bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images
Authors: Liu Hanhu , Lei Xiangqi , Liang Hui , Wang Xiao
Published: April 2023

Rice is China’s main crop and its output accounts for 30% of the world’s total annual rice production. Rice growth status is closely related to chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values). The determination of a SPAD value is of great significance to the health status of rice, agricultural irrigation and regulated fertilization. The traditional SPAD value measurement method is not only time-consuming, laborious and expensive but also causes irreparable damage to vegetation. The main aim of the present study is to obtain a SPAD value through the inversion of hyperspectral remote sensing images. In order to achieve this purpose, the hyperspectral image of rice at different growth stages at the canopy scale was first acquired using a hyperspectral imaging instrument equipped with a drone; the spectral characteristics of the rice canopy at different growth stages were analyzed and combined with a ground-level measured SPAD value, the bands with high correlation between the SPAD values and the spectra of the rice canopy at different fertility stages were selected. Subsequently, we combined the spectral characteristics with the continuous projection algorithm to extract the characteristic band and used the PLS method in MATLAB software to analyze and calculate the weight of each type of spectral value and the corresponding canopy SPAD value; we then used the wavelength corresponding to the spectral value with the highest weight as the used band. Secondly, the four methods of univariate regression, partial least squares (PLS) regression, support vector machine (SVM) regression and back propagation (BP) neural network regression are integrated to establish the estimation model of the SPAD value of rice canopy. Finally, the models are used to map the SPAD values of the rice canopy. Research shows that the model with the highest decision coefficient among the four booting stage models is “booting stage-SVR” (R2 = 0.6258), and the model with the highest decision coefficient among the four dairy maturity models is “milk-ripe stage-BP” (R2 = 0.6716), all of which can meet the requirement of accurately retrieving the SPAD value of rice canopy. The above results can provide a technical reference for the accurate, rapid and non-destructive monitoring of chlorophyll content in rice leaves and provide a core band selection basis for large-scale hyperspectral remote sensing monitoring of rice.
A Theoretical Model of Strategic Communication for the Sustainable Development of Sport Tourism
Authors: Mazza Barbara
Published: April 2023

Sustainability and sustainable development are two fundamental principles that currently influence the way sport tourism activities, like those of any other socioeconomic area, are managed. For this reason, for some time, especially in the last decade, scholars have been wondering not only about the application methods of these two concepts in sport tourism but also how to communicate good practices among operators, residents, and tourists, and how to encourage the adoption of sustainable behaviours. Pro-sustainable attitudes are widespread, but they do not always convert into sustainable behaviour. Communication can intervene to favour these socio-cultural changes at an individual and collective level. This research is inspired by several studies and the most recent systematic literature reviews to identify communication methods and strategies that determine the tourism–sports experience, influencing the assumption or reinforcement of sustainable behaviours. This study attempted to configure a theoretical model of sustainable communication that can support the sustainable development of sport tourism. The goal is to understand how to create effective communication actions to raise the awareness of institutions and businesses, residents, and tourists towards the adoption of sustainable practices, and to encourage the promotion of sustainable initiatives in territories in support of the socioeconomic development of localities from an increasingly sustainable perspective.
International Industrial Transfer, Green Technological Innovation, and Manufacturing Global Value Chain Status
Authors: Kou Minglong , Sun Hui , Xin Long , Men Keping , Yan Xinjie
Published: April 2023

Most of the existing literature focuses on how international industrial transfer (IIT) impacts the global value chain (GVC) status of the manufacturing industry from the perspective of narrowly defined FDI but ignores the objective fact that FDI and IIT are not equivalent. Based on theory analysis, we used the TiVA database, the input–output model, and global value chain-related methods to effectively measure the scale of generalized IIT and GVCs of manufacturing sectors in China from 1995 to 2018. On this basis, the relationship between IIT and the GVCs of manufacturing industries and the moderating effect of green technological innovation (GTI) were empirically investigated using industry panel data. The results show that (1) there is a nonlinear inverted U-shaped relationship between IIT and manufacturing GVCs; that is, a larger IIT scale is not better from the perspective of manufacturing GVCs. (2) GTI weakens the inverted U-shaped relationship between IIT and manufacturing GVCs. (3) The heterogeneity analysis found that both medium- and high-technology manufacturing IITs have a nonlinear inverted U-shaped relationship with GVCs, which does not exist in low-technology manufacturing IIT. (4) The benchmark regression results remain robust after replacing the GVC measure, excluding special years and endogeneity treatment and replacing the estimation method robustness test. The research in this paper has implications for optimizing the design of IIT policies to promote the upgrading of manufacturing GVC status.
Distribution Characteristics and Influence Factors of Carbon in Coal Mining Subsidence Wetland
Authors: Yuan Tao , Lu Ping , Liu Yijun , Ren Feng , Zhang Haoran
Published: April 2023

Coal mining subsidence wetlands, as important supplementary resources for wetlands, are of great significance for regulating climate change. This study investigated the distribution and influencing factors of carbon in the overlying water and sediment of coal mining subsidence wetlands in Xuzhou, China, during low-water, high-water, and dry seasons. The results revealed significant spatial and temporal variations in the physicochemical properties of the wetlands, with water hypoxia and a trend toward eutrophication due to excess nitrogen and phosphorus. Dissolved organic carbon (WDOC) and dissolved inorganic carbon (WDIC) in water exhibited opposite temporal trends, while sediment organic carbon (SOC) and dissolved organic carbon (SDOC) showed similar temporal and spatial variations. Inorganic carbon in sediment (SIC) and dissolved inorganic carbon (SDIC) showed consistent temporal changes but significant spatial differences. There was a significant positive correlation between WDOC and SDOC, and WDIC was positively correlated with SDOC and SDIC, indicating the interconnection and transformation of dissolved carbon between water and sediment. WDIC was strongly correlated with water temperature and dissolved oxygen, while WDOC was weakly correlated with the physicochemical properties of water. Overall, these findings contribute to our understanding of the carbon distribution and cycling in coal mining subsidence wetlands, which are crucial supplementary resources to natural wetlands for regulating climate change.
Estimating Suspended Sediment Concentration Using Remote Sensing for the Teles Pires River, Brazil
Authors: Paulista Rhavel Salviano Dias , de Almeida Frederico Terra , de Souza Adilson Pacheco , Hoshide Aaron Kinyu , de Abreu Daniel Carneiro , da Silva Araujo Jaime Wendeley , Martim Charles Campoe
Published: April 2023

Improving environmental sustainability involves measuring indices that show responses to different production processes and management types. Suspended sediment concentration (SSC) in water bodies is a parameter of great importance, as it is related to watercourse morphology, land use and occupation in river basins, and sediment transport and accumulation. Although already established, the methods used for acquiring such data in the field are costly. This hinders extrapolations along water bodies and reservoirs. Remote sensing is a feasible alternative to remedy these obstacles, as changes in suspended sediment concentrations are detectable by satellite images. Therefore, satellite image reflectance can be used to estimate SSC spatially and temporally. We used Sentinel-2 A and B imagery to estimate SSC for the Teles Pires River in Brazil’s Amazon. Sensor images used were matched to the same days as field sampling. Google Earth Engine (GEE), a tool that allows agility and flexibility, was used for data processing. Access to several data sources and processing robustness show that GEE can accurately estimate water quality parameters via remote sensing. The best SSC estimator was the reflectance of the B4 band corresponding to the red range of the visible spectrum, with the exponential model showing the best fit and accuracy.
The Sustainable Rural Industrial Development under Entrepreneurship and Deep Learning from Digital Empowerment
Authors: Gao Suwei , Yang Xiaobei , Long Huizhen , Zhang Fengrui , Xin Qin
Published: April 2023

This paper aims to realize the planning of resource utilization and development of rural industries endowed by digitalization under entrepreneurship. First, the global classic practical experience of digitizing rural industries is studied, and the development model of existing rural industries is captured from the perspective of entrepreneurship. Second, the influencing factors of rural industrial development are extracted, the structure of resource development is analyzed, and a Neural Network (NN) model of industrial development aiming at expected per capita annual income is established. In addition, a Genetic Algorithm (GA) is introduced to learn the weights of influencing factors in the model. The structure of the NN is determined through extensive experiments. Finally, conclusions are drawn through the simulation and experiment of NN and GA. Tourism, infrastructure, and transportation planning have weights of 7.79, 5.6, and 6.4, respectively, and these three sectors should be vigorously developed. In the future, the weight values of these factors can be used for reference, and the development of various aspects can be refined. This paper clarifies the core of industrial development in rural revitalization based on the perspective of entrepreneurship. The problem of how to realize the optimal utilization of resources is solved scientifically and rationally through the mathematical model. The introduction of deep learning algorithm models provides data support for resource allocation and industrial planning in the process of digital empowerment of traditional rural industries, which is of great value and significance for exploring digital models for rural industry development.
Life Cycle Assessment of High-Performance Railway Infrastructure, Analysis of Superstructures in Tunnels and on Open Tracks
Authors: Hausberger Lukas , Cordes Tobias , Gschösser Florian
Published: April 2023

Almost 25% of the environmental pollution, measured by the indicator of global greenhouse emissions, is emitted by transport. Changes in the mobility behavior of the population will be essential if the 17 UN Sustainable Development Goals (SDGs) and the goals of the EU Commission’s Green Deal are to be attained. Accordingly, the existing infrastructure has to transform into a sustainable transport infrastructure through further optimizations in the future. Therefore, continuous optimizations and improvements of designs, materials, and processes are crucial to achieving long-term sustainability. This study investigates different superstructures with the method of life cycle assessment using the example of the emerging high-performance infrastructure at the Brenner Base Tunnel (BBT). The study analyzes all relevant life cycle stages (A1–C4) and validates different effects of service lifetimes of superstructure elements on the open track and in the tunnel. The results, which are presented in the form of GWP, AP, and NRCED, show that there is environmental reduction potential, especially in the stage of use. As more frequent modernization cycles and the associated remanufacturing of superstructure elements account for a significant proportion of the total environmental impact, lifetime extending optimization of products yields improvements in the ecological footprint.
Biodiversity and Resilience to Tsunamis in Chilean Urban Areas: The Role of Ecoinformatics
Authors: Brüning-González Mariana , Villagra Paula , Samaniego Horacio
Published: April 2023

By definition, a smart city must improve its readiness for extreme events in order to confront the growing unpredictability of natural disasters. Doing this implies planning for resilience. That is, to enhance our capacity to cope, mitigate, adapt, and rebuild human settlements after a catastrophic event. Although scholars have argued that biodiversity can enhance resilience, there is a dearth of empirical research that specifically addresses this crucial issue. This research analyzes Nature’s Contributions to People related to tsunami resilience. Then, the relationship between biodiversity and community resilience indexes is examined for 50 coastal Chilean cities that are prone to tsunamis, using biodiversity data from an open access database. The resilience index “population living in the first kilometer from the shoreline” was found to be correlated with species richness (p = 0.48) and the evenness biodiversity index, Pielou (p = −0.47). These results suggest that biodiversity data availability is crucial for understanding nature’s contribution to human settlement resilience. Although this study was hindered by limited data availability, the potential use in other contexts remains valuable for the development of smart cities. The study highlights the need for increased biodiversity data collection on a national scale and emphasizes the use of ecoinformatics to create smart cities that can effectively respond to climate uncertainty in coastal urban areas.
Enset Production System Diversity across the Southern Ethiopian Highlands
Authors: Blomme Guy , Kearsley Elizabeth , Buta Sisay , Chala Alemayehu , Kebede Ruhama , Addis Temesgen , Yemataw Zerihun
Published: April 2023

Enset is a staple crop of the southern Ethiopian highlands. Small-holder farmers cultivate enset as part of mixed subsistence farming systems, in which enset provides substantial food security services. While its cultivation is unique to this region, enset production systems take on many forms, varying with environmental and agronomic conditions, crop diversity and (co-)staples produced, the importance of enset for the household, and socio-economic and cultural differences. Through extensive interviews with 375 households covering 20 communities, along an altitudinal range of 1500 to 3000 masl across the main enset-producing belt, the diversity in enset production systems was assessed. We show that the size of enset-producing land holdings and the overall cultivated farmland decreased with altitude. The economic status of households however drives the proportion of land allocated to enset, with relatively more land (45%) allocated to the cultivation of enset in poorer households compared to medium (38%) and to richer (23%) households. The food crop diversity, with an average of 6.4 different food crop species on a farm (ranging from 2 to 15 crops), did not vary with the wealth status of the households or with altitude. Enset-derived food items were a main component of multiple daily meals for most households, complemented with other crops produced on the farm. Supplemental food purchases mainly included meat and bread products, although the purchasing power of enset-growing households is predominantly low. The co-staples grown varied with altitude, according to crop productive cultivation boundaries. Maize was an important co-staple observed across the entire investigated altitudinal range. At the mid to upper altitudes, wheat and barley often supplemented or substituted maize as the main cereal crop, while at the mid to lower altitudes, teff was produced in addition to maize. Coffee was the main cash crop grown up to altitudes of 2300 m. Root and tuber crops, and legumes had a more moderate importance in these systems. At lower altitudes, yam, sweet potato and taro were the main roots and tubers produced, which shifted to Irish potatoes at the mid to high altitudes. The importance of beans was higher in several high-altitude kebeles. The food crop diversity, combined with livestock rearing are key for the self-reliance of the small-holder subsistence farms. The need for increased enset cultivation was highlighted by the farmers to ensure food availability and food security with population growth. On the other hand, enset cultivation was mainly threatened by Xanthomonas wilt.
Sustainable Logistics 4.0: A Study on Selecting the Best Technology for Internal Material Handling
Authors: Ferraro Saverio , Cantini Alessandra , Leoni Leonardo , De Carlo Filippo
Published: April 2023

Logistics is a vital activity for the economic growth of an organization as it manages the flow of materials and information within, into, and out of the organization, as well as reverse flow. Like many other industrial processes, logistics has also been impacted by the rise of Industry 4.0 technologies, which has highlighted the significance of Logistics 4.0. However, Logistics 4.0 is mainly focused on economic benefits, while overlooking environmental and social concerns. To address this, a method is proposed that takes into account the three goals of sustainable development when selecting the best technology for internal material handling activities. Firstly, a comprehensive literature review was conducted to examine the application of 4.0 technologies in logistics processes and their impact on economic, environmental, and social sustainability. Secondly, based on the findings of the review, a three-level analytic hierarchy process was proposed to identify the optimal 4.0 technology for internal logistics. To demonstrate the practicality of the proposed method, it was tested on three companies. The results showed that additive manufacturing, exoskeletons, and collaborative robots are the most suitable options for achieving sustainable development goals within Logistics 4.0.
ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review
Authors: Alghazzawi Daniyal M. , Alquraishee Anser Ghazal Ali , Badri Sahar K. , Hasan Syed Hamid
Published: April 2023

Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.
Development of Machine Learning Algorithms for Application in Major Performance Enhancement in the Selective Catalytic Reduction (SCR) System
Authors: Kim Sunghun , Park Youngjin , Yoo Seungbeom , Lim Ocktaeck , Samosir Bernike Febriana
Published: April 2023

Machine learning is used in this study to deal with the reduction in the design period and major performance improvement of the selective catalyst reduction system. The selective catalyst reduction system helps in the reduction in NOx emission in the diesel engine. The existing methods for the design and performance improvement of selective catalyst reduction systems tend to be inefficient, due to layout changes that require modification when mounting a vehicle based on previously designed models. There are some factors that can affect the design of the diesel engine selective catalyst reduction system that can be identified by applying an optimized design. The Taguchi orthogonal array design is used with the eight factors and three levels of the main design factors. The distance of the urea injector, the distance of the mixer, the inflow angle of the exhaust gas, the angle of the urea injector, the angle of the mixer, the mounting angle in the direction of rotation of the mixer inside the selective catalyst reduction pipe, the number of mixer blades, the and bending angle of the mixer blade are identified as the eight major factors involved. These factors can also be considered manufacturing factors and can be established through machine learning. Machine learning has the advantage of being more efficient compared to other methods in determining the relationship between the data for each mutual factor. Machine learning can help in reducing processing time, which can further decrease the cost of the design analysis and improve the performance of the selective catalyst reduction system. This study shows that the results are statistically significant as the p values of the mixer blade number and cone length are lower than 0.05.
Effective Deterministic Methodology for Enhanced Distribution Network Performance and Plug-in Electric Vehicles
Authors: Memon Zeeshan Anjum , Said Dalila Mat , Hassan Mohammad Yusri , Munir Hafiz Mudassir , Alsaif Faisal , Alsulamy Sager
Published: April 2023

The rapid depletion of fossil fuel motivates researchers and policymakers to switch from the internal combustion engine (ICE) to plug-in electric vehicles (PEVs). However, the electric power distribution networks are congested, which lowers the accommodation of PEVs and produces higher power losses. Therefore, the study proposes an effective deterministic methodology to maximize the accommodation of PEVs and percentage power loss reduction (%PLR) in radial distribution networks (RDNs). In the first stage, the PEVs are allocated to the best bus, which is chosen based on the loading capacity to power loss index (LCPLI), and the accommodation profile of PEVs is developed based on varying states of charge (SoC) and battery capacities (BCs). In the second stage, the power losses are minimized in PEV integrated networks with the allocation of DG units using a recently developed parallel-operated arithmetic optimization algorithm salp swarm algorithm (AOASSA). In the third stage, the charging and discharging ratios of PEVs are optimized analytically to minimize power losses after planning PEVs and DGs. The outcomes reveal that bus-2 is the most optimal bus for accommodation of PEVs, as it has the highest level of LCPLI, which is 9.81 in the 33-bus system and 28.24 in the 69-bus system. The optimal bus can safely accommodate the largest number of electric vehicles, with a capacity of 31,988 units in the 33-bus system and 92,519 units in the 69-bus system. Additionally, the parallel-operated AOASSA mechanism leads to a reduction in power losses of at least 0.09% and 0.25% compared with other algorithms that have been previously applied to the 33-bus and 69-bus systems, respectively. Moreover, with an optimal charging and discharging ratio of PEVs in the IEEE-33-bus radial distribution network (RDN), the %PLR further improved by 3.08%, 4.19%, and 2.29% in the presence of the optimal allocation of one, two and three DG units, respectively. In the IEEE-69-bus RDN, the %PLR further improved by 0.09%, 0.09%, and 0.08% with optimal charge and discharge ratios in the presence of one, two, and three DG units, respectively. The proposed study intends to help the local power distribution companies to maximize accommodation of PEV units and minimize power losses in RDNs.
On the Use of Reanalysis Data to Reconstruct Missing Observed Daily Temperatures in Europe over a Lengthy Period of Time
Authors: Varotsos Konstantinos V. , Katavoutas George , Giannakopoulos Christos
Published: April 2023

In this study, a methodology that can reconstruct missing daily values of maximum and minimum temperatures over a long time period under the assumption of a sparse network of meteorological stations is described. To achieve this, a well-established software used for quality control, homogenization and the infilling of missing climatological series data, Climatol, is used to combine a mosaic of data, including daily observations from 15 European stations and daily data from two high-resolution reanalysis datasets, ERA5-Land and MESCAN-SURFEX; this is in order reconstruct daily values over the 2000–2018 period. By comparing frequently used indices, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in studies of climate change assessment and goodness-of-fit measures, the reconstructed time series are evaluated against the observed ones. The analysis reveals that the ERA5-Land reconstructions outperform the MESCAN-SURFEX ones when compared to the observations in terms of biases, the various indices evaluated, and in terms of the goodness of fit for both the daily maximum and minimum temperatures. In addition, the magnitude and significance of the observed long-term temporal trends maintained in the reconstructions, in the majority of the stations examined, for both the daily maximum and daily minimum temperatures, is an issue of the greatest relevance in many climatic studies.
Adaptation to Climate Change in Adriatic Croatia—The View of Policymakers
Authors: Marić Ana Čehić , Čop Tajana , Oplanić Milan , Ban Smiljana Goreta , Njavro Mario
Published: April 2023

Changes in temperature, precipitation, and the occurrence of extreme weather events are increasingly present. Due to climate change, the Mediterranean Basin (the focus of this study is on Adriatic Croatia as part of the Mediterranean Basin) is more affected by production and economic losses compared to other parts of Europe. Policymakers are important individuals involved in shaping public policies. The main objective of this study was to assess the opinion of policymakers at regional and national levels in Adriatic Croatia regarding climate change adaptation strategies. The aims are (i) to rank the importance of adaptation measures, (ii) to examine measures that have already been implemented, and (iii) to examine future measures that need to be implemented at regional and national levels. Semi-structured in-depth interviews were conducted with representatives of sectoral agencies, the national government, and the regional government to answer the study questions. The main conclusions show that policymakers consider irrigation, organic agriculture, crop crossbreeding and optimization of agrotechnical practices, and agricultural insurance as the most important strategies. Currently and in the future, the most important actions of policymakers are the provision of education, agricultural extension, and information exchange.
Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States
Authors: Ma Xin , Cai Yubin , Yuan Hong , Deng Yanqiao
Published: April 2023

Energy forecasting based on univariate time series has long been a challenge in energy engineering and has become one of the most popular tasks in data analytics. In order to take advantage of the characteristics of observed data, a partially linear model is proposed based on principal component analysis and support vector machine methods. The principal linear components of the input with lower dimensions are used as the linear part, while the nonlinear part is expressed by the kernel function. The primal-dual method is used to construct the convex optimization problem for the proposed model, and the sequential minimization optimization algorithm is used to train the model with global convergence. The univariate forecasting scheme is designed to forecast the primary energy consumption of the electric power sector of the United States using real-world data sets ranging from January 1973 to January 2020, and the model is compared with eight commonly used machine learning models as well as the linear auto-regressive model. Comprehensive comparisons with multiple evaluation criteria (including 19 metrics) show that the proposed model outperforms all other models in all scenarios of mid-/long-term forecasting, indicating its high potential in primary energy consumption forecasting.
Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects
Authors: Benti Natei Ermias , Chaka Mesfin Diro , Semie Addisu Gezahegn
Published: April 2023

This article presents a review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.
Multinational Corporations and Technological Innovation Development of China’s High-Tech Industries: A Heterogeneity-Based Threshold Effect Analysis
Authors: Kou Yuanyuan , Chen Huiying , Liu Kai , Xu Huajie
Published: April 2023

Technological innovation of multinational corporations (MNC) is an important engine to drive the world’s scientific and technological progress, which plays an important role in the sustainable development of global innovation. With China’s high-tech industry from 2009 to 2020 as the research sample, this study empirically analyzes the impact of MNCs’ two-stage innovation on the innovation efficiency of China’s high-tech industry through the perspective of base regression, threshold effect, and regional heterogeneity. It was found that MNC research and development (R&D) efficiency and achievement transformation efficiency have a positive impact on the innovation efficiency of high-tech industries in China, with nonlinear features and regional heterogeneity. This study provides an empirical basis for the efficient use of MNC’s innovative resources and the sustainable development of China’s high-tech industry.
A View of Industrial Agglomeration, Air Pollution and Economic Sustainability from Spatial Econometric Analysis of 273 Cities in China
Authors: Han Chengyu , Hua Dongwen , Li Juan
Published: April 2023

Industrial agglomeration in a region changes the economic structure, strategic layout and resource status of a city, and has an important impact on sustainable economic development. The relationship between industrial agglomeration, air pollution and economic sustainability is a key issue concerning the high-quality development of national economy. China is a developing country that once experienced severe air pollution. Now, the Chinese government is aiming to achieve the goal of sustainable and high-quality economic development in China. In this paper, a spatial Dubin model was developed to study the relationship between industry, environment and the economy. The statistical analysis used the air pollutant data of 273 prefecture-level cities in China from 2015 to 2018. The results showed that: (1) there was a positive U-shaped nonlinear relationship between industrial agglomeration and sustainable economic development, and there was a spatial spillover effect. (2) There was a positive U-shaped nonlinear relationship between air pollution and sustainable economic development, and there was a spatial spillover effect between them. (3) The effect of industrial agglomeration on sustainable economic development was influenced by air pollution, an intermediary variable. The existence of air pollution weakens the promoting effect of industrial agglomeration on sustainable economic development.
A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information
Authors: Wang Tao , Li Sixuan , Li Wenyong , Yuan Quan , Chen Jun , Tang Xiang
Published: April 2023

With the development of smart cities and smart transportation, cities can gradually provide people with more information to facilitate their life and travel, and parking is also inseparable from both of them. Accurate on-street parking demand prediction can improve parking resource utilization and parking management efficiency, as well as potentially improve urban traffic conditions. Previous parking demand prediction methods seldom consider the correlation between the parking demand of a road section and its surroundings. Therefore, in order to capture the correlation of parking demand in the temporal and spatial dimensions as carefully as possible and enrich the relevant features in the prediction model so as to achieve more accurate prediction results, we designed a parking demand prediction structure that considers different features from two perspectives: overall and internal. We used gated recurrent units (GRU) to extract demand influences in the temporal dimension. The GRU is used in combination with a graph convolutional neural network (GCN) to extract demand influencing factors in the spatial dimension. Additionally, a more detailed representation is designed to express spatial dimensional features. Then, based on the historical parking demand features extracted using encoder–decoder, we fuse the extracted spatio-temporal features with them to finally obtain an on-street parking demand prediction model combining the overall and the internal information. By combining them, we can integrate more correlation factors to achieve a more accurate parking demand prediction. The performance of the model is evaluated by real parking data in Xiufeng District of Guilin. The results show that the proposed model achieves good prediction performance compared with other baselines. In addition, we also design feature ablation experiments. Through the comparison of the results, we find that each feature considered in the proposed model is important in parking demand prediction.
Seeding and Harvesting Times and Climate Conditions Are Important for Improving Nitrogen and Fiber Contents of Green Manure Sunn Hemp
Authors: Kaneko Makoto , Kato Naoki , Hattori Ikuo , Matsuoka Makoto , Vendramini Joao M. B.
Published: April 2023

The efficient use of green manure enhances sustainable nitrogen (N) cycling in agroecosystems. Appropriately utilizing the differences in N and fiber of green manure between cultivars or harvesting times strengthens the N use efficiency for subsequent crops. Therefore, it is necessary to clarify these differences. We clarified differences in nitrogen and fibers from four sunn hemp (Crotalaria juncea L.) commercial cultivars at two harvesting times after spring and summer seeding for three years. Only a few significant differences were found for any components between the four commercial cultivars. Although sunn hemp produced almost no flowers after the May seeding, the earliest flower opened at 50 days after seeding (DAS) and then significantly increased by 60 DAS (p < 0.05), after the July seeding. Sunn hemp DMY at 60 DAS had significantly larger values than those at 50 DAS all three years after the May and July seedings (p < 0.01). Significant differences between 50 DAS and 60 DAS were also observed for more than two years for the sunn hemp total carbon (TC), total nitrogen (TN), C/N ratio (CN), nitrate N (NO3-N), acid detergent fiber (ADF), and neutral detergent fiber (NDF) (p < 0.05). The sunn hemp N and ADF yields were significantly greater at 50 DAS than at 60 DAS in 2019 and 2020 (p < 0.05). These results suggest that the seeding and harvest time are important for decomposition estimations, although the differences between cultivars with equivalent flowering characteristics were not worth considering. Additionally, significant differences between years were found for all measurements except for NDF after the July seeding. This result suggests that the yearly effect of the difference in weather conditions, which causes differences in the yield, nitrogen, and ADF contents, is also an important factor for decomposition.
Study on the Distribution of Fresh Food Support System—An Example of Shanghai during the Epidemic Closure of 2022
Authors: Wang Jun , Fu Man-Jun
Published: April 2023

One of the key indicators to measure the sustainability and resilience of a city during a public health crisis is how well it can meet the daily needs of its residents. During the COVID-19 lockdown in Shanghai in 2022, e-commerce shopping and delivery became the most important method for ensuring the city’s material supplies. This article uses the distribution data of a fresh e-commerce platform’s pre-warehouse and static population distribution data to establish a basic material supply system evaluation model for the city and explore its resilience potential. Focusing on the central urban area of Shanghai, this study uses a population heat map with geographic coordinates to reflect the static distribution of residents and obtains the distribution data of the e-commerce pre-warehouses. Using kernel density analysis, the relationship between the pre-warehouses and the residents’ needs is established. Through analysis, it was found that the supply capacity of fresh food in different areas of Shanghai during the lockdown could be categorized as insufficient, adequate, or excessive. Based on these three categories, improvement strategies were proposed. Finally, this article suggests establishing a scientific supply security system to promote urban sustainability and prepare for future challenges.
Factors and Activities Considered by First Generation Agripreneurs for Agri-Business Sustainable Development: A Study of Haryana, India
Authors: Garima  , Dhingra Atul , Centobelli Piera , Cerchione Roberto
Published: April 2023

The main objective of the present study is to identify the factors and activities considered by first generation agripreneuers in managing agribusiness which plays a vital role in the success and sustainable development of any agribusiness. To accomplish this objective, a factor analysis method was adopted to gather and understand the findings. The data has been gathered from four geographical zones (East zone, West zone, North zone, South zone) of Haryana state comprising 22 districts through a purposive sampling strategy. The major findings of the study highlight that out of seven factors, effective leadership was considered the most important factor with the highest Cronbach value 0.812. It plays significant role in the success of any enterprise by ensuring supportive environment for the workers in agro-industries. Strategic planning was also important because becoming a successful agripreneur requires planning before converting all business activities into action.In addition, all the other factors such as scanning business opportunities, organizing and business activities, prior analysis, and credit facilities all play a vital role in the success of agro-industries.
Who Drives Circularity?—The Role of Construction Company Employees in Achieving High Circular Economy Efficiency
Authors: Dräger Philipp , Letmathe Peter
Published: April 2023

The circular economy in the construction industry is still in its infancy. It seems particularly difficult for companies in this sector to make strategic decisions that enable sustainable operations and ensure long-term business success. This article investigates factors such as employee involvement at the operational level that facilitate circular economy practices for companies in the construction industry. For this purpose, we conducted a company survey and analyzed it using a structural equation model. The results show that it is worthwhile for companies to empower their manufacturing employees to take actions independently in order to increase process quality and to reduce setup time, which together lead to better circular economy efficiency.
Evolutionary Game Analysis of Governmental Intervention in the Sustainable Mechanism of China’s Blue Finance
Authors: Chen Zhihan , Huang Weilun
Published: April 2023

This article is a case study of the blue finance mechanism (BFM) in China and makes use of evolutionary game theory and numerical simulation to show how the BFM plays a critical role in promoting the sustainable development of China’s marine economy, society, and environment. To ensure the perpetuation of the BFM, it is necessary for the Chinese government to attract private sector investment in the marine sector (PSIMS). By intervening in the BFM, the government can create a more favorable investment environment, which can then lead to greater private sector investment and contribute to the overall sustainability of the ocean. The goal of this article is to create an analytical model based on public finance and government management to examine the efficiency of Chinese governmental involvement in the BFM in order to boost the maritime industry by attracting private sector investment for funding the BFM. The results revealed the following: First, governmental involvement can have significant positive effects in promoting the sustainable development of the BFM in China. Second, the timeliness of governmental intervention in China can affect the private sector’s incentive to invest in the marine sector. Third, the Chinese government’s intervention in subsidizing costs can have significant impacts in engaging the private sectors to expand capital injection into marine investments. The minimization of potential risks of investment in the marine sector is critical to enhancing investor confidence and trust. The early intervention of the Chinese government is therefore crucial. Additionally, to further incentivize PSIMS, the Chinese government must make a concerted effort to increase subsidies and provide non-monetary rewards. This will help achieve sustainable development in the country’s economy, society, and environment.
The Origin of the Difference between Space and Time
Authors: Nikolić Hrvoje
Published: April 2023

All differences between the role of space and time in nature are explained by proposing principles in which none of the spacetime coordinates has an a priori special role. Spacetime is treated as a non-dynamical manifold, with a fixed global RD topology. The dynamical theory of gravity determines only the metric tensor on a fixed manifold. All dynamics is treated as a Cauchy problem, so it follows that one coordinate takes a special role. It is proposed that any boundary condition that is finite everywhere leads to a solution which is also finite everywhere. This explains the (1,D−1) signature of the metric, the boundedness of energy from below, the absence of tachyons, and other related properties of nature. The time arrow is explained by proposing that the boundary condition should be ordered. The quantization is considered as a boundary condition for field operators. Only the physical degrees of freedom are quantized.
Cable Coupling Characteristics of Wireless Network Convergence Nodes by Transient Electromagnetic Fields in Intelligent Substations
Authors: Yang Zhichao , Li Qian , Guo Xin , Zhao Mingmin , Zhao Peng , Lin Shanshan , Zhang Weidong
Published: April 2023

Switching operations in intelligent substations generate a wide range of extremely strong and asymmetric transient spatial electromagnetic fields, and this complex transient electromagnetic field can have a coupling effect with the wireless network sink node equipment cables. We conducted a study on the networking method of wireless network equipment in smart substations and analyzed the types of sink node cables used. In the GIS room of the 110 kV GIS smart substation, the transient spatial electromagnetic field generated by switching operation and the transient induced current of the sink node cable under different operating conditions were tested and obtained, and the time domain and frequency domain comparative analysis was performed. A near-field coupling model of the asymmetric and non-uniform cable transient spatial electric field at the sink node is established, and the validity of the model is verified by comparing and analyzing the measurement and simulation results. The results of the study can provide a reference for transient electromagnetic disturbances in 110 kV GIS intelligent substations and contribute to improving the reliability of wireless network equipment operation.
Numerical Modeling of Peridynamic Richards’ Equation with Piecewise Smooth Initial Conditions Using Spectral Methods
Authors: Difonzo Fabio V. , Di Lena Francesco
Published: April 2023

In this paper, we introduce peridynamic theory and its application to Richards’ equation with a piecewise smooth initial condition. Peridynamic theory is a non-local continuum theory that models the deformation and failure of materials. Richards’ equation describes the unsaturated flow of water through porous media, and it plays an essential role in many applications, such as groundwater management, soil science, and environmental engineering. We develop a peridynamic formulation of Richards’ equation that includes the effect of peridynamic forces and a piecewise smooth initial condition, further introducing a non-standard symmetric influence function to describe such peridynamic interactions, which turns out to provide beneficial effects from a numerical point of view. Moreover, we implement a numerical scheme based on Chebyshev polynomials and symmetric Gauss–Lobatto nodes, providing a powerful spectral method able to capture singularities and critical issues of Richards’ equation with piecewise smooth initial conditions. We also present numerical simulations that illustrate the performance of the proposed approach. In particular, we perform a computational investigation into the spatial order of convergence, showing that, despite the discontinuity in the initial condition, the order of convergence is retained.