bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction Models of ≥2 MeV Electron Daily Fluences for 3 Days at GEO Orbit Using a Long Short-Term Memory Network
Authors: Sun Xiaojing , Lin Ruilin , Liu Siqing , Luo Bingxian , Shi Liqin , Zhong Qiuzhen , Luo Xi , Gong Jiancun , Li Ming
Published: May 2023

Geostationary satellites are exposed to harsh space weather conditions, including ≥2 MeV electrons from the Earth’s radiation belts. To predict ≥2 MeV electron daily fluences at 75°W and 135°W at geostationary orbit for the following three days, long short-term memory (LSTM) network models have been developed using various parameter combinations. Based on the prediction efficiency (PE) values, the most suitable time step of inputs and best combinations of two or three input parameters of models for predictions are recommended. The highest PE values for the following three days with three input parameters were 0.801, 0.658 and 0.523 for 75°W from 1995 to August 2010, and 0.819, 0.643 and 0.508 for 135°W from 1999 to 2010. Based on yearly PE values, the performances of the above models show the solar cycle dependence. The yearly PE values are significantly inversely correlated with the sunspot number, and they vary from 0.606 to 0.859 in predicting the following day at 75°W from 1995 to 2010. We have proven that the poor yearly PE is related to relativistic electron enhancement events, and the first day of events is the most difficult to predict. Compared with previous models, our models are comparable to the top performances of previous models for the first day, and significantly improve the performance for second and third days.
Comparison of Various Annual Land Cover Datasets in the Yellow River Basin
Authors: Liu Bo , Zhang Zemin , Pan Libo , Sun Yibo , Ji Shengnan , Guan Xiao , Li Junsheng , Xu Mingzhu
Published: May 2023

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB.
DILRS: Domain-Incremental Learning for Semantic Segmentation in Multi-Source Remote Sensing Data
Authors: Rui Xue , Li Ziqiang , Cao Yang , Li Ziyang , Song Weiguo
Published: May 2023

With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time. Unfortunately, the domain shift between multi-source remote sensing data from various sensors and regions poses a significant challenge. Segmentation models face difficulty in adapting to incremental domains due to catastrophic forgetting, which can be addressed via incremental learning methods. However, current incremental learning methods mainly focus on class-incremental learning, wherein classes belong to the same remote sensing domain, and neglect investigations into incremental domains in remote sensing. To solve this problem, we propose a domain-incremental learning method for semantic segmentation in multi-source remote sensing data. Specifically, our model aims to incrementally learn a new domain while preserving its performance on previous domains without accessing previous domain data. To achieve this, our model has a unique parameter learning structure that reparametrizes domain-agnostic and domain-specific parameters. We use different optimization strategies to adapt to domain shift in incremental domain learning. Additionally, we adopt multi-level knowledge distillation loss to mitigate the impact of label space shift among domains. The experiments demonstrate that our method achieves excellent performance in domain-incremental settings, outperforming existing methods with only a few parameters.
Exploring the Spatial and Temporal Characteristics of China’s Four Major Urban Agglomerations in the Luminous Remote Sensing Perspective
Authors: Wang Jiahan , Chen Jiaqi , Liu Xiangmei , Wang Wei , Min Shengnan
Published: May 2023

This study addresses the knowledge gap regarding the spatiotemporal evolution of Chinese urban agglomerations using long time series of luminescence remote sensing data. The evolution of urban agglomerations is of great significance for the future development and planning of cities. Nighttime light data provide a window for observing urban agglomerations’ characteristics on a large spatial scale, but they are affected by temporal discontinuity. To solve this problem, this study proposes a ridge-sampling regression-based Hadamard matrix correction method and constructs consistent long-term nighttime light sequences for China’s four major urban agglomerations from 1992 to 2018. Using the Getis-Ord Gi* hot-cold spot, standard deviation ellipse method, and Baidu search index, we comprehensively analyze the directional evolution of urban agglomerations and the correlations between cities. The results show that, after correction, the correlation coefficient between nighttime light intensity and gross domestic product increased from 0.30 to 0.43. Furthermore, this study identifies unique features of each urban agglomeration. The Yangtze River Delta urban agglomeration achieved balanced development by shifting from coastal to inland areas. The Guangdong-Hong Kong-Macao urban agglomeration developed earlier and grew more slowly in the north due to topographical barriers. The Beijing-Tianjin-Hebei urban agglomeration in the north has Beijing and Tianjin as its core, and the southeastern region has developed rapidly, showing an obvious imbalance in development. The Chengdu-Chongqing urban agglomeration in the inland area has Chengdu and Chongqing as its dual core, and its development has been significantly slower than that of the other three agglomerations due to the influence of topography, but it has great potential. Overall, this study provides a research framework for urban agglomerations based on four major urban agglomerations to explore their spatiotemporal characteristics and offers insights for government urban planning.
CFRWD-GAN for SAR-to-Optical Image Translation
Authors: Wei Juan , Zou Huanxin , Sun Li , Cao Xu , He Shitian , Liu Shuo , Zhang Yuqing
Published: May 2023

Synthetic aperture radar (SAR) images have been extensively used in earthquake monitoring, resource survey, agricultural forecasting, etc. However, it is a challenge to interpret SAR images with severe speckle noise and geometric deformation due to the nature of radar imaging. The translation of SAR-to-optical images provides new support for the interpretation of SAR images. Most of the existing translation networks, which are based on generative adversarial networks (GANs), are vulnerable to part information loss during the feature reasoning stage, making the outline of the translated images blurred and semantic information missing. Aiming to solve these problems, cross-fusion reasoning and wavelet decomposition GAN (CFRWD-GAN) is proposed to preserve structural details and enhance high-frequency band information. Specifically, the cross-fusion reasoning (CFR) structure is proposed to preserve high-resolution, detailed features and low-resolution semantic features in the whole process of feature reasoning. Moreover, the discrete wavelet decomposition (WD) method is adopted to handle the speckle noise in SAR images and achieve the translation of high-frequency components. Finally, the WD branch is integrated with the CFR branch through an adaptive parameter learning method to translate SAR images to optical ones. Extensive experiments conducted on two publicly available datasets, QXS-SAROPT and SEN1-2, demonstrate a better translation performance of the proposed CFRWD-GAN compared to five other state-of-the-art models.
Effect of Bit Depth on Cloud Segmentation of Remote-Sensing Images
Authors: Liao Lingcen , Liu Wei , Liu Shibin
Published: May 2023

Due to the cloud coverage of remote-sensing images, the ground object information will be attenuated or even lost, and the texture and spectral information of the image will be changed at the same time. Accurately detecting clouds from remote-sensing images is of great significance to the field of remote sensing. Cloud detection utilizes semantic segmentation to classify remote-sensing images at the pixel level. However, previous studies have focused on the improvement of algorithm performance, and little attention has been paid to the impact of bit depth of remote-sensing images on cloud detection. In this paper, the deep semantic segmentation algorithm UNet is taken as an example, and a set of widely used cloud labeling dataset “L8 Biome” is used as the verification data to explore the relationship between bit depth and segmentation accuracy on different surface landscapes when the algorithm is used for cloud detection. The research results show that when the image is normalized, the effect of cloud detection with a 16-bit remote-sensing image is slightly better than that of an 8-bit remote sensing image; when the image is not normalized, the gap will be widened. However, using 16-bit remote-sensing images for training will take longer. This means data selection and classification do not always need to follow the highest possible bit depth when doing cloud detection but should consider the balance of efficiency and accuracy.
Assessment of Active LiDAR Data and Passive Optical Imagery for Double-Layered Mangrove Leaf Area Index Estimation: A Case Study in Mai Po, Hong Kong
Authors: Li Qiaosi , Wong Frankie Kwan Kit , Fung Tung , Brown Luke A. , Dash Jadunandan
Published: May 2023

Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in Mai Po, Hong Kong, China. LiDAR data were employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. A single red-edge VI derived from HSI data delivered the lowest RMSE of 0.12 and the highest R2adj of 0.79 for OLe model fitting. The synergetic use of LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R2adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. In addition to their penetration attributes, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation.
Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery
Authors: Kurkin Semen , Gordleeva Susanna , Savosenkov Andrey , Grigorev Nikita , Smirnov Nikita , Grubov Vadim V. , Udoratina Anna , Maksimenko Vladimir , Kazantsev Victor , Hramov Alexander E.
Published: May 2023

Experiments show activation of the left dorsolateral prefrontal cortex (DLPFC) in motor imagery (MI) tasks, but its functional role requires further investigation. Here, we address this issue by applying repetitive transcranial magnetic stimulation (rTMS) to the left DLPFC and evaluating its effect on brain activity and the latency of MI response. This is a randomized, sham-controlled EEG study. Participants were randomly assigned to receive sham (15 subjects) or real high-frequency rTMS (15 subjects). We performed EEG sensor-level, source-level, and connectivity analyses to evaluate the rTMS effects. We revealed that excitatory stimulation of the left DLPFC increases theta-band power in the right precuneus (PrecuneusR) via the functional connectivity between them. The precuneus theta-band power negatively correlates with the latency of the MI response, so the rTMS speeds up the responses in 50% of participants. We suppose that posterior theta-band power reflects attention modulation of sensory processing; therefore, high power may indicate attentive processing and cause faster responses.
A Quality Assessment Network for Failure Detection in 3D Printing for Future Space-Based Manufacturing
Authors: Tang Jianning , Wu Xiaofeng
Published: May 2023

The application of space manufacturing technology holds tremendous potential for the advancement of space exploration. With significant investment from respected research institutions such as NASA, ESA, and CAST, along with private companies such as Made In Space, OHB System, Incus, and Lithoz, this sector has recently experienced a notable surge in development. Among the available manufacturing technologies, 3D printing has been successfully tested in the microgravity environment onboard the International Space Station (ISS), emerging as a versatile and promising solution for the future of space manufacturing. In this paper, an automated Quality Assessment (QA) approach for space-based 3D printing is proposed, aiming to enable the autonomous evaluation on the 3D printed results, thus freeing the system from reliance on human intervention, an essential requirement for the operation of space-based manufacturing platforms functioning in the exposed space environment. Specifically, this study investigates three types of common 3D printing failures, namely, indentation, protrusion, and layering to design an effective and efficient fault detection network that outperforms its counterparts backboned with other existing networks. The proposed approach has achieved a detection rate of up to 82.7% with an average confidence of 91.6% by training with the artificial samples, demonstrating promising results for the future implementation of 3D printing in space manufacturing.
Dynamic Resource Allocation for Network Slicing with Multi-Tenants in 5G Two-Tier Networks
Authors: Lin Jia-You , Chou Ping-Hung , Hwang Ren-Hung
Published: May 2023

Virtualization is a core 5G network technology which helps telecom companies significantly reduce capital expenditure and operating expenses by deploying multiple services on the same hardware infrastructure. However, providing QoS-guaranteed services for multi-tenants poses a significant challenge due to multi-tenant service diversity. Network slicing has been proposed as a means of addressing this problem by isolating computing and communication resources for the different tenants of different services. However, optimizing the allocation of the network and computation resources across multiple network slices is a critical but extremely difficult problem. Accordingly, this study proposes two heuristic algorithms, namely Minimum Cost Resource Allocation (MCRA) and Fast Latency Decrease Resource Allocation (FLDRA), to perform dynamic path routing and resource allocation for multi-tenant network slices in a two-tier architecture. The simulation results show that both algorithms significantly outperform the Upper-tier First with Latency-bounded Overprovisioning Prevention (UFLOP) algorithm proposed in previous work. Furthermore, the MCRA algorithm achieves a higher resource utilization than the FLDRA algorithm.
Research on Error Correction Technology in Underwater SINS/DVL Integrated Positioning and Navigation
Authors: Li Jian , Gu Mingyu , Zhu Tianlong , Wang Zexi , Zhang Zhen , Han Guangjie
Published: May 2023

Underwater vehicles are key carriers for underwater inspection and operation tasks, and the successful implementation of these tasks depends on the positioning and navigation equipment with corresponding accuracy. In practice, multiple positioning and navigation devices are often combined to integrate the advantages of each equipment. Currently, the most common method for integrated navigation is combination of the Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL). Various errors will occur when SINS and DVL are combined together, such as installation declination. In addition, DVL itself also has errors in the measurement of speed. These errors will affect the final accuracy of the combined positioning and navigation system. Therefore, error correction technology has great significance for underwater inspection and operation tasks. This paper takes the SINS/DVL integrated positioning and navigation system as the research object and deeply studies the DVL error correction technology in the integrated system.
Modeling Cues May Reduce Sway Following Sit-To-Stand Transfer for People with Parkinson’s Disease
Authors: Martin Rebecca A. , Fulk George , Dibble Lee , Boolani Ali , Vieira Edgar R. , Canbek Jennifer
Published: May 2023

Cues are commonly used to overcome the effects of motor symptoms associated with Parkinson’s disease. Little is known about the impact of cues on postural sway during transfers. The objective of this study was to identify if three different types of explicit cues provided during transfers of people with Parkinson’s disease results in postural sway more similar to healthy controls. This crossover study had 13 subjects in both the Parkinson’s and healthy control groups. All subjects completed three trials of uncued sit to stand transfers. The Parkinson’s group additionally completed three trials of sit to stand transfers in three conditions: external attentional focus of reaching to targets, external attentional focus of concurrent modeling, and explicit cue for internal attentional focus. Body worn sensors collected sway data, which was compared between groups with Mann Whitney U tests and between conditions with Friedman’s Tests. Sway normalized with modeling but was unchanged in the other conditions. Losses of balance presented with reaching towards targets and cueing for an internal attentional focus. Modeling during sit to stand of people with Parkinson’s disease may safely reduce sway more than other common cues.
High-Gain Millimeter-Wave Beam Scanning Transmitarray Antenna
Authors: Yang Hao-Zheng , Qu Shi-Wei
Published: May 2023

In this article, a high-gain millimeter-wave transmitarray antenna (TAA) maintaining scanning ability is developed, integrating an array feed as the primary emitter. The work is achieved within a limited aperture area, avoiding the replacement or extension of the array. The addition of a set of defocused phases along the scanning direction to the phase distribution of the monofocal lens allows the converging energy to be dispersed into the scanning scope. The beam forming algorithm proposed in this article can determine the excitation coefficients of the array feed source, and is beneficial to improve the scanning capability in array-fed transmitarray antennas. A transmitarray based on the square waveguide element illuminated by an array feed is designed with a focal-to-diameter ratio (F/D) of 0.6. A 1-D scan with a scope of −5° to 5° is realized through calculation. The measured results show that the transmitarray can achieve a high gain, 37.95 dBi at 160 GHz, although a maximum 2.2 dB error appears compared with the calculation in the operating band of 150–170 GHz. The proposed transmitarray has been proven to generate scannable high-gain beams in the millimeter-wave band and is expected to demonstrate its potential in other applications.
Isolation of Peroxisomes from Frozen Liver of Rat by Differential and Iodixanol Gradient Centrifugation
Authors: Baz Lina , Al-thepyani Mona , Algarni Salha , Gashlan Hana
Published: May 2023

In the last decade, research has shown that most diseases are associated with organelle dysfunction in which metabolites play a crucial role or indicate specific processes. Peroxisomes are cellular organelles attracting an increasing amount of attention and are now recognized as essential players in physiological conditions and diseases. However, a limited amount of research focuses on isolating the organelles and studying their properties and the diseases resulting from organelle dysfunction. All methods for isolating peroxisomes are based on fresh tissue samples. To the best of our knowledge, this is the first work in which peroxisomes have been isolated from frozen rat liver. In our work, we isolated peroxisomes from frozen rat liver at −80 °C and evaluated the separation success and degree of purification of isolated peroxisomes by measuring the relative specific activity, purification fold, and percentage yield (Y%) of organelle marker enzymes in the isolated fractions. The percentage of protein distribution and density was also estimated. Our results showed that the purified peroxisome fraction (F3-peroxisome) had significantly higher relative specific activity, as well as the highest purification fold and percentage yield of catalase compared with the enzyme markers of other organelles in the postnuclear supernatant (PNS), postmitochondrial supernatant (PMS), and light mitochondria–peroxisome (LM-P) fractions. In addition, the percentage of protein distribution was significantly lower in the F3-peroxisome fraction compared with PNS, PMS, and LM-P fractions while the percentage of protein distribution and density of the F3-peroxisome fraction after iodixanol centrifugation were significantly higher than those of the F1 and F2 fractions. The present work demonstrates the possibility of isolating peroxisomes from frozen liver samples efficiently, which could pave the way for further research in the future on other subcellular organelles from frozen samples.
Community Solutions for Community Problems: Reflections on a Civic Organisation Colloquium for Resolving Gangsterism in the Northern Areas of Gqeberha, Eastern Cape, South Africa
Authors: Petrus Theodore , Davids Desira
Published: May 2023

In July 2022, the authors had the opportunity to facilitate, observe and participate in a community colloquium of civic organisations based in the Northern Areas of Gqeberha (Port Elizabeth), South Africa. The main purpose of the colloquium was to bring together civic organisations to discuss the challenge of gang subcultures in the Northern Areas, and how to address it. This article is a reflection on the colloquium, and the insights gained. As well as being the first time that a colloquium of this nature had taken place in the Northern Areas, it was the result of the authors’ ongoing research on gang subcultures in the community since 2013. Using participant observation, an engaged participatory action research (PAR) approach, and focus group discussions, the article provides some of the results that the colloquium contributed to the study. The results revealed that community-based strategies to address gangsterism would be more sustainable and effective in the long term.
Advancing Sustainable Development: Emerging Factors and Futures for the Engineering Field
Authors: Burleson Grace , Lajoie Jason , Mabey Christopher , Sours Patrick , Ventrella Jennifer , Peiffer Erin , Stine Emma , Stettler Kleine Marie , MacDonald Laura , Austin-Breneman Jesse , Javernick-Will Amy , Winter Amos , Lucena Juan , Knight David , Daniel Scott , Thomas Evan , Mattson Christopher , Aranda Iana
Published: May 2023

This study set out to identify emerging trends in advancing engineering for sustainable development, supporting the engineering workforce to address wicked problems, and strengthening pathways between engineering education, industry, and policy. The following question guided this work: What are the emerging factors impacting the future of global sustainability efforts within engineering, and how can these be amplified to increase the impact of engineering for sustainable development? Using an adapted Delphi method with surveys, focus groups, and member-checking interviews, we hosted the American Society of Mechanical Engineers (ASME) 2022 Engineering Global Development (EGD) Stakeholder Summit. The summit convened industry leaders, innovators, and academics to explore emerging factors impacting the future of global sustainability efforts in engineering. This manuscript synthesizes emerging trends and proposes recommendations for engineering, particularly in the specific focus area of engineering for sustainable development (e.g., ‘humanitarian engineering’, ‘global engineering’). Critical recommendations include the adoption of emerging cultural mindsets, which include: (1) take an interdisciplinary and multi-stakeholder approach, (2) consider dynamic and interconnected systems, (3) increase humility and intercultural competence, (4) prioritize diversity and inclusion, (5) increase localization and center community perspectives, (6) challenge the perception that engineering is neutral, and (7) broaden the goals of engineering. Ultimately, this study highlights pathways forward for the broader engineering community to more effectively contribute to advancing the United Nations Sustainable Development Goals.
Circular Economy Development in the Wood Construction Sector in Finland
Authors: Husgafvel Roope , Sakaguchi Daishi
Published: May 2023

Circular economy development is about a system level change towards enhanced sustainability and circularity covering both biological and technical cycles. This study aimed at exploring, identifying, analyzing and synthesizing the current state of and future outlook on CE development in the wood construction sector in Finland as perceived by various sectoral companies. This study focused on multiple themes related to the importance of the various aspects of a CE and associated approaches in this particular sector. This study applied a qualitative research approach, and a questionnaire survey was the specific method. The survey was sent to both architectural and construction sector companies. This study addressed a gap in research and contributes to the better understanding of the current state of and future outlook on CE development in the wood construction sector. The results indicate that the CE concept is mostly considered to be an important part of building design and construction. However, some respondents found that this concept is new to them. Interestingly, the cascading use of wood and the assessment and measurement of a CE were not at all familiar to many respondents. Particularly important CE aspects in the wood construction sector include, for example, sustainability and the long life cycles of products, components and materials; co-creation and cooperation covering the whole life cycle of construction and the whole supply chain; training and competence development; and design for a CE, sustainability and long life cycles. Many essential elements of a CE were in use, coming into use or in consideration by many of the sectoral companies.
Evaluation of Green Marketing Strategies by Considering Sustainability Criteria
Authors: Esmaelnezhad Danial , Lagzi Mohammad Dana , Antucheviciene Jurgita , Hashemi Shide Sadat , Khorshidi Sina
Published: May 2023

The purpose of this study is to identify and prioritize green marketing strategies in the food industry by taking sustainability goals into consideration since it is very difficult to prioritize and select green marketing strategies when considering sustainability criteria in their implementation. Therefore, it is necessary to use an appropriate solution to prioritize these strategies. For this purpose, sustainability criteria and green marketing strategies are recognized. Then, sustainability criteria are screened using the fuzzy Delphi method. Subsequently, using the fuzzy stepwise weight assessment ratio analysis method, the criteria are weighted according to expert recommendations. Next, green marketing strategies in the Iranian food industry are ranked using several fuzzy multiple criteria decision-making methods: fuzzy additive ratio assessment, fuzzy complex proportional assessment, fuzzy technique for order preference by similarity to the ideal solution, and fuzzy Viekriterijumsko Kompromisno Rangiranje methods. Finally, the results are integrated with the help of the Copeland method to choose the best strategies. The results indicated that the criteria of ‘minimizing waste’, ‘minimizing environmental impacts’, and ‘consumer participation’ had the highest weight. In addition, the strategies for the ‘development of waste reduction technology, ‘development of market research to detect green needs’, and ‘use of recycled or reusable materials in products’ received a higher rank.
A Review of Research on Embodied Carbon in International Trade
Authors: Wang Haoran , Fujita Toshiyuki
Published: May 2023

Nowadays, how to reduce carbon emissions is a hot issue in environmental economics research, and countries around the world are having extensive discussions on their respective carbon emission obligations. The embodied carbon contained in international trade plays a crucial role in controlling pollutant emissions but it is often overlooked, resulting in problems such as carbon displacement and avoidance of responsibility for pollutant emissions. Based on the Social Sciences Citation Index (SSCI) and Science Citation Index-Expanded (SCI-E) database, this paper adopts a bibliometric method to summarize 626 papers from 1994 to 2023 in six aspects, including the number of the literature, the literature citations, research region, journal, author, and research discipline. Meanwhile, the research method and model used in the collected papers are classified and reviewed. Then, this study briefly outlines the current status of embodied carbon emissions and the international pollutant identification laws and analyzes the shortcomings of existing research and the rationality of responsibility identification principles. Finally, we propose future research hotspots by combining carbon neutrality and carbon trading theory.
Urban Stream Temperature Surge—Streamwater Temperature Variability after Rainfall in Suceava City Metropolitan Area
Authors: Briciu Andrei-Emil , Mihăilă Dumitru , Oprea Dinu Iulian , Prisăcariu Alin
Published: May 2023

Suceava city is a territory under the urban heat island effect that leads to warm runoffs into small urban streamwaters that are highly impacted by the thermal pollution due to a combination of the small stream discharge and important runoff from impervious urban surfaces. This research detects the frequency and specificities of temperature surges in the Suceava city metropolitan area–40 surges were certainly identified in ~2 years-long time series. We analyzed the diurnal cycles of the stream temperature and correlated the atmospheric precipitation with some heated surface runoffs that resulted in aperiodic increases in the stream temperature. The high variability of rainfalls in the urban area meant that the stream temperature surges occurred rarely in all streams during the same rainfall event, despite the small distances between the watersheds (1–5 km between adjacent water monitoring points). The surges lasted up to 10 h and had an amplitude of up to 5.2 °C; they usually lasted 3.9–5.6 h after 5.8–7.7 mm rainfalls, causing temperature peaks of 0.5–1.4 °C. Additionally, they were easier to detect in the Cetăţii Creek due to its colder-than-natural waters during the warm season—a result of important wastewater discharge. Overall, the surges occurred mostly during the warm and wet semester of the year (87.5%) and especially during the summer (50%). Because Suceava is an average Romanian city, our findings could be taken into account as relevant data at least for cities located in the Moldavian Plateau.
Research on Indoor Thermal Environment Analysis and Optimization Strategy of Rural Dwellings around Xi’an Based on PET Evaluation
Authors: Qi Yingtao , Li Xiaodi , Wang Yupeng , Zhou Dian
Published: May 2023

Rural dwellings are an important group of residential buildings in China. With the continuous development of rural construction in China, the contradiction between the pursuit of a simple material space and the villagers’ demand for living quality, especially the indoor thermal comfort of rural dwellings, has become increasingly prominent. Therefore, it is particularly important to study the optimization strategies of the indoor thermal environment in rural dwellings. Current research on optimizing the indoor thermal environment of rural dwellings mainly focuses on analyzing the impact of individual factors, such as the envelope structures, building constructions, and building technology applications, but there is a lack of strategy development based on the comprehensive evaluation. This study aims to analyze the combined effects of multiple design elements on the indoor thermal environment and propose a comprehensive optimization strategy for rural dwellings. This study selects the rural dwellings around Xi’an as an example and establishes a basic model of the rural dwellings around Xi’an through field investigation and software simulation. Then, through univariate and compound-variable simulations, we analyze the influence of changes in passive architectural design indicators on the indoor physiological equivalent temperature (PET) of rural dwellings and obtain a comprehensive design indicator optimization strategy. This strategy can improve the indoor thermal comfort in winter and summer, especially in winter, achieving an average increase of 4.17 °C in the winter PET value and an average decrease of 0.66 °C in summer. This provides a reference for the design and renovation of rural dwellings in Xi’an and other rural areas in the cold regions of China.
Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems
Authors: Li Xiang , Yan Qipeng , Ma Yafeng , Luo Chen
Published: May 2023

Public transport, especially bus and metro, are fundamental elements of sustainable transport systems. However, a dearth of research has been devoted to exploring the correlation between the built environment and the intermodal transfer modes that link bus and metro. To address this research gap, this study aims to explore the relationship between the built environment and transfer ridership by examining transfer ridership across different modes. First, this study uses Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) data collected in the city of Chengdu to identify the ridership of Metro-to-Bus (M-B) and Bus-to-Metro (B-M) transfer passengers using dynamic transfer time thresholds. A multi-scale geographically weighted regression model (MGWR) is employed to examine the impact of the built environment on M-B and B-M transfer modes and their scale effects. The findings demonstrate that the MGWR model is effective in capturing the spatial heterogeneity and scale effects of the interrelationships between different built environment factors in the M-B and B-M modes. Furthermore, the impact of different built environment factors on transfer ridership varies. In particular, the number of bus stops and lines have a more pronounced positive effect on promoting transfer ridership, while the density of non-motorway lanes has a significant negative effect. This research provides valuable insights for public transportation management and supports the seamless integration of bus and metro systems to optimize transfer services.
Strategies for Sustainable Urban Renewal: Community-Scale GIS-Based Analysis for Densification Decision Making
Authors: Chen Jinliu , Pellegrini Paola , Yang Zhuo , Wang Haoqi
Published: May 2023

China is gradually shifting towards more sustainable urban development, and the local governments are increasingly promoting social and environmentally sustainable spatial planning practices. This article debates the potential contradiction between the goal of a constantly growing urban population and the limits to the consumption of land planned by this new direction of urban development. The analysis focuses on the wealthy city of Suzhou in the Yangtse River Delta region and explores the opportunities for densification of the residential areas as a possible solution for this contradiction, as already tested by some Chinese cases for land use efficiency. The research applies GIS-based spatial analysis and identifies some of the sites that can be efficiently redeveloped in the resettlement communities for their low floor area ratio (FAR) and obsolescent conditions, which do not correspond to the increasingly middle-class status of the residents in the urban region. The article investigates the different options of a densification strategy in the frame of the policies of urban renewal promoted in China in recent years for improving the quality of the built environment.
Emissions and Total Cost of Ownership for Diesel and Battery Electric Freight Pickup and Delivery Trucks in New Zealand: Implications for Transition
Authors: Lyu Zichong , Pons Dirk , Zhang Yilei
Published: May 2023

Road freight transport contributes to a large portion of greenhouse gas (GHG) emissions. Transitioning diesel to battery electric (BE) trucks is an attractive sustainability solution. To evaluate the BE transition in New Zealand (NZ), this study analysed the life-cycle GHG emissions and total cost of ownership (TCO) of diesel and BE trucks based on real industry data. The freight pickup and delivery (PUD) operations were simulated by a discrete-event simulation (DES) model. Spreadsheet models were constructed for life-cycle assessment (LCA) and TCO for a truck operational lifetime of 10 years (first owner), this being the typical usage of a tier-one freight company in New Zealand (NZ). The whole-of-life emissions from the diesel and BE trucks are 717,641 kg and 62,466 kg CO2e, respectively. For the use phase (first owner), the emissions are 686,754 kg and 8714 kg CO2e, respectively; i.e., the BE is 1.27% of the diesel truck. The TCO results are 528,124 NZ dollars (NZD) and 529,573 NZD (as of 2022), respectively. The battery price and road user charge are the most sensitive variables for the BE truck. BE truck transitions are explored for freight companies, customers, and the government. For the purchase of BE trucks, the break-even point is about 9.5 years, and straight-line depreciation increases freight costs by 8.3%. Government subsidy options are evaluated. The cost of emission credits on the emissions trading scheme (ETS) is not expected to drive the transition. An integrated model is created for DES freight logistics, LCA emissions, and TCO costs supported by real industry data. This allows a close examination of the transition economics.
Determinants of Green Innovation: The Role of Monetary Policy and Central Bank Characteristics
Authors: Spyromitros Eleftherios
Published: May 2023

The current global energy crisis has prompted a comprehensive investigation into its influencing factors. It is hypothesised that a set of monetary, macro-environmental, and institutional variables causally affect the transition to green development in a holistic model. Monetary expansion and central bank characteristics are required for economic and environmental development. The current study investigates and rigorously verifies the impact of expansionary monetary policy actions on green innovation, using a panel of 109 countries from 2010 to 2018. Overall, specific actions have a substantial positive effect on the performance of green innovation. A rise in per capita GDP, government spending, and improvement in bureaucracy all promote green economic activity. Green innovation is significantly affected by developing nations’ central bank independence and lower interest rates. Expansionary monetary policy, central bank transparency, and energy variables promote green growth in developed countries and green innovation in Latin American countries and in East Asian and Pacific countries. Finally, green innovation is more affected by expansionary monetary policy in countries with high institutional quality, industrial concentration, and energy intensity, and inflation and trade openness serve as deterrents in the monetary expansion–green development nexus.
Fiscal Decentralization, Environmental Regulation and High-Quality Economic Development
Authors: Yu Zhuoxi , Wu Yu , Zhu Zhichuan
Published: May 2023

Fiscal decentralization, as a policy with which to regulate the financial power relationships between central and local governments, undoubtedly has an impact on high-quality economic development, and in the current context of focusing on environmental quality its path may include environmental regulation, which is restricted by the degree of decentralization and directly affects high-quality economic development. Based on data from 30 Chinese provinces from 2010 to 2019, this paper empirically tests the moderating effect of environmental regulation on the relationship between fiscal decentralization and high-quality economic development through the use of an intermediary-effect model, a threshold model, and a quantile regression model. The following are the main conclusions: First, fiscal decentralization encourages high-quality economic development with a coefficient of 0.069, but environmental regulation dampens this effect, with a coefficient value of 0.0055 for the suppression effect; after removing endogenous effects, the conclusion remains valid. Second, the influence of fiscal decentralization has different manifestations under different environmental regulation intensities and different levels of high-quality economic development: fiscal decentralization has a larger regression coefficient when the intensity of environmental regulation exceeds 0.0108; it only shows a positive effect in regions with a higher quality of economic development (after the 25% quantile). The main contribution of this paper is the discussion of the impact of fiscal decentralization on high-quality economic development from the perspective of environmental regulation and revealing of a new path for fiscal decentralization to play its role, which is of certain significance in exploring the appropriate degree of decentralization and formulating environmental regulation policies.
Track-Index-Guided Sustainable Off-Road Operations Using Visual Analytics, Image Intelligence and Optimal Delineation of Track Features
Authors: Kalra Manoj Kumar , Shukla Sanjay Kumar , Trivedi Ashutosh
Published: May 2023

Visual-analytics-guided systems are replacing human efforts today. In many applications, movement in off-road terrain is required. Considering the need to negotiate various soft ground and desertic conditions, the beaten tracks of leading vehicles considered to be safe and suitable for guiding are used in such operations. During night, often, these tracks pass through low-contrast conditions posing difficulty in their identification. The maximization of track contrast is therefore desired. Many contrast enhancement techniques exist but their effectiveness varies as per the surrounding. Other than conventional techniques, the role of texture too becomes important for enhancing the differentiable track contrast. Gray-level co-occurrence matrix (GLCM)-based statistic measures are used here to evaluate the track texture. These measures are seen to improve the contrast of vehicle tracks significantly. A track-index-based technique is proposed to sort various images as per their effectiveness in increasing the track contrast. Different forms of track indices are proposed and compared. The proposed track index is seen as effective in sorting 88.8% of contrast images correctly. The proposed technique of creating and sorting images based on the contrast level is seen as a useful tool for improved fidelity in many difficult situations for making the off-road operations sustainable.
An Energy Management Strategy for Fuel-Cell Hybrid Commercial Vehicles Based on Adaptive Model Prediction
Authors: Xu Enyong , Ma Mengcheng , Zheng Weiguang , Huang Qibai
Published: May 2023

Fuel-cell hybrid electric vehicles have the advantages of zero pollution and high efficiency and are extensively applied in commerce. An energy management strategy (EMS) directly impacts the fuel consumption and performance. Moreover, model prediction control (MPC) is synchronous and has been a research hotspot of EMS in recent years. The existing MPC’s low-speed prediction accuracy, which results in considerable instability in EMS allocation, is solved by the proposed energy management strategy based on adaptive model prediction. Dynamic programming (DP) is used as the solver, improved condition recognition and a radial basis neural network (RBFNN) are used as the speed predictor, and hydrogen consumption and the state of charge (SOC) are used as the objective function. According to the simulation results, using a 5 s speed prediction improves the forecast accuracy by 9.75%, and compared with employing a rule-based energy management strategy, this strategy reduces hydrogen consumption and the power cell fluctuation frequency by 3.50%.