bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying Core Wavelengths of Oil Tree’s Hyperspectral Data by Taylor Expansion
Authors: Sun Zhibin , Jiang Xinyue , Tang Xuehai , Yan Lipeng , Kuang Fan , Li Xiaozhou , Dou Min , Wang Bin , Gao Xiang
Published: June 2023

The interference of background noise leads to the extremely high spatial complexity of hyperspectral data. Sensitive band selecting is an important task to minimize or eliminate the influence of non-target elements. In this study, Taylor expansion is innovatively used to identify core wavelengths/bands of hyperspectral data. Unlike other traditional methods, this proposed Taylor-CC method considers more local and global information of spectral function to estimate the linear/nonlinear correlation between two wavelengths. Using samples of hyperspectral data with a wavelength range of 350–2500 nm and SPAD for Camellia oleifera, this Taylor-CC method is compared with the traditional PCC method derived from the Pearson correlation coefficient. Using the 240 samples with their different 57 core wavelengths identified by the Taylor-CC method and PCC method, three machine models (i.e., random forest-RF, linear regression-LR, and artificial neural network-ANN) are trained to compare their performances. Their results show that the correlation matrix from the Taylor-CC method represents a clear diagonal pattern with near zero values at most locations away from the diagonal, and all three models confirm that the Taylor-CC method is superior to the PCC method. Moreover, the SPAD spectral response relationship based on machine learning algorithms is constructed, and ANN is the best prediction performance among the three models when using the core wavelengths identified by the Taylor-CC method. The Taylor-CC method proposed in this study not only lays a mathematical foundation for the next analysis of the response mechanism between spectral characteristics and nutrient content of Camellia leaf, but also provides a new idea for the correlation analysis of adjacent spectral bands for hyperspectral signals in many applications.
Weather Radar Echo Extrapolation with Dynamic Weight Loss
Authors: Zhang Yonghong , Geng Sutong , Tian Wei , Ma Guangyi , Zhao Huajun , Xie Donglin , Lu Huanyu , Lim Kam Sian Kenny Thiam Choy
Published: June 2023

Precipitation nowcasting is an important tool for economic and social services, especially for forecasting severe weather. The crucial and challenging part of radar echo image prediction is the focus of radar-based precipitation nowcasting. Recently, a number of deep learning models have been designed to solve the problem of extrapolating radar images. Although these methods can generate better results than traditional extrapolation methods, the issue of error accumulation in precipitation forecasting is exacerbated by using only the mean square error (MSE) and mean absolute error (MAE) as loss functions. In this paper, we approach the problem from the perspective of the loss function and propose dynamic weight loss (DWL), a simple but effective loss function for radar echo extrapolation. The method adds model self-adjusted dynamic weights to the weighted loss function and structural similarity index measures. Radar echo extrapolation experiments are performed on four models, ConvLSTM, ConvGRU, PredRNN, and PredRNN++. Radar reflectivity is predicted using Nanjing University C-band Polarimetric (NJU-CPOL) weather radar data. The quantitative statistics show that using the DWL method reduces the MAE of the four models by up to 10.61%, 5.31%, 14.8%, and 13.63%, respectively, over a 1 h prediction period. The results show that the DWL approach is effective in reducing the accumulation of errors over time, improving the predictive performance of currently popular deep learning models.
A Preliminary Study on the Inversion Method for the Refraction Structure Parameter from Vortex Electromagnetic Waves
Authors: Liao Qixiang , Sheng Zheng , Zhou Shudao , Guo Peng , Long Zhiyong , He Mingyuan , Guan Jiping
Published: June 2023

When vortex electromagnetic waves propagate through a turbulent atmosphere, the amplitude and phase of the electromagnetic waves are disturbed, creating the scintillation effect. According to the scintillation index of vortex waves, a new method of retrieving the turbulent refraction structure parameter was proposed using a genetic algorithm, and the feasibility of this method was verified by simulated experiments. The numerical results showed that the inversed value obtained by the genetic algorithm was close to the real parameter when the turbulent inner scale and outer scale were fixed. However, there was a gap between the inversed value and the real parameter when only the turbulent outer scale was fixed. These results suggest that vortex wave data can be used for turbulent refraction structure parameter inversion, and they provide new research directions for atmospheric remote sensing.
A High-Performance Automated Large-Area Land Cover Mapping Framework
Authors: Zhang Jiarui , Fu Zhiyi , Zhu Yilin , Wang Bin , Sun Keran , Zhang Feng
Published: June 2023

Land cover mapping plays a pivotal role in global resource monitoring, sustainable development research, and effective management. However, the complexity of the mapping process, coupled with significant computational and data storage requirements, often leads to delays between data processing and product publication, thereby bringing challenges to creating multi-timesteps large-area products for monitoring dynamic land cover. Therefore, improving the efficiency of each stage in land cover mapping and automating the mapping process is currently an urgent issue to be addressed. This study proposes a high-performance automated large-area land cover mapping framework (HALF). By leveraging Docker and workflow technologies, the HALF effectively tackles model heterogeneity in complex land cover mapping processes, thereby simplifying model deployment and achieving a high degree of decoupling between production models. It optimizes key processes by incorporating high-performance computing techniques. To validate these methods, this study utilized Landsat imagery data and extracted samples using GLC_FCS and FROM_GLC, all of which were acquired at a spatial resolution of 30 m. Several 10° × 10° regions were chosen globally to illustrate the viability of generating large-area land cover using the HALF. In the sample collection phase, the HALF introduced an automated method for generating samples, which overlayed multiple prior products to generate a substantial number of samples, thus saving valuable manpower resources. Additionally, the HALF utilized high-performance computing technology to enhance the efficiency of the sample–image matching phase, thereby achieving a speed that was ten times faster than traditional matching methods. In the mapping stage, the HALF employed adaptive classification models to train the data in each region separately. Moreover, to address the challenge of handling a large number of classification results in a large area, the HALF utilized a parallel mosaicking method for classification results based on the concept of grid division, and the average processing time for a single image was approximately 6.5 s.
Neural Network Compression via Low Frequency Preference
Authors: Zhang Chaoyan , Li Cheng , Guo Baolong , Liao Nannan
Published: June 2023

Network pruning has been widely used in model compression techniques, and offers a promising prospect for deploying models on devices with limited resources. Nevertheless, existing pruning methods merely consider the importance of feature maps and filters in the spatial domain. In this paper, we re-consider the model characteristics and propose a novel filter pruning method that corresponds to the human visual system, termed Low Frequency Preference (LFP), in the frequency domain. It is essentially an indicator that determines the importance of a filter based on the relative low-frequency components across channels, which can be intuitively understood as a measurement of the “low-frequency components”. When the feature map of a filter has more low-frequency components than the other feature maps, it is considered more crucial and should be preserved during the pruning process. We conduct the proposed LFP on three different scales of datasets through several models and achieve superior performances. The experimental results obtained on the CIFAR datasets and ImageNet dataset demonstrate that our method significantly reduces the model size and FLOPs. The results on the UC Merced dataset show that our approach is also significant for remote sensing image classification.
LEO Satellite Clock Modeling and Its Benefits for LEO Kinematic POD
Authors: Wang Kan , El-Mowafy Ahmed , Yang Xuhai
Published: June 2023

High-accuracy Low Earth Orbit (LEO) satellite clock and orbital products are preconditions to realize LEO augmentation for high-accuracy GNSS-based positioning on the ground. There is a high correlation between the orbit and clock parameters in the kinematic Precise Orbit Determination (POD) process. While future LEO satellites are planned to be equipped with better clocks, the benefits of modeling high-stability LEO satellite clocks are not yet thoroughly investigated, particularly when mid- to long-term systematic effects induced by the complex LEO relativistic effects and the external environment remain in the clocks. Through clock modeling, this study attempts to reduce not only the short-term noise of radial kinematic orbits, but also mis-modeled effects caused by, e.g., real-time GNSS orbital and clock errors. To explore the benefits of clock modeling, the clocks need to be first detrended by the mid- to long-term systematic effects. While over-detrending limits the orbital improvements, weak detrending would also hamper strong clock modeling and easily lead to performance degradations. A balance between the strengths of the detrending and the model thus needs to be investigated for different clock types. In this study, the Piece-Wise Linear (PWL) model of different time lengths and a 2.5-state filter with different strengths (h values) are tested using real data from GRACE FO-1 with an Ultra-Stable Oscillator (USO) on board. Using the CNES real-time GPS products, it was found that when detrending the clocks with a smoothing window of 300 to 500 s, one could generally expect an improvement larger than 10% in the estimation of radial orbits when applying a PWL model with a length from 300 to 1200 s. Improvements of this size can also be expected when using the 2.5-state model with h−1 (for Flicker Frequency Noise) from 10−28 to 10−30.
Coastal Ship Tracking with Memory-Guided Perceptual Network
Authors: Yang Xi , Zhu Haiyang , Zhao Hua , Yang Dong
Published: June 2023

Coastal ship tracking is used in many applications, such as autonomous navigation, maritime rescue, and environmental monitoring. Many general object-tracking methods based on deep learning have been explored for ship tracking, but they often fail to accurately track ships in challenging scenarios, such as occlusion, scale variation, and motion blur. We propose a memory-guided perception network (MGPN) to address these issues. MGPN has two main innovative improvements. The dynamic memory mechanism (DMM) in the proposed method stores past features of the tracked target to enhance the model’s feature fusion capability in the temporal dimension. Meanwhile, the hierarchical context-aware module (HCAM) enables the interaction of different scales, global and local information, to address the scale discrepancy of targets and improve the feature fusion capability in the spatial dimension. These innovations enhance the robustness of tracking and reduce inaccuracies in the bounding boxes. We conducted an in-depth ablation study to demonstrate the effectiveness of DMM and HCAM. Finally, influenced by the above two points, MGPN has achieved state-of-the-art performance on a large offshore ship tracking dataset, which contains challenging scenarios such as complex backgrounds, ship occlusion, and varying scales.
AHF: An Automatic and Universal Image Preprocessing Algorithm for Circular-Coded Targets Identification in Close-Range Photogrammetry under Complex Illumination Conditions
Authors: Shang Hang , Liu Changying
Published: June 2023

In close-range photogrammetry, circular-coded targets (CCTs) are a reliable method to solve the issue of image correspondence. Currently, the identification methods for CCTs are very mature, but complex illumination conditions are still a key factor restricting identification. This article proposes an adaptive homomorphic filtering (AHF) algorithm to solve this issue, utilizing homomorphic filtering (HF) to eliminate the influence of uneven illumination. However, HF parameters vary with different lighting types. We use a genetic algorithm (GA) to carry out global optimization and take the identification result as the objective function to realize automatic parameter adjustment. This is different from the optimization strategy of traditional adaptive image enhancement methods, so the most significant advantage of the proposed algorithm lies in its automation and universality, i.e., users only need to input photos without considering the type of lighting conditions. As a preprocessing algorithm, we conducted experiments combining advanced commercial photogrammetric software and traditional identification methods, respectively. We cast stripe- and lattice-structured light to create complex lighting conditions, including uneven lighting, dense shadow areas, and elliptical light spots. Experiments showed that our algorithm significantly improves the robustness and accuracy of CCT identification methods under complex lighting conditions. Given the perfect performance under stripe-structured light, this algorithm can provide a new idea for the fusion of close-range photogrammetry and structured light. This algorithm helps to improve the quality and accuracy of photogrammetry and even helps to improve the decision making and planning process of photogrammetry.
A Prediction Method of Ionospheric hmF2 Based on Machine Learning
Authors: Wang Jian , Yu Qiao , Shi Yafei , Yang Cheng
Published: June 2023

The ionospheric F2 layer is the essential layer in the propagation of high-frequency radio waves, and the peak electron density height of the ionospheric F2 layer (hmF2) is one of the important parameters. To improve the predicted accuracy of hmF2 for further improving the ability of HF skywave propagation prediction and communication frequency selection, we present an interpretable long-term prediction model of hmF2 using the statistical machine learning (SML) method. Taking Moscow station as an example, this method has been tested using the ionospheric observation data from August 2011 to October 2016. Only by inputting sunspot number, month, and universal time into the proposed model can the predicted value of hmF2 be obtained for the corresponding time. Finally, we compare the predicted results of the proposed model with those of the International Reference Ionospheric (IRI) model to verify its stability and reliability. The result shows that, compared with the IRI model, the predicted average statistical RMSE decreased by 5.20 km, and RRMSE decreased by 1.78%. This method is expected to provide ionospheric parameter prediction accuracy on a global scale.
Sharp Feature-Preserving 3D Mesh Reconstruction from Point Clouds Based on Primitive Detection
Authors: Liu Qi , Xu Shibiao , Xiao Jun , Wang Ying
Published: June 2023

High-fidelity mesh reconstruction from point clouds has long been a fundamental research topic in computer vision and computer graphics. Traditional methods require dense triangle meshes to achieve high fidelity, but excessively dense triangles may lead to unnecessary storage and computational burdens, while also struggling to capture clear, sharp, and continuous edges. This paper argues that the key to high-fidelity reconstruction lies in preserving sharp features. Therefore, we introduce a novel sharp-feature-preserving reconstruction framework based on primitive detection. It includes an improved deep-learning-based primitive detection module and two novel mesh splitting and selection modules that we propose. Our framework can accurately and reasonably segment primitive patches, fit meshes in each patch, and split overlapping meshes at the triangle level to ensure true sharpness while obtaining lightweight mesh models. Quantitative and visual experimental results demonstrate that our framework outperforms both the state-of-the-art learning-based primitive detection methods and traditional reconstruction methods. Moreover, our designed modules are plug-and-play, which not only apply to learning-based primitive detectors but also can be combined with other point cloud processing tasks such as edge extraction or random sample consensus (RANSAC) to achieve high-fidelity results.
The (Un)Equal Effect of Binary Socialisation on Adolescents’ Exposure to Pornography: Girls’ Empowerment and Boys’ Sexism from a New Representative National Survey
Authors: Cerbara Loredana , Ciancimino Giulia , Corsetti Gianni , Tintori Antonio
Published: June 2023

Due to the increasing trend in the use of pornography recorded in recent years, along with the decreasing age of its consumers, many studies have investigated its potential negative effects on adolescents, who are developing their social and sexual identity, often producing inconsistent results. With the present study, which is based on a large representative sample of Italian students attending public upper secondary schools, predictors as well as negative and positive effects of pornography use on adolescents’ attitudes, beliefs and well-being have been identified through a psychosocial approach. To achieve these results, the study involved a two-step analysis: first, the decision tree method for classification was applied to identify the main predictors of different frequencies in pornography consumption; subsequently, multinomial logistic regressions were carried out to detect the effects of pornography use. The results show a large difference between boys and girls in terms of frequency of pornography use, but also in the effects on adherence to gender roles, which is related to a still strongly stereotyped socialisation. Instead, similar negative effects on both girls and boys were found on self-esteem, body satisfaction, negative primary emotions and distress, suggesting the need for adequate sex education provided by both parents and schools to counteract taboos and blind internalisation of models of beauty and sexual behaviours provided by pornography.
The Empirical Relationship between Procedural Justice, Police Legitimacy, and Intimate Partner Violence Experiences among a Sample of Previously Adjudicated Youth
Authors: Zedaker Sara , Goodson Amanda
Published: June 2023

The impact of intimate partner violence on procedural justice has not received much attention in extant literature. As such, the current study uses data from the Pathways to Desistance Study to examine how elements of intimate partner violence affect trust in police and perceptions of legitimacy toward the criminal justice system. Results indicated several important findings regarding the effects of intimate partner violence on procedural justice. Limitations, future research, and policy recommendations are discussed.
Under My Skin: Reducing Bias in STEM through New Approaches to Assessment of Spatial Abilities Considering the Role of Emotional Regulation
Authors: Lennon-Maslin Michelle , Quaiser-Pohl Claudia Michaela , Ruthsatz Vera , Saunders Mirko
Published: June 2023

Reducing gender bias in STEM is key to generating more equality and contributing to a more balanced workforce in this field. Spatial ability and its components are cognitive processes crucial to success in STEM education and careers. Significant gender differences have consistently been found in mental rotation (MR), the ability to mentally transform two- and three-dimensional objects. The aim of this pilot study is to examine factors in psychological assessment which may contribute to gender differences in MR performance. Moreover, findings will inform the development of the new approaches to assessment using computer adaptive testing (CAT). (1) Background: The study examines the impact of emotional regulation on MR performance in primary school children whose mean age was 9.28 years old. (2) Methods: Skin conductance was measured to assess the impact of emotional reactivity (ER) on performance during an MR task. (3) Results: Patterns of ER influence response time (RT) on specific items in the task. (4) Conclusions: Identifying the effects of emotional arousal and issues of test construction such as stereotyped stimuli and item difficulty in tests of spatial ability warrants ongoing investigation. It is vital to ensure that these factors do not compromise the accurate measurement of performance and inadvertently contribute to the gender gap in STEM.
Research on the Performance of Recycled-Straw Insulating Concrete and Optimization Design of Matching Ratio
Authors: Deng Jun-Xi , Li Xiao , Li Xiao-Juan , Wei Tai-Bing
Published: June 2023

Construction solid waste and agricultural waste, as renewable resources, have gained increasing attention recently. This research aims to explore the mechanical and thermal properties of recycled-straw insulating concrete commonly made with construction waste and straw in northern Fujian, which can provide useful suggestions for the practical use of recycled-straw insulating concrete. The effects of recycled coarse aggregate, fly ash, and straw on the mechanical and thermal properties of recycled-straw insulating concrete were investigated by orthogonal tests. The results of the orthogonal tests were optimized by the total efficacy coefficient method to obtain the optimal mix ratio of recycled-straw insulating concrete. Combined with the finite element analysis software ANSYS Workbench, the heat transfer performance of the recycled-straw insulating concrete walls was analyzed to simulate the insulation performance of the walls. The compressive strength of the recycled-straw insulating concrete with the optimal ratio was found to be 30.93 MPa, and the thermal conductivity was 0.5051 W/(m·K). The steady-state thermal analysis of the recycled-straw insulating concrete wall and the plain concrete wall was carried out by finite element software, and the simulation results showed that the insulation performance of the recycled-straw insulating concrete walls was improved by 145% compared with the plain concrete wall. These results indicate that the recycled-straw insulating concrete wall has better thermal insulation performance and can be applied to building envelopes to save heating costs in winter and reduce carbon dioxide emissions, which has significant economic and environmental significance for areas with low outdoor temperatures in winter and long heating periods.
Mapping Policy Actors Using Social Network Analysis on Integrated Urban Farming Program in Bandung City
Authors: Widayat Yanne Yuniarti , Karlina Nina , Munajat Mas Dadang Enjat , Ningrum Sinta
Published: June 2023

This study aimed to produce a network structure in Integrated Urban Farming Program in Bandung City to map the involved policy actors to realize a food-smart city. In this study, a mixed method was used with an exploratory sequential strategy involving policy actors from the government, private sector, academia, community, and mass media. To obtain a network structure in Integrated Urban Farming toward determining the most important actors, the Social Network Analysis (SNA) approach was also employed through the Gephi application. From this context, the structure emphasized four dimensions, namely Degree, Betweenness, Closeness, and Eigenvector Centralities. The results showed that the actor with the most connections (degree of centrality) and best communication control (betweenness centrality) is Parahyangan Catholic University (academic). At the same time, the actor that plays the most important role (eigenvector) is at the lower level of the government’s Sub-District and Urban Village. This study is useful for explaining the importance of the position of actors in the urban farming policy network, which is the key to the success of a program.
The Impact of Logistics Corporate Social Responsibility on Supply Chain Performance: Using Supply Chain Collaboration as an Intermediary Variable
Authors: Chen Lu , Fu Yueyue , Liu Yujia , Wang Cui
Published: June 2023

In recent years, there has been an increasing lack of social responsibility, such as low credibility of information disclosure, product quality defects, food safety, and other issues. This has had a certain impact on supply chain performance and has become an important topic of sustainable supply chain research. This study considers the relationship between logistics corporate social responsibility and supply chain performance. Structural equation models were built to explore the relationship between logistics corporate social responsibility, supply chain collaboration, and supply chain performance, and the bootstrap method was used to build path models to explore whether there is a mediation effect between logistics corporate social responsibility and supply chain performance. The results show that logistics corporate social responsibility has a significant positive impact on supply chain coordination, and logistics corporate social responsibility and supply chain coordination also have a significant positive impact on supply chain performance. Supply chain coordination plays an intermediary role between logistics corporate social responsibility and supply chain performance. Based on these results, it is suggested that logistics enterprises improve their awareness of fulfilling corporate social responsibility, improve transparency, strengthen supply chain collaboration, and accept the role of supervision and management at the government level.
Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank
Authors: Lin Lan , Xiong Min , Jin Yue , Kang Wenjie , Wu Shuicai , Sun Shen , Fu Zhenrong
Published: June 2023

Age-related cognitive decline is a global phenomenon that affects individuals worldwide. The course and extent of this decline are influenced by numerous factors, such as genetics, lifestyle, education, and cognitive engagement. The theory of brain and cognitive reserve/maintenance posits that these factors have a significant impact on the degree of cognitive decline and overall brain health. However, the absence of standardized definitions and measurements for these terms creates ambiguity in research. To address this issue, we utilized a robust and systematic experimental paradigm, employing a considerably large subject pool comprising 17,030 participants from the UK Biobank. Utilizing advanced machine learning methodologies, we were able to accurately quantify both brain maintenance (BM) and cognitive maintenance (CM), making use of six distinct MRI modalities and nine distinct cognitive capabilities. Our study successfully identified several significant features that were meaningfully associated with both BM and CM outcomes. The results of our study demonstrate that lifestyle factors play a significant role in influencing both BM and CM through unique and independent mechanisms. Specifically, our study found that health status is a critical determinant of BM, while diabetes was found to be moderately associated with CM. Furthermore, our study revealed a positive correlation between BM/CM and cognitive reserve. By carefully considering the unique and independent mechanisms that govern both BM and CM, as well as their correlation with cognitive reserve, our study has provided valuable insight into the various strategies that may be leveraged to promote sustainable interventions to enhance cognitive and brain health across the lifespan.
Self-Application of the CCP Model among Socio-Labor Counseling Professionals: Evaluation of the Impact on Their Careers and Social Sustainability Actions
Authors: Suárez-Ortega Magdalena , Sánchez-García María Fe , Fernández-García Ana , García-Ripa María Inés
Published: June 2023

This study evaluates the impact of the professional career construction model (CCP) on the professional practice of counselors and the social sustainability actions put into play in their professional performance. A mixed methodology featuring a predominantly qualitative approach is used to examine the employment situation and professional development of this group. Forty-eight socio-labor counselors from the autonomous community of Andalusia (Spain) participated in the self-application of the CCP model. Subsequently, a questionnaire was administered via a discussion group. The data analysis combines descriptive and qualitative procedures with the support of computer software. Strengths, weaknesses, opportunities, and threats (SWOT) analysis and triangulation techniques are used as methodological integration strategies. The resulting data are discussed, later presenting a series of limitations and prospective of the study. Relevant conclusions are provided about the impact of the CCP model on the professional careers of counselors based on their satisfaction with the self-application of the model, their career progression and employment status, and their self-perceptions of the social impact of their counseling careers. These conclusions highlight the implications of the study regarding promoting the sustainable careers of socio-labor counselors, enhancing their initial and continuous training, and facilitating their occupational actions in a complex and constantly changing society.
Effect of Continuous Planting on Tree Growth Traits and Growth Stress in Plantation Forests of Eucalyptus urophylla × E. grandis
Authors: Wang Tianhui , Dai Qiongling , Fu Yunlin , Wei Penglian
Published: June 2023

Continuous planting is the primary method for managing Eucalyptus plantations. The “space-replacing time” approach assesses growth parameters of Eucalyptus trees in China across generations, including height, diameter at breast height (DBH), slenderness ratio, trunk oblateness, and longitudinal growth strain. The findings reveal: (1) significant variations in growth strain occur among generations, with average strain increasing noticeably; and (2) growth-linked traits of Eucalyptus urophylla × E. grandis are impacted, with negative correlation between slenderness ratio and growth strain, and positive correlation between height and trunk oblateness. Factors influencing growth strain include height, slenderness, and surface longitudinal growth strain at breast height, with strong correlations observed. These parameters serve as growth strain indicators. Continuous planting affects growth traits and strain in Eucalyptus plantations. It is advisable to select trees with stable or slow growth rates and to avoid continuous planting without limits.
Yeknemilis: Social Learning and Intercultural Transdisciplinary Collaboration for Sustainable Life
Authors: Bueno Isabel , Moreno-Calles Ana Isabel , Merçon Juliana
Published: June 2023

Intercultural transdisciplinary research reinforces sustainable social-ecological systems in Latin America. Social learning (SL) is a crucial process within this type of research as it fosters collaboration among diverse groups of people, communities of practice, and cultures. Buen vivir (‘well living’), of the popular movements in America, promotes collective responsibility and respect for life. Yeknemilis (‘a good life’) is a value framework of the Masewal people of the Sierra Madre Oriental, Mexico. Members of Tosepan, an organization of the Masewal and other indigenous peoples of this region, reflected on their cultural roots, ways of life, and relations with the territory to strengthen their alternative and self-determined lifeway. Involvement in participatory research within the transdisciplinary process allowed us to focus on the learnings and conditions that foster values and strategies for yeknemilis. Through participant observation, semi-structured interviews, and participatory activities, we identified five key social learning areas that foster conditions for yeknemilis and life-sustaining relationships in intercultural transdisciplinary collaboration (ITC): collective action agenda, community capacities, intercultural transdisciplinarity, creative reflexivity, and a relational ontology horizon. Finally, we show how the collaborative construction of yeknemilis and social learning practices can be crucial in scaling up collective action toward sustainability.
Rooftop Solar PV Policy Assessment of Global Best Practices and Lessons Learned for the Kingdom of Saudi Arabia
Authors: Al-Sharafi Emad Addin , Alhussein Musaed , Ali Amjad , Aurangzeb Khursheed
Published: June 2023

The growth in global electricity demand, price volatility, and global warming is diverting the attention of power producers to look for alternative green energy sources, more specifically, solar photovoltaic (SPV). Rooftop solar PV (RSPV) is a significant contributor to the successful development and deployment of SPV in any country. Therefore, developing countries such as the Kingdom of Saudi Arabia (KSA) are seeking alternative energy sources. According to climatological studies, Saudi Arabia has an average of 3230 sun hours annually, indicating significant potential for producing solar energy. The article investigated the characteristics of policies of countries that have had massive success in developing RSPV systems like China, the US, Germany, Italy, Spain, Japan, and India). Each country has its unique policies that result in many policy structures. Therefore, it is beyond the content of this report to provide a complete overview of all policies for the selected countries. Instead, information will focus on specific aspects of solar policy in each of the seven countries. As part of the RSPV policy assessment in Saudi Arabia, this study shows the key vectors of the selected countries’ success in their rooftop policies’ examination, and eventually, it presents a clear policy assessment of KSA’s rooftop solar PV policy.
An Assessment on Quality of Life and Happiness Indices of Project Affected People in Indian Coalfields
Authors: Sinha Archana , Chandra Bibhas , Mishra Arvind Kumar , Goswami Shubham
Published: June 2023

Coal deposits are generally found in either riverine and/or beneath forest area. The coerced displacement of project-affected people (PAPs) for coal mining endangers the in situ conservation of their life/livelihood. It accounts for a heavy price against the mineral value mostly harvested by distant stakeholders. A study on quality of life (QoL) enroute happiness indices of PAPs is undertaken with reference to CSR/resettlement and rehabilitation initiatives undertaken by mining companies in coalfields of Jharkhand. This study aims to analyze elements influencing quality of life and happiness indices such as job/income opportunity, housing affordability, health security, infrastructure, social relations, environment sustainability, inclusivity, equity and diversity. Data were collected directly from PAPs using a questionnaire survey method and almost 501 responses received to assist in development of the model. Multivariate statistical analysis has been used with application of structural equation modeling methodology for data analysis. The result shows vital relations among the constructs introduced based on human, social, natural, physical and economical concerns. The findings also indicate inadequate resettlement and rehabilitation initiatives undertaken by project proponents towards restoring life quality. Thus, the conceptual framework customized to mining area is developed and validated for rendering a qualitative life-ecosystems to the PAPs.
Novel High-Performance ETICS Coatings with Cool Pigments Incorporation
Authors: Curado António , Figueiras Ricardo , Gonçalves Hélder , Sambento Filipe , Nunes Leonel J. R.
Published: June 2023

External Thermal Insulation Composite Systems (ETICS) enhance building aesthetics and optimize thermal performance while offering protection against weather, fire, and harmful agents. Key to these capabilities are properties of ETICS rendering. We have applied specialized organic renderings, including modified acrylic resins, additives, and reflective pigments, to mitigate color bleaching and stress cracking induced by high surface temperatures, resulting in improved color stability and water protection. In a practical application at a shopping center in Portugal, we observed reduced coating layer failures, better thermal resistance, and lower maintenance costs over one year. Subsequent research reveals the benefits of Near Infrared Reflective (NIR) pigments and nanocomposites such as titanium dioxide, which increase solar reflectance, enhance resistance to dirt, and promote self-cleaning. Synthetic colored inorganic pigments improve heat stability, thermal inertia, and mechanical resistance. The application of cool pigments also reduces surface temperature by up to 10 °C. These advancements in ETICS technology mark a significant step towards sustainable building practices.
Spatial and Temporal Divergence in the Coupling Coordination of Digital Economy, Environmental Regulation and Sustainable Development: An Experience Study in China
Authors: Cheng Yunlong , Yang Jian
Published: June 2023

The digital economy and environmental regulation are important drivers of sustainable development, and exploring the coupling of the three is important for promoting the coordinated development of regional economy, society and environment. However, integrating the three into the same system and evaluating their coupling and coordination has been little studied in academia. This research employs the entropy method with objective weighting to measure the levels of digital economy, environmental regulation and sustainable development in 30 provinces (autonomous regions and municipalities directly under the central government) in China from 2011 to 2020, invokes the concept of coupling in physics, constructs a coupling coordination degree model and identifies the spatial and temporal divergence characteristics of the coupled and coordinated development of the three subsystems. The results of the study demonstrate that: (1) the levels of digital economy, environmental regulation and sustainable development reveal a fluctuating trend of growth, with environmental regulation having the highest overall level; (2) there are spatial and temporal differences in the degree of coupling and coordination of the three subsystems, with a national coupling and coordination degree at the temporal level lying between moderate and good coordination, and showing a “W”-shaped upward trend in general and a “chain” at the spatial level; (3) There is a significant spatial autocorrelation in the degree of coupling coordination, with a localized “high–high” and “high–high” pattern. Based on the above results, the article concludes with suggestions to enhance the development level of each subsystem, providing thoughts for the improvement of the coupling and coordination degree of the three in the later stage.
Railway Freight Demand Forecasting Based on Multiple Factors: Grey Relational Analysis and Deep Autoencoder Neural Networks
Authors: Liu Chengguang , Zhang Jiaqi , Luo Xixi , Yang Yulin , Hu Chao
Published: June 2023

The construction of high-speed rail lines in China has drastically improved the freight capacity of conventional railways. However, due to recent national energy policy adjustments, rail freight volumes, consisting mostly of coal, ore, and other minerals, have declined. As a result, the corresponding changes in the supply and demand of goods and transportation have led to a gradual transformation of the railway freight market from a seller’s market to a buyer’s market. It is important to carry out a systematic analysis and a precise forecast of the demand for rail freight transport. However, traditional time series forecasting models often lack precision during drastic fluctuations in demand, while deep learning-based forecasting models may lack interpretability. This study combines grey relational analysis (GRA) and deep neural networks (DNN) to offer a more interpretable approach to predicting rail freight demand. GRA is used to obtain explanatory variables associated with railway freight demand, which improves the intelligibility of the DNN prediction. However, the high-dimension predictor variable can make training on DNN challenging. Inspired by deep autoencoders (DAE), we add a layer of an encoder to the GRA-DNN model to compress and aggregate the high-dimension input. Case studies conducted on Chinese railway freight from 2000 to 2018 show that the proven GRA-DAE-NN model is precise and easy to interpret. Comparative experiments with conventional prediction models ARIMA, SVR, FC-LSTM, DNN, FNN, and GRNN further validate the performance of the GRA-DAE-NN model. The prediction accuracy of the GRA-DAE-NN model is 97.79%, higher than that of other models. Among the main explanatory variables, coal, oil, grain production, railway locomotives, and vehicles have a significant impact on the railway freight demand trend. The ablation experiment verified that GRA has a significant effect on the selection of explanatory variables and on improving the accuracy of predictions. The method proposed in this study not only accurately predicts railway freight demand but also helps railway transportation companies to better understand the key factors influencing demand changes.
The Impact of Smart City Pilots on Haze Pollution in China—An Empirical Test Based on Panel Data of 283 Prefecture-Level Cities
Authors: Zhang Liwei , Wu Chuanqing
Published: June 2023

The rapid pace of urbanization in China has led to a significant increase in haze pollution in its cities. However, there has been limited research on the dynamic impact and mechanisms of smart city pilots, which offer an innovative approach to urbanization, on haze pollution. This study selects panel data from 283 prefecture-level cities in China from 2007 to 2017 and uses a quasi-experimental approach based on the three batches of pilot construction of smart cities since 2012 to examine the impact of smart city pilots on haze pollution. The multi-phase difference-in-differences (DID) model is used for the analysis. The findings reveal: (1) Smart city pilots have a significant positive effect on reducing urban haze pollution. (2) Smart city pilots contribute to changes in the urban development model, where technological innovation, industrial structure adjustment, and resource allocation optimization under innovation-driven development significantly mitigate haze pollution. (3) Heterogeneity analysis shows regional differences in the effectiveness of smart city pilot policies in reducing haze pollution in China, with a decreasing trend from the eastern to the western regions. The haze-reducing effect of smart city pilots in the central region has yet to be observed. This research provides valuable theoretical and policy insights for improving urban ecological environments and promoting green transformations of production and lifestyle.
FDI Quality, Green Technology Innovation and Urban Carbon Emissions: Empirical Evidence from China
Authors: Fang Hui , Zhang Xiaoye , Lei Ting , Houadi Baya Lydia
Published: June 2023

High-quality introduction and utilization of foreign investment is a new engine for China’s further opening up and plays a significant role in urban carbon emission reduction in the context of carbon peaking and carbon neutrality strategies. By constructing a comprehensive assessment index of Foreign Direct Investment (FDI) quality and using panel data from 283 Chinese cities from 2006 to 2019, this paper systematically examines the impact of FDI quality on urban carbon emissions and identifies the mechanism of green technology innovation in order to clarify the correlation among the three. The results show that FDI quality upgrading can significantly reduce urban carbon emissions, which is mainly manifested by FDI management and FDI export capacity to suppress total carbon emissions. The above effect of FDI quality is more significant in cities with a low rationalization degree of industrial structure, high investment in science and technology, strong environmental regulation and high dependence on foreign capital. Green technology innovation is an intermediary mechanism for FDI quality to have a carbon emission reduction effect. Looking at different areas, foreign companies in the eastern regions can promote carbon emission reduction through independent innovation, while the central and western regions essentially rely on imitative innovation; however, there is a delay in the role of innovation quality. Further study suggests that the carbon emission reduction effect of FDI quality has an obvious characteristic of threshold, which will be slightly weakened with the increase in the quantity of imitative innovation but strengthened with the improvement of the quantity of independent innovation and the quality of innovation. The conclusion aims to provide policy implications for China to explore a sustainable pathway that combines promoted opening with carbon reduction targets.
Consumer Acceptance of Genome-Edited Foods in Japan
Authors: Shigi Ryoma , Seo Yuna
Published: June 2023

Genome-edited (GE) food is recently being introduced into the market with the promise of efficient food production and food waste reduction. Genetic editing aims to accelerate genetic progress by replacing the germ cell lineage of commercial breeding animals with cells derived from genetically elite lines. At the present early stage of the GE food market in Japan, this study investigated consumer acceptance of GE foods and constructed a GE foods acceptance model. This model illustrated that awareness of GE technology is essential for the stimulation of consumer acceptance, which is mediated by information credibility and the perceived usefulness of GE foods. It suggests that Japanese consumers may demand more information about how GE foods are produced and what their benefits are. Further research on effective food technology education and on communication about GE foods is needed.
CSR Communication Matters! An Examination of CSR, Organisational Pride, and Task-Related Pro-Environmental Behaviour Nexus
Authors: Fatima Tehreem , Badar Kamal , Waqas Muhammad , Ayub Arslan , Haris Muhammad
Published: June 2023

Corporate Social Responsibility (CSR) has many positive outcomes, yet how and when its impacts employees’ green behaviour is still to be examined. This research tested the effect of CSR initiatives on the Task Pro-environmental behaviour (TPB) of employees via the mediating role of organisational pride. Moreover, the role of CSR communication was considered as a moderator in CSR and pride relationships. The study was based on two theories: Social Identity Theory and Signalling Theory. The investigation was quantitative and based on the data collected at three points of time, i.e., T1 = CSR and internal CSR communication, T2 = organisational pride, and T3 = TPB with a one-week time lag from banking sector employees. 500 questionnaires were distributed, of which 361 responded, forming a response rate of 72.2%. The analysis was done using PROCESS mediation and moderation models. The results affirmed a positive relationship between CSR and organisational pride and also its subsequent impact on TBP. The moderating role of internal CSR communication between CSR and organisational pride was also supported. In addition, the indirect moderating role of internal CSR communication was also established between CSR and TPB via organisational pride.