bluuebunny/crossref_metadata_2025 huggingface dataset:
A Siamese Network with a Multiscale Window-Based Transformer via an Adaptive Fusion Strategy for High-Resolution Remote Sensing Image Change Detection
Authors: Tao Chao , Kuang Dongsheng , Wu Kai , Zhao Xiaomei , Zhao Chunyan , Du Xin , Zhang Yunsheng
Published: May 2023

Remote sensing image change detection (RS-CD) has made impressive progress with the help of deep learning techniques. Small object change detection (SoCD) still faces many challenges. On the one hand, when the scale of changing objects varies greatly, deep learning models with overall accuracy as the optimization goal tend to focus on large object changes and ignore small object changes to some extent. On the other hand, the RS-CD model based on deep convolutional networks needs to perform multiple spatial pooling operations on the feature map to obtain deep semantic features, which leads to the loss of small object feature-level information in the local space. Therefore, we propose a Siamese transformer change detection network with a multiscale window via an adaptive fusion strategy (SWaF-Trans). To solve the problem of ignoring small object changes, we compute self-attention in windows of different scales to model changing objects at the corresponding scales and establish semantic information links through a moving window mechanism to capture more comprehensive small object features in small-scale windows, thereby enhancing the feature representation of multiscale objects. To fuse multiscale features and alleviate the problem of small object feature information loss, we propose a channel-related fusion mechanism to model the global correlation between channels for display and adaptively adjust the fusion weights of channels to enable the network to capture more discriminative features of interest and reduce small object feature information loss. Experiments on the CDD and WHU-CD datasets show that SWaF-Trans exceeds eight advanced baseline methods, with absolute F1 scores as high as 97.10% and 93.90%, achieving maximum increases of 2% and 5.6%, respectively, compared to the baseline methods.
High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences
Authors: Alsafadi Karam , Bi Shuoben , Bashir Bashar , Sharifi Ehsan , Alsalman Abdullah , Kumar Amit , Shahid Shamsuddin
Published: May 2023

The inclusion of physiographic and atmospheric influences is critical for spatial modeling of orographic precipitation in complex terrains. However, attempts to incorporate cloud cover frequency (CCF) data when interpolating precipitation are limited. CCF considers the rain shadow effect during interpolation to avoid an overly strong relationship between elevation and precipitation in areas at equivalent altitudes across rain shadows. Conventional multivariate regression or geostatistical methods assume the precipitation–explanatory variable relationship to be steady, even though this relation is often non-stationarity in complex terrains. This study proposed a novel spatial mapping approach for precipitation that combines regression-kriging (RK) to leverage its advantages over conventional multivariate regression and the spatial autocorrelation structure of residuals via kriging. The proposed hybrid model, RK (GT + CCF), utilized CCF and other physiographic factors to enhance the accuracy of precipitation interpolation. The implementation of this approach was examined in a mountainous region of southern Syria using in situ monthly precipitation data from 57 rain gauges. The RK model’s performance was compared with conventional multivariate regression models (CMRMs) that used geographical and topographical (GT) factors and CCF as predictors. The results indicated that the RK model outperformed the CMRMs with a root mean square error of <8 mm, a mean absolute percentage error range of 5–15%, and an R2 range of 0.75–0.96. The findings of this study showed that the incorporation of MODIS–CCF with physiographic variables as covariates significantly improved the interpolation accuracy by 5–20%, with the largest improvement in modeling precipitation in March.
Inversion and Validation of FY-4A Official Land Surface Temperature Product
Authors: Dong Lixin , Tang Shihao , Wang Fuzhou , Cosh Michael , Li Xianxiang , Min Min
Published: May 2023

The thermal infrared data of Fengyun 4A (FY-4A) geostationary meteorological satellite can be used to retrieve hourly land surface temperature (LST). In this paper, seven candidate algorithms are compared and evaluated. The Ulivieri (1985) algorithm is determined to be optimal for the algorithm of FY-4A LST official products. The refined algorithm coefficients for distinguishing dry and moist atmosphere were established for daytime and nighttime, respectively. Then, FY-4A LST official products under clear-sky conditions are produced. The validation results show that: (1) Compared with in-situ measured LST data at the HeBi crop measurement network, the root mean square errors (RMSE) were 2.139 and 2.447 K. Compared with in-situ measured LST data at Naqu alpine meadow site of Tibet plateau, the RMSE was 2.86 K. (2) When compared with the MODIS LST product, the RMSE was 1.64, 2.17, 2.6, and 1.73 K in March, July, October, and December, respectively. By the bias long-time change at a single site, RMSE of the XLHT (city) and GZH (desert) sites were 2.735 and 2.97 K, respectively. Overall, the preferred algorithm exhibits good accuracy and meets the required accuracy of the FY-4A mission.
A Multi-GNSS/IMU Data Fusion Algorithm Based on the Mixed Norms for Land Vehicle Applications
Authors: Jiang Chen , Zhao Dongbao , Zhang Qiuzhao , Liu Wenkai
Published: May 2023

As a typical application of geodesy, the GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation technique was developed and has been applied for decades. For the integrated systems with multiple sensors, data fusion is one of the key problems. As a well-known data fusion algorithm, the Kalman filter can provide optimal estimates with known parameters of the models and noises. In the literature, however, the data fusion algorithm of the GNSS/INS integrated navigation and positioning systems is performed under a certain norm, and performance of the conventional filtering algorithms are improved only under this fixed and limited frame. The mixed norm-based data fusion algorithm is rarely discussed. In this paper, a mixed norm-based data fusion algorithm is proposed, and the hypothesis test statistics are constructed and adopted based on the chi-square distribution. Using the land vehicle data collected through the multi-GNSS and the IMU (Inertial Measurement Unit), the proposed algorithm is tested and compared with the conventional filtering algorithms. Results show that the influences of the outlying measurements and the uncertain noises are weakened with the proposed data fusion algorithm, and the precision of the estimates is further improved. Meanwhile, the proposed algorithm provides an open issue for geodetic applications with mixed norms.
Disentangling the Key Drivers of Ecosystem Water-Use Efficiency in China’s Subtropical Forests Using an Improved Remote-Sensing-Driven Analytical Model
Authors: Chen Tao , Tang Guoping , Yuan Ye , Xu Zhenwu , Jiang Nan
Published: May 2023

The subtropical forests in China play a pivotal part in the global and regional carbon–water cycle and in regulating the climate. Ecosystem water-use efficiency (WUE) is a crucial index for understanding the trade-off between ecosystem carbon gain and water consumption. However, the underlying mechanisms of the WUE in forest ecosystems, especially the different subtropical forests, have remained unclear. In this paper, we developed a simple framework for estimating forest WUE and revealing the underlying mechanisms of forest WUE changes via a series of numerical experiments. Validated by measured WUE, the simulated WUE from our developed WUE framework showed a good performance. In addition, we found that the subtropical forest WUE experienced a significant increasing trend during 2001–2018, especially in evergreen and deciduous broadleaf forests where the increasing rate was greatest (0.027 gC kg−1 H2O year−1, p < 0.001). Further analysis indicated that the atmospheric CO2 concentration and vapor pressure deficits (VPD), rather than leaf area index (LAI), were the dominant drivers leading to the subtropical forest WUE changes. When summed for the whole subtropical forests, CO2 and VPD had an almost equal spatial impact on annual WUE change trends and accounted for 45.3% and 49.1% of the whole study area, respectively. This suggests that future forest management aiming to increase forest carbon uptake and protect water resources needs to pay more attention to the long-term impacts of climate change on forest WUE.
Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability
Authors: Yang Lingbo , Huang Ran , Zhang Jingcheng , Huang Jingfeng , Wang Limin , Dong Jiancong , Shao Jie
Published: May 2023

Monitoring of rice planting areas plays an important role in maintaining food security. With powerful automatic feature extraction capability, crop mapping based on deep learning methods has become one of the most important research directions of crop remote sensing recognition. However, the training of deep learning models often requires a large number of samples, which restricts the application of these models in areas with a lack of samples. To address this problem, based on time-series Sentinel-1 SAR data, this study pre-trained the temporal feature-based segmentation (TFBS) model with an attention mechanism (attTFBS) using abundant samples from the United States and then performed an inter-continental transfer of the pre-trained model based on a very small number of samples to obtain rice maps in areas with a lack of samples. The results showed that an inter-continental transferred rice mapping model was feasible to achieve accurate rice maps in Northeast China (F-score, kappa coefficient, recall, and precision were 0.8502, 0.8439, 0.8345, and 0.8669, respectively). The study found that the transferred model exhibited a strong spatiotemporal generalization capability, achieving high accuracy in rice mapping in the three main rice-producing regions of Northeast China. The phenological differences of rice significantly affected the generalization capability of the transferred model, particularly the significant differences in transplanting periods, which could have resulted in a decrease in the generalization capability of the model. Furthermore, the study found that the model transferred based on an extremely limited number of samples could attain a rice recognition accuracy equivalent to that of the model trained from scratch with a substantial number of samples, indicating that the proposed method possessed strong practicality, which could dramatically reduce the sample requirements for crop mapping based on deep learning models, thereby decreasing costs, increasing efficiency, and facilitating large-scale crop mapping in areas with limited samples.
Acquisition of Weak BDS Signal in the Lunar Environment
Authors: Ju Zhanghai , Chen Liang , Yan Jianguo
Published: May 2023

Autonomous navigation using the GNSS has been increasingly used in lunar exploration missions, as it is considered an efficient method for spacecraft to operate without relying on ground facilities. However, so far, only GPS and Galileo have been studied and implemented in lunar missions, whereas the potential of BDS-3 for such missions remains largely unexplored. This paper presents an analysis and evaluation of the navigation service capabilities for spacecraft at lunar altitudes by utilizing existing GNSS satellite resources in orbit. In detail, we investigate the number of GNSS signals received by low lunar orbit (LLO) receivers, as well as the carrier-to-noise ratio (C/N0), Doppler shift, and geometric dilution of precision (GDOP) of the received signal. Additionally, a digital intermediate frequency (IF) signal simulation program to emulate the BDS B1I signal is utilized, which allows to freely set the carrier-to-noise ratio, Doppler shift, and code phase. Based on this framework, we discussed a high-sensitivity BDS B1I signal receiver and validated its performance with simulated signals. We verified the capabilities of coherent integration, non-coherent integration, differential integration, and semi-bit methods to detect weak BDS B1I signals in a lunar environment. The results indicate that the semi-bit coherent differential integration method is still capable of acquiring signals at a C/N0 of 15 dB-Hz and can effectively suppress the navigation data bit sign transition.
A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets
Authors: Liu Junfei , Liu Kai , Wang Ming
Published: May 2023

Identifying floods and flood susceptibility mapping are critical for decision-makers and disaster management. Machine learning and deep learning have emerged as powerful tools for flood prevention, whereas they confront the drawbacks of overfitting and biased prediction due to the difficulty in obtaining real data. Therefore, this study presents a novel approach for flood susceptibility prediction by integrating ResNet-18 with a 2D hydrological model for global flood susceptibility mapping using remote sensing datasets. The three main contributions of this study are outlined below. First, a new perspective integrating hydrological simulation and deep learning is presented to overcome the inherent drawbacks of deep learning. Second, the model performance is improved through physics-based initialization. Third, the pretrained model achieves better performance than the original model with incomplete training labels. This experiment demonstrates that the physics-based initialized ResNet-18 model achieves satisfactory prediction performance in terms of accuracy and area under the receiver operating characteristic (ROC) curve (0.854 and 0.932, respectively) and is extremely robust according to a sensitivity analysis.
Dual-Level Contextual Attention Generative Adversarial Network for Reconstructing SAR Wind Speeds in Tropical Cyclones
Authors: Han Xinhai , Li Xiaohui , Yang Jingsong , Wang Jiuke , Zheng Gang , Ren Lin , Chen Peng , Fang He , Xiao Qingmei
Published: May 2023

Synthetic Aperture Radar (SAR) imagery plays an important role in observing tropical cyclones (TCs). However, the C-band attenuation caused by rain bands and the problem of signal saturation at high wind speeds make it impossible to retrieve the fine structure of TCs effectively. In this paper, a dual-level contextual attention generative adversarial network (DeCA-GAN) is tailored for reconstructing SAR wind speeds in TCs. The DeCA-GAN follows an encoder–neck–decoder architecture, which works well for high wind speeds and the reconstruction of a large range of low-quality data. A dual-level encoder comprising a convolutional neural network and a self-attention mechanism is designed to extract the local and global features of the TC structure. After feature fusion, the neck explores the contextual features to form a reconstructed outline and up-samples the features in the decoder to obtain the reconstructed results. The proposed deep learning model has been trained and validated using the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric model product and can be directly used to improve the data quality of SAR wind speeds. Wind speeds are reconstructed well in regions of low-quality SAR data. The root mean square error of the model output and ECMWF in these regions is halved in comparison with the existing SAR wind speed product for the test set. The results indicate that deep learning methods are effective for reconstructing SAR wind speeds.
Investigating the Feasibility of Using Satellite Rainfall for the Integrated Prediction of Flood and Landslide Hazards over Shaanxi Province in Northwest China
Authors: Wang Sheng , Zhang Ke , Chao Lijun , Chen Guoding , Xia Yi , Zhang Chuntang
Published: May 2023

Rainfall-triggered flood and landslide hazards pose significant threats to human lives and infrastructure worldwide. This study aims to evaluate the applicability of three satellite rainfall data sets—namely, CMORPH, GPM, and TRMM—for the prediction of flood and landslide hazards using a coupled hydrological-slope stability model. The spatial distribution of annual rainfall from the three satellite data sets was similar to that of gauge rainfall, with an increasing trend from the north to the south of Shaanxi Province. The average annual rainfall of CMORPH was the lowest, while that of TRMM was the highest. The modeled discharges forcing by satellite rainfall generally matched the observed discharges at four hydrological stations for the period 2010–2012, with average correlation coefficients of 0.51, 0.61, and 0.57 for the CMORPH, GPM, and TRMM rainfall, respectively. The exceedance probabilities of modeled discharges for the three satellite rainfall data sets were close to those of the observations, particularly when the discharges were low. Moreover, the landslide prediction results demonstrated that the three satellite rainfall data sets could simulate the spatial distribution of landslide events well; these simulations were consistent with the information in the landslide inventory map. Furthermore, when compared to the classical Intensity-Duration (ID) rainfall threshold method, the physically based slope stability model presented higher global accuracy under all three satellite rainfall data sets. The global accuracy of GPM rainfall was the highest among the three data sets (0.973 for GPM vs. 0.951 for CMORPH and 0.965 for TRMM), indicating that GPM rainfall provides the highest quality compared to CMORPH and TRMM rainfall. These findings provide a crucial basis for the application of satellite rainfall data in the context of flood and landslide prediction.
P-Band UAV-SAR 4D Imaging: A Multi-Master Differential SAR Tomography Approach
Authors: Wang Zhen , Wei Yangkai , Ding Zegang , Zhao Jian , Sun Tao , Wang Yan , Li Han , Zeng Tao
Published: May 2023

Due to its rapid deployment, high-flexibility, and high-accuracy advantages, the unmanned-aerial-vehicle (UAV)-based differential synthetic aperture radar (SAR) tomography (D-TomoSAR) technique presents an attractive approach for urban risk monitoring. With its sufficiently long spatial and temporal baselines, it offers elevation and velocity resolution beyond the dimensions of range and azimuth, enabling four-dimensional (4D) SAR imaging. In the case of P-band UAV-SAR, a long spatial-temporal baseline is necessary to achieve high enough elevation-velocity dimensional resolution. Although P-band UAV-SAR maintains temporal coherence, it still faces two issues due to the extended spatial baseline, i.e., low spatial coherence and high sidelobes. To tackle these problems, we introduce a multi-master (MM) D-TomoSAR approach, contributing three main points. Firstly, the traditional D-TomoSAR signal model is extended to a MM one, which improves the average coherence coefficient and the number of baselines (NOB) as well as suppresses sidelobes. Secondly, a baseline distribution optimization processing is proposed to equalize the spatial–temporal baseline distribution, achieve more uniform spectrum samplings, and reduce sidelobes. Thirdly, a clustering-based outlier elimination method is employed to ensure 4D imaging quality. The proposed method is effectively validated through computer simulation and P-band UAV-SAR experiment.
Improvement of Lunar Surface Dating Accuracy Utilizing Crater Degradation Model: A Case Study of the Chang’e-5 Sampling Area
Authors: Zhao Feiyue , Zuo Wei , Li Chunlai
Published: May 2023

Taking the Chang’e-5 (CE-5) sampling area as an example, this study carried out an investigation on improving the crater size-frequency distribution (CSFD) dating accuracy of lunar surface geologic units based on the crater degradation model. We constructed a three-parted crater degradation model, which consists of the diffusion equation describing crater degradation and equations describing the original crater profile for small craters (D < 1 km) and larger craters (D ≥ 1 km). A method that can improve the accuracy of CSFD dating was also proposed in this study, which utilizes the newly constructed degradation model to simulate the degradation process of the craters to help determine the crater degradation process and screen out the craters suitable for CSFD analysis. This method shows a good performance in regional dating. The age determined for the CE-5 sampling area is 2.0 ± 0.2 Ga, very close to the 2.03 ± 0.004 Ga of isotopic dating result of the returned sample. We found that the degradation state of the craters simulated by our constructed degradation model is highly consistent with the real existing state of the craters in terms of their topographic, geomorphological, and compositional (e.g., FeO) features. It fully demonstrates that the degradation model proposed in this study is effective and reliable for describing and distinguishing the degradation state of craters over time due to the cumulative effects of small craters. The proposed method can effectively distinguish between diffusively degraded (which conform to the degradation model) and non-diffusively degraded (which do not conform to the degradation model) craters and improve the CSFD accuracy through the selection of the craters. This not only provides an effective solution to the problem of obtaining a more “exact” frequency distribution of craters, which has long plagued the practical application of the CSFD method in dating the lunar surface but also advances our understanding of the evolutionary history of the geologic units of the study area. The results of this work are important for the in-depth study of the formation and evolution of the moon, especially for lunar chronology.
A Priori Knowledge Based Ground Moving Target Indication Technique Applied to Distributed Spaceborne SAR System
Authors: Cai Bin , Hao Xiaolong , Chen Li , Liang Jia , Cheng Tianhao , Luo Ying
Published: May 2023

Through formation flying, the distributed spaceborne SAR(synthetic aperture radar) system can increase the number of spatial degree of freedoms (DOFs) and provide flexible multi-baselines for SAR-GMTI (ground moving target indication), which improves the system performance. This paper proposes an a priori knowledge-based adaptive clutter cancellation and moving target detection technique applied to the distributed spaceborne SAR-GMTI systems. Firstly, the adaptive clutter cancellation technique is exploited to suppress the ground clutter. A priori knowledge, such as road network information, is integrated to the adaptive clutter cancellation processor to reduce any moving target steering vector mismatch. Secondly, adaptive matched filter (AMF) and adaptive beamformer orthogonal rejection test (ABORT) are exploited as adaptive detection techniques for moving target detection. Due to the dense road network, the moving target steering vector estimation may be ambiguous for the different position and orientation of the roads. The multiple hypothesis testing (MHT) technique is proposed to detect the moving targets and resolve the potential ambiguities. A scheme is exploited to detect, classify, and relocate the moving targets. Finally, simulation experiments and performance analysis have demonstrated the effectiveness and robustness of the proposed technique.
FEPVNet: A Network with Adaptive Strategies for Cross-Scale Mapping of Photovoltaic Panels from Multi-Source Images
Authors: Su Buyu , Du Xiaoping , Mu Haowei , Xu Chen , Li Xuecao , Chen Fang , Luo Xiaonan
Published: May 2023

The world is transitioning to renewable energy, with photovoltaic (PV) solar power being one of the most promising energy sources. Large-scale PV mapping provides the most up-to-date and accurate PV geospatial information, which is crucial for planning and constructing PV power plants, optimizing energy structure, and assessing the ecological impact of PVs. However, previous methods of PV extraction relied on simple models and single data sources, which could not accurately obtain PV geospatial information. Therefore, we propose the Filter-Embedded Network (FEPVNet), which embeds high-pass and low-pass filters and Polarized Self-Attention (PSA) into a High-Resolution Network (HRNet) to improve its noise resistance and adaptive feature extraction capabilities, ultimately enhancing the accuracy of PV extraction. We also introduce three data migration strategies by combining Sentinel-2, Google-14, and Google-16 images in varying proportions and transferring the FEPVNet trained on Sentinel-2 images to Gaofen-2 images, which improves the generalization performance of models trained on a single data source for extracting PVs in images of different scales. Our model improvement experiments demonstrate that the Intersection over Union (IoU) of FEPVNet in segmenting China PVs in Sentinel-2 images reaches 88.68%, a 2.37% increase compared to the HRNet. Furthermore, we use FEPVNet and the optimal migration strategy to extract photovoltaics across scales, achieving a precision of 94.37%. In summary, this study proposes the FEPVNet model with adaptive strategies for extracting PVs from multiple image sources, with significant potential for application in large-scale PV mapping.
Fractionation of Marigold Waxy Extract Using Supercritical CO2
Authors: Zanotti Alessandra , Baldino Lucia , Scognamiglio Mariarosa , Reverchon Ernesto
Published: May 2023

Marigold oil is a product of great industrial interest thanks to its wide range of medicinal and wound-healing properties. In this work, supercritical carbon dioxide was used to recover marigold essential oil from the hexane solvent extract of marigold flowers, the floral “concrete”. This starting material was mixed with synthetic paraffinic waxes to heighten its melting point and viscosity, thus, improving material processability. Supercritical fluid extraction and fractionation of the modified marigold “concrete” was carried out, and the effect of pressure and CO2 mass flow rate was studied. The pressure was varied from 80 to 180 bar, keeping the temperature constant at 40 °C: the higher the pressure, the larger the CO2 solvent power and extraction yield (up to 9.40% w/w). Nevertheless, the optimum between productivity and process selectivity was found at 100 bar. By changing the CO2 mass flow rate (from 1.20 to 1.50 kg/h), we noted that mass transfer resistance was located externally. GC-MS analysis showed that the most abundant compounds in the oil were δ-cadinene (25%), γ-cadinene (16%), τ-muurolol (6.5%), and α-muurolene (6%). Moreover, the traces of oil and waxes showed no mutual contamination between lighter species and waxes, meaning that the fractionation step was successful.
Everyday Virtuality: A Multimodal Analysis of Political Participation and Newsworthiness
Authors: Yepez-Reyes Veronica , Cevallos Patricio , Carrillo-Andrade Andrea , Cruz-Silva Jorge , López-Paredes Marco , González-Quincha Alejandra
Published: May 2023

Due to the COVID-19 pandemic, digital interactions ceased to be “just another form of communication”; indeed, they became the only means of social interaction, mediated and driven by information and communication technologies (ICTs). Consequently, working in a digital context switched from being a phenomenon to be studied to the primary means of socializing and the primary workspace for researchers. This study explores four different methodologies to question how discursive interactions related to power and newsworthiness may be addressed in digital contexts. The multimodal approach was reviewed through the affordances of critical discourse analysis, issue ownership and salience, morphological discourse analysis, and protest event analysis. It starts by theoretically addressing concepts of multimodality and phenomenology by focusing on the implications of both perspectives. It examines publications and interactions in digital contexts in Ecuador from March 2017 to December 2020 within three political phenomena. The results of the analysis of these publications and interactions suggest that when analyzing political participation and newsworthiness, the virtual becomes a subjective space. Moreover, qualitative research is one of the primary ways to combine multimodality with other forms of discourse analysis. This paper concludes that perceptions, practices, and meanings assigned to social online representations can be better analyzed through multimodality, which tackles the intertwined characteristics of virtual discourses.
The Inner Functioning of Local Governance Networks in Centralized Countries: A ‘Brave New World’?
Authors: Silva Patrícia , Mota Luís F. , Carneiro Raúl , Valentim Raquel , Teles Filipe
Published: May 2023

Local governance networks are increasingly seen as the big idea to cope with issues that are complex enough in scope and scale to require a diversity of expertise and resources. While conventional narrative has posited that local networks are optimal for addressing a range of policy problems, and enhancing democratic participation, scarce attention has been devoted to understanding their inner working. A relevant gap in the literature pertains to the impact of central government intervention in igniting such arrangements on the diversity of actors, the intensity of interactions among actors, or their coordination practices. Such assessment is particularly relevant in centralized contexts. This article seeks to map and characterize the inner working of local networks in such a context—Portugal. The empirical analysis highlights the crucial role of central government in igniting local networks and in ensuring higher levels of formal intensity of collaboration to reduce transaction costs. Keeping such arrangements under the radar of central government, however, may curtail the diversity of actors, policy areas, and curb stakeholders’ commitment in local governance arrangements.
A Cross-Comparative Analysis of Transportation Safety Research
Authors: Blišťanová Monika , Koščák Peter , Tirpáková Michaela , Ondicová Magdaléna
Published: May 2023

This article aims to identify the main trends of safety research in relation to different transportation modes (aviation, rail, road, and ship) based on the chosen methods, emphasizing the same and different features in the research topics. In the context of the main goal, quantitative content analysis was chosen, which identified the trends and domains of safety research, and text-mining software Leximancer was used for processing concept maps and reports showing semantic structures. The article found that the dominant topics in all modes of transportation are safety issues, mostly related to incidents/accidents. All modes of transportation have similarities in research (e.g., management, safety, and legislation are discussed), but differences regarding the specific operation prevail. The article emphasizes what trends in safety research dominate and how they differ. It has also been proven that, even though it is safety research, security issues can also appear because the two concepts form an indivisible complex in many ways.
Analysing Multiple Paths of Urban Low-Carbon Governance: A Fuzzy-Set Qualitative Comparative Analysis Method Based on 35 Key Cities in China
Authors: Li You-Dong , Yan Chen-Li , Zhao Yun-Hui , Bai Jia-Qi
Published: May 2023

The city is a crucial space carrier for the country to carry out low-carbon construction and solve sustainable–development problems. However, existing research lacks an in-depth discussion of the complex mechanisms and governance paths of urban low-carbon transformation. Therefore, this study explores multiple paths of urban low-carbon governance (ULCG). This study constructs a theoretical model of ULCG based on the technology–organisation–environment (TOE) framework. It uses fuzzy-set qualitative comparative analysis (fsQCA) to analyse the overall and sub-regional paths of 35 key cities in China to explore various ULCG approaches. The following three conclusions are drawn. First, a single antecedent condition is not a necessary condition for ULCG. Second, five differentiated paths have been formed under the joint action of the TOE conditions to improve ULCG. It can be divided into three types: the ULCG model dominated by ‘big data + market’, ‘big data’, and ‘market’. Third, apparent differences exist in the ULCG paths in China’s eastern, central and western regions. The study deepens the rational understanding of multiple factors interacting in the complex mechanism behind urban low-carbon transformation and provides differentiated ULCG paths, enabling cities in eastern, central, and western China to choose low-carbon governance paths tailored to their local conditions based on both a comprehensive perspective and a regional perspective.
Saudi Fertilizers and Their Impact on Global Food Security: Present and Future
Authors: Emam Abda
Published: May 2023

This study was designed to assess the long-run association between the Kingdom of Saudi Arabia (KSA) and world export fertilizer (urea and DAP) prices. It also aims to disclose the effect of KSA fertilizer export quantities on global food security. KSA and world fertilizer prices and quantities were collected from various sources for the period ranging from 2002 to 2020. The results from the autoregressive distributed lag bounds and Engle–Granger two-step procedure tests discovered that there are long-run associations between KSA and world urea and DAP prices, respectively. Fully modified ordinary least squares and dynamic ordinary least squares model results were compatible with the results of the autoregressive distributed lag model. From the ECM results, the coefficient of adjustment parameter for KSA urea and DAP prices (as a dependent variable) proved positive and insignificant, leading to the conclusion that the model was powerless to right its previous time instability. This may be due to the short time interval of the series under study, and indicate that it requires more than 12 months to return to its long-run equilibrium. Likewise, the results showed that KSA fertilizer (urea and DAP) export quantities have a positive, highly significant impact on world urea and DAP export quantities. Reference to forecasting analysis results, the growth rates of urea exports quantities equal to 0.002 and 0.004 for the period from 2002 to 2018 and from 2019 to 2026, respectively. In addition, the growth rates of DAP export quantities equal to 0.45 and 0.07 for the period 2002 to 2018 and for the forecast period 2019 to 2026, respectively. With reference to KSA fertilizer exports, the importer countries rank among the main countries producing wheat, sorghum, maize, rice, and millet (chief food crops) in the world, showing that the KSA fertilizer (urea and DAP) export quantities contribute directly to global food security through fertilizer exportation and indirectly through the contribution of the importer’s countries in production of chief food crops. Recommendations drawn from the results are to increase the contribution of the KSA to global food security through lowering the time required to absorb fertilizer price shocks in the world (less than 12 months) and increase the KSA’s fertilizer export quantities.
Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model
Authors: Al-Nefaie Abdullah H. , Aldhyani Theyazn H. H.
Published: May 2023

Burning fossil fuels results in emissions of carbon dioxide (CO2), which significantly contributes to atmospheric changes and climate disturbances. Consequently, people are becoming concerned about the state of the environment, and governments are required to produce precise projections to develop efficient preventive measures. This study makes a significant contribution to the area by modeling and predicting the CO2 emissions of vehicles using advanced artificial intelligence. The model was constructed using the CO2 emission by vehicles dataset from Kaggle, which includes different parameters, namely, vehicle class, engine size (L), cylinder transmission, fuel type, fuel consumption city (L/100 km), fuel consumption hwy (L/100 km), fuel consumption comb (L/100 km), fuel consumption comb (mpg), and CO2 emissions (g/km). To forecast the CO2 emissions produced by vehicles, a deep learning long short-term memory network (LSTM) model and a bidirectional LSTM (BiLSTM) model were developed. Both models are efficient. Throughout the course of the investigation, the researchers employed four statistical assessment metrics: the mean square error (MSE), the root MSE (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Based on the datasets of experiments carried out by Kaggle, the LSTM and BiLSTM models were created and implemented. The data were arbitrarily split into two phases: training, which included 80% of the total data, and testing, which comprised 20% of the total data. The BiLSTM model performed best in terms of accuracy and achieved high prediction values for MSE and RMSE. The BiLSTM model has the greatest attainable (R2 = 93.78). In addition, R% was used to locate a connection between the dataset’s characteristics to ascertain which characteristics had the highest level of association with CO2 emissions. An original strategy for the accurate forecasting of carbon emissions was developed as a result of this work. Consequently, policymakers may use this work as a potentially beneficial decision-support tool to create and execute successful environmental policies.
Review of Energy-Saving Technologies for Electric Vehicles, from the Perspective of Driving Energy Management
Authors: Wang Deping , Guan Changyang , Wang Junnian , Wang Haisheng , Zhang Zhenhao , Guo Dachang , Yang Fang
Published: May 2023

The driving range of electric vehicles (EVs) is still an important factor restricting their development. Although the rising battery energy density has reached a bottleneck, which is a key constraint, the drive energy management strategy also has a significant effect and can improve the driving range of EVs, since wheel traction torque control can directly optimize the driving energy consumption of EVs. In order to comprehensively analyze the current research status of driving energy management and clarify its development direction, this review focuses on the driving energy management strategy of EVs and systematically summarizes the configurations and power distribution strategies of the dual-motor coupling drive system (DCDS), as well as torque vectoring control strategies of the decentralized drive system. Firstly, driving energy losses are analyzed in detail, which mainly include electric loss, tire slip energy dissipation, and the power of cornering resistance. Secondly, typical configurations of the DCDS are introduced, and the power distribution strategies of the DCDS are comprehensively reviewed. Finally, as an interesting energy-saving technology, energy-saving torque vectoring, generally applied to decentralized drive systems, is reviewed in detail in terms of its energy-saving pathways and control strategies, which are classified as front-and-rear torque vectoring and left-and-right torque vectoring. Research findings indicate that the driving range of EVs can be effectively increased by applying a driving energy management strategy based on several novel multi-power source drive systems. The development of a driving energy management strategy and the required novel drive systems will be a valuable and crucial direction for further energy conservation in EVs.
Role of Innovations to Mitigate CO2e: Theory and Evidence for European Economies
Authors: Umar Farooq Muhammad , Amin Azka , Peng Sun , Işık Cem , Aqdas Ramaisa , Akbar Muhammad , Sabahat Gul , Ongan Serdar
Published: May 2023

Even though numerous researchers have analyzed the factors of carbon emissions, technological innovation’s linear and non-linear effects on carbon emissions have not been thoroughly examined in the energy–environment literature with the Environmental Kuznets Curve framework for European economies. For this purpose, this study has employed linear and non-linear autoregressive distributed lagged models, the novel bounds testing methodologies of dynamic simulations. Renewable energy and resident and non-resident patents are the indicators of technological innovations. The findings of this study demonstrate a significant negative association of renewable energy use and technological innovation with carbon emissions, while economic growth, non-renewable energy, and urbanization have depicted a positive relationship. These findings confirm the validity of the Environmental Kuznets hypothesis for the sampled countries. It is suggested that research and development facilities are required to mitigate environmental pollution by using innovation and discouraging more use of coal in electricity generation. This study also provides policymakers with particular statistics on sector-based renewable energy initiatives, highlighting the greenhouse gas impacts in European countries.
Ex Situ Catalytic Pyrolysis of Invasive Pennisetum purpureum Grass with Activated Carbon for Upgrading Bio-Oil
Authors: Reza Md Sumon , Afroze Shammya , Kuterbekov Kairat , Kabyshev Asset , Zh. Bekmyrza Kenzhebatyr , Taweekun Juntakan , Ja’afar Fairuzeta , Saifullah Abu Bakar Muhammad , Azad Abul K. , Roy Hridoy , Islam Md. Shahinoor
Published: May 2023

Energy demands keep increasing in this modern world as the world population increases, which leads to a reduction in fossil fuels. To resolve these challenges, Pennisetum purpureum, an invasive grass in Brunei Darussalam, was examined as the feedstock for renewable energy through a catalytic pyrolysis process. The activated carbon was applied as the catalyst for a simple and economical solution. The catalytic pyrolysis was executed at 500 °C (the temperature for the highest biofuel yield) for both reactors to produce the highest amount of upgraded biofuels. The biochar produced from the non-catalytic and catalytic pyrolysis processes showed a consistent yield due to stable operating conditions, from which the activated carbon was generated and used as the catalyst in this work. A significant amount of improvement was found in the production of biofuels, especially bio-oil. It was found that for catalysts, the number of phenolic, alcohol, furans, and ketones was increased by reducing the amount of acidic, aldehyde, miscellaneous oxygenated, and nitrogenous composites in bio-oils. The highest amount of phenolic compounds was produced due to a number of functional groups (-C=O and -OH) in activated carbon. The regenerated activated carbons also showed promising outcomes as catalysts for upgrading the bio-oils. The overall performance of synthesized and regenerated activated carbon as a catalyst in catalytic pyrolysis was highly promising for improving the quality and stability of bio-oil.
Study on the Behavior Strategy of the Subject of Low-Carbon Retrofit of Residential Buildings Based on Tripartite Evolutionary Game
Authors: Zhang Zihan , Song Junkang , Wang Wanjiang
Published: May 2023

Under carbon peaking and neutrality constraints, low-carbon retrofitting of residential buildings (LRRB) has become a strategic need for most countries worldwide. However, the development of China’s LRRB market still relies on government guidance without moving towards the goal of autonomous orientation. This area is still a concern for academics. Moreover, many stakeholders are involved in the LRRB, and the secondary stakeholders’ behavioral strategies do not substantially impact the LRRB. So, this paper adopts Mitchell’s score-based approach to identify the core stakeholders, followed by a tripartite evolutionary game model of government, ESCOs, and owners. Based on the system dynamics (SD) model, the evolution rules of the three parties’ behavior strategies and evolution stabilization strategies are analyzed, and the key factors influencing the equilibrium are found. The results of the study show that under the condition that the government adopts the same level of subsidy for ESCOs and owners, ESCOs are more sensitive to the subsidy; with the introduction of penalties under the premise of subsidy, ESCOs can reach evolutionary equilibrium faster; and when the benefits of owners accepting LRRB outweigh the losses, owners will eventually choose to accept retrofit regardless of whether the government subsidizes owners or not. Finally, the paper ends with suggestions for developing an LRRB market. The game model proposed in this paper can provide a scientific reference for stakeholders’ carbon reduction decisions.
Regional Differences and Convergence of Technical Efficiency in China’s Marine Economy under Carbon Emission Constraints
Authors: Li Gen , Wang Jingwen , Liu Fan , Wang Tao , Zhou Ying , Tian Airui
Published: May 2023

With the continuous development of China’s marine economy and the increasing pollution in marine-related industries, how to implement a sustainable development strategy in the marine economy has become an important issue. Under the stochastic frontier analysis framework, this paper measures the technical efficiency of the marine economy in 11 coastal provinces in China under carbon emission constraints from 2006 to 2016 and analyzes regional differences and the dynamic evolution of technical efficiency and its influencing factors. Panel unit root test is applied to analyze the stochastic convergence of technical efficiency of the inter-regional marine economy. The result shows that: in the reference period, the technical efficiency of the marine economy is on the rise. Guangdong and Shanghai are in the lead. Technical level and industrial structure have a positive impact on technical efficiency, while the structure of property rights, FDI, energy prices, and energy structure have a negative effect on it. On the whole, the changes in the technical efficiency of coastal provinces present a process from concentration to differentiation. There is a stochastic convergence between the Pan-Pearl River Delta and the Yangtze River Delta. Raising the technological level, promoting low-carbon production in the marine industry, and strengthening inter-regional cooperation have a certain effect on the improvement of the technical efficiency of the marine economy.
Empowering Educators to Sustain Reflective Teaching Practices: The Validation of Instruments
Authors: Oo Tun Zaw , Habók Anita , Józsa Krisztián
Published: May 2023

This study aimed to investigate the sustainability of an instructional design utilizing the novel approach of the Reflective Teaching Model for Reading Comprehension (RTMRC), and to continuously develop and validate instruments for reflective measures to ensure the sustainable teaching of reading comprehension. The RTMRC design was featured based on two main parts: the reflective teaching process (planning, acting, reflecting and evaluating) and the reading comprehension process (reader, strategy, text and task). Then, a quasi-experiment (the pre- and post-test control group design) was conducted with 168 grade-9 students to assess the performance of the RTMRC in practice. The reflective questionnaire was also applied as an instrument to gather feedback from students about the instructional context, with the aim of supporting the sustainable reflective practices of the teacher. The reading achievement in the experimental group was evaluated and fostered using this model. Through the analyses of t-tests and Cohen’s d effect size, it was found that the experimental group teaching with RTMRC outperformed the control group without RTMRC teaching. Using exploratory factor analysis, confirmatory factor analysis, Rasch analysis and testing measurement invariance across groups, we report that the instruments were found to be valid for measuring the effect of the RTMRC in teaching reading comprehension in English Language Teaching (ELT) in a sustainable manner.