Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact Solutions on MHD Flow Past an Accelerated Porous Plate in a Rotating Frame

We present an analysis of an electrically conducting viscous fluid over a porous plate in a rotating system. The plate starts impulsively from rest relative to the rotating fluid moving with uniform acceleration in its own plane. Exact solutions are developed for both large and small times. In addition, skin friction is computed. The variations of magnetic field and porosity are displayed and discussed. It is noted that steady state behavior is not achieved even under the simultaneous effects of suction, magnetic field and rotation.
A Combined Acute and Chronic Risk Assessment Rolling Window for Type 1 Diabetes

Monitoring the control of persons with type 1 diabetes based on their history of blood glucose levels is essential for self-management. Persons with diabetes must keep their blood glucose levels in a very narrow glycaemic region (70–180 mg/dl) to avoid hypoglycaemia and hyperglycaemia. An extended period of time in the hypoglycaemic or hyperglycaemic region can lead to short-term and long-term complications, respectively. Many measures have been proposed for the management of diabetes, such as the Glucose Management Indicator (GMI) and the Average Daily Risk Range (ADRR). A major drawback of these measures is that they only address acute (ADRR) or chronic (GMI) complications and provide no information on the trend. This paper proposes a rolling window to calculate ADRR and GMI. Calculating ADRR and GMI using a rolling window results in new data, which provide information on the efficacy of self-management of an individual and their risk trend. Use of a rolling window for the risk analysis provides novel information about the glycaemic variability and can be used for improved personal diabetes management plans. Furthermore, ADRR and GMI are combined to propose four new risk levels, which represents the lowest to the highest probable risk of complications. The analysis was performed on 12 subjects from the OhioT1DM data set. The results presented include a detailed examination and summary of all risks to the subjects and the information about their ADRR and GMI trend.
Design of Operational Transconductance Amplifier using gm/Id method in 180nm Technology

The reduction in transistor size has led to the failure of the conventional square-law devices in describing the Short Channel and Moderate Weak inversion devices. The shortcomings of these devices are addressed by the gm/Id method that traverses between the hand calculations and circuit simulation. In this literature, a structured process for the design of a Two-Stage Operational Transconductance Amplifier is presented. The graphical approach is used to investigate the link between transconductance by current versus normalized drain current. The geometry of the transistors is calculated from the graph. The Operational Transconductance Amplifier is designed using 180 nm Predictive Technology Model using various parameters like Slew Rate, DC Open-Loop Gain, Common-Mode Rejection Ratio, Input Range, and Output Range. In this paper, the Two-Stage Operational Transconductance Amplifier gives 55 dB gain and 220 µW of power consumption.
An Evolutionary Pathway of Linear Power Flow Equation: Breaking of Empirical Assumptions

Linearization of the power flow equation is widely-used in power system analysis to meet the computational efficiency demand of the power industry. However, existing linear power flow equations are proposed based on empirical assumptions. The optimality of these power flow equations cannot be proven. The evolutionary pathway of the linear power flow equation is not clear. In this paper, we review the widely-used linear power flow equation from the perspective of their zero-error assumption of linearization approximation. Then, the improvement of the linear power flow equation is revealed as constantly breaking the zero-error assumption. Finally, we propose an enhanced linear power flow equation based on the arithmetic-geometric mean inequality for optimal power flow (OPF) calculation. The performance is verified in IEEE 30, 118 and Polish 2383 test systems.
Bespoke Mitigation Framework for False Data Injection Attack-Induced Contingency Events

Perpetrators of large-scale Stealth False Data Injection Attacks (FDIA), who desire actual and accurate controllability of target Cyber Physical Power System (CPPS), often utilize Machine Learning (ML)-based systems to ascertain the minimum/optimal Control Signal Energy Cost (CSECopt) and Controllability Gramian Matrix (CGMopt)/principal submatrices (a.k.a., CGSMopt), which turn out to be directly related to the involved convex relaxation. Likewise, those charged with FDIA mitigation are seeking to solve the same problem to anticipate optimal Target Nodes/Links (TNopt/TLopt) for Targeted Control (TCopt). During an attack, wherein conditions are fluid, it is a contest at machine speed between the ML systems of the attackers and defenders. As a Robust Convex Relaxation (RCR) is central to the described scenario, this paper proposes an FDIA mitigation framework, which is based upon a sequence of transformations — Nonnegative Matrix Factorization (NMF) to Multiresolution Matrix Factorization (MMF) to Corresponding Wavelet Transform (CORWT) to an Enhanced CORWT (ECORWT) — and a bespoke Robust Constriction Factor Particle Swarm Optimization (RCF-PSO)-based RCR (RPR), which leverages translation-invariant Continuous Wavelet Transforms (CWTs) to achieve enhanced insight into the prospective CGMopt/CGSMopt.
MEMS Shock Absorbers Integrated with Al2O3-Reinforced, Mechanically Resilient Nanotube Arrays

This paper presents MEMS-integrated nanotube arrays as an in-plane shock absorber to ensure device reliability to external mechanical shocks. The geometrically structured nanotube arrays, consisting of vertically-aligned carbon nanotubes (CNTs) coated with alumina (Al2O3) layer, are directly integrated onto designated areas, allowing them to be compatible with conventional batch-fabrication processes for MEMS. The deformable carbon nanotube arrays with an exceptionally high aspect ratio dissipate the abruptly applied kinetic energy, while the reinforcing Al2O3 layer provides outstanding mechanical resilience to withstand extreme deformations. Therefore, in the shock-drop test, specimens with the nanotube arrays showed approximately 115 % and 80 % higher survival rate (with an average fracture acceleration of 10,000 g) compared to control groups (without the nanotubes) with a hard stop and compliant spring-based shock absorbers, respectively.
Multiple Feature based Classification for the Prediction of Brain Tumor

Recognition of brain tumor type is a critical task and advice of medical experts is essential for its detection and diagnosis purpose. Another complex process is to classify the type of identified brain tumor for its accurate treatment. In this paper, using machine learning concept, a multiple feature based classification (FBC) scheme will be introduced that finds diverse features from input samples and extracts them to form a feature knowledgebase and it also prepares a training model. During testing phase, existing knowledgebase is utilized to make the prediction for input dataset. Its performance is analyzed using different methods (Random Forest/ Decision Tree/Support Vector Machine and Gaussian Native Bayes etc.) under the various parameters (Accuracy/ Sensitivity/Precision/ReCall and True Positive Ratio etc). Analysis shows that its outcome varies w.r. t. different approaches and acceptable results can be obtained using Random Forest method as compared with Decision Tree method as compared to SVM/ Gaussian Native Bayes.
High Performance Computing Infrastructure in Technical University Research

A technical university needs high performance computing resources to support the research activity carried out by interdisciplinary teams, through heterogeneous, various and flexible configurations. The CloudUT infrastructure has been designed and developed to meet the requirements and performances necessary for research projects in the scientific and engineering fields specific to the Technical University of Cluj-Napoca such as big data, artificial intelligence, spatial data, satellite data, internet of things, complex simulation, and computer assisted design. This paper analysis and highlight the challenges and issues on finding efficient solutions for the distributed architecture, parallel computing, massive data, flexible architectures, virtualization, cloud applications and services for covering a large range of technical domains of engineering such as mechanics, automation and computers, civil and building services, electric, architecture and urban planning, electronics, robotics, and mechatronics.
Event-Triggered Model Reference Adaptive Control for Linear Partially Time-Variant Continuous-Time Systems With Nonlinear Parametric Uncertainty

In this work, we develop an event-triggered adaptive control approach for solving the state tracking problem of linear partially time-variant continuous-time systems with the nonlinear state-dependent matched parametric uncertainty under unknown system dynamics. First, an event-triggered model reference adaptive controller is designed, which is composed of event-triggered adaptive laws based on the event-updated information and an event-triggering condition depending on the state tracking error of the controlled plant and reference model. Then, the state-tracking error and the error between control parameters and ideal ones of the resulting closed-loop system are proven to be uniformly ultimately bounded. Moreover, based on the designed event-triggering condition, the interevent time between two consecutive triggering points is proven to have a positive lower bound. Finally, a simulation example is provided to show the effectiveness of the proposed approach.
An Image Processing Based Classifier to Support Safe Dropping for Delivery-by-Drone

Autonomous delivery-by-drone of packages is an active area of research and commercial development. However, the assessment of safe dropping/ delivery zones has received limited attention. Ensuring that the dropping zone is a safe area for dropping, and continues to stay safe during the dropping process is key to safe delivery. This paper proposes a simple and fast classifier to assess the safety of a designated dropping zone before and during the dropping operation, using a single onboard camera. This classifier is, as far as we can tell, the first to address the problem of safety assessment at the point of delivery-by-drone. Experimental results on recorded drone videos show that the proposed classifier provides both average precision and average recall of 97% in our test scenarios.
Rotor Shape Design Method of Non-flux Barriers Synchronous Reluctance Motor

Compared with the flux barriers synchronous reluctance motor (FB-SynRM), the non-flux barriers synchronous reluctance motor (NFB-SynRM) has the advantages of higher mechanical strength and smaller d-axis magnetic resistance which makes the parameters of NFB-SynRMs more linear compared with the FB-SynRMs. The rotor shape of the NFB-SynRMs is very critical for the motor performance. An improper rotor shape design may cause large torque ripple and decrease the motor torque density. To solve this problem, a convenient rotor shape design method of NFB-SynRMs is proposed and the rotor profile function is derived. The effectiveness of the proposed method is verified by electromagnetic finite element analysis.
Fully-Flexible Micro-Scale Actuator Array with the Liquid-Gas Phase Change Materials

This paper reports a fully flexible micro-actuator array utilizing a liquid-to-gas phase change mechanism. A stretchable membrane of the actuator is deformed by volume expansion and contraction by inducing vaporization and liquefaction of the phase change materials. The phase change mechanism has a high energy density inducing large displacement even in a small volume at the microscale, which is advantageous for downscaling compared to other actuation mechanisms, such as electrostatic and pneumatic actuation. Additionally, this device could be a candidate for next-generation wearable devices due to the flexibility originating from polymer (chamber and membrane) and liquid (phase change material).
Conditional Style-Based Generative Adversarial Networks for Renewable Scenario Generation

Day-ahead scenario generationof renewable power plays an important role in short-term power system operations due to considerable output uncertainty included. In this paper, a deep renewable scenario generation model using style-based generative adversarial networks followed by a sequence encoder network, is developed to generate accurate and reliable day-ahead scenarios directly from historical data through different-level scenario style controlling and mixing, thus achieving better characterization of renewable spatial-temporal dynamics. Meanwhile, the integration of meteorological information serving as conditions enables our model to precisely capture the complex diurnal pattern and seasonality difference of renewable power. From wind and photovoltaic power perspectives, the effectiveness of the proposed model is validated on two real-world datasets reflecting region aggregation level and distributed power station level respectively. Numerical results demonstrate the superiority of model performance through both the statistical and power system scheduling analysis, compared to three benchmarks.
Detecting Clickbaits Using Deep Forest

News outlets employ catchy headlines that have the main purpose of luring the reader to click on the article and thus to visit the media site. These headlines are known as Clickbait. Although these baits may trick the readers into clicking, they often fail to deliver when it comes to the content of the article, leaving the reader disappointed. Furthermore, clickbaits can help the spread of misinformation, in the form of fake news, and have the power to influence people’s opinion.Our task is to automatically detect clickbaits from genuine article headlines. The developed system is based on Deep Forest, that is a non differentiable deep learning algorithm. As theoretical contribution, we extending the default Deep Forest implementation with a boosting mechanism. The Chakraborty dataset was used that comprises 32,000 headlines of news articles. Three approaches were used for feature engineering, namely word embeddings, headline extracted features and a combination of both.
IoT based Energy Efficient using Wireless Sensor Network Application to Smart Agriculture

With the use of WSNs, farmers may access a wealth of data in the agricultural sector. To boost yields and quality, farmers are increasingly turning to a management method known as precision agriculture (PA), which makes use of technological advances in information gathering and analysis. Developing extremely efficient and environmentally friendly farming via the use of wireless sensor technology and management systems. With PA management, farmers no longer have to tend to their crops in the same way, no matter the conditions at their plots. This study presented an IoT based smart agriculture by using the platform for energy efficient wate management (EEWMP). The effectiveness of these models is assessed in terms of energy usage and network stability. According to the testing findings, the energy-efficient water management platform doubles network stability while using 25% less energy than the current approach. With little modifications, the proposed model can be best working with different models of the irrigation such as irrigation in terms pf drip, irrigation in terms of sparkler, and surface irrigation. Additionally, it can be used in small farms in developing nations with their already-existing 2G or 3G connection infrastructures.
Weight Discretized BP Algorithm Based on Synapse Transistor with Symmetric/Asymmetric Memory Curve

In the field of brain-like computing, the synaptic transistor is a core device that can simulate the computing patterns of the human brain, and evaluating its performance is important for the subsequent construction of neural networks. In this paper, based on the synaptic transistor memory characteristic curve, the influence of discreteness, symmetry/asymmetry and non-linearity on the performance of weight discretized back propagation (BP) neural network algorithm are investigated. The results show that since the conductance of the device is discrete, the effect of this discreteness on its performance is not negligible until the number of discrete points reaches a threshold value. More interesting, this threshold can be reduced by an asymmetric model and a lower degree of nonlinearity. Compared with symmetry model, the complementarity of the asymmetric model leads to more uniform values of discrete weights, which can improve the recognition accuracy of the neural network. This research has a guiding significance for the hardware selection and modeling of artificial intelligence algorithm.
High Order Current Harmonics Suppression for Five-Phase Permanent Magnet Synchronous Machine Based on MSRFTs

To suppress the high-order current harmonic in five-phase permanent magnet synchronous machines (FPMSMs), the mathematical model of FPMSMs with high-order harmonics is proposed. Then, based on this model, the multiple synchronous rotating frame transformations (MSRFTs) algorithm of FPMSM is proposed to convert the 7th-order and 9th-order harmonics in the stationary coordinate system into the DC quantities in the 7th-order and 9th-order SRFs, respectively. In order to extract current harmonics accurately, this paper further designed a frequency adaptive low-pass filter to achieve good filtering effect in the full speed range of the motor. Simulation results show that the proposed algorithm can suppress the current harmonics effectively.
Dynamical Primal-Dual Nesterov Accelerated Method and Its Application to Network Optimization

This article develops a continuous-time primal-dual accelerated method with an increasing damping coefficient for a class of convex optimization problems with affine equality constraints. This article analyzes critical values for parameters in the proposed method and prove that the rate of convergence in terms of the duality gap function is $O(\frac{1}{t^2})$ by choosing suitable parameters. As far as we know, this is the first continuous-time primal-dual accelerated method that can obtain the optimal rate. Then, this article applies the proposed method to two network optimization problems, a distributed optimization problem with consensus constraints and a distributed extended monotropic optimization problem, and obtains two variant distributed algorithms. Finally, numerical simulations are given to demonstrate the efficacy of the proposed method.
A Pipette Tip Integrated with a Capacitive Microsensor Fabricated by Combined 3D Printing and MEMS Process for Cell Detection and Transportation

This paper reports on the detection and manipulation of cells using a pipette tip with a capacitive microsensor fabricated by combined 3D printing and MEMS processes. This pipette tip with microsensor was fabricated by 3D printing directly onto a MEMS device. First, a pair of comb electrode capacitive sensor was fabricated using the traditional MEMS process. Then, the pipette tip structure was 3D-printed directly on the sensor substrate by projection micro stereolithography. This process is possible to form nm- to cm-scale structures integrally with high precision and three-dimensionally. The fabricated device successfully detected cell suction and ejection. In addition, cell transfer was performed to show the possibility of applying the device to cell manipulation by a robot.
Optimal Configuration of Shared Energy Storage Considering the Incentive-Based Demand Response

In the context of electricity market, low energy utilization and high investment costs in energy storage constrain the development of microgrids (MGO) and policy guidance is urgently needed. Therefore, this paper proposes an optimal configuration strategy of shared energy storage (ESS) capacity in MGO considering incentive demand response (DR). First, a load guidance mechanism based on scheduling priority was constructed, and the DR results were calculated by elasticity coefficients. Then, with the ESS operator as the leader and the MGO as the follower, a Stackelberg game model between the MGO and the ESS operator considering incentive DR is established. Finally, a combination of heuristic algorithm and solver was used to solve the game model. The results of the calculations show that the total user cost is reduced by 1.03% with incentive DR compared to no policy incentive. Meanwhile, ESS revenue increased by 17.35% after considering DR. It can be seen that the model effectively balances the interests of MGO users and shares the energy storage operator, achieving a win-win situation.
Building fast and reliable reverse engineering tools with Frida and Rust

Reverse engineering binary applications is a key process for black-box security auditing and malware analysis. Frida is a reverse engineering framework based on dynamic binary instrumentation that allows the user to create agents, which are injected in the analyzed process, and can communicate with the user’s program. Frida is written in C and Vala and offers high level bindings in Python and JavaScript. Dynamic languages allow fast development iteration, a key requirement when trying to discover the inner workings of an application or protocol. The main disadvantages of such languages include performance limitations and their error-prone nature due to lack of type checking. In this paper we address these limitations by building bindings in Rust, which aims to offer high performance and numerous correctness guarantees while still maintaining reasonable development iteration speed. We show examples of performance improvements and present a real use case to validate the usability of the library.
An Empirical Analysis of Digital Image Color Quantization using Partition Based Clustering Techniques

In today’s technical world, dependency on digital devices as well as the generation of digital data has increased by many times. Out of the total data produced through various digital devices, image data occupies a large section. Devices can create images in different formats such as monochrome, gray-scale, and color. Color images again can have different color models out of which the red green blue (RGB) model is one of the most widely used models. The total number of colors that can be present in an RGB color image can be as high as 224 = 16777216. All devices may not be able to efficiently process these high numbers of colors and most importantly, all these colors may not be of much significance from the perspective of human vision or various types of applications. In this paper, we have used partition-based clustering algorithms such as K-Means++, K-Medoids, Fuzzy-C Means, and Self-Organizing Maps to reduce the number of colors in RGB images from 16777216 to as small as 512 (i.e from 224 to 29) colors. According to the experimental results, K-Means++ clustering gives better performance in comparison to the other three clustering methods for color quantization.
Asymmetric multiview image coding based on feature matching

In this paper, we propose a method of feature matching based asymmetric three-dimensional (3D) image coding with hierarchical reconstruction quality. At the encoder, for the main view the standard intra coding can be applied to obtain high reconstruction quality while for its neighboring views extracted feature descriptors can be utilized to calculate transformation matrix between views. The parameters of transformation matrix can be transmitted by very low bit rate and achieve the preliminary reconstruction. Furthermore, the residues can be exploited to improve the performance. The experimental results have shown that the proposed scheme can reach a very high compression ratio.