theoretical basis for traffic management departments to take targeted measures, which is of great significance to improving road traffic safety in snowy and icy weather.
Multiphase Equilibrium Relationships between Copper Matte and CaO-Al2O3-Bearing Iron Silicate Slags in Combined Smelting of WEEE and Copper Concentrates
Authors: Tian Miao , Wang Qiongqiong , Wang Songsong , Wan Xingbang , Wang Qinmeng , Guo Xueyi
Published: January 2024

Waste electrical and electronic equipment (WEEE) contains various valuable metals, making it a potential secondary resource for sustainable metal usage. Pyrometallurgical smelting is an efficient technique to recycle WEEE by extracting precious metals into copper matte and removing impurities into slags. The impact of WEEE impurities such as CaO and Al2O3 on the phase compositions of the smelting products attracts great attention for industrial metal recovery. This study clarified the impact of CaO and Al2O3 on the equilibrium phase compositions of copper matte and SiO2-saturated FeOx-SiO2-Al2O3-CaO slags. The high-temperature smelting experiments were taken at a controlled p(SO2) of 0.1 atm and 1300 °C, followed by quenching and electron probe microanalysis. The results showed that the copper and sulfur in the smelting system were highly deported into copper matte, and their distribution in matte was enhanced by increasing CaO and Al2O3 concentrations introduced by WEEE. The chemical copper dissolution in slags increased with increasing matte grade but decreased by adding CaO and Al2O3. The iron was preferentially concentrated in slags, and higher matte grades improved the iron distribution in slags. The current experimental results enrich fundamental thermodynamic data and help optimize WEEE smelting operations for efficient recovery of valuable metals.
Assessing User Acceptance of Automated Vehicles as a Precondition for Their Contribution to a More Sustainable Mobility
Authors: Martínez-Díaz Margarita , Montes Carbó Maximilià-Miquel
Published: January 2024

Autonomous vehicles (AVs) have been proposed as vectors for more sustainable mobility, the adjective “sustainable” being understood as the union of usefulness for society (i.e., efficiency), environmental friendliness, and economic viability. The realization of social usefulness implies a primary condition, which is AVs’ adoption on the part of the population. This paper is aimed at identifying factors contributing to society’s willingness to shift to two different AV implementations: as private vehicles or as the basis for public transportation systems. To this end, data collected from a survey were analyzed using exploratory data analysis, exploratory factor analysis, and ordinal regression. Safety-related variables as well as the a priori appeal of AVs turned out to be the most influential for the adoption of both solutions. Therefore, informative campaigns focusing on the safety benefits of AVs could boost the modal shift. Further research starting from these hypotheses and accounting for the limitations of the sample used could help to resolve remaining doubts.
Parametric Study and Optimization of Hydrogen Production Systems Based on Solar/Wind Hybrid Renewable Energies: A Case Study in Kuqa, China
Authors: Yang Tianqi , Yan Xianglin , Cai Wenchao , Luo Hao , Xu Nianfeng , Tong Liang , Yan Fei , Chahine Richard , Xiao Jinsheng
Published: January 2024

Based on the concept of sustainable development, to promote the development and application of renewable energy and enhance the capacity of renewable energy consumption, this paper studies the design and optimization of renewable energy hydrogen production systems. For this paper, six different scenarios for grid-connected and off-grid renewable energy hydrogen production systems were designed and analyzed economically and technically, and the optimal grid-connected and off-grid systems were selected. Subsequently, the optimal system solution was optimized by analyzing the impact of the load data and component capacity on the grid dependency of the grid-connected hydrogen production system and the excess power rate of the off-grid hydrogen production system. Based on the simulation results, the most matched load data and component capacity of different systems after optimization were determined. The grid-supplied power of the optimized grid-connected hydrogen production system decreased by 3347 kWh, and the excess power rate of the off-grid hydrogen production system decreased from 38.6% to 10.3%, resulting in a significant improvement in the technical and economic performance of the system.
Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models
Authors: Che Guanghui , Zhou Daocheng , Wang Rui , Zhou Lei , Zhang Hongfu , Yu Sheng
Published: January 2024

In recent years, the energy crisis has become increasingly severe, and global attention has shifted towards the development and utilization of wind energy. The establishment of wind farms is gradually expanding to encompass forested regions. This paper aims to create a Weather Research and Forecasting (WRF) model suitable for simulating wind fields in forested terrains, combined with a long short-term time (LSTM) neural network enhanced with attention mechanisms. The simulation focuses on capturing wind characteristics at various heights, short-term wind speed prediction, and wind energy assessment in forested areas. The low-altitude observational data are obtained from the flux tower within the study area, while high-altitude data are collected using mobile radar. The research findings indicate that the WRF simulations using the YSU boundary layer scheme and MM5 surface layer scheme are applicable to forested terrains. The LSTM model with attention mechanisms exhibits low prediction errors for short-term wind speeds at different heights. Furthermore, based on the WRF simulation results, a wind energy assessment is conducted for the study area, demonstrating abundant wind energy resources at the 150 m height in forested regions. This provides valuable support for the site selection in wind farm development.
Sustainability in the Manufacturing of Eco-Friendly Aluminum Matrix Composite Materials
Authors: Wąsik Anna , Leszczyńska-Madej Beata , Madej Marcin
Published: January 2024

The purpose of this work was to consolidate the eco-friendly Al–SiC composites prepared with various weight fractions of ceramic particles (0; 2.5; 5; 10; 15 wt.% SiC) in the energy-saving sintering process under vacuum and in a nitrogen atmosphere at 600 °C. The density of the manufactured composites was determined using Archimedes’ method. The mechanical properties and strength characteristics of the metal–ceramic interface were measured using three-point flexural and uniaxial compression tests, as well as the Brinell hardness measurement. The tribological properties were evaluated by determining the coefficients of friction and weight losses of the tested materials and identifying the tribological wear mechanisms. Advanced microstructural observation methods, such as scanning electron microscopy (SEM) and transmission electron microscopy (TEM), were used to analyze the microstructure of the composites in detail, including the identification of the phase composition using X-ray analysis methods. Low-cost composites with a porosity not exceeding 7% were successfully produced via energy-saving production routes. Simultaneously, owing to the formation of aluminum nitrides during sintering in a nitrogen atmosphere, these composites exhibited mechanical and tribological properties superior to those of materials sintered under vacuum.
A Spatial Multicriteria Analysis for a Regional Assessment of Eligible Areas for Sustainable Agrivoltaic Systems in Italy
Authors: Fattoruso Grazia , Toscano Domenico , Venturo Andrea , Scognamiglio Alessandra , Fabricino Massimiliano , Di Francia Girolamo
Published: January 2024

Agrivoltaics’ share of renewable generation is relevant for countries to achieve their energy transition targets. Agrivoltaics is the dual and synergistic use of the land by agricultural crop production and photovoltaic (PV) systems. For their development around a country, a fundamental step is to determine which areas are suitable. This research work has developed a methodological framework for a reliable agrivoltaic land eligibility analysis at regional scale based on a spatial multicriteria analysis (i.e., GIS/MCDM-AHP). The challenging step has been to select a set of relevant criteria, also based on experts’ knowledge, able to capture the factors that can affect both the solar PV potential and agriculture-oriented factors. On the basis of these criteria, properly weighted, a 30 m-resolution land eligibility map for agrivoltaic systems has been generated for the NUTS-2 regions of Italy. We have found that Italy has an eligible area of 10.7 million hectares and a capacity potential of 6435 GW, assuming an installed power density of 0.6 MW/ha. Thus, a land coverage of only 1.24% of this area would allow to reach the national 80 GW target of new renewable capacity to achieve the country’s decarbonisation and energy transition objectives by 2030. The potential of installed capacity results at GW scale for the main land categories of arable land and permanent crops if they are just covered by only 5% with agrivoltaic systems. Thus, the impact of agrivoltaic systems development on land occupation can be considered marginal especially in relation to benefits generated for both the energy and agricultural sectors. Such a method is essentially targeted to regional authorities for planning the sustainable development of agrivoltaic systems at the local level.
Sustainable Development Goals and Gender Equality: A Social Design Approach on Gender-Based Violence
Authors: Lima Raquel , Guedes Graça
Published: January 2024

Gender equality is a central human aspect of the Sustainable Development Goals. Among its multiple and complex issues, this research highlights gender-based violence as a domain that affects women’s empowerment and the guarantees of an effective equality on numerous levels. To address such a complex structure, which perpetuates inequalities between men and women, generating multiple effects and jeopardising social changes, social design can provide contributions on cultural and social levels. To achieve social systemic changes, one needs to activate profound cultural transformations. Thus, how can we change culture without rejecting the need to empower women and promote equality? The Montréal Design Declaration (2017) recognised social design’s potential to achieve the Sustainable Development Goals (SDG), to contribute to global challenges, and to accept a calling for stakeholders’ integration and agency promotion. This review explores how social design can provide contributions with regard to SDG5 and gender-based violence, presenting relevant domains that actively contribute to cultural transformation to address interventions in this systemic phenomenon.
Classroom Emotion Monitoring Based on Image Processing
Authors: Llurba Cèlia , Fretes Gabriela , Palau Ramon
Published: January 2024

One challenge of teaching and learning the lack of information during these processes, including information about students’ emotions. Emotions play a role in learning and processing information, impacting accurate comprehension. Furthermore, emotions affect students’ academic engagement and performance. Consideration of students’ emotions, and therefore their well-being, contributes to building a more sustainable society. A new way of obtaining such information is by monitoring students’ facial emotions. Accordingly, the purpose of this study was to explore whether the use of such advanced technologies can assist the teaching–learning process while ensuring the emotional well-being of secondary school students. A model of Emotional Recognition (ER) was designed for use in a classroom. The model employs a custom code, recorded videos, and images to identify faces, follow action units (AUs), and classify the students’ emotions displayed on screen. We then analysed the classified emotions according to the academic year, subject, and moment in the lesson. The results revealed a range of emotions in the classroom, both pleasant and unpleasant. We observed significant variations in the presence of certain emotions based on the beginning or end of the class, subject, and academic year, although no clear patterns emerged. Our discussion focuses on the relationship between emotions, academic performance, and sustainability. We recommend that future research prioritise the study of how teachers can use ER-based tools to improve both the well-being and performance of students.
Biosorption of Hexavalent Chromium by Freshwater Microalgae Craticula subminuscula from Aqueous Solutions
Authors: Sbihi Karim , Elhamji Sara , Lghoul Siham , Aziz Khalid , El Maallem Abdelali , Mabrouki Jamal , El-Sheekh Mostafa , Aziz Faissal
Published: January 2024

Recently, microalgae have tended to be used as a biological treatment for wastewater decontamination. The present study aimed to investigate the Cr(VI) removal using the freshwater microalgae ‘Craticula subminuscula’ and their biobased adsorbant, isolated from a Moroccan river in the High Atlas Mountain. The optimum operational conditions for maximum Cr(VI) biosorption by the biobased adsorbent form (95.32%) were determined at (pH = 1.09, adsorbent dose = 10.91 mg L−1, and treatment duration = 129.47 min) using response surface methodology (RSM). Under those optimal conditions, the biosorption process of Cr(VI) by C. subminuscula is endothermic, spontaneous and follows Langmuir and a pseudo-second-order model with a constant rate; the theoretical and experimental biosorption capacity of 0.0004 g/mg/min was 289.01 mg g−1 and 277.57 mg g−1, respectively. Fourier Transform Infrared Spectroscopy (FTIR) analyses of the biomass and scanning electron microscopy (SEM) revealed that the principal mechanism to remove Cr(VI) by C. subminuscula was the affinity of Cr(VI) by the cell walls of microalgae. Thus, the positive results of desorption cycles promise increased potential utilization of these algae in continuous systems within industrial processes. The findings contribute valuable insights into the effectiveness of C. subminuscula as a biobased remediation agent for Cr(VI) in wastewater treatment.
Change in Sustainable Waste Management Behaviour in Oil Palm Community: Application of the Theory of Planned Behaviour
Authors: Sapawi Safraa , Ahmad Albattat , Valeri Marco , Azman Norhidayah
Published: January 2024

The inability to effectively manage waste has evolved into one of the most significant challenges faced in oil palm plantations, and this failure has had an impact on the operations within the plantation. This study examines how attitudes (ATT), subjective norms (SN), and perceived behavioural control (PBC) affect both behavioural intention (BI) and sustainable waste management behaviour (SWMB) in the palm oil mills of Sabah, Malaysia. The theory of planned behaviour (TPB) serves as the primary theory in this research, which uses a conceptual framework. Distributed questionnaires to the top management of 133 oil palm mills were used to gather the data, and then they were processed using Smart-PLS V4. This research found out that SN and PBC have an influence on individuals’ BI and SWMB adaptations in Sabah’s oil palm mills. The SN was shown to have the strongest association between the SWMB and BI. However, attitudes resulted in no impact on either the intention to engage in sustainable waste management behaviour or on the actions themselves. Ultimately, the findings of this study will give a framework for the governance of palm oil waste in Malaysia by contributing to the development of a legislative and regulatory framework in this area that is more effective.