bluuebunny/crossref_metadata_2025 huggingface dataset:
Epoch-Wise Estimation and Analysis of GNSS Receiver DCB under High and Low Solar Activity Conditions
Authors: Zhang Xiao , Xia Linyuan , Lin Hong , Li Qianxia
Published: April 2023

Differential code bias (DCB) is one of the main errors involved in ionospheric total electron content (TEC) retrieval using a global navigation satellite system (GNSS). It is typically assumed to be constant over time. However, this assumption is not always valid because receiver DCBs have long been known to exhibit apparent intraday variations. In this paper, a combined method is introduced to estimate the epoch-wise receiver DCB, which is divided into two parts: the receiver DCB at the initial epoch and its change with respect to the initial value. In the study, this method was proved feasible by subsequent experiments and was applied to analyze the possible reason for the intraday receiver DCB characteristics of 200 International GNSS Service (IGS) stations in 2014 (high solar activity) and 2017 (low solar activity). The results show that the proportion of intraday receiver DCB stability less than 1 ns increased from 72.5% in 2014 to 87% in 2017, mainly owing to the replacement of the receiver hardware in stations. Meanwhile, the intraday receiver DCB estimates in summer generally exhibited more instability than those in other seasons. Although more than 90% of the stations maintained an intraday receiver DCB stability within 2 ns, substantial variations with a peak-to-peak range of 5.78 ns were detected for certain stations, yielding an impact of almost 13.84 TECU on the TEC estimates. Moreover, the intraday variability of the receiver DCBs is related to the receiver environment temperature. Their correlation coefficient (greater than 0.5 in our analyzed case) increases with the temperature. By contrast, the receiver firmware version does not exert a great impact on the intraday variation characteristics of the receiver DCB in this case.
Blind Spots Analysis of Magnetic Tensor Localization Method
Authors: Xu Lei , Huang Xianyuan , Dai Zhonghua , Yuan Fuli , Wang Xu , Fan Jinyu
Published: April 2023

In order to compare and analyze the positioning efficiency of the magnetic tensor location method, this paper studies the blind spots of the magnetic tensor location method. By constructing two magnetic tensor localization models, the localization principles of the single-point magnetic tensor localization method (STLM) and the two-point magnetic tensor linear localization method (TTLM) are analyzed. Furthermore, the eigenvalue analysis method is studied to analyze the blind spots of STLM, and the spherical analysis method is proposed to analyze the blind spots of TTLM. The results show that when the direction of any measuring point is perpendicular to the direction of the target magnetic moment, blind spots of STLM appear. However, TTLM still has good positioning performance in the blind spot.
Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters
Authors: Campos Ricardo M.
Published: April 2023

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate spatial and temporal criteria for collocating altimeter data to fixed-point positions and to provide practical guidance on altimeter collocation in deep waters. The results show that a temporal criterion of 30 min and a spatial criterion between 25 km and 50 km produce the best results for altimeter collocation, in close agreement with buoy data. Applying a 25 km criterion leads to slightly better error metrics but at the cost of fewer matchups, whereas using 50 km augments the resulting collocated dataset while keeping the differences to buoy measurements very low. Furthermore, the study demonstrates that using the single closest altimeter record to the buoy position leads to worse results compared to the collocation method based on temporal and spatial averaging. The final validation of altimeter data against buoy observations shows an RMSD of 0.21 m, scatter index of 0.09, and correlation coefficient of 0.98 for Hs, confirming the optimal choice of temporal and spatial criteria employed and the high quality of the calibrated AODN altimeter dataset.
Fast Neutron Measurement System Using Prompt Gamma Neutron Activation Solid Converter: Monte Carlo Study
Authors: Walg Jonathan , Orion Itzhak
Published: April 2023

Measuring fast neutron emission around accelerators is important for purposes of environmental monitoring and radiation safety. It is necessary to detect two types of neutrons: thermal and fast. Fast neutron spectroscopy is commonly employed using a hydrogen-recoil proportional-counter; however, its threshold is 2 MeV. The aim of this study was to expand PGNA converters based on KCl to fulfil the need to detect neutron energies ranging from 0.02 MeV to 3 MeV. In our previous research, we established a counting system comprised of a large converter of KCl with a NaI(Tl) gamma radiation spectrometer. The KCl converter is efficient for fast neutron prompt gamma emission. The potassium naturally includes a radioisotope that emits 1.460 MeV gamma rays. The presence of the constant level of 1.460 MeV gamma ray counts offers an advantage, providing a stable background for the detector. The study was carried out using MCNP simulations of the counting system with a variety of PGNA converters based on KCl. We concluded that KCl mixtures combined with other elements, such as PGNA converters, demonstrated improved detection performance for fast neutron emissions. Furthermore, an explication of how to add materials to KCl to provide a proper converter for fast neutrons was introduced.
Effects of AR-Based Home Appliance Agents on User’s Perception and Maintenance Behavior
Authors: Baba Takeru , Isoyama Naoya , Uchiyama Hideaki , Sakata Nobuchika , Kiyokawa Kiyoshi
Published: April 2023

Maintenance of home appliances can be tedious. Maintenance work can be physically demanding and it is not always easy to know the cause of a malfunctioning appliance. Many users need to motivate themselves to perform maintenance work and consider it ideal for home appliances to be maintenance-free. On the other hand, pets and other living creatures can be taken care of with joy and without much pain, even if they are difficult to take care of. To alleviate the hassle associated with the maintenance of home appliances, we propose an augmented reality (AR) system to superimpose an agent over the home appliance of concern who changes their behavior according to the internal state of the appliance. Taking a refrigerator as an example, we verify whether such AR agent visualization motivates users to perform maintenance work and reduces the associated discomfort. We designed a cartoon-like agent and implemented a prototype system using a HoloLens 2, which can switch between several animations depending on the internal state of the refrigerator. Using the prototype system, a Wizard of Oz user study comparing three conditions was conducted. We compared the proposed method (Animacy condition), an additional behavior method (Intelligence condition), and a text-based method as a baseline for presenting the refrigerator state. In the Intelligence condition, the agent looked at the participants from time to time as if it was aware of them and exhibited help-seeking behavior only when it was considered that they could take a short break. The results show that both the Animacy and Intelligence conditions induced animacy perception and a sense of intimacy. It was also evident that the agent visualization made the participants feel more pleasant. On the other hand, the sense of discomfort was not reduced by the agent visualization and the Intelligence condition did not improve the perceived intelligence or the sense of coercion further compared to the Animacy condition.
A Method for Spatially Registered Microprofilometry Combining Intensity-Height Datasets from Interferometric Sensors
Authors: Mazzocato Sara , Daffara Claudia
Published: April 2023

A recognized problem in profilometry applied to artworks is the spatial referencing of the surface topography at micrometer scale due to the lack of references in the height data with respect to the “visually readable” surface. We demonstrate a novel workflow for spatially referenced microprofilometry based on conoscopic holography sensors for scanning in situ heterogeneous artworks. The method combines the raw intensity signal collected by the single-point sensor and the (interferometric) height dataset, which are mutually registered. This dual dataset provides a surface topography registered to the artwork features up to the precision that is given by the acquisition scanning system (mainly, scan step and laser spot). The advantages are: (1) the raw signal map provides additional information about materials texture, e.g., color changes or artist marks, for spatial registration and data fusion tasks; (2) and microtexture information can be reliably processed for precision diagnostic tasks, e.g., surface metrology in specific sub-domains and multi-temporal monitoring. Proof of concept is given with exemplary applications: book heritage, 3D artifacts, surface treatments. The potential of the method is clear for both quantitative surface metrology and qualitative inspection of the morphology, and it is expected to open future applications for microprofilometry in heritage science.
A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics
Authors: Samimi Hamed , Dajani Hilmi R.
Published: April 2023

Traditional cuff-based sphygmomanometers for measuring blood pressure can be uncomfortable and particularly unsuitable to use during sleep. A proposed alternative method uses dynamic changes in the pulse waveform over short intervals and replaces calibration with information from photoplethysmogram (PPG) morphology to provide a calibration-free approach using a single sensor. Results from 30 patients show a high correlation of 73.64% for systolic blood pressure (SBP) and 77.72% for diastolic blood pressure (DBP) between blood pressure estimated with the PPG morphology features and the calibration method. This suggests that the PPG morphology features could replace the calibration stage for a calibration-free method with similar accuracy. Applying the proposed methodology on 200 patients and testing on 25 new patients resulted in a mean error (ME) of −0.31 mmHg, a standard deviation of error (SDE) of 4.89 mmHg, a mean absolute error (MAE) of 3.32 mmHg for DBP and an ME of −4.02 mmHg, an SDE of 10.40 mmHg, and an MAE of 7.41 mmHg for SBP. These results support the potential for using a PPG signal for calibration-free cuffless blood pressure estimation and improving accuracy by adding information from cardiovascular dynamics to different methods in the cuffless blood pressure monitoring field.
Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach
Authors: Jurdana Vedran , Lopac Nikola , Vrankic Miroslav
Published: April 2023

Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the density-based spatial clustering algorithm. Moreover, an appropriate criterion for the performance of the method is formalized, i.e., component concentration and preservation, as well as interference suppression, are measured utilizing the information obtained from the short-term and the narrow-band Rényi entropies, while component connectivity is evaluated using the number of regions with continuously-connected samples. The CS-AF area selection and reconstruction algorithm parameters are optimized using an automatic multi-objective meta-heuristic optimization method, minimizing the here-proposed combination of measures as objective functions. Consistent improvement in CS-AF area selection and TFD reconstruction performance has been achieved without requiring a priori knowledge of the input signal for multiple reconstruction algorithms. This was demonstrated for both noisy synthetic and real-life signals.
Student Cheating Detection in Higher Education by Implementing Machine Learning and LSTM Techniques
Authors: Alsabhan Waleed
Published: April 2023

Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model’s superior performance.
Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project
Authors: Buchman Aron S. , Wang Tianhao , Oveisgharan Shahram , Zammit Andrea R. , Yu Lei , Li Peng , Hu Kun , Hausdorff Jeffrey M. , Lim Andrew S. P. , Bennett David A.
Published: April 2023

This study characterized person-specific rates of change of total daily physical activity (TDPA) and identified correlates of this change. TDPA metrics were extracted from multiday wrist-sensor recordings from 1083 older adults (average age 81 years; 76% female). Thirty-two covariates were collected at baseline. A series of linear mixed-effect models were used to identify covariates independently associated with the level and annual rate of change of TDPA. Though, person-specific rates of change varied during a mean follow-up of 5 years, 1079 of 1083 showed declining TDPA. The average decline was 16%/year, with a 4% increased rate of decline for every 10 years of age older at baseline. Following variable selection using multivariate modeling with forward and then backward elimination, age, sex, education, and 3 of 27 non-demographic covariates including motor abilities, a fractal metric, and IADL disability remained significantly associated with declining TDPA accounting for 21% of its variance (9% non-demographic and 12% demographics covariates). These results show that declining TDPA occurs in many very old adults. Few covariates remained correlated with this decline and the majority of its variance remained unexplained. Further work is needed to elucidate the biology underlying TDPA and to identify other factors that account for its decline.
Introducing a Rapid DNA Analysis Procedure for Crime Scene Samples Outside of the Laboratory—A Field Experiment
Authors: de Roo Rosanne , Mapes Anna , van Cooten Merel , van Hooff Britt , Kneppers Sander , Kokshoorn Bas , Valkenburg Thalassa , de Poot Christianne
Published: April 2023

Technological innovations enable rapid DNA analysis implementation possibilities. Concordantly, rapid DNA devices are being used in practice. However, the effects of implementing rapid DNA technologies in the crime scene investigation procedure have only been evaluated to a limited extent. In this study a field experiment was set up comparing 47 real crime scene cases following a rapid DNA analysis procedure outside of the laboratory (decentral), with 50 cases following the regular DNA analysis procedure at the forensic laboratory. The impact on duration of the investigative process, and on the quality of the analyzed trace results (97 blood and 38 saliva traces) was measured. The results of the study show that the duration of the investigation process has been significantly reduced in cases where the decentral rapid DNA procedure was deployed, compared to cases where the regular procedure was used. Most of the delay in the regular process lies in the procedural steps during the police investigation, not in the DNA analysis, which highlights the importance of an effective work process and having sufficient capacity available. This study also shows that rapid DNA techniques are less sensitive than regular DNA analysis equipment. The device used in this study was only to a limited extent suitable for the analysis of saliva traces secured at the crime scene and can mainly be used for the analysis of visible blood traces with an expected high DNA quantity of a single donor.
TN-GAN-Based Pet Behavior Prediction through Multiple-Dimension Time-Series Augmentation
Authors: Kim Hyungju , Moon Nammee
Published: April 2023

Behavioral prediction modeling applies statistical techniques for classifying, recognizing, and predicting behavior using various data. However, performance deterioration and data bias problems occur in behavioral prediction. This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series augmentation to minimize the data bias problem. The prediction model dataset in this study used nine-axis sensor data (accelerometer, gyroscope, and geomagnetic sensors). The ODROID N2+, a wearable pet device, collected and stored data on a web server. The interquartile range removed outliers, and data processing constructed a sequence as an input value for the predictive model. After using the z-score as a normalization method for sensor values, cubic spline interpolation was performed to identify the missing values. The experimental group assessed 10 dogs to identify nine behaviors. The behavioral prediction model used a hybrid convolutional neural network model to extract features and applied long short-term memory techniques to reflect time-series features. The actual and predicted values were evaluated using the performance evaluation index. The results of this study can assist in recognizing and predicting behavior and detecting abnormal behavior, capacities which can be applied to various pet monitoring systems.
Heterogeneous Fusion of Camera and mmWave Radar Sensor of Optimizing Convolutional Neural Networks for Parking Meter System
Authors: Sun Chi-Chia , Lin Yong-Ye , Hong Wei-Jia , Jan Gene-Eu
Published: April 2023

In this article, a novel heterogeneous fusion of convolutional neural networks that combined an RGB camera and an active mmWave radar sensor for the smart parking meter is proposed. In general, the parking fee collector on the street outdoor surroundings by traffic flows, shadows, and reflections makes it an exceedingly tough task to identify street parking regions. The proposed heterogeneous fusion convolutional neural networks combine an active radar sensor and image input with specific geometric area, allowing them to detect the parking region against different tough conditions such as rain, fog, dust, snow, glare, and traffic flow. They use convolutional neural networks to acquire output results along with the individual training and fusion of RGB camera and mmWave radar data. To achieve real-time performance, the proposed algorithm has been implemented on a GPU-accelerated embedded platform Jetson Nano with a heterogeneous hardware acceleration methodology. The experimental results exhibit that the accuracy of the heterogeneous fusion method can reach up to 99.33% on average.
Graph Representation Learning and Its Applications: A Survey
Authors: Hoang Van Thuy , Jeon Hyeon-Ju , You Eun-Soon , Yoon Yoewon , Jung Sungyeop , Lee O-Joun
Published: April 2023

Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional vectors while preserving graph structure and entity relationships. Over the decades, many models have been proposed for graph representation learning. This paper aims to show a comprehensive picture of graph representation learning models, including traditional and state-of-the-art models on various graphs in different geometric spaces. First, we begin with five types of graph embedding models: graph kernels, matrix factorization models, shallow models, deep-learning models, and non-Euclidean models. In addition, we also discuss graph transformer models and Gaussian embedding models. Second, we present practical applications of graph embedding models, from constructing graphs for specific domains to applying models to solve tasks. Finally, we discuss challenges for existing models and future research directions in detail. As a result, this paper provides a structured overview of the diversity of graph embedding models.
The Positive Effect of Four-Week Combined Aerobic–Resistance Training on Body Composition and Adipokine Levels in Obese Females
Authors: Rejeki Purwo Sri , Pranoto Adi , Rahmanto Ilham , Izzatunnisa Nabilah , Yosika Ghana Firsta , Hernaningsih Yetti , Wungu Citrawati Dyah Kencono , Halim Shariff
Published: April 2023

Obesity is a metabolic disease that is caused by a lack of physical activity and is associated with an increased risk of chronic inflammation. A total of 40 obese adolescent females with an average age of 21.93 ± 1.35 years and average body mass index (BMI) of 30.81 ± 3.54 kg/m2 were enrolled in this study, randomized, and divided into four groups, i.e., control (CTL; n = 10), moderate intensity aerobic training (MAT; n = 10), moderate intensity resistance training (MRT; n = 10), and moderate intensity combined aerobic–resistance training (MCT; n = 10). The enzyme-linked immunosorbent assay (ELISA) kits method was used to analyze the adiponectin and leptin levels between pre-intervention and post-intervention. Statistical analysis was conducted using a paired sample t-test, while correlation analysis between variables used the Pearson product–moment correlation test. Research data showed that MAT, MRT, and MCT significantly increased adiponectin levels and decreased leptin levels compared to the CTL (p ≤ 0.05). The results of the correlation analysis of delta (∆) data showed that an increase in adiponectin levels was significantly negatively correlated with a decrease in body weight (BW) (r = −0.671, p ≤ 0.001), BMI (r = −0.665, p ≤ 0.001), and fat mass (FM) (r = −0.694, p ≤ 0.001) and positively correlated with an increase in skeletal muscle mass (SMM) (r = 0.693, p ≤ 0.001). Whereas, a decrease in leptin levels was significantly positively correlated with a decrease in BW (r = 0.744, p ≤ 0.001), BMI (r = 0.744, p ≤ 0.001), and FM (r = 0.718, p ≤ 0.001) and negatively correlated with an increase in SMM (r = −0.743, p ≤ 0.001). In summary, it can be concluded that our data show that adiponectin levels increased and leptin levels decreased after the intervention of aerobic, resistance, and combined aerobic–resistance training.
High-Intensity Training for Knee Osteoarthritis: A Narrative Review
Authors: Tarantino Domiziano , Theysmans Tine , Mottola Rosita , Verbrugghe Jonas
Published: April 2023

Knee osteoarthritis (OA) is the most common joint disease worldwide. Exercise therapy has been identified as a first-line treatment option in patients suffering from knee OA. High-intensity training (HIT) is an innovative exercise modality with potential in improving various disease-related outcomes. The purpose of this review is to explore the impact of HIT on knee OA symptoms and physical functioning. A comprehensive search of scientific electronic databases was conducted to identify articles on the effects of HIT on knee OA. Thirteen studies were included in this review. Ten compared the effects of HIT with those of low-intensity training, moderate-intensity continuous training, or a control group. Three evaluated the effects of HIT alone. Eight reported a decrease in knee OA symptoms (especially pain), and eight reported an increase in physical functioning. HIT was shown to improve knee OA symptoms and physical functioning, but also aerobic capacity, muscle strength, and quality of life with minimal or no adverse events. However, compared with other exercise modalities, no clear superiority of HIT was found. HIT is a promising exercise strategy in patients with knee OA; nonetheless, the actual quality of evidence remains very low, and more high-quality studies are needed to confirm these promising outcomes.
The Impact of Digital Technology Innovation Network Embedding on Firms’ Innovation Performance: The Role of Knowledge Acquisition and Digital Transformation
Authors: Ge Chengwei , Lv Wendong , Wang Junli
Published: April 2023

In the digital economy context, enterprises’ competitive environment is changing rapidly. Historically, enterprises rely on a solitary fight to occupy the market. Now, enterprises should actively embed into digital technology innovation networks to maximize access to external digital technology knowledge resources through organizational cooperation and achieve the absorption of digital resources and technologies. However, the relationship between digital technology innovation network embedding and innovation performance still needs to be clarified. Therefore, this study adopts the “structure–behavior–performance” research paradigm to extend innovation network research to the digital technology innovation network context, aiming to explore the impact of digital technology innovation network embedding on enterprise innovation performance and to analyze the mediating effect of knowledge acquisition and the moderating effect of digital transformation. This study conducts an empirical study based on Chinese A-share listed firms that undertook digital technology innovation from 2010–2021. The findings show that digital technology innovation networks’ relational and structural embedding positively affects firm innovation performance. Knowledge acquisition mediates digital technology innovation network embedding and innovation performance. Digital transformation has a moderating role between digital technology innovation network embedding and innovation performance, and different levels of digital transformation will have different effects on firms’ innovation performance. Overall, the relational and structural embedding of digital technology innovation networks can encourage enterprises to acquire more social capital and tacit knowledge and reduce R&D costs, thus improving their innovation performance. Firms should focus on building external cooperation networks, actively establishing an excellent corporate image, strengthening communication and cooperation with network members, establishing mutually beneficial cooperation beliefs, and promoting digital transformation. The present results will help companies understand the impact of digital technology innovation networks and provide a reference for companies to utilize in digital transformation to improve their innovation performance.
Vehicle Intersections Prediction Based on Markov Model with Variable Weight Optimization
Authors: He Zhihui , Ning Lei , Jiang Baihui , Li Jiajia , Wang Xin
Published: April 2023

In this study, a new algorithm for predicting vehicle turning at intersections is proposed. The method is based on the Markov chain and can predict vehicle trajectories using GPS location sequences. Unlike traditional Markov models, which use preset weights, we created the Markov model using a data-driven weight selection method. The proposed model can dynamically adjust the weights of each intersection’s influence on current trajectories based on the data, in contrast to the fixed weights in traditional models. The study also details how to process trajectory data to identify whether a vehicle has passed through an intersection and how to determine the adjacency relationship of intersections, thus providing a reference for implementing a model of the classification problem. The data-driven algorithm was applied and compared to the fixed-weight algorithm on the same trajectory dataset, and the superiority of the weight selection algorithm was proven. The prediction accuracy of the traditional method was 49.61%, while the proposed method achieved a prediction accuracy of 60.66% for 100,000 trajectory datasets, nearly an 11% increase. Volunteer participation in the second dataset collected on the university campus showed that the accuracy of the proposed method could be further improved to 79.31% as the GPS sampling frequency increased. Simulation results show that the algorithm provides accurate prediction and that the prediction effect is improved with the expansion of the trajectory data set and the increase in GPS sampling frequency. The proposed algorithm has the potential to provide a location-based optimization of network resource allocation.
Clustering Analysis of Classified Performance Evaluation of Higher Education in Shanghai Based on Topsis Model
Authors: Xia Yan , You Jianxin , Feng Xiumeng , Xu Yingjie , Feng Hui
Published: April 2023

Diversification is a fundamental attribute of higher education. With the continuous expansion of the scale, universities and colleges have paid more attention to developing in diversified ways. Diversification is an important way to promote sustainable development of universities and colleges. Sustainable development is the endogenous impetus for the long-term development of higher education. The implementation of classified performance evaluation on higher education is beneficial to optimize fund and resource allocation for different types of universities and colleges, to effectively promote the diversified construction and sustainable development of higher education. Therefore, it becomes extremely important in the reform of higher education in China. It classifies universities and colleges into different types and then implements performance evaluation on the objects of the same type. In this paper, a classified performance evaluation indicator system is established for different types of universities and colleges. Topsis model is used to calculate the relative adjacency between the evaluated objects and the optimal and inferior solutions of all objects. A systematic clustering algorithm is made to analyze and evaluate the performance of universities and colleges in the same type. An automatic system is developed to analyze data from 62 universities and colleges in Shanghai. It provides advice and guidance for the development strategy of higher education.
Building Resilient Communities: The Environmental Observatory for Mining Projects and Climate Change Indicators
Authors: Bergamini Kay , Ángel Piroska , Rugiero Vanessa , Medina José Ignacio , Mollenhauer Katherine
Published: April 2023

Public environmental information can improve industry performance, reduce environmental conflicts, and foster informed citizenship. The latter is directly linked to resilience because it is a “process that enables people to learn together, support experimentation, and increase the potential for (social and technological) innovation”. Importantly, the transparency and disclosure of environmental information alone do not have the desired impact; the general public may have access to information but not understand the content. It is necessary to reframe the technical language of information to reach broader stakeholder understanding. The Environmental Observatory for Mining Projects is an applied research project that aims to provide a public information access system for diverse stakeholders. It integrates data from various public services and makes them available to a variety of stakeholders, including the general public, through a web server and application that facilitate accessibility and understanding by using the co-creation methodology for public services. As a result of the project, the authors identified 25 indicators, six of which relate to climate change, including greenhouse gas emissions, water pollution, air pollution, hazardous waste, and tailing deposit locations. These indicators are relevant for decision making through the combined knowledge of public policies, information priorities on the impacts and vulnerabilities of climate change, and more practical issues related to data availability. The authors conclude that environmental information systems must provide people with essential data, but that such information must also be understandable, manageable, comparable, and interoperable so as to promote access to crucial information for resilient communities.
Comparing Traditional Teaching and the Personal and Social Responsibility Model: Development of Values in Secondary Education Students
Authors: Manzano-Sánchez David
Published: April 2023

Personal and Social Responsibility Model (TPSR) is considered an ideal methodology for promoting values while teaching academic or physical activity contents. The purpose of this study was to carry out an intervention in an educational center with TPSR to exceed 70% of the total teaching time. A total of 105 students participated in the present study as experimental group and 147 as control group (12 to 16 years old). A series of questionnaires were passed to check their responsibility (PSRQ questionnaire) and for the experimental group, a questionnaire to evaluate their TPSR satisfaction (ECVA-12 questionnaire). On the other hand, we check with interviews teachers opinion about the experience with TPSR intervention. The results showed that TPSR allowed after six months a favorable opinion including a values near to 4 (3.78 and 3.72) in a 1–5 scale in the satisfaction with TPSR and values acquisition comparing this methodology with their previous way of receiving classes. At the same time, personal and social responsibility values had significative scores after intervention in both personal (p < 0.05) and social responsibility (p < 0.01) and taking into account the control group. In addition, the teachers interviewed showed high satisfaction with the methodology, valuing the importance of adapting very well to the most complex students, and the need to carry out continuous training in the methodology. It is concluded that TPSR is an adequate methodology to improve students and teachers satisfaction with teaching, as well as allow an improvement in the responsibility values.
Blockchain-Based Design of a Government Incentive Mechanism for Manufacturing Supply Chain Data Governance
Authors: Wei Jinyu , Yi Xiuping , Yang Xin , Liu Yaoxi
Published: April 2023

In the context of “double carbon”, constructing green supply chains is the only way to implement sustainable development strategies in the manufacturing industry. This paper, therefore, examines the manufacturing supply chain for low-carbon products. More recently, the lack of technical information flow due to data barriers up and down the supply chain has led to high energy consumption, the serious waste of raw materials, and the substandard production of green products. Therefore, the level of supply chain data governance must be improved to enhance the sustainability of the supply chain. By studying blockchain-based data governance and government policy incentives for manufacturing supply chains, this study constructed an evolutionary game model based on prospect theory for the tripartite relation of government, manufacturers, and retailers. The difference between the perceived and actual value was introduced into a three-way evolutionary game model based on prospect theory to optimize the practical implications of the model. The model was then simulated using system dynamics. Through the simulation, it could be concluded that the ability of the three-way evolutionary game to reach the optimal stability point is only related to the sensitivity of the retailer’s perceived value. Additionally, the outcome of the three-way evolutionary game can be unstable, with changes in perceived value sensitivity. Finally, relevant policy recommendations are made. The innovation of this study is establishing a data governance platform that uses data governance to build green supply chains. Additionally, the government was added to the subjects of the game to explore the role of government policy in data governance and sustainable development. In addition, the evolutionary game model was incorporated with prospect theory and traditional expected utility theory, and the rational deficits and preferences of decision makers were taken into account, which brings the results closer to the reality of the situation.
Study on the Spatial and Temporal Evolution of High-Quality Development in Nine Provinces of the Yellow River Basin
Authors: Zhang Jing , Liu Yang , Liu Cuicui , Guo Su , Cui Junfu
Published: April 2023

High-quality development (HQD) is a positive initiative in China to promote sustainable development. Promoting HQD in the Yellow River Basin (YRB) is a major national strategy for China. The YRB is an important ecological barrier and economic zone in China, which comprises nine provinces, including Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong. The objective of this paper is to provide a comprehensive evaluation of the HQD of the nine provinces in the YRB. It clarifies the basic connotation of HQD and constructs a comprehensive evaluation index system for HQD according to the principles of comprehensiveness, distinctiveness, openness, refinement and feasibility. The comprehensive evaluation index system includes four dimensions: economic development, technology innovation, society and livelihood, and ecological security, which contain 28 secondary indicators. The combined method of coupling development and entropy weight termed the “technique for order preference by similarity to an ideal solution (TOPSIS)” was selected to make a comprehensive evaluation of the HQD of the nine provinces in the YRB from the time dimension and the space dimension, respectively. It was found that there are still problems, such as the differentiation of HQD, the low comprehensive development degree (CDD), and the low development coupling degree (DCD) in some provinces.
Effects of Knowledge Anxiety and Cognitive Processing Bias on Brand Avoidance during COVID-19: The Mediating Role of Attachment Anxiety and Herd Mentality
Authors: Chen Rui , Yan Haolan
Published: April 2023

The COVID-19 pandemic has led to an increase in users’ knowledge anxiety, which has been further intensified by the diversity of information platforms and the emphasis on digital personal branding. While previous research has examined the relationship between digital personal branding and negative emotions, little is known about the mechanisms behind negative reactions to digital personal branding from non-direct factors or users’ spontaneous negative emotions. To address this gap, this study draws on cognitive appraisal theory (CAT) and social identity theory (SIT) to explore the relationships between users’ knowledge anxiety, cognitive processing biases, and brand avoidance, and the impacts of herding behavior and attachment anxiety on these relationships. A sample of 530 consumers completed an online survey, and the data were analyzed using a partial least squares path model. The results revealed that user knowledge anxiety directly and indirectly influenced brand avoidance behavior through cognitive processing bias, and attachment anxiety moderated the path between cognitive processing bias and user knowledge anxiety. However, herding behavior was not found to be significant in online knowledge sharing communities.
Characterization of Tasks and Skills of Workers, Middle and Top Managers in the Industry 4.0 Context
Authors: Torres Diamantino , Pimentel Carina , Matias João C. O.
Published: April 2023

This study intends to make a characterization of the main tasks and skills needed to face the Industry 4.0. Moreover, it gives special attention to the different company’s hierarchical levels. To achieve the goals of this paper, a methodology based on Collaborative Decision Making method was used. Firstly, thirty participants were interviewed to understand their point of views. Next, they were split into Group Works to refine the results. Finally, a Group Concordance took place to create the final list of tasks and skills. The skills were organized into four categories: Technical, Social, Methodological and Personal. The results show different requirements in each level. In the Workers level, due to the presence of more technical tasks, eight of the top ten skills belong to the Technical category. In the Middle Managers level, there are tasks of both leading and analyzing nature and thus the top ten skills needed are split through the four categories. Finally, in the Top Managers level, due to the presence of mainly strategy tasks, the top ten skills are split between the Methodological and Social categories. To the authors’ knowledge, this is one of the first studies to use a Collaborative Decision Making methodology to develop a set of tasks and skills that future work environments will demand on the different hierarchical levels of the organization.
The Dynamic Evolution of the Digital Economy and Its Impact on the Urban Green Innovation Development from the Perspective of Driving Force—Taking China’s Yangtze River Economic Belt Cities as an Example
Authors: Lu Juchun , Zhou Siqun , Xiao Xiaohan , Zhong Meng , Zhao Yifan
Published: April 2023

The digital economy is an essential driving force supporting high-quality green development. Based on the panel data of cities in China’s Yangtze River from 2011 to 2020, this paper builds the digital economy evaluation index system from the five driving forces of market demand, information application, innovative development, industrial foundation, and the policy system. The Kernel density function, Moran index, and spatial econometric model are used to investigate the spatial and temporal evolution of the digital economy, as well as its driving effect and spatial effect on urban green innovation development. The results show that: (1) In China’s Yangtze River economic belt, the gap and the urban spatial dependence of the urban digital economy development are increasing; (2) The digital economy directly drives urban green innovation development, and also indirectly drives it by stimulating intra-city industrial structure upgrading and inter-city knowledge spillover, both of which have significant structural and regional differences; (3) Through the spatial spillover effect, it affects the urban green innovation development in its’ own city and in the surrounding ones, varying according to urban administrative levels and urban development types. The results provide a theoretical and practical reference for future policy formation to support high-quality green innovation development and fully utilize the driving force of the digital economy.
Do Live Streaming and Online Consumer Reviews Jointly Affect Purchase Intention?
Authors: Qin Chaoyong , Zeng Xinyu , Liang Shichang , Zhang Ke
Published: April 2023

Social commerce has become a mainstream online shopping phenomenon. The effects of single social-commerce modes, such as live streaming and online consumer reviews (OCRs), on consumers’ purchase intention have attracted much attention. However, the existing literature overlooks the combined impact and complementary mechanisms of multiple social business modes on purchase intention. Drawing on the previous research, we identified the characteristics of live streaming and the characteristics of OCRs. Drawing inspiration from channel complementarity theory, a dual-channel influence model is presented. We collected 448 online questionnaires from several social-commerce platforms. The data were processed via structural equation modeling. The results show that the social-commerce modes of live streaming and OCRs can jointly influence consumers’ purchase intention, sense of community, interactivity, perceived usefulness, and perceived trust as antecedents of purchase intention, with customer engagement playing a mediating role. However, emotional support had no impact on purchase intention. This study provides useful insight into the mechanisms behind how multiple social-commerce channels influence purchase intention.
Sustainable Maritime Freight Transportation: Current Status and Future Directions
Authors: Singh Suneet , Dwivedi Ashish , Pratap Saurabh
Published: April 2023

Maritime freight has gained popularity among researchers and practitioners due to its cost efficiency and eco-friendly nature. It was initially developed for cargo transfer, but its widespread adoption has made it the backbone of global economy. Despite its favourable nature, some of its serious negative effects have attracted the attention of researchers and scholars. Therefore, the present study reviews the extensive literature available on maritime freight logistics, and evaluates the existing access distance between sustainability practices and maritime freight logistics. A systematic three-stage review process including review planning, review conduct and evaluation is followed in this study. VOSviewer and the R language are used to evaluate relevant issues and changes in the literature. Thereafter, the content analysis highlights the major themes of the subject. This study underscores the impact of innovative technologies discovered to make maritime freight sustainable and also examines maritime freight transport in terms of three pillars of sustainability. The result has implications for policymakers to facilitate the smooth implementation of sustainable practices in maritime freight transportation.
The Effect of Power Flow Entropy on Available Load Supply Capacity under Stochastic Scenarios with Different Control Coefficients of UPFC
Authors: Ou Zhongxi , Lou Yuanyuan , Wang Junzhou , Li Yixin , Yang Kun , Peng Sui , Tang Junjie
Published: April 2023

With the sharp increase in fluctuant sources in power systems, the deterministic power flow (DPF) calculation has been unable to meet the demands of practical applications; thus, the probabilistic method becomes indispensable for the reliable and stable operation of power systems. This paper adopts the probabilistic power flow (PPF) method, which is a Monte Carlo simulation (MCS) based on the Latin hypercube sampling (LHS) method, to analyze the uncertainties of power systems. Specifically, the available load supply capability (ALSC) based on the branch loading rate is used to analyze the safety margin of the whole system, while the improved power flow entropy is introduced to quantify the equilibrium of power flow distribution. The repeated power flow (RPF) calculation is combined with the PPF method, and, hence, the probabilistic repeated power flow (PRPF) method is proposed to calculate the power flow entropy at the initial state and the probabilistic ALSC. To flexibly control the power flow, the unified power flow controller (UPFC) is added to the AC power system. The different control coefficients of UPFC are set to reveal the relationship between power flow entropy and available load supply capability under the stochastic scenarios. Finally, the modified IEEE14 test system is used to study the adjustment abilities of UPFC. With consideration of uncertainties in the test case, the positive effect of UPFC on the power flow entropy and the probabilistic ALSC under stochastic scenarios is deeply studied.