Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of distributed practice and sleep on laparoscopic skill acquisition and retention

Most laparoscopic skill training is still given as a full day trainings course. However, this might lead learners to overload their cognitive capacity and therefore negatively influence learning, performance and motivation. There is compelling evidence that distributed training and sleep have a positive influence on skill acquisition and retention. In this study, we will examine the effect of two different distributed training schedules and the effect of a powernap on laparoscopic skill acquisition and retention. Results showed no effect between a one day spaced training and a 45 minute spaced training on skill acquisition on the basic tasks. This was also the case between the nap and the spaced-II group. But, there was a significant effect in favor of one day spaced training and nap on skill retention. Although this study found a lack of effect during main analyses, we did replicate some effects seen in previous studies on distributed training. However, more research in needed to analyse the effect of distribution time and amount of sleep on complex laparoscopic skill acquisition and retention. SPACING AND SLEEP ON LAPAROSCOPIC SKILL AQUISITION 3
Contextual Dependence of Human Preference for Complex Objects: A Bayesian Statistical Account

Understanding how humans perceive the likability of highdimensional "objects" such as faces is an important problem in both cognitive science and AI/ML. Existing models of human preferences, in both machine learning and psychology, generally assume these preferences to be fixed. However, human assessment of facial attractiveness appears to be highly contextdependent. Specifically, the classical Beauty-in-Averageness (BiA) phenomenon, whereby a face morphed from two original faces is judged to be more attractive than the originals, significantly diminishes when (1) the original faces are recognizable, or (2) when the attractiveness judgment is preceded by a similarity judgment of the morph relative to the originals, or (3) when the morph is bi-racial and the attractiveness judgment is preceded by a race categorization. Intriguingly, doing the similarity judgment first (2) significantly increases the perceived attractiveness of the originals. Here, we present a statistically grounded explanation of contextual dependence of human attractiveness judgment.
Far Right Parties and the Educational Performance of Children

There has been recent resurgence in right wing political parties with (anti) immigration as a core platform. While the recent ‘Brexit’ referendum and US Presidential Election involved immigration as a major theme. A feature of all has been an increase in mainstream anti-immigrant rhetoric and the vilification of minorities more generally. This has led to an increase in hate crimes against immigrants, and minorities more generally. This paper examines the influence of far-right political parties on the outcomes of immigrant children. We focus on immigrant test scores in Northern Italy which has seen the rise of Lega Nord. We focus on mayors who have been elected with narrow margins of victory in a Regression Discontinuity framework. The election of a Lega Nord mayor leads to a marked reduction in the educational performance of children, concentrated amongst 1st and 2nd generation immigrant children.
Validity of the low-loss approximation in the estimation of dielectric constant and water content from borehole radar data

We derive expressions to determine the error on the estimation of the permittivity (ε) due to the use of the low-loss approximation under lossy conditions. We then examine the consequences on water content (θ ) estimation. For that purpose, the well-known Topp equation and the CRIM model are used as water content estimators. We extend this study by looking at the influence of the effective conductivity obtained for various pore fluid conductivity and porosity scenarios, as well as the impact of varying clay content. Mixing rules based on the Maxwell-Garnet and Hanai-Bruggeman models are used to compute the effective parameters of the sand/clay/water compound. The Cole-Cole model is used to describe the behaviour of clay and water considered in the aforementioned step. We find that the errors can be significant for typical hydrogeophysical applications involving crosshole radar measurements. It is true that soils and materials exhibiting high loss tangent are usually unsuitable for GPR surveying due to lack of penetration. However, for transmission measurements made in relatively closely spaced boreholes, the propagation range often yields sufficient coverage despite high losses. In applications requiring a high degree of accuracy in the determination of ε and θ , we therefore propose to systematically perform attenuation tomography in addition to velocity tomography in order to compute the loss tangent over the investigated area. If proven necessary, the sought parameters can then be determined more accurately using lossy formulation. We also propose to supplement GPR measurements with electrical resistivity tomography (ERT) in order to constrain the radar amplitude processing steps required by attenuation tomography. Keywords–Water content, borehole radar, low-loss approxima-
Against the F-score

In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test’s accuracy. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results, and r is the number of correct positive results divided by the number of positive results that should have been returned. The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst at 0.
Chuckers: Measuring Lineup Shot Distribution Optimality Using Spatial Allocative Efficiency Models

In the 2017-18 NBA playoffs, Russell Westbrook scored 46 points on 43 shot attempts in a 96-91 loss to the Utah Jazz, ending the Oklahoma City Thunder's season. At the time, many popular media figures conjectured that having one player dominate field goal attempts in this way would limit the Thunder's success. While scoring 46 points in a playoff basketball game is an impressive feat for any one player, its impact on the win column is moderated by the fact that it required 43 attempts. At its core, this critique is about efficiency.
Development of The Universal Apparatus for Induction Motors with Combined Winding Design

The main objective of this article is the development of the universal apparatus for the design of high efficiency electric motors with combined stator windings by means of computer modeling in ANSYS Maxwell. Based on the developed models the advantage of induction motors with combined windings design is shown. The universal design apparatus reduces the development time of induction motors with combined windings in wide range of power capacity and speeds.
An Integrated Detection Circuit for Transmission Coefficients

As the applications of radio-frequency (RF) circuits continue to prosper, scattering parameters (S-parameters) play an essential role in the verification of a variety of chips. The traditional way to measure the S-parameters of RF integrated circuits (RFICs) is by using vector network analyzers (VNA). However, measuring RFICs with VNAs is very expensive and likely to reduce the profits of IC products. An implementation of the embedded circuit for S-parameter measurement can greatly reduce the costs of using expensive VNAs. Another reason to embed the circuit for S-parameter measurement is to increase the portion of a chip that can be measured. Besides, novel technologies, such as three-dimensional ICs, will require advanced methods for on-chip verifications of RF circuits since many RF nodes may be buried deep inside a chip stack. In view of these needs, this paper proposes a simple network that can realize on-chip S21 measurements. The greatest advantages of this circuit are the easy implementation and technology independence. To verify the feasibility of the circuit, we fabricated the test chips by using the 0.18- $\mu \text{m}$ IBM 7RF process. The measurement results show the expected behavior and demonstrate the feasibility of the design concept.
Methods for rapid diagnosis of responsible units operators state

The rapid diagnosis problems of responsible units operator's state are considered. The methods of increasing the results reliability of acupuncture points electrical parameters measurement are discussed. It is shown that the proposed hardware solutions based on measuring generators of a specified electric power make it possible to obtain a measurement result invariant to the electrical resistance of biological tissue and increased convergence of the obtained data (compared to the modes of a specified electric current and voltage drop – by 40% and more than an order of magnitude, respectively, for the electrical power exposure of 30-60 microwatts). For the first time, the proposed joint use of the Nakatani technique and measuring generators of a specified electric power will improve the reliability of diagnosis results of responsible units operators state.
Design and Basic Control of Extra Robotic Legs for Dynamic Walking Assistance

This paper presents an Extra Robotic Legs (XRL) system which is designed to assist in the human walking with carrying loads. The design and basic control of the XRL system are proposed, and performances of the system are verified by primary experiments. The main aim of the design is to enable the XRL system to generate enough power for assistance during the natural human walking with low self-weight. Two robotic legs are designed for the XRL system, and each leg has three degrees of freedom (DOFs): two rotational DOFs intersecting at one point on its basis, and one prismatic one along its length generating assistant forces. The prismatic DOF is driven by synchronous belts along a linear slider, and has a high linear velocity for dynamic walking assistance. A force control method is used to generate assistant forces. The final weight of our autonomous XRL system is 8.3 kg, lower than other relative autonomous systems in present. Primary experiments verify the functional performances of the XRL system. This work may inspire other XRL designs and can expand the applications of wearable robots in daily life.
Efficient Human Detection Algorithm using Color & Depth information with Accurate Outer Boundary Matching

Foreground segmentation has a critical role in image processing and computer vision-based applications. Extracting accurate foreground pixels, especially in the boundary region is still an unsaturated area in image analysis. The proposed method explains a segmentation scheme to extract human area from an image or from video frames taken by RGB-Depth sensors like Microsoft Kinect camera. The pre-segmentation is achieved by using the depth information. Here in this algorithm, more focus is given for the enhancement of the pre-segmented image for accurate boundary matching of the segmented human factor. Since the depth analysis have limitations in detecting accurate border pixels, post processing steps are involved here for restoring the missing foreground portions and to avoid background pixels from the final segmented output. Initially hair area is restored using Chan-Vese active contour detection along with Viola-Jones face detection. For making the segmentation quality enough to use in multimedia applications like film editing, fine outer boundary estimation is performed using digital matting procedures.
Modeling with SoaML Applied for Smart Room Based on Internet of Things

The IoT is designing to have unlimited applications for example is IoT system for controlling things in a room. Service-modeling is key both for viewpoint and just for detailed model. The Service Oriented Architecture Modelling Language (SoaML) is utilized to clarify the architecture including the roles and interactions between service participants. In this paper, we investigate how SoaML can be used to represent model for the Smart Room based on Internet of Things (SaRIOT) system. The result of SaRIOT system model is discussed within this paper as presented in section IV. The proposed model may be used to generate software code which would enable to develop the system for the future work.
A Basic Study on Railway Facility Extraction Using a Single-Shot Multi-Box Detector

In railway facilities, there are numerous types and electric train-line facilities. It is difficult to visually inspect all of them, so automatic visual inspection is expected. To achieve automatic inspection, it is important to extract and diagnose the target facilities. This study focuses on facilities extraction by utilizing single-shot multi-box detector (SSD), which can be used as a discriminator for human, car and boat object detection, etc. Diagnosis using Local Subspace Classifier (LSC) is proposed. Herein, we present the evaluation results and the issues applying SSD to the equipment called hangers connecting overhead lines. Some diagnosis results are also explained.
The Channel Mode for a Supply Chain Selling Products with Network Extemalities

We develop a dynamic game model of a supply chain in which the manufacturer sells products with network externalities both through direct and retail channels, and investigate the equilibrium pricing and channel selection strategy of the supply chain members. We analyze the effects of the intensity of network externality on the equilibrium outcome and channel selection strategy of the supply chain members. It reveals that, compared to the single channel mode, the manufacturer and the retailer charges a lower wholesale price and retail price under dual channel mode. With network externalities, the direct channel incurs positive demand and profit under certain conditions. The profit of the manufacturer is always positively correlated to the network externality. The manufacturer prefers dual channel mode to single channel mode, if the network externality is sufficiently large. In contrast, the retailer prefers dual channel to single channel, when the network externality is sufficiently small. Therefore, there exits Pareto range about the network externality, in which both the manufacturer and the retailer both obtains a higher profit under dual channel mode than single channel mode.
Bad-Scenario-Set Robust Power Economic Dispatch with Wind Power

This paper studies an uncertain economic dispatch problem for power systems with wind power. Uncertain wind power is described by discrete scenarios. A threshold of wind power is introduced to define a new concept called a bad scenario set. The bad-scenario-set robust optimization model is established to minimize the total penalty caused by the increase of the generation cost under bad scenarios. A hybrid particle swarm optimization algorithm is developed by incorporating particle swarm optimization with simulated annealing operators to solve the established problem. The simulated annealing operators are performed in a neighborhood which is defined based on the unit commitment constraints. Finally, the established model and the developed algorithm are verified by a 10-unit system with a wind farm. The computational results show that the established model can provide robust solutions with less conservativeness, and the developed hybrid algorithm outperforms other possible alternative algorithms.
Link Prediction by Multiple Motifs in Directed Networks

Link prediction which can restore and predict missing links has wide applications in complex networks. In existing researches on link prediction of directed networks, most methods only consider the information of a single motif, in which the effects of multiple motifs are not included, especially the role of each node forming different motif structures. In order to solve the above problems, firstly we propose a single motif naive Bayes model beyond calculating the number of edge-dependent motifs. We also investigate a two-motif naive Bayes model and a machine learning framework based on multi-motif features to further improve the performance of link prediction. The new framework of link prediction by multiple motifs is superior to the state-of-the-art methods such as potential theory, local path and superposed random walk. Experimental results on real-life networks show the highest performance improvement is 64.3%. Finally, we use maximal information coefficients to reveal the topology correlation between different motifs, which is helpful to understand the evolutionary mechanism of directed networks.
Cotton Temporary Storage Policy and Spot-Futures Interaction

This paper investigates the impact of the government temporary storage policy on the price interactions between the cotton spot and futures markets. We divide the sample into three periods according to the implementation and the abolishment of the temporary storage policy. The empirical evidence indicates that the policy weakens the pricing function of the futures market. The tests based on VAR model show that (1) The temporary storage policy reduces the price reactions in the spot (futures) market to the information shock from its own; (2) The policy decreases the effect of the futures market on the spot market and changes the direction of the influence of the spot market on the futures market; (3) There is a long-term equilibrium between the prices in the futures market and spot market. (4) When investors are more heterogeneous in their beliefs, the impacts of the futures market on the spot market are enhanced.
An Adaptive Distributed Averaging Integral Control Scheme for Micro-Grids With Renewable Intermittency and Varying Operating Cost

The increasing penetration of intermittent renewable energy resources in micro-grids poses several issues, such as stochastic power generation, demand and supply miss-match, frequency fluctuation, and economic dispatch problems. To address such critical issues, a distributed secondary control scheme based for micro-grids with varying operating cost and intermittent renewable energy resources is proposed for frequency regulation and economic load dispatch. The paper presents an adaptive distributed averaging integral control scheme with conditional uncertainties, namely varying operating costs, and renewable intermittency. The proposed control scheme adapts to the uncertainties by updating the control law parameters dynamically and can maintain overall network stability. The distributed control scheme employs communication channels for exchange of generation data from the neighboring power units for optimal power sharing and consensus among the power units. An additional controller at tertiary control layer of the hierarchical control architecture is also augmented in the control structure to economically dispatch the load and the consensus-based algorithm guarantees optimal load sharing. The proposed communication based control scheme reveals the best combination of performance and flexibility. A performance-based comparative analysis is also presented, validating the effectiveness of the proposed control scheme compared to the prior works. The robustness and performance of the proposed control scheme is illustrated through computer simulations.
Visual Navigation Features Selection Algorithm Based on Instance Segmentation in Dynamic Environment

Ego-motion estimation, as one of the core technologies of unmanned systems, is widely used in autonomous robot navigation, unmanned driving, augmented reality and other fields. With the development of computer vision, there has been considerable interest in ego-motion estimation with visual navigation. One of the core technologies in Visual navigation is using the matching feature points between consecutive image frames to estimate pose. Since the feature-based method performed under the assumption of a static environment, it susceptive to the dynamic targets. Visual navigation in the dynamic environment has become an important research issue. This paper proposed a practical and robust features selection algorithm of visual navigation which avoids using the feature points on dynamic objects. Firstly, according to the instance segmentation of deep neural network, the objects are classified into potential dynamic and static categories. Subsequently, the matching features on the potential moving objects are used to update vehicle state respectively, meanwhile, the relevant reprojection error of other feature points on the background could be calculated. Eventually, the result of whether the target is moving or not will be judged by the reprojection error, and the features on dynamic targets are removed. To illustrate the effectiveness of the features selection method in the dynamic environment, the proposed algorithm is merged into an MSCKF based on tri-focal tensor geometry, and it has been evaluated in a public dataset. Experimental results demonstrated the effectiveness of the proposed method.
Prediction Stock Price Based on Different Index Factors Using LSTM

Predicting stock price has been a challenging project for many researchers, investors, and analysts. Most of them are interested in knowing the stock price trend in the future. To get a precise and winning model is the wish of them. Recently, Neural Network has been a prevalent means for stock prediction. However, there are many ways and different predicting models such as Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). In this paper, we propose a novel idea that average previous five days stock market information (open, high, low, volume, close) as a new value then use this value to predict, and use the predicted value as the average of the stock price information for the next five days. Moreover, we utilize Technical Analysis Indicators to consider whether to buy stocks or continue to hold stocks or sell stocks. We use Foxconn company data collected from Taiwan Stock Exchange for testing with the Neural Network Long Short-Term Memory (LSTM).
An Automated Identification Tool for LC-MS Based Metabolomics Studies

Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.
Frequency selective surfaces with hexagonal elements for millimeter waves applications

Numerical investigations on a frequency selective surface using metalized strips deposited on high resistive silicon in a millimeter waves are presented in this paper. High resistivity Si substrate was chosen due to the very good transparence in millimeter waves. For simulations, normal incidence of millimeter waves alongside Floquet modes and ports are considered. Dependence of the frequency response with the geometrical parameters of the unit cell is discussed.