bluuebunny/crossref_metadata_2025 huggingface dataset:
Balance Control of a Configurable Inverted Pendulum on an Omni-Directional Wheeled Mobile Robot
Authors: Kao Sho-Tsung , Ho Ming-Tzu
Published: October 2022

This paper considers the balance control problems of a configurable inverted pendulum with an omni-directional wheeled mobile robot. The system consists of two parts. One is an inverted pendulum, and another one is an omni-directional wheeled mobile robot. The system can be configured as a rotary inverted pendulum or a spherical inverted pendulum. The objective is to control the omni-directional wheeled mobile robot to provide translational force on the plane to balance the spherical inverted pendulum and to provide the moment to balance the rotary inverted pendulum. Detailed dynamic models of these two systems are derived for the control strategy design and simulation studies. Stabilizing controllers based on the second-order sliding mode control are designed for both systems. The closed-loop stability is proved based on the passivity properties. The proposed control schemes can guarantee semi-globally asymptotical stability over the upper-half plane. In addition, the conventional sliding mode controllers proposed in our previous work and Linear-Quadratic Regulator (LQR) controllers based on the linearized system models about its upright equilibrium point are also used for performance comparison. The effectiveness of the control strategies is investigated and verified using simulation and experimental studies. In the simulation studies, different sources of uncertainty and disturbance are investigated. It is shown that the second-order sliding mode control outperforms the conventional sliding mode control and LQR control without any uncertainty and disturbance. For robustness to the matched disturbance, the simulation results show that the second-order sliding mode controller has a less significant steady-state oscillation in the pendulum’s angular displacement than other controllers. The simulation results also show that only the second-order sliding mode controller can stabilize the system with a significant initial deviation from the pendulum’s upright position. Finally, the experimental results demonstrate that second-order sliding mode control outperforms conventional sliding mode control and LQR control.
Machine Learning Based Design of Railway Prestressed Concrete Sleepers
Authors: Kaewunruen Sakdirat , Sresakoolchai Jessada , Huang Junhui , Zhu Yingyu , Ngamkhanong Chayut , Remennikov Alex M.
Published: October 2022

The state-of-the-art design methods for railway prestressed concrete sleepers are currently based on the quasi-static stresses resulting from a simplification of dynamic wheel loads, and subsequently the quasi-static responses of concrete sleepers. This method has been widely used in practices to overcome the complexity of dynamic analysis and testing. A single load factor (or called dynamic impact factor) for a partial safety-factored design (or k factors for the test criteria) is commonly used to crudely account for dynamic train–track interactions over different levels of track irregularities. The dynamic impact factors for either design or testing are usually obtained from either (i) railway infrastructure managers (i.e., in EN 13230), or (ii) prescribed standardised factors (i.e., AS 1085.14, AREMA Chapter 30, JSA—JIS E 1201). The existing design concepts for prestressed concrete sleepers using either (i) an allowable stress design or (ii) the limit state design method require many iterations for calculations and optimisations. The design process to achieve optimal products suitable for track, operational, and environmental parameters is, thus, very time-consuming. On this ground, this study investigates the potential capability of machine learning (ML) to learn from large amounts of design data sets and then to facilitate the design and capacity prediction of railway prestressed concrete sleepers. Three ML algorithms are developed, namely deep learning, Bayesian Neural Network, and random forest. Through a combination of hand-calculated design data, industry design data, and experimental investigations in compliance with EN 13230, over 3000 sets of design data have been collected. These data sets are used to assimilate a comprehensive database for machine learning. Four indicators, namely mean squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and R2 are used to benchmark the accuracy and precision of machine learning models. Our results reveal that the random forest algorithm offers the best performance. The values of MSE, RMSE, MAE, and R2 are 0.54, 0.74, 0.25, and 0.99, respectively. Note that the Bayesian neural network also performs very well. In contrast, the deep learning algorithm performs worse than the others. The insight demonstrates machine learning’s capability to aid in the design of railway prestressed concrete sleepers, to satisfy both serviceability and ultimate limit states
The Effects of High-Performance Cloud System for Network Function Virtualization
Authors: Chung Wu-Chun , Wang Yun-He
Published: October 2022

Since ETSI introduced the architectural framework of network function virtualization (NFV), telecom operators have paid more attention to the synergy of NFV and cloud computing. With the integration of the NFV cloud platform, telecom operators decouple network functions from the dedicated hardware and run virtualized network functions (VNFs) on the cloud. However, virtualization degrades the performance of VNF, resulting in violating the performance requirements of the telecom industry. Most of the existing works were not conducted in a cloud computing environment, and fewer studies focused on the usage of enhanced platform awareness (EPA) features. Furthermore, few works analyze the performance of the service function chain on a practical cloud. This paper facilitates the OpenStack cloud with different EPA features to investigate the performance effects of VNFs on the cloud. A comprehensive test framework is proposed to evaluate the verification of functionality, performance, and application testing. Empirical results show that the cloud system under test fulfills the requirements of service level agreement in Rally Sanity testcases. The throughput of OVS-DPDK is up to 8.2 times as high as that of OVS in the performance test. Meanwhile, the hardware-assisted solution, SR-IOV, achieves the throughput at near the line rate in the end-to-end scenario. For the application test, the successful call rate for the vIMS service is improved by up to 14% while applying the EPA features on the cloud.
Multi-Objective Optimization Design of Vehicle Side Crashworthiness Based on Machine Learning Point-Adding Method
Authors: Gao Dawei , Yao Bufan , Chang Gaoshuang , Li Qiang
Published: October 2022

Multi-objective optimization problems are often accompanied by complex black-box functions which not only increases the difficulty of solving, but also increases the solving time. In order to reduce the computational cost of solving such multi-objective problems, this paper proposes an ARBF-MLPA (Adaptive Radial Basis Function neural network combined with Machine Learning Point Adding) method, which uses an ABRF (Adaptive Radial Basis Function) neural network and OLHS (Optimized Latin Hypercube Sampling) to establish the first generation metamodel and uses the NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm to obtain the optimal front edge of Pareto. The ARBF-MLPA method is continuously used to select and add points to update the meta-model, then dynamically improve the accuracy of the meta-model until the optimal front edge converges. Then the ARBF-MLPA method and RBF-UDPA (Radial Basis Function neural network combined with Uniform Point Adding) method are compared using the test functions of three different frontier features. The performance evaluation indexes of Inverted Generation Distance (IGD), Hypervolume (HV) and Spacing Metric are superior to RBF-UDPA. Finally, ARBF-MLPA method combined with the NSGA-II optimization algorithm is applied in the multi-objective optimization design of vehicle-side crashworthiness. The model converges after 6 iterations. Comparing the results obtained by the ARBF-MLPA method with the finite element simulation results, the error is within 5%, which meets the error requirements. The optimized model reduces chest intrusion by 4.32%, peak collision force by 2.11% and reduces mass by 14.05%.
Code Smell Detection Using Ensemble Machine Learning Algorithms
Authors: Dewangan Seema , Rao Rajwant Singh , Mishra Alok , Gupta Manjari
Published: October 2022

Code smells are the result of not following software engineering principles during software development, especially in the design and coding phase. It leads to low maintainability. To evaluate the quality of software and its maintainability, code smell detection can be helpful. Many machine learning algorithms are being used to detect code smells. In this study, we applied five ensemble machine learning and two deep learning algorithms to detect code smells. Four code smell datasets were analyzed: the Data class, the God class, the Feature-envy, and the Long-method datasets. In previous works, machine learning and stacking ensemble learning algorithms were applied to this dataset and the results found were acceptable, but there is scope of improvement. A class balancing technique (SMOTE) was applied to handle the class imbalance problem in the datasets. The Chi-square feature extraction technique was applied to select the more relevant features in each dataset. All five algorithms obtained the highest accuracy—100% for the Long-method dataset with the different selected sets of metrics, and the poorest accuracy, 91.45%, was achieved by the Max voting method for the Feature-envy dataset for the selected twelve sets of metrics.
The Role of XAI in Advice-Taking from a Clinical Decision Support System: A Comparative User Study of Feature Contribution-Based and Example-Based Explanations
Authors: Du Yuhan , Antoniadi Anna Markella , McNestry Catherine , McAuliffe Fionnuala M. , Mooney Catherine
Published: October 2022

Explainable artificial intelligence (XAI) has shown benefits in clinical decision support systems (CDSSs); however, it is still unclear to CDSS developers how to select an XAI method to optimize the advice-taking of healthcare practitioners. We performed a user study on healthcare practitioners based on a machine learning-based CDSS for the prediction of gestational diabetes mellitus to explore and compare two XAI methods: explanation by feature contribution and explanation by example. Participants were asked to make estimates for both correctly and incorrectly predicted cases to determine if there were any over-reliance or self-reliance issues. We examined the weight of advice and healthcare practitioners’ preferences. Our results based on statistical tests showed no significant difference between the two XAI methods regarding the advice-taking. The CDSS explained by either method had a substantial impact on the decision-making of healthcare practitioners; however, both methods may lead to over-reliance issues. We identified the inclination towards CDSS use as a key factor in the advice-taking from an explainable CDSS among obstetricians. Additionally, we found that different types of healthcare practitioners had differing preferences for explanations; therefore, we suggest that CDSS developers should select XAI methods according to their target users.
Numerical Analysis of Fluid Stream Division to Supply Multiple Hydraulic Receivers
Authors: Filo Grzegorz , Lisowski Edward
Published: October 2022

This article presents a proposal and a numerical analysis of a hydraulic system consisting of one constant capacity pump supplying multiple receivers in the form of hydraulic motors, based on the example of a rail grinder. The proposed approach requires splitting the stream of the working fluid, which was achieved using 2-way flow control valves. As part of the preliminary CFD studies, the pressure and velocity distributions of the flow control valve were obtained, and the discharge coefficient of the valve nozzles was determined as a function of the valve spool position. Then, a mathematical model of the system was formulated, which was used to build a simulation model in the Matlab/Simulink environment. Next, the ability of the system to achieve the assumed operating parameters and its energy efficiency in a given load range were tested. The results indicate that the system can effectively perform the required work cycles with sufficient accuracy.
Research on Sound Imagery of Electric Shavers Based on Kansei Engineering and Multiple Artificial Neural Networks
Authors: Lin Zhe-Hui , Woo Jeng-Chung , Luo Feng , Chen Yu-Tong
Published: October 2022

The electric shaver market in China reach 26.3 billion RMB by 2021. Nowadays, in addition to functional satisfaction, consumers are increasingly focused on the emotional imagery conveyed by products with multiple-senses, and electric shavers are not only shaped to attract consumers, but their product sound also conveys a unique emotional imagery. Based on Kansei engineering and artificial neural networks, this research explored the emotional imagery conveyed by the sound of electric shavers. First, we collected a wide sample of electric shavers in the market (230 types) and obtained the consumers’ perceptual vocabulary (85,710 items) through a web crawler. The multidimensional scaling method and cluster analysis were used to condense the sample into 34 representative samples and 3 groups of representative Kansei words; then, the semantic differential method was used to assess the users’ emotional evaluation values. The sound design elements (including item and category) of the samples were collected and classified using Heardrec Devices and ArtemiS 13.6 software, and, finally, multiple linear and non-linear correlation prediction models (four types) between the sound design elements of the electric shaver and the users’ emotional evaluation values were established by the quantification theory type I, general regression neural network, back propagation neural network, and genetic algorithm-based BPNN. The models were validated by paired-sample t-test, and all of them had good reliability, among which the genetic algorithm-based BPNN had the best accuracy. In this research, four linear and non-linear Kansei prediction models were constructed. The aim was to apply higher accuracy prediction models to the prediction of electric shaver sound imagery, while giving specific and accurate sound design metrics and references.
Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
Authors: Zhao Junhong , Li Bin , Wei Xinyu , Lu Huazhong , Lü Enli , Zhou Xingxing
Published: October 2022

(1) Background: Forklifts are used widely in factories, but it shows the problem of large uncertainties when using an RGB-D sensor to recognize and locate pallets in warehouse environments. To enhance the flexibility of current autonomous forklifts in unstructured environments, the improved labeled template matching algorithm was proposed to recognize pallets. (2) Methods: The algorithm comprises four steps: (i) classifying each pixel of a color image with the color feature and obtaining the category matrix; (ii) building a labeled template containing the goods, pallet, and ground category information; (iii) compressing and matching the category matrix and template to determine the region of the pallet; and (iv) extracting the pallet pose from information in respect of the pallet feet. (3) Results: The results show that the proposed algorithm is robust against environmental influences and obstacles and that it can precisely recognize and segment multiple pallets in a warehouse with a 92.6% detection rate. The time consumptions were 72.44, 85.45, 117.63, and 182.84 ms for detection distances of 1000, 2000, 3000, and 4000 mm, respectively. (4) Conclusions: Both static and dynamic experiments were conducted, and the results demonstrate that the detection accuracy is directly related to the detection angle and distance.
Low and Moderate Doses of Caffeinated Coffee Improve Repeated Sprint Performance in Female Team Sport Athletes
Authors: Karayigit Raci , Forbes Scott C. , Osmanov Zakir , Yilmaz Canan , Yasli Burak Caglar , Naderi Alireza , Buyukcelebi Hakan , Benesova Daniela , Gabrys Tomasz , Esen Ozcan
Published: October 2022

The aim of this study was to determine the effect of low and moderate doses of caffeine ingestion via caffeinated coffee on repeated sprint test (RST) and plasma catecholamine concentration in trained female team-sport athletes. In a randomized, double-blind, crossover design, 13 female team-sport athletes (VO2max: 48.7 ± 4 mL·kg·min−1) completed three RST trials, separated by 4-day, 60 min post-ingestion of either 3 mg·kg−1 (LCOF) or 6 mg·kg−1 (MCOF) or placebo (PLA). The RST consisted of 12 × 4 s sprints on a cycle ergometer interspersed with 20 s of active recovery. Blood lactate (BLa) and glucose (GLU) and epinephrine and norepinephrine concentrations were collected before and 60 min after coffee ingestion, and after RST. Heart rate (HR) and ratings of perceived exertion (RPE) were measured at the beginning of RST, and after the 6th and 12th sprints. Average peak power score during RST was significantly improved after LCOF (p = 0.016) and MCOF (p = 0.041) compared to PLA, but peak and mean power output of the individual sprints, and fatigue index were not different between trials (all p > 0.05). Epinephrine and norepinephrine concentrations were significantly higher before and after RST in LCOF and MCOF compared to PLA (all p < 0.05). BLa was also higher after RST in both LCOF and MCOF compared to PLA (p = 0.005). HR, RPE, and GLU were not different between conditions (p > 0.05). In conclusion, low and moderate dose of caffeine ingestion can enhance the average peak power score during repeated sprints. These findings partly support low and moderate doses of caffeine supplementation via coffee as a nutritional ergogenic aid for trained female team-sport players during repeated sprint exercise.
Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches
Authors: Zheng Linfeng , Qin Xiangyang , Wang Jiao , Zhang Mengying , An Quanlin , Xu Jinzhi , Qu Xiaosheng , Cao Xin , Niu Bing
Published: October 2022

Alzheimer’s disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model of MAO-B inhibitors. The results showed that the identification model for MAO-B inhibitors with　K-nearest neighbor(KNN) algorithm had a prediction accuracy of 94.1% and 88.0% for the 10-fold cross-validation test and the independent test set, respectively. Secondly, a quantitative activity prediction model for MAO-B was investigated with the Topomer CoMFA model. Two separate cutting mode approaches were used to predict the activity of MAO-B inhibitors. The results showed that the cut model with q2 = 0.612 (cross-validated correlation coefficient) and r2 = 0.824 (non-cross-validated correlation coefficient) were determined for the training and test sets, respectively. In addition, molecular docking was employed to analyze the interaction between MAO-B and inhibitors. Finally, based on our proposed prediction model, 1-(4-hydroxyphenyl)-3-(2,4,6-trimethoxyphenyl)propan-1-one (LB) was predicted as a potential MAO-B inhibitor and was validated by a multi-spectroscopic approach including fluorescence spectra and ultraviolet spectrophotometry.
Comparative Genomic Analysis Uncovers the Chloroplast Genome Variation and Phylogenetic Relationships of Camellia Species
Authors: Lin Ping , Yin Hengfu , Wang Kailiang , Gao Haidong , Liu Lei , Yao Xiaohua
Published: October 2022

Camellia is the largest genus in the family Theaceae. Due to phenotypic diversity, frequent hybridization, and polyploidization, an understanding of the phylogenetic relationships between Camellia species remains challenging. Comparative chloroplast (cp) genomics provides an informative resource for phylogenetic analyses of Camellia. In this study, 12 chloroplast genome sequences from nine Camellia species were determined using Illumina sequencing technology via de novo assembly. The cp genome sizes ranged from 156,545 to 157,021 bp and were organized into quadripartite regions with the typical angiosperm cp genomes. Each genome harbored 87 protein-coding, 37 transfer RNA, and 8 ribosomal RNA genes in the same order and orientation. Differences in long and short sequence repeats, SNPs, and InDels were detected across the 12 cp genomes. Combining with the complete cp sequences of seven other species in the genus Camellia, a total of nine intergenic sequence divergent hotspots and 14 protein-coding genes with high sequence polymorphism were identified. These hotspots, especially the InDel (~400 bp) located in atpH-atpI region, had sufficient potential to be used as barcode markers for further phylogenetic analysis and species identification. Principal component and phylogenetic analysis suggested that regional constraints, rather than functional constraints, strongly affected the sequence evolution of the cp genomes in this study. These cp genomes could facilitate the development of new molecular markers, accurate species identification, and investigations of the phylogenomic relationships of the genus Camellia.
Changing the Tendency to Integrate the Senses
Authors: Quintero Saul I. , Shams Ladan , Kamal Kimia
Published: October 2022

Integration of sensory signals that emanate from the same source, such as the visual of lip articulations and the sound of the voice of a speaking individual, can improve perception of the source signal (e.g., speech). Because momentary sensory inputs are typically corrupted with internal and external noise, there is almost always a discrepancy between the inputs, facing the perceptual system with the problem of determining whether the two signals were caused by the same source or different sources. Thus, whether or not multisensory stimuli are integrated and the degree to which they are bound is influenced by factors such as the prior expectation of a common source. We refer to this factor as the tendency to bind stimuli, or for short, binding tendency. In theory, the tendency to bind sensory stimuli can be learned by experience through the acquisition of the probabilities of the co-occurrence of the stimuli. It can also be influenced by cognitive knowledge of the environment. The binding tendency varies across individuals and can also vary within an individual over time. Here, we review the studies that have investigated the plasticity of binding tendency. We discuss the protocols that have been reported to produce changes in binding tendency, the candidate learning mechanisms involved in this process, the possible neural correlates of binding tendency, and outstanding questions pertaining to binding tendency and its plasticity. We conclude by proposing directions for future research and argue that understanding mechanisms and recipes for increasing binding tendency can have important clinical and translational applications for populations or individuals with a deficiency in multisensory integration.
A Comprehensive Overview on Corrosion in RCC and Its Prevention Using Various Green Corrosion Inhibitors
Authors: Ahmed E S Junaid , Ganesh G. Mohan
Published: October 2022

Reinforced cement concrete (RCC) is a versatile material that deteriorates over time due to corrosion when exposed to any surrounding aggressive environment. In order to avoid this ramification, many researchers have carried out different work to find the most feasible way to reduce corrosion. To tackle this at the initial stage itself, one such convenient method is through the application of inhibitors. These inhibitors have most applications in their use as an admixture. This review paper mainly focuses on the inhibitor and its types, the drawbacks of inorganic, commercial organic, and hybrid inhibitors, and application to RCC, and also provides detailed information on green corrosion inhibitors (GCI). In recent years, widespread attention has been paid towards the utilization of different natural plant components/parts in concrete structures. This review paper consolidates the work done by various researchers using different GCIs with the main focus on plant extracts as an additive to concrete or RCC specimens which will be significant to sustainable progress in the field of corrosion.
Influence of Synthesis Conditions on Physicochemical and Photocatalytic Properties of Ag Containing Nanomaterials
Authors: Al-Malwi Salwa D. , Al-Ammari Rahmah H. , Alshehri Abdulmohsen , Narasimharao Katabathini
Published: October 2022

Silver (Ag) containing nanomaterials were successfully prepared by varying synthesis conditions to understand the influence of preparation conditions on the physicochemical and photocatalytic properties of these materials. Different analytical techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Scanning electron microscopy (SEM), Diffuse reflectance UV-vis spectra (DR UV-vis), X-ray photoelectron spectroscopy (XPS) measurements, and N2-physisorption were used to investigate the physicochemical properties of synthesized Ag containing nanomaterials. The samples (Ag-1 and Ag-2) prepared using AgNO3, NaHCO3, and polyvinylpyrrolidone (PVP) template exhibited pure Ag metal nanorods and nanoparticles; the morphology of Ag metal is influenced by the hydrothermal treatment. The Ag-3 sample prepared without PVP template and calcined at 250 °C showed the presence of a pure Ag2O phase. However, the same sample dried at 50 °C (Ag-4) showed the presence of a pure Ag2CO3 phase. Interestingly, subjecting the sample to hydrothermal treatment (Ag-5) has not resulted in any change in crystal structure, but particle size was increased. All the synthesized Ag containing nanomaterials were used as photocatalysts for p-nitrophenol (p-NP) degradation under visible light irradiation. The Ag-4 sample (pure Ag2CO3 with small crystallite size) exhibited high photocatalytic activity (86% efficiency at pH 10, p-NP concentration of 16 mg L−1, 120 min and catalyst mass of 100 mg) compared to the other synthesized Ag containing nanomaterials. The high photocatalytic activity of the Ag-4 sample is possibly due to the presence of a pure Ag2CO3 crystal structure with nanorod morphology with a low band gap energy of 1.96 eV and relative high surface area.
PPAR-γ Partial Agonists in Disease-Fate Decision with Special Reference to Cancer
Authors: Ballav Sangeeta , Biswas Bini , Sahu Vishal Kumar , Ranjan Amit , Basu Soumya
Published: October 2022

Peroxisome proliferator-activated receptor-γ (PPAR-γ) has emerged as one of the most extensively studied transcription factors since its discovery in 1990, highlighting its importance in the etiology and treatment of numerous diseases involving various types of cancer, type 2 diabetes mellitus, autoimmune, dermatological and cardiovascular disorders. Ligands are regarded as the key determinant for the tissue-specific activation of PPAR-γ. However, the mechanism governing this process is merely a contradictory debate which is yet to be systematically researched. Either these receptors get weakly activated by endogenous or natural ligands or leads to a direct over-activation process by synthetic ligands, serving as complete full agonists. Therefore, fine-tuning on the action of PPAR-γ and more subtle modulation can be a rewarding approach which might open new avenues for the treatment of several diseases. In the recent era, researchers have sought to develop safer partial PPAR-γ agonists in order to dodge the toxicity induced by full agonists, akin to a balanced activation. With a particular reference to cancer, this review concentrates on the therapeutic role of partial agonists, especially in cancer treatment. Additionally, a timely examination of their efficacy on various other disease-fate decisions has been also discussed.
Mechanical Properties Test and Enhancement Mechanism of Lime Soil Modified by High Content Soda Residue for Road Use
Authors: Zhu Shengxue , Tang Yiting , Xu Quan , Zhang Kun , Li Hui , Zhu Zhiling , Yin Wei
Published: October 2022

In order to solve the problem of solid waste soda residue (SR) environmental pollution and resourceful utilization, lime soil modified by high content soda residue (LSMHCSR) is prepared by solid waste SR, lime and soil. In this paper, the basic characteristics of SR and the mechanical properties of LSMHCSR were tested, and the enhancement mechanism of LSMHCSR was analyzed. The test results showed that: (1) SR is mainly composed of CaCO3, Ca(OH)2 and CaCl2, with high natural moisture content, which is not recommended as a separate engineering filler; (2) As the SR content gradually increased, the optimum moisture content of LSMHCSR gradually increased, with the maximum dry density first increasing and then decreasing; (3) With SR content in the range of 10% to 30%, both 7d and 28d UCS of LSMHCSR increased first and then decreased, reaching the maximum under 15% SR, with the increase as high as 36.9% and 37.2%, respectively. The optimal material mix ratio was SR:lime:soil = 15%:6%:85%; (4) An appropriate amount of SR could effectively promote the physical filling, cementation, crystallization and carbonization, and pozzolanic reactions of the material, and the resulting cementing substances, such as CaCO3 crystals, C-H-S and N-A-S-H, could improve the material strength. However, under excessive SR amounts, the excess SR did not react, with the material strength reduced. Based on the engineering benefit analysis of the test section, lime soil modified by high content SR can effectively alleviate the road material supply shortage, soil erosion and solid waste pollution problems, demonstrating remarkable technical, economic and social benefits and enjoying a good prospect of application and promotion.
Total and Differential Sputtering Yields Explored by SRIM Simulations
Authors: Mahne Nastja , Čekada Miha , Panjan Matjaž
Published: October 2022

Total sputtering yield and spatial distributions of sputtered atoms are important for numerous deposition techniques. We performed SRIM (Stopping and Range of Ions in Matter) simulations to analyze the total sputtering yield and angular distribution of sputtered atoms for a range of single-element target materials. The simulations were conducted for normal argon ion incidence in the 300–1200 eV range and at an oblique angle for selected ion energies. We examined the total and differential sputtering yields for the transition metals in the periods 4–6 and groups 4–6 (Ti, V, Cr; Zr, Nb, Mo; Hf, Ta, and W) and group 11 (Cu, Ag, and Au) of the periodic table, and other materials that are relevant to sputtering (B and C; Al and Si). For the transition metals, the total sputtering yield increases with the group of the periodic table. The elements in group 4 (i.e., Ti, Zr, and Hf) have the lowest sputtering yield, while the elements in group 11 (i.e., Cu, Ag, and Au) exhibit the highest sputtering yield. The angular distribution of the sputtered atoms shows a cosine distribution for the transition metal atoms. The angular distribution of the sputtered atoms for the oblique ion incidence is more asymmetric for the lower ion energies, while for the higher ion energies, the atoms are sputtered more symmetrically. The symmetry also depends on the group of the periodic table and the atomic mass of the target material. The elements in group 11 show the most symmetric distribution, while the elements in group 4 experience the most asymmetric distribution. Furthermore, in an individual group, the distribution becomes more symmetric with heavier target elements. We also examined in detail the influence of the surface binding energy, atomic mass, and ion energy on the total sputtering yield. These parameters were analyzed with regard to the simplified analytical formula for the total sputtering yield, which was derived by Sigmund. This formula was modified by introducing a power fitting parameter, which accounts for the non-linear sputtering yield dependence on the ion energy. The equation provided good estimates for the total sputtering yield of the transition metals that were sputtered by argon ions with energies up to 1200 eV.
Plasmacytic Pleural Effusion as a Major Presentation of Angioimmunoblastic T-Cell Lymphoma: A Case Report
Authors: Li Borui , Nong Lin , Zhang Jianhua , Wang Wensheng , Wang Qian , Zhang Yang , Ren Shaomin , Wang Mangju
Published: October 2022

Angioimmunoblastic T-cell lymphoma is one of the peripheral T-cell lymphomas. Reactive plasma cells can occasionally be observed in AITL patients’ peripheral blood and bone marrow. Plasmacytic pleural effusion as the presentation of AITL has not been reported before. The mechanisms of plasmacytic pleural effusion are not fully understood. Here we present an 82-year-old male with exuberant plasma cells in his pleural effusion in addition to his peripheral blood and bone marrow aspiration. By presenting this case, we would like to expand the spectrum of disease presentations in AITL and discuss the significance of flow cytometry in the differential diagnosis of pleural effusion. To our knowledge, this is the first case report in the literature, which will be crucial to assist the hematopathologist in accurate diagnosis and treatment.
Viscosity Plane-Wave UltraSound (Vi PLUS) in the Evaluation of Thyroid Gland in Healthy Volunteers—A Preliminary Study
Authors: Petea-Balea Diana-Raluca , Solomon Carolina , Muntean Delia , Dulgheriu Ioana-Teofana , Silaghi Cristina , Dudea Sorin
Published: October 2022

Viscosity and elasticity represent biomechanical properties of soft tissues that suffer changes during the pathophysiological alterations of the tissue in various conditions. This study aimed to determine average viscosity values for the thyroid gland and to evaluate the potential influences of age, gender and body mass index (BMI), using a recent technique Viscosity Plane-wave UltraSound (Vi PLUS). A total of 85 healthy Caucasian volunteers (56 women and 29 men, median age of 29 years, range 17–81 years) were included in this prospective monocentric study conducted between January 2022 and March 2022. Thyroid viscosity was measured using the SuperSonic MACH 30® Ultrasound system (Aixplorer, SuperSonic Imagine, Aix-en-Provence, France), equipped with a curvilinear C6-IX transducer that allows simultaneous quantification of the viscosity and stiffness. The mean thyroid viscosity measurement value was 2.63 ± 0.47 Pa.s. No statistically significant differences were detected between the left and the right lobes of the thyroid gland. A significant positive correlation was found between thyroid viscosity and elasticity (r = 0.685, p < 0.0001). There was no statistically significant correlation between body mass index (BMI) and thyroid gland viscosity and elasticity values (r = 0.215, p = 0.053; r = 0.106, p = 0.333). No correlation between viscosity and gender was established (p > 0.05). Vi PLUS represents a new and promising ultrasonographic technique that can provide helpful information for evaluating the thyroid parenchyma, similar to elastography. The effect of the potential confounding factors on thyroid viscosity was negligible, except for BMI.
Application of Deep Learning to IVC Filter Detection from CT Scans
Authors: Gomes Rahul , Kamrowski Connor , Mohan Pavithra Devy , Senor Cameron , Langlois Jordan , Wildenberg Joseph
Published: October 2022

IVC filters (IVCF) perform an important function in select patients that have venous blood clots. However, they are usually intended to be temporary, and significant delay in removal can have negative health consequences for the patient. Currently, all Interventional Radiology (IR) practices are tasked with tracking patients in whom IVCF are placed. Due to their small size and location deep within the abdomen it is common for patients to forget that they have an IVCF. Therefore, there is a significant delay for a new healthcare provider to become aware of the presence of a filter. Patients may have an abdominopelvic CT scan for many reasons and, fortunately, IVCF are clearly visible on these scans. In this research a deep learning model capable of segmenting IVCF from CT scan slices along the axial plane is developed. The model achieved a Dice score of 0.82 for training over 372 CT scan slices. The segmentation model is then integrated with a prediction algorithm capable of flagging an entire CT scan as having IVCF. The prediction algorithm utilizing the segmentation model achieved a 92.22% accuracy at detecting IVCF in the scans.
Stock Index Spot–Futures Arbitrage Prediction Using Machine Learning Models
Authors: Sheng Yankai , Ma Ding
Published: October 2022

With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot–futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly retrospective, rather than anticipatory of arbitrage opportunities. To close the gap, this study uses machine learning approaches based on historical high-frequency data to forecast spot–futures arbitrage opportunities for the China Security Index (CSI) 300. Firstly, the possibility of spot–futures arbitrage opportunities is identified through econometric models. Then, Exchange-Traded-Fund (ETF)-based portfolios are built to fit the movements of CSI 300 with the least tracking errors. A strategy consisting of non-arbitrage intervals and unwinding timing indicators is derived and proven profitable in a back-test. In forecasting, four machine learning methods are adopted to predict the indicator we acquired, namely Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). The performance of each algorithm is compared from two perspectives. One is an error perspective based on the Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and goodness of fit (R2). Another is a return perspective based on the trade yield and the number of arbitrage opportunities captured. Finally, a performance heterogeneity analysis is conducted based on the separation of bull and bear markets. The results show that LSTM outperforms all other algorithms over the entire time period, with an RMSE of 0.00813, MAPE of 0.70 percent, R2 of 92.09 percent, and an arbitrage return of 58.18 percent. Meanwhile, in certain market conditions, namely both the bull market and bear market separately with a shorter period, LASSO can outperform.
Systematic Literature Review of Innovative Schools: A Map and a Characterization from Which We Learn
Authors: Lomba Eduardo Amaral , Alves José Matias , Cabral Ilídia
Published: October 2022

The traditional school model is subject to growing social pressure. The collective awareness of the need to find new pedagogical and organizational approaches has fueled the dynamics of school innovation. Over the last two decades, several schools have emerged worldwide bearing innovative models. The present study pursued two objectives: first—map the innovative schools worldwide, with students between 10/11 and 17/18 years old, referenced by academic publications; second—identify the dimensions of school innovation that those same academic publications indicate in the referenced schools. A systematic literature review was carried out in English, Portuguese, and Spanish between 2000 and 2021 in the search engines SCOPUS, Web of Science (WoS), EBSCO, Google Scholar, and RCAAP (Open Access Scientific Repositories of Portugal). There may be an increasingly broad consensus on the need for a change in the current school model, but are those so-called innovative schools more effective in promoting learning? The results obtained may raise further research to answer this question.
No Budge for any Nudge: Information Provision and Higher Education Application Outcomes
Authors: Ilie Sonia , Maragkou Konstantina , Brown Ashton , Kozman Eliza
Published: October 2022

Despite increasing efforts to improve their access, students facing socio-economic disadvantages are still underrepresented in UK higher education. In this paper, we study whether behavioural nudging with information provision through text messages, embedded within a larger programme of widening participation activities, can be effective at increasing higher education application rates. We conducted two randomised control trials in which final year students in schools and further education colleges in areas with low higher education participation rates in the East of England region received a series of text messages that prompted thinking and/or action regarding the process of applying to higher education. We find null and statistically insignificant effects on application outcomes, suggesting that behavioural nudging in a setting where it is implemented as part of a more intensive widening participation programme is not effective at increasing higher education application rates. These results add to recent evidence regarding the potential impact of nudging in education by studying such interventions within a busy intervention space.
Modulation Recognition of Digital Signal Using Graph Feature and Improved K-Means
Authors: Li Guodong , Qin Xvan , Liu He , Jiang Kaiyuan , Wang Aili
Published: October 2022

Automatic modulation recognition (AMR) has been wildly used in both military and civilian fields. Since the recognition of digital signal at low signal-to-noise (SNR) ratio is difficult and complex, in this paper, a clustering analysis algorithm is proposed for its recognition. Firstly, the digital signal constellation is extracted from the received waveform (digital signal + noise) by using the orthogonal decomposition and then, it is denoised by using an algorithm referred to as auto density-based spatial clustering technique in noise (ADBSCAN). The combination of density peak clustering (DPC) algorithm and improved K-means clustering is used to extract the constellation’s graph features, the eigenvalues are input into cascade support vector machine (SVM) multi-classifiers, and the signal modulation mode is obtained. BPSK, QPSK, 8PSK, 16QAM and 32QAM five kinds of digital signals are trained and classified by our proposed method. Compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR (less than 4dB), which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.
Virtual Hairstyle Service Using GANs & Segmentation Mask (Hairstyle Transfer System)
Authors: Abdallah Mohamed S. , Cho Young-Im
Published: October 2022

The virtual hair styling service, which now is necessary for cosmetics companies and beauty centers, requires significant improvement efforts. In the existing technologies, the result is unnatural as the hairstyle image is serviced in the form of a ‘composite’ on the face image, image, extracts and synthesizing simple hair images. Because of complicated interactions in illumination, geometrical, and occlusions, that generate pairing among distinct areas of an image, blending features from numerous photos is extremely difficult. To compensate for the shortcomings of the current state of the art, based on GAN-Style, we address and propose an approach to image blending, specifically for the issue of visual hairstyling to increase accuracy and reproducibility, increase user convenience, increase accessibility, and minimize unnaturalness. Based on the extracted real customer image, we provide a virtual hairstyling service (Live Try-On service) that presents a new approach for image blending with maintaining details and mixing spatial features, as well as a new embedding approach-based GAN that can gradually adjust images to fit a segmentation mask, thereby proposing optimal styling and differentiated beauty tech service to users. The visual features from many images, including precise details, can be extracted using our system representation, which also enables image blending and the creation of consistent images. The Flickr-Faces-HQ Dataset (FFHQ) and the CelebA-HQ datasets, which are highly diversified, high quality datasets of human faces images, are both used by our system. In terms of the image evaluation metrics FID, PSNR, and SSIM, our system significantly outperforms the existing state of the art.
A Comprehensive Study of Random Forest for Short-Term Load Forecasting
Authors: Dudek Grzegorz
Published: October 2022

Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we use RF for short-term load forecasting (STLF), focusing on data representation and training modes. We consider seven methods of defining input patterns and three training modes: local, global and extended global. We also investigate key RF hyperparameters to learn about their optimal settings. The experimental part of the work demonstrates on four STLF problems that our model, in its optimal variant, can outperform both statistical and ML models, providing the most accurate forecasts.
A Mixed Algorithm for Integrated Scheduling Optimization in AS/RS and Hybrid Flowshop
Authors: Lu Jiansha , Xu Lili , Jin Jinghao , Shao Yiping
Published: October 2022

The integrated scheduling problem in automated storage and retrieval systems (AS/RS) and the hybrid flowshop is critical for the realization of lean logistics and just-in-time distribution in manufacturing systems. The bi-objective model that minimizes the operation time in AS/RS and the makespan in the hybrid flowshop is established to optimize the problem. A mixed algorithm, named GA-MBO algorithm, is proposed to solve the model, which combines the advantages of the strong global optimization ability of genetic algorithm (GA) and the strong local search ability of migratory birds optimization (MBO). To avoid useless solutions, different cross operations of storage and retrieval tasks are designed. Compared with three algorithms, including improved genetic algorithm, improved particle swam optimization, and a hybrid algorithm of GA and particle swam optimization, the experimental results showed that the GA-MBO algorithm improves the operation efficiency by 9.48%, 19.54%, and 5.12% and the algorithm robustness by 35.16%, 54.42%, and 39.38%, respectively, which further verified the effectiveness of the proposed algorithm. The comparative analysis of the bi-objective experimental results fully reflects the superiority of integrated scheduling optimization.