Miracle-dz/UniFAR-FTUs huggingface dataset:
A study over Hybrid Electric Vehicles role in vehicle concept design

The hybrid electric vehicles (HEVs) have become increasingly popular in recent years and their market share is expected to rapidly increase in the near future. The success relies on the capability to provide a compromise solution with respect to conventional combustion engine vehicles and battery electric vehicles. Energy consumption savings, lower emissions and range are the key factors. The study provided in this paper shows the role of HEVs in the vehicle design considering energy consumption, performance and the effectiveness of a specific powertrain layout compared to conventional combustion engine and battery electric vehicles. A relevant aspect concerning HEVs design is the level of hybridization of the vehicle, thus the relative power provided by the electric traction unit compared to the combustion engine. The analysis of different vehicles, by varying the vehicle power and the level of hybridization, allows to provide guidelines towards the most effective solutions to be adopted.
High-sensitive troponin T increase after hemodialysis is associated with left ventricular global longitudinal strain and ultrafiltration rate.

BACKGROUND Circulating troponin levels are both stable and higher in patients with end-stage renal disease, even in the absence of acute coronary syndrome. These patients commonly have underlying cardiac problems that frequently cause troponin elevation. The effect of hemodialysis (HD) on troponin levels has not been well elucidated. Thus, investigated herein is the relationship between the changes in troponin levels along with left ventricular deformation and volume depletion in patients with end-stage renal disease. METHODS Patients included were between 18 and 85 years of age and were receiving hemodialysis for at least 6 months. High sensitive cardiac troponin T (hs-cTnT) levels were studied in blood samples taken at the beginning and end of HD. Two-dimensional speckle tracking strain imaging was used to evaluate myocardial contractility. RESULTS Seventy patients (50.7 ± 16.9 years of age, 27 women) were included in study. The mean volume of ultrafiltration was 3260 ± 990 mL. A significant increase in circulating hs-cTnT levels was observed, as well as a prominent decrease in left ventricular global longitudinal strain (GLS) after HD (52.4 ± 40.2 ng/L vs. 66.8 ± 48.5 ng/L, p < 0.001 and 20.1 ± 3.6% vs. 16.8 ± 3.8% p < 0.001, respectively). Moreover, ultrafiltration rate and GLS were found as the strongest independent variables in relation to the relative increase in hs-cTnT. CONCLUSIONS Hemodialysis can cause a significant increase in hsTnT. This can jeopardize the accuracy of clinical diagnoses based on hs-TnT measurements. GLS may be used as a determinant of this hs-TnT increase. The influence of HD on the cardiovascular system should be kept in mind to prevent unnecessary interventions.
Magnetic and Microstructural Properties of [FePt–Mg(Ti, Ta, Zr, Nb, B)O] Granular Films

Magnetic and microstructural properties of granular FePt films mixed with a novel Mg(Ti, Ta, Zr, Nb, B)O multiple-oxide segregant material were investigated. Two different series of samples were prepared. In series (I), a Mg(Ti, Ta, Zr, Nb, B)O(<inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>) (<inline-formula> <tex-math notation="LaTeX">$t = 2$ </tex-math></inline-formula>, 4, and 6 nm) interlayer was deposited under a 10 nm-thick FePt layer; in series (II), FePt(2 nm)/[Mg(Ti, Ta, Zr, Nb, B) O(<inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>)/FePt(4 nm)]<sub>2</sub> (<inline-formula> <tex-math notation="LaTeX">$t = 0.5$ </tex-math></inline-formula>, 1, and 1.5) multilayer stacks consisting of FePt films interleaved with Mg(Ti, Ta, Zr, Nb, B)O layers were prepared. Overall, the granular FePt films present a preferential perpendicular anisotropy, the coercivity being, however, lower than that of the reference FePt films, likely because of diffusion of metallic elements from the segregant interlayer into the FePt grains. Such an effect is more extended in sample series (II) because of the multilayer structure, and lower coercivity values are achieved. However, using a multilayer stack allows obtaining FePt films with a better (001) crystallographic texture and a dense dome-like morphology that is closed to the expected columnar grain structure, thus indicating that such a strategy and the proposed segregant material are of potential interest for the development of the FePt-based magnetic recording media.
A Deep Convolutional Neural Networks-Based Method for Inversion of Rough Surface Parameters

Deep convolution networks (CNN) is applied to inverse the rough surface parameters, including the root-me an-square height and the correlation length, from microwave images. We employ computational electromagnetic method to simulate the training data for deep CNN. The simulated backward scattering data is converted into microwave images as the inputs to the CNN. An inversion network of deep convolution neural networks with five cascaded convolutional-maxpooling layers and two fully connected layers is designed, including feature extraction and data regression by using convolution layers and fully connected layers. The simulated results demonstrate the feasibility to inverse the sough surface parameters from electromagnetic scattering fields by using deep CNN.
Simple and Tightly Approximated Integrals Over $\kappa$ -$\mu$ Shadowed Fading Channel With Applications

In this correspondence, simple and tight bound approximations to the integrals representing the average of some useful forms of the Gaussian <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> function, over a <inline-formula><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> shadowed fading channel are proposed. These are then used to calculate the symbol error probability of various digital modulation schemes over <inline-formula> <tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula> shadowed fading channels, which include a majority of the widely used classical fading models. The accuracy of the analysis is further confirmed with the help of computer simulations.
Protecting privacy on the web: A study of HTTPS and Google Analytics implementation in academic library websites

Purpose The purpose of this paper is to examine the extent to which HTTPS encryption and Google Analytics services have been implemented on academic library websites, and discuss the privacy implications of free services that introduce web tracking of users. Design/methodology/approach The home pages of 279 academic libraries were analyzed for the presence of HTTPS, Google Analytics services and privacy-protection features. Findings Results indicate that HTTPS implementation on library websites is not widespread, and many libraries continue to offer non-secured connections without an automatically enforced redirect to a secure connection. Furthermore, a large majority of library websites included in the study have implemented Google Analytics and/or Google Tag Manager, yet only very few connect securely to Google via HTTPS or have implemented Google Analytics IP anonymization. Practical implications Librarians are encouraged to increase awareness of this issue and take concerted and coherent action across five interrelated areas: implementing secure web protocols (HTTPS), user education, privacy policies, informed consent and risk/benefit analyses. Originality/value Third-party tracking of users is prevalent across the web, and yet few studies demonstrate its extent and consequences for academic library websites.
Single-Frame Remote Sensing Image Super-Resolution Reconstruction Algorithm Based on Two-Dimensional Wavelet

The obtained precisely high frequency information is the key of single-frame image super-resolution reconstruction by using two-dimensional wavelet. Because the bicubic interpolation of high frequency components decomposed by wavelet will introduce noise, it will affect reconstruction effect. An improved algorithm using Fourier transform and zero-padding resampling instead of bicubic interpolation is proposed in this paper. The advantage of frequency domain interpolation is obtained by using Fourier transform and zero-padding resampling. And high frequency components obtained by wavelet decomposition of the original image can be interpolated optimally without introducing noise, which makes the high frequency details more precise in the reconstruction process. The experimental results show that the improved algorithm is superior to the traditional two-dimensional wavelet reconstruction algorithm, which can be applied to the single-frame remote sensing image super-resolution reconstruction.
Autonomous Driving System based on Deep Q Learnig

This paper deals with the simulation results of an autonomous car learning to drive in a simplified environment containing only lane markings and static obstacles. Learning is performed using the Deep Q Network. For a given input image of the street captured by the car front camera, the Deep Q Network computes the Q values (rewards) corresponding to the actions available to the autonomous driving car. These actions are discrete angles through which the car can steer for a fixed speed. The autonomous driving system in the car enforces the action that has the highest reward. Our simulation results show high accuracy in learning to drive by observing the lanes and bypassing obstacles.
Cyber Threat Information Sharing System for Industrial Control System (ICS)

In pursuit of enhanced cyber security capability in the whole industry, an information sharing scheme for ICS-related security information is sought after. It is suggested that exchanging cyber threat information described in a specific machine-readable format can partly automate the security measure processes and eventually lighten the workload of technical personnel. This will also enable information sharing across the sector and accordingly the effective implementation of security measures (detect infection, block suspicious communication etc.) in an early stage. This paper proposes a system to share cyber threat information with a wide range of organisations and format of the data to be exchanged through the system.
Meta-Analysis of the Prospective Relation Between Alliance and Outcome in Child and Adolescent Psychotherapy

In the youth treatment literature, the alliance has been defined and measured as a consensual or collaborative bond. In this article, we review varied definitions of the alliance, enumerate its frequent measures, and present clinical examples. We provide a meta-analytic review on the relation between the therapeutic alliance and treatment outcome in child and adolescent psychotherapy. In particular, this review only includes prospective studies of youth therapy that used an explicit measure of alliance. The meta-analysis of 28 studies revealed a weighted random effect size of r = . 19 (k = 28, N = 2419, p < .01, 95% confidence interval [.13, .25]), which is a small to medium effect (equivalent to d = 0.39) consistent with the adult alliance literature and with prior youth meta-analyses. Given that a medium-large amount of heterogeneity was observed in effect sizes (I2 = 64.19%), theory- and method-based moderators were examined. Multiple moderators of the alliance–outcome association were found, including diagnosis class, type of therapy, study design (randomized controlled trials [RCT] vs. nonrandomized trials [non-RCT]), and treatment setting (inpatient vs. outpatient). Research limitations, patient contributions, and diversity considerations follow. The article concludes with research-informed practices for building and maintaining the therapeutic alliance with youth.
Magnetic Recording Using a Spin Torque Oscillator

Simulations of the switching of magnetization using only the field from a spin torque oscillator (STO) are presented. Single phase and exchange coupled composite (ECC) grains were used and the switching probability calculated as a function of the STO position, frequency, and the magnetic properties of the grains. The results show that ECC grains can be reliably switched by an STO for a wide range of STO positions and frequencies.
Research on V2V Communication Based on Peer to Peer Network

Vehicle networking is an important part of modern intelligent transportation system. Most of the existing vehicle networking is based on the central structure, this structure is prone to single point of failure, resulting in paralysis of the entire system. Therefore, in order to increase the network fault tolerance and maintain the stability of the network system, this paper proposes a vehicle network model based on peer to peer network. In this model, the base station and the roadside units will no longer be necessary facilities. The vehicles can be used as relay to directly participate in the exchange and transfer of information. In this paper, the feasibility of the vehicle network is proved by time delay analysis. In comparison with the traditional centralized network structures, the efficiency and cost effectiveness of the proposed peer to peer network is better.
A Hybrid Approach to Optimization in Answer Set Programming

Answer set programming (ASP) is today a successful approach to knowledge representation and reasoning in various real-world problem domains. ASP offers an expressive rule-based constraint modelling language, supporting concise declarative modelling of both decision and optimization problems within the first or the second level of the polynomial hierarchy. In this paper, we propose a new approach to solving optimization problems via ASP, i.e., to the problem of finding optimal solutions (in terms of optimal answer sets or stable models) under a given weight function over soft atoms (weak constraints). Our approach constitutes the first adaptation of the so-called implicit hitting set approach in the context of ASP. In particular, in contrast to the earlier proposed family of core-guided algorithms for optimization in answer set programming, we present a hybrid approach which makes use of interactions between an ASP decision solver (as an unsatisfiable core extractor) and an integer programming solver (as a minimum-cost hitting set algorithm). We explain how various concepts and features specific to ASP and IP can be harnessed within the approach, including several ways for obtaining better upper and lower bounds during search, with the aim of speeding up the computation of an optimal answer set. By a careful integration of the interactions between state-of-the-art ASP and IP solvers, we show that already our first implementation provides a complementary approach when empirically compared to the currently available solvers supporting optimization in answer set programming.
Collecting and Delivering Progress Feedback: A Meta-Analysis of Routine Outcome Monitoring

This systematic review and meta-analysis examines the impact of measuring, monitoring, and feeding back information on client progress to clinicians while they deliver psychotherapy. It considers the effects of the 2 most frequently studied routine outcome monitoring (ROM) practices: The Partners for Change Outcome Management System and the Outcome Questionnaire System. Like other ROM practices, they typify attempts to enhance routine care by assisting psychotherapists in recognizing problematic treatment response and increasing collaboration between therapist and client to overcome poor treatment response. A total of 24 studies were identified and considered suitable for analysis. Two-thirds of the studies found that ROM-assisted psychotherapy was superior to treatment-as-usual offered by the same practitioners. Mean standardized effect sizes indicated that the effects ranged from small to moderate. Feedback practices reduced deterioration rates and nearly doubled clinically significant/reliable change rates in clients who were predicted to have a poor outcome. Clinical examples, diversity considerations, and therapeutic advances are provided.
Driver Risk Assessment Using Traffic Violation and Accident Data by Machine Learning Approaches

Along with high speed urbanization and motorization, road traffic accidents have become a severe problem in China. Drivers' operation error and risk-taking behavior is a leading cause of traffic accidents. Under this condition, the demand of drivers' traffic safety assessment keeps increasing, especially for professional drivers like passenger drivers and freight drivers. This work proposes a data mining framework of drivers' traffic safety assessment using drivers' personal information, traffic violations and traffic accident data records. Model validation and result interpretation are given, showing the rationality and usefulness of our proposed approach.
A Visual Marker based Image Annotation Tool with Projective Transformation

Although template matching has been studied for decades, difficult problems still exist, such as template matching with projective transformation. Projective template matching is challenging not only because of its own hardness, but also owing to the high cost in creating ground truths. Focusing on the problem in creating ground truths, in this paper, we propose a novel method to automatically create ground truth by augmented reality (AR) technology. Specifically, when creating dataset, around each target image we attach AR markers. Then we shoot videos containing target image with the markers from different angles and backgrounds. Finally we locate the target images by detecting the markers. We evaluate our method by deviation, intersection over union (IoU), and necessary correction rate, indicating that our method is available.
Vultures respond to challenges of near-ground thermal soaring by varying bank angle

ABSTRACT Many large birds rely on thermal soaring flight to travel cross-country. As such, they are under selective pressure to minimise the time spent gaining altitude in thermal updrafts. Birds should be able to maximise their climb rates by maintaining a position close to the thermal core through careful selection of bank angle and airspeed; however, there have been few direct measurements of either parameter. Here, we apply a novel methodology to quantify the bank angles selected by soaring birds using on-board magnetometers. We couple these data with airspeed measurements to parameterise the soaring envelope of two species of Gyps vulture, from which it is possible to predict ‘optimal’ bank angles. Our results show that these large birds respond to the challenges of gaining altitude in the initial phase of the climb, where thermal updrafts are weak and narrow, by adopting relatively high, and conserved, bank angles (25–35 deg). The bank angle decreased with increasing altitude, in a manner that was broadly consistent with a strategy of maximising the rate of climb. However, the lift coefficients estimated in our study were lower than those predicted by theoretical models and wind-tunnel studies. Overall, our results highlight how the relevant currency for soaring performance changes within individual climbs: when thermal radius is limiting, birds vary bank angle and maintain a constant airspeed, but speed increases later in the climb in order to respond to decreasing air density. Summary: Empirical data on soaring behaviour of vultures reveal currency trade-offs through the thermal climb.
Fast and High-performance Multi-convolution Deep Neural Network Structure with Residuals

Very deep convolution neural networks show a great improvement over competitive benchmarks. But the depth also brings extremely high computational cost. In this paper, inspired by Inception module, we introduce a new convolutional neural network module that combines residual structure and multiple convolution. A residual structure is mainly adopted to solve the gradient vanishing problem. And unlike the concatenation in the inception module, multiple convolution is used to find the most proper feature maps through self-optimizing training. With this module, we no longer need to carefully optimize the convolution structure of network, but can attain state-of-the-art results on CIFAR-10, MNIST and CIFAR-100 with 26 layers and only 19k parameters.
Optimization of the Cut-Off Rate of Generalized Spatial Modulation With Transmit Precoding

Spatial modulation (SM) and generalized spatial modulation (GSM) are emerging multiple input multiple output (MIMO) schemes that use transmitter (TX) antenna switching for data transmission. Their operating principle makes optimization of channel capacity and mutual information usually more difficult than for conventional MIMO schemes which are not based on antenna switching. We propose to use channel cut-off rate as a relevant and more tractable metric for performance optimization of spatial modulation (SM)/GSM systems, as it constitutes a practical lower-bound of channel capacity. In particular, we propose four TX precoding schemes for increasing the cut-off rate of SM/GSM systems. We show that those TX precoding schemes which are designed for increasing array gain provide the largest improvement of cut-off rate for low signal-to-noise ratio (SNR). On the other hand, the TX precoding schemes that are designed for increasing the minimum Euclidean distance of GSM symbols are more suitable for application to medium to high SNR setups and correlated channels. The proposed precoding schemes are shown to be able to enhance mutual information, and the gain is shown to be of the same order of magnitude as the gain of the corresponding channel cut-off rate.
Synthesis of Sparse Near-Field Focusing Antenna Arrays Based on Bayesian Compressive Sensing

A mathematical model for near-field sparse array antennas synthesis is established in this work for the first time. An algorithm based on the multi-task compressive sensing method is employed to obtain the element position and excitation coefficient fitting a desired near-field radiation pattern. Numerical results validate the effectiveness of the proposed method to synthesize different types of desired patterns with more than 30% of elements saved compared with uniformly spaced array.
Polar Transformed Subdomain Modeling for Double-Stator Permanent Magnet Linear Synchronous Machine

This paper presents a developed 2-D analytical subdomain model for design and analysis of the double-stator permanent magnet linear synchronous machine (DS-PMLSM) to account for both the primary and secondary end effects. First, considering the time constraint, polar transformed method is adopted to establish the 2-D subdomain analytical model, the DS-PMLSM is deformed into a ring-segment PMLSM, and the analytical model is calculated in polar coordinates instead of Cartesian coordinates. Then, the subdomain method is adopted to analyze the analytical model by solving Laplace’s equation and Poisson’s equation, and 2-D relative permeance function is used to consider the slot effect. The flux density and back electromotive force are calculated based on the developed analytical model. Finally, the analytical results are verified by the finite-element method and experiment. The results show that the proposed analytical model can accurately predict the electromagnetic performance, which is useful for the initial design of the DS-PMLSM.
Model Checking-based Safety Verification of a Petri Net Representation of Train Interlocking Systems

This paper describes an implementation of formal methods for safety verification of a railway interlocking system model based on model checking techniques. In the proposed method, a model for the interlocking system is constructed using timed-arc petri net and the safety specifications are expressed as computation tree logic formulas. These model and specification are then used in TAPAAL model checker to perform the model checking of safety verification task. Simulation results are presented to illustrate the advantages of the proposed verification method.