bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design
Authors: Mak Hugo Wai Leung , Han Runze , Yin Hoover H. F.
Published: March 2023

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possible use and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering has also been minimally discussed in the literature neither. This study first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model to generate new game levels based on two well-known game settings; and to validate the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments are also utilized to evaluate the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and graphical results, several potential deficiencies, for example, difficulties in handling high-dimensional and vast datasets, as well as insufficient clarity of outputs are discussed; then measures of future enhancement, such as tokenization and the combination of VAE and GAN models, are also outlined. Hopefully, this can ultimately maximize the strengths and advantages of VAE for future game design tasks and relevant industrial missions.
High-Level CNN and Machine Learning Methods for Speaker Recognition
Authors: Costantini Giovanni , Cesarini Valerio , Brenna Emanuele
Published: March 2023

Speaker Recognition (SR) is a common task in AI-based sound analysis, involving structurally different methodologies such as Deep Learning or “traditional” Machine Learning (ML). In this paper, we compared and explored the two methodologies on the DEMoS dataset consisting of 8869 audio files of 58 speakers in different emotional states. A custom CNN is compared to several pre-trained nets using image inputs of spectrograms and Cepstral-temporal (MFCC) graphs. AML approach based on acoustic feature extraction, selection and multi-class classification by means of a Naïve Bayes model is also considered. Results show how a custom, less deep CNN trained on grayscale spectrogram images obtain the most accurate results, 90.15% on grayscale spectrograms and 83.17% on colored MFCC. AlexNet provides comparable results, reaching 89.28% on spectrograms and 83.43% on MFCC.The Naïve Bayes classifier provides a 87.09% accuracy and a 0.985 average AUC while being faster to train and more interpretable. Feature selection shows how F0, MFCC and voicing-related features are the most characterizing for this SR task. The high amount of training samples and the emotional content of the DEMoS dataset better reflect a real case scenario for speaker recognition, and account for the generalization power of the models.
Accurate Calibration of a Large Field of View Camera with Coplanar Constraint for Large-Scale Specular Three-Dimensional Profile Measurement
Authors: Lu Rongsheng , Wang Zhizhuo , Zou Zhiting
Published: March 2023

In the vision-based inspection of specular or shiny surfaces, we often compute the camera pose with respect to a reference plane by analyzing images of calibration grids, reflected in such a surface. To obtain high precision in camera calibration, the calibration target should be large enough to cover the whole field of view (FOV). For a camera with a large FOV, using a small target can only obtain a locally optimal solution. However, using a large target causes many difficulties in making, carrying, and employing the large target. To solve this problem, an improved calibration method based on coplanar constraint is proposed for a camera with a large FOV. Firstly, with an auxiliary plane mirror provided, the positions of the calibration grid and the tilt angles of the plane mirror are changed several times to capture several mirrored calibration images. Secondly, the initial parameters of the camera are calculated based on each group of mirrored calibration images. Finally, adding with the coplanar constraint between each group of calibration grid, the external parameters between the camera and the reference plane are optimized via the Levenberg-Marquardt algorithm (LM). The experimental results show that the proposed camera calibration method has good robustness and accuracy.
High Selectivity Hydrogen Gas Sensor Based on WO3/Pd-AlGaN/GaN HEMTs
Authors: Nguyen Van Cuong , Cha Ho-Young , Kim Hyungtak
Published: March 2023

We investigated the hydrogen gas sensors based on AlGaN/GaN high electron mobility transistors (HEMTs) for high temperature sensing operation. The gate area of the sensor was functionalized using a 10 nm Pd catalyst layer for hydrogen gas sensing. A thin WO3 layer was deposited on top of the Pd layer to enhance the sensor selectivity toward hydrogen gas. At 200 °C, the sensor exhibited high sensitivity of 658% toward 4%-H2, while exhibiting only a little interaction with NO2, CH4, CO2, NH3, and H2S. From 150 °C to 250 °C, the 10 ppm hydrogen response of the sensor was at least eight times larger than other target gases. These results showed that this sensor is suitable for H2 detection in a complex gas environment at a high temperature.
A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications
Authors: Mcmurray Samuel , Sodhro Ali Hassan
Published: March 2023

Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement.
A PUF-Based Key Storage Scheme Using Fuzzy Vault
Authors: Yang Jinrong , Chen Shuai , Cao Yuan
Published: March 2023

Physical Unclonable Functions (PUFs) are considered attractive low-cost security anchors in the key generation scheme. The helper data algorithm is usually used to transform the fuzzy responses extracted from PUF into a reproducible key. The generated key can be used to encrypt secret data in traditional security schemes. In contrast, this work shows that the fuzzy responses of both weak and strong PUFs can be used to secretly store the important data (e.g., the distributed keys) directly by an error-tolerant algorithm, Fuzzy Vault, without the traditional encryption algorithm and helper data scheme. The locking and unlocking methods of our proposal are designed to leverage the feature of weak and strong PUFs relatively. For the strong PUFs, our proposal is a new train of thought about how to leverage the advantage of strong PUFs (exponential number of challenge–response pairs) when used in the field. The evaluation was performed on existing weak PUF and strong PUF designs. The unlocking rate and runtime are tested under different parameters and environments. The test results demonstrate that our proposal can reach a 100% unlocking rate by parameter adjustment with less than 1 second of locking time and a few seconds of unlocking time. Finally, the tradeoff between security, reliability, and overhead of the new proposal is discussed.
Event-Triggered Sliding Mode Neural Network Controller Design for Heterogeneous Multi-Agent Systems
Authors: Shen Xinhai , Gao Jinfeng , Liu Peter X.
Published: March 2023

A class of heterogeneous second-order multi-agent consensus problems is studied, in which an event-triggered method is used to improve the feasibility of the control protocol. The sliding mode control method is used to achieve the robustness of the system. A special type of general radial basis function neural network is applied to estimate the uncertainties. The event-triggered mechanism is introduced to reduce the update frequency of the controller and the communication frequency among the agents. Zeno behavior is avoided by ensuring a lower bound between two adjacent trigger instants. Finally, the simulation results are provided to demonstrate that the time evolution of consensus errors eventually approaches zero. The consensus of multi-agent systems is achieved.
Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20
Authors: Zangenehnejad Farzaneh , Gao Yang
Published: March 2023

In recent years, numerous smartphones have been equipped with global navigation satellite system (GNSS) technology, enabling individuals to utilize their own devices for positioning and navigation purposes. In 2016, with the launch of a mobile app by Google, namely GnssLogger, smartphone users with Android version 7 or higher were able to record raw GNSS measurements (i.e., pseudorange, carrier phase, Doppler, and carrier-to-noise density ratio (C/N0)). Since then, enhancing the accuracy and efficiency of smartphone positioning has become an interesting area of research. Precise point positioning (PPP) is a powerful method providing precise real-time positioning of a single receiver, and it can be applied to smartphone observations as well. Achieving high-precision PPP requires selecting appropriate functional and stochastic models. In this study, we investigate the development of more reliable stochastic models for smartphone GNSS observations. The least-square variance component estimation (LS-VCE) method is applied to double-difference (DD) pseudorange and carrier phase observations from two Samsung S20s to obtain appropriate variances for GPS and GLONASS. According to the results, there is no significant correlation between the pseudorange and carrier phase observations of GPS and GLONASS on the L1 frequency. Furthermore, the quality of GLONASS carrier phase observations is comparable to that of GPS. The model’s performance is then assessed with respect to single-frequency precise point positioning (SF-PPP) using a dataset collected in kinematic mode from a Samsung S20 smartphone. A significant improvement of 25.1% and 32.7% on the root-mean-square (RMS) of horizontal positioning and the 50th percentile error, respectively, was achieved when employing the obtained stochastic model.
The Effect of Nitrogen Annealing on the Resistive Switching Characteristics of the W/TiO2/FTO Memory Device
Authors: Yu Zhiqiang , Han Xu , Xu Jiamin , Chen Cheng , Qu Xinru , Liu Baosheng , Sun Zijun , Sun Tangyou
Published: March 2023

In this paper, the effect of nitrogen annealing on the resistive switching characteristics of the rutile TiO2 nanowire-based W/TiO2/FTO memory device is analyzed. The W/TiO2/FTO memory device exhibits a nonvolatile bipolar resistive switching behavior with a high resistance ratio (RHRS/RLRS) of about two orders of magnitude. The conduction behaviors of the W/TiO2/FTO memory device are attributed to the Ohmic conduction mechanism and the Schottky emission in the low resistance state and the high resistance state, respectively. Furthermore, the RHRS/RLRS of the W/TiO2/FTO memory device is obviously increased from about two orders of magnitude to three orders of magnitude after the rapid nitrogen annealing treatment. In addition, the change in the W/TiO2 Schottky barrier depletion layer thickness and barrier height modified by the oxygen vacancies at the W/TiO2 interface is suggested to be responsible for the resistive switching characteristics of the W/TiO2/FTO memory device. This work demonstrates the potential applications of the rutile TiO2 nanowire-based W/TiO2/FTO memory device for high-density data storage in nonvolatile memory devices.
Quantification and Rehabilitation of Unilateral Spatial Neglect in Immersive Virtual Reality: A Validation Study in Healthy Subjects
Authors: Faity Germain , Sidahmed Yasmine , Laffont Isabelle , Froger Jérôme
Published: March 2023

Unilateral spatial neglect is a common sensorimotor disorder following the occurrence of a stroke, for which prismatic adaptation is a promising rehabilitation method. However, the use of prisms for rehabilitation often requires the use of specific equipment that may not be available in clinics. To address this limitation, we developed a new software package that allows for the quantification and rehabilitation of unilateral spatial neglect using immersive virtual reality. In this study, we compared the effects of virtual and real prisms in healthy subjects and evaluated the performance of our virtual reality tool (HTC Vive) against a validated motion capture tool. Ten healthy subjects were randomly exposed to virtual and real prisms, and measurements were taken before and after exposure. Our findings indicate that virtual prisms are at least as effective as real prisms in inducing aftereffects (4.39° ± 2.91° with the virtual prisms compared to 4.30° ± 3.49° with the real prisms), but that these effects were not sustained beyond 2 h regardless of exposure modality. The virtual measurements obtained with our software showed excellent metrological qualities (ICC = 0.95, error = 0.52° ± 1.18°), demonstrating its validity and reliability for quantifying deviation during pointing movements. Overall, our results suggest that our virtual reality software (Virtualis, Montpellier, France) could provide an easy and reliable means of quantifying and rehabilitating spatial neglect. Further validation of these results is required in individuals with unilateral spatial neglect.
Spatial Multi-Source Information Fusion Localization Algorithm in Non-Line-of-Sight Environments
Authors: Wang Jianhui , Li Jingjing , Cui Weijia , Liu Qing
Published: March 2023

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications.
Head-Mounted Projector for Manual Precision Tasks: Performance Assessment
Authors: Mamone Virginia , Ferrari Vincenzo , D’Amato Renzo , Condino Sara , Cattari Nadia , Cutolo Fabrizio
Published: March 2023

The growing interest in augmented reality applications has led to an in-depth look at the performance of head-mounted displays and their testing in numerous domains. Other devices for augmenting the real world with virtual information are presented less frequently and usually focus on the description of the device rather than on its performance analysis. This is the case of projected augmented reality, which, compared to head-worn AR displays, offers the advantages of being simultaneously accessible by multiple users whilst preserving user awareness of the environment and feeling of immersion. This work provides a general evaluation of a custom-made head-mounted projector for the aid of precision manual tasks through an experimental protocol designed for investigating spatial and temporal registration and their combination. The results of the tests show that the accuracy (0.6±0.1 mm of spatial registration error) and motion-to-photon latency (113±12 ms) make the proposed solution suitable for guiding precision tasks.
DSM Extraction Based on Gaofen-6 Satellite High-Resolution Cross-Track Images with Wide Field of View
Authors: Yin Suqin , Zhu Ying , Hong Hanyu , Yang Tingting , Chen Yi , Tian Yi
Published: March 2023

Digital Surface Model (DSM) is a three-dimensional model presenting the elevation of the Earth’s surface, which can be obtained by the along-track or cross-track stereo images of optical satellites. This paper investigates the DSM extraction method using Gaofen-6 (GF-6) high-resolution (HR) cross-track images with a wide field of view (WFV). To guarantee the elevation accuracy, the relationship between the intersection angle and the overlap of the cross-track images was analyzed. Cross-track images with 20–40% overlaps could be selected to conduct DSM extraction. First, the rational function model (RFM) based on error compensation was used to realize the accurate orientation of the image. Then, the disparity map was generated based on the semi-global block matching (SGBM) algorithm with epipolar constraint. Finally, the DSM was generated by forward intersection. The GF-6 HR cross-track images with about 30% overlap located in Taian, Shandong Province, China, were used for DSM extraction. The results show that the mountainous surface elevation features were retained completely, and the details, such as houses and roads, were presented in valleys and urban areas. The root mean square error (RMSE) of the extracted DSM could reach 6.303 m, 12.879 m, 14.929 m, and 19.043 m in valley, ridge, urban, and peak areas, respectively. The results indicate that the GF-6 HR cross-track images with a certain overlap can be used to extract a DSM to enhance its application in land cover monitoring.
Bright Molecular Strain Probe Templates for Reporting Protein–Protein Interactions
Authors: Kim Sung-Bae , Furuta Tadaomi , Kamiya Genta , Kitada Nobuo , Paulmurugan Ramasamy , Maki Shojiro A.
Published: March 2023

Imaging protein–protein interactions (PPIs) is a hot topic in molecular medicine in the postgenomic sequencing era. In the present study, we report bright and highly sensitive single-chain molecular strain probe templates which embed full-length Renilla luciferase 8.6-535SG (RLuc86SG) or Artificial luciferase 49 (ALuc49) as reporters. These reporters were deployed between FKBP-rapamycin binding domain (FRB) and FK506-binding protein (FKBP) as a PPI model. This unique molecular design was conceptualized to exploit molecular strains of the sandwiched reporters appended by rapamycin-triggered intramolecular PPIs. The ligand-sensing properties of the templates were maximized by interface truncations and substrate modulation. The highest fold intensities, 9.4 and 16.6, of the templates were accomplished with RLuc86SG and ALuc49, respectively. The spectra of the templates, according to substrates, revealed that the colors are tunable to blue, green, and yellow. The putative substrate-binding chemistry and the working mechanisms of the probes were computationally modeled in the presence or absence of rapamycin. Considering that the molecular strain probe templates are applicable to other PPI models, the present approach would broaden the scope of the bioassay toolbox, which harnesses the privilege of luciferase reporters and the unique concept of the molecular strain probes into bioassays and molecular imaging.
Vertical Farming Monitoring: How Does It Work and How Much Does It Cost?
Authors: Morella Paula , Lambán María Pilar , Royo Jesús , Sánchez Juan Carlos
Published: March 2023

Climate change, resource scarcity, and a growing world population are some of the problems facing traditional agriculture. For this reason, new cultivation systems are emerging, such as vertical farming. This is based on indoor cultivation, which is not affected by climatic conditions. However, vertical farming requires higher consumption of water and light, since in traditional agriculture those resources are free. Vertical cultivation requires the use of new technologies and sensors to reduce water and energy consumption and increase its efficiency. The sensorization of these systems makes it possible to monitor and evaluate their performance in real time. In addition, vertical farming faces economic uncertainty since its profitability has not been studied in depth. This article studies the most important variables when monitoring a vertical farming system and proposes the sensors to be used in the data acquisition system. In addition, this study presents a cost model for the installation of this type of system. This cost model is applied to a case study to evaluate the profitability of installing this type of infrastructure. The results obtained suggest that the investment made in VF installations could be profitable in a period of three to five years.
Removal of Azo Dyes from Wastewater through Heterogeneous Photocatalysis and Supercritical Water Oxidation
Authors: Vaiano Vincenzo , De Marco Iolanda
Published: March 2023

Azo dyes are synthetic organic dyes used in the textile, leather, and paper industries. They pose environmental problems due to their toxic and persistent nature. The toxicity is due to the presence of azo groups in the dye molecule that can break down into aromatic amines, which are highly toxic to aquatic organisms and humans. Various treatment methods have been developed to remove azo dyes from wastewater. Conventional wastewater treatments have some drawbacks, such as high operating costs, long processing times, generation of sludge, and the formation of toxic by-products. For these reasons, a valid alternative is constituted by advanced oxidation processes. Good results have been obtained using heterogeneous photocatalysis and supercritical water oxidation. In the former method, a photocatalyst is in contact with wastewater, a suitable light activates the catalyst, and generated reactive oxygen species that react with pollutants through oxidative reactions to their complete mineralization; the latter involves pressurizing and heating wastewater to supercritical conditions in a reactor vessel, adding an oxidizing agent to the supercritical water, and allowing the mixture to react. In this review paper, works in the literature that deal with processing wastewater containing azo dyes through photocatalysts immobilized on macroscopic supports (structured photocatalysts) and the supercritical water oxidation technique have been critically analyzed. In particular, advancement in the formulation of structured photocatalysts for the degradation of azo dyes has been shown, underlying different important features, such as the type of support for the photoactive phase, reactor configuration, and photocatalytic efficiency in terms of dye degradation and photocatalyst stability. In the case of supercritical water oxidation, the main results regarding COD and TOC removal from wastewater containing azo dyes have been reported, taking into account the reactor type, operating pressure, and temperature, as well as the reaction time.
Disinformation and Verification in a Digital Society: An Analysis of Strategies and Policies Applied in the European Regional TV Broadcasters of the CIRCOM Network
Authors: Rúas-Araújo Jose , Rodríguez-Martelo Talia , Fontenla-Pedreira Julia
Published: March 2023

The recent COVID-19 health crisis has shone a spotlight on disinformation as the circulation of false information became more and more prominent. What the World Health Organization (WHO) has defined as an ‘infodemic’ poses a great risk for democracies and for society in general. In this context, public television channels, with their regional scope, actively participate in the fight against misinformation. This research aims to identify and classify the different verification initiatives and technological tools, as well as the different strategies and codes used in fact-checking tasks by European broadcasters belonging to the CIRCOM network. The methodology undertakes an exploratory approach and employs a questionnaire that is applied to a sample of the members of the network. Managers and professionals with executive profiles were asked about the management, operation and strategies used in the verification process. In light of the results obtained, it can be concluded that the current verification processes are based on human efforts, rather than technological tools, amounting to a total dependence on content curation by the writing teams in the newsroom. Thus, it is evidenced that in most cases, there is neither a specific department for verification, nor sufficient resources, despite the fact that all those surveyed regard disinformation as a priority issue, a threat to democratic integrity and a responsibility of public service media.
The Acute and Chronic Effects of Resistance and Aerobic Exercise in Hemostatic Balance: A Brief Review
Authors: Skouras Apostolos Z. , Antonakis-Karamintzas Dimitrios , Tsantes Andreas G. , Triantafyllou Athanasios , Papagiannis Georgios , Tsolakis Charilaos , Koulouvaris Panagiotis
Published: March 2023

Hemostatic balance refers to the dynamic balance between blood clot formation (coagulation), blood clot dissolution (fibrinolysis), anticoagulation, and innate immunity. Although regular habitual exercise may lower the incidence of cardiovascular diseases (CVD) by improving an individual’s hemostatic profile at rest and during exertion, vigorous exercise may increase the risk of sudden cardiac death and venous thromboembolism (VTE). This literature review aims to investigate the hemostatic system’s acute and chronic adaptive responses to different types of exercise in healthy and patient populations. Compared to athletes, sedentary healthy individuals demonstrate similar post-exercise responses in platelet function and coagulatory and fibrinolytic potential. However, hemostatic adaptations of patients with chronic diseases in regular training is a promising field. Despite the increased risk of thrombotic events during an acute bout of vigorous exercise, regular exposure to high-intensity exercise might desensitize exercise-induced platelet aggregation, moderate coagulatory parameters, and up-regulate fibrinolytic potential via increasing tissue plasminogen activator (tPA) and decreasing plasminogen activator inhibitor (PAI-1) response. Future research might focus on combining different types of exercise, manipulating each training characteristic (frequency, intensity, time, and volume), or investigating the minimal exercise dosage required to maintain hemostatic balance, especially in patients with various health conditions.
Study on Carbon Emission Measurement in Building Materialization Stage
Authors: Cheng Shengdong , Zhou Xin , Zhou Huan
Published: March 2023

The construction industry plays a pivotal role in energy conservation and emission reduction. Therefore, it is of great significance to conduct research on quantifying carbon emissions in this industry to accelerate the establishment of a standardized carbon emission accounting system and achieve the goals of carbon peak and carbon neutrality. In this study, the focus is on the building materialization stage, and a carbon emission accounting system is established using the carbon emission factor method. This system is applied to calculate the carbon emissions of 15 residential buildings in Shaanxi Province. Results indicate that the carbon concentration during the materialization stage ranges from 372.43 kgCO2/m2 to 525.88 kgCO2/m2, and the building material production stage accounts for 94.27% of the total emissions. Additionally, by analyzing the carbon emission composition of the sample buildings, the primary factors that influence carbon emissions during the residential building materialization stage are identified, and corresponding carbon reduction strategies are proposed. The sensitivity coefficients of carbon reduction strategies vary significantly across different stages, with the building material production stage exhibiting the highest sensitivity coefficient. Thus, it is imperative to prioritize carbon reduction strategies that target the building material production stage.
Optimal Elasto-Plastic Analysis of Prestressed Concrete Beams by Applying Residual Plastic Deformation Limitations
Authors: Khaleel Ibrahim Sarah , Movahedi Rad Majid
Published: March 2023

This work introduces elasto-plastic analysis of prestressed reinforced concrete beams under different prestressing conditions by limiting the residual plastic behaviour inside the steel bars using complementary strain energy. A non-linear optimal method was used to limit residual plastic deformations in steel bars, including prestressed tendons, used to reinforce beams from two previous research investigations. This was considered by using an optimization approach with an objective function to find the maximum loading while applying constraints on the complementary strain energy of residual internal forces in steel elements to control residual plastic deformations. Thus, an elasto-plastic optimization programme was linked to models simulated by ABAQUS, as concrete was calibrated by the concrete damage plasticity (CDP) model. Some variables were considered regarding the force applied inside prestressed tendons and the number of tendons used inside the models. Thus, limits on complementary strain energy affected load values and model damage where an increase in the permissible strain energy value leads to an increase in the corresponding loading values produced; thus, this produces a higher stress intensity in steel and tension-damaged areas in concrete. Based on these data, many comparisons have been made to determine when beams behaved elastically and how they turned into plastic.
A Study on Local Scour of Large-Diameter Monopile under Combined Waves and Current
Authors: Liu Hanqiu , Tao Zijian , Zhang Rongsheng , Zhang Cailiang , Sun Xiang , Fan Shaotao , Zhu Ronghua
Published: March 2023

The complex-load environment of offshore monopile foundations makes the combination of waves and current, and the variation in water depth, important factors for local scour. A flume test model of flow–pile–soil coupling for large-diameter monopile foundations is established, which comprehensively considers the combined reciprocating action of wave/current and the influence of tide depth. The precision of the experiment is ensured by the extension method of series models. The results show that the local scour caused by tides and the wave–current combination is obviously different from the unidirectional wave–current combination. The equilibrium scour depth obtained by the test is found to have some deviation from the predicted scour-depth equation. The maximum scour depth decreased with the increase in water depth within the range of 3 to 6 times the pile diameter. The bed shear-stress equation was generally consistent with the scouring-depth rule measured by the model, which can be considered to evaluate the rationality of the test results.
What Happens to the Entrepreneurial Intentions of Gen Z in a Crony Capitalist Economy Amidst the COVID-19 Pandemic?
Authors: Pranić Ljudevit
Published: March 2023

Despite years of research, scholars still have a limited understanding of the factors that lead individuals to start their own businesses. Drawing upon the crisis decision theory (CDT), the theory of planned behavior (TPB), the entrepreneurial event model (EEM), and previous research on entrepreneurial intentions (EIs), this study investigates the impact of a set of predictors (i.e., perceived crisis severity, entrepreneurial disposition, support from family/friends, university affiliation, gender, year of study, work experience, presence of a role model, and completion of an entrepreneurship class) on the outcome variable (i.e., EIs) of Generation Z in a highly tourism-dependent transitional economy. Pearson’s correlation and multiple regression were employed to analyze the data collected in May/June 2020 via a self-administered questionnaire from 300 tourism and hospitality students enrolled at five public universities in Croatia. The results indicate that entrepreneurial disposition, work experience, and gender are directly related to EI. The perceived crisis severity does not affect EI. These findings contribute to filling gaps in the existing research on entrepreneurship during major crises, on EIs of Gen Z, on the role of perceived crisis severity in EIs, and on the state of EIs in mono-industrial (i.e., dominated by one industry) crony capitalist ex-communist economies.
The Formation and Transformation Mechanisms of Deep Consumer Engagement and Purchase Behavior in E-Commerce Live Streaming
Authors: Liu Chenglin , Sun Kai , Liu Luchuan
Published: March 2023

With the intensification of competition, it is of great significance for businesses and platforms to explore the formation and transformation mechanisms of deep consumer engagement and purchase behavior in an e-commerce live streaming environment. With the help of Hovland’s persuasion theory and the uses and gratifications theory, this study constructed a model of these formation and transformation mechanisms via the grounded theory coding of interview data from 42 consumers. Our study shows that demand drives consumers to use e-commerce live streaming, and attraction factors, such as the e-commerce anchor (source), the product message (message), and the live streaming medium (channel), can influence consumers’ attitudes, thus reinforcing deep engagement and purchase behavior. This behavior creates feedback to consumer demand generates new purchase motivation in the consumer, and eventually, forms new purchase behavior. Finally, the theoretical contribution of this study to understanding consumer behavior in e-commerce live streaming is discussed; it could be of practical use for merchants and platforms and also highlights directions for future research.
Transforming Challenges into Opportunities for Qatar’s Food Industry: Self-Sufficiency, Sustainability, and Global Food Trade Diversification
Authors: Al-Abdelmalek Noora , Kucukvar Murat , Onat Nuri C. , Fares Enas , Ayad Hiba , Bulak Muhammet Enis , Ekren Banu Y. , Kazancoglu Yiğit , Ertogral Kadir
Published: March 2023

Food trade restrictions pose a serious risk for countries that are heavily reliant on food imports, potentially leading to food crises, inequality, and geopolitical conflicts on a global scale. However, such restrictions may also have transformative effects in promoting food supply chain resilience, security, and self-sufficiency. In this study, a novel econometric analysis is presented, utilizing a data-driven analytical model to investigate the impact of a food embargo on the industry, using Qatar as a case study. A structured and automated food trade database is created using Microsoft Management Server Studio and data visualization software is integrated for automated data discovery. By using a global, trade-based sustainability assessment model, which combines the multi-region input-output (MRIO) analysis with transportation mode-based (sea, road, and air) emissions, the carbon footprint of the dairy food production sector could be estimated. The study shows that the trade embargo on Qatar’s food industry can lead to significant reductions in the annual import of food products, promoting self-sufficiency, and reducing the net carbon emissions of the dairy food sector by nearly 40%. This reduction is not only achieved through food supply chain changes, such as transportation modes, but also by restrictions pushing the country to increase domestic production. Overall, the study demonstrates that a trade embargo, with the support of a well-designed national food security strategy, trade/import diversification, and the use of different modes of transportation for food products, can improve the resilience of global supply chains, self-sufficiency, and environmental sustainability.
Hybrid K-Medoids with Energy-Efficient Sunflower Optimization Algorithm for Wireless Sensor Networks
Authors: Al-Otaibi Shaha , Cherappa Venkatesan , Thangarajan Thamaraimanalan , Shanmugam Ramalingam , Ananth Prithiviraj , Arulswamy Sivaramakrishnan
Published: March 2023

Wireless sensor network (WSN) sensor nodes should have adequate energy. Reduced energy usage is essential to maximize the endurance of WSNs. Combining WSN with a more significant energy source, a cluster head (CH), is another effective strategy for extending WSN durability. A CH is dependent on the communication inside and between clusters. A CH’s energy level extends the cluster’s life for the complete WSN. Determining the energy required in WSNs while developing clustering algorithms is challenging. For maintaining energy efficiency in WSNs, this research offers K-medoids with sunflower-based clustering and a cross-layer-based optimal routing approach. An efficient fitness function generated from diverse objectives is used to choose the CH. After CH selection, sunflower optimization (SFO) indicates the best data transmission line to the sink node. The proposed protocol, SFO-CORP, increased the network lifetime by 19.6%, 13.63%, 11.13%, and 4.163% compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, respectively. The experimental results showed that it performed better for packet delivery ratio, energy consumption, end-to-end delay, network lifetime, and computation efficiency.
Spatial and Temporal Heterogeneity of Rural Habitat Level Evolution and Its Influencing Factors—A Case Study of Rural Villages in Nature a Reserve of China
Authors: Wang Yaobin , Zhao Ruitao , Li Ying , Yao Rong , Wu Ruoxue , Li Wenlin
Published: March 2023

Taking China’s Qilian Mountains Nature Reserve as an example, entropy, hierarchical analysis and spatial autocorrelation analysis methods were combined with geographically and temporally weighted regression to construct an evaluation index system of a rural habitat environment. The spatiotemporal characteristics of the evolution of the rural habitat environment in the Qilian Mountains Nature Reserve from 2000 to 2020 were revealed, and the spatiotemporal heterogeneity of factors affecting the evolution of the rural habitat environment in the Qilian Mountains were analyzed. The results show that during the research period, the rural habitat environment of the Qilian Mountains Nature Reserve obviously improved. The advantaged areas, such as Liangzhou and Shandan, are located mainly on flat terrain, with perfect supporting resources and convenient transportation. The disadvantaged areas, such as Qilian and Menyuan, are concentrated in the mountainous areas with poor natural conditions and inconvenient transportation. The rural habitat in the Qilian Mountains Nature Reserve shows an obvious positive spatial correlation, and areas with similar habitat are adjacent to each other. From 2000 to 2015, the high–high agglomeration area was located in Liangzhou, and the low–low agglomeration area was located in Menyuan. The rural habitat environment in the Qilian Mountains Nature Reserve is influenced by the average temperature, investment in fixed assets, the proportion of secondary and tertiary industries, PM2.5 concentration and CO2 emissions. The influence of various factors on the rural habitat showed obvious spatial and temporal heterogeneity. In rural revitalization, it is necessary to allocate resources to local conditions to promote the continuous improvement of the rural habitat environment in nature reserves.
An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems
Authors: Zhang Meihang , Zhang Hua , Yan Wei , Jiang Zhigang , Zhu Shuo
Published: March 2023

Large and extensive manufacturing systems consume a large proportion of manufacturing energy. A key component of energy efficiency management is the accurate prediction of energy efficiency. However, the nonlinear and vibration characteristics of machining systems’ energy consumption (EC) pose a challenge to the accurate prediction of system EC. To address this challenge, an energy consumption prediction method for machining systems is presented, which is based on an improved particle swarm optimization (IPSO) algorithm to optimize long short-term memory (LSTM) neural networks. The proposed method optimizes the LSTM hyperparameters by improving the particle swarm algorithm with dynamic inertia weights (DIWPSO-LSTM), which enhances the prediction accuracy and efficiency of the model. In the experimental results, we compared several improved optimization algorithms, and the proposed method has a performance improvement of more than 30% in mean absolute error (MAE)and mean error(ME).
Realizing Sustainable Value from ERP Systems Implementation
Authors: Anaya Luay , Flak Leif , Abushakra Ahmad
Published: March 2023

This paper investigates enterprise resource planning (ERP) implementations to improve the ability to realize outstanding value from such systems. In particular, it aims to provide a deep understanding of realizing sustainable value from ERP systems and to examine the relevance of benefits management (BM) in this context. To do so, this research applied a qualitative case study approach to investigate the implementation of Tier-1 ERP systems in two firms. Key findings initially suggest five considerations to better understand the realization of benefits from ERP implementation. Consequently, this research outlines the key activities undertaken by the investigated organizations and aligns them with activities suggested by benefits management literature. In conclusion, this research conjectures that while benefits management is a good practice and a systematic approach to realizing benefits from information systems, it may be ineffective in addressing the benefits that emerge in practice, i.e., when integrating the ERP system with modern digital technologies. Therefore, this research advocates either revisiting the current BM techniques or improving the implementation of digital technologies, including ERP systems with BM concepts and principles by incorporating such BM concepts within the implementation process. This study responds to research calls for maximizing the returned value from the implemented ERP systems by providing insightful recommendations.
Consumer Participation in CSR: Spending Money versus Spending Time
Authors: Fang Yaping , Liu Feng , Kim Sunmin , Pyo Minchan
Published: March 2023

Consumer participation plays a more active role in corporate social responsibility (CSR) than ever before. However, a framework describing how participation approaches generate cognitive and behavior responses in consumers is still lacking. The purpose of this study is to investigate the different effects among consumers’ participation approaches (i.e., spending money versus spending time) on inspiring consumers to engage in CSR. Additionally, we explore consumers’ cognitive mechanisms by identifying the key mediating role of perceived value. A total of 429 participants were recruited using an inter-group between-subjects design, and hypotheses were tested by a structural equation model, including path analysis and bootstrapping procedure method. The results show that consumers tend to have a more positive perception of CSR and greater intention to participate when they spend time rather than money to engage in CSR activities. We also confirmed the importance of perceived value in CSR, as the link between consumer participation type and behavioral intention is fully mediated by perceived value. These findings shed a light on the importance of the participation approach in CSR, contributing to CSR and consumer participation research. Our study also provides meaningful implications for companies to encourage consumers to use their time to participate in CSR activities.
SMCEWS: Binary Robust Multicentre Features
Authors: Xu Ying , Chen Mingwei , Zhang Wenjie , He Li , Yang Rong , Wang Yun
Published: March 2023

The Oriented FAST and Rotated BRIEF (ORB) algorithms have been improved, and a new method for calculating descriptors based on symmetrical multicentric weighted binary encoding is proposed, which enhances the robustness of feature points. This method employs a string of binary descriptors to encode the features and uses the multiple descriptor centre strategy to sample descriptors at the feature point and on the symmetrical circumference around it. Furthermore, a weighted summation is introduced in the descriptor calculation process to address the noise in the image during the sampling process. Specifically, the pixel values around the sampled point and the sampled point itself are combined using a certain weight to produce the final pixel value of the sampled point. The reliability of the descriptor is enhanced by introducing the pixel information around the sample point while solving the noise problem. Our method makes full use of the pixel information in the various parts of the descriptor sampling region to improve the distinguishability of the descriptors. We compare it with the ORB algorithm and experimentally show that the feature extraction method achieves better matching results with almost constant computation time.
Research on the Game Relationship and Behavior Optimization between Banks and Customers in the Omni-Channel Environment
Authors: Xu Jianzhong , Cui Xiaolei
Published: March 2023

In an omni-channel environment of banks, the information transmission between banks and customers is the basis for decision-making on both sides. This dissertation analyzes the characteristics of signals sent between banks and customers at different stages and proposes a game model of bank–customer signals in an omni-channel environment. The model explores the types of banks and customers and the influence of six signals on both parties’ action decisions. Building on this model, a genetic algorithm of the signaling game between banks and customers is developed. This algorithm improves the adaptability of customers to the bank’s omni-channel environment through the “selection–crossover–mutation” process. The algorithm determines the signal that brings the greatest utility among multiple bank–customer combinations. This is carried out by calculating the choices made, resulting in the greatest total utility. Finally, a case study is carried out on the omni-channel transformation of Agricultural Bank of China, illustrating the validity of the research results of the game relationship and action optimization. Overall, this study provides a quantitative tool for the action decision-making of banks and customers and the optimization of the relationship between the two. It also provides a reference for how banks should manage customer relationships in an omni-channel environment.