bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of ICESat-2’s Horizontal Accuracy Using an Iterative Matching Method Based on High-Accuracy Terrains
Authors: Gao Ming , Xing Shuai , Zhang Guoping , Zhang Xinlei , Li Pengcheng
Published: April 2023

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data support for further study. The conventional terrain-matching methods regard the laser ground track as a whole, ignoring the individual differences caused by the interaction of photons during flight. Therefore, a novel terrain-matching method using a two-dimensional affine transformation model was proposed to describe the deformation of laser tracks. The least-square optimizes the model parameters with the high-accuracy terrain data to obtain the best matching result. The results in McMurdo Dry Valley (MDV), Antarctica, and Zhengzhou (ZZ), China, demonstrate that the proposed method can verify geolocation accuracy and indicate that the average horizontal accuracy of ICESat-2 V5 data is about 3.86 m in MDV and 4.67 m in ZZ. It shows that ICESat-2 has good positioning accuracy, even in mountainous areas with complex terrain. Additionally, the random forest (RF) model was calculated to analyze the influence of four factors on geographic location accuracy. The slope and signal-to-noise ratio (SNR) are considered the crucial factors affecting the accuracy of ICESat-2 data.
Overview of Comprehensive Risk Assessment Methods and Hazards Early Warning System for Geological Hazards in the Mountain Area
Authors: Zhu Chun , Xu Yingze , Tao Zhigang , Zhu Hong-Hu , Cao Chen , He Manchao
Published: April 2023

Many major projects are under construction in the mountain and surrounding areas [...]
Synergistic Integration of Time Series Optical and SAR Satellite Data for Mariculture Extraction
Authors: Wang Shuxuan , Huang Chong , Li He , Liu Qingsheng
Published: April 2023

Mariculture is an important part of aquaculture, and it is important to address global food security and nutrition issues. However, seawater environmental conditions are complex and variable, which causes large uncertainties in the remote sensing spectral features. At the same time, mariculture types are distinct because of the different types of aquaculture (cage aquaculture and raft aquaculture). These factors bring great challenges for mariculture extraction and mapping using remote sensing. In order to solve these problems, an optical remote sensing aquaculture index named the marine aquaculture index (MAI) is proposed. Based on this spectral index, using time series Sentinel-1 and Sentinel-2 satellite data, a random forest classification scheme is proposed for mapping mariculture by combining spectral, textural, geometric, and synthetic aperture radar (SAR) backscattering. The results revealed that (1) MAI can emphasize the difference between mariculture and seawater; (2) the overall accuracy of mariculture in the Bohai Rim is 94.10%, and the kappa coefficient is 0.91; and (3) the area of cage aquaculture and raft aquaculture in the Bohai Rim is 16.89 km2 and 1206.71 km2, respectively. This study details an effective method for carrying out mariculture monitoring and ensuring the sustainable development of aquaculture.
Eddy Detection in the Marginal Ice Zone with Sentinel-1 Data Using YOLOv5
Authors: Khachatrian Eduard , Sandalyuk Nikita , Lozou Pigi
Published: April 2023

The automatic detection and analysis of ocean eddies in the marginal ice zone via remote sensing is a very challenging task but of critical importance for scientific applications and anthropogenic activities. Therefore, as one of the first steps toward the automation of the eddy detection process, we investigated the potential of applying YOLOv5, a deep convolutional neural network architecture, to specifically collected and labeled high-resolution synthetic aperture radar data for a very dynamic area over the Fram Strait. Our approach involved fine-tuning pre-trained YOLOv5 models on a sparse dataset and achieved accurate results with minimal training data. The performances of the models were evaluated using several metrics, and the best model was selected by visual examination. The experimental results obtained from the validation and test datasets consistently demonstrated the robustness and effectiveness of the chosen model to identify submesoscale and mesoscale eddies with different structures. Moreover, our work provides a foundation for automated eddy detection in the marginal ice zone using synthetic aperture radar imagery and contributes to advancing oceanography research.
Assessment of a New Solar Radiation Nowcasting Method Based on FY-4A Satellite Imagery, the McClear Model and SHapley Additive exPlanations (SHAP)
Authors: Jia Dongyu , Yang Liwei , Gao Xiaoqing , Li Kaiming
Published: April 2023

The global warming effect has been accelerating rapidly and poses a threat to human survival and health. The top priority to solve this problem is to provide reliable renewable energy. To achieve this goal, it is important to provide fast and accurate solar radiation predictions based on limited observation data. In this study, a fast and accurate solar radiation nowcasting method is proposed by combining FY-4A satellite data and the McClear clear sky model under the condition of only radiation observation. The results show that the random forest (RF) performed better than the support vector regression (SVR) model and the reference model (Clim-Pers), with the smallest normalized root mean square error (nRMSE) values (between 13.90% and 33.80%), smallest normalized mean absolute error (nMAE) values (between 7.50% and 24.77%), smallest normalized mean bias error (nMBE) values (between −1.17% and 0.7%) and highest R2 values (between 0.76 and 0.95) under different time horizons. In addition, it can be summarized that remote sensing data can significantly improve the radiation forecasting performance and can effectively guarantee the stability of radiation predictions when the time horizon exceeds 60 min. Furthermore, to obtain the optimal operation efficiency, the prediction results were interpreted by introducing the latest SHapley Additive exPlanation (SHAP) method. From the interpretation results, we selected the three key channels of an FY-4A and then made the model lightweight. Compared with the original input model, the new one predicted the results more rapidly. For instance, the lightweight parameter input model needed only 0.3084 s (compared to 0.5591 s for full parameter input) per single data point on average for the 10 min global solar radiation forecast in Yuzhong. Meanwhile, the prediction effect also remained stable and reliable. Overall, the new method showed its advantages in radiation prediction under the condition that only solar radiation observations were available. This is very important for radiation prediction in cities with scarce meteorological observation, and it can provide a reference for the location planning of photovoltaic power stations.
Learning-Based Near-Infrared Band Simulation with Applications on Large-Scale Landcover Classification
Authors: Yuan Xiangtian , Tian Jiaojiao , Reinartz Peter
Published: April 2023

Multispectral sensors are important instruments for Earth observation. In remote sensing applications, the near-infrared (NIR) band, together with the visible spectrum (RGB), provide abundant information about ground objects. However, the NIR band is typically not available on low-cost camera systems, which presents challenges for the vegetation extraction. To this end, this paper presents a conditional generative adversarial network (cGAN) method to simulate the NIR band from RGB bands of Sentinel-2 multispectral data. We adapt a robust loss function and a structural similarity index loss (SSIM) in addition to the GAN loss to improve the model performance. With 45,529 multi-seasonal test images across the globe, the simulated NIR band had a mean absolute error of 0.02378 and an SSIM of 89.98%. A rule-based landcover classification using the simulated normalized difference vegetation index (NDVI) achieved a Jaccard score of 89.50%. The evaluation metrics demonstrated the versatility of the learning-based paradigm in remote sensing applications. Our simulation approach is flexible and can be easily adapted to other spectral bands.
Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity
Authors: Dang Van Anh , Vu Khanh Quy , Nguyen Van-Hau , Nguyen Tien , Nguyen Dinh C.
Published: April 2023

Health is gold, and good health is a matter of survival for humanity. The development of the healthcare industry aligns with the development of humans throughout history. Nowadays, along with the strong growth of science and technology, the medical domain in general and the healthcare industry have achieved many breakthroughs, such as remote medical examination and treatment applications, pandemic prediction, and remote patient health monitoring. The advent of 5th generation communication networks in the early 2020s led to the Internet of Things concept. Moreover, the 6th generation communication networks (so-called 6G) expected to launch in 2030 will be the next revolution of the IoT era, and will include autonomous IoT systems and form a series of endogenous intelligent applications that serve humanity. One of the domains that receives the most attention is smart healthcare. In this study, we conduct a comprehensive survey of IoT-based technologies and solutions in the medical field. Then, we propose an all-in-one computing architecture for real-time IoHT applications and present possible solutions to achieving the proposed architecture. Finally, we discuss challenges, open issues, and future research directions. We hope that the results of this study will serve as essential guidelines for further research in the human healthcare domain.
Decentralized Real-Time Anomaly Detection in Cyber-Physical Production Systems under Industry Constraints
Authors: Goetz Christian , Humm Bernhard
Published: April 2023

Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.
GMM-Based Adaptive Extended Kalman Filter Design for Satellite Attitude Estimation under Thruster-Induced Disturbances
Authors: Kim Taeho , Zewge Natnael S. , Bang Hyochoong , Yoon Hyosang
Published: April 2023

Star images from star trackers are usually defocused to capture stars over an exposure time for better centroid measurements. While a satellite is maneuvering, the star point on the screen of the camera is affected by the satellite, which results in the degradation of centroid measurement accuracy. Additionally, this could result in a worse star vector outcome. For geostationary satellites, onboard thrusters are used to maintain or change orbit parameters under orbit disturbances. Since there is misalignment in the thruster and torque is generated by an impulsive shape signal from the torque command, it is difficult to generate target torque; in addition, it also impacts the star image because the impulsive torque creates a sudden change in the angular velocity in the satellite dynamics. This makes the noise of the star image non-Gaussian, which may require introducing a method for dealing with non-Gaussian measurement noise. To meet this goal, in this study, an adaptive extended Kalman filter is implemented to predict measurement vectors with predicted states. The GMM (Gaussian mixture model) is connected in this sequence, giving weighting parameters to each Gaussian density and resulting in the better prediction of measurement vectors. Simulation results show that the GMM-EKF exhibits a better performance than the EKF for attitude estimation, with 30% improvement in performance. Therefore, the GMM-EKF could be a more attractive approach for use with geostationary satellites during station-keeping maneuvers.
Coastal Bathymetric Sounding in Very Shallow Water Using USV: Study of Public Beach in Gdynia, Poland
Authors: Makar Artur
Published: April 2023

The bathymetric surveys executed with a use of small survey vessels in limited water areas, including offshore areas, require precise determination of the geospatial coordinates of the seabed which is a synthesis of, among others, determining the position coordinates and measuring the depth. Inclination of the seabed and the declining depth make manoeuvring of the sounding vessel, e.g., a hydrographic motorboat or Unmanned Survey Vehicle (USV), in shallow water impossible. Therefore, it is important to determine the minimal depth for the survey resulting from the draught of the sounding vessel and the limits of the sounding area. The boundaries also result from the dimensions of the sounding vessel, its manoeuvring parameters and local water level. Type of the echosounder used in the bathymetric survey is a decisive factor for the sounding profile planning and the distances between them and the survey vessel for the possibility performing the measurements in shallow water. Electronic Navigational Chart (ENC) was used to determine the water area’s boundaries, and the safety contours were determined on the basis of the built Digital Sea Bottom Model (DSBM). The safety contour, together with the vessel’s dimensions, its manoeuvring parameters and the hydrometeorological conditions, limit the offshore area in which the measurement can be performed. A method of determining boundaries of the survey performed by a USV equipped with SingleBeam EchoSounder (SBES) on survey lines perpendicular to the coastal line are presented in the paper in order to cover the water area with the highest amount of measurement data, with the USV’s navigational safety taken into consideration. The measurements executed on the municipal beach served verification of the DSBM.
Stackelberg Game Approach for Service Selection in UAV Networks
Authors: Hadjkouider Abdessalam Mohammed , Kerrache Chaker Abdelaziz , Korichi Ahmed , Sahraoui Yesin , Calafate Carlos T.
Published: April 2023

Nowadays, mobile devices are expected to perform a growing number of tasks, whose complexity is also increasing significantly. However, despite great technological improvements in the last decade, such devices still have limitations in terms of processing power and battery lifetime. In this context, mobile edge computing (MEC) emerges as a possible solution to address such limitations, being able to provide on-demand services to the customer, and bringing closer several services published in the cloud with a reduced cost and fewer security concerns. On the other hand, Unmanned Aerial Vehicle (UAV) networking emerged as a paradigm offering flexible services, new ephemeral applications such as safety and disaster management, mobile crowd-sensing, and fast delivery, to name a few. However, to efficiently use these services, discovery and selection strategies must be taken into account. In this context, discovering the services made available by a UAV-MEC network, and selecting the best services among those available in a timely and efficient manner, can become a challenging task. To face these issues, game theory methods have been proposed in the literature that perfectly suit the case of UAV-MEC services by modeling this challenge as a Stackelberg game, and using existing approaches to find the solution for such a game aiming at an efficient services’ discovery and service selection. Hence, the goal of this paper is to propose Stackelberg-game-based solutions for service discovery and selection in the context of UAV-based mobile edge computing. Simulations results conducted using the NS-3 simulator highlight the efficiency of our proposed game in terms of price and QoS metrics.
Characterization of Inclination Analysis for Predicting Onset of Heart Failure from Primary Care Electronic Medical Records
Authors: Guida Federica , Lenatti Marta , Keshavjee Karim , Khatami Alireza , Guergachi Aziz , Paglialonga Alessia
Published: April 2023

The aim of this study is to characterize the performance of an inclination analysis for predicting the onset of heart failure (HF) from routinely collected clinical biomarkers extracted from primary care electronic medical records. A balanced dataset of 698 patients (with/without HF), including a minimum of five longitudinal measures of nine biomarkers (body mass index, diastolic and systolic blood pressure, fasting glucose, glycated hemoglobin, low-density and high-density lipoproteins, total cholesterol, and triglycerides) is used. The proposed algorithm achieves an accuracy of 0.89 (sensitivity of 0.89, specificity of 0.90) to predict the inclination of biomarkers (i.e., their trend towards a ‘survival’ or ‘collapse’ as defined by an inclination analysis) on a labeled, balanced dataset of 40 patients. Decision trees trained on the predicted inclination of biomarkers have significantly higher recall (0.69 vs. 0.53) and significantly higher negative predictive value (0.60 vs. 0.55) than those trained on the average values computed from the measures of biomarkers available before the onset of the disease, suggesting that an inclination analysis can help identify the onset of HF in the primary care patient population from routinely available clinical data. This exploratory study provides the basis for further investigations of inclination analyses to identify at-risk patients and generate preventive measures (i.e., personalized recommendations to reverse the trend of biomarkers towards collapse).
The Effects of Social Presence and Familiarity on Children–Robot Interactions
Authors: Chen Yi-Chen , Yeh Su-Ling , Lin Weijane , Yueh Hsiu-Ping , Fu Li-Chen
Published: April 2023

In children–robot interactions, an impression of a robot’s “social presence” (i.e., an interactive agent that feels like a person) links positively to an improved relationship with the robot. However, building relationships takes many exposures, and there is an intellectual gap in how social presence and familiarity collaborate in modulating children–robot relationships. We investigated whether social presence altered over time, how repeated exposure and social presence affected rapport, and how social presence would modulate children’s attitudes toward the robot. Fourteen children (four female, age = 10.79 ± 1.12) interacted with a companion robot for four days in spontaneous interactions. The findings revealed that children who perceived the robot as having a higher social presence developed a stronger rapport than those who perceived a lower social presence. However, repeated encounters did not change the children’s perceptions of the robot’s social presence. Children rated higher rapport after repeated interactions regardless of social presence levels. This suggests that while a higher social presence initially elevated the positive relationship between children and the robot, it was the repeated interactions that continued solidifying the rapport. Additionally, children who perceived a higher social presence from the robot felt less relational uneasiness about their relationship with robots. These findings highlight the importance of robots’ social presence and familiarity in promoting positive relationships in children–robot interaction.
Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems
Authors: Kolosov Dimitrios , Fengou Lemonia-Christina , Carstensen Jens Michael , Schultz Nette , Nychas George-John , Mporas Iosif
Published: April 2023

Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively.
Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm
Authors: Zhao Wentao , Wu Dasheng , Zheng Xinyu
Published: April 2023

Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper proposed an improved CR-YOLOv5s to recognize flower buds and blooms for chrysanthemums by emphasizing feature representation through an attention mechanism. The coordinate attention mechanism module has been introduced to the backbone of the YOLOv5s so that the network can pay more attention to chrysanthemum flowers, thereby improving detection accuracy and robustness. Specifically, we replaced the convolution blocks in the backbone network of YOLOv5s with the convolution blocks from the RepVGG block structure to improve the feature representation ability of YOLOv5s through a multi-branch structure, further improving the accuracy and robustness of detection. The results showed that the average accuracy of the improved CR-YOLOv5s was as high as 93.9%, which is 4.5% better than that of normal YOLOv5s. This research provides the basis for the automatic picking and grading of flowers, as well as a decision-making basis for estimating flower yield.
Virtual Sensor: Simultaneous State and Input Estimation for Nonlinear Interconnected Ground Vehicle System Dynamics
Authors: Sentouh Chouki , Fouka Majda , Popieul Jean-Christophe
Published: April 2023

This paper proposes a new observer approach used to simultaneously estimate both vehicle lateral and longitudinal nonlinear dynamics, as well as their unknown inputs. Based on cascade observers, this robust virtual sensor is able to more precisely estimate not only the vehicle state but also human driver external inputs and road attributes, including acceleration and brake pedal forces, steering torque, and road curvature. To overcome the observability and the interconnection issues related to the vehicle dynamics coupling characteristics, tire effort nonlinearities, and the tire–ground contact behavior during braking and acceleration, the linear-parameter-varying (LPV) interconnected unknown inputs observer (UIO) framework was used. This interconnection scheme of the proposed observer allows us to reduce the level of numerical complexity and conservatism. To deal with the nonlinearities related to the unmeasurable real-time variation in the vehicle longitudinal speed and tire slip velocities in front and rear wheels, the Takagi–Sugeno (T-S) fuzzy form was undertaken for the observer design. The input-to-state stability (ISS) of the estimation errors was exploited using Lyapunov stability arguments to allow for more relaxation and an additional robustness guarantee with respect to the disturbance term of unmeasurable nonlinearities. For the design of the LPV interconnected UIO, sufficient conditions of the ISS property were formulated as an optimization problem in terms of linear matrix inequalities (LMIs), which can be effectively solved with numerical solvers. Extensive experiments were carried out under various driving test scenarios, both in interactive simulations performed with the well-known Sherpa dynamic driving simulator, and then using the LAMIH Twingo vehicle prototype, in order to highlight the effectiveness and the validity of the proposed observer design.
Signal Amplification-Based Biosensors and Application in RNA Tumor Markers
Authors: Li Haiping , Zhang Zhikun , Gan Lu , Fan Dianfa , Sun Xinjun , Qian Zhangbo , Liu Xiyu , Huang Yong
Published: April 2023

Tumor markers are important substances for assessing cancer development. In recent years, RNA tumor markers have attracted significant attention, and studies have shown that their abnormal expression of post-transcriptional regulatory genes is associated with tumor progression. Therefore, RNA tumor markers are considered as potential targets in clinical diagnosis and prognosis. Many studies show that biosensors have good application prospects in the field of medical diagnosis. The application of biosensors in RNA tumor markers is developing rapidly. These sensors have the advantages of high sensitivity, excellent selectivity, and convenience. However, the detection abundance of RNA tumor markers is low. In order to improve the detection sensitivity, researchers have developed a variety of signal amplification strategies to enhance the detection signal. In this review, after a brief introduction of the sensing principles and designs of different biosensing platforms, we will summarize the latest research progress of electrochemical, photoelectrochemical, and fluorescent biosensors based on signal amplification strategies for detecting RNA tumor markers. This review provides a high sensitivity and good selectivity sensing platform for early-stage cancer research. It provides a new idea for the development of accurate, sensitive, and convenient biological analysis in the future, which can be used for the early diagnosis and monitoring of cancer and contribute to the reduction in the mortality rate.
Photoprotector Effect of Emulsions with Yerba-Mate (Ilex paraguariensis) Extract
Authors: Andriolli Ribeiro Juliana , Magri Ederlan , Gonçalves Itamar Luís , Paese Karina , Roman Juliana , Valduga Alice Teresa
Published: April 2023

Yerba-mate contains in its composition a high concentration of phenolic compounds. This class of secondary metabolites exhibits strong values of molar absorptivity on ultraviolet and visible wavelengths. This study evaluated the effect of yerba-mate extracts on the in vitro solar protection factor (SPF) value of sunscreen formulations. The sunscreen formulations were prepared to have non-ionic lotion as a basis and yerba-mate extract and/or avobenzone as active agents. The SPF and resveratrol protective effect of the formulations were determined by UV-vis spectrometry. A synergic effect between the yerba-mate extract and avobenzone on the SPF was found. Yerba-mate extract at 5% improved the SPF of the avobenzone 5% formulation from 28.46 ± 5.45 to 40.48 ± 0.84. Yerba-mate extract at 5% avoided resveratrol degradation by ultraviolet radiation. At this same concentration, avobenzone produced a smaller effect than yerba-mate extracts in resveratrol protection. The formulations with yerba-mate + avobenzone presented smaller changes in pH values during 12 days of storage. The spreadability profile of yerba-mate and avobenzone formulations was similar to the profile of avobenzone formulations. The results reported here show the suitability of the yerba-mate extract use in photoprotective formulations, highlighting their in vitro effect and opening possibilities for new investigations exploring this property.
Reconceptualizing Customer Perceived Value in Hotel Management in Turbulent Times: A Case Study of Isfahan Metropolis Five-Star Hotels during the COVID-19 Pandemic
Authors: Ghorbani Amir , Mousazadeh Hossein , Akbarzadeh Almani Farahnaz , Lajevardi Masoud , Hamidizadeh Mohammad Reza , Orouei Mehrdad , Zhu Kai , Dávid Lóránt Dénes
Published: April 2023

The COVID-19 pandemic has significantly impacted the tourism and hospitality industry. This study aims to reconceptualize the concept of customer perceived value (CPV) in Isfahan’s five-star hotels during the pandemic using the grounded theory (GT) approach in the context of qualitative research. The objective of this study is to explore the key dimensions of CPV and identify the main strategies that enhance the value perceived by hotel customers. Data were collected with interviews with 30 experts in the hotel and hospitality industry, and MAXQDA software was used to analyze the data. The dominant themes that emerged from the content analysis included health-oriented self-gratification value, financial value, quality value, emotional value, social value, epistemic value, information value, and health security value. Additionally, the subthemes identified were trust, satisfaction, and information access. All these themes could potentially be employed by hotel sectors as solutions to enhance customer satisfaction during the pandemic. From a practical standpoint, this study provides insights to hotel managers or practitioners to implement updated strategies that contribute to knowledge development about customer perceived value in the hotel sector, which can lead to enhanced customer satisfaction and loyalty. The article makes theoretical contributions to the literature on CPV by identifying new dimensions that are relevant to the pandemic. This extension of the literature provides a more comprehensive understanding of CPV in crisis situations and can inform future research on the topic. The article also discusses future research directions.
Game Theory Analysis of Chinese DC/EP Loan and Internet Loan Models in the Context of Regulatory Goals
Authors: Tu Yuting , Yan Xin , Wang Huan
Published: April 2023

The issuance of digital currency electronic payments (DC/EP), under the supervision of the People’s Bank of China, will have a certain impact on commercial banks, and will further affect the areas of internet finance and traditional financing. This paper studies the regulatory performance of DC/EP in the post-lending market under the models of internet finance and bank financing, exploring their theoretical and practical significance. Through the construction of an enterprise profit function and the regulatory utility function under the models of internet finance and bank financing, this study explores the impact of using DC/EP on the post-lending market. The study proves the following:In the absence of government regulation, internet finance platforms will increase the leverage ratio of loans, in order to obtain excess profits. The emergence of regulatory authorities and the use of DC/EP will control the leverage ratio of internet finance platforms, purify market performance, and stabilize the market order. The application of DC/EP provides a risk control method for the financial market, and coupled with government regulatory measures, it will effectively regulate the existing market order of internet finance platforms and increase the credibility of DC/EP. Therefore, the management insights obtained from this study of the application of DC/EP and the government regulation of existing financing and lending methods in the financial sector have practical significance for the current market.
Analysis on the Effectiveness and Mechanisms of Public Policies to Promote Innovation of High-Tech Startups in Makerspaces
Authors: Li Youjia , Li Yi , Qiu Shunli
Published: April 2023

In the context of digital transformation and the rapid development of artificial intelligence, corporate innovation has become increasingly important in leading industrial development and improving national competitiveness. In 2015, China launched a “mass entrepreneurship and innovation program”; in response to the policy, many makerspaces have been established, resulting in clusters of high-tech startups. High-tech startups are the pioneers in innovation development. However, there is still a lack of empirical evidence for whether these firms’ innovative activities, capabilities, and performance can be effectively stimulated by public policy. Drawing from the institutional theory and resource-based view, this paper develops a model of policy perception on innovation response. Using a sample of 500 startups located in the three representative makerspaces in China, this work verifies the effectiveness of innovation and entrepreneurship incentives on startups’ innovative activities, capabilities, and performance, and successfully identifies the mediating role of policy adaptation in the policy perception-innovative responses’ link and the moderating role of makerspace support.
First-Year Performance of the Pervious Oyster Shell Habitat (POSH) along Two Energetic Shorelines in Northeast Florida
Authors: Mathews Hunter , Uddin Mohammad J. , Hargis Craig W. , Smith Kelly J.
Published: April 2023

Novel living shoreline methods are being developed to minimize negative environmental impact while maintaining strength and effectiveness in high-energy systems. The “Pervious Oyster Shell Habitat” (POSH) is a novel structure composed of oyster shells bound by a thin layer of Portland cement into the shape of a dome. The structure’s makeup greatly reduces its environmental impact while providing optimal substrate for the provision of oyster reef habitat. Previous laboratory testing has demonstrated that the structure is robust, and this follow-up study assesses the structure’s performance in the estuarine environment. Oyster and barnacle densities were compared between POSH modules and the industry standard “Oyster Ball” model Reef BallTM along two energetic shorelines in northeast Florida. Oyster densities on the POSH were high and significantly greater than on the Oyster Ball at both sites. Barnacle densities did not differ between structures and did not appear to affect oyster recruitment. The size distribution of oysters on POSH and Oyster Ball modules was measured to assess the demographics and growth of oysters over time. Overall, demographics were similar among the two structures. Differences in oyster densities and demographics were greater at our more energetic site. Results show that the POSH can be an optimal structure for early oyster recruitment and reef development in energetic systems and should be considered by restoration stakeholders.
Good Governance within Public Participation and National Audit for Reducing Corruption
Authors: Su Huishui , Lu Yu , Lyulyov Oleksii , Pimonenko Tetyana
Published: April 2023

The globalization process and acceptance of a new paradigm for world development aimed at attaining sustainable development require extending the concept of good governance, which allows the improvement of institutional quality. At the same time, governance performance depends on corruption and public participation in national audits. The paper aimed to analyze the impact of public participation in national audits on corruption governance. The object of investigation was 30 Chinese provinces for 2008–2017. The study applied a regression model with the preventive, exposed, and defensive functions of audits. The findings showed that the “governance view” of the audit is the root cause of the audit. In addition, the “immune system” function of national audits has an inhibitory effect on corruption, among which the deterrent effect of the defense function is the most-influential. The influence of public participation in national audits on corruption governance is mainly reflected in the deterrence and resistance function of national audits. The paper contributes to audit theory by incorporating the public participation mechanism, improving the governance path of “national audit—public participation—corruption governance”. It allows improving the national audit “immune system” function by considering the democratic connotation of decision-making and developing a good governance environment.
The Tragedy of the Nurdles: Governing Global Externalities
Authors: Murtazashvili Ilia , Rayamajhee Veeshan , Taylor Keith
Published: April 2023

Nurdles have been referred to by some as a global environmental disaster. However, relative to the controversies surrounding industrial fracking practices, such as public health and safety associated with extraction of shale gas (as well as shale oil), the problems with nurdles are not as widely known. In this article, we highlight that fracking and nurdles are interrelated: fracking processes are a major source of the raw materials used to produce nurdles, which are tiny plastic pellets polluting our waters. Our contention is that a key question for analysis of fracking is how to regulate the externalities associated with downstream products produced in the fracking process. This article takes insights from Elinor Ostrom and scholars of the Bloomington School of Political Economy—such as polycentricity, diversity of collective action problems (CAPs), coproduction, and institutional diversity—to analyze nurdles pollution as a global commons problem. Nurdles generate widespread, large-scale negative externalities that are difficult to contain and address within a fixed geographical boundary governed by a static jurisdictional authority. Using the case of the Royal Dutch Shell cracker plant in Beaver County, Pennsylvania, we show that nurdles present complex and nested challenges that require coproduction, with citizen monitoring playing an essential role in mitigating negative externalities. We demonstrate the efficacy of applying polycentric approaches toward addressing CAPs associated with nurdles.
Evaluating Urban Sustainability in Uzbekistan: A Novel Formula for Empirical Analysis
Authors: Veckalne Regina , Tambovceva Tatjana
Published: April 2023

Urban sustainability has become a critical issue in the past few decades due to rising urbanisation and mounting environmental problems. This article aims to develop a novel formula for assessing urban sustainability in Uzbekistan, a country with very little recent research in the field of sustainable development. The formula was created specifically for the setting of Uzbekistan to evaluate urban sustainability by taking into account a variety of socioeconomic and environmental aspects specific to the discussed region. The article provides a thorough review of the research on urban sustainability, with an emphasis on evaluation techniques and their use in the Uzbek context, which not only contributes to the development of the theoretical framework for the research but also identifies the knowledge gaps in the assessment of urban sustainability in Uzbekistan. Utilising this newly developed formula, an empirical analysis of urban sustainability in Uzbekistan urban settings was conducted, offering comprehensive insights and suggestions for urban planning and policymaking. The results of this research are expected to advance the discussion about urban sustainability on a global scale as well as act as a catalyst for additional research in the area.
Multilayer Perceptron for the Future Urban Growth of the Kharj Region in 2040
Authors: Alshahrane Abear Safar , Altuwaijri Hamad Ahmed
Published: April 2023

Urban growth is described as an increase in the size and use of cities, which is frequently the consequence of an increase in the number of residents due to internal or external migration and an increase in economic activity rates. In recent decades, modern technology and mathematical models have been used to determine future urban growth on a large scale and develop sustainable urban policies in the long term. The cities of the Kingdom of Saudi Arabia have witnessed economic growth in recent decades, which has resulted in urban expansion, as is evident in this case study of the Kharj region. Since most of the previous studies have not applied mathematical models to predict the urban growth of the Kharj region, this study aims at simulating urban growth over the next two decades, between 2020 and 2040, by monitoring the growth during the past thirty years, which is the period between 1990 and 2020. This study relies on the satellite visualizations of the Landsat satellites 5, 7, and 8 for classifying the land cover by applying the land change model (LCM) and comparing the land-use maps for the years 2000 and 2020. Then, the factors affecting urban growth, such as distance from the city center, the road network, valleys, and land slopes, are determined to monitor the prediction of urban growth. The results showed that the urban areas extended significantly toward the south, southeast, southwest, and northwest, with an area of 269 km². The results further revealed a significant decline in agricultural and vacant lands due to their transformation into residential areas, educational establishments, and industrial facilities. The model’s accuracy was tested to confirm the mathematical model’s validity. The Kappa index findings indicated a high percentage, ranging from 89% in 2010 to 90% in 2020.
Sustaining Performance of Wheat–Rice Farms in Pakistan: The Effects of Financial Literacy and Financial Inclusion
Authors: Raza Ali , Tong Guangji , Erokhin Vasilii , Bobryshev Alexey , Chaykovskaya Lyubov , Malinovskaya Natalya
Published: April 2023

The unceasingly augmenting emission of greenhouse gases (GHG) has contributed substantially to the degradation of the key environmental parameters and the deterioration of performance of the agricultural sector globally. The food insecurity problem has been aggravated consequently. Due to a variety of economic and social issues, many farmers in developing economies use low-efficient and environmentally unfriendly agricultural practices. Adopting innovative technologies and practices linked with sustainable farming remains a complex issue across the developing world. In particular, financial exclusion and low financial literacy are commonly cited as the critical obstacles to achieving sustainable development. To contribute to resolving this development problem, the study attempts to investigate the effects of financial inclusion and literacy on establishing a sustainable performance of farms. A structured questionnaire was used to collect the data from 220 farmers within the Central Punjab region of Pakistan. Small-scale farms demonstrated a potential to sustain their performance by improving financial inclusion and financial literacy. Trust in extension services for agriculture plays a significant role in moderating the impact of eight independent variables (knowledge, skills, attitude, behavior, access, usage, quantity, welfare) on sustainable farm performance. The study implies that increased trust in financial services is essential for improving sustainable performance in the agricultural sector. The effect imposed by financial actors is crucial for establishing trust in financial services linked to sustainability within the agricultural industry.
Evaluation of Microwave- and Ultrasound-Assisted Extraction Techniques for Revalorization of Black Chokeberry (Aronia melanocarpa) Fruit Pomace Anthocyanins
Authors: Elez Garofulić Ivona , Repajić Maja , Zorić Zoran , Jurendić Tomislav , Dragović-Uzelac Verica
Published: April 2023

Black chokeberry juice production results in a large amount of pomace, which is usually regarded as waste. Nevertheless, it contains significant amounts of anthocyanins, which can be utilized as health-promoting components, but also as food colorants. To take advantage of their benefits, green extraction methods such as microwave-assisted (MAE) and ultrasound-assisted extraction (UAE) are widely used for their isolation. This study aimed to evaluate the effects of MAE and UAE parameters (solvent, treatment time, temperature, or ultrasound amplitude) on the extraction yield of anthocyanins from black chokeberry pomace and to compare the effectiveness of these two green extraction methods with conventional reflux extraction, both in terms of total anthocyanins yield and effects on individual compounds. In both techniques, acidification of the extraction solvent did not show a significant effect on anthocyanin content. For MAE, a temperature increase from 40 to 60 °C positively affected the extraction yield, while 4 min was a substantial treatment time for the extraction. Conversely, UAE required 10 min of treatment time with no effect on amplitude. UPLC ESI-MS2 analysis confirmed the presence of 6 anthocyanins in the obtained extracts, with significantly higher levels of cyanidin-3-O-xyloside and cyanidin-3-O-arabinoside were in ones isolated by green extraction techniques.
The Moderating Role of Digital Environmental Management Accounting in the Relationship between Eco-Efficiency and Corporate Sustainability
Authors: Abdelhalim Abeer M. , Ibrahim Nahla , Alomair Mohammed
Published: April 2023

In response to concerns about environmental issues and the role of manufacturing corporations in maintaining eco-efficiency, this study aimed to investigate the moderating role of digitally supported environmental management accounting (EMA) in the relationship between eco-efficiency and corporate sustainability performance. A quantitative approach was applied by using a survey distributed to a sample consisting of 77 individuals from senior and executive financial and operational positions in large Saudi manufacturing corporations. The findings of the linear regression analysis revealed that there is an insignificant direct relationship between eco-efficiency and corporate sustainability performance, while there is a significant moderating impact of EMA on the linkage between eco-efficiency and corporate environmental sustainability, and this significant moderating impact also applied to the linkage between digital applications and corporate environmental sustainability. This study provides good insights into the domain of environmental sustainability performance on the business scale in the Saudi context as an emerging economy, as it could be considered an innovative contribution to theoretical and practical aspects in the recent green issue adoption context; theoretically, it provides additional evidence of the role of digital EMA in improving environmental sustainability performance, and practically, the study findings can be beneficial for strategy and policymakers in corporations and regulators of environmental sustainability performance.