Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Slotless and Coreless Permanent Magnet Synchronous Machines for Electric Aircraft Propulsion

Electrification of the propulsion motor for electric aircraft has become an emerging research hotspot recently. In fact, the direct-drive system on the electric aircraft is to coaxially connect the motor with the propeller to produce the thrust or lift force. Also, aircraft propulsion requires high speed, high power density, high efficiency and ultra-light weight for direct drive motors. This paper focuses on the slotless and coreless stator topologies to achieve smooth propulsion. First, the machine models are proposed and compared, with explanations of unique designs. Then, since the slotless windings are very different with slotted stator windings, the winding analysis is carried out, including the arrangement, flux linkage and induced voltage. Furthermore, the no-load and rated load performances for the proposed machines are implemented by FEA tools. Finally, comparison results are used to validate the superiority of the proposed machines.
Fractional Abundance Estimation of Mixed and Compound Materials by Hyperspectral Imaging.

The mechanical and chemical properties of a compound material are determined by the fractional abundances of its components. In this work, we present a spectral unmixing technique to estimate the fractional abundances of the components of mixed and compound materials from hyperspectral images. The estimation of fractional abundances in mixed materials faces the main challenge of intimate mixing. In compound materials, the mixing with water causes changes in chemical properties resulting in spectral variability and non-linearity. To address these challenges, a supervised method is proposed that learns a mapping from the hyperspectral data to spectra that follow the linear mixing model. Then, a linear unmixing technique is applied on the mapped spectra to estimate the fractional abundances. To demonstrate the potential of the proposed method, experiments are conducted on hyperspectral images from mixtures of red and yellow clay powders and hardened mortar samples with varying water to cement ratios.
Surgical Treatment of Nasopharyngeal Malignancies: Role of Endoscopic Endonasal Approaches

Development of endoscopic techniques has progressed onward with the recent enrichment of anatomical knowledge and experience; as well as the development of advanced technologies. Expansion of the clinical indications for endoscopic endonasal approaches has produced a paradigm shift in the surgical management of nasopharyngeal malignancies similar to that of other areas of the ventral skull base. Endoscopic endonasal nasopharyngectomy is a feasible and minimal access technique for the resection of select recurrent and radioresistant nasopharyngeal malignancies. It bestows the advantages of lack of external incisions or scars, decreased trauma to adjacent soft tissues and bone, reduced risk of neurological damage, and superior postoperative quality of life. A comprehensive review of the available literature shows that endoscopic results are better than re-irradiation or surgery via conventional open approaches. We acknowledge that these results may be influenced by a bias in patient selection. However, they suggest that, in properly selected cases, an endoscopic endonasal nasopharyngectomy affords low morbidity rates without compromising oncologic outcomes. Nonetheless, one should be fully aware of the indications and limitations of using endoscopic endonasal approach to achieve optimal outcomes. Endoscopic endonasal approaches plays a strong role in the treatment of nasopharyngeal malignancies
Designing, Modeling, and Control Allocation Strategy of a Novel Omnidirectional Aerial Robot

A novel omnidirectional aerial robot is introduced in this paper. It is driven by four 2-DOF tilting modules mounted at four vertices of a regular tetrahedron. This configuration ensures that at least three rotors remain on one horizontal plane while the frame rotates at any angle. The robot is thus capable of flying at any orientation in SE (3). This significantly expands application areas of the robot. Reliability is enhanced thanks the over-actuation design and configuration of the rotors. Dynamic equations of motion are derived based on the mechanical design. A control strategy is represented that allows decoupling of rotational and translational dynamics. A conceptual method for control allocation is also proposed. This takes into consideration the input saturation as well as other physical constraints. Furthermore, robustness is provided by re-configuration of rotors if some actuator encounters failures. Preliminary simulation and experiments demonstrate feasibility of the novel design and capability of this aerial robot.
Robustness Improvement of Two-Vector-Based Model Predictive Current Control for Permanent Magnet Synchronous Motor

The conventional model predictive current control (MPCC) suffers from the errors caused by parameter uncertainties, which will deteriorate the motor performance. This paper proposes a two-vector-based predictive current control with the improved parameter robustness. Two arbitrary voltage vectors are applied in each sampling period to enhance steady-state performance. By adding the prediction error to the prediction stage of the proposed two-vector-based MPCC, the errors related to the prediction stage and vector duration calculation can be effectively eliminated. The simulated and experimental results are given to verify the proposed method.
Semantic Automation Systems, a Suitable Approach for Automation Networks in the Industry 4.0

The Industry 4.0 demands a change ofparadigm for the automation engineering and some new requirements that has to be accomplished like data integration, big data processing, semantics, communication protocols, and standardization. The interoperability is another essential concept, it is a key for the development of the Industry 4.0. This work presents a semantic approach to model automation systems through an ontology, and its applicability in industrial automation networks using OPC UA. The Automation I4.0 Ontology, the model proposed in this project, provides a semantic tool to represent high-, middle, and low-level concepts of automation systems, and offers a way to integrate directly in software applications and semantic-based communications protocols such as OPC UA. It demonstrates the suitability of the semantic-based automation systems to be implemented by semantic-based or object-based communication protocols in Industry 4.0 applications. This initiative intends to empower the development of the Industry 4.0 and the Internet of Things for the automation engineering.
BiN: A Two-Level Learning-Based Bug Search for Cross-Architecture Binary

With the popularity of IoT (Internet of Things) devices, the security risks of these devices are increasing. However, due to the multisource heterogeneity of IoT devices, there are significant differences between the vulnerability detection of the Internet of Things and the PC-based vulnerability search method. Therefore, determining how to accurate search for vulnerabilities in large-scale cross-platform binary executable files is an urgent problem to be solved. At present, the solution to this problem mostly calculates code similarities by generating a CFG (control flow graph) from binary code, but due to the choice of architecture, OS (operating system) or compilation options, the same source code will be compiled into different assembly codes. The performance of existing vulnerability search methods for cross-architecture binaries has been challenged. To alleviate the vast differences in the assembly codes caused by different compilation scenarios, this paper proposes a cross-platform large-scale binary vulnerability search method based on two-level feature semantic learning. The contribution is that we have defined a new functional structured signature method to mitigate the massive grammatical and structural differences of binary files caused by different compilation environments. Moreover, we reasonably integrate the hierarchical model of Structure2Vec and GAT (graph attention network) and implement training from the internal control flow characteristics of the function and the call relationship between functions to obtain a more accurate functional semantic expression.
Machine Learning Assisted Content Delivery at Edge of Mobile Social Networks

The demand of content delivery (for video streaming, photos, mobile web pages) has explosively grown in recent years, leading to an excessive volume of network traffic at the edge of mobile networks. Mobile social networking (MSN) is widely recognized as the most popular mobile application, as it attracts the largest number of users, and consumes the highest daily time of each user on average. However, mobile users can be distantly located, and their preferences to content may vary over time, leading to highly dynamic distribution of network traffic. Hence, it is necessary to allocate network edge resources for meeting the growing user demand to different types of contents spatially and temporally. To study this problem, we establish a machine learning framework for predicting how users' location and their consumed content can affect the traffic distribution in the underlying content distribution network. Given the predicted distribution of content demands, the proposed smart content delivery scheme is able to instruct the edge servers to dynamically deliver the content to edge users. We evaluate the performance of the conventional regression models and the proposed content delivery scheme over a real-world content distribution network for MSNs. Results reveal that the neural network model outper-forms other approaches by increasing the hit ratio by 30%, and saving the network cost by 15%.
Closed-Loop Data-Driven Estimation on Passivity Property

In this paper, the estimation of passivity based on data is considered. In recent works, a sampling scheme to estimate the passivity level of unknown systems from iterative open-loop experiments was introduced. However, there are many cases where it is useful to estimate the passivity level of a sub-system in closed-loop systems; therefore, we extend this method to estimate the passivity level from closed-loop experiments.
Deep Belief Network-Based Gas Path Fault Diagnosis for Turbofan Engines

A gas path fault diagnosis scheme for turborfan engines based on deep belief network (DBN) is presented. The scheme is constructed according to the diagnosis principles of gas path faults and is composed of a turbofan engine reference model and a DBN diagnosis model. The DBN diagnosis model is a stacked network of several restricted Boltzmann machines (RBM) and was trained with the contrastive divergence algorithm and the back propagation algorithm. To optimize the DBN performance, the orthogonal tests L25 (57) were adopted to determine the hyper-parameters, such as learning rate, hidden layer number, hidden layer neuron number, etc. The proposed DBN-based scheme was applied to diagnose the gas path faults of a turbofan engine model and compared with BP-based and SVM-based schemes. The results show that the fault diagnosis accuracy of the DBN-based scheme is as high as 96.59%, and the DBN-based scheme has dramatic performance advantages over the other two schemes.
BalletVR: a Virtual Reality System for Ballet Arm Positions Training

In a ballet studio, the teacher plays an important role in the process of learning positions and movements. However, there are occasions where the student needs to practice while the teacher is unavailable. In this situation, the student has no orientation and is likely to create bad habits during training, which may lead to worse results than before. In this work, we present the BalletVR, a Virtual Reality Kinect-based application for guiding ballet dancers through the practice of basic ballet arm positions. The results show our system's potential to be used by students as an alternative for autonomous ballet basic positions training.
What Do Developers Discuss about Biometric APIs?

With the emergence of biometric technology in various applications, such as access control (e.g. mobile lock/unlock), financial transaction (e.g. Alibaba smile-to-pay) and time attendance, the development of biometric system attracts increasingly interest to the developers. Despite a sound biometric system gains the security assurance and great usability, it is a rather challenging task to develop an effective biometric system. For instance, many public available biometric APIs do not provide sufficient instructions / precise documentations on the usage of biometric APIs. Many developers are struggling in implementing these APIs in various tasks. Moreover, quick update on biometric-based algorithms (e.g. feature extraction and matching) may propagate to APIs, which leads to potential confusion to the system developers. Hence, we conduct an empirical study to the problems that the developers currently encountered while implementing the biometric APIs as well as the issues that need to be addressed when developing biometric systems using these APIs. We manually analyzed a total of 500 biometric API-related posts from various online media such as Stack Overflow and Neurotechnology. We reveal that 1) most of the problems encountered are related to the lack of precise documentation on the biometric APIs; 2) the incompatibility of biometric APIs cross multiple implementation environments.
V-PSC: A Perturbation-Based Causative Attack Against DL Classifiers' Supply Chain in VANET

DL Based classifiers can attain a higher accuracy with less storage requirement, which suits perfectly with the VANET. However, it has been proved that DL models suffer from crafted perturbation data, a small amount of such can misguide the classifier, thus backdoors can be created for malicious reasons. This paper studies such a causative attack in the VANET. We present a perturbation-based causative attack which targets at the supply chain of DL classifiers in the VANET. We first train a classifier using VANET simulated data which meets the standard accuracy for identifying malicious traffic in the VANET. Then, we elaborate on the effectiveness of our presented attack scheme on this pre-trained classifier. We also explore some feasible approaches to ease the outcome brought by our attack. Experimental results show that the scheme can cause the target DL model a 10.52% drop in accuracy.
Preterm birth and cardiometabolic risk factors in adolescence and early adulthood

About 11% of infants are born preterm (before 37 weeks of gestation) worldwide. Adults born preterm with very low birth weight show enhancement of cardiometabolic risk factors such as elevated blood pressure and impaired glucose regulation compared with their peers born at term. Not all the cardiometabolic risk factors related to preterm birth are known, or whether they apply to those born less preterm, although about 80% of premature infants are born late preterm. The association between preterm birth and cardiometabolic risk factors in adolescence and adulthood was investigated in three cohort studies: The Helsinki Study of Very Low Birth Weight Adults, the Northern Finland Birth Cohort 1986, and the ESTER study. Preterm birth over its whole range has a long-term impact on a child’s health in later life: adults born preterm with very low birth weight had lower resting energy expenditure, but higher resting energy expenditure per unit lean body mass than their peers born at term. Adolescent girls born before 34 weeks of gestation had higher blood pressure and boys have elevated levels of LDL cholesterol and apolipoprotein B. Adults born preterm were more likely to be obese and to have hypertension or metabolic syndrome than their peers born at term. In addition to conventional biomarkers of cardiometabolic disorders, they had alterations in other cardiometabolic biomarkers, such as uric acid and liver transaminases. Adolescents and adults born preterm are at greater risk of developing cardiometabolic disorders than their peers born at term. Most of the cardiometabolic risk factors related to preterm birth are modifiable. Favorable early life circumstances of premature infants, such as optimal nutrition and reduction of stress in neonatal intensive care units, might reduce the risk of later cardiometabolic disorders. In addition, children and adults born preterm might particularly benefit from primary prevention such as screening for additional risk factors and promotion of healthy lifestyles.
Security and Fairness in IoT Based e-Health System: A Case Study of Mobile Edge-Clouds

Through IoT, humans and objects can be connected seamlessly, to guaranty improved quality of service (QoS). IoT-driven e-Health systems benefit from such rich network setting, to transmit health information and deliver health services. It is expected to grow massively in scale, but for that to happen, several issues need to be addressed, including security and trust. Edge computing paradigms, such as Fog computing and Cloudlet, are already popular in IoT based e-Health domain. Fog nodes are leveraged to reduce latency between IoT devices and remote cloud computing infrastructure. In this work, we explain how Mobile edge-clouds, which is a less popular edge computing paradigm, can be employed to achieve similar or lower latency, at a lower cost. We also propose a lightweight mechanism for security and fairness in e-Health protocols that are based on mobile edge-clouds and other paradigms. Detailed simulation experiments show that the proposed method is scalable and can efficiently mitigate attacks that are targeted at e-Health information and the network.
Research on novel high torque density transverse-flux permanent magnet motor

A new transverse flux permanent magnet motor(TFPMM) with skewed PMs and shared-border pole stator is presented in the paper, which can make full use of laminations instead of soft magnetic composite (SMC) material. This new TFPMM also has a higher torque density by advantageously utilizing the laminations and taking full advantage of the radial space. To clarify the distribution of inner magnetic field and the optimal design rule for the new TFPMM, the Maxwell 3D finite element method(FEM) has to be adopted, and a simplified model for analysis and optimizations is presented. A prototype scheme is presented. The theoretical analysis was verified by finite element analysis and prototype test results.
Overview of IoT Security Architecture

In the Internet of Things, services can be provisioned using centralized architectures, where central entities acquire, process, and provide information. Alternatively, distributed architectures, where entities at the edge of the network exchange information and collaborate with each other in a dynamic way, can also be used. In order to understand the applicability and viability of this distributed approach, it is necessary to know its advantages and disadvantages not only in terms of security and privacy challenges. The purpose of this paper is to show that IoT three-layer security logic architecture and the distributed approach has various challenges that need to be solved, but also various interesting properties and strengths. It also focuses on security issues such as privacy protection and intrusion detection, etc. We also propose a high-level security management scheme based on blockchain for different IoT devices in the full life cycle. It also summarizes the main causes of the deficiencies and security problems in existing research work.
OSAIFU: A Source Code Factorizer on Android Studio

Programs written in the event-driven style that are typical of mobile and/or Web applications are not easy to read through. For the purpose of reducing the burden put on software developers, we propose a tool for supporting program understanding, named OSAIFU, implemented as a plugin for Android Studio. OSAIFU automatically factorizes source code at hand, i.e., extracts implemented features from source code by analyzing relations among program elements and shows the summary to the user as a list of clickable tag clouds. The user can check how each feature is implemented by clicking a tag cloud and looking at the corresponding part in the source code that is highlighted to make it easy to spot. The results of preliminary case studies show the effectiveness of OSAIFU. OSAIFU is available at https://imozuru.github. io/OSAIFU/.
Tratamiento de la Artritis Reumatoidea con AntiTNF Experiencia en Uruguay Treating Rheumatoid Arthritis with Anti-TNF. Experience in Uruguay

Arch med Interna 2012 - 34(2): 38-41 Rheumatoid Arthritis (RA) is a systemic autoimmune disease, associated to an impairment of quality of life and increased morbimortality. Remissive the- rapies of RA include therapies with Disease modifying Anti Rheumatic Drugs (DmARDs) and biologic Agents. Therapy with the Anti-Tumor Necrosis Factor (Anti-TNF) was incorporated to the drugs reimbursed by the National Resou- rce Fund (FNR) in march 2010. The study assessed a cohort of patients that started therapy before may 1 2011. Objectives: to characterize the population and to evaluate the efficacy outcomes of therapy, based on the evolution of DAs 28 scores. Results: 69 patients were enrolled. The mean pre and post therapy DAs 28 scores showed improvement (p < 0.05). Improvement criteria in 76.4% (low grade activity 37.3% and remission 14.9%). Adverse effects in 20 patients (in 3 cases it led to discontinuation of therapy). Drug changes, primary cause: treatment failure (8/9). Conclusions: Over 70% achieve im- provement criteria; hence, although the results in this first evaluation of the response may be classified as good, they need to be monitored with time.
An Integrated Drive Power Converter Topology for Plug-in Hybrid Electric Vehicle with G2V, V2G and V2H Functions

Plug-in hybrid electrical vehicle (PHEV) is an attractive means of transportation due to the less carbon dioxide emissions and high fuel efficiency. This paper presents an integrated electrified drive converter with a permanent magnet synchronous generator (PMSG) and a switched reluctance motor (SRM) for PHEV with flexible charging functions. In driving mode, four different driving modes are achieved by controlling the front-end and middle converters according to the state-of-charge (SOC) of the battery bank and road conditions. In charging mode, the integrated drive converter can be rearranged to perform the grid to vehicle (G2V), vehicle to grid (V2G) and vehicle to home (V2H) functions. In G2V mode, the battery bank are charged from the grid by utilizing the font-end converter. During V2G mode, the real power in battery bank can be sent back to grid. For V2H mode, the front-end converter can be used as an inverter to power home appliances.
Data Fusion of Physical Variables as Bearing for User Thermal Comfort Analysis in Closed Environments

Context-aware systems use data obtained from variables, for example environmental, to adapt to changes in the environment. Due to this, it is necessary to analyze these variables to maintain healthy environmental levels and ensure users thermal-comfort in work spaces. In this sense, data fusion provides a way to unify data as a support for analysis of users thermal comfort. Thus, for this work, a sensor network was implemented to acquire data on environmental variables, such as temperature, humidity, air quality, light intensity, and noise level. While for the data fusion, Bayesian estimation was used as a method to generate knowledge about integrated values as a support for the analysis of users thermal comfort. This analysis was based on the specifications of the ASHRAE 55–2017 standard, which defines the thermal comfort zones according to users activities, and season. Results showed that the values of environmental variables were close to the thermal zone considered comfortable in summer for users. So, to improve thermal comfort conditions, other ventilation, cooling and air extraction systems could be installed in the work area. The importance of these analyses is to ensure a healthy and comfortable environment for users while performing daily activities.