Miracle-dz/UniFAR-FTUs huggingface dataset:
Examination of optimal cognitive task for Near-infrared spectroscopy-based brain-computer interfaces

Brain-computer interface (BCI) is a new technology that enables communication using brain activities for patients in totally locked-in state. In this research, we examined optimal mental task for near-infrared spectroscopy-based BCI from among six tasks. SVM was used for learning, and the classification accuracy for each tasks through k-fold cross validation was calculated. As a result, it was suggested that the mental figure rotation is a task with high discrimination accuracy and less sense of burden.
DC Motor Control based on Robust run-time Optimization Algorithm

This conference paper suggests an interesting application of a Robust Extremum Seeking Algorithm for dynamic plants with uncertainties and perturbations. The main idea is develop a signal such drives a DC motor to a specific angular velocity, taking into account a perturbation of the inertia (change of mass of the load, density, spatial distribution, etc) and viscous friction and optimizing an specific functional in real-time to reach some angular velocity.
A Novel Reliability Analysis Method for Fuzzy Multi-State Systems Considering Correlation

Fuzzy multi-state systems (FMSSs) exist widely in practical engineering. It is usually difficult to evaluate the reliability of FMSSs because the reliability data is usually fuzzy due to the inaccuracy or imperfection of information, and there is often correlation between the main components and others which constitute the FMSSs. Although many research works with respect to the independent failure of components have been carried out, the master-slave relationship between the main components and others of the FMSSs is often ignored, thus unrealistic results are often obtained with this treatment. Based on fuzzy universal generating function (FUGF), an effective reliability analysis method of FMSSs considering the correlation and fuzziness is proposed in this paper. In the novel method, the fuzziness of reliability data and the master-slave relationship between the main and other components are taken into consideration, and the performance levels and corresponding probabilities of the non-main components are considered as conditional probability distributions. A case study with respect to the reliability analysis of hydraulic system is presented to illustrate the application of the proposed method.
Markov Random Field Function minimized by Stochastic Gradient Descent

A Markov Random Field Function (MRF) is used to evaluate optical flow from two consecutive images in time, it requires to be minimized or maximized an error output in order to accept or reject a optical flow value for one pixel intensity in the first image with respect a pixel intensity in the second one. In the present paper, Stochastic Gradient Descent is proposed as an optimization method to minimized the error in the Markov Random Field Function, Gradients are computed in horizontal and vertical axis to get a direction which is used to compute the following value to be evaluated by the function until convergence is reach respect a stop condition. Experiments used a pair of images taken using only one camera from the same scene, one image taken after the other with overlapping and non overlapping image sections. A pixel is selected from the first image, then evaluating the correspondence in the second image with the MRF Function, if it returns an aceptable minimum error value, and optical flow value is obtained. The method proposed in the present paper is used to compute this minimum error value using Stochastic Gradient Descent (SGD) as a minimization method, experiments will probe SGD can be used for this goal and the proposed method will be discussed describing how it works locating the optical flow in a localized area in the pixel neighborhood of the first image.
Seasonal Theanine Accumulation and Related Gene Expression in the Roots and Leaf Buds of Tea Plants (Camellia Sinensis L.)

Theanine, a unique and abundant non-proteinogenic amino acid in tea, confers to the tea infusion its umami taste and multiple health benefits. Its content in new tea shoots is dynamic in winter and spring. However, its seasonal accumulation pattern and the underlying regulation mechanism of tea plants remain largely unknown. In this study, we measured the theanine contents in the roots and leaf buds of 13 tea cultivars at four time points from winter to spring (Dec. 12, Mar. 1, Mar. 23, and Apr. 13). We found theanine accumulated significantly in the roots to as high as ∼6% dry weight. We found theanine content in the roots was constant or slightly decreased on Mar. 1 compared with Dec.12 but increased consistently on Mar. 23 and then decreased on Apr. 13 in all 13 cultivars. In the leaf buds, theanine content kept increasing from Mar. 1 to Mar. 23 and decreasing from Apr. 13 in most of the 13 cultivars, meaning it was probably both season- and developmental stage-dependent. The expression of theanine biosynthesis and amino acid transporter genes in the roots and buds at the four time points was then examined. The correlation analyses between the gene expression and theanine content suggested the expression of theanine-biosynthesis genes was generally and negatively correlated with theanine content; however, the expression of amino acid transporter genes including CsLHT was generally and positively correlated with theanine contents. Finally, we showed that CsLHT has theanine transport activity. Taken together, this study provided insight into the seasonal regulation of theanine biosynthesis and transport in tea plants during winter and spring.
Comparative Study of Core Loss Models in Three-Phase Self-Excited Reluctance Generators

To avoid the environmental impact of the hazardous waste of conventional power generation, green renewable energy sources have been introduced as alternative clean-energy sources. One of these remarkable, exploited sources is wind energy. Self-excited reluctance generators (SERG) are used to convert wind power into electric power. Extensive research studies have analyzed the dynamic, transient, and steady-state performance of SERG. In most of these studies, core loss was neglected. However, different methods have been developed that take the core loss into consideration by adding a resistance (Re) to the model of the generator. The value of Re is considered as either (i) fixed, (ii) linearly proportional to the direct axis magnetizing reactance (Xd), or (iii) a polynomial function of Xd. This paper presents a comparative study of the above-mentioned methods under different operation conditions. The effect of neglecting the core loss on the analysis of the generator is also included.
Static Detection of Control-Flow-Related Vulnerabilities Using Graph Embedding

Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges.
Experimental study on thermal characteristics during leader discharge and its recovery procedure

Long air gap discharge is the main form of external insulation in UHV power grid. The in-depth study of its discharge characteristics is beneficial to optimize the insulation level configuration in the construction to improve the safety and reliability of power grid operation and save costs. The thermodynamic characteristics of the leader discharge are the important characteristics that distinguish it from other discharge stages. The study of the thermodynamic characteristics of the leader discharge and its recovery process can provide the basis for the reliable operation of the energy Internet. In this article, a multi-parameter comprehensive observation experimental platform for long air gap leader discharge and its recovery process was built, which realized the simultaneous measurement of thermal characteristics, morphological parameters and electrical parameters. The effect of voltage amplitude on the thermal characteristics of the leader discharge and its recovery process was investigated under positive polarity standard operating impulse voltage.
Construction of superhydrophobic surfaces for antifouling and drag reduction applications

Superhydrophobic surfaces (water contact angle $\theta\gt 150^{\circ}$) with a low contact angle hysteresis have attracted much attention as a new route for antifouling and drag reduction applications. The last decade has witnessed the significant development in the design of superhydrophobic surfaces in Cassie-Baxter state inspired by the lotus leaf in nature. In this study, a simple fabrication method was developed to prepare a superhydrophobic surfaces in CassieBaxter state. The approach involved the formation of a rough structure and fluorination treatment on substrate. The asprepared surface became superhydrophobic based on the combination of rough structures and low-energy compositions. The air layer captured within the rough structures effectively prevent microorganisms’ adhesion. Meanwhile, the results indicate that the superhydrophobic surfaces show drag reduction for the air/liquid contact. The work provides a simple and highly efficient method to fabricate superhydrophobic surfaces for potential antifouling and drag reduction applications.
CMAR_A_203425 8181..8190

*These authors contributed equally to this work Objectives: To examine the predictive role of Glasgow Prognostic Score (GPS) on longterm survival in esophageal cancer. Method: Comprehensive searches of electronic databases were performed to identify potential studies that evaluated the prognostic value of pretreatment GPS in esophageal cancer patients. We combined the hazard ratios (HRs) with 95% confidence intervals (CIs) to assess the association of GPS with overall survival (OS), disease-free survival (DFS) and cancer-specific survival (CSS). Results: A total of 21 studies including 6115 patients were analyzed. Compared with patients with GPS 0, patients with elevated GPS had poorer OS (HR =2.12, 95% CI: 1.83–2.45, P<0.001) and CSS (HR =2.16, 95% CI: 1.56–2.98, P<0.001); but no significant relationship was observed between the elevated GPS and DFS (HR=2.14, 95% CI:1.00–4.61, P=0.051). Subgroup analysis outcomes were similar to overall analyses. Conclusion: Pretreatment GPS could serve as a valuable factor in predicting the prognosis of patients with esophageal cancer. More well-designed prospective studies are warranted to confirm our findings.
Towards a Knowledge-Based Learning System for The Quranic Text

In this research, an attempt to create a knowledge-based learning system for the Quranic text has been performed. The knowledge base is made up of the Quranic text along with detailed information about each chapter and verse, and some rules. The system offers the possibility to study the Quran through web-based interfaces, implementing novel visualization techniques for browsing, querying, consulting, and testing the acquired knowledge. Additionally the system possesses knowledge acquisition facilities for maintaining the knowledge base.
Causal analysis of network logs with layered protocols and topology knowledge

To detect root causes of failures in large-scale networks, we need to extract contextual information from operational data automatically. Correlation-based methods are widely used for this purpose, but they have a problem of spurious correlation, which buries truly important information. In this work, we propose a method for extracting contextual information in network logs by combining a graph-based causal inference algorithm and a pruning method based on domain knowledge (i.e., network protocols and topologies). Applying the proposed method to a set of log data collected from a nation-wide R & E network, we demonstrate that the pruning method reduced processing time by 74% compared with a single-handed causal analysis method, and it detected more useful information for troubleshooting compared with an existing area-based method.
Applications of е-Health Standards in Personalized Healthcare Systems

The paper presents an overview of e-Health standards used for implementation of Personal Healthcare systems (PHSs). Interoperability issues on the technical level are discussed addressing data formats and transmission protocols standardization. At the end, some recommendations for application of specific standards for development of PHSs are given. The directions for future development of e-Health standards are discussed.
Intent-Driven Path State Monitoring to Enable Centralized State-Aware Flow Steering

Running state monitoring for network switches and links enables the derivation of a path-based data view. Thereby, various monitoring intents like utilization and latency awareness can be targeted during data collection. Whereas each of these objectives relies on a particular set of state metrics, different monitoring methods may be run to gather the data basis serving as decision input for subsequent analysis purposes. In addition, path conditions have an impact on the state observed for individual packet streams being forwarded along a specific path. While path level state data is of relevance, e.g., to evaluate past load ratios in order to run state-aware and efficient path determination, flow level state helps to monitor flow experience conditions like achieved throughput or perceived latency, e.g., to track the compliance with flow-based requirements. This paper presents a modular architecture for path state monitoring that considers port counter query, network probing and in-band network telemetry as methods for demand-driven data collection and focuses on utilization and latency awareness as monitoring intents. State data is collected by a centralized controller in collaboration with distributed modules deployed in a switch’s data plane to run data tracking, wherefore programmable switches are used as operational basis to ensure a flexible monitoring protocol. Evaluations show that continuously collected data snapshots allow to track accurate path state trends that – w.r. t. path state-aware traffic steering – can be leveraged to improve flow-based load distribution across available path capacities and to resolve inefficiencies like imbalanced path load or congestion.
Switched-Capacitor Boost Converter for Low Step-Up Applications

The paper presents a new switched-capacitor step-up dc-dc converter which exhibits smooth output current and employs one active switch and four diodes. Compared to classical boost converter, at the same duty cycle, the proposed topology has a lower conversion ratio, thus being suitable for applications where a small step-up of the input voltage is required. The measured efficiency of about 90% over a wide duty cycle range is another feature of this new converter. As the topology belongs to the switched-capacitor family, for the first time the dc analysis is extended to the situation when unequal capacitor values are assumed. The main equations and the semiconductor stresses are evaluated. An experimental prototype of this new converter was first simulated and then practically tested and the results confirmed the theoretical considerations.
Secure Authentication and Group Key Distribution Scheme for WBANs Based on Smartphone ECG Sensor

As one of the crucial components in the emerging internet of things (IoT), wireless body area networks (WBANs) is capable of monitoring vital physiological and behavioral information of users through wearable sensors, offering a new paradigm for the next-generation healthcare systems. However, due to the inherent open wireless communicating characteristics, security and privacy issues for WBANs communication remain unsolved. Note that the deployed WBANs sensors are resource-restrained entities, which restricts its wide applications in medical environment. In this case, effective authentication scheme with efficient group key management strategy is of great significance. Moreover, with comparatively large computation ability and storage capacity, smartphone is able to perform as the vital data processing gateway for WBANs, especially in the upcoming 5G network implementation with superior transmission quality and speed. Furthermore, the WBAN sensors are responsible for continuous physiological monitoring, where the acquired biometric features could be adopted to the authentication process. For the above consideration, a secure certificateless biometric authentication and group key management for WBAN scenarios is proposed in this paper. In our design, user’s smartphone takes the role of personal controller (PC) in traditional WBANs structure. The representative features of the gathered electrocardiogram (ECG) records are applied as the distinctive biometric parameter during authentication procedure. Hence efficient authentication towards participating sensors is enabled. Subsequently, fast group key management among all validated sensors is presented, where small modification is required for dynamic key updating mechanism in sensor side. Security analysis indicates that the proposed protocol can achieve desired security properties and provide resistance to various attacks. Performance analysis demonstrates that the proposed protocol is efficient compared with the state-of-the-art WBAN authentication schemes.
Statistical Analyses of Telugu Text Corpora

Corpora and corpus based studies are relatively recent and limited in Indian languages as compared to other major languages of the world. This paper is about statistical analyses of Telugu text corpora. Telugu is one of the major languages of India, ranking third position in terms of number of speakers in the country, spoken mainly in the southern state of Andhra Pradesh. Telugu belongs to the Dravidian family of languages, characterized by a rich system of morphology resulting in long and complex word forms. In this paper, we analyze a nearly 39 Million word text corpus of Telugu developed by us. This is perhaps for the first time that such a detailed statistical analysis of a large corpus is being carried out for any Dravidian language. We highlight the complexity of word forms and the effect of this on the statistical characterization of the Telugu language.
Design and Analysis of a Half-Bridge Converter-Based Hierarchical Battery Equalizer

In this manuscript, a half-bridge converter-based hierarchical battery equalizer is proposed and analyzed. On the low-voltage side of this equalizer, two adjacent battery cells and corresponding switches and transformer windings form a half-bridge unit, and the cells in this unit can be equalized by means of the magnetizing inductance of the transformer. Further, the secondary sides of these transformers are linked to a high-voltage half-bridge unit, whose output connects the entire battery string to realize the equalization between the low-voltage half-bridge units. The proposed scheme achieves time-division multiplexing of power devices in the equalization process, which effectively reduces the cost and volume of the equalizer, and improves its modularity and practicality. Besides, the two-stage balancing structure and continuously controllable equalization current ensure the equalization speed and equalization efficiency. The operating state and principle, parameter calculation and control strategy of the equalizer are thoroughly analyzed. Finally, the equalization verification results based on four series-connected battery cells fully validate the feasibility and correctness of the proposed two-stage hierarchical battery equalizer.
Dynamic Behavior of Constant Speed WT based on Induction Generator Directly connect to Grid

The objective of this paper is determinate dynamic behavior of constant step wind turbine with squirrel cage as generator directly connected to a grid. Initially, all models and parameter used for simulation are shown; then a test system is simulated on dynamic state for evaluate the implications of wind speed fluctuations over wind turbine performance. Short circuits on terminals of wind turbines were simulated to evaluate the impact of these technologies on fault conditions. Several implications over clearing time was found on and related in this paper.
Dynamic 2D patterns for shading 3D scenes

We describe a new way to render 3D scenes in a variety of non-photorealistic styles, based on patterns whose structure and motion are defined in 2D. In doing so, we sacrifice the ability of patterns that wrap onto 3D surfaces to convey shape through their structure and motion. In return, we gain several advantages, chiefly that 2D patterns are more visually abstract - a quality often sought by artists, which explains their widespread use in hand-drawn images. Extending such styles to 3D graphics presents a challenge: how should a 2D pattern move? Our solution is to transform it each frame by a 2D similarity transform that closely follows the underlying 3D shape. The resulting motion is often surprisingly effective, and has a striking cartoon quality that matches the visual style.
CMAR_A_218686 8845..8853

Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Air Force Military Medical University, Xi’an, Shaanxi 710032, People’s Republic of China Aims: Surgery is the primary treatment option for patients with gastric cancer, however the rate of postoperative complications are still high. The implementation of surgical safety checklists (SSCs) has been shown to reduce morbidity and mortality. This study aimed to evaluate the effect of SSCs on the clinical outcomes of gastric cancer. Methods: A total of 881 gastric cancer patients who underwent D2 gastrectomy from May 2009 to April 2011 in a large teaching hospital in China were included in this retrospective study. Patients were matched and divided into the control group (SSC nonimplementation) and intervention group (SSC implementation). The outcomes including intraoperative condition, postoperative complications, and prognosis were then compared between the groups. Results: The control group comprised 414 patients (47.0%), and the intervention group included 467 patients (53.0%). Patients in the intervention group had a significantly shorter length of postoperative stay (P < 0.001). Operation time, blood loss, blood transfusion, and hospital charges were comparable between the two groups (all P > 0.05). SSC was not associated with postoperative complications (all P > 0.05). Overall survival was also comparable between patients in the two groups (P > 0.05). Conclusion: The implementation of an SSC was associated with a decreased length of postoperative stay in gastric cancer patients following D2 gastrectomy but did not significantly affect the other outcomes.