Miracle-dz/UniFAR-FTUs huggingface dataset:
Light Yield and Timing Characteristics of Lu₀.₈Gd₂.₂(Al5–xGax)O₁₂:Ce,Mg Single Crystals

The scintillation characteristics of Lu<sub>0.8</sub>Gd<sub>2.2</sub> (Al<sub>5–<italic>x</italic></sub>Ga<sub><italic>x</italic></sub>)O<sub>12</sub>:Ce,Mg (<inline-formula> <tex-math notation="LaTeX">$x = 2.6$ </tex-math></inline-formula> and 3) single crystals grown by the micropulling down method are investigated. The light yield of 27600 photons/MeV obtained for Lu<sub>0.8</sub>Gd<sub>2.2</sub>Al<sub>2</sub>Ga<sub>3</sub>O<sub>12</sub>:Ce,Mg is higher than that of 25 400 photons/MeV obtained for Lu<sub>0.8</sub>Gd<sub>2.2</sub>Al<sub>2.4</sub>Ga<sub>2.6</sub>O<sub>12</sub>:Ce,Mg. The photoluminescence and scintillation decays measured at room temperature (RT) for Lu<sub>0.8</sub>Gd<sub>2.2</sub>Al<sub>2</sub>Ga<sub>3</sub>O<sub>12</sub>:Ce,Mg are faster than those for Lu<sub>0.8</sub>Gd<sub>2.2</sub>Al<sub>2.4</sub>Ga<sub>2.6</sub>O<sub>12</sub>:Ce,Mg. Temperature dependence of radioluminescence (RL) intensity indicates a lower quenching temperature for Lu<sub>0.8</sub>Gd<sub>2.2</sub>Al<sub>2</sub>Ga<sub>3</sub>O<sub>12</sub>:Ce,Mg. Coincidence time resolution of a Lu<sub>0.8</sub>Gd<sub>2.2</sub>Al<sub>2</sub>Ga<sub>3</sub>O<sub>12</sub>:Ce,Mg detector in coincidence experiment with a BaF<sub>2</sub> detector was measured to be 410 ps. The afterglow and thermally stimulated luminescence (TSL) characteristics below RT are also measured.
Research Landscape of Patterns and Architectures for IoT Security: A Systematic Review

We have entered a tremendous computerized rev-olution of the Internet of Things (IoT) era when everything is connected. The popularity of IoT systems makes security for the IoT of paramount importance. Security patterns consist of domain-independent time-proven security knowledge and expertise. Would they be applicable to develop secure IoT systems? We aim to draw a research landscape of patterns and architectures for IoT security by conducting a systematic literature review. From more than a thousand of candidate papers, we have systematically distinguished and analyzed twenty-two (22) papers that have been published around patterns and architectures for IoT security (and privacy). Our analysis shows a rise in the number of publications tending to security patterns and architectures in the last two years. Within this rise, we see that most patterns and architectures are applicable for all IoT systems, while some are limited within specific domains. However, there are gaps in this research area that can be filled in to promote the utilization of patterns for IoT security and privacy.
An Integrated, Back-Drivable Electro-Hydrostatic Actuator for a Knee Prosthesis

This paper focuses on the analysis, development and experimental evaluation of an electro-hydrostatic actuator for knee prosthetic devices. In contrast to traditional actuation, the goal is to obtain a highly-integrated and back-drivable actuator with both active and regenerative capabilities. A design procedure is established with the purpose of yielding a fully-customized compact actuation unit. Specifications are defined initially from a biomechanical perspective. Further constraints are imposed by the limitations of the actuation system components. Then, the methodology is applied to develop a custom prototype. From a control standpoint, admittance control is implemented to show actuation functionality and to make a preliminary evaluation of the ability to suit the application. Experimental results demonstrate the validation of the proposed technology and its high potential in active prostheses applications.
W-ERA: One-Time Memory Repair with Wafer-Level Early Repair Analysis for Cost Reduction

Since the probability of fault occurrence on memory has increased with the advance of memory density and capacity, memory repairs in wafer-level and package-level are widely used with redundancy analysis (RA) to improve memory yield. However, as the costs for memory repair also have increased in proportion to the memory density and capacity, the repair costs have occupied a significant portion of the total costs. To address the problem, one-time memory repair with wafer-level early repair analysis (W-ERA) for cost reduction is proposed in this paper. The proposed W-ERA facilitates that all unrepairable memories are classified rapidly without searching memory repair solutions in wafer-level and repairable memory faults occurred in wafer-level are repaired in package-level with additional faults occurred in package-level simultaneously. It means, as the costs of memory repair can be highly reduced since memory repair is skipped in wafer-level, the total costs also can be highly reduced. In addition, memory redundancies can be efficiently used for memory repair in package-level and it results a high repair rate achievement.
Taking advantage of unsupervised learning in incident response

This paper looks at new ways to improve the necessary time for incident response triage operations. By employing unsupervised K-means, enhanced by both manual and automated feature extraction techniques, the incident response team can quickly and decisively extrapolate malicious web requests that concluded to the investigated exploitation. More precisely, we evaluated the benefits of different visualization enhancing methods that can improve feature selection and other dimensionality reduction techniques. Furthermore, early tests of the gross framework have shown that the necessary time for triage is diminished, more so if a hybrid multi-model is employed. Our case study revolved around the need for unsupervised classification of unknown web access logs. However, the demonstrated principals may be considered for other applications of machine learning in the cybersecurity domain.
Continuous Debt Valuation Approach (CoDVA) for Technical Debt Prioritization

This paper proposes a technical debt prioritization technique to be used throughout software development lifecycle. Our method stresses a broader perspective of the product, so the focus is kept on optimizing the software artifacts supporting the complete flow of the value for the customer (including e.g. test environment or deployment pipelines). Many technical debt approaches are focused on static code analysis and misconsider business aspects as a separate activity. On the contrary, our method, based on the New Solution Selling Process, strongly advocates for a business perspective. It facilitates technical debt valuation against a predicted product roadmap by a close alignment with a structured sales process and the advancement of sales opportunities. Taking into account prediction of sales, progress of conversations with potential clients and bids, probability that a given functionality will be prioritized for the development, this approach may serve as a reference point for measuring business value of technical debt continuously. This valuation serves as a relative comparison of technical debt items enabling prioritization of effort to pay off technical debt. The method was evaluated on a release of a wireless telecommunication system. The results confirm that the suggested priorities may address the most profitable product areas from the company’s perspective and ultimately optimize the value for customers.
Estimating Wrist Joint Torque Using Regression Ensemble of Bagged Trees under Multiple Wrist Postures

Powered prostheses and orthoses are expected to significantly improve quality of life of individuals with motor impairment or limb amputation. Despite the advances in the mechatronic design of powered prostheses and orthoses, their use is not yet widespread because of limitations in their control schemes. Methods that use Electromyography (EMG) measurements to estimate a physical signal related to joint kinematics or kinetics to be used as a control variable are promising as these methods may enable robust control schemes for high-dimensional powered prostheses and orthoses. In this study, we propose a method to estimate joint torques about two degrees of freedom of the wrist joint. The developed method uses regression ensemble of bagged trees, and is applied to isometric tasks under multiple wrist postures. The proposed regression model estimated Flexion/Extension and Radial/Ulnar Deviation torques with coefficient of determination equal to 0.94 and 0.88, respectively, when training and testing at the neutral wrist posture. The analysis showed that even using only one wrist posture for training, good results can be obtained in estimating joint torque under different postures. The outcome of this study suggests that a single-posture calibration combined with the proposed estimation method can be used as a paradigm to derive a linear multi-dimensional control signal that could be suitable for EMG control of prosthetic or exoskeletal devices.
Modelling Data Pipelines

Data is the new currency and key to success. However, collecting high-quality data from multiple distributed sources requires much effort. In addition, there are several other challenges involved while transporting data from its source to the destination. Data pipelines are implemented in order to increase the overall efficiency of data-flow from the source to the destination since it is automated and reduces the human involvement which is required otherwise.Despite existing research on ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) pipelines, the research on this topic is limited. ETL/ELT pipelines are abstract representations of the end-to-end data pipelines. To utilize the full potential of the data pipeline, we should understand the activities in it and how they are connected in an end-to-end data pipeline. This study gives an overview of how to design a conceptual model of data pipeline which can be further used as a language of communication between different data teams. Furthermore, it can be used for automation of monitoring, fault detection, mitigation and alarming at different steps of data pipeline.
A Wireline Termination Embedded Energy Harvesting System With 300-μW Extracted

An energy harvesting system with a three-phase rotating and <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula>time-interleaving switched capacitor (SC) is proposed to be embedded in a wireline I/O interface. By a 50-<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">${Z} _{0}$ </tex-math></inline-formula>) mimicking at the termination to harvest the energy from the wireline received signal, the harvesting system can extract 300-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> net power. The proposed energy harvester is fabricated in a 40-nm CMOS technology.
DIVERSITÉ GÉNÉTIQUE DE TALPA EUROPAEA ET DE L'HANTAVIRUS NOVA (NVAV) EN FRANCE GENETIC DIVERSITY OF TALPA EUROPAEA AND NOVA HANTAVIRUS (NVAV) IN FRANCE

Nova hantavirus (NVAV) was first identified in a captured European mole (Talpa europaea) in Hungary. Analysis of lung tissues from 94 moles captured in France revealed NVAV in 50% of the animals. Based on the genetic diversity of the cytochrome b mtDNA, moles collected in Poitiers and Bordeaux were more closely related to the Iberian mole (T. occidentalis), a species previously assumed to be restricted to the Iberian Peninsula. Several hypotheses are discussed to explain these observations: 1) the presence of hitherto unnoticed T. occidentalis in southwestern France; 2) the existence of an ancient mitochondrial introgression phenomenon between the two Talpaspecies, producing a particular phenotype in some hybrids; 3) the existence of a hybrid zone between the two species; and 4) the existence of a new Talpa species. NVAV was not detected in the southwestern moles, which raises the question of the possible presence of a particular Hantavirus species in this population and/or in the Iberian moles. SUMMARY
Prediction of Recurrence and Non-recurrence Events of Breast Cancer using Bagging Algorithm

Breast cancer is a type of cancer which prescribed life-threading cancer of women. Cancer growth begins when cells start to out of control. Breast cancer is basically found in family sources, routinely on account of DNA changes. Normal or inside parts, for instance: age, sex, procured inherited deformations and skin type. For this study, 275 instances with 12 features and outcome of having recurrence and non-recurrence event were collected. 10-fold cross-validation had applied for training the data and the accuracy for Bagging algorithm is 73.8182 %. Naive Bayes classifier had been used as base learner of Bagging algorithm.
SMA based Reduced-order Model for DFIGs

As the integration of wind power increases in the power system, it is the basic requirement to model wind generators properly for the power system stability analysis and transient simulation. In this paper, a reduced-order method based on selective modal analysis (SMA) is proposed for constructing a reduced model of DFIGs. The whole system model with DFIGs is introduced firstly. Then the reduced-order method using SMA is presented in detailed about how to constructing a reduced model using the key modes and participate factors from the modal analysis. The comparison between the detailed model and the reduced model is carried out in the theoretical analysis and the transient simulation. Both the theoretical and simulation results verify that the reduced-order model can well fit the original steady and transient behavior of the system, which significantly reduces the computational complexity and thus improves the efficiency.
A Multilingual Framework of CNN and Bi-LSTM for Emotion Classification

Speech emotion recognition is a crucial task for developing human-computer based interfaces. However, many significant studies have focused on extracting low-level features for classification. In this paper, we propose a language-independent, deep learning-based framework for speech emotion classification. We extract the MFCC based features along with deep high-level features by developing a unique combination of 1D CNN and Bi-LSTM units. The proposed framework can extract the local information from the signals with the help of CNN, and the Bi-LSTM layer helps in modeling the long-term contextual dependencies of the signal. We validate our proposed framework on two multilingual datasets, namely: EmoDB for German language and RAVDESS for the English language. Experimental results indicate that EMODB and RAVDESS datasets achieve an average accuracy of 94% and 73%, respectively. We also compare our results with state-of-the-art and alternative methods to validate our framework and measure the performance of the algorithm in terms of precision, recall, F1 score, and accuracy.
Development of an Air-filled SIW Filter with Wideband Spurious Suppression

In this paper, we propose an air-filled substrate integrated waveguide (SIW) bandpass filter (BPF) to realize the wideband attenuation. The proposed filter has a direct-coupling structure via an iris. In the area where the TE103 and TE104 modes appear as electric fields of spurious modes are concentrated, the dielectric of the substrate is removed to make a hollow structure. The effective dielectric constant of the substrate decreases as a part of the substrate becomes hollow, and the spurious modes move to a higher frequency band. The designed BPF achieved −20 dB out-of-band rejection from 6.0 to 11.7 GHz. The hollow structure reduced insertion loss to −0.3 dB.
A Machine Learning Approach to Performance and Dropout prediction in Computer Science: Bangladesh Perspective

This is an era of computers and technology. Nowadays Computer Science (C.S.) and other technology-related subjects are a hot cake for the students. Due to a good job market for these subjects, students are taking computer science and other related topics without thinking about their capability and without knowing the curriculum of these subjects. So the dropout rate is getting high day by day in these subjects. Especially developing countries like Bangladesh. In this work, we have used current computer science students' data to predict their and also prospective C.S. students' future performance and the chance of dropout using machine learning algorithms like SVM, naïve Bayes, neural network, etc. We have also predicted the crucial factors that are strongly correlated to the performance of a C.S. student.
Cable-Driven 3-DOF Wrist Rehabilitation Robot with optimized Human-Robot Interaction Performance

This paper developed a cable-driven 3-DOF (degree of freedom) wrist rehabilitation robot with optimized human-robot interaction performance. The workspace and cable tension efficiency of the robot are increased by adding the dynamic adaptive structure of cable attachment points, which improves the safety and comfort of human-robot interaction. In addition, the DASA (distributed active semi-active) system based on MRs(magnetorheological) is adopted, which reduces the effects of elastic elements (e.g., cables or Bowden cables) in the system, improves the force-feedback performance. The robot is compact and can be used independently or transplanted to any upper limb rehabilitation robot without wrist rehabilitation function. The performance of the system and control algorithm is verified by several experiments on healthy subjects. The results show that the system can meet the needs of rehabilitation training for workspace and force-feedback. This system also has the potential to be a force-feedback device for the healthy person.
Magnetic Fault–Tolerant Navigation Filter for a UAV

The heading state of an unmanned aerial vehicle (UAV) often diverges due to magnetic field disturbances, although the magnetic field can provide information about the heading. In this paper, we propose a novel magnetic fault–tolerant navigation filter (MFTNF) for UAVs, which combines a primary filter and a magnetometer-free attitude and heading reference system (AHRS) as a secondary backup filter to obtain an accurate and robust heading under an unknown magnetic disturbance. In the MFTNF framework, the primary filter that uses the magnetic field is the main filter used in normal situations for estimating the attitude, position, and velocity states. When an anomaly in the magnetic field is detected, the magnetometer-free AHRS replaces the heading of the primary filter. To detect magnetic disturbances, a novel decision-making algorithm is proposed in this paper. Flight experiments with UAVs demonstrate that the MFTNF is accurate and reliable under an unknown magnetic disturbance and that in such circumstances, the conventional approaches typically exhibit degraded accuracy in estimating the heading and the position. In addition, the advantage of the proposed MFTNF is that it always produces a robust heading estimate, regardless of the degree of magnetic interference, due to the magnetometer-free AHRS.
Point Estimation Method Considering Estimator Boundary Constraints to Calculate Probabilistic Power Flow

Point estimation method is commonly used to calculate probabilistic power flow due to its accuracy and efficiency. However, it may cause relatively large errors when dealing with some probabilistic models, such as wind farms which have cut-in and cut-out constraints. This paper proposes the idea of " equal constraint transformation " and power transformation to solve this problem. First, the mathematical model of electricity system is constructed. Second, the idea of power transformation is utilized to transform wind speed from asymmetry into symmetry to ensure that the estimated point locates within the limit. Third, equal constraint transformation method is used to estimate the power generated by wind farms. Finally, time-domain simulations are performed to verify the effectiveness of the proposed two methods.
Sensorless Control of PMSM using Backstepping Control and ESO-type Observer

This article presents a Permanent Magnet Synchronous Motor (PMSM) sensorless control system using backstepping control. The Extended State Observer (ESO) is also used to perform the sensorless control, and to estimate the load torque which is considered an external disturbance of the control system. Starting from the classic Field Oriented Control (FOC) type PMSM control structure, the classic PI speed and current controllers id, iq are replaced by a backstepping control structure. The main blocks used for the implementation and numerical simulation in the Matlab/Simulink graphical programming environment are presented. The very good static and dynamic performance obtained through numerical simulations even at high speeds, step variations of the value of the load torque, uniformly distributed noise, and variations of the PSMS nominal parameters can be explained by the fact that the backstepping control is applied to the non-linear system of the PMSM and by the step-by-step method of achieving each iqref, ud and uq control by using a Lyapunov-type function. Future articles propose the approach of real-time implementation of the PMSM sensorless control by using the backstepping control.
ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs

The outcome-oriented predictive process monitoring is a family of predictive process mining techniques that have witnessed rapid development and increasing adoption in the past few years. Boosted by the recent successful applications of deep learning in predictive process mining, we propose ORANGE, a novel deep learning method for learning outcome-oriented predictive process models. The main innovation of this study is that we adopt an imagery representation of the ongoing traces, which delineates potential data patterns that arise at neighbour pixels. Leveraging a collection of images representing ongoing traces, we train a Convolutional Neural Network (CNN) to predict the outcome of an ongoing trace. The empirical study shows the feasibility of the proposed method by investigating its accuracy on different benchmark outcome prediction problems in comparison to state-of-art competitor methods. In addition, we show how ORANGE can be integrated as an Intelligent Assistant into a CVM realized by MTM Project srl company to support sales agents in their negotiations. This case study shows that ORANGE can be effectively used to smartly monitor the outcome of ongoing negotiations by early highlighting negotiations that are candidate to be completed successfully.
An Identification Method for Sympathetic Inrush Based on Substation-Area Information

Sympathetic inrush is a phenomenon that occurs in an in-service transformer (IT) when a nearby transformer is energized. The phenomenon can cause transformer differential protection to maloperate. Identification methods for sympathetic inrush have been proposed to deal with the problem. However, these methods are based on local information from the IT, while sympathetic inrush involves two or more transformers. Therefore, an identification method is proposed based on substation-area information. A substation-area differential current is defined and its second harmonic component is utilized to identify the sympathetic inrush. Simulation results by PSCAD/EMTDC show the proposed method can distinguish sympathetic inrush from internal faults and energization inrush effectively.