bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
Authors: Jia Lin , Cai Jingsen , Wu Li , Qin Tiange , Song Kun
Published: December 2022

Analysis of the stability of slopes in fractured rock masses is not trivial and is fraught with uncertainty and risk. A DFN-DEC model (Discrete Fracture Network-Distinct Element Code) based on the MATLAB platform is developed to evaluate the stability of rock slopes with random fractures. Then, the influences of mean values of geometric characteristics (i.e., the trace length T, the dip D, and the spacing S) for both the horizontal (denoted with 1) and inclined (denoted with 2) fractures on the mean slope stability are investigated. The results indicate that the proposed DFN-DEC model based on the MATLAB platform is adequate, robust, and can generate more realistic fracture networks. By means of probabilistic analysis (i.e., Monte Carlo simulation), we can obtain a more accurate stability assessment result of fractured rock slopes. In addition, it is found that the μFs (the mean of Fs) of slopes decreases with the increase in μT1, μT2 (the mean trace length of horizontal and inclined fractures, respectively) and μD1 (the mean dip of horizontal fractures), and increases gradually with the increase in μD2 (the mean dip of inclined fractures), μS1 and μS2 (the mean spacing of horizontal and inclined fractures, respectively). Furthermore, the geometric characteristics related to inclined fractures have a much greater influence on μFS than that related to horizontal fractures. This study can be well applied in engineering practice, e.g., preliminary evaluation of the slope stability according to the statistics of fracture geometric characteristics.
Dynamics Analysis and Chaos Identification of Compound Pendulum Jaw Crusher with Joint Clearance
Authors: Wang Shenpeng , Cui Yan , Wang Chune
Published: December 2022

A dynamic model of the working mechanism of a compound pendulum jaw crusher with clearance is established by the Lagrange multiplier method (LMD) based on the L-N contact force model and modified Coulomb friction force model. The correctness of the dynamic model is verified by MATLAB and Adams comparison simulation, and the best selection range of driving speed and clearance size is determined according to the laminating crushing theory (LCT). The effects of driving speed and clearance size on the shock and chaos phenomenon in the clearance mechanism are analyzed, as well as the vibration force applied to the foundation. The results show that, under the premise of meeting the requirements of crusher production capacity, increasing the driving speed and reducing the clearance size can weaken the shock and chaos phenomenon during the operation of the mechanism, improve the dynamic performance and enhance the operation stability, and the service life of foundation is prolonged with the stability of vibration force. Furthermore, the optimal combination of driving speed and clearance size is determined, which provides a theoretical basis for the optimization of the vibration force of the jaw crusher.
CNN Based Image Classification of Malicious UAVs
Authors: Brown Jason , Gharineiat Zahra , Raj Nawin
Published: December 2022

Unmanned Aerial Vehicles (UAVs) or drones have found a wide range of useful applications in society over the past few years, but there has also been a growth in the use of UAVs for malicious purposes. One way to manage this issue is to allow reporting of malicious UAVs (e.g., through a smartphone application) with the report including a photo of the UAV. It would be useful to able to automatically identify the type of UAV within the image in terms of the manufacturer and specific product identification using a trained image classification model. In this paper, we discuss the collection of images for three popular UAVs at different elevations and different distances from the observer, and using different camera zoom levels. We then train 4 image classification models based upon Convolutional Neural Networks (CNNs) using this UAV image dataset and the concept of transfer learning from the well-known ImageNet database. The trained models can classify the type of UAV contained in unseen test images with up to approximately 81% accuracy (for the Resnet-18 model), even though 2 of the UAVs represented in the UAV image dataset are visually similar, and the fact that the UAV image dataset contains images of UAVs that are a significant distance from the observer. This provides a motivation to expand the study in the future to include more UAV types and other usage scenarios (e.g., UAVs carrying loads).
Foreword to the Special Issue on Sorption of Environmental Pollutants: Thermodynamic and Kinetic Aspects
Authors: Salvestrini Stefano
Published: December 2022

Although new technologies are continuously proposed for water pollutant removal, sorption is still a very effective process used today for this purpose, largely due to its relatively low cost, tunability, and ease of use [...]
Crowd Density Level Estimation and Anomaly Detection Using Multicolumn Multistage Bilinear Convolution Attention Network (MCMS-BCNN-Attention)
Authors: Ekanayake E. M. C. L. , Lei Yunqi , Li Cuihua
Published: December 2022

The detection of crowd density levels and anomalies is a hot topic in video surveillance. Especially in human-centric action and activity-based movements. In some respects, the density level variation is considered an anomaly in the event. Crowd behaviour identification relies on a computer-vision-based approach and basically deals with spatial information of foreground video information. In this work, we focused on a deep-learning-based attention-oriented classification system for identifying several basic movements in public places, especially, human flock movement, sudden motion changes and panic events in several indoor and outdoor places. The important spatial features were extracted from a bilinear CNN and a multicolumn multistage CNN with preprocessed morphological video frames from videos. Finally, the abnormal and crowd density estimation was distinguished by using an attention feature combined with a multilayer CNN feature by modifying the fully connected layer for several categories (binary and multiclass). We validate the proposed method on several video surveillance datasets including PETS2009, UMN and UCSD. The proposed method achieved an accuracy of 98.62, 98.95, 96.97, 99.10 and 98.38 on the UCSD Ped1, UCSD Ped2, PETS2009, UMN Plaza1 and UMN Plaza2 datasets, respectively, with the different pretrained models. We compared the performance between recent modern approaches and the proposed method (MCMS-BCNN-Attention) and achieved the highest accuracy. The anomaly detection performance on the UMN and PETS2009 datasets was compared with that of a state-of-the-art method and achieved the best AUC results as 0.9953 and 1.00 for both scenarios, respectively, with a binary classification.
Parameter Estimation of Different Photovoltaic Models Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm
Authors: Gupta Jyoti , Hussain Arif , Singla Manish Kumar , Nijhawan Parag , Haider Waseem , Kotb Hossam , AboRas Kareem M.
Published: December 2022

The performance of a typical solar energy-based system can be improved by accurately modeling the current versus voltage characteristics of the involved solar cells. However, estimating the exact value of parameters related to solar cells is quite challenging. The optimization function, considering the current–voltage characteristics of solar cells, requires the solution of sophisticated non-linear and multi-modal optimization methods. So far, various optimization approaches have been reported. This paper proposes the application of a new hybrid algorithm, i.e., Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), which is a combination of two algorithms, i.e., Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) method. The hybrid PSOGSA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. Moreover, the developed hybrid algorithm is benchmarked using ten standard test functions to verify its efficiency. In this manuscript, monocrystalline and polycrystalline solar cells are considered. The parameter optimization results are obtained using PSOGSA and further compared with those obtained using other algorithms presented in the literature, such as PSO, GSA, MVO, HBO, PO and SCA. The complete error analysis is carried out for the modified single-diode model (MSDM), the modified double-diode model (MDDM), and the modified three-diode model (MTDM) of photovoltaic (PV) cells to prove the superiority of the PSOGSA. Moreover, statistical results are carried out based on Friedman’s ranking and Wilcoxon’s rank sum test. The comparison results show that the proposed PSOGSA is better than other algorithms in estimating the unknown PV model parameters.
Terahertz Broadband Adjustable Absorber Based on VO2 Multiple Ring Structure
Authors: Wang Xiaoxin , Wu Guozhang , Wang Yuandong , Liu Jianguo
Published: December 2022

A broadband adjustable absorber operating in the terahertz (THz) range is presented based on a vanadium dioxide (VO2) multiple ring structure with a certain gap design. The dynamic absorption regulation of the absorber is realized by utilizing the phase-change characteristics of VO2, which is easily affected by external temperature. The simulation results show that when the external temperature reaches 350 K, the conductivity of VO2 can reach 2 × 105 S/m, and the absorber can obtain an absorption efficiency of over 90% from 3.01 THz to 7.27 THz. At this time, the absorption bandwidth reaches 4.26 THz with 82.9% of the relative bandwidth. When the external temperature reaches 300 K, the conductivity changes to 200 S/m, and the absorption efficiency is less than 4%, indicating the strong THz absorption dynamic adjustable ability. Further, through analyzing the optimal impedance matching and the electric field distribution under different conductivities, the broadband absorption mechanism of the absorber can be obtained. Finally, this paper shows that the absorption spectrum cannot be influenced by small angle incidences in both polarization modes. Therefore, the ultra-wideband adjustable absorber is expected to have applications in the terahertz fields of detecting, modulating, and switching.
A Nighttime and Daytime Single-Image Dehazing Method
Authors: Tang Yunqing , Xiang Yin , Chen Guangfeng
Published: December 2022

In this study, the requirements for image dehazing methods have been put forward, such as a wider range of scenarios in which the methods can be used, faster processing speeds and higher image quality. Recent dehazing methods can only unilaterally process daytime or nighttime hazy images. However, we propose an effective single-image technique, dubbed MF Dehazer, in order to solve the problems associated with nighttime and daytime dehazing. This technique was developed following an in-depth analysis of the properties of nighttime hazy images. We also propose a mixed-filter method in order to estimate ambient illumination. It is possible to obtain the color and light direction when estimating ambient illumination. Usually, after dehazing, nighttime images will cause light source diffusion problems. Thus, we propose a method to compensate for the high-light area transmission in order to improve the transmission of the light source areas. Then, through regularization, the images obtain better contrast. The experimental results show that MF Dehazer outperforms the recent dehazing methods. Additionally, it can obtain images with higher contrast and clarity while retaining the original color of the image.
Modeling Liquid Thermal Conductivity of Low-GWP Refrigerants Using Neural Networks
Authors: Pierantozzi Mariano , Tomassetti Sebastiano , Di Nicola Giovanni
Published: December 2022

The thermal conductivity of refrigerants is needed to optimize and design the main components of HVAC&R systems. Consequently, it is crucial to have reliable models that are able to accurately calculate the temperature and pressure dependence of the thermal conductivity of refrigerants. For the first time, this study presents a neural network specifically developed to calculate the liquid thermal conductivity of various low-GWP-based refrigerants. In detail, a feed-forward network algorithm with 5 input parameters (i.e., the reduced temperature, the critical pressure, the acentric factor, the molecular weight, and the reduced pressure) and 1 hidden layer was applied to a large dataset of 3404 experimental points for 7 halogenated alkene refrigerants. The results provided by the neural network algorithm were very satisfactory, achieving an absolute average relative deviation of 0.389% with a maximum absolute relative deviation of 6.074% over the entire dataset. In addition, the neural network ensured lower deviations between the experimental and calculated data than that produced using different literature models, proving its accuracy for the liquid thermal conductivity of the studied refrigerants.
Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
Authors: Zhu Linan , Xu Yifei , Zhu Zhechao , Bao Yinwei , Kong Xiangjie
Published: December 2022

Sentiment-controlled text generation aims to generate texts according to the given sentiment. However, most of the existing studies focus only on the document- or sentence-level sentiment control, leaving a gap for finer-grained control over the content of generated results. Fine-grained control allows a generated review to express different opinions toward multiple aspects. Some previous works attempted to generate reviews conditioned on aspect-level sentiments, but they usually suffer from low adaptability and the lack of an annotated dataset. To alleviate these problems, we propose a novel pre-trained extended generative model that can dynamically refer to the prompt sentiment, together with an auxiliary classifier that extracts the fine-grained sentiments from the unannotated sentences, thus we conducted training on both annotated and unannotated datasets. We also propose a query-hint mechanism to further guide the generation process toward the aspect-level sentiments at every time step. Experimental results from real-world datasets demonstrated that our model has excellent adaptability in generating aspect-level sentiment-controllable review texts with high sentiment coverage and stable quality since, on both datasets, our model steadily outperforms other baseline models in the metrics of BLEU-4, METETOR, and ROUGE-L etc. The limitation of this work is that we only focus on fine-grained sentiments that are explicitly expressed. Moreover, the implicitly expressed fine-grained sentiment-controllable text generation will be an important puzzle for future work.
A Comparison of Machine Learning Approaches for Predicting Employee Attrition
Authors: Guerranti Filippo , Dimitri Giovanna Maria
Published: December 2022

Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, can provide HR departments with a valuable decision support system and, as a result, prevent a large waste of time and resources. In this paper, we propose a preliminary exploratory analysis of the application of machine learning methodologies for employee attrition prediction. We compared several classification models with the goal of finding the one that not only performs best, but is also well interpretable, in order to provide companies with the possibility of improving those aspects that have been shown to produce the quitting of their employees. Among the proposed methods, Logistic Regression performs the best, with an accuracy of 88% and an AUC-ROC of 85%.
Mobile Robot Tracking with Deep Learning Models under the Specific Environments
Authors: Zhang Tongpo , Song Yunze , Kong Zejian , Guo Tiantian , Lopez-Benitez Miguel , Lim Enggee , Ma Fei , Yu Limin
Published: December 2022

Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design.
A Survey on 6G Enabled Light Weight Authentication Protocol for UAVs, Security, Open Research Issues and Future Directions
Authors: Khan Adnan Shahid , Sattar Muhammad Ali , Nisar Kashif , Ibrahim Ag Asri Ag , Annuar Noralifah Binti , Abdullah Johari bin , Karim Memon Shuaib
Published: December 2022

This paper demonstrates a broad exploration of existing authentication and secure communication of unmanned aerial vehicles (UAVs) in a ‘6G network’. We begin with an overview of existing surveys that deal with UAV authentication in 6G and beyond communications, standardization, applications and security. In order to highlight the impact of blockchain and UAV authentication in ‘UAV networks’ in future communication systems, we categorize the groups in this review into two comprehensive groups. The first group, named the Performance Group (PG), comprises the performance-related needs on data rates, latency, reliability and massive connectivity. Meanwhile, the second group, named the Specifications Group (SG), is included in the authentication-related needs on non-reputability, data integrity and audit ability. In the 6G network, with blockchain and UAV authentication, the network decentralization and resource sharing would minimize resource under-utilization thereby facilitating PG targets. Furthermore, through an appropriate selection of blockchain type and consensus algorithms, the SG’s needs of UAV authentication in 6G network applications can also be readily addressed. In this study, the combination of blockchain and UAV authentication in 6G network emergence is reviewed as a detailed review for secure and universal future communication. Finally, we conclude on the critical identification of challenges and future research directions on the subject.
No Movie to Watch: A Design Strategy for Enhancing Content Diversity through Social Recommendation in the Subscription-Video-On-Demand Service
Authors: Kim Sangyeon , Huh Insil , Lee Sangwon
Published: December 2022

Increasing diversity is becoming crucial in recommender systems to address the “filter bubble” issue caused by accuracy-based algorithms. Diversity-oriented algorithms have been developed to solve this problem. However, this diversification has made it difficult for users to discover what they really want from the variety of information provided by the algorithm. Users spend their time wandering around the recommended content space but fail to find content they want to watch. Therefore, they rely on external services to gather information that does not appear on the recommended list. This could lead to a reduction in the services’ ability to compete with other subscription video on-demand (SVOD) services. To address this problem, this study proposes a human-centered approach to diversification through social recommendations. We conducted an experiment to understand how perceived diversity affects user perceptions and attitudes. Specifically, by incorporating social recommendations into the SVOD service, this experiment was changed to examine the following conditions: (1) influencers vs. online friends, and (2) human recommendation lists vs. algorithmic recommendation lists. The findings indicated that perceived diversity influences the manner in which the users perceive information quality and playfulness, both of which have a positive effect on their intention to use. Additionally, the participants’ perceptions of information quality were greater in the scenario with the human recommendation than in that with the algorithmic recommendation. This study contributes to the development of a theoretical framework based on perceived diversity through social recommendations and the design of an SVOD interface with social recommendations to provide better user experiences.
An Improved Particle Swarm Optimization Algorithm for Data Classification
Authors: Bangyal Waqas Haider , Nisar Kashif , Soomro Tariq Rahim , Ag Ibrahim Ag Asri , Mallah Ghulam Ali , Hassan Nafees Ul , Rehman Najeeb Ur
Published: December 2022

Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques.
A Computational Framework for 2D Crack Growth Based on the Adaptive Finite Element Method
Authors: Alshoaibi Abdulnaser M. , Fageehi Yahya Ali
Published: December 2022

As a part of a damage tolerance assessment, the goal of this research is to estimate the two-dimensional crack propagation trajectory and its accompanying stress intensity factors (SIFs) using the adaptive finite element method. The adaptive finite element code was developed using the Visual Fortran language. The advancing-front method is used to construct an adaptive mesh structure, whereas the singularity is represented through construction of quarter-point single elements around the crack tip. To generate an optimal mesh, an adaptive mesh refinement procedure based on the posteriori norm stress error estimator is used. The splitting node strategy is used to model the fracture, and the trajectory follows the successive linear extensions for every crack increment. The stress intensity factors (SIFs) for each crack extension increment are calculated using the displacement extrapolation technique. The direction of crack propagation is determined using the theory of maximum circumferential stress. The present study is carried out for two geometries, namely a rectangular structure with two holes and one central crack, and a cracked plate with four holes. The results demonstrate that, depending on the position of the hole, the crack propagates in the direction of the hole due to the unequal stresses at the crack tip, which are caused by the hole’s influence. The results are consistent with other numerical investigations for predicting crack propagation trajectories and SIFs.
Unmanned Aerial Vehicle Computation Task Scheduling Based on Parking Resources in Post-Disaster Rescue
Authors: Zhu Jinqi , Zhao Hui , Wei Yanmin , Ma Chunmei , Lv Qing
Published: December 2022

Natural disasters bring huge loss of life and property to human beings. Unmanned aerial vehicles (UAVs) own the advantages of high mobility, high flexibility, and rapid deployment, and are important equipment during post-disaster rescue. However, UAVs usually have restricted battery and computing power. They are not fit for performing compute-intensive tasks during rescue. Since there are widespread parking resources in a city, multiple parked vehicles working together to compute the applications from UAVs in a post-disaster rescue is investigated to ensure the quality of experience (QoE) of the UAVs. To execute uploaded task effectively, surviving parked vehicles within the monitoring range of an UAV are arranged into a cluster as much as possible. Then, the task execution cost is analyzed. Furthermore, a deep reinforcement learning (DRL)-based offloading policy is constructed, which interacts with the environment in an intelligent way to achieve optimization goals. The simulation experiments show that the proposed offloading scheme has a higher task completion rate and a lower task execution cost than other baselines schemes.
Preliminary Study on the Phenomenon and Mechanism of Granite Core Discing in Laboratory Drilling Test
Authors: Tang Hua , Liang Dongcai , Wu Zhenjun , Zhang Yonghui , Cheng Xu
Published: December 2022

It is generally believed that core discing is a rock failure phenomenon under high in-situ stress, and high stress is closely related to the characteristics of core discing. The core drilling tests of intact granite were conducted on the laboratory servo-controlled drilling test platform. It was observed that the granite core showed discing phenomenon at a high drilling rate (> 15 mm/min) under no confining pressure (0 MPA). The fluctuation of the drilling parameter was consistent with the core discing section along the core. The failure mode of the core discs was determined by morphology and surface fractal dimension characteristics. Numerical simulation models considering the interaction between the drill bit and the rock were established using ANSYS software. Based on core drilling tests, failure morphology analysis of the core discs, and numerical simulation, the mechanism of granite core discing under low confining pressure is as follows: Tensile failure is caused by the stress concentration of the bit at the core root. The drilling rate is the critical factor affecting core discing.
The Influence of Brushing Motion on the Cutting Efficiency of Two Heat-Treated Endodontic Files: An In-Vitro Micro Computed Tomography Study
Authors: Zebouni Cynthia , Nehme Walid , Hachem Claire El , La Rosa Giusy Rita Maria , Conte Gianluca , Kaloustian Marc Krikor , Neelakantan Prasanna , Pedullà Eugenio
Published: December 2022

Background: To assess the cutting efficiency of two heat-treated endodontic files with a brushing motion in oval-shaped canals. Methods: A total of 10 intact lower molars with a single oval distal canal were selected and randomly divided into two groups according to the system used for shaping: 2Shape and Hyflex CM. The instrumentation was performed in two parts: a first shaping according to the manufacturer’s instructions and final shaping using 5 brushing motions on the distal, lingual, and buccal walls, avoiding the danger zones. Micro computed tomography (micro-CT) scans before and after the brushing motion were superimposed at all three levels: coronal, middle, and apical. Canal changes in buccolingual (ΔBL) and mesiodistal (ΔMD) dimensions were measured. Data were statistically analyzed by repeated-measures analyses of variance and the student t-test (p < 0.05). Results: For ΔMD, no significant difference emerged between the 2Shape and Hyflex CM in the apical and coronal thirds. However, the cutting efficiency was significantly greater with the 2Shape in the middle third (p < 0.05). Regarding ΔBL, no statistically significant difference was detected between 2Shape and Hyflex CM comparing the anatomical thirds, while 2Shape had a significant higher cutting efficiency considering the total BL diameter (p < 0.05). Conclusions: Under the limits of a vitro study, the shaping procedure with 2Shape and brushing motion was more efficient than the Hyflex CM in the midroot levels in terms of ΔMD, and in total canal space for ΔBL. Both files ensured an effective mechanical preparation.
Research on Mechanical Characteristics of Slope Reinforcement by Spatial Arc Crown Beam Composite Supporting Structure
Authors: Deng Yousheng , Yao Zhigang , Peng Chengpu , Li Wenjie , Zhang Keqin
Published: December 2022

To effectively optimize the mechanical behavior of a traditional anti-slide pile and reduce environmental destruction, a new method for slope reinforcement by a spatial arc crown beam composite supporting structure was proposed. First, a numerical model was validated through lab-scale model test data obtained herein, and then a full-scale numerical model was created for an in-depth understanding of the distribution regularity of displacement along the pile, the soil pressure, the crown beam stiffness, and so on. The results demonstrated that: (1) The spatial arc crown beam is simplified to a two-hinged arch, and the maximum value of the bending moment in the arc crown beam is about one-third of the straight crown beam through theoretical calculation. (2) The spatial arc crown beam redistributes the load sharing among different piles, and the extreme bending moment of other piles varies within 10% along the downhill direction except for the piles at the slope foot. (3) Bending moments are close to zero at the pile end, and the anti-slide pile can be simplified as a vertical beam with one end fixed and the other end hinged. (4) The axial force in the spatial arc crown beam is always presented as pressure, so the crown beam can make full utilization of the compression resistance of concrete. (5) The distribution characteristic of soil pressure in front of the pile at the arch foot is different from that in other positions, and the stable soil at the slope foot provides greater soil resistance for anti-piles. (6) As the crown beam stiffness is above five times the reference value, the axial force of the crown beam tends to be stable, and as the crown beam stiffness increases continually, the maximum value of My is −1013.13 kN·m, and the constraining effect of the crown beam is gradually weakened.
Design and Implementation of Three-Phase Smart Inverter of the Photovoltaic Power Generation Systems
Authors: Huang Kuo-Hua , Chao Kuei-Hsiang , Sun Zhi-Yao , Ho Cheng-Yi
Published: December 2022

The main purpose of this paper is to conduct design and implementation on three-phase smart inverters of the grid-connected photovoltaic system, which contains maximum power point tracking (MPPT) and smart inverter with real power and reactive power regulation for the photovoltaic module arrays (PVMA). Firstly, the piecewise linear electrical circuit simulation (PLECS) power electronic real-time control system was applied to construct the simulation and actual test environment for the three-phase mains parallel photovoltaic system, where the KC200GT photovoltaic module was used to form a 1600 W system for conducting the simulation. For enabling the PVMA to output the maximum power in terms of both insolation and ambient temperature, where the perturbation and observation (P&O) method was used for MPPT. Then, the voltage-power control technology was added to the grid-connected photovoltaic inverter. When the grid voltage p.u. value is between 1.0 and 1.03, the smart inverter starts voltage-power regulation, reducing the real power output to 1440 W, and absorbing the system’s reactive power to 774 VAr. The power factor of the grid system end is controlled to 0.9 (lagging), and the grid voltage is reduced to norminal value 220 V. If the grid voltage p.u. value is between 0.97 and 1.0, the smart inverter starts voltage-power regulation, controlling the output real power to 1440 W and the reactive power to the system to 774 VAr, so that the power factor of the system end is controlled to 0.9 (leading), and the grid voltage is increased to norminal value 220 V. Finally, the results from the simulation and actual test were used to demostrate the effectiveness of the regulation performance of the smart inverter.
Improving Graph Neural Network Models in Link Prediction Task via A Policy-Based Training Method
Authors: Shang Yigeng , Hao Zhigang , Yao Chao , Li Guoliang
Published: December 2022

Graph neural network (GNN), as a widely used deep learning model in processing graph-structured data, has attracted numerous studies to apply it in the link prediction task. In these studies, observed edges in a network are utilized as positive samples, and unobserved edges are randomly sampled as negative ones. However, there are problems in randomly sampling unobserved edges as negative samples. First, some unobserved edges are missing edges that are existing edges in the network. Second, some unobserved edges can be easily distinguished from the observed edges, which cannot contribute sufficiently to the prediction task. Therefore, using the randomly sampled unobserved edges directly as negative samples is difficult to make GNN models achieve satisfactory prediction performance in the link prediction task. To address this issue, we propose a policy-based training method (PbTRM) to improve the quality of negative samples. In the proposed PbTRM, a negative sample selector generates the state vectors of the randomly sampled unobserved edges and determines whether to select them as negative samples. We perform experiments with three GNN models on two standard datasets. The results show that the proposed PbTRM can enhance the prediction performance of GNN models in the link prediction task.
Home Balconies during the COVID-19 Pandemic: Future Architect’s Preferences in Lisbon and Warsaw
Authors: Duarte Carlos C. , Cortiços Nuno D. , Stefańska Anna , Stefańska Aneta
Published: December 2022

The COVID-19 pandemic lockdowns altered people’s perception of the relationship between indoor and outdoor living spaces, pushing the home balcony concept into new levels of ambiguity. Particularly in European contexts, people’s perspectives on its existence, functions, features, and borders are now unclear territory. Therefore, our study intends to assess the balcony design features and activities during pandemic times through the lens of selected key informants’ preferences in Lisbon and Warsaw, resorting to a post-occupancy evaluation of an online survey and a subsequent statistical analysis using a Mixed Model Analysis of Variance. Differences in cultural/social background, climate, and geography appear to be irrelevant during the pandemic, as both locations’ respondents show similar outcomes concerning balcony preferences. Activities previously practised elsewhere are now preferential to occur on balconies, such as having meals, gathering people, or reading and writing. The study concludes that the importance of home balconies increased during the pandemic, demanding an update of its design features to accommodate the newly rediscovered activities with direct implications on its users’ health and life quality.
Exploring the Effect of Virtual Environments on Passive Haptic Perception
Authors: Kim Daehwan , Kim Yongwan , Jo Dongsik
Published: December 2022

Recent advances in virtual reality (VR) technologies such as immersive head-mounted display (HMD), sensing devices, and 3D printing-based props have become much more feasible for providing improved experiences for users in virtual environments. In particular, research on haptic feedback is being actively conducted to enhance the effect of controlling virtual objects. Studies have begun to use real objects that resemble virtual objects, i.e., passive haptic, instead of using haptic equipment with motor control, as an effective method that allows natural interaction. However, technical difficulties must be resolved to match transformations (e.g., position, orientation, and scale) between virtual and real objects to maximize the user’s immersion. In this paper, we compare and explore the effect of passive haptic parameters on the user’s perception by using different transformation conditions in immersive virtual environments. Our experimental study shows that the participants felt the same within a certain range, which seems to support the “minimum cue” theory in giving sufficient sensory stimulation. Thus, considering the benefits of the model using our approach, haptic interaction in VR content can be developed in a more economical way.
10th Anniversary of Applied Sciences: Invited Papers in Electrical, Electronics and Communications Engineering Section
Authors: Benbouzid Mohamed
Published: December 2022

Applied Sciences has reached a remarkable milestone by publishing its 10th volume in 2020 [...]
Development of a Quantitative Colour-Based Software Method to Evaluate the Effectiveness of Active Antioxidant Packaging on Fresh Sliced Mushrooms
Authors: Wrona Magdalena , Pezo Davinson , Salafranca Jesús , Nerín Cristina , Ihler Alexander
Published: December 2022

A software-based procedure consisting of colour measurement of mushrooms was developed for assessing the effectiveness of a new antioxidant active packaging. As active agents, sodium metabisulphite combined with citric acid, green tea extract combined with α-tocopherol and purple carrot extract were evaluated. After obtaining the image of mushrooms with a conventional flatbed scanner, a MATLAB code was specially designed to obtain and compare histograms. Then, data were processed using principal component analysis (66% of the accumulated variance explained) and a successful classification of samples according to their age and antioxidant system was obtained. The developed procedure greatly exceeded the discrimination capabilities of other commonly used methods such as CIE L*a*b* (which was unable to find any significant difference between blank and antioxidant-treated mushrooms; L*blank = 70 ± 3; L*metabisulphite = 75 ± 5; L*green tea = 75 ± 5; L*carrot = 69 ± 5) and visual panel (where only sodium metabisulphite sample was perceived to be significantly different from the rest of the samples, with 27 positive votes out of 50). The developed method classified correctly up to 90% of blank samples according to their age. Besides, 100% and 70% of the mushrooms treated with sodium metabisulphite and green tea, respectively, were found to be fresher than their corresponding blanks. Among samples with sodium metabisulphite, 33% behaved as one-day blank samples, and 67% acted as 4-day blank samples, while among samples with green tea, 10% were found in the 1-day group, and 60% were found in the 4-day group. In contrast, purple carrot proved to be inefficient as an antioxidant. Thus, the difference between blank mushroom samples and those treated with antioxidants was objectively measured.
Area-Selective Defect-Related Modulation of Optical and Electrical Properties of Monolayer Molybdenum Disulfide by Focused-Laser Irradiation
Authors: Ko Changhyun
Published: December 2022

Molybdenum disulfide (MoS2) has been actively explored as a direct bandgap semiconductor in the monolayer (ML) limit for various applications due to its prominent physical properties and stability. In order to broaden its application range further, diverse treatments have been developed to modulate the properties of ML-MoS2. The native point defects, such as S vacancies, are known to activate surface charge transfer doping in ML-MoS2. Unlike conventional semiconductors, ML-MoS2 shows distinct excitonic transitions that can be exploited for controlling its optical, optoelectronic, and electric characteristics via coupling with defect-driven doping. Here, the ambient photoluminescence (PL) of ML-MoS2 could be increased by ~1500% at the center of focused-laser irradiation (FLI). Expectedly, the PL intensity varied spatially along with exciton–trion transitions across the irradiation spot due to the Gaussian profile of laser intensity. Then, nano-Auger electron spectroscopy (n-AES) revealed that the spectral fraction of exciton PL increased by ~69.2% while that of trion PL decreased by ~49.9% with increasing S deficiency up to ~13.4 ± 3.5%. Cryogenic PL and field-effect transistor experiments were also performed to understand the defect-related phenomena comprehensively. This novel experimental combination of FLI with an n-AES probe provides a facile, effective, and cost-efficient approach for exploring defect effects in two-dimensional structures.
Effective Optical Scattering Range Determination Based on Ray Tracing
Authors: Leng Rongkuan , Chen Zhiwei , Wang Shang , Wang Zhi , Fang Chao
Published: December 2022

Surface imperfections or contamination on an optical smooth surface is usually inevitable and causes scattering. The directional information of the scattered ray can be related to the spatial frequency through the grating equation. On the other hand, the layout of the optical system determines whether a scattered ray will finally reach the detector according to the positions and directions of the scattered rays. Therefore, the scattering propagation in an optical system is usually band-limited and the effective optical range differs for different systems. In this paper, a method based on ray tracing is described that can statistically determine the optical scattering and the band of roughness. The results can be an essential reference for optical surface polishing and contamination control.
Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
Authors: Wu Xinye , Fu Shude , He Zujie
Published: December 2022

Traffic flow has the characteristics of randomness, complexity, and nonlinearity, which brings great difficulty to the prediction of short-term traffic flow. Based on considering the advantages and disadvantages of various prediction models, this paper proposes a short-term traffic flow prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and machine learning. Firstly, CEEMDAN is used to decompose the original traffic flow time series to obtain multiple component sequences with huge complexity differences. In order to measure the complexity of each component sequence, the permutation entropy of each component sequence is calculated. According to the permutation entropy, the component sequence is divided into three types: high-frequency components, intermediate-frequency components, and low-frequency components. Secondly, according to the different volatility of the three types of components, the high-frequency components, intermediate-frequency components, and low-frequency components are predicted by long short-term memory (LSTM), support vector machine (SVM), and k-nearest neighbor (KNN), respectively. Finally, the accurate traffic flow prediction value can be obtained by the linear superposition of the prediction results of the three component prediction models. Through a measured traffic flow data, the combined model proposed in this paper is compared to the binary gray wolf algorithm–long short-term memory (BGWO-LSTM) model, the improved gray wolf algorithm–support vector machine (IGWO-SVM) model, and the KNN model. The mean square error (MSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 41.26, 44.98, and 57.69, respectively. The mean absolute error (MAE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.33, 2.44, and 2.70, respectively. The root mean square error (RMSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.89, 3.11, and 3.80, respectively. The three error indexes of the combined model are far smaller than those of the other single models. At the same time, the decision coefficient (R2) of the combined model is also closer to 1 compared to the other models, indicating that the prediction result of the combined model is the closest to the actual traffic flow.
Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
Authors: Khwairakpam Omorjit Singh , Mariotti Emilio , Scarpa Daniele , Nicolosi Piergiorgio , Khanbekyan Alen , Ferracane Salvatore , Arzenton Alberto , Andrighetto Alberto
Published: December 2022

We report on a laser photo-ionization study of silver in relation to the Selective Production of Exotic Species (SPES) project at INFN-LNL in the off-line laser laboratory. In this study, two dye lasers and an ablation laser operating at 10 Hz are used alongside a time-of-flight mass spectrometer (TOF-MS). Isotopic separation of the natural, stable isotopes 107Ag and 109Ag was clearly observed in the TOF signal. Resonant photo-ionization of silver was achieved with the use of the scheme 4d105s 2S1/2→ 4d105p 2P3/2o→ 4d106d 2D3/2 with transition wavelengths of 328.163 nm and 421.402 nm, respectively. Doppler-suppressed spectroscopy of these transition lines was performed in an ablation plume. Doppler broadening with collinear injection of excitation lasers and the effect of the linewidths of the excitation lasers were investigated. The fine-structure splitting of the level 4d106d 2D (J = 5/2 and J = 3/2) was confirmed to be 186 ± 2 pm, corresponding to 314 ± 3 GHz.
Watch Out for the Skin Deep: Medieval Icelandic Transformations
Authors: Jakobsson Ármann
Published: December 2022

Icelandic prose narratives from the 13th and 14th centuries are replete with instances of metamorphosis, humans transforming into animals, or changing shape in an indeterminate way. Sources indicate that the transformation is not merely corporeal but is also spiritual. In this paper, the Old Norse lexicon of transformation, such as the words hamrammr, hamslauss, and hamstoli, will be closely examined.