Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Simulation of a Wireless Charging System for Electric Vehicles

In order to reduce pressure on fossil fuel and reduce environmental pollution, the use of electric vehicles (EV) is rapidly increasing, instead of combustion engine vehicles. The main concern issue of the EV customers is being addressed through various charging methods. The key difficulty in implementation of EV is arrangement of charging infrastructure. The wireless charging system (WCS) is a favorite option in the growing EV market. In this paper, a WCS for charging electric vehicles is developed via inductively coupled power transfer technology. A simulation system is made in the MATLAB/SIMULINK, and the performance of the system is verified by the simulation results.
Asymptotic Stability Constraints for Direct Horizon-One Model Predictive Control of SPMSM Drives

This letter proposes asymptotic stability (A.S) constraints embedded into a new horizon-one model predictive control (H1-MPC) of voltage source inverter (VSI)-fed surface-mounted permanent magnet synchronous motor (SPMSM) drives. The relevant A.S constraints are systematically developed to ensure the asymptotic convergence of controlled outputs. Then, a new direct H1-MPC is proposed with the A.S constraints to optimize the control performance. Unlike other direct MPC methods, both A.S and optimality are targeted by considering the equivalent feedback control laws and predictive optimization problem. Furthermore, the average switching frequency can be reduced by retaining the previous switching state provided that it satisfies all constraints in the next period. Comparative studies and experimental validation on a prototype VSI-fed SPMSM drive with a TI TMS320F28335 digital signal processor are conducted to prove the superior performance of the proposed H1-MPC on zero steady-state error, few output ripples, and low average switching frequency.
De-anonymizing Social Networks User via Profile Similarity

Nowadays people are likely to reveal different aspects of life on different websites. Given a user's account information on one site, we can find his or her other accounts on other websites with the help of profile matching, which can benefit multiple application domains, including recommendation, privacy, security and so on. Traditional profile matching method makes use of as many attributes as possible and calculates the similarity of attribute values one by one, which is not fit for the realistic scenario with large-scale data and lack of common attributes. Therefore, in this paper, we proposed a new profile matching method. We extracted information about user identities on about.me, Twitter and Github; We used real name, username, location, and url to generate the feature of the user. We applied several similarity measures (e.g. Jaro Winkler similarity, Levenshtein distance and N-gram distance) to measure the similarity of the user profiles on different websites. Using the most promising similarity measure and parameters, we achieved a high reliability with a recall over 95% for a 98% precision, similar to the basic method and greatly reduced the running time.
Course Concept Extraction in MOOC via Explicit/Implicit Representation

Massive Open Online Courses(MOOCs) provide convenient access to knowledge for learners all over the world. Concept Extraction is a basic requirement in MOOCs. However, textual content in MOOCs, such as video subtitles and quizzes, are generally presented as semi-structured or unstructured format. Thus it is hard to extract important concepts with simple methods from MOOCs. In this paper, we design a graph-based propagation method to solve the concept extraction problem. Our method utilize textual and structured data on Wikipedia, to generate implicit and explicit representation for concepts respectively. Experiments show that our method outperforms alternative methods on Chinese dataset(+0.054-0.062 in terms of MAP).
Web Service Applying Moving Target Defense

This paper focuses on studying how Moving Target Defense technology will bring impact about the security and the cost on web service. We propose a novel design named MTD-applying Web Service, a kind of web service based on Moving Target Defense technology, for security enhancement. MTD-applying Web Service unlike traditional web service owning only one static web server and one constant database server, it diversifies its backend sources. Especially, diversification of web servers and database servers makes a great increment of difficulty for attacker to intrude. Finally, a series of experiments also prove our theory effective. According to the result of experiment of evaluating the MTD-applying Web Service system, we can announce that our design can significantly improve the system's security with acceptable cost.
Genetic diversity and population structure of local and exotic sheep breeds in Jordan using microsatellites markers

Aim: This study was conducted to study the genetic and population structure of local (Awassi) and exotic (Romanov, Charollais, Assaf, Awassi, and Suffolk) sheep breeds in Jordan using eight microsatellite markers. Materials and Methods: A total of 125 sheep were used (25 from each breed) in the study. The number of alleles (A), the mean values of observed (Ho) and expected (He) heterozygosity, polymorphism information content (PIC), fixation index as a measure of heterozygote deficiency or excess, and Hardy–Weinberg equilibrium (HWE) were analyzed using PopGen and CERVUS softwares. Nei’s standard genetic distances among breeds and dendrogram of unweighted pair group method with arithmetic mean (UPGMA) were calculated and constructed using PopGen software. Results: A total of 40 alleles were detected with an average number of alleles of 5. The mean Ho value was higher than the mean He value for all breeds. Awassi breed showed the highest average PIC value while Romanov had the lowest. There was a significant (p<0.05) deviation from HWE at each locus within and between breeds. Deviations from HWE were found to be highly significant for all markers except OARFCP304 locus. The genetic distance estimates revealed a close relationship between Romanov and Charollais and between Awassi and Charollais. In the UPGMA dendrogram, Charollais, Romanov, and Awassi breeds were placed together in one main cluster while Assaf was in a different subcluster. Awassi was placed alone in a second main cluster. Conclusion: Results of this study offer insight toward the genetic conservation of the studied breeds and a base on which breeding plans can be made.
Stochastic Alternating Direction Method of Multipliers with Conjugate Gradient

The Alternating Direction Method of Multipliers(ADMM) is an important method for machine learning. A large number of stochastic versions of ADMM continue to emerge. But almost all the algorithm focused on the Steepest Descent, which cause slow convergence rate. In our work, we propose a new stochastic alternative direction method of multipliers with conjugate gradient(SCG-ADMM), and prove its convergence rate in ergodic sense. Conjugate gradient descent has a better performance in traditional cases. Experiments on LASSO and Logistics Regression show that our proposed algorithm can achieve state-of-the-art performance in real applications. And we use the linear search method to replace the manual setting of the step length.
Privacy-Aware Personal Information Propagation Management in Social Networks

Online social network becomes the most important medium of information propagation. As the number of social network users grows rapidly and information sharing behavior becomes more frequent, the propagation range of personal information becomes more widespread. This phenomenon gradually arouses the user's high concern for the privacy protection of personal information. At present, the main contradiction is formed between users' privacy awareness demands for information sharing and unknowns of information propagation range. In order to solve this contradiction, an open framework called PPIPM(Privacy-aware Personal Information Propagation Management) is proposed in this paper. It measures user's privacy awareness from the perspective of dynamic propagation, and then achieves privacy protection for sharing personal information by propagation constraints based on RBAC(Role Based Access Control) model. Role mining and permission assignment are two key technologies in deployment of PPIPM, and they are implemented in a separate manner with relative independence, respectively called RMPM (Role Mining for Propagation Management) and MAPM (Maximum-permission Authorization for Propagation Management). The Facebook dataset is used in experiments to evaluate the feasibility of PPIPM and compare the effect of personal information propagation management while plugging different role mining algorithms into PPIPM. The results indicate that PPIPM has good applicability for satisfying users' privacy awareness demands in general social networks.
Scan Code Injection Flaws in HTML5-Based Mobile Applications

HTML5-based mobile apps are becoming popular in the development of a cross-platform mobile. They are also built using web technologies, including HTML5, CSS, and JavaScript, so it may face with code injection attacks like web apps. However, code injection attacks are exploited in web apps and in mobile web apps are distinguished. The code injection attacks in web apps are often exploited by attackers throughout the cross-site scripting. In HTML5-based mobile apps, attackers can deploy attacks by various code injection channels such as inter-apps, inter-components, inter-devices communication, and local device resources such as WiFi, SMS, Contact. The plugin APIs are implemented for the code injection channels are defined as the sensitive plugin APIs. The previous approaches aimed at modeling known sensitive plugin APIs, and applying the data flow analysis to detect sensitive information flows from such modeled sensitive plugin APIs to vulnerable APIs. However, their method can miss code injection flaws caused by unknown sensitive plugin APIs. Besides, analyzing information flows in JavaScript is challenging. We found that the previous approaches are not able to analyze various contexts of callback functions. In this paper, we developed a static analysis tool called SCANCIF to scan code injection flaws. SCANCIF identifies the sensitive plugin APIs based on code injection tags, and analyzes flows of information based on modeling contexts of callback functions passed in function calls. We evaluated our approach on a data set of 3,204 HTML5-based mobile apps, as a result, SCANCIF scanned 220 vulnerable apps. We manually reviewed them and found 4 new code injection channels.
Unidirectional Optical Transmission in a Single-Layer Metallic Grating Consisting of Cambered Resonators

In order to realize unidirectional optical transmission (UOT), a single-layer metallic grating (SMG) consisting of cambered resonators (CRs) is proposed. Numerical simulation results show that the transmittance contrast ratio of the SMG can reach about 5000 with a high unidirectional transmittance of 39%. UOT results from diffraction enhancement or suppression in transmission direction. Diffraction is manipulated by the interference of electromagnetic radiation from local surface plasmons on CRs. Our single-layer structure has sub-100 nm thickness and can be used in ultracompact optical devices to manipulate light transmission.
Model-Predictive Sliding-Mode Control for Three-Phase AC/DC Converters

This paper presents a model-predictive sliding-mode control (MPSMC) scheme for a three-phase ac/dc converter to achieve better stability and dynamic performances. In the conventional model-predictive control method, a proportional–integral (PI) controller is used to generate the active power reference. This traditional model-predictive PI control (MPPIC) scheme, however, produces a large overshoot/undershoot, a long settling time, and a large steady-state error under disturbances. To overcome these deficiencies, a sliding-mode controller is employed to replace the PI controller. Since the control law and the controller are designed based on the system model, the proposed MPSMC scheme can reduce the effects of unexpected disturbances, such as the output voltage demand and the resistance load variations. Both methods have been simulated in MATLAB/Simulink during various disturbances. Compared with the performances of MPPIC, the results obtained from MPSMC show that the settling time of the dc voltage can be minimized by about 91%, and the overshoot can be eliminated from 9.13% during the steady-state progress. The active and reactive power from MPSMC can also be controlled to the desired values, respectively, with a much smaller overshoot/undershoot and a faster response speed. Similar dynamic improvements can be achieved with MPSMC when the dc voltage demand varies. The simulation results are validated by experimental results.
Advancements of Air, Water and Nanofluid Based Photovoltaic Thermal (PV/T) Technologies

At present world, due to the increasing demand of heat and electrical energy, photovoltaic thermal (PV/T) systems have become very popular to the modern researchers. The PV/T system generates heat and electricity simultaneously. To obtain more electric power and thermal energy from the PV/T systems, PV cell cooling is very important. Usually air and liquid are used as heat carrying medium to cool the PV panels. In commercial market, efficient PV/T systems are not available. From the last few decades, several researchers are working on the PV/T systems to increase the system performance. This paper describes the development of conventional and newly designed air, water and nanofluid based PV/T systems.
PHAX: Physical Characteristics Aware Ex-Situ Training Framework for Inverter-Based Memristive Neuromorphic Circuits

In this paper, we propose a training framework for an inverter-based memristive neuromorphic hardware. The framework, which is called PHAX, is a physical characteristics aware one relying on an <italic>ex-situ</italic> training approach. The considered neuromorphic circuit is highly energy efficient hybrid CMOS-memristive implementation of neuromorphic circuits. To solve the problem of high sensitivity of the training to the mismatches between the high-level mathematical modeling of the neurons and the corresponding physical characteristics, an approach for analytical yet accurate modeling of the memristive crossbar and neuron circuits is suggested. The approach, which is based on SPICE simulations, models the inverter-based neurons using a hyperbolic tangent function. To increase the training efficacy, the backpropagation training algorithm is modified by considering some constraints based on the physical characteristics of the memristive circuit. This modification along with the accurate back-annotation of the physical characteristics considerably improve the effectiveness of the <italic>ex-situ</italic> training method of the neuromorphic circuit. The results of this paper show an average reduction of <inline-formula> <tex-math notation="LaTeX">$1805{\times }$ </tex-math></inline-formula> in the training runtime compared to that of the <italic>in-situ</italic> training approach. Furthermore, the results of applying the approach on the kernels of some applications such as image recognition, image processing, and financial analysis reveal that the designed neuromorphic circuits provide an average power saving (speed up) of <inline-formula> <tex-math notation="LaTeX">$1478{\times }$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$5.2{\times }$ </tex-math></inline-formula>) over the ASIC implementation in a 90-nm CMOS technology.
Representation Learning Based on Influence of Node for Multiplex Network

The research of social network and mining of multi-source/ multi-view network has gradually been a focus in the field of social network recently. Existing studies on building social network are mainly based on the single source data, instead of the multi-source data. In this paper, we use multiplex network (e.g. multi-relation network) to model multi-source data, then propose a node learning representation of multiplex network. First, we propose a method of extracting node influence of multiplex network. Next, taking account into the influence of node and the random walk in multiplex network, we propose a biased random walk method to learn the embedding of node in multiplex network. Finally, we compare existing state-of-the-art techniques on edge reconstruction accuracy and link prediction in several real-world networks from diverse domains. Experiments on real datasets validate the effectiveness of our network representation method, enrich the quantity of conserving multilayer network structure information, and make the description of the node embedding in multiplex network more accurate.
Investigating the effect of visfatin on ERalpha phosphorylation (Ser118 and Ser167) and ERE-dependent transcriptional activity

Obesity is associated with higher postmenopausal breast cancer incidence. Visfatin level alteration is one of the mechanisms by which obesity promotes cancer. Ligand-independent activation of estrogen receptor alpha (ERα) is also associated with carcinogenesis. The activity of ERα is modulated through phosphorylation on multiple sites by a number of protein kinases. Here we investigated the effect of visfatin as a novel adipocytokine on the phosphorylation and activity of ERα in MCF-7 breast cancer cells. We showed that exogenous administration of visfatin significantly increased the phosphorylation of ERα at serine 118 (Ser118) and 167 (Ser167) residues. Visfatin-induced Ser118 phosphorylation was diminished after treatment of cells with U0126 (MEK1/2 inhibitor). Furthermore, our results showed that visfatin-induced Ser167 phosphorylation is mediated through both MAPK and PI3K/Akt signaling pathways. Inhibition of the enzymatic activity of visfatin by FK866 had no effect on phosphorylation of ERα. We also showed that visfatin enhanced the estrogen response element (ERE)-dependent activity of ER in the presence of 17-β estradiol (E2). Additional study on T47D cells showed that visfatin also increased Ser118 and Ser167 phosphorylation of ERα and enhanced ERE-dependent activity in the presence of E2 in these cells.
Expert Identification Based on Dynamic LDA Topic Model

In recent years, human society is transferring from information society to knowledge society. Experts mastering professional knowledge are becoming more and more valuable resources in the society, therefore Expert Identification, also known as Expert Finding, became an important research field. Existed Expert Identification work is mainly based on traditional information retrieval, or standard topic models. Experts finding still faces a lot of problems, such as the missing of semantic information or the inaccuracy without changes over time taken into consideration. This paper presents a domain Expert Identification method with the improved dynamic LDA algorithm which solves these shortcomings of existed methods. Based on the standard LDA model, this method divides the corpus with large time span according to time to apply the dynamic LDA model and combines profile modelling and file modelling for expert modelling. In addition, this method considers both the semantic information of the domain and expert authority. Experiments show its feasibility and effectiveness, and its advantage over the traditional static topic model. It has opened up new application fields of dynamic topic model.
Cost-effectiveness of edoxaban compared to warfarin for the treatment and secondary prevention of venous thromboembolism in the UK

ABSTRACT Background: Venous thromboembolism (VTE), which includes pulmonary embolism (PE) and deep vein thrombosis (DVT), is the third most common acute cardiovascular disease and represents an important burden for patients and payers. Objective: The aim was to estimate the cost-effectiveness of edoxaban, a non-VKA oral anticoagulant vs. warfarin, the currently most prescribed treatment for VTE in the UK. Study design: A Markov model was built using data from the Hokusai-VTE randomised controlled trial to estimate the lifetime costs and quality-adjusted life years (QALYs) in patients with VTE treated with edoxaban or warfarin over a lifetime horizon, from the UK National Health Services perspective. The model included VTE recurrences, VTE-related complications (post-thrombotic syndrome and chronic thromboembolic pulmonary hypertension), and several types of bleeds associated with anticoagulation treatment. Patients were treated during a period of 6 months after the first VTE event, followed by flexible treatment duration (from 6 months to lifetime) after recurrence, i.e., tertiary prevention. Results: Edoxaban was found dominant vs. warfarin with 0.033 additional QALY and £55 less costs. The reduction of patient management costs, specifically monitoring costs, outweighed the higher drug costs. Edoxaban was dominant in all subgroups (index DVT only, all PE cases (PE with or without DVT), PE without DVT and PE with DVT). Cost-savings ranged from £54 to £81 while additional QALYs ranged from 0.031 to 0.046. Edoxaban was found dominant in 88.6% of cases and cost-effective in additional 10.9% of cases considering a £20,000 threshold in the probabilistic sensitivity analysis. Conclusion: Edoxaban may improve patients’ quality of life in a lifetime horizon without additional costs for the healthcare system due to lower bleeding risk and no monitoring cost compared to warfarin.
A Carrier-Based PWM for Three-Level T-Type Inverter to Tolerate Open-Circuit Fault

This paper investigates the open-circuit faults of switching devices in the three-level T-Type inverter and proposes a new tolerant control method. The three-phase current and variation of the neutral-point voltage are detected to determine the fault switch. A carrier-based pulse width modulation is developed to tolerate switch fault by transiting from three-level to two-level switching. This makes the modulation scheme easier to be implemented compared to space vector modulation. For the neutral-point fault, the fault phase is operated by two-level modulation to produce the same rating voltage. On the other hand, the inverter is able to produce balanced voltage with reduced rating by keeping the phase of half-bridge fault to the neutral-point voltage. The neutral-point voltage is also able to be balanced by using two-level modulation since it does not contribute the neutral-point current. The performance and effectiveness of the proposed tolerant control are verified by a laboratory build.
Prognostic value of 2-hour lactate level and lactate clearance for 30-day mortality and comparison with trauma scores in multi-trauma patients

Objective: Trauma scores are prone to misreading. Therefore, a readily available, objective way to estimate the mortality of the trauma patients is needed. We aimed to evaluate the prognostic utility of lactate levels, and clearance for 30-days mortality, and compare with the physiological trauma scores. Methods: All adult trauma patients (two hundred) admitted to ED were enrolled. Initial and 2-hour serum lactate levels were measured and components of GAP, MGAP, RTS, VIEWS and VIEWS-L trauma scores were calculated. Results: Final study population was 200 patients with a median age of 33 years. Mortality was 7/200 (3.5%) in 30-days. Both initial (2.3 vs. 7.7 mmol/L) and 2h-lactate (1.7 vs. 8.4 mmol/L) levels were significantly lower, and lactate clearance was significantly higher (23.8% vs. -12.0%) in survivors. Also, the change in the lactate level from 0h to 2h (2.3 vs. 1.7mmol/L) was significant in survivors, contrary to non-survivors (7.7 vs. 8.4mmol/L). VIEWS-L, VIEWS, two hour-lactate level and EMTRAS showed high specificity at the 100% sensitivity cut-offs, therefore, were the most valuable prognostic parameters in this study. Conclusion: Calculation of 2h-lactate clearance and evaluation of a 2h-lactate level may not be needed to predict long-term mortality if the initial lactate level is below 2.8mmol/L.