bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Maize Yield Predictions Using Machine Learning on Multi-Source Data
Authors: Croci Michele , Impollonia Giorgio , Meroni Michele , Amaducci Stefano
Published: December 2022

Timely yield prediction is crucial for the agri-food supply chain as a whole. However, different stakeholders in the agri-food sector require different levels of accuracy and lead times in which a yield prediction should be available. For the producers, predictions during the growing season are essential to ensure that information is available early enough for the timely implementation of agronomic decisions, while industries can wait until later in the season to optimize their production process and increase their production traceability. In this study, we used machine learning algorithms, dynamic and static predictors, and a phenology approach to determine the time for issuing the yield prediction. In addition, the effect of data reduction was evaluated by comparing results obtained with and without principal component analysis (PCA). Gaussian process regression (GPR) was the best for predicting maize yield. Its best performance (nRMSE of 13.31%) was obtained late in the season and with the full set of predictors (vegetation indices, meteorological and soil predictors). In contrast, neural network (NNET) and support vector machines linear basis function (SVMl) achieved their best accuracy with only vegetation indices and at the tasseling phenological stage. Only slight differences in performance were observed between the algorithms considered, highlighting that the main factors influencing performance are the timing of the yield prediction and the predictors with which the machine learning algorithms are fed. Interestingly, PCA was instrumental in increasing the performances of NNET after this stage. An additional benefit of the application of PCA was the overall reduction between 12 and 30.20% in the standard deviation of the maize yield prediction performance from the leave one-year outer-loop cross-validation, depending on the feature set.
Polarimetric SAR Decomposition Method Based on Modified Rotational Dihedral Model
Authors: Chen Yifan , Zhang Lamei , Zou Bin , Gu Guihua
Published: December 2022

Polarimetric decomposition is an effective way to analyze the scattering mechanism of targets in polarimetric synthetic aperture radar (PolSAR) images. However, the analysis of urban areas is frequently a challenge. Most decomposition methods use a rotated dihedral derived via rotation matrix to model the double-bounce scattering mechanism of buildings. However, according to electromagnetic theory, the existing dihedral model is not accurate, especially when the orientation angle of the dihedral is large. Therefore, the double-bounce scattering contribution in urban areas with large orientation angles will be difficult to extract. To address this problem, based on physical optics (PO) and geometric optics (GO), the interaction process of electromagnetic waves and the rotational dihedral is analyzed, and then a modified rotational dihedral model (MRDM) is proposed for the accurate representation of the rotational double-bounce scattering mechanism. Accordingly, MRDM is introduced to a five-component decomposition method (MRDM-5SD) to analyze the scattering components in an urban area. The validity of MRDM-5SD is demonstrated using several data sets. The experimental results show that the power contributions of double-bounce scattering in urban areas with large orientation angles increase by using MRDM-5SD. Therefore, MRDM can provide support for feature extraction and target detection in urban areas.
ERA5 Reanalysis for the Data Interpretation on Polarization Laser Sensing of High-Level Clouds
Authors: Kuchinskaia Olesia , Bryukhanov Ilia , Penzin Maxim , Ni Evgeny , Doroshkevich Anton , Kostyukhin Vadim , Samokhvalov Ignatii , Pustovalov Konstantin , Bordulev Iurii , Bryukhanova Valentina , Stykon Alexander , Kirillov Nikolay , Zhivotenyuk Ivan
Published: December 2022

Interpreting the results of a high-level clouds (HLCs) lidar study requires a comparison with the vertical profiles of meteorological quantities. There are no regular radiosonde measurements of vertical profiles of meteorological quantities in Tomsk. The nearest aerological stations are several hundred kilometers away from the lidar and perform radiosonde measurements only a few times a day, whereas lidar experiments are performed continuously throughout the day. To estimate meteorological conditions at the HLC altitudes, we propose to use the ERA5 reanalysis. Its reliability was tested by comparing with the data from five aerological stations within a radius of 500 km around Tomsk. A labeled database of the lidar, radiosonde, and ERA5 data (2016–2020) for isobaric levels 1000–50 hPa was created. The temperature reconstruction error over the entire altitude range was characterized by an RMSE of 0.8–2.8 °C, bias of 0–0.9, and Corr ~1. The accuracy of the relative vertical profiles (RMSE 25–40%, Bias 10–22%, and Corr <0.7) and specific humidity (RMSE 0.2–1.2 g/kg, Bias ~0 g/kg, and Corr ~0) at the HLC altitudes were unsatisfying. The ERA5 data on wind direction and speed for the HLC altitudes were promising.
Tree Species Classifications of Urban Forests Using UAV-LiDAR Intensity Frequency Data
Authors: Gong Yulin , Li Xuejian , Du Huaqiang , Zhou Guomo , Mao Fangjie , Zhou Lv , Zhang Bo , Xuan Jie , Zhu Dien
Published: December 2022

The accurate classification of tree species is essential for the sustainable management of forest resources and the effective monitoring of biodiversity. However, a literature review shows that most of the previous unmanned aerial vehicle (UAV) light detection and ranging (LiDAR)-based studies on fine tree species classification have used only limited intensity features, accurately identifying relatively few tree species. To address this gap, this study proposes developing a new intensity feature—intensity frequency—for the LiDAR-based fine classification of eight tree species. Intensity frequency is defined as the number of times a certain intensity value appears in the individual tree crown (ITC) point cloud. In this study, we use UAV laser scanning to obtain LiDAR data from urban forests. Intensity frequency features are constructed based on the extracted intensity information, and a random forest (RF) model is used to classify eight subtropical forest tree species in southeast China. Based on four-point cloud density sampling schemes of 100%, 80%, 50% and 30%, densities of 230 points/m2, 184 points/m2, 115 points/m2 and 69 points/m2 are obtained. These are used to analyze the effect of intensity frequency on tree species classification accuracy under four different point cloud densities. The results are shown as follows. (1) Intensity frequencies of trees are not significantly different for intraspecies (p > 0.05) values and are significantly different for interspecies (p < 0.01) values. (2) The intensity frequency features of LiDAR can be used to classify different tree species with an overall accuracy (OA) of 86.7%. Acer Buergerianum achieves a user accuracy (UA) of over 95% and a producer accuracy (PA) of over 90% for four density conditions. (3) The OA varies slightly under different point cloud densities, but the sum of correct classification trees (SCI) and PA decreases rapidly as the point cloud density decreases, while UA is less affected by density with some stability. (4) The priori feature selected by mean rank (MR) covers the top 10 posterior features selected by RF. These results show that the new intensity frequency feature proposed in this study can be used as a comprehensive and effective intensity feature for the fine classification of tree species.
Driver of the Positive Ionospheric Storm over the South American Sector during 4 November 2021 Geomagnetic Storm
Authors: Zhai Changzhi , Tang Shenquan , Peng Wenjie , Cheng Xiaoyun , Zheng Dunyong
Published: December 2022

During geomagnetic storms, ionospheric storms can be driven by several mechanisms. Observations performed using ground- and space-based instruments were used to reveal the driver of the positive ionospheric storm over the South American sector during the 4 November 2021 geomagnetic storm. The positive storm appeared from 10:30 UT to 18:00 UT and covered the region from 40°S to 20°N. The maximum magnitudes of TEC (Total Electron Content) enhancement and relative TEC enhancement were about 20 TECU and 100%, respectively. Defense Meteorological Satellite Program (DMSP) also observed a significant electron density increase over South America and the eastern Pacific Ocean. In the meantime, about 50% ∑O/N2 enhancement was observed by the Global-scale Observations of the Limb and Disk (GOLD) satellite at low latitudes. Ionosonde observations (AS00Q and CAJ2M) registered an ~80 km uplift in F2 peak height (HmF2) and a prominent F2 peak electron density (NmF2) increase ~3 h after the uplift. A prominent enhancement in the cross-polar cap potential (CPCP) in the southern hemisphere was also observed by Super Dual Auroral Radar Network (SuperDARN) one hour earlier than the HmF2 uplift. Measurements of the Ionospheric Connection Explorer satellite (ICON) showed that the outward E×B drift was enhanced significantly and that the horizontal ion drift was poleward. According to the ICON ion drift observations, the HmF2 uplift was caused by an electric field rather than equatorward neutral wind. We propose that the enhanced eastward electric field dominated the positive ionospheric storm and that the thermospheric composition variation may have also contributed.
Attributing the Impacts of Vegetation and Climate Changes on the Spatial Heterogeneity of Terrestrial Water Storage over the Tibetan Plateau
Authors: Han Yuna , Zuo Depeng , Xu Zongxue , Wang Guoqing , Peng Dingzhi , Pang Bo , Yang Hong
Published: December 2022

Terrestrial water storage (TWS) is of great importance to the global water and energy budget, which modulates the hydrological cycle and then determines the spatiotemporal distributions of water resources availability. The Tibetan Plateau is the birthplace of the Yangtze, Yellow, and Lancang–Mekong River, where the water resources are directly related to the life of the Eastern and Southeastern Asian people. Based on multi-source datasets during the period 1981–2015, the long-term spatiotemporal variabilities of the TWS over the Tibetan Plateau were investigated by the Sen’s slope and Mann–Kendall test trend analysis methods; the changing mechanisms were explored from two perspectives of components analysis and the hydrological cycle. The water conservation capacity of vegetation in the alpine mountainous areas was also discussed by geostatistical methods such as correlation analysis, extracted by attributes and zonal statistics. The results show that the TWS of the Tibetan Plateau increased with the speed of 0.7 mm/yr as the precipitation accumulated and the glaciers melted during the period 1981–2015. The TWS values were low and generally present a trend of obvious accumulation over the northern Tibetan Plateau, while the high and decreasing values were distributed in the south of Tibetan Plateau. The results of the components analysis indicate that the TWS mainly consisted of soil moisture at one-fourth layers, which are 0–200 cm underground in most areas of the Tibetan Plateau. The precipitation is mainly lost through evapotranspiration over the northern Tibetan Plateau, while in the northwestern corner of the Tibetan Plateau, the Himalayas, and northeastern Yarlung Zangbo River basin, the runoff coefficients were larger than 1.0 due to the influence of snow melting. In the alpine mountains, different climate and vegetation conditions have complex effects on water resources. The results are helpful for understanding the changing mechanism of water storage over the Tibetan Plateau and have scientific meaning for the development, utilization, and protection of regional water resources.
A Rapid Self-Supervised Deep-Learning-Based Method for Post-Earthquake Damage Detection Using UAV Data (Case Study: Sarpol-e Zahab, Iran)
Authors: Takhtkeshha Narges , Mohammadzadeh Ali , Salehi Bahram
Published: December 2022

Immediately after an earthquake, rapid disaster management is the main challenge for relevant organizations. While satellite images have been used in the past two decades for building-damage mapping, they have rarely been utilized for the timely damage monitoring required for rescue operations. Unmanned aerial vehicles (UAVs) have recently become very popular due to their agile deployment to sites, super-high spatial resolution, and relatively low operating cost. This paper proposes a novel deep-learning-based method for rapid post-earthquake building damage detection. The method detects damages in four levels and consists of three steps. First, three different feature types—non-deep, deep, and their fusion—are investigated to determine the optimal feature extraction method. A “one-epoch convolutional autoencoder (OECAE)” is used to extract deep features from non-deep features. Then, a rule-based procedure is designed for the automatic selection of the proper training samples required by the classification algorithms in the next step. Finally, seven famous machine learning (ML) algorithms—including support vector machine (SVM), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), decision trees (DT), k-nearest neighbors (KNN), and adaBoost (AB)—and a basic deep learning algorithm (i.e., multi-layer perceptron (MLP)) are implemented to obtain building damage maps. The results indicated that auto-training samples are feasible and superior to manual ones, with improved overall accuracy (OA) and kappa coefficient (KC) over 22% and 33%, respectively; SVM (OA = 82% and KC = 74.01%) was the most accurate AI model with a slight advantage over MLP (OA = 82% and KC = 73.98%). Additionally, it was found that the fusion of deep and non-deep features using OECAE could significantly enhance damage-mapping efficiency compared to those using either non-deep features (by an average improvement of 6.75% and 9.78% in OA and KC, respectively) or deep features (improving OA by 7.19% and KC by 10.18% on average) alone.
A Mesh Mapping-Based Cooperative Inversion Strategy for Airborne Transient Electromagnetic and Magnetic Methods
Authors: Ren Hao , Lei Da , Wang Zhongxing , Fu Changmin
Published: December 2022

Cooperative inversion is a powerful underground imaging technique that can overcome the limitations of a single detection method. However, due to the different grid divisions used by various geophysical methodologies, imposing structural constraints between grids of different scales is challenging. This paper proposes a new cooperative inversion strategy and applies it to the inversion of the quasi-two-dimensional aerial transient electromagnetic method (ATEM) with the induced polarization (IP) effect and the two-dimensional magnetic method to solve the problem of applying cross-gradient constraints under grids of different scales. The mesh mapping method is incorporated into the iterative process of cooperative inversion in this inversion strategy. The inversion of synthetic data shows that this technique can effectively employ data complementarity to increase the accuracy of the results for describing the medium boundary. The mesh mapping methodology may be applied to the cooperative inversion of geophysical methods under any grid division and successfully solves the problem of grid division mismatch in cooperative inversion.
Hyperspectral Feature Selection for SOM Prediction Using Deep Reinforcement Learning and Multiple Subset Evaluation Strategies
Authors: Zhao Linya , Tan Kun , Wang Xue , Ding Jianwei , Liu Zhaoxian , Ma Huilin , Han Bo
Published: December 2022

It has been widely certified that hyperspectral images can be effectively used to monitor soil organic matter (SOM). Though numerous bands reveal more details in spectral features, information redundancy and noise interference also come accordingly. Due to the fact that, nowadays, prevailing dimensionality reduction methods targeted to hyperspectral images fail to make effective band selections, it is hard to capture the spectral features of ground objects quickly and accurately. In this paper, to solve the inefficiency and instability of hyperspectral feature selection, we proposed a feature selection framework named reinforcement learning for feature selection in hyperspectral regression (RLFSR). Specifically, the Markov Decision Process (MDP) was used to simulate the hyperspectral band selection process, and reinforcement learning agents were introduced to improve model performance. Then two spectral feature evaluation methods were introduced to find internal relationships between the hyperspectral features and thus comprehensively evaluate all hyperspectral bands aimed at the soil. The feature selection methods—RLFSR-Net and RLFSR-Cv—were based on pre-trained deep networks and cross-validation, respectively, and achieved excellent results on airborne hyperspectral images from Yitong Manchu Autonomous County in China. The feature subsets achieved the highest accuracy for most inversion models, with inversion R2 values of 0.7506 and 0.7518, respectively. The two proposed methods showed slight differences in spectral feature extraction preferences and hyperspectral feature selection flexibilities in deep reinforcement learning. The experiments showed that the proposed RLFSR framework could better capture the spectral characteristics of SOM than the existing methods.
Synergy of Geospatial Data from TLS and UAV for Heritage Building Information Modeling (HBIM)
Authors: Klapa Przemysław , Gawronek Pelagia
Published: December 2022

Currently, Building Information Modeling (BIM) is increasingly entering the operational level in terms of creating a model for newly constructed facilities. For existing objects, and objects of Culture Heritage (CH), the creation of coherent and qualitative BIM models depends on the quality of the data constituting the basis for modelling. What’s more, BIM of CH is not only a challenge to obtain high-quality three-dimensional data, but also a time-consuming study of object documentation and photographic documentation in order to create a faithful library of parametric objects. In the article, the authors presented the synergy of spatial data with TLS and UAV as the basis for creating a BIM model for two CH objects. The aim of the article was to make such a synergy of TLS and UAV data that the geospatial database, developed for the needs of modelling historic objects in the HBIM trend, would have a specific amount of information without the frequently used redundancy. In principle, the acquired 3D database should be expressed in a global reference system with the degree of georeferencing accuracy for situational and altitude measurements and should be consistent to provide comprehensive information about the object. The analyses led to conclusions in which the authors assign superior importance to the accuracy of measurement information and the integration of individual data groups in the process of developing the HBIM model with the desired accuracy in opposition to the appropriate selection of the level of detail, which is usually assigned a superior role, which in turn results from the quality of the data geospatial modelling.
Indoor 3D Point Cloud Segmentation Based on Multi-Constraint Graph Clustering
Authors: Luo Ziwei , Xie Zhong , Wan Jie , Zeng Ziyin , Liu Lu , Tao Liufeng
Published: December 2022

Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation.
Warming Has Accelerated the Melting of Glaciers on the Tibetan Plateau, but the Debris-Covered Glaciers Are Rapidly Expanding
Authors: Hu Mingcheng , Zhou Guangsheng , Lv Xiaomin , Zhou Li , Wang Xiaoliang , He Xiaohui , Tian Zhihui
Published: December 2022

Glacier changes on the Tibetan Plateau are of great importance for regional climate and hydrology and even global ecological changes. It is urgent to understand the effect of climate warming on both clean and debris-covered glaciers on the Tibetan Plateau. This study used the double RF method and Landsat series images to extract clean glaciers and debris-covered glaciers on the Tibetan Plateau from 1985 to 2020 and analyzed their temporal and spatial changes under the background of climate change. The total area of glaciers on the Tibetan Plateau showed a retreating trend from 1985 to 2020, with an average retreat rate of −0.5 % yr−1. The area of clean glaciers showed a significant retreating trend, with a retreat rate of −0.55 % yr−1. The area of debris-covered glaciers showed an expanding trend, with an expanding rate of 0.62 % yr−1. The clean glaciers retreated faster in the southeast and slower in the northwest, while the debris-covered glaciers expanded in most basins. The debris-covered glaciers were generally located at lower elevation areas than those of the clean glaciers. The slopes of clean glaciers were mainly in the range of 0–50°, while the slopes of debris-covered glaciers were mainly in the range of 0–30°. Climate warming was a main driver of glacier change. The clean glacier area was correlated negatively with average temperature in summer and positively with average precipitation in winter, while the debris-covered glacier area was correlated positively with both. The results of the study may provide a basis for scientific management of glaciers on the Tibetan Plateau in the context of climate warming.
Self-Supervised Depth Completion Based on Multi-Modal Spatio-Temporal Consistency
Authors: Zhang Quan , Chen Xiaoyu , Wang Xingguo , Han Jing , Zhang Yi , Yue Jiang
Published: December 2022

Due to the low cost and easy deployment, self-supervised depth completion has been widely studied in recent years. In this work, a self-supervised depth completion method is designed based on multi-modal spatio-temporal consistency (MSC). The self-supervised depth completion nowadays faces other problems: moving objects, occluded/dark light/low texture parts, long-distance completion, and cross-modal fusion. In the face of these problems, the most critical novelty of this work lies in that the self-supervised mechanism is designed to train the depth completion network by MSC constraint. It not only makes better use of depth-temporal data, but also plays the advantage of photometric-temporal constraint. With the self-supervised mechanism of MSC constraint, the overall system outperforms many other self-supervised networks, even exceeding partially supervised networks.
Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1/2 on the GEE
Authors: Liu Jiantao , Li Yexiang , Zhang Yan , Liu Xiaoqian
Published: December 2022

The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize the level of urbanization and measure the intensity of human activities, and hence, the timely understanding of ISA dynamic changes is of great significance to protect the ecological safety of the YRDHEEZ. Based on the multi-source and multi-modal Sentinel-1/2 remotely sensed data provided by Google Earth Engine (GEE) cloud computing platform, this study developed a novel approach for the extraction of time-series ISA in the YRDHEEZ through a combination of random forest algorithm and numerous representative features extracted from Sentinel-1/2. Subsequently, we revealed the pattern of the ISA spatial-temporal evolution in this region over the past five years. The results demonstrated that the proposed method has good performance with an average overall accuracy of 94.84% and an average kappa coefficient of 0.9393, which verified the feasibility of the proposed method for large-scale ISA mapping with 10 m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km2 in 2018 to 5147.02 km2 in 2022 with an average rate of −16.09 km2/year in the last 5 years, suggesting that the ISA of YRDHEEZ has decreased while its overall pattern was not significantly changed over time. The presented workflow can provide a reference for large-scale ISA mapping and its evolution analysis, especially in regions on estuarine deltas.
TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data
Authors: Huang Qiqiao , Chen Sheng , Tan Jinkai
Published: December 2022

Currently, most deep learning (DL)-based models for precipitation forecasting face two conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect of forecasting precipitation intensity. Therefore, this study proposes “time series residual convolution (TSRC)”, a DL-based convolutional neural network for precipitation nowcasting over China with a lead time of 3 h. The core idea of TSRC is it compensates the current local cues with previous local cues during convolution processes, so more contextual information and less uncertain features would remain in deep networks. We use four years’ radar echo reflectivity data from 2017 to 2020 for model training and one year’s data from 2021 for model testing and compare it with two commonly used nowcasting models: optical flow model (OF) and UNet. Results show that TSRC obtains better forecasting performances than OF and UNet with a relatively high probability of detection (POD), low false alarm rate (FAR), small mean absolute error (MAE) and high structural similarity index (SSIM), especially at longer lead times. Meanwhile, the results of two case studies suggest that TSRC still introduces smoothing effects and slightly outperforms UNet at longer lead times. The most considerable result is that our model can forecast high-intensity radar echoes even for typhoon rainfall systems, suggesting that the degenerate effect of forecasting precipitation intensity can be improved by our model. Future works will focus on the combination of multi-source data and the design of the model’s architecture to gain further improvements in precipitation short-term forecasting.
An Evaluation of Immobilized Poly-(S)-N-(1-phenylethyl)acrylamide Chiral Stationary Phases
Authors: Lu Guangying , Miao Yiyuan , Zhao Jianchao , Chen Xin , Ke Yanxiong
Published: December 2022

In this study, brush type and polymer type stationary phases were prepared based on (S)-N-(1-phenylethyl) acrylamide, and the polymeric stationary phase demonstrated superior chiral recognition ability. The two polymeric stationary phases were synthesized by two strategies, one was the “grafting from” method, which obtained polymer CSP by initiating monomer polymerization on the surface of 3-methacrylatepropyl silica gel, and the other was “grafting to”, which fixed the copolymer of (S)-N-(1-phenylethyl) acrylamide and trimethoxysilylpropyl methacrylate on silica gel. A comparison of these two bonding modes revealed that the stationary phase produced by “grafting to” had higher chiral recognition ability. Further improvement can be achieved by the end-capping of silanol groups with trimethylchlorosilane to reduce non-enantioselective retention caused by residual silanol groups and improve the peak shape of enantiomers. Chiral separation in subcritical fluid chromatography was also studied. Similar enantioselectivity results with higher resolution were observed due to the improvement of peak shape.
Smart City: Sharing of Financial Services
Authors: Popova Yelena , Cernisevs Olegs
Published: December 2022

Contemporary life is closely interconnected with numerous phenomena, which have appeared in our life in recent decades. The concepts of a smart city, digitalization of the economy, and the sharing economy are among them. These factors create new opportunities for businesses operating in modern markets. The article considers the sharing services in digital payment operations for achieving the Key Performance Indicators (KPI) of a smart city. The goal of the research is to determine the costs of sharing economy implementation in the financial sector of a smart city. The study takes the example of Rome’s experience. The authors consider KPIs selected by the municipality of Rome as a measure of smart city implementation and their provision by sharing services in financial operations. The authors specify the structure of the costs of shared financial services for a smart city and for Fintech companies operating with open banking, which is followed by the cost functions peculiar to these operations of Fintech companies. The authors demonstrate the point at which a Fintech company starts earning a positive profit on these services via operating leverage.
The Implementation of Dating Violence Prevention Programmes in Portugal and Their Effectiveness: Perspectives of Professionals
Authors: Neves Sofia , Santos Inês , Topa Joana
Published: December 2022

The aim of the present study is to learn the perspectives of professionals on the effectiveness of implementing dating violence prevention programmes in Portugal, based on their experiences. For data collection, a questionnaire survey built specifically for this purpose was used, which was completed by a total of 33 participants, 31 female and two male, with an average age of 39 years (SD = 10.10). The results indicate that the implementation of dating violence prevention programmes is generally perceived as effective, although a very limited number of participants mention the reduction of violence as a resultant gain. Limitations of a different nature are recognized, namely in terms of specificity, sustainability and the evaluation systems, which may compromise the consolidation of long-term objectives. Thus, the study presents important indications on how to redefine strategies to prevent and combat dating violence.
Keys to Build an Inclusive University System: The Case of Spanish Public Universities
Authors: Diaz-Vega Miriam , Moreno-Rodriguez Ricardo , López-Díaz José María , López-Bastías José Luis
Published: December 2022

Sustainable Development Goals and the targets described in each of them can only be fully achieved with the commitment and involvement of universities. These institutions are the perfect environment to promote and enhance sustainable human development, in such a way that from their own structure and operation they favor the transition towards the building of peaceful, fair, and inclusive societies. To achieve the goals described in SDG4, universities must address three areas in a combined and parallel way: “Institutional Management and Governance”, “Education”, and “Leadership”. Following the development of four quantitative and qualitative studies that address these areas individually, the main common conclusions are presented, aimed at showing the path that the university system must follow to achieve these goals within 10 years. The results reveal the need to the focus on the development of an inclusive university culture based on the foundations of each institution (mission, vision, and values), as well as the need to strengthen the link with the NGO sector. Similarly, this inclusive culture must be transversal in university strategic plans to ensure that all actions developed in this context are aligned with the precepts of the 2030 Agenda.
“Voodoo” Science in Neuroimaging: How a Controversy Transformed into a Crisis
Authors: Sauvayre Romy
Published: December 2022

Since the 1990s, functional magnetic resonance imaging (fMRI) techniques have continued to advance, which has led researchers and non specialists alike to regard this technique as infallible. However, at the end of 2008, a scientific controversy and the related media coverage called functional neuroimaging practices into question and cast doubt on the capacity of fMRI studies to produce reliable results. The purpose of this article is to retrace the history of this contemporary controversy and its treatment in the media. Then, the study stands at the intersection of the history of science, the epistemology of statistics, and the epistemology of science. Arguments involving actors (researchers, the media) and the chronology of events are presented. Finally, the article reveals that three groups fought through different arguments (false positives, statistical power, sample size, etc.), reaffirming the current scientific norms that separate the true from the false. Replication, forming this boundary, takes the place of the most persuasive argument. This is how the voodoo controversy joined the replication crisis.
Effect of Gear Ratio and Cadence on Gross Efficiency and Pedal Force Effectiveness during Multistage Graded Cycling Test Using a Road Racing Bicycle
Authors: Kamba Mutsumi , Naito Hisashi , Ozaki Hayao , Machida Shuichi , Katamoto Shizuo
Published: December 2022

Gross efficiency (GE) and the index of pedal force effectiveness (IFE) are important factors that enhance cyclists’ performance; however, the effects of changing pedal force (gear ratio) and cadence on these indices while riding on a road racing bicycle are poorly investigated. This study aimed to examine the effect of changing gear ratio or cadence on GE and IFE using a road racing bicycle. Nine male cyclists completed graded submaximal cycling tests (five stages of 4 min submaximal cycling sessions with 1 min passive rest intervals). The work rate of each stage was determined using two principles: changing gear ratio at a fixed cadence and changing cadence at a fixed gear ratio. We determined GE and IFE using respiratory variables and pedal reaction forces, respectively. Increasing the gear ratio improved GE, and was associated with the IFE. Although increasing the cadence slightly improved GE from the initial level, the increased values then mostly maintained. IFE was almost stable even when cadence increased. Moreover, no significant correlation was observed between the changes in GE and IFE accompanied by increasing cadence. Our data indicate that an increasing gear ratio, but not cadence, may affect GE and IFE while riding on a road racing bicycle.
Coordination vs. Competitiveness of Effective Average Tax Rates in Relation to FDI: The Case of Emerging EU Economies
Authors: Glavaški Olgica , Beker Pucar Emilija , Beljić Marina , Stojkov Stefan
Published: December 2022

Due to the single market within the European Union (EU), capital mobility has created many challenges in the field of tax policy, especially whether taxes should be coordinated or governments should retain fiscal sovereignty for the sake of tax competitiveness. In order to attract foreign direct investment (FDI), emerging EU economies most often choose the policy of tax reduction and particularly lowering the effective average tax rates (EATR). The purpose of this paper is to empirically assess the impact of changes in the EATR on the decision to localize FDI in emerging EU economies in the period 1998–2021, in the framework of cross-sectional dependent, non-stationary, heterogeneous panels. Using the (Pooled) Mean Group estimator, the long-run relationship between the EATR and the FDI is revealed. The error-correction parameters are significant and heterogeneous, showing that the speed of adjustments towards equilibrium is different, but the highest in Estonia. Accession to the EU contributed faster adjustments to the long-run relationship in the majority of the emerging EU economies, while a broader set of potential determinants of FDI localization is taken into consideration and estimated using the Panel-corrected standard error method. Results showed de facto tax competitiveness in the group of emerging EU economies, instead of de jure tax coordination in the EU.
Corporate Sustainable Development, Corporate Environmental Performance and Cost of Debt
Authors: Sun Haiyan , Wang Guangyang , Bai Junwei , Shen Jianfei , Zheng Xinyuan , Dan Erli , Chen Feiyu , Zhang Ludan
Published: December 2022

High environmental performance of enterprises may reduce financing costs, while good environmental performance can promote sustainable development of enterprises. Therefore, this paper examines the impact mechanism of China’s corporate environmental performance on financing costs, and whether corporate sustainable development plays a regulatory role in the research of heavy pollution industries. This study is conducted through the Breusch and Pagan Lagrange multiplier test for random effects and the Hausman test to determine whether to adopt Fixed-effects regression or Random-effects GLS regression as an estimation method to control individual effects and endogenous problems brought by time. By collecting the samples of listed companies in China from 2010 to 2021, the empirical results show that corporate environmental performance is negatively related to financing costs. Sustainable development, as a moderator variable, is negatively related to financing costs and has weakened the inhibition of corporate environmental performance on financing costs. Although the existing literature shows that environmental performance will lead to changes in debt costs, this study has made contributions to the literature by revealing the sustainable development mechanism in the relationship between corporate environmental performance and financing costs and has verified that sustainable development is one of the important factors affecting financing costs.
The Conflict between Technology and Scale: Evidence from China’s Wooden Furniture Industry
Authors: Lin Meng
Published: December 2022

The Chicago School believes that more efficient firms will earn higher profits, leading to higher market concentration. How does this apply to situations in specific industrial environments? The output value and import and export volume of China’s wooden furniture industry rank among the highest in the world, but the industry’s profit growth rate is decreasing. Is this phenomenon caused by a problem in the growth mode of the industry itself? It is especially important to understand the real characteristics of the industry. The purpose of this study is to discover the characteristics and trends of China’s wooden furniture industry and to clarify the direction of the future development of the industry. Market concentration index, data envelopment analysis and Engel-Granger cointegration test were applied to this study respectively. Based on the theory of industrial organization, this study conducted an empirical analysis in three stages. The results show that China’s wooden furniture industry is still a labor-intensive industry, the increase in market concentration mainly depends on the expansion of the labor force, and there is a conflict between industrial technological progress and scale expansion. It has also been confirmed in theory that the market structure is affected by efficiency. However, the way and degree of impact depend on the level of industrial development. This study can provide a reference for the formulation and adjustment of macro policies for industrial development, improve the understanding of industry characteristics and trends at the enterprise level to help enterprises achieve transformation, and offer international and domestic investors a reference point for investment decisions at the market level. This study combines macro-level "industry" with micro-level "enterprise" to make the study more comprehensive and systematic, and fills in the gaps in related research in the field.
An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings
Authors: Rastbod Samira , Rahimi Farnaz , Dehghan Yara , Kamranfar Saeed , Benjeddou Omrane , Nehdi Moncef L.
Published: December 2022

Recent developments in indirect predictive methods have yielded promising solutions for energy consumption modeling. The present study proposes and evaluates a novel integrated methodology for estimating the annual thermal energy demand (DAN), which is considered as an indicator of the heating and cooling loads of buildings. A multilayer perceptron (MLP) neural network is optimally trained by symbiotic organism search (SOS), which is among the strongest metaheuristic algorithms. Three benchmark algorithms, namely, political optimizer (PO), harmony search algorithm (HSA), and backtracking search algorithm (BSA) are likewise applied and compared with the SOS. The results indicate that (i) utilizing the properties of the building within an artificial intelligence framework gives a suitable prediction for the DAN indicator, (ii) with nearly 1% error and 99% correlation, the suggested MLP-SOS is capable of accurately learning and reproducing the nonlinear DAN pattern, and (iii) this model outperforms other models such as MLP-PO, MLP-HSA and MLP-BSA. The discovered solution is finally expressed in an explicit mathematical format for practical uses in the future.
The Influence of the COVID-19 Pandemic on Mortality of Patients Hospitalized in Surgical Services in Romania: A Cross-Sectional Study of a National Survey
Authors: Bîrlă Rodica , Panaitescu Eugenia , Ceban Cornelia , Radu Andra-Victoria , Hoară Petre , Constantin Adrian , Păduraru Dan Nicolae , Bordianu Anca , Cristian Daniel Alin , Constantinoiu Silviu
Published: December 2022

Surgical practice worldwide has changed rapidly in response to the coronavirus disease 2019 (COVID-19) pandemic. The study aimed to analyze the impact of the COVID-19 pandemic on mortality, in hospitalized patients, in Romanian surgical services. We have developed our research on a national survey of the consecutive records of hospitalizations, surgical interventions and deaths performed in Romania between March and August 2020. Results show that 47 surgical departments responded to the request. The admissions in the period March–August 2020 represented 52% of that from the similar period of 2019. In the studied period, the share of surgical interventions in patients admitted to non-COVID-19 centres was 98.7%, respectively 78.2% in COVID-19 support hospitals (p < 0.05), and emergency interventions of 43.4% in non-COVID-19 hospitals, respectively 84.8% in COVID-19 support hospitals (p < 0.05). Overall mortality in this period was 5.82%, compared to 3.28% in a similar period in 2019, (p < 0.05). Postoperative mortality in COVID-19-positive patients was 19%. In conclusion, in the hospitals in Romania included in the survey, the overall mortality in the studied period was higher than in a similar period in 2019. In patients with COVID-19 positive, the recorded postoperative mortality was higher than overall mortality.
The Quality of Corporate Social Responsibility Information Disclosure and Enterprise Innovation: Evidence from Chinese Listed Companies
Authors: Ban Qi
Published: December 2022

The Chinese government has implemented a series of corporate social responsibility (CSR)-related policies in recent years, aiming to achieve high-quality economic development and transformation. To assess the effectiveness of these CSR policies, this study empirically explores the relationship between the quality of CSR information disclosure and corporate innovation performance using data from A-share listed companies in Shanghai and Shenzhen, China, from 2009 to 2020. The results show that: (1) Improvement in the quality of CSR information can significantly improve corporate innovation performance. (2) Further analysis reveals that the improvement of CSR information quality not only helps to reduce the agency costs and alleviate the financing constraints of enterprises but also reduces the cost of external interpretation of corporate innovation projects and conveys a good corporate image by attracting the attention of analysts and the media, which all contribute to the improvement of corporate innovation performance. This study further refines the mechanism of the impact of CSR disclosure on firms’ innovation performance, which may provide guidance to governments in developing and improving CSR disclosure systems in the future.
Rural Tourism in Marginimea Sibiului Area—A Possibility of Capitalizing on Local Resources
Authors: Adamov Tabita , Iancu Tiberiu , Peț Elena , Popescu Gabriela , Șmuleac Laura , Feher Andrea , Ciolac Ramona
Published: December 2022

The current research was motivated by observed particularities of local communities and the necessity of local rural economy diversification. Development through rural tourism is a possible sustainable response that can be implemented in many of Europe’s rural areas, including Marginimea Sibiului. Thus, the purpose of this research, and its main contribution, was to identify the specific features of the Marginimea Sibiului area and possible strategies for capitalizing on local resources through rural tourism activities. We also sought to understand the extent to which rural tourism is seen by the owners of tourist structures as a developmental possibility. We utilized a case study research method to address these questions. The research has major implications due to the questionnaires applied to the owners of rural tourism structures during August 2022, which allowed us to determine: the motivation of tourist structure owners to carry out tourism activity, the degree of development, aspects concerning the operation and management of the tourist structure, and future development and improvement of the owned products. Because the area is representative at the national level in terms of rural tourism activity, an X-ray was sketched after the conclusions were drawn, which issued some proposals for “mixing” the resources through rural tourist activities as a “presentation modality”, which may potentially have favorable future consequences.
Airline Cabin Crew Members’ Ambidexterity as the Sustainable Attitude for Prosocial Passenger Service
Authors: Choi Hyung-Min , Kessler Daniel
Published: December 2022

The current study aims to explore the intrinsic and extrinsic motivators for cabin crew members’ ambidexterity and to investigate the influence of ambidexterity on prosocial service behavior. The authors employed PLS-SEM to examine the model, using 428 valid samples from cabin crew members, their pursers, and managers working for airlines in South Korea. The study found that challenge, empowerment, and learning goal orientation, among the four dimensions of the intrinsic motivators, have positive and significant influences on cabin crew members’ ambidexterity. The findings also demonstrate that only service climate, among the three extrinsic motivators studied, is associated with ambidexterity. Moreover, the findings reveal a strong relationship between cabin crew members’ ambidexterity and prosocial service behaviors. Based on the findings, the authors addressed theoretical and practical implications related to sustainability literature and the industry.