bluuebunny/crossref_metadata_2025 huggingface dataset:
GBB-Nadir and KLIMA: Two Full Physics Codes for the Computation of the Infrared Spectrum of the Planetary Radiation Escaping to Space
Authors: Dinelli Bianca , Del Bianco Samuele , Castelli Elisa , Di Roma Alessio , Lorenzi Giacomo , Premuda Margherita , Barbara Flavio , Gai Marco , Raspollini Piera , Di Natale Gianluca
Published: May 2023

In 2019 the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission was selected to be the 9th Earth Explorer mission of the European Space Agency (ESA). In the preparatory phase of the mission there was the need for accurate and versatile codes to compute the spectrally resolved Earth radiation escaping to space ( outgoing long-wave radiation, OLR), targets for the FORUM measurements.Moreover, for the study of planetary atmospheres, several instruments measuring the planetary radiation escaping to space have been deployed (i.e., the planetary Fourier spectrometer on Mars express or composite infrared spectrometer on Cassini). For both the analysis of the measurements of these instruments and the design of new instruments, reliable radiative transfer codes need to be available. In this paper, we describe two full physics codes, Geofit broadband-Nadir (GBB-Nadir) and Kyoto protocol-informed management of adaptation (KLIMA), both able to compute the OLR spectrum, while GBB-Nadir is only a forward model, and therefore computes the spectra only, KLIMA implements the computation of spectral radiance derivatives with respect to atmospheric parameters and therefore it is suitable to be used in retrieval codes. The GBB-Nadir code can be interfaced with radiative transfer solvers that include representations of multiple scatterings, making it suitable to compute the radiances in all-sky conditions. KLIMA has been extensively validated comparing its radiances to ones generated by the widely used line-by-line radiative transfer model (LBLRTM) code. In this paper, we describe the latest version of both codes and their comparison. We compared the optical depth computed by GBB-Nadir and KLIMA for given values of pressure, temperature and gas columns for most gases active in the far-infrared and thermal-infrared spectral regions. We show that the optical depths computed by the two codes are in very good agreement. We compared the simulated spectra in clear sky conditions for three different atmospheres (equatorial, mid-latitude and polar) at resolutions of the FORUM instrument. The differences found are well below the expected noise of the FORUM instrument. The KLIMA code has already been used to simulate the observations of the Mars atmosphere, while the limb version of the GBB code has been used to simulate the radiances measured in the limb geometry of planetary atmospheres (Titan and Jupiter). Therefore, we may safely affirm that both codes can be used to simulate the nadir measurements of planetary atmospheres.
Constrained MEMS-Based INS/UWB Tightly Coupled System for Accurate UGVs Navigation
Authors: Mi Jing , Wang Qing , Han Xiaotao
Published: May 2023

To enhance the navigation performance and robustness of navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) under complex indoor environments, an improved navigation method—Allan variance (AV) to assist a modified adaptive extended Kalman Filter based on the dynamic weight function (DWF-MAEFF)—is proposed. Firstly, AV is used to improved INS error dynamics by modeling the stochastic noise of an inertial sensor; which can compensate for inertial sensor error caused by stochastic noise during integrated navigation. Secondly, the MAEKF is developed by designing the weight function to adjust the weight of measurement noise reasonably and dynamically, which can further improve the robustness of the AEKF algorithm. Field tests were conducted to verify the effectiveness of the proposed navigation method. The result indicated that an improvement of up to 60% over the existing integrated navigation method based on EKF and AEKF can be obtained by the proposed method.
Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model
Authors: Zhang Qiaoqiao , Luo Hao , Min Chao , Xiu Yongwu , Shi Qian , Yang Qinghua
Published: May 2023

Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro genetic algorithm, the North Atlantic/Arctic Ocean–Sea Ice Model (NAOSIM) has already demonstrated advantages in Arctic sea ice simulations. However, its performance in simulating pan-Arctic SITs remains unclear. In this study, a further evaluation of Arctic SITs from NAOSIM was conducted based on a comparison with satellite and in situ observations. Generally, NAOSIM can reproduce the annual cycle and downward trend in the sea ice volume. However, deficiencies can still be found in the simulation of SIT spatial patterns. NAOSIM overestimates the SIT of thinner ice (<1.5 m) in the Beaufort Sea, underestimates the SIT of thick ice (>1.5 m) in the central Arctic and is unable to capture the upward trend in the SIT in the north of the Canadian Archipelago as well as to reproduce the intensity of the observed SIT variability. In terms of SIT simulation, NAOSIM performs better as the time approaches the optimization window (2000–2012). Therefore, in the context of rapid changes in Arctic sea ice, how to optimize this model based on limited observations still remains a challenge.
Agreement and Disagreement-Based Co-Learning with Dual Network for Hyperspectral Image Classification with Noisy Labels
Authors: Zhang Youqiang , Sun Jin , Shi Hao , Ge Zixian , Yu Qiqiong , Cao Guo , Li Xuesong
Published: May 2023

Deep learning-based label noise learning methods provide promising solutions for hyperspectral image (HSI) classification with noisy labels. Currently, label noise learning methods based on deep learning improve their performance by modifying one aspect, such as designing a robust loss function, revamping the network structure, or adding a noise adaptation layer. However, these methods face difficulties in coping with relatively high noise situations. To address this issue, this paper proposes a unified label noise learning framework with a dual-network structure. The goal is to enhance the model’s robustness to label noise by utilizing two networks to guide each other. Specifically, to avoid the degeneration of the dual-network training into self-training, the “disagreement” strategy is incorporated with co-learning. Then, the “agreement” strategy is introduced into the model to ensure that the model iterates in the right direction under high noise conditions. To this end, an agreement and disagreement-based co-learning (ADCL) framework is proposed for HSI classification with noisy labels. In addition, a joint loss function consisting of a supervision loss of two networks and a relative loss between two networks is designed for the dual-network structure. Extensive experiments are conducted on three public HSI datasets to demonstrate the robustness of the proposed method to label noise. Specifically, our method obtains the highest overall accuracy of 98.62%, 90.89%, and 99.02% on the three datasets, respectively, which represents an improvement of 2.58%, 2.27%, and 0.86% compared to the second-best method. In future research, the authors suggest using more networks as backbones to implement the ADCL framework.
Delineating Polynya Area Using Active and Passive Microwave Sensors for the Western Ross Sea Sector of Antarctica
Authors: Burada Girija Kalyani , McDonald Adrian , Renwick James , Jolly Ben
Published: May 2023

A polynya is an area of open water or reduced concentration of sea ice surrounded by either concentrated sea ice or land ice. They are often seen as sites of intense ocean–atmosphere heat exchange and as ice production factories. Given their importance, it is crucial to quantify the accuracy of satellite-derived polynya information. Polynyas in their early evolution phase are generally narrow and occur at scales likely too fine to be detected by widely used passive microwave (PMW) radiometric sensors. We derived 40 m scale polynya information over the western Ross Sea from high-resolution Synthetic Aperture Radar (SAR) Sentinel-1 C-band data and examined discrepancies with larger-scale estimates. We utilized two automated algorithms, supervised (a rule-based approach) and unsupervised (a combination of texture analysis with k-means clustering), to accurately identify the polynya areas. We generated data for validation using Sentinel-1 data at instances where polynyas can be visually delineated. Results from PMW sensors (NSIDC and AMSR2) and SAR-based algorithms (rule-based and texture-based) are compared with manually delineated polynya areas obtained through Sentinel-1. Analysis using PMW sensors revealed that NSIDC overestimates larger polynyas and underestimates smaller polynyas compared to AMSR2. We were more accurately able to identify polynya presence and area using Sentinel-1 SAR observations, especially in clear cases and cases when PMW data miscalculates the polynya’s presence. Of our SAR-based algorithms, the rule-based approach was more accurate than the texture-based approach at identifying clear polynyas when validated against manually delineated regions. Altogether, we emphasize the need for finer spatio-temporal resolution data for polynya studies.
Forward and Backward Walking: Multifactorial Characterization of Gait Parameters
Authors: Donno Lucia , Monoli Cecilia , Frigo Carlo Albino , Galli Manuela
Published: May 2023

Although extensive literature exists on forward and backward walking, a comprehensive assessment of gait parameters on a wide and homogenous population is missing. Thus, the purpose of this study is to analyse the differences between the two gait typologies on a relatively large sample. Twenty-four healthy young adults participated in this study. By means of a marker-based optoelectronic system and force platforms, differences between forward and backward walking were outlined in terms of kinematics and kinetics. Statistically, significant differences were observed in most of the spatial–temporal parameters, evidencing some adaptation mechanisms in backward walking. Differently from the ankle joint, the hip and knee range of motion was significantly reduced when switching from forward to backward walking. In terms of kinetics, hip and ankle moment patterns for forward and backward walking were approximately mirrored images of each other. Moreover, joint powers appeared drastically reduced during reversed gait. Specifically, valuable differences in terms of produced and absorbed joint powers between forward and backward walking were pointed out. The outcomes of this study could represent a useful reference data for future investigation evaluating the efficacy of backward walking as a rehabilitation tool for pathological subjects.
High Acoustic Impedance and Attenuation Backing for High-Frequency Focused P(VDF-TrFE)-Based Transducers
Authors: Toffessi Siewe Sean , Callé Samuel , Vander Meulen François , Valente Damien , Grégoire Jean-Marc , Banquart Aline , Chevalliot Stéphanie , Capri Arnaud , Levassort Franck
Published: May 2023

Backing materials with tailored acoustic properties are beneficial for miniaturized ultrasonic transducer design. Whereas piezoelectric P(VDF-TrFE) films are common elements in high-frequency (>20 MHz) transducer design, their low coupling coefficient limits their sensitivity. Defining a suitable sensitivity–bandwidth trade-off for miniaturized high-frequency applications requires backings with impedances of >25 MRayl and strongly attenuating to account for miniaturized requirements. The motivation of this work is related to several medical applications such as small animal, skin or eye imaging. Simulations showed that increasing the acoustic impedance of the backing from 4.5 to 25 MRayl increases transducer sensitivity by 5 dB but decreases the bandwidth, which nevertheless remains high enough for the targeted applications. In this paper, porous sintered bronze material with spherically shaped grains, size-adapted for 25–30 MHz frequency, was impregnated with tin or epoxy resin to create multiphasic metallic backings. Microstructural characterizations of these new multiphasic composites showed that impregnation was incomplete and that a third air phase was present. The selected composites, sintered bronze–tin–air and sintered bronze–epoxy–air, at 5–35 MHz characterization, produced attenuation coefficients of 1.2 and >4 dB/mm/MHz and impedances of 32.4 and 26.4 MRayl, respectively. High-impedance composites were adopted as backing (thickness = 2 mm) to fabricate focused single-element P(VDF-TrFE)-based transducers (focal distance = 14 mm). The center frequency was 27 MHz, while the bandwidth at −6 dB was 65% for the sintered-bronze–tin–air-based transducer. We evaluated imaging performance using a pulse-echo system on a tungsten wire (diameter = 25 μm) phantom. Images confirmed the viability of integrating these backings in miniaturized transducers for imaging applications.
Correlation of Boreholes through Well Logs: Application to the Western Sector of Madrid
Authors: Díaz-Curiel Jesús , Arévalo-Lomas Lucía , Biosca Bárbara , Miguel María Jesús , Caparrini Natalia
Published: May 2023

This study correlates the results obtained from the resistivity and spontaneous potential well logs in six boreholes for water extraction, located in the multilayer siliciclastic basin in the Madrid region, in the center of the Iberian Peninsula. Given the small lateral continuity that the layers considered in isolation show in this type of multilayer aquifer, geophysical stretches, with their corresponding average lithological assignments, have been established to achieve this objective from the well logs. These stretches allow for mapping the internal lithology in the studied area, obtaining a correlation of greater geological scope than that provided by the correlation between layers. Subsequently, the possible correlation of the lithological stretches selected in each of the boreholes was analyzed, verifying their lateral continuity and establishing an NNW-SSE section in the study area. In this work, the transcendence of the correlation of wells up to great distances (about 8 km in total, and average distance of 1.5 km between wells) is focused on the fact that, if there is a presence of pollutant in certain aquifer stretches in a part of the studied area, overexploitation in the Madrid basin will cause the mobilization of this pollutant to the whole basin, with a possible impact on non-polluted areas.
Categorization of Marijuana Suspected Policies’ Seizures in Southeast Serbia According to Cannabinoids Content
Authors: Kostic Emilija , Djukic Mirjana , Antovic Aleksandra , Zdravkovic Miodrag , Milic Miroslav , Stojanovic Ivan , Todorovic Stevan , Vujovic Maja
Published: May 2023

Background: The cannabis plant or marijuana has been used since ancient times for different purposes. An increase in the content of THC in cannabis has been observed worldwide, whereas the CBD content is dropping. This study’s main goal was to categorize marijuana suspected policies’ seizures (MSPS) based on the cannabinoid component concentrations concerning their potency and age. Methods: The samples were MSPS seized in southeast Serbia from April 2019 to April 2020. The cannabinoid content was determined using gas chromatography with mass spectrometry. Descriptive statistics were performed using Linux LibreOffice Calc. Results: All of the samples had a THC content higher than 0.3%, thus classifying the samples as marijuana according to the Serbian Law on Psychoactive Controlled Substances. The highest concentration of THC was 16.10%, while the lowest was 4.90%. The highest average concentration of THC (12.39%) was found in the buds. Only 11.81% of the samples had a CBD concentration higher than 1%, making all other marijuana samples extremely psychoactive. Most of the samples were older than 2 years, according to the calculated CBN/THC ratio. Conclusion: Our results confirm trends regarding the dominance of THC content in contemporary cannabis worldwide. It is necessary to monitor trends of cannabis potency in Serbia continuously because of its location.
Impact of Nano Additives in Heat Exchangers with Twisted Tapes and Rings to Increase Efficiency: A Review
Authors: Assaf Younus Hamoudi , Akroot Abdulrazzak , Abdul Wahhab Hasanain A. , Talal Wadah , Bdaiwi Mothana , Nawaf Mohammed Y.
Published: May 2023

The heat exchanger is crucial to all systems and applications that use it. Researchers are primarily focused on improving this component’s thermal conductivity to improve its efficiency. This was achieved by using one or more of the following strategies: inserting tapes with various shapes and numbers, inserting rings of various shapes and spacing between each, and transforming a basic liquid into a nanoliquid by adding nanomaterials with high conductivity and ultra-small particle sizes. Different types of nanomaterials were added in varying concentrations. In earlier studies, it was found that every increase in heat transfer was accompanied by a pressure drop at both ends of the exchanger. The amount of heat transferred and the pressure drop are affected by many factors, such as the torsion tape ratio, the pitch of the ring, and whether the pitch faces the direction of flow or not. Heat transfer rates can also be impacted by factors such as the length and angle of the wings, how many rings and tapes there are, and whether the rings and tapes contain holes or wings. In addition, the Reynolds number, the type, conductivity, and size of nanomaterials, and the base fluid used in the nanofluid affect this. It is possible for the shape of the exchanger tube, as well as varying rates of rise, to introduce such impacts. In this study, the factors, costs, and benefits of using any technology to increase the efficiency of the heat exchanger are reviewed so that the user can make an informed decision about the technology to use.
Study on the Impact and Mechanism of Industrial Internet Pilot on Digital Transformation of Manufacturing Enterprises
Authors: He Jianjia , Liu Xuchen
Published: May 2023

In the era of digital economy, Industrial Internet is promoting the deep integration of manufacturing industry and the Internet through the new generation of information technology, which has become a new driving force to encourage the development of digital transformation of manufacturing enterprises. In this paper, we use the China Industrial Internet Pilot Demonstration Project as a proposed natural experiment to assess the impact and mechanism of action of the Industrial Internet pilot on the digital transformation of manufacturing enterprises using score propensity matching and double-difference methods based on panel data of annual reports of manufacturing listed companies from 2012 to 2020. The empirical results show that the Industrial Internet has a significant empowering effect on the digital transformation of manufacturing enterprises and positively promotes the digital transformation of manufacturing enterprises through the mechanism of digitalization, technology absorption, talent introduction, and innovation integration of industry cohort enterprises; the heterogeneity analysis shows that the empowering effect of the Industrial Internet is more substantial for technology-intensive enterprises, diversified business enterprises, and enterprises in the eastern region. This study provides empirical evidence for understanding the digital transformation of manufacturing enterprises enabled by the Industrial Internet. It provides theoretical support for promoting the development of Industrial Internet projects and achieving sustainable development of the manufacturing industry.
Research on Agricultural and Rural Public Governance and Sustainable Development: Evidence from 2350 Data
Authors: Huang Tingting , Huang Qinghua
Published: May 2023

Sustainable agriculture and good governance are part of the UN Sustainable Development Goals (SDGs), which have attracted great attention from all nations around the world. A scientific metrological and knowledge map analysis was conducted on the spatial–temporal evolution, collaboration network, research hotspots, cluster labels, frontier detection, and evolution path of 2350 pieces of data in this paper. The main results show that research hotspots such as sustainable development, rural development, agriculture, and others have influenced the development of the entire research process and have evolved into larger topic cluster groups such as ecosystem service, sustainable agriculture, land consolidation, and agricultural intensification. Research frontiers such as agriculture, integrated systems, smallholder systems, rural sustainable development, and land consolidation play key roles. Based on the findings, it is necessary to focus on the UN 2030 SDGs, combine the countries’ regional development needs and reality, and further clarify and refine the topics that need to be studied and the problems that need to be solved. More scientific demonstration and more feasible measures should be adopted to jointly deal with and enhance awareness of the current problems and practical challenges and further promote practical development by cohering academic consensus and expanding and innovating the governance models from the comprehensive dimensions of economy, politics, society, culture, and ecological environment so as to achieve good agricultural and rural governance.
Bioeconomy in Romania: Investigating Farmers’ Knowledge
Authors: Balan Emilia Mary , Zeldea Cristina Georgiana
Published: May 2023

The approach of studying the perceptions of Romanian farmers regarding the bioeconomy brings an element of novelty, and the study intends to add value to works in the field. The literature regarding the attitudes of farmers towards the adoption of new bioeconomic practices is quite limited at the European level and even more so in Romania. However, Romania’s agricultural potential is recognized nationally and internationally. This article aimed to explore the attitudes of Romanian farmers towards the bioeconomy and to take a step forward in determining a set of scientific actions necessary for the initiation of a national strategy dedicated to the bioeconomy. The quantitative research presented is based on a comprehensive survey. The analysis revealed the role of the bioeconomy in agricultural activities and the expectations of respondents in relation to the main aspects addressed by the concept of bioeconomy. The findings pointed out the contribution of Romanian public institutions in explaining and promoting this complex phenomenon to agricultural workers. The results led to three main conclusions: (i) farmers’ attitudes towards the bioeconomy are generally positive, although their knowledge is limited; (ii) public authorities are not sufficiently involved in supporting and promoting the bioeconomy; (iii) the bioeconomy is underfinanced at the national level. The findings draw attention to a great investment potential in the agricultural field that could foster job creation and regional development in Romania. A closer collaboration between researchers, decision-makers, local authorities, and farmers as well as the expansion of technological research are the conditions needed for the development of the bioeconomy in the agriculture of Romania.
Analysis of Key Factors for Green Supplier Selection: A Case Study of the Electronics Industry in Vietnam
Authors: Tsai Jung-Fa , Wu Sheng-Che , Pham Thi Khanh Linh , Lin Ming-Hua
Published: May 2023

In recent years, green supply chain management (GSCM) and green supplier selection (GSS) have become significant trends and have received increasing attention around the globe and in Vietnam. This research aimed to explore the key factors for GSS in the case of the electronics industry in Vietnam. Modified Delphi and analytic hierarchy process (AHP) methods were used to determine the factors and measure their importance, with data collected from experts in Vietnam’s electronics industry. According to the analytical results, the ranking of importance of five dimensions for selecting green suppliers was product quality, cost and service performance, environmental management, technology capability, and supplier risks. Key factors for GSS included the capability of handling abnormal quality, product price, quality assurance, quality-related certificates and lead time. These research findings could help electronics companies in Vietnam and other developing countries to make strategic decisions when selecting suppliers and offer guidance for suppliers on increasing their competitive advantages. Additionally, this study provides a comprehensive overview picture of environmental management and GSCM implementation to the government.
Modeling and Optimization of Combined Heating, Power, and Gas Production System Based on Renewable Energies
Authors: Chen Tzu-Chia , Alvarez José Ricardo Nuñez , Dwijendra Ngakan Ketut Acwin , Kadhim Zainab Jawad , Alayi Reza , Kumar Ravinder , PraveenKumar Seepana , Velkin Vladimir Ivanovich
Published: May 2023

Electrical energy and gas fuel are two types of energy needed that increase environmental pollution by burning fossil fuels in power plants to produce electrical energy and direct combustion of gas fuel. In this research, an attempt has been made to model the electrical energy network in the presence of renewable energy sources and gas production systems. The advantage of this model compared to other models of similar studies can be found in providing a mixed integer linear optimization model of distributed generation sources with gas fuel, energy storage systems, and gas power systems, along with electric vehicles in an integrated electricity and gas system. In addition to the energy consumption of buildings, an electric vehicle is also considered a base load, which is one of the limitations in optimizing the maximum charging of an electric vehicle. Among the important results of this research, it can be mentioned that the investment cost of USD 879,340 in the first scenario, in which 37,374 kW of electric energy was purchased from the network to supply the electric load, and 556,233 m3 was purchased from the gas network to supply the required gas.
Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP
Authors: Bekdaş Gebrail , Cakiroglu Celal , Kim Sanghun , Geem Zong Woo
Published: May 2023

The optimal design of prestressed concrete cylindrical walls is greatly beneficial for economic and environmental impact. However, the lack of the available big enough datasets for the training of robust machine learning models is one of the factors that prevents wide adoption of machine learning techniques in structural design. The current study demonstrates the application of the well-established harmony search methodology to create a large database of optimal design configurations. The unit costs of concrete and steel used in the construction, the specific weight of the stored fluid, and the height of the cylindrical wall are the input variables whereas the optimum thicknesses of the wall with and without post-tensioning are the output variables. Based on this database, some of the most efficient ensemble learning techniques like the Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Gradient Boosting (CatBoost) and Random Forest algorithms have been trained. An R2 score greater than 0.98 could be achieved by all of the ensemble learning models. Furthermore, the impacts of different input features on the predictions of different machine learning models have been analyzed using the SHapley Additive exPlanations (SHAP) methodology. The height of the cylindrical wall was found to have the greatest impact on the optimal wall thickness, followed by the specific weight of the stored fluid. Also, with the help of individual conditional expectation (ICE) plots the variations of predictive model outputs with respect to each input feature have been visualized. By using the genetic programming methodology, predictive equations have been obtained for the optimal wall thickness.
The Effect of State Ownership on a Cross-Border Acquisition Wave
Authors: Le Crystal , Pu Jing , Choi Seungho
Published: May 2023

More enterprises from developing countries are conducting cross-border acquisitions (CBA) in the global market. Inter-industry acquisitions take the form of waves, and the position in an acquisition wave has been confirmed to be related to acquisition performance in developed countries. This paper examines the relationship between state ownership and position in a CBA wave using a sample of Chinese firms’ CBA events from 2008 to 2019. This article also attempts to examine the moderating effect of the number of board members with overseas work experiences and educational backgrounds.
Vertical Profiles of Microplastics in the Hyporheic Zone Sediment: A Case Study in the Yangtze River, Nanjing Section
Authors: Bao Kun , Jiang Hui , Su Pengpeng , Lu Ping , Yan Zhenhua
Published: May 2023

Microplastics are ubiquitous in the river environment, although their abundance in sediment profiles has received little attention. The river hyporheic zone (HZ) sediment is the area influenced by surface and groundwater flow dynamics, and pollutants are more likely to be transported vertically in this area, thus entering the groundwater. Understanding the microplastic abundance and composition in the HZ sediment is crucial for microplastic pollution management. Hence, this study investigated the vertical distribution and characteristics of microplastic in the HZ sediment of the Yangtze River (Nanjing section). The results show that the abundance of microplastics in the HZ sediment ranged from 207 ± 95 to 1817 ± 467 items/kg dry wet in a vertical profile. With the increase of sediment depth, the abundance of microplastics decreased obviously in most sites, whereas the proportions of pellet shapes and smaller sizes of microplastics increased only at S1. No significant variation was found in the microplastic colors between different depth layers. Polypropylene and polyethylene were the dominant polymer types in all sediment samples. These results provided insights into the understanding of the microplastic fates in a river HZ region.
Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules
Authors: Aribia Houssem Ben , El-Rifaie Ali M. , Tolba Mohamed A. , Shaheen Abdullah , Moustafa Ghareeb , Elsayed Fahmi , Elshahed Mostafa
Published: May 2023

One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.
Impacts of Digital Economy on Urban Entrepreneurial Competencies: A Spatial and Nonlinear Perspective
Authors: Zhao Kai , Yang Jiaqi , Wu Wanshu
Published: May 2023

The vigorous rise of the digital economy not only affects the dynamic system and development path of entrepreneurial activities, but also brings new opportunities to enhance urban entrepreneurial competencies. The purpose of this paper is to investigate whether the digital economy supported by digital technologies can become a new kinetic energy that can enhance urban entrepreneurial competencies in the context of the “New Normal”. Based on the sample of 286 cities in China, this paper investigates the temporal and spatial characteristics of urban entrepreneurial competencies and analyzes the spatial effect of the digital economy on urban entrepreneurial competencies using the spatial dynamic panel Durbin model. Furthermore, this paper examines whether the impact of the digital economy on urban entrepreneurial competencies has a “threshold effect” in different business environments by using threshold spatial dynamic panel model. It is found that: (1) Urban entrepreneurial competencies have obvious spatial dependence; (2) The digital economy harms the entrepreneurial competencies of neighboring cities; (3) In different business environments, the impact of the digital economy on urban entrepreneurial competencies shows obvious non-linear characteristics.
Convolutional Long Short-Term Memory Two-Dimensional Bidirectional Graph Convolutional Network for Taxi Demand Prediction
Authors: Cao Yibo , Liu Lu , Dong Yuhan
Published: May 2023

With the rise of the online ride-hailing market, taxi demand prediction has received more and more research interest in intelligent transportation. However, most traditional research methods mainly focused on the demand based on the original point and ignored the intention of the passenger’s destination. At the same time, many forecasting methods need sufficient investigation and data processing, which undoubtedly increases the complexity and operability of forecasting problems. Therefore, we regard the current taxi demand prediction as an origin–destination problem in order to provide more accurate predictions for the taxi demand problem. By combining a spatial network based on graph convolutional network (GCN) and a temporal network of convolutional long short-term memory (Conv-LSTM), we propose a new spatial-temporal model of Conv-LSTM two-dimensional bidirectional GCN (CTBGCN) to uncover the potential correlation between origin and destination. We utilize the temporal network for effective temporal information and the spatial network of multi-layers to get the implicit origin–destination correlation. Numerical results suggest that the proposed method outperforms the state-of-the-art baseline and other traditional methods.
Transport/Detachment Regimes of Different Size Class Sediment Particles and Enlightenments for Transport Capacity Prediction for Rain-Induced Overland Flow Erosion
Authors: Wang Shanqi , Fan Ying , Liu Lin , Qu Jiuqi
Published: May 2023

The transport/detachment regimes of each particle size vary with size. Moreover, the changing transport/detachment regimes of sediments with size and their related mechanisms considerably affect sediment transport capacity (Tc). To investigate the transport/detachment regimes of each particle size and their explanation for Tc prediction, 27 simulated rainfall experiments were conducted under slope gradients of 8.7%, 17.6%, and 26.7% and rainfall intensities of 60, 90, and 120 mm h−1. The experimental soil was Cumulic Anthrosols, and the duration of each rainfall was 1 h. Results showed that for rain-induced overland flow erosion, the average transport ratios (Trs) of clay, fine silt, and coarse sand (<0.002, 0.002–0.02, and >0.25 mm, respectively) for rainfall, were greater than 1.0, and their erosion regimes were detachment-limited. The Trs of coarse silt and fine sand (0.02–0.05 mm and 0.05–0.25 mm, respectively) were less than 1.0, and their erosion regimes were transport-limited. The transport-/detachment-limited degree of each size class of particles, except for coarse sand, increased with the slope gradient, but slightly and complexly changed with rainfall intensity. The Trs of each size class of particles on the gentle slope (8.7%, 17.6%) increased with the Trs of total sediments. In the case of the steep slope (27.6%), however, the Trs of fine silt and clay increased with a decrease in the Trs of the total sediment particles. Different prediction equations were established to simulate the Tcs of sediments with different sizes in the two slope ranges (R2 > 0.823, p < 0.01). The findings will help to elucidate the selective transport mechanisms of all sizes of sediment particles and improve the prediction of Tc in the future.
Blockchain Technology Adoption for Improved Environmental Supply Chain Performance: The Mediation Effect of Supply Chain Resilience, Customer Integration, and Green Customer Information Sharing
Authors: Mohamed Summer K. , Haddad Sandra , Barakat Mahmoud , Rosi Bojan
Published: May 2023

Due to the complexity of building supply chain resilience (SCR) towards long-term environmental sustainability amendments, the use of emerging technologies such as Blockchain Technology (BCT) can be adopted as an innovative tool to enhance the sustainability and resilience of supply chains, especially in uncertain environments. Drawing on the Knowledge-Based View (KBV) and Dynamic Capability View (DCV), this research aims to demonstrate how the adoption of BCT can enhance the environmental supply chain performance (SCP). A total of 603 valid surveys were collected from respondents from manufacturing and service organizations in Egypt. The collected data were analyzed using structural equation modelling, and results revealed that BCT adoption alone had a negative direct impact on environmental SCP. However, when this relationship was mediated by SCR and sequentially mediated by customer integration and green customer information sharing, the results were positive. This research presents insights on how organizations can adapt to dynamic business environments, and, in addition, it extends the theories of KBV and DCV in an empirical contribution by filling the gap in understanding regarding how environmental SCP can be enhanced through the adoption of BCT.
Analysis of the Influencing Factors of Power Demand in Beijing Based on the LMDI Model
Authors: Su Deguo , Tan Beibei , Zhang Anbing , Hou Yikai
Published: May 2023

Since the reform and opening-up, under the new economic situation and policy, the rapid growth of power demand in Beijing is threatening the sustainable development of China’s economy and environment. To recognize the driving factors of electricity consumption growth and offer policy implications, based on the data of electricity consumption, the Gross Domestic Product (GDP) and the resident population in Beijing from 1990 to 2021, this research used the Kaya-equation and logarithmic mean divisia index (LMDI) model to decompose the growth of power demand in Beijing into the quantitative contribution of each driving factor from the perspective of industrial electricity consumption and residential electricity consumption. The results of the decomposition analysis show that, as far as industrial electricity consumption is concerned, the contribution rates of economic growth, electricity consumption intensity and output value structure to industrial electricity growth are 234.26%, −109.01% and −25.25%, respectively, which shows that economic growth is the primary driving force promoting the growth of industrial electricity demand. Power consumption intensity is the main reason for restraining the growth of industrial power demand, the growth rate is sliding and the contribution of the industrial structure is relatively small; as far as residential power consumption is concerned, the contribution rates of per capita power consumption and population size to residential power growth are 68.13% and 31.87%, respectively, which indicates that per capita power consumption is the main factor promoting the growth of residential power demand, followed by the total population. The study results show that the consumption of electric power would increase if Beijing’s economy and urbanization keep developing, and optimizing the industry structure, improving the efficiency of electric energy utilization and adopting clean power energy are the main approaches to making Beijing’s consumption of electric power decrease.
A Moderated Mediation Analysis of the Relationship between Cultural Embeddedness of Regional Brand Products and Behavior Loyalty: A Case Study of Wudang Mountains in Hubei Province of China
Authors: Xu Na , Zhao Yanpu
Published: May 2023

This study examines how sustainable development of the cultural embeddedness of regional brand products (CERBP) can be maintained, which has received limited attention. Accordingly, it proposes a novel conceptual model by integrating the theory of planned behavior with local cultural resources to gain global competitive advantage. Based on the model, this study explores both the direct and indirect effects of CERBP, perceived value (PV), and attitude on behavior loyalty (BL) (H1–H5); it also examines how perceived authenticity (PA) might moderate the relationship between CERBP and PV (H1a). The model’s validity was confirmed using a partial least squares–structural equation modeling technique based on the data collected from 397 survey responses. The results are as follows: (1) CERBP significantly and positively influenced PV and attitude. (2) PV significantly and positively influenced attitude and BL. (3) Attitude significantly and positively influenced BL. (4) PV and attitude significantly and positively mediated the relationship between CERBP and BL; compared with attitude, PV had more significant influence on BL, either directly or indirectly. (5) However, PA was an insignificant moderator of the relationship between CERBP and PV. These research findings from analysis results provide useful and important theoretical and practical implications for the sustainable development of cultural resources and regional economies.
Deep-Learning-Based Strawberry Leaf Pest Classification for Sustainable Smart Farms
Authors: Kim Haram , Kim Dongsoo
Published: May 2023

This paper presents a deep-learning-based classification model that aims to detect diverse pest infections in strawberry plants. The proposed model enables the timely identification of pest symptoms, allowing for prompt and effective pest management in smart farms. The present research employed an actual dataset of strawberry leaf images collected from a smart farm test bed. To expand the dataset, open data from sources such as Kaggle were utilized, while diseased leaf images were obtained through web crawling with the aid of the Python library. Subsequently, the expanded and added data were resized to a uniform size, and Pseudo-Labeling was implemented to ensure stable learning for both the training and test datasets. The RegNet and EfficientNet models were selected as the primary CNN-based image network models for repetitive learning, and ensemble learning was employed to enhance prediction accuracy. The proposed model is anticipated to facilitate the early identification and treatment of pests on strawberry leaves during the seedling period, a pivotal phase in smart farm development. Furthermore, it is expected to boost production in the agricultural industry and strengthen its competitive edge.
Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation
Authors: Ye Qing , Li Yi , Shen Wenzhe , Xuan Zhaoze
Published: May 2023

This study focuses on identifying accident-prone areas and analyzing the factors contributing to the distribution of traffic accidents near highway ramps. A combined method of kernel density estimation, spatial autocorrelation analysis, and multivariate logistic regression analysis helped to identify accident hotspots. Through data collection and analysis, the clustering characteristics of traffic accidents in the diversion and merging areas were identified. Four levels of accident-prone areas were divided according to the accident rates. The factors influencing the spatial distribution of accidents were analyzed. The results showed that traffic accidents in the diversion area were concentrated near the exit, but the accidents in merging areas had a wider range of distribution. The analysis of this phenomenon was conducted using the multinomial logit model results. The important factors of different accident-prone areas were clarified. The temperature, the accident lane, weather conditions, and the time of day had significant impacts on the spatial distribution of traffic accidents. The study’s findings provide an important decision-making basis for highway accident prevention management.
Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms
Authors: Solano Edna S. , Affonso Carolina M.
Published: May 2023

This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. A clustering algorithm is used to group data according to the weather, and feature selection is applied to choose the most-related inputs and their past observation values. Prediction performance is evaluated by several metrics using a real-world Brazilian database, considering different prediction time horizons of up to 12 h ahead. Numerical results show the weighted average voting approach based on random forest and categorical boosting has superior performance, with an average reduction of 6% for MAE, 3% for RMSE, 16% for MAPE, and 1% for R2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models.