Miracle-dz/UniFAR-FTUs huggingface dataset:
Sedimentation in a dilute polydisperse system of interacting spheres. Part 2. Numerical results

Analytical formulae or the effect of interaction between pairs of rigid spherical particles on the mean velocity of each species in a statistically homogeneous dilute polydisperse system were given in Part 1 (Batchelor 1982), and are here evaluated numerically. We have calculated the pair-distribution function and the associated value of the sedimentation coefficient for a wide variety of conditions of the two interacting species, including different values of the ratio of the radii of the spheres (λ), different values of the ratio of their (reduced) densities (γ), small and large values of the Péclet number of the interaction, and different forms of the potential of the mutual force exerted directly between the two spheres. Values of λ and γ such that some of the trajectories of one sphere centre moving under gravity alone relative to another are of finite length lie outside the scope of the calculations at large Péclet number, and the change of behaviour across the boundary of this excluded set of values leads to a complicated dependence of the sedimentation coefficient on λ and γ. At small Péclet number the behaviour is simpler, and a formula which represents the calculated values of the sedimentation coefficient over the whole range of values of λ and γ (on which the dependence is known to be linear) with fair accuracy in the absence of interparticle forces is devised. Our calculations of the effect of an interparticle force were based on the assumption of a high Coulomb barrier at a certain sphere separation which could be varied, and a van der Waals attractive force at larger separations. It appears that the direct contribution to the sedimentation coefficient made by gravity is always appreciably larger than that made either by relative Brownian diffusion of the two interacting spheres or by the interparticle force. However, all three of these (effective) forces normally have a significant influence on the pair-distribution function and thereby also affect the sedimentation coefficient indirectly. Some published observations of the mean particle velocity in monodisperse systems are interpreted in the light of the present calculations of the effect of interparticle forces.
Formation of a bimodal eutectic structure in Ti-Fe-Sn alloys with enhanced plasticity

Microstructural investigations on a series of (Ti70.5Fe29.5)100−xSnx alloys with x=5, 7, and 9 reveal that Sn addition is effective in introducing both structural and spatial heterogeneities in ultrafine eutectic composites stemming from a large temperature difference between two eutectic temperatures upon solidification. The microstructural heterogeneities in these ultrafine eutectic composites strongly enhance the room temperature compressive plasticity up to ∼15.7%.
Snapshot hyperspectral imaging to measure oxygen saturation in the retina using fiber bundle and multi-slit spectrometer

We have developed a snapshot fiber bundle technique that circumvents the issue of saccades of the non-immobilized eye. In this technology, 458 individual fibers are assembled in a two-dimensional array where each fiber represents a portion of the image. These fibers are redistributed into two separate one-dimensional fiber rows interfaced into a two-slit spectrometer. The light from each fiber is decomposed into its spectral components by the spectrometer. Using this innovative technology, we have been able to detect the whole spectrum of hemoglobin using the single light exposure capabilities of a fundus camera. The hemoglobin signature of the retinal arteries, veins, and retina tissue can be recorded. The final result is a complete, 3-dimensional representation of the spectral and spatial information from a single exposure of the patient. By adjusting the field of view on the imaging portion of the fundus camera, the fiber optic cable may encompass a larger area. However, this causes a decrease in spatial resolution, so we increased the area of the fiber array by increasing the number of the fibers from 458 to 648, increased the size of each individual fiber from 10 μm to 20 &μm, and increased the number of slits to four.
Lagrange M-th band filters and the construction of smooth M-band wavelets

This paper develops a new explicit formula for the construction of M-th band maximally flat lowpass filters based on Lagrange interpolation. Both the minimum length and arbitrary length solutions are obtained. This leads to the construction of M-band wavelet filters with N vanishing wavelet moments as spectral factors of the new M-th band filters. One can then vary the free parameters of the general (non-minimal length) M-th band filter to obtain filters with better stopband attenuation than the minimal length filters, and wavelet bases with greater smoothness than the corresponding minimal length solutions. Remarkably, in the M-band case (M>2), one can simultaneously improve the stopband attenuation of the lowpass filters and obtain smoother wavelet bases. <<ETX>>
Modeling Rossby Wave Breaking in the Southern Spring Stratosphere

AbstractRossby wave breaking (RWB) plays a central role in the evolution of stratospheric flows. The generation and evolution of RWB is examined in the simple dynamical framework of a one-layer shallow-water system on a sphere. The initial condition represents a realistic, zonally symmetric velocity profile corresponding to the springtime southern stratosphere. Single zonal wavenumber Rossby waves, which are either stationary or traveling zonally with realistic speeds, are superimposed on the initial velocity profile. Particular attention is placed on the Lagrangian structures associated with RWB. The Lagrangian analysis is based on the calculation of trajectories and the application of a diagnostic tool known as the “M” function. Hyperbolic trajectories (HTs), produced by the transverse intersections of stable and unstable invariant manifolds, may yield chaotic saddles in M. Previous studies associated HTs with “cat’s eyes” generated by planetary wave breaking at the critical levels. HTs, and hence RWB, .
Reliability analysis and allocation for electric and hybrid vehicles

Starting from a general literature review of the most common techniques for the reliability analysis and assessment, subdivided into bottom-up and top-down approaches, an application to different configurations of electric and hybrid vehicles was developed. In particular, the focus was the powertrain, considering the critical aspects to achieve the reliability goal for the whole system, through Fault Tree Analysis (FTA). The considered top event was the vehicle stop. A sensitivity analysis and a comparison between the different configurations were conducted, getting the basis for future developments and deeper analyses.
Emotion Recognition based on Heart Rate Detection using Logistic Regression for Automation Food Healing Application

Daily pressure activity especially in work place or any society condition may causing stress or pressure for the people deal with that condition. This stress may causing of decreasing of work performance and achievement in the daily activity. Furthermore in worse condition, people may went to psychiatrist consulting regarding the condition in order to heal the stress or pressure feeling. In this work, food recommendation healing in order to improve the mood based on mood recognition using heartbeat rate detection system is developed. Mood detection results will be used to recommend foods that can improve the mood. the mood detection system work based on automatic intelligent heartbeat rate identification. This system installed on an Android cellphone, so its make the system more flexible and portable. the system has also connected to the web, in order used by doctors to monitor the patient's condition if the patient is being monitoring due to health mental diseases treatment. This system has worked properly with mood detection based on heartbeat rate with 96% and 97%, training and testing accuracy, respectively. Furthermore, this system is connected to the website for monitoring purposes for the psychiatrist to monitor their patient intern of mood changing fluctuation
Improving Company Performance by The Correctness of Management Decision through Implementation Dashboard using Power BI Tools (Case Study at Company Y)

Making the right decisions is one of the important things in supporting the performance of a company. Companies need good data analysis in order to produce insightful information that can be used as the basis for making the right decisions. Data needs to be extracted, processed and visualized on analytical panels (dashboard) so that it can be analyzed and become useful information for company management in making decisions. This paper is about dashboard implementation using Power BI (Business Intelligence) in a case study company Y. The goals is to understand how dashboard implementation using Power BI and improve business performance with precision management decisions that can help business enterprises to look back in history and assist with forward planning.
Leveraging Channel Capacity of a Macro Diversity MIMO System in Gamma-Shadowed Nakagami-m Fading Channel for QoS estimation using Quantum Machine Learning

In this paper we consider a system of one macro diversity SC receiver (MD SC) and two micro MRC (mD MRC) receivers, operating in a Nakagami-m multipath environment with gamma shadowing. The combination of the maximum L-branch SNR signal-to-noise ratio is realized at the micro level, and the selector combiner (SC) with two base stations is realized at the macro level. A closed-form expression for the channel capacity (CC) of the envelope of the SC macro diversity output signal is calculated. Graphical results show the effects of different system parameters on system performance, as well as the improvement due to the use of a combination of the micro and macro diversity. The derived expressions are used within GPU-based mobile network for modeling, planning and simulation of the system to estimate the quality of service (QoS).
Smart Agricultural System using Internet of Things

The radical mutation of technology throughout the years and developments in lifestyle needed substantial improvement in divergent sectors such as houses, agriculture, health care, traffic management, and so on. Agriculture is important among these, as satisfying the increasing need for food caused by a quickly rising population is particularly challenging. Future agricultural problems include climate change, a scarcity of fertile land, insect prevention and management for increased output, and computerized monitoring of large farms. The use of a Smart Agriculture System created utilizing the new technology, Internet of Things, and Cloud storage could assist the farmers in addressing these issues. This design outperforms the conventional agricultural system idea by integrating IoT technology with cutting-edge innovations such as Temperature and Humidity sensors, Moisture sensors, and microcontrollers that continually monitor the field and detect even minute changes. Few basic issues faced by the farmer in the process of fulfilling the demand are addressed by the use of these sensors, which also help in generating a better yield, therefore, saving extra expenditures, unnecessary pesticides, and fertilizers, thus making debt less farmers who have been the backbone of developing nations for years. As a result, farmers can monitor everything using a customized software application and webpage. The fundamental goal of this concept is to use energy-efficient equipment and hence reduce energy expenses. This study proposes unique ideas such as delivering information on the growing state and farm soil qualities based on environmental conditions to enable optimal crop production. This paper also provides a real-time prototype that has been created and implemented.
Composite Filtering and Morphology for Optic Disc Segmentation from Retinal Images

Optic Disc is one of the important attributes which signifies the status of Different eye related diseases like Diabetic Retinopathy, Glaucoma etc. For this purpose, accurate segmentation of Optic Disc from retinal images is essential. Towards such prospect, this paper proposes a new Optic Disc segmentation mechanism based on Composite Filtering and Mathematical Morphology. The complete methodology is accomplished under two-fold; they are (1) Optic Disc Pixel (ODP) identification and (2) Optic Disc Segmentation. In the first fold, ODP is located as an average of candidate pixel locations which are obtained through three different filters namely Median, Gaussian and Variance filters. Next, the final Optic Disc is segmented after the removal of blood vessels with the help of Linear and Rotational Structuring element. For experimental validation, two standard datasets namely DRIVE and MESSIDOR are used, and the performance is measured through Accuracy, sensitivity, specificity and overlap score. The average sensitivity, specificity and overlap score of proposed method is observed as 0.8865, 0.8722, and 0.8472 respectively. Further, the average improvement in the accuracy gained by proposed method is observed as 5.31%.
Research on Underwater Acoustic Target Recognition Method Based on DenseNet

Under the statistical mode, underwater acoustic target recognition relies on heavy feature engineering, and the manually extracted features are sometimes not necessarily effective. At the same time, for confidentiality reasons, the lack of underwater acoustic data will also seriously affect the performance of the underwater acoustic target recognition system. In view of the above problems, the convolutional neural network, residual neural network and densely connected convolutional neural network are introduced and improved, and a Res-DenseNet-based network model is proposed and applied to the underwater acoustic target recognition task. An experimental study was carried out on the dataset. The experimental results show that, compared with the traditional method of MFCC+SVM, using the ResNet network alone and the DenseNet network alone, the correct recognition rates of the new model proposed in this paper are increased by 9.48%, 5.09% and 5.06%, respectively. The method in this paper can be effectively used for underwater acoustic target recognition.
Interpretation of Latent Codes in InfoGAN to Manipulate Rotation of SAR Targets

Despite the success of InfoGAN’s latent codes in class-varying and attribute-editing in synthetic aperture radar (SAR) images, there lacks enough interpretation of the relation between classes/attributes and latent codes. In this work, we show that such a relation can be described as an approximate Gaussian function. By introducing the rotation angle estimators, we can quantify this relation. Various qualitative and quantitative results reveal that the rotation (view angle) can be calculated by a set of certain latent codes, inversely, the satisfying latent codes can also be estimated when a desired viewing angle is given. In this case, both classes and viewing angles of generated SAR images are completely under users’ control.
Limiting the Failure Impact of Interdependent Power-Communication Networks via Optimal Partitioning

The mutual dependency between the power grid and communication network increases the risk of large-scale cascading failure, making the mitigation of outages a difficult task. In this paper, we use the concept of joint partitioning of the power and communication subsystems by identifying the most vulnerable typed-graphlets. This results in higher-order partitions, which prevent cascading failures from propagating globally in the system. For this purpose, we first formulate the problem of partitioning the interdependent system as a large-scale optimization problem with the objective of minimizing the overall load shedding subject to: i) power flow convergence, ii) power stability, iii) partition connectivity, iv) communication congestion/delay control, and v) a high partition quality. Then, using Benders’ method, we solve the problem by decomposing it into a relaxed master problem that consists of constrained spectral clustering, and a linear subproblem that consists of minimizing the load shedding subject to power flow convergence, stability, and congestion/delay control. Simulations are conducted on an IEEE 118-bus supported by a 118-node communication network. Our investigations reveal an average of 62% decrease in damage when the system is optimally partitioned.
Technology Analysis of Network Anomalous Behavior Detection Based on Machine Learning

In the increasingly changing network environment, network anomalous behaviors show a trend of concealment and complexity, and it is crucial to detect anomalous behaviors and eliminate hidden dangers to enhance network system security. Researchers have applied machine learning methods to anomaly detection and achieved good results. This paper presents a review of these studies. First, the concept of anomalous behavior, its sources and its analysis methods are introduced, and then an overview of the application of machine learning methods is given, detailing in order the commonly used datasets, data pre-processing methods and the application of machine learning in anomaly detection. Finally, the advantages and disadvantages of different methods are summarized, and the main challenges and solution ideas faced in the current anomaly detection tasks are analyzed.
Pricing for Electric Vehicle Charging Stations Based on the Responsiveness of Demand

Electric vehicles (EVs) have the promising potential to be effective in mitigating greenhouse gas emissions in the transportation sector. Hence, the penetration of EVs in some countries experiences steady growth. However, the proliferation of EVs can cause negative impacts on the power distribution network (PDN) and the transportation network (TN). Thus, in this paper, a dynamic pricing strategy for electric vehicle charging stations (EVCSs) is proposed to facilitate EVCSs to earn higher profits while alleviating the potential negative impacts on both PDN and TN. First, a pricing strategy, considering the competition effect established based on the TN, is formulated to facilitate the EVCS to attract the defined competitive charging demand. Second, a two-step approach is proposed to mathematically formulate the responsiveness of demand towards the charging price. Third, EV user behaviors are incorporated based on both an admission control scheme and a queueing model to further adjust the charging demand. We have conducted simulations to verify the effectiveness of the pricing strategy in a PDN and TN coupled system, which contains approximately 2000 EVs. Results show that the proposed pricing strategy can facilitate the EVCS to gain 14.2% more net charging profit on average compared with the other three cases. Moreover, the operational stability of the EVCS can be ensured because the proposed pricing strategy can result in the least queueing length volatility with moderate profit variance compared with the other three cases. Furthermore, the proposed pricing strategy can achieve spatial load shifting by incentivizing EVs to alter their station-selection decisions to avoid possible power congestion in the electricity network.
How should we design violin plots?

One way to illustrate distributions of samples is through the use of violin plots. The original design is a combination of boxplot and density plot mirrored and plot around the boxplot. However, there are other designs in literature. Although they seem a powerful way to illustrate distributions, the fact that they encode distributions makes them difficult to read. Users have problems comparing two different distributions, and certain basic statistics such as the mean can be difficult to estimate properly. To get more insights on how people interprets violin plots, we have carried out an experiment to analyze how the different configurations affect judgments over values encoded in those plots.
Prevention Techniques of Grid-tied Solar Photovoltaic System under Islanded Mode

In islanding conditions, a micro grid is isolated from the main grid at the Point of Common Coupling (PCC). The purpose of this study is to determine the islanding condition of the micro grid from the main grid and then analyze the different anti - islanding prevention schemes under the islanding mode of a micro grid. Different methods include the switching of circuit breakers and applying various grid faults. Different relays are used to check the performance of various anti-islanding techniques. This study also compares the detection time of different relays for anti-islanding protection devices and their effect on grid voltage, current, and power.
Emotional Expressions Recognize Ability in Children with Autism: Intervention Using Emotion Recognition Assistance Application

The ability to recognize emotional expression is the basis for developing social relationship skills. Children with autism spectrum disorders have difficulties in recognizing other people’s emotional expressions. In this study, an experiment was conducted to improve the skills of recognizing basic emotional expressions (angry, happy, sad and afraid) using assistive technology based on the Emotion Recognition Assistance (ERA) android application. There are three levels of activity interactively carried out by research subjects in the ERA application, namely recognizing basic emotions, matching emotional expressions with the context and finally imitating emotional expressions by taking photos of themselves to then be responded to by the system regarding the accuracy of emotional expressions shown. The results of data analysis with Wilcoxcon showed the results of a significant increase in skills between before and after treatment in 10 research subjects with ages between 10 and d. 17 years.
A Q-Learning and Data Importance Rating-Based MAC Protocol for Dynamic Clustering Underwater Acoustic Networks

When an underwater acoustic sensor network (UASN) is applied to underwater data collection, different data importance rating (DIR) of sensor nodes will affect the scheduling time slot of data collection. In this paper, we propose a Q-learning and DIR-based media access control (Q-DIR MAC) protocol for dynamic clustering underwater acoustic sensor networks (UASNs), in which the nodes in the network may drift with the movement of ocean currents. We use k-mean algorithm to divide the nodes into several clusters. Each partitioned cluster is composed of one cluster head (CH) and several cluster members (CMs). The CMs can be divided into three levels according to the DIR: non-urgent, normal, and very urgent. The number of three types of nodes follows normal distribution. The data importance of each node is introduced into reward function design of Q-learning. The results show that, in the dynamic clustering UASNs, the proposed Q-DIR MAC protocol can ensure that important data can be sent to the destination node in time without reducing the data success rate under the condition of priority transmission mechanism.
Improved Pulsating Signal Injection Based Sensorless Control of Dual Three Phase IPMSMs Considering Inductance Asymmetry

Since the last decade, dual three phase (DTP) interior permanent magnet synchronous machine (IPMSM) becomes increasingly popular. For dual three phase IPMSMs, most conventional sensorless control methods only consider symmetrical parameters. Therefore, based on DTP IPMSM, this article investigates high frequency pulsating sinusoidal signal injection based sensorless control under inductance asymmetry and proposes a method to compensate the second order harmonic position error because of inductance asymmetry. The proposed suppression method is validated experimentally.