bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images
Authors: Guo Anting , Ye Huichun , Li Guoqing , Zhang Bing , Huang Wenjiang , Jiao Quanjun , Qian Binxiang , Luo Peilei
Published: March 2023

Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model’s accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R2 of 0.567 and an RMSE of 5.598, and the model with AL had an R2 of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R2 = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model’s robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R2 = 0.792, RMSE = 2.8; CCC: R2 = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth.
Dielectric and Biological Characterization of Liver Tissue in a High-Fat Diet Mouse Model
Authors: Buisson Clément , Mounien Lourdes , Sicard Flavie , Landrier Jean-François , Tishkova Victoria , Sabouroux Pierre
Published: March 2023

Hepatic steatosis may be caused by type 2 diabetes or obesity and is one of the origins of chronic liver disease. A non-invasive technique based on microwave propagation can be a good solution to monitor hepatic tissue pathologies. The present work is devoted to the dielectric permittivity measurements in healthy and fatty liver in the microwave range. A mouse model following normal and high sugar/glucose (HFS) diets was used. We demonstrated the change in the triglyceride and glucose concentration in the hepatic tissue of HFS diet mice. The difference in the dielectric permittivity of healthy and fatty liver was observed in the range from 100 MHz to 2 GHz. The dielectric permittivity was found to be 42 in the healthy tissue and 31 in the fatty liver tissue at 1 GHz. The obtained results demonstrate that dielectric permittivity can be a sensitive tool to distinguish between healthy and fatty hepatic tissue.
Deep Reinforcement Learning for Articulatory Synthesis in a Vowel-to-Vowel Imitation Task
Authors: Shitov Denis , Pirogova Elena , Wysocki Tadeusz A. , Lech Margaret
Published: March 2023

Articulatory synthesis is one of the approaches used for modeling human speech production. In this study, we propose a model-based algorithm for learning the policy to control the vocal tract of the articulatory synthesizer in a vowel-to-vowel imitation task. Our method does not require external training data, since the policy is learned through interactions with the vocal tract model. To improve the sample efficiency of the learning, we trained the model of speech production dynamics simultaneously with the policy. The policy was trained in a supervised way using predictions of the model of speech production dynamics. To stabilize the training, early stopping was incorporated into the algorithm. Additionally, we extracted acoustic features using an acoustic word embedding (AWE) model. This model was trained to discriminate between different words and to enable compact encoding of acoustics while preserving contextual information of the input. Our preliminary experiments showed that introducing this AWE model was crucial to guide the policy toward a near-optimal solution. The acoustic embeddings, obtained using the proposed approach, were revealed to be useful when applied as inputs to the policy and the model of speech production dynamics.
Charging Protocol for Partially Rechargeable Mobile Sensor Networks
Authors: Hung Li-Ling
Published: March 2023

Wireless sensor networks (WSNs) have wide applicability in services used in daily life. However, for such networks, limited energy is a critical issue. The efficiency of a deployed sensor network may be subject to energy supply. Wireless rechargeable sensor networks have recently been proposed and discussed. Most related studies have involved applying static rechargeable sensors to an entire rechargeable environment or having mobile chargers patrol the environment to charge sensors within it. For partially rechargeable environments, improving the recharge efficiency and extending the lifetime of WSNs are considerable challenges. Scientists have devoted attention to energy transmission technologies and mobile sensor network (MSN) applications. In this paper, we propose a flexible charging protocol in which energy can be transmitted from certain energy supply regions to other regions in an MSN. Mobile rechargeable sensors are deployed to monitor the environment. To share energy in a certain region, the sensors move to replenish their energy and transmit energy to sensors outside the energy supply region. The efficiency of the proposed protocol is also discussed in the context of various situations. The evaluation results suggest that the flexible protocol is more efficient than other charging protocols in several situations.
Detection of Plastic Granules and Their Mixtures
Authors: Kulko Roman-David , Pletl Alexander , Hanus Andreas , Elser Benedikt
Published: March 2023

Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400–1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400–1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used.
Theoretical and Experimental Studies on Sensitivity and Bandwidth of Thickness-Mode Driving Hydrophone Utilizing A 2-2 Piezoelectric Single Crystal Composite
Authors: Je Yub , Sim Minseop , Cho Yohan , Lee Sang-Goo , Seo Hee-Seon
Published: March 2023

Piezoelectric composites, which consist of a piezoelectric material and a polymer, have been extensively studied for the applications of underwater sonar sensors and medical diagnostic ultrasonic transducers. Acoustic sensors utilizing piezoelectric composites can have a high sensitivity and wide bandwidth because of their high piezoelectric coefficient and low acoustic impedance compared to single-phase piezoelectric materials. In this study, a thickness-mode driving hydrophone utilizing a 2-2 piezoelectric single crystal composite was examined. From the theoretical and numerical analysis, material properties that determine the bandwidth and sensitivity of the thickness-mode piezoelectric plate were derived, and the voltage sensitivity of piezoelectric plates with various configurations was compared. It was shown that the 2-2 composite with [011] poled single crystals and epoxy polymers can provide high sensitivity and wide bandwidth when used for hydrophones with a thickness resonance mode. The hydrophone element was designed and fabricated to have a thickness mode at a frequency around 220 kHz by attaching a composite plate of quarter-wavelength thickness to a hard baffle. The fabricated hydrophone demonstrated an open circuit voltage sensitivity of more than −180 dB re 1 V/μPa at the resonance frequency and a −3 dB bandwidth of more than 55 kHz. The theoretical and experimental studies show that the 2-2 single crystal composite can have a high sensitivity and wide bandwidth compared to other configurations of piezoelectric elements when they are used for thickness-mode hydrophones.
Inverse LGAD (iLGAD) Periphery Optimization for Surface Damage Irradiation
Authors: Doblas Albert , Flores David , Hidalgo Salvador , Moffat Neil , Pellegrini Giulio , Quirion David , Villegas Jairo , Maneuski Dzmitry , Ruat Marie , Fajardo Pablo
Published: March 2023

Pixelated LGADs have been established as the baseline technology for timing detectors for the High Granularity Timing Detector (HGTD) and the Endcap Timing Layer (ETL) of the ATLAS and CMS experiments, respectively. The drawback of segmenting an LGAD is the non-gain area present between pixels and the consequent reduction in the fill factor. To overcome this issue, the inverse LGAD (iLGAD) technology has been proposed by IMB-CNM to enhance the fill factor and provide excellent tracking capabilities. In this work, we explore the use of iLGAD sensors for surface damage irradiation by developing a new generation of iLGADs, the periphery of which is optimized to improve the performance of irradiated sensors. The fabricated iLGAD sensors exhibit good electrical performances before and after X-ray irradiation.
Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System
Authors: van der Merwe Johannes Rossouw , Contreras Franco David , Hansen Jonathan , Brieger Tobias , Feigl Tobias , Ott Felix , Jdidi Dorsaf , Rügamer Alexander , Felber Wolfgang
Published: March 2023

Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.
Convolutional Model with a Time Series Feature Based on RSSI Analysis with the Markov Transition Field for Enhancement of Location Recognition
Authors: Lee Hyunji , Lee Jaeho
Published: March 2023

Although numerous schemes, including learning-based approaches, have attempted to determine a solution for location recognition in indoor environments using RSSI, they suffer from the severe instability of RSSI. Compared with the solutions obtained by recurrent-approached neural networks, various state-of-the-art solutions have been obtained using the convolutional neural network (CNN) approach based on feature extraction considering indoor conditions. Complying with such a stream, this study presents the image transformation scheme for the reasonable outcomes in CNN, obtained from practical RSSI with artificial Gaussian noise injection. Additionally, it presents an appropriate learning model with consideration of the characteristics of time series data. For the evaluation, a testbed is constructed, the practical raw RSSI is applied after the learning process, and the performance is evaluated with results of about 46.2% enhancement compared to the method employing only CNN.
A Stress Test for Robustness of Photo Response Nonuniformity (Camera Sensor Fingerprint) Identification on Smartphones
Authors: Martín-Rodríguez Fernando , Isasi-de-Vicente Fernando , Fernández-Barciela Mónica
Published: March 2023

In the field of forensic imaging, it is important to be able to extract a camera fingerprint from one or a small set of images known to have been taken by the same camera (or image sensor). Note that we are using the word fingerprint because it is a piece of information extracted from images that can be used to identify an individual source camera. This technique is very important for certain security and digital forensic situations. Camera fingerprint is based on a certain kind of random noise present in all image sensors that is due to manufacturing imperfections and is, thus, unique and impossible to avoid. Photo response nonuniformity (PRNU) has become the most widely used method for source camera identification (SCI). In this paper, a set of attacks is designed and applied to a PRNU-based SCI system, and the success of each method is systematically assessed both in the case of still images and in the case of video. An attack method is defined as any processing that minimally alters image quality and is designed to fool PRNU detectors or, in general, any camera fingerprint detector. The success of an attack is assessed as the increment in the error rate of the SCI system. The PRNU-based SCI system was taken from an outstanding reference that is publicly available. Among the results of this work, the following are remarkable: the use of a systematic and extensive procedure to test SCI methods, very thorough testing of PRNU with more than 2000 test images, and the finding of some very effective attacks on PRNU-based SCI.
Towards the Use of Individual Fluorescent Nanoparticles as Ratiometric Sensors: Spectral Robustness of Ultrabright Nanoporous Silica Nanoparticles
Authors: Iraniparast Mahshid , Peng Berney , Sokolov Igor
Published: March 2023

Here we address an important roadblock that prevents the use of bright fluorescent nanoparticles as individual ratiometric sensors: the possible variation of fluorescence spectra between individual nanoparticles. Ratiometric measurements using florescent dyes have shown their utility in measuring the spatial distribution of temperature, acidity, and concentration of various ions. However, the dyes have a serious limitation in their use as sensors; namely, their fluorescent spectra can change due to interactions with the surrounding dye. Encapsulation of the d, e in a porous material can solve this issue. Recently, we demonstrated the use of ultrabright nanoporous silica nanoparticles (UNSNP) to measure temperature and acidity. The particles have at least two kinds of encapsulated dyes. Ultrahigh brightness of the particles allows measuring of the signal of interest at the single particle level. However, it raises the problem of spectral variation between particles, which is impossible to control at the nanoscale. Here, we study spectral variations between the UNSNP which have two different encapsulated dyes: rhodamine R6G and RB. The dyes can be used to measure temperature. We synthesized these particles using three different ratios of the dyes. We measured the spectra of individual nanoparticles and compared them with simulations. We observed a rather small variation of fluorescence spectra between individual UNSNP, and the spectra were in very good agreement with the results of our simulations. Thus, one can conclude that individual UNSNP can be used as effective ratiometric sensors.
EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures
Authors: Tripathi Bhaskar , Sharma Rakesh Kumar
Published: March 2023

Day traders in the financial markets are under constant pressure to make rapid decisions and limit capital losses in response to fluctuating market prices. As such, their emotional state can greatly influence their decision-making, leading to suboptimal outcomes in volatile market conditions. Despite the use of risk control measures such as stop loss and limit orders, it is unclear if these strategies have a substantial impact on the emotional state of traders. In this paper, we aim to determine if the use of limit orders and stop loss has a significant impact on the emotional state of traders compared to when these risk control measures are not applied. The paper provides a technical framework for valence-arousal classification in financial trading using EEG data and deep learning algorithms. We conducted two experiments: the first experiment employed predetermined stop loss and limit orders to lock in profit and risk objectives, while the second experiment did not employ limit orders or stop losses. We also proposed a novel hybrid neural architecture that integrates a Conditional Random Field with a CNN-BiLSTM model and employs Bayesian Optimization to systematically determine the optimal hyperparameters. The best model in the framework obtained classification accuracies of 85.65% and 85.05% in the two experiments, outperforming previous studies. Results indicate that the emotions associated with Low Valence and High Arousal, such as fear and worry, were more prevalent in the second experiment. The emotions associated with High Valence and High Arousal, such as hope, were more prevalent in the first experiment employing limit orders and stop loss. In contrast, High Valence and Low Arousal (calmness) emotions were most prominent in the control group which did not engage in trading activities. Our results demonstrate the efficacy of our proposed framework for emotion classification in financial trading and aid in the risk-related decision-making abilities of day traders. Further, we present the limitations of the current work and directions for future research.
Infrared Camera Geometric Calibration: A Review and a Precise Thermal Radiation Checkerboard Target
Authors: ElSheikh Ahmed , Abu-Nabah Bassam A. , Hamdan Mohammad O. , Tian Gui-Yun
Published: March 2023

Different infrared (IR) planar geometric calibration targets have been developed over the years that exploit a well-established and flexible optical camera geometric calibration procedure following the pinhole approximation. This geometric calibration is typically neglected in IR cameras, due to the relatively low resolution of thermal images and the complex IR targets needed for the geometric calibration in comparison to the optical targets. In this study, a thorough literature review of numerous IR camera geometric calibration targets, along with their respective outcomes, were summarized and leveraged to deliver a practical checkerboard target for less experienced end users, while offering the lowest reprojection errors. It was concluded that the fabrication of high emissivity contrast and precise square points of intersection within a checkerboard pattern extends the accuracy of capturing these control points in a thermal image for an optimized IR camera geometric calibration. Accordingly, two simple planar checkerboard targets were fabricated using laser engraving and ultraviolet (UV) printing technologies on a polished stainless steel (SS304) plate. The UV-printed checkerboard target on a polished metallic alloy delivered the lowest mean reprojection error (MRE) of 0.057 pixels and the lowest root mean square error (RMSE) of reprojection of 0.063 pixels, with a standard deviation lower than 0.003 pixels. The UV-printed design offers better accuracy than any other checkerboard calibration target, and comparable results to the best prominent circular pattern results reported in the literature.
Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing
Authors: Singhal Saurabh , Athithan Senthil , Alomar Madani Abdu , Kumar Rakesh , Sharma Bhisham , Srivastava Gautam , Lin Jerry Chun-Wei
Published: March 2023

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.
Editorial for Special Issue: Machine Health Monitoring and Fault Diagnosis Techniques
Authors: Sun Shilong , Shen Changqing , Wang Dong
Published: March 2023

Machine health monitoring and fault diagnosis have played crucial roles in automatic and intelligent industrial plants [...]
Ecosystems and Their Digital Models as Factors of Algocognitive Culture and Transition to New Technological Order
Authors: Kuimov Vasilii V. , Simonov Konstantin V. , Shcherbenko Eva V. , Iushkova Liudmila V.
Published: March 2023

In the paradigm of dissipative structures based on theories of economic and regional development, business networks, and the theory of algocognitive culture, the authors studied the processes of the formation of ecosystem formats for business development in the region as the core of a new technological paradigm. The research aimed to identify the relationship between current development formats in digital transformation with the basic foundations of business ecosystems and the possibility and willingness to use ESG principles and network practices to replicate cognitive forms of human brain hypernetworks. In this research, the authors used the methods of systemic and comparative analysis, grouping, and generalization including the authors’ practices of functional environmental analysis. As a result, the primary trends showed cooperative-network multi-level formats of interaction between business partners in the space of value formation, the development of the business ecosystem environment of the regional economy, and guidelines for the development of service practices. The new ecosystem formats implemented in the economy are considered as a manifestation of the patterns of dissipative structures and new realities of algocognitive culture including the formation of algorithmic thinking of a contemporary person. The compliance of the development of mass businesses in the region with the primary basic characteristics of ecosystems and their active use of basic digital technologies in business processes is shown. The authors revealed the readiness and need for the development of the economy of the region in the formats of ecosystems and a sufficient degree of development of this kind of format by various businesses of the territory. The possibility and necessity of using digital models of business ecosystems were established and the primary hypothesis of the research was confirmed, which forms the assumption about the active development of modern formats of interactions in the environment of small and medium-sized businesses and their cooperative associations. It provides a basis for the proposal and development of digital models of such interactions, which should significantly increase the effectiveness and speed of qualitative changes in the transition to a new technological way of life.
Effect of Residential Parking Policy Derogations on Sustainability of Streets: The Case of Gaziantep, Türkiye
Authors: Tercan Şafak Hengirmen
Published: March 2023

Rapid urbanization causes severe parking problems in developing cities. Parking policies cannot be strictly applied in some cases because of varying claims by developers. The variables such as household density, car ownership, and availability of public transport, and the new challenges such as sustainability, climate change, and sustainable mobility still remain insignificant in parking policies. The appropriate parking policies are immediately needed; however, the question is “which policy is appropriate for which city?” In this study, the evolution of residential parking policies is analyzed through the policy analysis process and parking survey methods. In conclusion, maintaining the status quo and not monitoring the applied policy results during the peak period of urbanization caused an irreversible spillover problem. In particular, some exemptions, such as the derogation payment and lack of public district parking lots, increased the pressure on the on-street parking capacity and sustainability of streets. A case study parking survey in a large-city center shows that parking demand is exceeding the capacity by 166% for average daily occupancy on weekdays and 111% on weekends in 2022, compared to 61% in 2015. Five policy alternatives are developed and the “zone parking policy” is concluded to be the most appropriate one for districts while the “mobility level of zones” needs more action at the city level.
Exploring the Relationship between Cultural Intelligence (CQ) and Management Competencies (MC)
Authors: Garamvölgyi Judit , Rudnák Ildikó
Published: March 2023

The aim of this study is to demonstrate the importance of Cultural Intelligence (CQ) in management performance through measurable performance assessment. In an empirical study, “Cultural Intelligence and Management Competencies”, managers provided a self-assessment and their subordinates also reported on their performance. Correlation analysis of the results of this study was used to examine the relationship between managers’ CQ and their Management Competencies. Among the factors examined (CQ, gender, origin, age, work experience, position and education), the level of CQ of managers correlates significantly with their management competencies. Our findings indicate that managers with higher CQ are more effective in their leadership roles, and we conclude that the CQ of leaders contributes to a more positive perception of leadership performance in teams that are characterized by significant cultural diversity.
The Impact of Green Capital Structure on Enterprise Development and the Regulation of Technological Innovation under Carbon Peaking
Authors: Zhou Maochun , Fan Rui
Published: March 2023

The introduction of the “double carbon” target has placed higher and newer demands on China’s economic development, which must rely on investment in green capital. In today’s knowledge-based economy, one of the most important factors influencing the growth of enterprises is technological innovation. Based on the data of A-share listed companies in China from 2016 to 2020, this paper empirically tests whether the green capital structure of enterprises can promote the development of enterprises through regression analysis. The study shows that green capital structure has a significant promotion effect on enterprise development; technological innovation will weaken the promotion effect of green capital structure on enterprise development to a certain extent. The paper further shows that the higher the cash flow from operating activities, the higher the concentration of equity and the non-state-owned enterprises, the more conducive the green capital structure is to enterprise development. Finally, the article’s findings are supported by robustness tests. It is conducive to promoting the development of a green economy and facilitating the transformation and upgrading of China’s economy while protecting the environment.
Effects of Polyacrylamide, Biochar, and Palm Fiber on Soil Erosion at the Early Stage of Vegetation Concrete Slope Construction
Authors: Xia Lu , Zhao Bingqin , Luo Ting , Xu Yakun , Guo Shiwei , Xu Wennian , Xia Dong
Published: March 2023

The goal of this research is to investigate strategies to increase the erosion resistance of the slope surface during the early stages of vegetation concrete construction, as well as to offer a scientific foundation for improving vegetation concrete formulation. Simulated rainfall experiments were carried out at 2 different slope gradients (50° and 60°), 2 different rainfall intensities (60 and 120 mm·h−1), and 4 treatments (CK-no additive, 0.4% P-polyacrylamide, 4% C-biochar, and 0.4% F-palm fiber). PAM, palm fiber, and biochar significantly reduced the initial runoff time of the vegetation concrete slope by an average of 47.03%, 46.41%, and 22.67%, respectively (p < 0.05). The runoff rate of each slope under different conditions increased with the expansion of rainfall duration and then fluctuated and stabilized, whereas the erosion rate decreased and then fluctuated and stabilized. PAM and palm fiber both increased runoff rates while decreasing erosion rates, but biochar increased both runoff rates and erosion rates. The runoff reduction benefits of PAM, palm fiber, and biochar were −69.84~−1.97%, −68.82~−14.28% and −63.70~−6.80%, respectively, while the sediment reduction benefits were 69.21~94.07%, −96.81~−50.35%, and 36.20~60.47%, respectively. PAM and palm fiber both have obvious sediment reduction benefits and can be used in the ecological restoration of high and steep slopes in areas with heavy rainfall.
Intelligent Risk Assessment of Ecological Agriculture Projects from a Vision of Low Carbon
Authors: Chang Yanhua , Liang Yi
Published: March 2023

Ecological agriculture projects have achieved a growing development in the context of low carbon. However, because of the great difference in these issues from traditional types, there exist risks in progression quality and sustainability. To better identify the risk, this paper proposes a novel hybrid approach that integrates the analytic hierarchy process (AHP) with technique for order preference by similarity to an ideal solution (TOPSIS), as well as an improved support vector machine (SVM) based on the brainstorming algorithm (BSO). First, a risk evaluation index framework is developed and elaborated in terms of the natural environment, society, market economy, management, technology, and finance. Then the traditional assessment can be derived from AHP with TOPSIS. In addition, BSO is applied to improve SVM for rapid computation. Finally, a case study is implemented to verify the accuracy of the proposed technique. In this research, based on the low-carbon perspective, artificial intelligence algorithm and risk assessment are introduced into the field of ecological agriculture project management, which is conducive to the rapid and effective evaluation of ecological agriculture project risk. It can improve managers’ risk awareness and risk management ability, reduce investment blindness, and help ecological agriculture projects achieve healthy and sustainable development under the background of low carbon, thus contributing to the development of a low-carbon economy.
A Bibliometric Analysis of Digital Literacy Research from 1990 to 2022 and Research on Emerging Themes during the COVID-19 Pandemic
Authors: Wang Chen , Si Li
Published: March 2023

Due to the rapid advancement of digital technology and its contribution to sustainable development, digital literacy has become an increasingly significant research topic. However, the uneven distribution of new technologies has caused emerging inequalities, which have been exacerbated by the COVID-19 pandemic, one of the most significant public health crises of the century. This paper aims to conduct an in-depth analysis of scientific production using bibliometric methods to comprehend the current research status of digital literacy studies and evaluate the pandemic’s impact on such research. In total, 7523 documents published between 1990 and 2022 were identified and analyzed using bibliometric research methods in the Web of Science database. These methods included growth trend analysis, network analysis, highly cited literature analysis, factor analysis, and time-series-based analysis of frequently discussed topics. Additionally, a word cloud analysis of the keywords in digital literacy literature from 2020 to 2022, during the COVID-19 pandemic, was created. The study’s outcomes explore digital literacy research, including current trends, significant publications, and institutions involved in the field. This study emphasizes the importance of digital literacy in today’s society, particularly during the COVID-19 pandemic. It also highlights the potential of bibliometric analysis as a tool for identifying research gaps.
Research Context and Prospect of Green Railways in China Based on Bibliometric Analysis
Authors: Chen Weiya , Shi Xiaoqi , Fang Xiaoping , Yu Yongzhuo , Tong Shiying
Published: March 2023

The CiteSpace bibliometric software was used to quantitatively analyze the research papers on green railways retrieved from the China National Knowledge Infrastructure (CNKI) database during 1985–2021. Combined with content association analysis, the development stages, frontier hotspots, and evolutionary trends of green railway research in China were summarized in the Chinese context. The results show that in the past 36 years, China’s green railway research has experienced four main stages: the emerging stage (1985–1997), the horizontal expansion stage (1998–2010), the vertical deepening stage (2010–2015), and integrated expansion stage (2016–present). The research topics emerging in the four stages are green design and green construction, green channel and green logistics, energy conservation and emission reduction and green evaluation, multimodal transportation, and green development. In general, the research topics are diversified, but green construction of railway infrastructures and green manufacturing of railway equipment have been the research hotspots all the time. Both external and internal paths drive the transmutation of academic frontiers, and the push effect of the external path is more evident than the internal path. Interdisciplinary integration and innovation gradually become a new force to promote green railway research. As the railway development slowly enters a “big operation era”, it can be inferred that the development trend of green railway research could throw light on the following three areas: from the research perspective and topics, it should be based on a framework of life cycle management to explore, systematically and deeply, the correlation and integration of railway green design, green construction, green operation and maintenance; in terms of the research content, more focus should be on new theories, new methods, and new technologies of railway green operation and green maintenance on the basis of railway green design, construction, and manufacturing research, such as railway green operation strategies and evaluation systems and green transportation organization theories and methods; from innovation paths, academic progress still needs both external and internal paths, interdisciplinary integration and innovation as the primary internal driving force to promote green railway research, and more focus on the use of big data and artificial intelligence and other technologies to innovate green railway development.
Entrepreneurial Leadership and Entrepreneurial Success: The Mediating Role of Entrepreneurial Opportunity Recognition and Innovation Capability
Authors: Taleb Taleb S. T. , Hashim Norashidah , Zakaria Norria
Published: March 2023

Micro-, small, and medium enterprises (MSMEs) play a crucial role in the economic growth of emerging markets. This study examines the effect of entrepreneurial leadership (EL) on the entrepreneurial success (ES) of MSMEs in Malaysia through the mediating effect of entrepreneurial opportunity recognition (EOR) and innovation capability (IC). To examine the proposed mediation model, we utilized a structured questionnaire to collect data for this quantitative research. Partial least square-structural equation modeling (SEM) was used to test the hypothesis on a sample of 401 micro-business owners/managers in Malaysia. The findings reveal that an EL has a significant positive effect on ES but is conditional on the mediating role of EOR and IC. However, the results show that EOR and IC partially mediate the link between EL and ES. Additionally, EOR and IC serially mediated the relationship between EL and ES. This research has valuable contributions to MSMEs by explaining EL, EOR, and IC’s critical role in gaining competitive advantage and achieving success. This research develops a theory-based mediation model to demonstrate how the EL and ES of MSMEs are related. Further, the model in this study adds to the body of knowledge by examining whether or not serial mediation occurs through EOR and IC. Hence, this research sheds new light on the relationship between EL and ES.
Grid-Vehicle-Grid (G2V2G) Efficient Power Transmission: An Overview of Concept, Operations, Benefits, Concerns, and Future Challenges
Authors: Hossain Sagar , Rokonuzzaman Md. , Rahman Kazi Sajedur , Habib A. K. M. Ahasan , Tan Wen-Shan , Mahmud Md , Chowdhury Shahariar , Channumsin Sittiporn
Published: March 2023

Electric vehicles (EVs) are proportionally increasing day-by-day with the inclusion of upgraded technology toward considered zero carbon emission efforts. To mitigate greenhouse gas emissions from the transportation sector, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are getting significant attention nowadays. EVs equipped with modern technology can help to stabilize the power grids through load-balancing topology during peak hours. The improvement in EVs can support the surroundings in numerous ways, such as power grid voltage and frequency regulations, harmonics distortions, accessible solar energy implemented to the grids, and peak load stabilizations. This literature review analyzes G2V and V2G impacts in more depth, namely opportunities, improvements in strategies, operation, control, issues, and new technology adoptions. This paper emphasizes the possibilities of bringing advancements in EV technology, smooth operations between grids and EVs, fast bidirectional charging and discharging scopes, control of grids and EVs structures, issues, benefits, pitfalls, challenges, and recommendations.
How can the Digital Economy Boost the Performance of Entrepreneurs? A Large Sample of Evidence from China’s Business Incubators
Authors: Chen Peng , Yan Zhenjun , Wang Pengfei
Published: March 2023

The digital economy enabled by digital technologies is reshaping economic and social development, bringing a digital revolution to entrepreneurship and innovation. Does the digital economy realistically translate into excellence in microentrepreneurial ventures, and do business incubators still play an important role in the digital era? There is a lack of sufficient evidence in this area. This study combines macro and micro perspectives, economics, and management perspectives and examines the relationship between regional digital economy development and incubates performance using a panel fixed effects model based on a large sample of data from Chinese technology business incubators and their incubates. Robustness tests were also conducted by the instrumental variable’s method and other conventional methods, and the stepwise regression method was used to set up a mediating effect model of incubation service support to test the mechanism of the impact of the digital economy on the performance of incubated enterprises. The results of this study show that the development of the digital economy in cities helped improve the revenue capacity of startups, and the more developed the digital economy is, the better the financial performance of startups performs. From a resource-based view, resource service support from incubators, such as capital, technology, human resources, and knowledge, is an important channel through which the digital economy promotes the performance of startups. This study provides new perspectives and additions to theoretical and empirical studies of the digital economy and entrepreneurship development and provides policy and management insights for the development of the business incubation industry from the digital economy perspective.
Radioactivity: Sustainable Materials and Innovative Techniques
Authors: Saleh Hosam M. , Hassan Amal I.
Published: March 2023

Radioisotopes, also known as radionuclides, are atoms with unstable nuclei that emit ionizing radiation [...]
Financial Risk and Environmental Sustainability in Poland: Evidence from Novel Fourier-Based Estimators
Authors: Addai Kwaku , Genç Sema Yılmaz , Castanho Rui Alexandre , Couto Gualter , Orhan Ayhan , Umar Muhammad , Kirikkaleli Dervis
Published: March 2023

Using Fourier-based ARDL and Fourier Toda Yamamoto causality methods, this study aims to detect the impact of financial risk on environmental sustainability in Poland while controlling economic growth, primary energy consumption, and renewable energy consumption from 1990Q1 to 2019Q4. The outcomes of the Fourier ADL cointegration test indicate all variables are cointegrated. Additionally, the Fourier ARDL long-form estimates indicate both renewable energy consumption and financial risk contribute to reducing environmental degradation in Poland. However, the Fourier ARDL long-form estimates show that both primary energy consumption and economic growth have detrimental impacts on the economy’s level of environmental degradation. Analysis of causality direction with the Fourier Toda Yamamoto causality test indicates both financial risk and renewable energy consumption for the period had a one direction causal effect on environmental degradation. For policy insight, the government could support environmental policies requiring companies to factor climate-related risks into their supervision and financial stability monitoring. The Polish government requires financial institutions to prioritize climate risk scenarios in stress testing to control catastrophic weather-related losses.
Learning-by-Doing Methodology towards Urban Decarbonisation: An Application in Valletta (Malta)
Authors: Maccanti Matteo , D’Ascanio Romina , Di Pietrantonio Federica , Marchi Michela , Vargas Molina Jesús , Pulselli Riccardo Maria , Poldrugovac Andrea , Schembri Cassar Diane , Barbieri Lorenzo , López Galdeano Josefina , Niccolucci Valentina , Gioia Carmela , Mondelli Francesca Paola , Xuereb Jesmond , Palazzo Anna Laura , Bastianoni Simone
Published: March 2023

Since approximately 75% of Europeans currently live in cities, and this number will rise, urban areas are the most important testbeds for energy transition, climate change adaptation measures, and decarbonisation models, on which studies and efforts for concrete change must focus. The teaching of mitigation and adaptation measures to climate change and decarbonisation models has gradually taken up space within university courses. However, the complexity of the decarbonisation issue is raising awareness on the urgency of an interdisciplinary approach that can be conveyed by spatial planning. Currently, this approach is not widespread in Higher Education Institutions in Europe but is nonetheless necessary to let new professional profiles emerge who are able to coordinate different stakeholders, data, and information sources. The Erasmus+ project CITY MINDED (2020–2022) has worked in this direction, by developing and testing a methodology for the design of a structured ordinary practice for teaching urban decarbonisation to students in Higher Education. This practice (at the same time, interdisciplinary, collaborative, experiential, and place-based) aims to offer students a combination of different approaches and working methods to investigate and improve urban neighbourhoods and districts, resulting in the definition of an operative roadmap for decarbonisation in the medium-to-long-term. The aim of this article is to highlight the learning-by-doing experience developed by the project consortium, with reference to the testing of the methodology conducted within an Intensive Course in the City of Valletta (Malta). In particular, the paper illustrates how this experience succeeded in stimulating students with different academic backgrounds to establish connections across disciplines, in raising their awareness about the complexity of city decarbonisation processes. Overcoming the strict time and budget constraints of an EU-funded project, such an approach can be further developed, replicated on theoretical grounds, and implemented within different degree programmes dealing with urban sustainability.