bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Depolymerization of Glass Fiber Reinforced PET Composites
Authors: Rubio Arias Jose Jonathan , Thielemans Wim
Published: November 2022

The transition to an eco-friendly circular materials system for garbage collected after use from end-users is a serious matter of concern for current society. One important tool in this challenge to achieve a truly circular economy is the chemical recycling of polymers. It has previously been demonstrated that chemical recycling is a feasible alternative to reach carbon circularity, which promotes the maximization of carbon recovery through all possible means. Among the advantages of chemical recycling, one must highlight its ability to selectively attack one or several target functionalities inside a complex mixed stream of polymers to obtain pure monomers, which can then be used to prepare virgin-like polymers as a final product. In previous works from our group, we used a microwave-heated potassium hydroxide in methanol (KMH) system to instantaneously depolymerize PET bottles. The KMH system was also effective for polycarbonate (PC), and intimately mixed PET/PC blends. In the present study, glass fiber reinforced (GFR) PET composites were submitted to depolymerization using the KMH system, and it was verified that more strict conditions were required for full depolymerization of GFR pellets than for pure PET pellets. Evidence of the reorganization of PET chains leading to increased crystallinity were obtained through DSC and WAXD. Surface adhesion of PET and crystallization onto glass fibers led to a different crystalline phase that seems to be more protected against the depolymerization solution, thus increasing the time required for full depolymerization when compared to unreinforced PET. An activation energy of 123 kJ/mol was estimated, in the same range of pristine PET pellets and PET bottles. The optimization of depolymerization conditions permitted 100% depolymerization within 5 min of reaction at 120 °C using 30 mL of KMH solution per g of composite. The green chemistry metrics reflect that our system is more efficient than most of the depolymerization systems found in the literature. The optimal depolymerization conditions here reported for GFR PET composites represent another step towards a total recycling system that includes not only pure polymers but also composites, commonly present in daily life.
Experimental Study on the Possibilities of FDM Direct Colour Printing and Its Implications on Mechanical Properties and Surface Quality of the Resulting Parts
Authors: Tamașag Ioan , Suciu Cornel , Beșliu-Băncescu Irina , Dulucheanu Constantin , Cerlincă Delia-Aurora
Published: November 2022

The present paper aims to contribute to the methodology of 3D printing in-process colouring and study its implications and impact on the tensile strength and surface quality of the obtained parts. The proposed study was based on a Taguchi L27 DOE plan using standardised EN ISO 527-2 type 1B-shaped specimens, in which four factors on three levels were considered. The obtained results highlight the possibility of using the presented in-process colouring method. Different materials (PLA, PLA+, and PETG) with varying infill densities (15%, 30%, and 50%), colour distribution (33%, 66%, and 99%), and colour pigments (blue, green, and red) were studied and the results highlighted that the most influential parameter on the tensile strength of the parts was infill density, followed by the tested material, colour pigment, and colouring percentage; regarding surface roughness, the most influential parameter was infill density, followed by colouring percentage, colour pigment, and material. Moreover, the values resulting from the Taguchi DOE were compared to uncoloured parts, from which it could be concluded that the colouring of the parts had direct implications (negative for tensile strength and positive for surface roughness).
Hybrid TDOA/AOA Hypocenter Localization Using the Constrained Least Squares Method with Deep Learning P-Onset Picking
Authors: Ahn Hyeongki , Kim Hyunchang , Choi Ahyeong , You Kwanho
Published: November 2022

In this study, we propose a hypocenter localization algorithm that uses the time difference of arrival (TDOA) and angle of arrival (AOA) as a hybrid model. The hypocenter measurements are detected by the accelerator sensors of the four separate observatories that are closest to the origin of an earthquake. The measurements are calibrated by the proposed deep learning P-onset picking system with short-time Fourier transform (STFT) signal analysis because the accurate detection of Primary waves (P-waves) is limited by seismic environmental noise. The revised measurements are used to calculate the precise distances between the observatories and hypocenters. The proposed hybrid TDOA/AOA is represented by a linear matrix equation that includes the unknowns of the precise distances, coordinates, and arrival angles to the observatories. We estimate a hypocenter using the constrained least squares method (CLS) under the constraints of the TDOA/AOA. The objective function with the constraints is optimized using the Lagrange function, and the asymptotic optimum is obtained by specifying the optimal Lagrange multipliers. Simulations show the performance of the proposed hypocenter localization method.
Self-Attention and Convolution Fusion Network for Land Cover Change Detection over a New Data Set in Wenzhou, China
Authors: Zhu Yiqun , Jin Guojian , Liu Tongfei , Zheng Hanhong , Zhang Mingyang , Liang Shuang , Liu Jieyi , Li Linqi
Published: November 2022

With the process of increasing urbanization, there is great significance in obtaining urban change information by applying land cover change detection techniques. However, these existing methods still struggle to achieve convincing performances and are insufficient for practical applications. In this paper, we constructed a new data set, named Wenzhou data set, aiming to detect the land cover changes of Wenzhou City and thus update the urban expanding geographic data. Based on this data set, we provide a new self-attention and convolution fusion network (SCFNet) for the land cover change detection of the Wenzhou data set. The SCFNet is composed of three modules, including backbone (local–global pyramid feature extractor in SLGPNet), self-attention and convolution fusion module (SCFM), and residual refinement module (RRM). The SCFM combines the self-attention mechanism with convolutional layers to acquire a better feature representation. Furthermore, RRM exploits dilated convolutions with different dilation rates to refine more accurate and complete predictions over changed areas. In addition, to explore the performance of existing computational intelligence techniques in application scenarios, we selected six classical and advanced deep learning-based methods for systematic testing and comparison. The extensive experiments on the Wenzhou and Guangzhou data sets demonstrated that our SCFNet obviously outperforms other existing methods. On the Wenzhou data set, the precision, recall and F1-score of our SCFNet are all better than 85%.
Visual/Inertial/GNSS Integrated Navigation System under GNSS Spoofing Attack
Authors: Gu Nianzu , Xing Fei , You Zheng
Published: November 2022

Visual/Inertial/GNSS (VIG) integrated navigation and positioning systems are widely used in unmanned vehicles and other systems. This VIG system is vulnerable to of GNSS spoofing attacks. Relevant research on the harm that spoofing causes to the system and performance analyses of VIG systems under GNSS spoofing are not sufficient. In this paper, an open-source VIG algorithm, VINS-Fusion, based on nonlinear optimization, is used to analyze the performance of the VIG system under a GNSS spoofing attack. The influence of the visual inertial odometer (VIO) scale estimation error and the transformation matrix deviation in the transition period of spoofing detection is analyzed. Deviation correction methods based on the GNSS-assisted scale compensation coefficient estimation method and optimal pose transformation matrix selection are proposed for VIG-integrated system in spoofing areas. For an area that the integrated system can revisit many times, a global pose map-matching method is proposed. An outfield experiment with a GNSS spoofing attack is carried out in this paper. The experiment result shows that, even if the GNSS measurements are seriously affected by the spoofing, the integrated system still can run independently, following the preset waypoint. The scale compensation coefficient estimation method, the optimal pose transformation matrix selection method and the global pose map-matching method can depress the estimation error under the circumstances of a spoofing attack.
Two-Step Matching Method Based on Co-Occurrence Scale Space Combined with Second-Order Gaussian Steerable Filter
Authors: Wan Genyi , Zhong Ruofei , Wu Chaohong , Xu Yusheng , Ye Zhen , Yu Ke
Published: November 2022

Multimodal images refer to images obtained by different sensors, and there are serious nonlinear radiation differences (NRDs) between multimodal images for photos of the same object. Traditional multimodal image matching methods cannot achieve satisfactory results in most cases. In order to better solve the NRD in multimodal image matching, as well as the rotation and scale problems, we propose a two-step matching method based on co-occurrence scale space combined with the second-order Gaussian steerable filter (G-CoFTM). We first use the second-order Gaussian steerable filter and co-occurrence filter to construct the image’s scale space to preserve the image’s edge and detail features. Secondly, we use the second-order gradient direction to calculate the images’ principal direction, and describe the images’ feature points through improved GLOH descriptors. Finally, after obtaining the rough matching results, the optimized 3DPC descriptors are used for template matching to complete the fine matching of the images. We validate our proposed G-CoFTM method on eight different types of multimodal datasets and compare it with five state-of-the-art methods: PSO-SIFT, CoFSM, RIFT, HAPCG, and LPSO. Experimental results show that our proposed method has obvious advantages in matching success rate (SR) and the number of correct matches (NCM). On eight different types of datasets, compared with CoFSM, RIFT, HAPCG, and LPSO, the mean SRs of G-CoFSM are 17.5%, 6.187%, 30.462%, and 32.21%, respectively, and the mean NCMs are 5.322, 11.503, 8.607, and 16.429 times those of the above four methods.
Trends in Forest Greening and Its Spatial Correlation with Bioclimatic and Environmental Factors in the Greater Mekong Subregion from 2001 to 2020
Authors: He Bing , Wu Xi , Liu Kang , Yao Yuanzhi , Chen Wenjiang , Zhao Wei
Published: November 2022

Understanding trends of vegetation evolution and its spatial characteristics is critical for sustainable social development in the Greater Mekong Subregion (GMS), which is densely populated and still has uneven economic development. Through Theil–Sen/Mann–Kendall tests, polynomial regression and bivariate local autocorrelation analyses, we investigated vegetation greening trends and their spatial correlation with bioclimatic and environmental variables. The study yielded the following results: (1) Land cover in the GMS has changed significantly over the last 20 years. Conversion between forest and grassland was the main type of change. (2) The upward trend in the forest enhanced vegetation index (EVI) significantly exceeded the downward trend in countries over 20 years. In GMS, the spatial variation in forest trend slope values ranged from −0.0297 a−1 to 0.0152 a−1. (3) Anthropogenic activities have played an important role in forest greening; planted, plantation and oil palm forests exhibit the largest contributions to greening. (4) Changes in forest EVI were most spatially correlated with radiation (12.19% for surface net solar radiation and 12.14% for surface solar radiation downwards) and least spatially correlated with seasonality precipitation (8.33%) and mean annual temperature (8.19%). The results of the analysis of EVI trends in vegetation and their spatial correlation with bioclimatic and environmental variables can provide a reference for strategies aimed for protecting the vegetation ecology.
A New Spatiotemporal Estimator to Downscale GRACE Gravity Models for Terrestrial and Groundwater Storage Variations Estimation
Authors: Fatolazadeh Farzam , Eshagh Mehdi , Goïta Kalifa , Wang Shusen
Published: November 2022

This study proposes a new mathematical approach to downscale monthly terrestrial water storage anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) and estimates groundwater storage anomalies (GWSA) at a daily temporal resolution and a spatial resolution of 0.25° × 0.25°, simultaneously. The method combines monthly 3° GRACE gravity models and daily 0.25° hydrological model outputs and their uncertainties in the spectral domain by minimizing the mean-square error (MSE) of their estimator to enhance the quality of both low and high frequency signals in the estimated TWSA and GWSA. The Global Land Data Assimilation System (GLDAS) was the hydrological model considered in this study. The estimator was tested over Alberta, Saskatchewan, and Manitoba (Canada), especially over the Province of Alberta, using data from 65 in-situ piezometric wells for 2003. Daily minimum and maximum GWS varied from 14 mm to 32 mm across the study area. A comparison of the estimated GWSA with the corresponding in-situ wells showed significant and consistent correlations in most cases, with r = 0.43–0.92 (mean r = 0.73). Correlations were >0.70 for approximately 70% of the wells, with root mean square errors <24 mm. These results provide evidence for using the proposed spectral combination estimator in downscaling GRACE data on a daily basis at a spatial scale of 0.25° × 0.25°.
Recent Response of Vegetation Water Use Efficiency to Climate Change in Central Asia
Authors: Hao Haichao , Hao Xingming , Xu Jianhua , Chen Yaning , Zhao Hongfang , Li Zhi , Kayumba Patient Mindje
Published: November 2022

Quantifying the coupled cycles of carbon and water is essential for exploring the response mechanisms of arid zone terrestrial ecosystems and for formulating a sustainable and practical solution to issues caused by climate change. Water use efficiency (WUE), one of the comprehensive indicators for assessing plant growth suitability, can accurately reflect vegetation’s dynamic response to changing climate patterns. This study assesses the spatio-temporal changes in WUE (ecosystem water use efficiency, soil water use efficiency, and precipitation water use efficiency) from 2000 to 2018 and quantifies their relationship with meteorological elements (precipitation, temperature, drought) and the vegetation index (NDVI). The study finds that the sensitivity of NDVI to WUE is highly consistent with the spatial law of precipitation. The εPre threshold range of different types of WUE is about 200 mm or 1600 mm (low-value valley point) and 300 mm or 1500 mm (high-value peak point), and the εTem threshold value is 3~6 °C (high-value peak point) and 9~12 °C (low-value valley point). The degree to which vegetation WUE is influenced by precipitation is positively correlated with its time lag, whereas the degree to which temperature influences vegetation is negatively correlated. The WUE time lag is very long in hilly regions and is less impacted by drought; it is quite short in plains and deserts, where it is substantially affected by drought. These findings may be of great significance in responding to the severe situation of increasingly scarce water resources and the deterioration of the ecological environment across Central Asia.
The Early Identification and Spatio-Temporal Characteristics of Loess Landslides with SENTINEL-1A Datasets: A Case of Dingbian County, China
Authors: Jiang Zhuo , Zhao Chaoying , Yan Ming , Wang Baohang , Liu Xiaojie
Published: November 2022

Loess landslides represent an important geohazard in relation to the deformation of unstable loess structures occurred on the slope of loess-covered area. It has become one of the important topics to accurately identify the distribution and activity of loess landslides and describe the spatio-temporal kinematics in the western-project construction in China. Interferometric synthetic aperture radar (InSAR) proves to be effective for landslides investigation. This study proposes an improved InSAR-based procedure for large-area landslide mapping in loess-hilly areas, including tropospheric-delay correction based on quadtree segmentation and automatic selection of interferograms based on minimum-error boundary. It is tested in Dingbian County in Shaanxi Province, China. More than 200 SAR images were processed and a total of 50 potential loess landslides were detected and mapped. Results show that the landslides are mainly distributed along the river basins and concentrated in areas with elevation ranging from 1450 m to 1650 m, and with slope angles of 10–40°. Then, a total of eight (16%) loess landslides are classified as active ones based on three parameters derived from InSAR-deformation rates: activity index (AI), mean deformation rate, and maximum deformation rate. Moreover, we characterize the segmentation of detected landslides and describe the discrepancy of local topography and deformation rates by coupling the peak in probability-density curves of deformation rates and profiles of the elevation and deformation rates. Finally, correlation between landslide deformation and rainfall is given through wavelet analysis.
Terrestrial Water Storage Component Changes Derived from Multisource Data and Their Responses to ENSO in Nicaragua
Authors: Jian Guangyu , Xu Chuang , Li Jinbo , Zhang Xingfu , Feng Li
Published: November 2022

Approximately 3.5 million people in Nicaragua have experienced food insecurity due to the El Niño-Southern Oscillation (ENSO)-induced drought from 2014 to 2016. It is essential to study terrestrial water storage component (TWSC) changes and their responses to ENSO to prevent the water crisis in Nicaragua influenced by ENSO. In this paper, we investigate the TWSC changes in Nicaragua and its sub-basins derived from the Gravity Recovery and Climate Experiment (GRACE)’s temporal gravity field, hydrological model, and water level data, and then determine the connection between the TWSC and ENSO from April 2002 to April 2021 by time series analysis. The research results show that: (1) The estimated TWSC changes in Nicaragua are in good agreement with the variation of precipitation and evaporation, and precipitation is the main cause of TWSC variation. (2) According to the cross-correlation analysis, there is a significant negative peak correlation between the interannual TWSC and ENSO in western Nicaragua, especially for interannual soil moisture (−0.80). The difference in peak correlation between the western and eastern sub-basins may be due to the topographic hindrance of the ENSO-inspired precipitation process. (3) The cross-wavelet analysis indicates that the resonance periods between TWSC and ENSO are primarily 2 and 4 years. These resonance periods are related to the two ENSO modes (the central Pacific (CP) mode with a quasi-2-year period and the eastern Pacific (EP) mode with a quasi-4-year period). Furthermore, their resonance phase variation may be due to the transition to ENSO mode. This study revealed the relationship between ENSO and TWSC in Nicaragua, which can provide a certain reference for water resources regulation.
Cultivated Land Quality Evaluated Using the RNN Algorithm Based on Multisource Data
Authors: Zhou Wu , Zhao Li , Hu Yueming , Liu Zhenhua , Wang Lu , Ye Changdong , Mao Xiaoyun , Xie Xia
Published: November 2022

Cultivated land quality (CLQ) is associated with national food security, benign economic development, social harmony, and stability. The scientific evaluation of CLQ provides the basis for achieving the “trinity” protection of cultivated land quantity, and quality, as well as ecology. However, the current research on CLQ evaluation has some limitations, mainly the poor consideration of evaluation indicators, time-consuming and labor-intensive data acquisition, and low precision of evaluation at the regional scale. Therefore, this study introduced multisource data to evaluate CLQ and proposed a new method for CLQ evaluation (natural grade evaluation, utilization grade evaluation, and economic grade evaluation), combining multisource data and the recurrent neural network (RNN) algorithm. Initially, optimal indicators were determined by correlation analysis and generalized linear regression coefficient methods based on factors related to CLQ acquired from multisource data. Then, CLQ evaluation models were constructed with the RNN algorithm on the basis of the aforementioned optimal indicators. Finally, the models were adopted to map CLQ. The present study was carried out in Guangzhou City, Guangdong Province, China. According to the results: (1) CLQ showed close relationship to pH, effective soil layer thickness (EST), chemical fertilizer application rate (CHFE), organic matter content (OMC), annual accumulated temperature (TEMA), 5–15 cm soil depth soil cation exchange capacity (CEC515), 0–5 cm soil depth soil cation exchange capacity (CEC05), 5–15 cm soil depth soil organic carbon content (SOC515), 0–5 cm soil depth soil organic carbon content (SOC05), field slope (FS), groundwater level (GWL), and terrain slope (TS). (2) All modeling accuracies (R2) were greater than 0.80 for the CLQ evaluation models constructed based on the RNN algorithm. The area and spatial distribution of each grade of CLQ evaluation were consistent with the actual situation. The best and the worst quality cultivated land occupied a small area, and the area without a gap with the actual CLQ was as high as 76%, indicating that the model results were reliable. The study shows the suitability of the method for evaluating CLQ at the regional scale, offering a scientific foundation for the rational utilization and management of cultivated land resources, as well as a reference for evaluating CLQ in the future.
An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks
Authors: Chen Chen , Fu Rufei , Ai Xiaojian , Huang Chengbin , Cong Li , Li Xiaohuan , Jiang Jiange , Pei Qingqi
Published: November 2022

Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This article proposes a composite method applied in the Wuyuan City, Jiangxi Province, in China. This method can detect water gauge areas and number areas from complex and changeable scenes, accurately detect the water level line from various water gauges, and finally, obtain the accurate water level value. Firstly, FCOS is improved by fusing a contextual adjustment module to meet the requirements of edge computing and ensure considerable detection accuracy. Secondly, to deal with scenes with indistinct water level features, we also apply the contextual adjustment module for Deeplabv3+ to segment the water gauge area above the water surface. Then, the area can be used to obtain the position of the water level line. Finally, the results of the previous two steps are combined to calculate the water level value. Detailed experiments prove that this method solves the problem of water level recognition in complex hydrological scenes. Furthermore, the recognition error of the water level by this method is less than 1 cm, proving it is capable of being applied in real river scenes.
Evaluation of Mangrove Wetlands Protection Patterns in the Guangdong–Hong Kong–Macao Greater Bay Area Using Time-Series Landsat Imageries
Authors: He Tingting , Fu Yingchun , Ding Hu , Zheng Weiping , Huang Xiaohui , Li Runhao , Wu Shuting
Published: November 2022

The protection of mangroves through nature reserves has been demonstrated to be effective. There were many studies evaluating the mangrove protection effect. However, the evaluation of mangrove growth quality with positive or negative growth trends, as well as restoration potential against disturbance in nature reserves, is still lacking. Thus, this study proposed a hierarchical evaluation framework for mangrove protection in nature reserves, which takes long-term metrics at three levels of loss and gain areas, patch pattern dynamics, and pixel growth trends into account. The continuous change detection and classification (CCDC) was utilized to identify the change condition of mangroves in six nature reserves of the Guangdong–Hong Kong–Macao Greater Bay Area. The Entropy Weight Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was utilized for scores evaluation of protection effort comparison from 2000 to 2020. The study results had the following three main findings. Firstly, the mangrove forest area increased by about 294.66 ha in four reserves and slightly decreased by about 58.86 ha in two. Most reserves showed an improved patches intact pattern and more positive growth trends. Secondly, the establishment of nature reserves and afforestation were the main causes of mangrove area gain. Until 2010, aquaculture, agriculture, and urban development were the biggest threats to mangroves. Finally, the protection of the reserves was successful in the early decades, but the general evaluation scores showed a decline in recent years once we considered the growth trends for quality. The proposed hierarchical evaluation methods provide a new sight to research the impacts of abrupt change and protection resilience status of the gradual restoration of nature reserves.
Food Insecurity Levels among University Students: A Cross-Sectional Study
Authors: Marques Bebiana , Azevedo Jorge , Rodrigues Isilda , Rainho Conceição , Gonçalves Carla
Published: November 2022

Background: University students may be vulnerable to food insecurity (FI) due to limited financial resources, lower purchasing power, and increasing housing and food costs. This study aimed to assess the prevalence and severity of FI and its associated factors among university students attending a public Portuguese university. Methods: A cross-sectional survey was designed, and data were collected using a self-reported online questionnaire validated for the Portuguese population. The study population included students from all levels of study and the assessments were conducted during the winter semester (December 2021 to February 2022). Chi-square tests were used to determine the associations between FI and sociodemographic variables. Bivariate logistic regression was further used to assess factors associated with FI. Results: From the 284 participants, 17.3% of students were classified as food insecure, consisting of 14.1% with mild FI, 2.1% with moderate FI, and 1.1% with severe FI. FI was significantly associated with nationality (p = 0.028) and the number of unemployed household members (p = 0.001). In comparison to Portuguese students, students of other nationalities were 4.1 times more likely to be food insecure (OR = 4.089, 95% CI:1.057–15.821, p = 0.041). Students in households with a higher number of members (OR = 2.537, 95% CI:1.231–5.230, p = 0.012) and a higher number of unemployed members (OR = 3.192, 95% CI:1.681–6.059, p < 0.001) were also 2.5 and 3.1 times more likely, respectively, to be food insecure. Conclusions: This study provides an insight into the FI levels of university students in a Portuguese university. Further future studies are needed to use objective measures of food insecurity (availability, access, and utilization of food) and explore interventions addressing food insecurity in this population.
Exploring My Life Path by Asking 600 People What They Love about Theirs
Authors: Crombie Angus Fionn , Angus Jonathan
Published: November 2022

A young man with Down syndrome and an early adopter of self-managed supports wanted to have a great life, but he needed to be sure what a great life was for him. He decided to ask successful people, “What do you love about your life?”, which he felt was a question that gets right to the heart of the matter. In this paper the young man and his father will outline their journey as co-researchers in collecting/filming 600 stories across ten countries, with some interviews prearranged but many occurring spontaneously at music, film, and arts festivals. The initial findings of “what people loved about their lives” fall into the following categories: Family, Work, Home, Travel, Health, Pets, and Life Itself. Positive side effects for the young man covered: self-confidence, ability to connect with people, opportunities, working relationships, and ongoing friendship. The implications of taking a social journalism approach to collecting stories will be discussed in line with the question “What makes for good inclusive research”? The journey of the co-researchers speaks to the outcome that it is worthwhile to research your life path, accessing elders’ wisdom. The tools of inclusive research are powerful when applied to a citizen’s life questions. Each person values their life in unique ways, yet there are many commonalities.
The Digital Automatic Coupler (DAC): An Effective Way to Sustainably Increase the Efficiency of Freight Transport in Europe
Authors: Cantone Luciano , Durand Thierry , Ottati Andrea , Russo Giulia , Tione Roberto
Published: November 2022

This paper employs and improves TrainDy software to simulate the in-train forces of single and coupled freight trains with a Digital Automatic Coupler (DAC), having a length from 740 m to 1500 m and hauled mass between 1600 t and 8000 t. Software improvements have focused on the ability to natively simulate the electro-pneumatic (EP) brake, which will be a feature of the future DAC. The various types of DAC and their main features are outlined in the paper. The study shows that it is also possible to increase the trains hauled mass by using radio communication between traction units (TUs). The “relative approach” method is used to demonstrate the safety of these new trains. The paper’s main results are reported in the conclusions and demonstrate the effectiveness of both technologies in increasing freight transport efficiency, even when applied to coupled trains where one train is “classic” and the other is equipped with mechanical DAC.
Developing DPSIR Framework for Managing Climate Change in Urban Areas: A Case Study in Jakarta, Indonesia
Authors: Kristiadi Yusuf , Sari Riri Fitri , Herdiansyah Herdis , Hasibuan Hayati Sari , Lim Tiong Hoo
Published: November 2022

From an environmentally conscious and ecological perspective, the sustainability of cities within the effects of climate change are closely related to the wise use of resources and modifications in the ecological status of the environment. In terms of the ecological environment, the sustainability of smart cities entails meeting present and future societal demands for the environment of the water, land, and air, among others. Environmental and the ecological concerns that arise from rapid climate change and monetary developments are shown in the inconsistency between ecological assets, environmental pollution, and the destruction of nature. In this study, the authors aim to develop a strategy to deal with climate change in urban areas using Remote Sensing and the Driver-Pressure-State-Impact-Response (DPSIR) Framework with a case study in Jakarta Smart City. The DPSIR framework, which will be developed and implemented in the city of Jakarta, is a smarter and more sustainable framework that is evaluated through a systematic evaluation of sustainability with quantitative research using the entropy weight method and Partial Least Square-Structural Equation Modeling (PLS-SEM). These methods evaluate 58 representative elements of environments at the urban level, including the shortcomings of earlier research such as data availability, spatial and temporal constraints, and several related ecological indicators, such as soil pH, wind speed, air quality index as well as land changes in the spatial (spatiotemporal) time series. The results of the study show that in the metropolitan city of Jakarta, the Drivers that are related to climate change are the rate of population growth and the rate of industrial growth which, although increases people’s income and GRDP in Jakarta; it also creates Pressures, namely an increase in the amount of water consumption and in the amount of wastewater. Based on these pressures, the environmental conditions (State) of Jakarta city have undergone several environmental changes, such as loss of water supply, changes in wind speed, changes in rainfall, and increasing concentrations of the Air Pollutant Standard Index. The Impact of these three elements resulted in the increase in household and industrial water consumption, an increase in annual electricity consumption, and deteriorating air quality. Hence, the Response to these four interrelated causal variables is that the Jakarta Provincial Government must increase annual funds for the construction of urban community facilities, increase the production capacity of clean water supply, build environment-friendly wastewater treatment facilities, increase the capacity of waste processing infrastructure and transportation fleets, and educate people to use water wisely to reduce the level of water use.
Modeling the Barriers Surrounding Digital Government Implementation: Revealing Prospect Opportunities in Saudi Arabia
Authors: Makki Anas A. , Alqahtani Ammar Y.
Published: November 2022

Digital government (DG) is key to more efficient, transparent, and successful governance processes in meeting beneficiaries’ demands. However, its implementation challenges traditional conceptions. This research study aims at modeling barriers surrounding DG implementation in Saudi Arabia. The interpretive structural modeling (ISM) approach was followed. Thirteen barriers to DG implementation were identified and used to collect DG experts’ opinions on the barriers. The developed model classifies the barriers based on their dependence and driving powers and reveals interrelationships among them on multiple levels. Key findings showed that institutional habits are a foundational barrier affecting political coordination. Both, in turn, lead to ethical concerns and perceived barriers related to law, organizational practice, finances, and technological resources, which all lead to risk aversion and capacity and skills barriers, consequently resulting in a lack of engagement with and demand from users/citizens, a lack of awareness/strategic thinking, and legal framework issues, thereby resulting in technological infrastructure issues, difficulty articulating benefits to beneficiaries, and political management support and leadership barriers. Implications of the developed model include providing a better understanding of the contextual interrelationships between the barriers, which will, in turn, assist in fostering current implementation successes and opening prospects for future opportunities in Saudi Arabia.
Dynamic Influence of Digital and Technological Advancement on Sustainable Economic Growth in Belt and Road Initiative (BRI) Countries
Authors: Zhao Sainan , Zhang Yichao , Iftikhar Huma , Ullah Atta , Mao Jie , Wang Tiantian
Published: November 2022

Digital and technological transformation has gained significant attention not only due to the exposure of the latest technologies but also due to its considerable impact on sustainable economic growth. This research determines the influence of digital and technological advancement on sustainable economic growth. Digital and technological advancement is composed of three variables; E-government Development Index (EGDI), Internet Users’ (IU) growth, and information and communications technology (ICT) exports. Besides that, the urbanization and unemployment rate have been considered as control variables. The dataset consists of the year 2004–2020 for 21 Asian region partner countries along Belt and Road (BRI) region. The conclusions of the two-step system GMM were validated through the D-K fixed effect regression technique. Findings indicate that increase in EGDI, ICT exports, and internet users’ growth has a significant and positive influence on sustainable economic growth which leads that digital and technological advancement having a positive influence on sustainable economic growth. Moreover, urbanization has a partial positive impact, while unemployment has a negative influence on sustainable economic growth as Asian regions are emerging economies and the rate of unemployment is very high, which is affecting the real GDP per capita. It is evident and suggested that improvement in the EGDI index, internet users’ growth, ICT exports, and reduction in the unemployment rate would enhance the balanced sustainable economic growth for all Asian countries of the BRI region.
Biogenic Synthesis of Silver-Iron Oxide Nanoparticles Using Kulekhara Leaves Extract for Removing Crystal Violet and Malachite Green Dyes from Water
Authors: Kolya Haradhan , Kang Chun-Won
Published: November 2022

Crystal violet and malachite green, cationic dyes, are widely used in various industries. Water-containing dye molecules affect human health and aquatic life. Here, we synthesized silver-iron oxide nanoparticles using an aqueous extract of kulekhara leaves. The main advantage of this synthesis is that no iron salts were used to prepare Ag-iron oxide nanoparticles. Iron-rich Kulekhara leaves provide iron oxide during the in situ formation of silver nanoparticles. Synthesized Ag-Fe2O3 nanoparticles were characterized by UV-Vis, FTIR, XRD, and STEM-Cs. The dye-degradation studies were performed using synthesized nanoparticles in the presence of sodium borohydride. In the catalytic reaction, the color of crystal violet and malachite green disappeared (100%) within three minutes, and the same results were obtained in their mixtures (1:1 v/v). The presence of Fe2O3 in AgNPs may boost the rapid reduction in azo bonds due to the higher exposed surface area. The color changes were monitored using UV-Vis spectroscopy. Comparative literature studies showed that the performance of Ag-Fe2O3 is superior regarding the degradation of malachite green and crystal violet. These findings could entice researchers to design and develop various dye degradation using this eco-friendly process.
Meso-Mechanical Simulation of the Mechanical Behavior of Different Types of Steel Fibers Reinforced Concretes
Authors: Wang Haifeng , Jiang Yicheng , Liu Ling
Published: November 2022

Introducing steel fibers into traditional concrete can improve its mechanical properties and crack resistance, but few studies have considered how the steel fiber shape and the bond-slip effect between fibers and matrix affect the mechanical behavior of concrete. This paper establishes a three-dimensional representative volume element (3D RVE) of steel fiber-reinforced concrete (SFRC) with random distribution, different shapes, and different interfacial strengths of steel fibers using Python, Abaqus and Hypermesh. Uniaxial tensile behaviors and failure modes of the SFRC are systematically simulated and analyzed. The results show that when the interfacial strength of steel fiber/concrete is changed from 1 to 3 MPa, the tensile strength of the SFRC increases accordingly. When the interfacial strength is greater than 3 MPa, it has no effect on tensile strength. Additionally, if the interfacial strength is 1 MPa, the tensile strength of the SFRC with end-hook steel fibers is increased by 7% when compared to the SFRC with straight steel fibers, whereas if the interfacial strength reaches 2.64 MPa (strength of pure concrete), the fiber shape has little effect on the tensile strength of the SFRC. Moreover, the simulation results also show that interfacial damage dominates when the interfacial strength is less than 1 MPa, and the crack propagation rate in the end-hook steel fiber-modified SFRC is lower than that in a straight steel fiber-modified SFRC. Therefore, this research reveals that using end-hook steel fibers can improve the strength of the SFRC under low interfacial strength, but the ideal strength of the SFRC can be achieved only by using straight fibers when the interfacial strength between steel fibers and concrete is relatively high.
Progress in Ecosystem Health Research and Future Prospects
Authors: Wang Jingwei , Zhang Jinhe , Wang Peijia , Ma Xiaobin , Yang Liangjian , Zhou Leying
Published: November 2022

Since the Anthropocene, drastic changes in the relationship between humans and the earth have prompted human beings to pay more attention to the living environment. As a metaphor to reveal the state of humanity and nature, ecosystem health has gradually become an important issue closely related to global sustainable development ever since it was widely discussed in the 1990s. This study reviewed 4354 publications about ecosystem health from the Web of Science Core Collection by using CiteSpace software. In addition, 135 empirical papers were selected to further analyze the evaluation methods and characteristics of ecosystem health. This study was able to answer four questions: (1) What are the definitions, research content, and characteristics of ecosystem health? (2) Are there any features during the different periods of the development of the ecosystem health? Are there geographical differences in the research? (3) What are the methods and cases selected for studying ecosystem health, and under what kind of conditions do they apply? Are there any patterns or characteristics in the use of these methods? (4) What are the gaps and deficiencies in ecosystem health research, and where should we set our focus on in the future? In general, this study combined bibliometric analysis with a systematic review method, aiming to summarize the current status of ecosystem health research, make up for the deficiencies in the current review, and put forward new insights regarding the concept cognition, evaluation, and future outlook of ecosystem health research.
Analysis of the Environmental and Economic Effect of the Co-Processing of Waste in the Cement Industry in Korea
Authors: Kim Dowan , Phae Chaegun
Published: November 2022

Recently, the amount of waste generated in Korea has been increasing, and there have been difficulties disposing of it. As an energy-intensive and raw-materials-oriented industry, the cement industry is facing challenges including overcoming the climate crisis and achieving carbon neutrality. Co-processing was suggested as a solution, but the environmental effects of this have not been specifically studied in Korea. In this study, the effects of using alternative resources (limestone, silica stone, iron ore, and gypsum) and fuel on greenhouse gas (GHG) emissions in the cement industry in Korea were analyzed. GHGs generated from mineral mining were compared to GHGs of alternative resources. The reduction in GHGs by using alternative fuel was calculated via the amount of heat from waste instead of that from bituminous coal. Co-processing can reduce approximately 106.9 kg of CO2 in one ton of cement. The cost savings were estimated to be about USD 3815 million. In addition, the lifespans of landfills would be extended by 7.55 years.
Research on Urban Distribution Routes Considering the Impact of Vehicle Speed on Carbon Emissions
Authors: Yao Zhiying , Cao Haiqing , Cui Zhenliang , Wang Yuru , Huang Ning
Published: November 2022

To solve the low-carbon vehicle routing problem (VRP) with sustainable development background, the VRP considering the impact of vehicle speed on carbon emissions is studied. A vehicle routing optimization model aims to minimize the total cost of distribution, penalty cost and carbon emission cost. In the model, the driving time and carbon emissions are calculated on the basis of the varying vehicle speed matching the real road network. Moreover, the improved genetic algorithm is applied to optimize vehicle routing in this paper. Using the Solomon standard datasets, the experimental results validate the carbon emission cost and the total cost from the dynamic road network are lower than those from the static network; furthermore, the carbon emission cost and the total cost from the improved GE algorithm are much lower than those from the general GE algorithm in the dynamic road network. Obviously, the built model and the improved algorithm are more feasible and effective.
Novel Sentiment Lexica Derived from User Generating Content by Chinese Tourists in Pacific Islands
Authors: Zhang Ying , Song Jiehang , Sciacca Angelo , Chan Jin , Qi Xiaoguang
Published: November 2022

Identification of tourists’ sentiments is relevant to the destination’s planning. Tourists generate extensive User Generated Content (UGC)—embedding their sentiments—in the form of textual data when sharing experiences on the Internet. These UGC tend to influence tourists’ decision-making, thus, representing an important data source for tourism research and planning. By obtaining data from Mafengwo and Ctrip, sentiment analysis was conducted to shed light on the sentiment tendency of Chinese tourists in seven Pacific Island Countries and Territories (PICTs). Eleven thousand two hundred four reviews were obtained between January and March 2021. The data shows that Chinese tourists’ sentiments towards the PICTs are overall positive. Yet, they pay more attention to practical issues such as transportation, visa and fees, and their sentiment orientations are influenced by tourism resources, weather, and perceived safety. Moreover, the study demonstrates that the needs of Chinese tourists in the region are influenced by their physiology, security, self-esteem, belonging, and self-actualisation needs. The study contributes to theory and practice by constructing an exclusive set of Chinese sentiment lexicons for tourism research based on data from the PICTs. This lexicon complements but also contradicts previous studies. In addition to being relevant for the studied region, it can inform similar destinations that may or may not have a relevant Chinese tourism market.
A Comparison of Research Methods to Determine the Sustainability of Mineral Resources in Henan Province Based on Cloud Analysis
Authors: Gong Shuang , Yao Shibin , Xi Furui , Liu Juan , Ren Xingyang , Niu Dengyun , Ding Jiaxing , Zhang Hao , Wang Jingkuo , Wang Shuai
Published: November 2022

In order to improve the fuzzy comprehensive evaluation model of mineral resource sustainability and enhance its scientific and objective nature, in this paper, a cloud model-based risk assessment method is introduced to determine the sustainability of mineral resources in a comprehensive comparison, while using a combination of subjective and objective weighting method combining improved hierarchical analysis and the entropy weighting method. Compared with the previous single-assignment evaluation method, the method used in this paper has the advantages of more reasonable determination of weights, more accurate results and better visualization. On this basis, the combined weight method, cloud model method and hierarchical fuzzy evaluation method are organically combined to conduct a comprehensive evaluation of the sustainability of mineral resources in Henan Province. The case analysis shows that the comprehensive evaluation results of the sustainability of mineral resources obtained according to the method are scientifically reasonable and have important reference value and promotion significance for quantitative research in related fields.