Miracle-dz/UniFAR-FTUs huggingface dataset:
Elucidating Cryptocurrency with Trading Dashboard

With the rise of interest in cryptocurrency in the recent decade, an ocean of financial news data has surfaced in articles, tweets, and even Reddit posts. Due to the sheer volume, it is not practical for the casual trader to read through all these news sources manually. However, only going through one or two sources alone may result in receiving biased information, or no useful information at all. With the current rise in cryptocurrency, accurately predicting market trends becomes highly beneficial to the user, providing a major opportunity for lower-income households to have a higher chance of profiting and living a substantially more comfortable lifestyle. In this study, a developer's API key was obtained for three news sources to scrape financial news from. Then, the TensorFlow Keras model and Gensim model's doc2vec NLP tool were utilized to process the data scraped online. The data is then saved as a . model and . sav file, and a website was constructed using the Flask framework. The website is now deployed and is available for all users. However, because the data obtained was too small to be utilized well, only a weak linear model that could give us a correlation between price and news sentiment was able to be constructed. The dashboard passed its functional and UAT tests with 100%, and via the usability test with SUS, the dashboard is considered to be easy to use. In all, the website summarizes the main details and sentiment of the coins and will benefit users who are just being introduced to the cryptocurrency space.
Study on the interests of nature reserves and their surrounding residents

In order to strike a balance between protecting wild animals and other natural resources and safeguarding the interests of people in the surrounding areas, we developed a dynamic game model that considers both factors. We used the number of wild animals and per capita GDP as observation targets, with the goal of optimizing the income of the surrounding residents while also maximizing the number of wild animals. The objective is to achieve a harmonious state where both benefits are balanced.While punishing residents for destructive behavior can be effective, it is important to protect their economic and living interests. This can be accomplished through increased awareness and education on the importance of preserving wild nature reserves, as well as by improving the employment and income opportunities of the residents. Developing local scenic spots can also help to increase the income of the surrounding communities and improve their overall living conditions. By taking these measures, we can create a sustainable and harmonious environment where both wild animals and people can thrive.
A numerical method of tracing a vortical axis along local topological axis line

A new numerical method is presented to trace or identify a vortical axis in flow, which is based on Galilean invariant flow topology. We focus on the local flow topology specified by the eigenvalues and eigenvectors of the velocity gradient tensor, and extract the axis component from its flow trajectory. Eigen-vortical-axis line is defined from the eigenvector of the real eigenvalue of the velocity gradient tensor where the tensor has the conjugate complex eigenvalues. This numerical method integrates the eigen-vortical-axis line and traces a vortical axis in terms of the invariant flow topology, which enables to investigate the feature of the topology-based vortical axis.
ROBUST M-ESTIMATES FOR THE PARTIAL LINEAR MODELS

In the paper the robust M-estimates of the unknown function and the unknown parameter of the partial linear models are discussed. The robust M-estimate of the unknown function is proposed by local linear method, and the robust M-estimate of the unknown parameter by tow step method. The weak consistence and the asymptotic normality of the robust M-estimate of the unknown function are given, and the weak consistence of the robust M-estimate of the unknown parameter is established.
Note on goodness-of-fit measures for the revealed preference test: The computational complexity of the minimum cost index

The purpose of this paper is to show that computing the minimum cost index (MCI) for a given price-amount data set, proposed by Dean and Martin (2010, 2015) as a goodness-of-fit measure for the revealed preference test, is NP-hard. Our proof uses a polynomial reduction from the feedback arc set problem, which is a decision problem known to be NP-complete. Our result refines the NP-hardness result in Dean and Martin (2010), which is presented in a more abstract framework than our economic data setting. Thus the computation of MCI is NP-hard even if we restrict our attention to the revealed preference setting for economic data. We also discuss computational procedures for MCI and provide a way of approximating MCI in polynomial-time using approximation algorithms for the (weighted) feedback arc set problem.
Vortex formation of the lvad-assisted left ventricle studied in a cardiac simulator

OF THE THESIS Vortex Formation of the LVAD-Assisted Left Ventricle Studied in a Cardiac Simulator by Kin Wong Masters of Science in Bioengineering San Diego State University, 2013 Heart transplantation remains the primary treatment for heart failure (HF), a deadly disease in which the heart fails to pump sufficient blood to meet the body’s needs. Unfortunately, of the 600,000 new HF patients per year, only 2200 (0.3%) of them receive heart transplantation. The critical organ shortage presents a need for artificial devices. Left ventricular assist devices (LVADs) are implantable blood pumps that serve as an alternative treatment for HF. Unfortunately, stroke is a devastating complication that occurs in about 1720% of LVAD patients. Since stroke is the result of blood vessel blockage, we hypothesize that LVADs create flow disturbances such as localized stagnation regions that encourages excessive blood clotting (thrombosis) in the LV, which detach and release into the bloodstream. Experiments were performed using the in vitro SDSU cardiac simulator, which reproduces the flow and hemodynamics of a left ventricle attached to a HeartMate II LVAD. The effect of LVAD support on LV flow patterns was assessed by varying the LVAD speed from low (6500rpm) to high (12500rpm). Particle Image Velocimetry was used to image 2-D fluid structures in the LV model. Briefly, fluorescent tracer particles were seeded into the simulator, the particles were illuminated by a laser plane and their motion captured by a high frame-rate camera. Localized stagnation was measured by the stagnicity (SI), defined as the reciprocal of average velocity within the region of interest. The flow patterns resemble a HF patient as the ventricular inflow jet follows an asymmetric recirculation pattern that redirects the momentum of the fluid stream to the LV outflow tract (LVOT). Once the LVAD is incorporated into the circulation, the fluid stream bifurcates into ventricular outflow and LVAD outflow. As LVAD speed increases further, more fluid is ejected through the LVAD until finally there is a complete series flow. We observe a local stagnation region in the LVOT regardless of LVAD speed. The level of stagnation increases progressively from Pre-LVAD (SI =4.20(m/s) -1 ), parallel (SI =4.75 (m/s) 1 ), to series flow (SI =7.75 (m/s) -1 ). The results suggest that LVAD support alters the cardiac ejection pathways and increases the risk of thrombosis within the left ventricle.
Calculation of Mass Attenuation Coefficients of Beta Particles

Using the semi-empirical equations for the transmission coefficient of monoenergetic electrons, the mass attenuation coefficient of beta particles has been calculated. Beta spectra for 'allowed' and 'first forbidden' transitions are considered. The calculated mass attenuation coefficients for β - emitters determined in the present study agree within 10% of those measured. However, the calculated value for 22 Na (β + emitter) deviates by as much as 20% from the measured value.
Set-Membership State Estimation for Unmanned Surface Vehicle Steering Motion With Try-Once-Discard Protocol

This article proposes a set-membership state estimation issue for unmanned surface vehicle (USV) steering motion under wireless sensor networks with try-once-discard (TOD) scheduling protocol. The discrete-time USV steering motion model with unknown but bounded noises and uncertain parameters is established based on the set-membership approach and norm bounded uncertainties. TOD protocol is employed to schedule sensor measurements dealing with the problem of medium access constraint in networked communication. A remote set-membership state estimator with a time-varying gain is designed for onshore human supervisors to aware the USV state in sway, yaw, and roll. Then a sufficient condition is established by the mathematical induction, ensuring that the estimation error is constrained by a time-varying ellipsoid, and the ellipsoid is minimized by a recursive optimization algorithm. Finally, the validity of the proposed estimation method is verified via a numerical example.
Analysis of Nonrandomly Censored Ordered Categorical Longitudinal Data from Analgesic Trials: Comment

A clinical trial of an analgesic agent compares pain relief scores over time among groups of patients. All subjects experience the same painful procedure, but different subjects are given different randomly assigned doses of active agent or placebo when they first request it. The data are short individual time series of ordered categorical pain relief scores subsequent to dosing. Nonrandom right censoring may be present because patients can elect to remedicate with an active agent if their pain relief is insufficient. The trial is meant to address two questions: (a) Is there proof that the drug relieves pain? If so, (b) What dosage patterns should be investigated further, or recommended for use by a typical patient? Marginal models of human pharmacology are basically empirical models, and although an analysis of a study based on such a model can adequately address the first question, such is not the case for the second question, because this question requires extrapolation to untested dosing patterns. We propose to analyze study data using a hierarchical model so as to address both questions. The analysis uses a semimechanistic subject-specific pain-relief model for the distribution of all (uncensored and censored) observations, conditional on individual random effects, and an empirical model for the censoring outcome, remedication, conditional on observed pain relief and individual random effects. We estimate the parameters of the foregoing (nonlinear mixed effects) model via maximum likelihood, assuming normally distributed random effects. Monte Carlo integration with respect to the random effects is used to compute marginal statistics relevant to the dosing question. Of particular note is that this formulation encourages use of subject matter information in model specification so that the extrapolations required to address the dosing question are credible. An example is given of the application of the analysis to analgesic trial data for the drug ketorolac.
A Study on Evaluation Method for Beam Profile of Phased Array by Using Two-Dimensional Measurement Equipment

SUMMARY Recently, “Both-Side Retrodirective System” was proposed, as a beam convergence technique, for microwave high power transmission. To demonstrate the effectiveness of the both-side retrodirective system by experiment, the authors propose a 2-dimensional measurement equipment. Propagation in the parallel plate waveguide was analogized based on free-space propagation, and the theory and characteristics were clarified by simulation. The electric field distribution in the waveguide was measured by electric probe with the proposed equipment. Two types of measurement equipment were developed. One is a 4-element experiment system, which is a small-scale device for principle verification. The other is a 16-element measurement equipment, which is intended to evaluate beam convergence of a both-side retrodirective system in the next step. The measured results were compared with simulation results. As a result, it was confirmed that the beam formed in the waveguide was successfully measured. Thus, the effectiveness of 2-dimensional measurement equipment for evaluation of beam convergence was shown.
Necropolitical Law and the Justification of Violence in the War on Terror

This superb study investigates one fundamental question: how does the United States legally justify the killing of people around the world in the battles of the war on terror? By analyzing a series of selected and signi ﬁ cant materials, Jothie Rajah shows how this process of justi ﬁ cation involves a complex interaction of codi ﬁ ed law and nonlegal texts, images, events, and perceptions. The validation of killing and of “ discounting life ” activates a system of “ necropolitical law ” that simultaneously de ﬁ nes a state operation, a legal process, a military strategy, a capitalist enterprise, the articulation of a salvi ﬁ c ideology, a quest for cultural hegemony, and a general form of neocolonial control. As a result of this combination of rationales, law is rede ﬁ ned “ as a compound and relational fabric, with violence and sovereign power inevitably part of the weave ” (5). This fabric expands beyond the conventional sphere of the law and is “ discernible in sovereign power ’ s plural, everyday, and embodied expressions ” (5). Rajah ’ s study is thus not a standard analysis of speci ﬁ c problems of the rule of law. Rather, she approaches necropolitical law as a type of “ cultural text ” (26) that describes the multiple discourses and relations tied in with the US war on terror.
Equality of critical densities in continuum percolation

Consider a homogeneous Poisson process in with density ρ, and add the origin as an extra point. Now connect any two points x and y of the process with probability g(x − y), independently of the point process and all other pairs, where g is a function which depends only on the Euclidean distance between x and y, and which is nonincreasing in the distance. We distinguish two critical densities in this model. The first is the infimum of all densities for which the cluster of the origin is infinite with positive probability, and the second is the infimum of all densities for which the expected size of the cluster of the origin is infinite. It is known that if , then the two critical densities are non-trivial, i.e. bounded away from 0 and ∞. It is also known that if g is of the form , for some r > 0, then the two critical densities coincide. In this paper we generalize this result and show that under the integrability condition mentioned above the two critical densities are always equal.
Sarcoidosis Associated with Enlarged Mediastinal Lymph Nodes with the Detection of Streptococcus gordonii and Cutibacterium acnes Using a Clone Library Method

A 77-year-old Japanese woman with mediastinal lymphadenopathy and uveitis was diagnosed with sarcoidosis. The bacterial flora in biopsied samples from mediastinal lymph nodes was analyzed using a clone library method with Sanger sequencing of the 16S rRNA gene, and Streptococcus gordonii (52 of 71 clones) and Cutibacterium acnes (19 of 71 clones) were detected. No previous study has conducted a bacterial floral analysis using the Sanger method for the mediastinal lymph node in sarcoidosis, making this case report the first to document the presence of S. gordonii and C. acnes in the mediastinal lymph node of a patient with sarcoidosis.
Ionic Liquid-Optoelectronics-Based Multimodal Soft Sensor

Multimodal soft sensors have attracted significant attention due to their ability to simultaneously measure multiple physical properties of objects, offering immense potential for the use of smart wearable devices and soft robots. However, achieving simultaneous detection of multiple physical quantities and integrating these functions in a limited sensor space remains a challenge. In this article, a novel multimodal soft sensor is proposed for the simultaneous detection of mechanical stretching and bending deformations, which is based on room temperature ionic liquid (RTIL) optoelectronics. The method employs RTIL as the light propagation medium and shares the same geometric unit with optoelectronics, effectively reducing the geometry of the sensor. It is capable of detecting three deformation modes, namely, stretching, bending, and simultaneous stretching and bending. The compound deformation of stretching and bending is decoupled by detecting the difference between RTIL and optoelectronic signals, thus combining with the deep learning of long and short memory (LSTM) neural networks. Moreover, the proposed multimodal sensor exhibits high sensitivity, wide measurement range, and good stability, making it a promising option for future applications in soft robots and wearable devices.
A 3-D Convolution-Based Burn-Through Point Multistep Prediction Model for Sintering Process

Predicting burn-through point (BTP) in advance is a quite critical task for the sintering process. However, sintering is a complex physicochemical reaction process, and the strong spatial–temporal correlations of data make the multistep prediction task very challenging. The previous BTP multistep prediction model only extracts spatial features in the high-level layers, leaving the spatial features in the low-level layers not learned. Specifically, the previous model only considers the relationships between the process variables and BTP, ignoring the spatial coupling relations among process variables. Further, the existing loss function is mainly based on Euclidean distance, which cannot learn dynamic information of multistep prediction sequence. To tackle these problems, in this article, we propose a 3-D convolution-based BTP multistep prediction model to simultaneously capture spatial–temporal features. First, the 3-D convolution is employed to capture the spatial–temporal features from low-level to high-level layers at the same time. Second, a spatial–temporal recalibration block is proposed to further refine the extracted features to increase the contributions of informative features and suppress the less useful ones. Finally, we design a time-aware multistep prediction loss function to dynamically weigh the similarity between the actual sequence and the predicted sequence. The experimental results on two real-world BTP datasets demonstrate the effectiveness and feasibility of the proposed model on the BTP multistep prediction task.
ULTRASOUND-ASSISTED AND ENZYMATIC-BASED METHOD FOR ISOLATION OF β-GLUCANS FROM OAT BRAN

β-Glucans are a group of non-starchy polysaccharides, or (1,3),(1,4)-β-D-glucans, that can be found in the cell walls of several species of bacteria, algae, lichens, fungi, and cereal grains. These carbohydrates are extensively used in food industry, cosmetics, pharmaceuticals and healthcare, therefore optimization of the extraction and isolation of β-glucans from grain sources has an especial importance in various fields of biotechnology, drug design, food science and technology. The aim of the study was to develop an optimized technological scheme for isolation of β-glucans from oat bran based on ultrasonic and enzymatic processing of raw material. Materials and methods. β-Glucans were isolated from grinded oat cereals during multi-stage process, which includes extraction of grain fats, hydrobarothermic processing, ultrasonification, enzymatic hydrolysis of concomitant starch and proteins, precipitation of β-glucan fraction by ethanol, centrifugation, and dry-freezing. Yield of β-glucans from raw material and its concentration in the final product were determined after hydrolysis by sulfuric acid or enzymatic cleavage by endo-1,3(4)-β-glucanase. Results. As shown by acidic hydrolysis of the final product, the yield of β-glucans was 10.8 ± 0.23% and concentration was 79.6 ± 3.89%, while enzymatic hydrolysis gave 8.7 ± 0.82% and 65.1 ± 4.72%, respectively. Thus, the use of hydrobarothermic and ultrasound pre-treatment of raw material in combination with proteolytic digestion of ballast lipids and proteins allowed producing oat β-glucans in amounts comparable with those in case of acid- or alkali-based procedures. Conclusions. The described technological scheme of β-glucan isolation from oat bran based on sequential hydrobarothermic processing, ultrasonification, and enzymatic removing starch and proteins can be widely used for routine β-glucan production for various purposes in food technology, pharmacological industry, and medicine.
Implementation of coordinate transformations in periodic finite‐element method for modeling rough surface scattering problems

The coordinate transformation technique (with its current name of transformation electromagnetics) is applied to the finite‐element method (FEM) with periodic boundary conditions for efficient Monte Carlo simulation of one‐dimensional random rough surface scattering problems. In a unit cell of periodic structure, two coordinate transformations are used, one of which is a real transformation designed to model the rough surface with flat surface, and the other is a complex transformation used to design a perfectly matched layer for mesh truncation. The approach is easily implementable because only the nodal coordinates are replaced by the transformed coordinates in a standard FEM. Since a simple and single mesh with flat surface is used, Monte Carlo simulations are performed in an efficient manner. Numerical results are presented for various rms surface heights, correlation lengths, and angles of incidence. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:322–329, 2016.
A Software-Defined UAV Network Using Queueing Model

To increase the redundancy and to provide seamless connectivity of the conventional communication systems, an unmanned aerial vehicle (UAV) enabled on-demand forwarding base station approach can be a flexible and dynamic solution, particularly for emergency services. However, managing and controlling these UAVs in changing scenarios can be challenging specifically in large-scale network scenarios. As a promising method of administering these networks, software-defined networking (SDN) can be a good choice compared to traditional networking due to its automated, centralized, and intellectual controllability. Therefore, to meet the future sixth-generation wireless communication requirements with high network availability, improved communication convergence, and intelligent features, a software-defined UAV (SDUAV) networking framework is proposed in this work. To enhance the reliability and scalability, and to reduce the single-point failure issues of this network, a multi-SDN controller-based approach is also deployed in this newly designed architecture. Besides, to solve the load balancing and fault tolerance problems like controller overhead or cascading failure, an adaptive load balancing algorithm as well as a robust hybrid routing algorithm are developed, accordingly. In addition, to evaluate the performance of the proposed architecture, a mathematical model is proposed by using the M/M/1 and M/M/c queueing systems at the primary and secondary controllers, respectively. Simulation results show that the proposed model reduces the packet processing time by 60%, 44%, and 25% in terms of packet arrival rate, service rate, and utilization factor, respectively, compared to the existing control-domain adjustment algorithm.