Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinematic Control of the Hexapod Robot

To design a control system for a walking robot, it is necessary to have kinematic and dynamic models of this robot. The aim of the study is to develop a kinematic model for a six-legged walking robot, which allows you to get a visual representation of its movement in space when creating control algorithms. Results: a mathematical description of the kinematic model of a six-legged walking robot (hexapod) was found in the form of systems of equations for determining the position of the robot nodes at given angles of rotation of the limb links. Based on the developed model, various types of robot movements were visualized in the MatLab package. To assess the possibilities of moving the robot, the area of reachability of the foot was constructed and the analytical boundaries of this area were found. Practical significance: the developed model can be used to build a dynamic model of motion control algorithms for a walking robot, as well as to control a machine model in a servo drive mode.
Parameter identification of degrading and pinched hysteretic systems using a modified Bouc–Wen model

Abstract The Bouc–Wen (BW) model is a successful differential equations model used to describe a wide range of nonlinear hysteretic systems. However, it is unable to describe force degradation, stiffness degradation and pinching effects. Therefore, Baber and Noori proposed a generalisation, developing the Bouc–Wen–Baber–Noori (BWBN) model. Nevertheless, it is composed of many parameters and complex pinching and degrading functions. Thus, it is necessary to develop a simpler and reliable model to be used for practical applications. In this paper, a modified BW model is proposed. It involves a more direct physical meaning of each parameter and allows achieving a substantial reduction of computational effort and numerical deficiencies. This is obtained through simpler pinching and degrading functions that entail a decrease of the number of parameters. The result is a straightforward model, capable of predicting the behaviour of degrading and pinched hysteretic systems. An application of the proposed scheme to a real case is also presented, in which reinforced concrete bridge piers that were physically tested in the laboratory are considered. The force–displacement data are used to perform the identification process of the model parameters via a Genetic Algorithm. The numerical results are accurate since they coincide with the experimental ones.
Manually or Autonomously Operated Drones: Impact on Sensor Data towards Machine Learning

The growing need for autonomous systems in offshore industries has contributed to the increased use of machine learning methods. These systems promise to improve safety in operations. However, the methods as enablers of autonomy are susceptible to various failures while interpreting data and making decisions. Several studies have highlighted the lack of research on the reliability and resilience of autonomous systems powered by these standard methods. Recent research provides sets of data interpretation methods. Despite the popularity of machine learning, there is a significant drop in knowledge when these methods result in failures. These failures further support autonomous systems in making wrong decisions. For autonomous systems, resilience and safety management should be an integrated functionality for recovery from risky situations and reporting of incidents. This research proposes an overview of machine learning methods for interpreting sensor data captured by drones operated manually and autonomously. We apply Isolation Forest for anomaly detection analysis and evaluate the Decision tree, Random forest, kNN, Logistic Regression, SVM, and, Naive Bayes for classification analysis. The methods are chosen based on their adequacy and comparative research prevalence. Comparison between the two drone operation modes contributes to understanding the reliability level for autonomously collected data. This research’s results provide an evaluation of machine learning methods’ performance across sensor data.
Protective Effects of Circulating TIMP3 on Coronary Artery Disease and Myocardial Infarction: A Mendelian Randomization Study

Tissue inhibitor of metalloproteinase 3 (TIMP3) is a protease with high expression levels in the heart and plays an essential role in extracellular matrix turnover by maintaining equilibrium with matrix metalloproteinases. Considerable data in experimental models have demonstrated a protective role of TIMP3 in coronary artery disease (CAD) and myocardial infarction (MI). However, causality remains unexplored in population studies. Here, we sought to decipher the potential causality between TIMP3 and CAD/MI using the Mendelian randomization (MR) method. We extracted summary−level datasets for TIMP3 and CAD/MI from the genome−wide association studies performed in the KORA study and CARDIoGRAMplusC4D consortium, respectively. Seven independent SNPs were obtained as instrumental variables for TIMP3. The MR analyses were replicated using FinnGen datasets, and the main results were combined in meta−analyses. Elevated genetically predicted serum TIMP3 levels were causally associated with a lower risk of CAD [odds ratio (OR), 0.97; 95% confidence interval (CI), 0.95, 0.98; p = 5.29 × 10−5] and MI (OR, 0.96; 95% CI, 0.95, 0.98; p = 3.85 × 10−5). The association patterns persisted in the meta−analyses combining the different datasets (CAD: OR, 0.97; 95% CI, 0.96, 0.99; p = 4.37 × 10−5; MI: OR, 0.97; 95% CI, 0.96, 0.99; p = 9.96 × 10−5) and was broadly consistent across a set of complementary analyses. Evidence of heterogeneity and horizontal pleiotropy was limited for all associations considered. In conclusion, this MR study supports inverse causal associations between serum TIMP3 and the risk of CAD and MI. Strategies for raising TIMP3 levels may offer new avenues for the prevention strategies of atherosclerotic cardiovascular diseases.
A Robust Field-Weakening Approach for Direct Torque and Flux Controlled Reluctance Synchronous Motors With Extended Constant Power Speed Region

This paper proposes a new robust field-weakening approach for reluctance synchronous motors regulated by direct torque and flux control. Compared to the existing direct torque and flux control based field-weakening methods that cannot achieve maximized DC-link voltage utilization and are parameter dependent, the proposed approach contributes to improve the field-weakening performance of reluctance synchronous motor in two aspects. First, it extends the constant power speed range through autonomous stator flux reference adjustment, which maximizes the dc-link voltage utilization. Smooth transition between the maximum torque per ampere trajectory and field-weakening trajectory is also realized. Second, it enhances the parameter robustness of drives in very high-speed region by employing a torque reference adjustment scheme. This effectively avoids the instability of drives caused by machine parameter variations. The proposed approach is verified experimentally on a laboratory setup.
APPLICATION OF DEMONSTRATION STRATEGY AND COMPUTER AIDED INSTRUCTION ON STUDENTS’ ACQUISITION OF KEYBOARDING SKILLS: A REVIEW OF ITS EFFECTS

Purpose: The advent of information and communication technology has brought to the front burner the need for students and teachers alike to master the craft of keyboarding. This study investigates the effects of demonstration strategy and computer aided instruction on students’ acquisition of keyboarding skills in colleges of education in South-west, Nigeria. Methodology: Quasi experimental research was adopted for the study. Specifically, the pre-test, post-test non-equivalent control group design was used. The population consisted of 1,729 NCE one business education students in 11 public colleges of education in South-west, Nigeria. The sample size comprised 620 NCE one students out of which 582 (94%) participated fully in the study. Simple random sampling technique was adopted and intact classes were used as experimental and control groups. The instrument for data collection was Keyboarding Skill Acquisition Test (KSAT) developed by the researcher and validated by four experts. The reliability of the instrument was determined using Spearman Rank Order Correlation which yielded a coefficient of 0.80. The instrument was administered as pre-test to both experimental and control groups. The treatment was applied for six weeks after which the post-test was administered. Mean and standard deviation were used in answering the research questions while Analysis of Covariance (ANCOVA) was used to test the hypotheses at 0.05 level of significance. The results were presented in tables. Findings: Findings revealed that teaching with computer aided instruction (mean = 53.69, SD = 9.47) had a significant higher positive effect on students’ acquisition of keyboarding skills than demonstration strategy (mean = 47.87, SD = 11.04) and lecture strategy (mean = 40.78, SD = 12.56). Unique contribution to theory, practice and policy: Teaching with demonstration strategy and computer aided instruction have salutary effect on students’ acquisition of keyboarding skills but computer aided instruction is more effective in the teaching and learning of keyboarding and similar business education skills than other strategies. Therefore, business educators should always endeavour to teach keyboarding with computer aided instruction to improve students’ learning outcome in tertiary institutions.
Optical Properties and Fluence Distribution in Rabbit Head Tissues at Selected Laser Wavelengths

The accurate estimation of skin and skull optical properties over a wide wavelength range of laser radiation has great importance in optogenetics and other related applications. In the present work, using the Kubelka–Munk model, finite-element solution of the diffusion equation, inverse adding-doubling (IAD), and Monte-Carlo simulation, we estimated the refractive index, absorption and scattering coefficients, penetration depth, and the optical fluence distribution in rabbit head tissues ex vivo, after dividing the heads into three types of tissues with an average thickness of skin of 1.1 mm, skull of 1 mm, and brain of 3 mm. The total diffuse reflectance and transmittance were measured using a single integrating sphere optical setup for laser radiation of 532, 660, 785, and 980 nm. The calculated optical properties were then applied to the diffusion equation to compute the optical fluence rate distribution at the boundary of the samples using the finite element method. Monte-Carlo simulation was implemented for estimating the optical fluence distribution through a model containing the three tissue layers. The scattering coefficient decreased at longer wavelengths, leading to an increase in optical fluence inside the tissue samples, indicating a higher penetration depth, especially at 980 nm. In general, the obtained results show good agreement with relevant literature.
The stability of the radial solution to the ginzburg-landau equation

In this paper, we consider the the Ginzburg-Landau equation -{del}u = 1/2 u (1 - {vert_bar}u{vert_bar}{sup 2}) in B u = g on {partial_derivative}B where B is the unit ball in R{sup 2} with center at the origin, g(e{sup i0} = e{sup id)}, d = 1,2,3,. , u:B {yields} C is smooth, and {epsilon} > 0 is a small parameter.
Multimarket contacts with trade costs and distance cost

This paper presents a critical comparison of the four major papers on territorial allocation by Bernheim and Whinston, Lommerud and Sorgard, Schroder, and Bhattacharjea and Sinha. The paper adds a new dimension to the analysis as it also studies the impact of a change in distance on collusion and establishes the distance paradox which states that collusion is facilitated with a unit fall in the distance
TsRss: A Practical Stream data Cleaning Method based on Local Shape Feature

Stream data which is common usually suffers from dirty data points due to noise interference, unreliable sensor reading, erroneous extraction of stock prices or other various reasons. Existing smoothing filter based data cleaning methods seriously alter the data without preserving the original information. And the others such as SCREEN need to be guided by some semantic constraints in specific application scenarios. To improve the usability, we propose a method called TsRss, which is a practical stream data cleaning method based on local shape feature (Shape-Sheet). TsRss is based on the basic idea that data points failing to match its local shape features are more likely to be dirty. To this end, we first study the methods of generating and representing unequal-length Shape-Sheets based on the local shape features. Then the method for finding dirty data via anomaly detection is proposed based on Shape-Sheet. Finally, experiments were conducted on several real datasets. The result showed that TsRss was more practical in use on various types of data, more accurate or more time-saving compared with state-of-the-art methods.
Hydrocarbons in Meteorites, the Milky Way, and Other Galaxies

The distribution, chemical structure, and formation of organic matter in the interstellar medium are important to our understanding of the overall evolution of dust. The exchange of dust between the dense and diffuse interstellar media, and the effects of processing on dust within dense clouds will affect the inventory of material available for incorporation into newly forming star and planetary systems. A series of absorption bands near 3.4 μm has been observed towards bright infrared objects which are seen through large column densities of interstellar dust. These aliphatic hydrocarbons carry the -CH2and -CH3 functional groups in the abundance ratio CH2/CH3 ∼ 2.5, and the amount of carbon tied up in this component is greater than 4% of the available cosmic carbon. The 3.4 μm band has been detected in absorption along more than two dozen lines-of-sight throughout the diffuse interstellar medium of our own Galaxy, as well as in the dusty spectra of other galaxies. Aliphatic hydrocarbons are also seen in the acid insoluble residue and organic extracts from carbonaceous meteorites, and the strong similarity between these features and those found in the diffuse interstellar dust, amid other signatures of a stellar history, suggests that interstellar organic material survives the formation of planetary systems. Comparisons of laboratory residues to the interstellar dust features provide clues to the origin of the aliphatic component. The evidence supports an origin in the outflow of carbon stars, as well as a formation site in the diffuse ISM itself, rather than an origin within dense molecular cloud ices. Perhaps the most significant advance in the field comes from nearby ultraluminous galaxies which sometimes show greater potential for band profile studies than sources within our Galaxy. The redshift to wavelengths further removed from atmospheric interference makes ground-based observations of the entire L band of these galaxies feasible for z<0.3.
Effect of Mixup Enhancement on CNN network for Brain Tumor Classification

This paper focuses on utilizing the mixup enhancement on brain tumor MRI dataset and comparing its performance with other two basic methods with the same CNN network, and compares the impact of these data preprocessing methods on overfitting. Mixup enhancement is a new image processing method for this dataset. The experiment used RandomColor, Flip or Rotation and mixup methods to respectively enhance the image in dataset, and trained a typical CNN network to classify the types of brain tumors. The results show that the accuracy in test data of the base model is 90.3 %. Then, the RandomColor enhancement reaches 81.7%. The Flip or Rotation method reaches 92.1 %. The effect of mixup is the best, reaching 94.7%. In summary, the mixup enhancement method can effectively improve accuracy of brain tumor classification and overcome the overfitting problem.
Speed and Current Vector Controlled Permanent Magnet Synchronous Motor Drive Based on Particle Swarm Optimization

In this paper, particle swarm optimization has been designed and implemented to enhance the dynamic performance of vector controlled permanent magnet synchronous motor drive. The block diagram of the control scheme for the permanent magnet synchronous motor drive is proposed. Practical swarm optimization has been applied in order to reach the optimal gains of the speed and current controllers of the permanent magnet synchronous motor drive. The optimal gains of proportional-integral controllers for speed and currents are determined using the proposed control technique. Furthermore, a tuning method for proportional-integral controllers is developed depending on the linearized models of the permanent magnet synchronous motor. The method of calculating the gains of current and speed controllers is shown on the example of a real electric machine. The performance of the proposed control scheme with optimal gains has been verified using Matlab software package. The results indicated that the robustness of the drive is satisfactory over a wide range of operating conditions. In addition, the performance of the drive system with these parameters is fast.
Test model for database architectures: an assessment for job search engine systems

Data is a fundamental part of information and management systems; thus, controlling its complexity is an essential step nowadays. NoSQL databases adopt new approaches to data management differing from relational structures. For this study, two database systems are considered, MongoDB as a NoSQL data storage model and PostgreSQL as a relational data model, they are compared and evaluated on a job search system. For this purpose, a dataset was defined, and its representation was constructed in databases based on each technology. This process allowed the data modeling in terms of the best practices, then, the development of a test plan prepared the environment for the determination of the comparison metrics of both databases under the methodology specified by the International Software Testing Qualifications Board (ISTQB) and the types of database testing. This study determined that the SQL schema provides greater functionality, that ensures the support of transactions and data integrity, the opposite happened with the NoSQL schemas, resulting in more efficient but lacking functionalities that are characteristic and required for data representation of a consistent system.
Evaluating Roadblocks to Implementing a Green Freight Transportation System: An Interval Valued Intuitionistic Fuzzy Digraph Matrix Approach

Nowadays, freight logistics industries are seeking to adopt green freight practices in their transport systems to reduce environmental concerns; efficient green freight practices lead to reductions in greenhouse gas emissions while using less energy and material. The freight logistics industry, despite its significant contribution to a country's monetary development and economic welfare, is not well regarded because of its role in contaminating the climate. However, the freight logistics industry is trying to implement a green freight transportation system to improve their image with buyers. However, they are facing a lot of obstacles. Therefore, this article seeks to identify the barriers to the implementation of green freight in first world and third world nations and formulate them into a single numeric index. The interval-valued intuitionistic fuzzy set based graph theory and matrix approach technique has been used in this research to derive the green freight barrier impact index value. The PERMAN algorithm is used to compute the permanent function of matrices. Findings suggest that the freight logistics industries in third world nations should pay more attention to societal and managerial barriers for successful implementation of green freight. This research will assist policymakers and managers of freight logistics industries to create strategies to overcome the hurdles in properly implementing green freight practices.
Neural Networks Reveal the Impact of the Vibrational Dynamics in the Prediction of the Long-Time Mobility of Molecular Glassformers

Two neural networks (NN) are designed to predict the particle mobility of a molecular glassformer in a wide time window ranging from vibrational dynamics to structural relaxation. Both NNs are trained by information concerning the local structure of the environment surrounding a given particle. The only difference in the learning procedure is the inclusion (NN A) or not (NN B) of the information provided by the fast, vibrational dynamics and quantified by the local Debye–Waller factor. It is found that, for a given temperature, the prediction provided by the NN A is more accurate, a finding which is tentatively ascribed to better account of the bond reorientation. Both NNs are found to exhibit impressive and rather comparable performance to predict the four-point susceptibility χ4(t) at τα, a measure of the dynamic heterogeneity of the system.
A Real-Time Maximum Efficiency Tracking for Wireless Power Transfer Systems Based on Harmonic-Informatization

With the increased requirements of dynamic wireless charging scenarios, the industry puts forward higher technical specifications for system efficiency. However, the traditional millisecond level response time cannot meet the requirements of dynamic maximum efficiency tracking (MET) system for real-time control. Therefore, this article proposes a wireless power transfer system based on harmonic-informatization, which simplifies the circuit through the harmonic and bandstop filter, so that the mutual inductance can be extracted by harmonic, and then MET can be implemented in real time. Compared with the traditional fundamental system, the harmonic-informatization system has the advantages of no iteration, no communication, and simple calculation. Because the operation of MET under harmonic is simple, the experimental system in this article successfully achieves the microsecond level response time. At the same time, the experiment shows that the system efficiency and power change little after adding the filter, which is due to the low equivalent reactance of the filter under the fundamental.
Machine Learning Guides the Solution of Blocks Relocation Problem in Container Terminals

The blocks relocation problem (BRP) is a well known and important combinatorial optimization problem, in which the initial storage state and retrieval priority of containers are known, and the containers should be picked up in the retrieval order with the goal of minimizing container relocations. This paper studied how machine-learning techniques guide the solution of this Non-deterministic Polynomial problem(NP-hard problem) NP-hard problem. Through our self-developed data generator, we generated initial state stacking matrices and extracted 22 influencing factors for container relocation operations. The supervised learning method and attribution technique were used to verify the relationship between significant container relocated influence features and the number of container relocations using the unrestricted BRP and restricted BRP models. We characterize the potential patterns in the data based on 22 container relocated influence features using four supervised learning models: random forest (RF), extra trees (ET), support vector machine (SVM), and logistic regression (LR). The experimental results demonstrate that RF has a classification accuracy rate of up to 94% on the restricted BRP model. The attribution technique identifies the most sensitive features to the number of container relocations. We organically integrate machine learning into the BRP problem and propose an interactive iterative framework that may provide a novel method for studying the BRP problem.