Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Approach to Development and Validation of Artificial Intelligence Systems for Drilling

Drilling operations for geothermal and hydrocarbon energy involves technology that controls a highly dynamic and complex process. A transition from assisted control to a higher level of automation not only requires a step-change in technology but also in infrastructure for development and validation of these technologies. The lack of realistic and scalable test environments for automated drilling systems delays qualification of new technology and limits the potential for the industry to reduce costs and minimize the carbon footprint. Since 2016, a high-fidelity drilling simulator has been established and tested for development and validation of Artificial Intelligence (AI) systems for drilling operations. The simulator can be accessed through a web Application Programming Interface (API) and run from a web client or as a Hardware-in-the-loop (HIL) simulator from a control system environment with programmable logic controllers (PLCs). The web enablement makes the simulator suitable for testing AI systems from anywhere in the world without any installation of software. The HIL functionality enables a workflow from early development stages to industrial pilots involving testing in a realistic environment. This paper describes the objectives of the project, the technical solutions, and the results obtained.
PKCε stimulation of TRPV1 orchestrates carotid body responses to asthmakines

We have previously shown that carotid body stimulation by lysophosphatidic acid elicits a reflex stimulation of vagal efferent activity sufficient to cause bronchoconstriction in asthmatic rats. Here, we show that pathophysiological concentrations of asthma‐associated prototypical Th2 cytokines also stimulate the carotid bodies. Stimulation of the carotid bodies by these asthmakines involves a PKCε–transient receptor potential vanilloid 1 (TRPV1) signalling mechanism likely dependent on TRPV1 S502 and T704 phosphorylation sites. As the carotid bodies’ oxygen sensitivity is independent of PKCε–TRPV1 signalling, systemic blockade of PKCε may provide a novel therapeutic target to reduce allergen‐induced asthmatic bronchoconstriction. Consistent with the therapeutic potential of blocking the PKCε–TRPV1 pathway, systemic delivery of a PKCε‐blocking peptide suppresses asthmatic respiratory distress in response to allergen and reduces airway hyperresponsiveness to bradykinin.
A Review on Emotional Speech Recognition: Resources, Features, and Classifiers

One of the fundamental elements with respect to communication amid human beings is emotion. To outspread the responsibility of sentiment in the direction of HCI, it is necessary for central processing units to possess certain inherent abilities for recognizing various emotional states of a user. This study aims to review emotional speech recognition taking into consideration three aspects. First, to offer latest records of obtainable emotional speech data that addresses numerous emotional states, languages, speakers, and speech type. Second, to provide different types of features, use of prominent features for emotional speech recognition, and how accessing emotions influences features. Third, to review different techniques for categorizing a speech into emotional states.
Bacterial OTU deubiquitinases regulate substrate ubiquitination upon Legionella infection

Legionella pneumophila causes a severe pneumonia known as Legionnaires’ disease. During the infection, Legionella injects more than 300 effector proteins into host cells. Among them are enzymes involved in altering the host-ubiquitination system. Here, we identified two LegionellaOTU (ovarian tumor)-like deubiquitinases (LOT-DUBs; LotB [Lpg1621/Ceg23] and LotC [Lpg2529]). The crystal structure of the LotC catalytic core (LotC14-310) was determined at 2.4 Å. Unlike the classical OTU-family, the LOT-family shows an extended helical lobe between the Cys-loop and the variable loop, which defines them as a unique class of OTU-DUBs. LotB has an additional ubiquitin-binding site (S1’), which enables the specific cleavage of Lys63-linked polyubiquitin chains. By contrast, LotC only contains the S1 site and cleaves different species of ubiquitin chains. MS analysis of LotB and LotC identified different categories of host-interacting proteins and substrates. Together, our results provide new structural insights into bacterial OTU-DUBs and indicate distinct roles in host–pathogen interactions.
Basketball Footwork Recognition using Smart Insoles Integrated with Multiple Sensors

In the basketball training, statistical data of basketball footwork can be used to improve training level. However, most basketball motion recognition systems are with high cost, and the recognition of footwork is often overlooked. In this paper, we propose a system to recognize basketball footwork using smart insoles. The system collects data through the three-axis accelerometer and three-axis angular velocity meter embedded in smart insoles. Then, five kinds of basic basketball footwork such as sideslip step, back step, cross step, jab step and jump step can be recognized. In this paper, K-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are used for footwork recognition. The experimental results show that the proposed system can recognize five kinds of basketball footwork effectively.
Impedance spectroscopy: From laboratory instrumentation to field sensors

Impedance spectroscopy is a measurement method applied in many fields of science and technology, such as electrochemistry, material science, biology and medicine [1]. For impedance spectroscopy (IS), knowledge from several fields of science and technology about measurement procedures, electrochemical and physical phenomena, modeling, signal processing and electronic systems is indispensable. For this purpose, the IEEE Instrumentation and Measurement Society launched a technical committee on this field, acting as a platform for promoting experience exchange and networking between scientists and experts from industry. It serves to share the knowledge and experience between scientists and to support new comers aiming to specialize in the field of impedance spectroscopy.
Miniaturized Implantable Rectenna for Far-Field Wireless Power Transfer

A compact rectifier-antenna (i.e. rectenna), integrating a compact patch antenna with a rectifier, is designed for the far-field wireless power transfer of implantable medical devices. The proposed rectenna operates in the Industrial, Scientific and Medical (ISM) band of 902–928 MHz. The sizes of the antenna are miniaturized to π × 42 × 1.27 mm3 by etching a meandering structure on the radiation patch. Through exciting two adjacent resonant frequencies, the simulated impedance bandwidth is extended to 36.6% (0.78–1.13 GHz) with |S11| below −10 dB. Furthermore, on the back of the antenna, a voltage doubled rectifier circuit is designed and analyzed. The simulated RF-to-DC conversion efficiency can be up to 58.3% at −5 dBm.
Ensemble Learning With Attention-Integrated Convolutional Recurrent Neural Network for Imbalanced Speech Emotion Recognition

This article addresses observation duplication and lack of whole picture problems for ensemble learning with the attention model integrated convolutional recurrent neural network (ACRNN) in imbalanced speech emotion recognition. Firstly, we introduce Bagging with ACRNN and the observation duplication problem. Then Redagging is devised and proved to address the observation duplication problem by generating bootstrap samples from permutations of observations. Moreover, Augagging is proposed to get oversampling learner to participate in majority voting for addressing the lack of whole picture problem. Finally, Extensive experiments on IEMOCAP and Emo-DB samples demonstrate the superiority of our proposed methods (i.e., Redagging and Augagging).
A Novel Polar Rapid Transfer Alignment for Shipborne SINS Under Arbitrary Misalignments

The traditional polar rapid transfer alignment (RTA) are generally modeled under small angle misalignment assumption. Those linearized and nonlinear polar RTA error models presented, so far, are not capable of accurately representing the nonlinear properties of the system where the all-orientation large attitude errors happen between the master and slave strap-down inertial navigation systems (SINS). It cannot satisfy the accuracy for the SINS initialization and has a singularity in computation. In this article, the misalignment quaternion in the grid frame was selected as the state and observation. An innovative polar RTA algorithm using the quaternion matching and the augmented unscented Kalman filter (UKF) was then proposed to estimate all-orientation large attitude misalignments in polar regions. Furthermore, the associated error propagation equations were redesigned in the grid frame and with compensations for the lever-arm effect. The augmented UKF model was further adjusted to apply. Simulation and semi-physical experiment results demonstrated that the performance of the innovative polar RTA algorithm proposed here outperformed those via traditional polar RTA techniques, especially with 3-axis large-angle misalignments, and was even robust with the disturbance of lever-arm effect aggravated by the harsh polar environment.
The Determinants for Food Safety Push Notifications on Continuance Intention in an E-Appointment System for Public Health Medical Services: The Perspectives of UTAUT and Information System Quality

Compared to other appointment methods in public hospitals, registering through the Internet or utilizing e-appointments, or registering online as an outpatient, can provide more information to the user. This research investigated the integration of unified theory of the acceptance and use of technology and information system quality in determining factors that influence the adoption of e-appointments by patients, based on the requirements of food safety consultation in Taiwan. Empirical data from 369 valid samples were assessed using Partial Least Squares (PLS). The key findings of this study indicated that patients’ acceptance of e-appointments was influenced by users’ perceptions (i.e., performance expectancy and facilitating conditions), along with information quality and service quality. The practical and academic implications are provided for future practitioners and scholars, and to enhance patients’ usage of e-appointments in their healthcare activities.
The core clock transcription factor BMAL1 drives circadian β-cell proliferation during compensatory regeneration of the endocrine pancreas

In this study, Petrenko et al. examined the role of pancreatic islets in β-cell regeneration after the massive ablation of β cells by doxycycline-induced expression of diphtheria toxin A (DTA) in Insulin-rtTA/TET-DTA mice. Using reporter genes expressing α- and β-cell-specific fluorescent proteins in these mice they could follow the fate of α and β cells separately, and they found that DTA induction resulted in an acute hyperglycemia, which was accompanied by dramatic changes in gene expression in residual β cells, and in arrhythmic Bmal1-deficient mice, which lack circadian clocks, no compensatory β-cell proliferation was observed, and the β-cell ablation led to aggravated hyperglycemia, hyperglucagonemia, and fatal diabetes.
MRI Brain Tumour Segmentation using a CNN Over a Multi-parametric Feature Extraction

A Brain tumour is a collection of an abnormal mass of tissue that can be grown as cancerous. This pathology can be detected using noninvasive techniques such as CT and MR. Despite CT can form a three-dimensional computer model by taking multiple X-rays shots, the MRI scans are highly preferred since they do not use ionizing energy on its captures and they also provide sufficient information to confirm a diagnosis, however, MRI scans have a lot of noise which can reduce the accuracy of the diagnosis. Therefore, many works in the state of the art try to solve these issue using first a filtering method to clear the noise and then a semantic classification algorithm such feature pyramid network, mask R CNN and random forest classifiers trained over the images acquired with MRI technique extracting grayscale intensity, spatial proximity and texture similarity features, however, segmentation image using these methods does not have sufficient accuracy. Thus, this work proposes to look forward over the FLAIR images on the BRATS 2015 training dataset that is composed by 155 captures of axial cuts from where the principal and adjacent layers that have the highest amount of information are used to reformulate and increase data features that lead on a pixel-based classifier U-net proposed performs a semantic segmentation with a precision of 76%, which improves in up to 23% precision compared with the random forest-based method that obtained a 53% of precision.
Comparative Analysis of Five Different PWM Techniques on Three-Phase Voltage Source Inverter fed Induction Motor Drive

Demand for induction motor is ever-growing. With increasing demand, a necessity arises to find efficient motor controlling method. PWM technique is efficient method used to vary voltage and frequency within inverter. A comparative study of five different PWM techniques of three-phase inverter for best induction motor drive performance is presented here using Simulink simulation. Sinusoidal PWM, Sixty-Degree PWM, Trapezoidal PWM, Third Harmonic Injected PWM (THIPWM), Space Vector PWM techniques are presented here. Motor parameters like rotor speed, rotor steady speed obtaining time etc. along with inverter output voltage THD & current THD were observed by varying modulation index, carrier frequency and load torque of motor drive. The simulation result shows that THIPWM exhibits the best induction motor drive performance compared to other PWM techniques. Using THIPWM, effect of high carrier frequency on rotor current, stator current, electromagnetic torque etc. is also presented here.
Vasohibin-2 Aggravates Development of Ascending Aortic Aneurysms But Not Abdominal Aortic Aneurysms Nor Atherosclerosis in ApoE Deficient Mice.

BACKGROUND Vasohibin-2 (VASH2) has been isolated as a homologue of vasohibin-1 (VASH1) that promote angiogenesis counteracting with VASH1. Chronic angiotensin II (AngII) infusion promotes both ascending and abdominal aortic aneurysms (AAs) in mice. The present study aimed to investigate whether exogenous VASH2 influenced AngII-induced vascular pathology in apolipoprotein E deficient (ApoE -/-) mice. METHODS Male, ApoE -/- mice (9 to 14 weeks old) were injected with Ad LacZ or Ad VASH2. After a week, saline or AngII (1,000 ng/kg/min) was infused into the mice subcutaneously via mini-osmotic pumps for 3 weeks. Consequently, all these mice were divided into 4 groups: saline + LacZ (n=5), saline + VASH2 (n=5), AngII + LacZ (n=18), and AngII + VASH2 (n=17). RESULTS Exogenous VASH2 had no significant effect on ex vivo maximal diameters of abdominal aortas (AngII + LacZ; 1.67±0.17 mm, AngII + VASH2; 1.52±0.16 mm, n.s.) or elastin fragmentation and accumulation of inflammatory cells. Conversely, exogenous VASH2 significantly increased intima areas of aortic arches (AngII + LacZ; 16.6±0.27 mm 2, AngII + VASH2; 18.6±0.64 mm 2, p=0.006). VASH2 effect of AngII-induced ascending AAs was associated with increased cleaved caspase-3 abundance. AngII-induced atherosclerosis was not altered by VASH2. CONCLUSION The present study demonstrated that augmented VASH2 expression had no effect of AngII-induced abdominal AAs or atherosclerosis, while increasing dilation in the ascending aorta.
LogTransfer: Cross-System Log Anomaly Detection for Software Systems with Transfer Learning

System logs, which describe a variety of events of software systems, are becoming increasingly popular for anomaly detection. However, for a large software system, current unsupervised learning-based methods are suffering from low accuracy due to the high diversity of logs, while the supervised learning methods are nearly infeasible to be used in practice because it is time-consuming and labor-intensive to obtain sufficient labels for different types of software systems. In this paper, we propose a novel framework, LogTransfer, which applies transfer learning to transfer the anomalous knowledge of one type of software system (source system) to another (target system). We represent every template using Glove, which considers both global word co-occurrence and local context information, to address the challenge that different types of software systems are different in log syntax while the semantics of logs should be reserved. We apply an LSTM network to extract the sequential patterns of logs, and propose a novel transfer learning method sharing fully connected networks between source and target systems, to minimize the impact of noises in anomalous log sequences. Extensive experiments have been performed on switch logs of different vendors collected from a top global cloud service provider. LogTransfer achieves an averaged 0.84 F1-score and outperforms the state-of-the-art supervised and unsupervised log-based anomaly detection methods, which are consistent with the experiments conducted on the public HDFS and Hadoop application datasets.
Research Topic Modeling: A Use Case for Data Analytics on Research Project Data

To provide assistance in effectively planning and designing Thailand’s research direction, a topic modeling algorithm is performed on the country’s research project data in order to extract their information in greater details. For this study, publications from the records of government-funded research projects over the period of 12 years (2008-2019) were collected. The projects’ abstracts were preprocessed and categorized based on their OECDs. Then, Latent Dirichlet Allocation (LDA) was applied to the abstracts in each category and suitable numbers of research topics were found. The examples of the topic modeling results are presented and analyzed. Overall, the work described was able to uncover current research themes and trends within the country. The information obtained are expected to be beneficial for developing national research strategies in the future.
Correlating UI Contexts with Sensitive API Calls: Dynamic Semantic Extraction and Analysis

The Android framework provides sensitive APIs for Android apps to access the user’s private information, e.g., SMS, call logs and locations. Whether a sensitive API call in an app is legitimate or not depends on whether the app has provided enough natural-language semantics to reflect the need for the permission. The prior efforts on analyzing description-to-permission fidelity in an app are all static. Some check whether the permissions requested (or sensitive APIs used) by the app are consistent with the functionalities described by the app. These app-level techniques are too coarse-grained, as they cannot tell if a sensitive API call under a certain runtime context, such as a UI state, is legitimate or not. Others attempt to establish this connection by performing a data-flow analysis, but such finegrained API-level static analyses are too imprecise to handle a variety of dynamic language features used in Android apps, including dynamic class loading, reflection and code obfuscation.We introduce APICOG, an automated fine-grained API-level approach, representing the first dynamic description-to-permission fidelity analysis for an Android app that can check if a sensitive API call is legitimate or not under a given runtime context. APICOG relates each sensitive API call with a UI state, called its UI context, under which the call is made via dynamic analysis and then extracts the text-based semantics for each UI context from its associated text- and image-typed attributes by applying a natural language processing (NLP) technique. Finally, APICOG relies on machine-learning to deduce if a sensitive API call under a UI context is legitimate or not. We have evaluated APICOG with thousands of Android apps drawn from a third-party market and a malware dataset, achieving an accuracy of 97.7%, a precision of 94.1% and a recall of 92.8% overall, outperforming the prior art in all the three metrics.
Secured Hybrid Precoder Design Based on Generalized Triangular Decomposition Method

The researchers and scientists suggest the use of millimeter-waves (mmWaves) for upcoming communication systems that can provide 200 times more bandwidth than the existing technology to meet all the challenging demands of modern mobile users. Since the demands of mobile users are exponentially increasing with the passage of time, exploiting the upper part of electromagnetic spectrum become vital to attain high data rate, broadband device connectivity, extreme capacity and nominal latency for upcoming communication systems. However, certain degradation problems are realized by the higher frequency bands. Thanks to the shorter wavelengths of mmWaves, which allow the employment of multiple-antenna technology to generate narrow and directed beam, and enhance the propagation gain. Still security is considered as one of the primary issues and has got lot of attention from the researchers in the recent times. In this study we aim to develop a hybrid precoder design that can ensure secure mmWave massive multiple-input multiple-output (MIMO) communications by exploiting generalized triangular decomposition (GTD) method in digital domain. Our simulation studies also confirms the effectiveness of GTD scheme.
Enhancing the State Estimation for Low Voltage Distribution Grids

The increasing integration of distributed energy resources (DERs) in distribution grids imposes an imperative need for reliable and accurate monitoring of the grids’ operating condition. Consequently, electric utilities have recently started considering the deployment of Phasor Measurement Units (PMUs) in Medium Voltage Distribution Grids (MVDGs) in order to enhance and improve the monitoring capabilities of the operator. In this paper, a fast reporting measurement device is considered at a MV-LV transformer substation for enhancing the monitoring of the MVDG. A monitoring scheme is then proposed that utilizes the capabilities of this device not only for enhancing the monitoring of the MVDG, but also to simultaneously improve the monitoring of the Low Voltage Distribution Grid (LVDG). The results of the numerical simulations indicate that the proposed scheme exhibits good performance in all the considered scenarios.
A Simple Measurement Method of Electromagnetic Field Distribution using Machine-Learning

In this paper, the improvement of the visualization method of the electromagnetic field distribution using a simple meter is described. Visualization is important to correctly understand the biological effects from electromagnetic fields. In this study, it is possible to measure more easily by using a simple meter instead of the conventional high-performance meter. In addition, a method has been proposed in which the measurement position, which used to be an IR marker or depth sensor until now, can be measured more easily by using object detection by machine-learning. Furthermore, user-friendly measurement is possible by correcting the deviation due to the scanning angle of the magnetic field sensor using machine-learning.
Research on Real-time Curve Drawing Method Based on VB.NET

Usually when designing a monitoring system, it is often necessary to use real-time fluctuation curves to represent the real-time situation of the current monitoring area. In order to achieve the representation of dynamic curves in various monitoring systems, this article uses VB.Net language on the VS2008 platform and uses GDI+ The class library realizes the drawing of the curve and the display of the coordinate axis. It conducts key research on the drawing and refreshing of the dynamic curve, solves the problem of the coordinate axis flickering, etc., and verifies this method of drawing the dynamic curve in conjunction with the example program. Experimental results show that the dynamic real-time curve class designed by this method has good real-time performance, strong versatility, and can be widely used.