Miracle-dz/UniFAR-FTUs huggingface dataset:
Uji Aktivitas Antihiperglikemik Ekstrak Daun Petai Cina (Leucaena leucocephala (Lam.) de Wit) pada Mencit Induksi Aloksan

Diabetes melitus merupakan suatu penyakit dengan adanya gangguan sekresi insulin baik karena penurunan sensitifitas maupun kerusakan pada sel beta. Tujuan penelitian ini adalah membuktikan ekstrak daun petai cina (Leucaena leucocephala (Lam.) de Wit) memiliki aktivitas menurunkan kadar gula darah mencit hiperglikemik pada dosis yang efektif. Penelitian antihiperglikemik ini menggunakan mencit putih sebanyak 30 ekor. Pengujian aktivitas antihiperglikemik dilakukan pada 6 kelompok perlakuan yaitu kelompok I (kontrol normal), kelompok II (kontrol negatif, induksi aloksan 200 mg/kg BB dan akuades), kelompok III (induksi aloksan dan glibenklamida 10 mg/kg BB), kelompok IV (induksi aloksan dan ekstrak daun petai cina 200 mg/kg BB), kelompok V (induksi aloksan dan ekstrak daun petai cina 400 mg/kg BB), kelompok VI (induksi aloksan dan ekstrak daun petai cina 300 mg/kg BB). Semua kelompok perlakuan diberikan perlakuan tersebut selama 14 hari dan dilakukan pengukuran kadar gula darah pada hari ke-0, 3, 10, dan 17. Aktivitas antihiperglikemik ekstrak daun petai cina ditunjukkan dengan cara menghitung daya hipoglikemik masing-masing perlakuan. Hasil penelitian menunjukkan bahwa ekstrak etanol daun petai cina mempunyai aktivitas antihiperglikemik pada mencit yang diinduksi aloksan. Pada dosis uji ekstrak etanol daun petai cina 600 mg/kg BB mempunyai aktivitas antihiperglikemik yang efektif dibanding dengan dosis 400 mg/kg BB dan 200 mg/kg BB yang sebanding dengan kelompok kontrol positif.
Research on Multi-Target Stable Tracking Algorithm Based on Detection and Tracking Fusion

Vision-based dynamic multi-objective detection and stable tracking has been a hot topic in recent years. This paper proposed a novel multi-target detection and tracking algorithm based on false-alarms elimination with classification and detection tracking fusion. Firstly, adaptive threshold segmentation and multiple morphological processing were used to finish target detecting, and center-peripheral gray scale difference classification method was proposed to remove false alarm target; secondly, the improved spatial phase correlation algorithm was used to complete the tracking of the target one by one; and thirdly, the target detection and tracking fusion algorithm based on a minimum cost function was also proposed to reduce the false alarm rate of the target. Experiment tests show that compared with the traditional method, the target detection accuracy of the detection and tracking fusion algorithm improves by 7% and the algorithm is able to adaptively adjust the tracking frame size and recapture the target after the target is lost. It can satisfy multiple targets tracking at the same time.
Spatiotemporal Applications of Big Data

Big data has emerged as a field of study and gained huge importance these days for both industry and researcher’s point of view. Initially database management systems (DBMS) developed to solve data management and relevant queries. Relational DBMS (RDBM) gave another innovation to the database field. Through the passage of time, it observed that some issues remained unsolved and required some more dimensions be added to the data. One of those was time and the other was location. Spatiotemporal aspects of data gained importance and scientists thought of incorporating these in the upcoming databases. This paper covers the inclusion of these dimensions in a database and its applications in today’s world. It also compares some of the tools used these days and suggests a combination for better results in an efficient and cost-effective way.
Pengukuran Speed dan Impedansi Akustik pada Tanah Liat dengan Memanfaatkan Sinyal Echo Ultrasonik

Each material has its own characteristics, which are represented by the value of speed/ultrasonic wave propagation speed (C) and acoustic impedance/material resistance (Ztl). One technique that can be used to obtain these characteristics is by applying ultrasonic testing. This technique utilizes two ultrasonic sensors as transmitter (UST) and receiver (USR) to get signal properties from each material. The measurement mechanism is nondestructive testing (NDT) where the material tested is not damaged so it does not change the character of the sample. In this research, material characteristics are represented by reflected signals from material (echo). To process the echo signal data and get the characteristics of the sample, we need a number of data processing algorithms such as Fast Fourier Transform (FFT), Peak Detection, and Grid Search. By processing echo from reflected signals, C and Ztl values can be obtained. From the experimental results obtained, the values of C and Ztl in sample 1 with a density of 1856.97573 g/m3 are C = 636 m/s and Ztl = 474640 Ns/m3, samples 2 with a density of 1792.94208 g/m3 of C = 491 m/s and Ztl = 408080 Ns/m3, while the sample 3 with a density of 1663.85025 g/m3 is C = 434 m/s and Ztl = 405639 Ns/m3. The value of material characterization shown that a dense clay also has higher C and Ztl.
Extending firing rate models to include ionic effects

Spiking models have been widely used to describe single neuron oscillation behaviors. However, these models can be quite complex so that in order to incorporate them in networks, one approach is to use so-called firing rate models where the dynamics of the neuron are reduced to the rate at which it fires when presented with a constant stimulus. In pathological conditions such as epilepsy or when the neurons are driven too strongly, they can stop firing due to a phenomenon known as depolarization block, which can come about due to the accumulation of potassium ions in the intracellular space. In the project, we used a well-known Wang-Buzsaki (WB) spiking model but also included an additional equation considering extracellular potassium effects. Given that the extracellular potassium effects is slow and synapses can be reasonably assumed as slow, we applied a slow-fast technique on the WB model and derived a firing rate model describing the synapse-potassium system qualitatively. The bifurcation of the reduced model suggests that the depolarization block threshold can be viewed as the homoclinic bifurcation in the synapse-potassium system, which would be depended upon the potassium sensitivity and the drift rate. In addition, we implement our firing rate model into a two-nearest neighbor spatial model. The spatial-temporal plots suggest our model behavior is consistent with experimental results. The synaptic connectivity has positive effects on seizure propagation and somewhat negative effects on synchronization. On the other hand, the potassium diffusion has a positive influence on synchronization. However, the influence of potassium sensitivity might be more complex. While the neurons under normal physiological conditions can be driven into the seizure-like oscillations in the network, the neurons under depolarization block seem to be a little bit more complicated and require further explanation.
A Transformless Electric Spring with Decoupled Real and Reactive Power Control

The electric spring has emerged as a promising technique to stabilize electrical grids with high penetration of renewable energy sources. In response to the grid frequency deviation, electric springs can flexibly regulate the noncritical load power. However, the previous versions of electric springs either suffer from the coupling effect between real and reactive powers, or require associated energy storage systems, or need an extra low-frequency transformer for the isolation purpose. To overcome these shortcomings, a transformless electric spring and its associated control strategy are proposed in this paper. The proposed electric spring can independently regulate the real and reactive power without using batteries for maintaining the DC-link voltage or handling power flow. Finally, real time hardware-in-loop (HIL) simulation results are provided for verification.
A Study on Damped Elastic Systems in Banach Spaces

Abstract In this article, we concern with the existence of mild solutions for damped elastic systems. The discussion is based on the operator semigroups theory and fixed point theorem. New forms of the corresponding first order evolution equation are introduced, and their well-posed property is proved by means of the operator semigroup theory. Further, we consider analyticity and exponential of the semigroup associated with the elastic system (1.1). Finally, two examples are given to illustrate availability of our results.
Topographic Effects on the Luzon Diurnal Cycle during the BSISO

Cloud-resolving simulations are used to evaluate the importance of topography to the diurnal cycle (DC) of precipitation (DCP) over Luzon, Philippines, and surrounding ocean during the July–August 2016 boreal summer intraseasonal oscillation (BSISO) event. Composites of surface precipitation for each 30-min time increment during the day are made to determine the mean DCP. The mean DCP is computed separately for suppressed and active BSISO conditions and compared across three simulations with varying topography—flat, true, and doubled topographic height. The magnitude of the topographic height helps to dictate the timing, intensity, and location of diurnal precipitation over and near Luzon. For example, the mean DCP in the true topography run peaks 1.5 h later, is broader by 1 h, and has a 9% larger amplitude during active conditions relative to suppressed conditions. By contrast, the flat run mean DCP is earlier and narrower by 0.5 h with a 5% smaller amplitude during active conditions versus suppressed conditions. Within the suppressed or active BSISO conditions, the mean DCP peak and amplitude increase as the topographic height increases. The presence of elevated topography focuses precipitation over the coastal mountains during suppressed conditions, while dictating which side of the domain (i.e., east Luzon and the Philippine Sea vs west Luzon and the South China Sea) more precipitation occurs in during active conditions. These topographic-induced changes are discussed in terms of mechanical and thermodynamic forcing differences between the two large-scale BSISO regimes for the three runs.
A single-shot pose estimation approach for a 2D laser rangefinder

Two-dimensional (2D) laser rangefinders (LRFs) are widely utilized in various mobilized systems, such as robots, intelligent vehicles (IVs) and mobile mapping. Pose estimation for a 2D LRF has important applications for exterior calibration of multiple sensors, registering light detection and ranging (LIDAR) data from multiple LRFs or from a spinning/nodding LRF, etc. This paper shows that the pose of a 2D LRF can be uniquely determined from a single-shot of a trirectangular trihedron. The pose is estimated by solving a simplified perspective-three-point (P3P) problem and a three-point-registration problem. Compared to the raw noisy LIDAR data, the three control points in the P3P problem are estimated from line fitting of the sensed LIDAR data to guarantee good and reliable estimation results. The proposed method has been validated with both simulation and real data. The results show that the method is accurate and practical. To the best of our knowledge, this paper solves the pose estimation of a 2D LRF by using LIDAR data from one scan for the first time.
Design and modeling of a walking mechanism for the self-adapting pipeline robot

Self-adapting pipeline robot is widely used in many industrial fields and the walking mechanism is its critical part. According to the actual dimensional demand of a certain pipeline and the requirement of self-adapting pipeline robot, a walking mechanism is designed based on the double-cross structure and modelled through utilizing 3D modeling software. The walking mechanism can adjust its size to adapt to pipeline with different diameters. The workbench can be kept in the center of the pipeline by optimization of size of the double-cross structure. There are two driving wheels in the bottom and one driving wheel in the top to constitute three-point type, which can improve the stability and load capacity of the robot. The H-type driving wheel is adopted to improve stability and reliability of the contact between the wheel and the guide. Through optimizing parameters of the double-cross structure, the designed walking mechanism can meet the requirement of the pipeline with diameters from 480mm to 600mm. Furthermore, constructions of the walking mechanism are modelled by the Solidworks modeling software, which can verify the feasibility of the design. Design and modeling of the walking mechanism are propitious to give technical support to maintenance and repair of the pipeline.
Modifikasi Default-Boxes Pada Model SSD Untuk Meningkatkan Keakuratan Deteksi

Modifikasi Default-boxes Pada Model SSD Untuk Meningkatkan Keakuratan Deteksi adalah penelitian untuk meningkatkan keakuratan pendeteksian metode Single Shot Multibox Detector. Pada penelitian ini default-boxes dimodifikasi dengan tambahan aspek rasio 2:3 dan 3:2.Dataset yang digunakan untuk penelitian ini adalah PASCAL VOC 2007. Model yang digunakan untuk mengklasifikasikan objek adalah Single Shot Multibox Detector. Layer pada convolutional network dibuat dengan mendefinisikannya dalam bentuk protocol buffer. Untuk melakukan proses training dan testing, digunakan framework caffe. Kesimpulan yang didapatkan dari penelitian ini adalah penambahan default-boxes dapat meningkatkan keakuratan secara keseluruhan. Namun pada kelas-kelas tertentu keakuratan justru mengalami penurunan.Kata kunci: SSD, CNN, neural network, pendeteksian objek
Two combined methods for the global solution of implicit semilinear differential equations with the use of spectral projectors and Taylor expansions

Two combined numerical methods for solving semilinear differential-algebraic equations (DAEs) are obtained and their convergence is proved. The comparative analysis of these methods is carried out and conclusions about the effectiveness of their application in various situations are made. In comparison with other known methods, the obtained methods require weaker restrictions for the nonlinear part of the DAE. Also, the obtained methods enable to compute approximate solutions of the DAEs on any given time interval and, therefore, enable to carry out the numerical analysis of global dynamics of mathematical models described by the DAEs. The examples demonstrating the capabilities of the developed methods are provided. To construct the methods we use the spectral projectors, Taylor expansions and finite differences. Since the used spectral projectors can be easily computed, to apply the methods it is not necessary to carry out additional analytical transformations.
Risk Control Technology for Water Inrush during the Construction of Deep, Long Tunnels

Water inrush is one of the main disasters occurring during tunnel construction in complex geological areas: once it happens, it can cause economic losses, casualties, and delay. Based on risk analysis and management, a risk control scheme is proposed as an effective means to control the risk of such a disaster; however, there are some deficiencies in existing research because the impacts of human factors on the risk of water inrush, dynamic changes in risk information during construction, and the diversity of types of water inrush are neglected. To enrich the research results of water inrush risk control and improve the effect of water inrush risk control, we first use the advantages of Bayesian network to analyse risk events, construct a Bayesian network structure chart of water inrush risk during construction, and propose a fuzzy probability risk analysis model for water inrush. The model can quickly track changes in risk information and diagnose the cause of water inrush disasters while providing an early warning thereof. In addition, considering that the diversity of water inrush types leads to differences in water inrush mechanisms, we believe that the formulation of any water inrush risk control scheme must be combined with water inrush mechanism analysis; therefore, we take a nondefect generated water inrush in front of the tunnel as a representative case and analyse the possible mechanism of water inrush through the stability analysis of the water-resisting strata. Then, based on the results of risk analysis and an analysis of the water inrush mechanism, a reasonable risk control scheme for water inrush is derived.
An Application of a Three-Stage XGBoost-Based Model to Sales Forecasting of a Cross-Border E-Commerce Enterprise

Sales forecasting is even more vital for supply chain management in e-commerce with a huge amount of transaction data generated every minute. In order to enhance the logistics service experience of customers and optimize inventory management, e-commerce enterprises focus more on improving the accuracy of sales prediction with machine learning algorithms. In this study, a C-A-XGBoost forecasting model is proposed taking sales features of commodities and tendency of data series into account, based on the XGBoost model. A C-XGBoost model is first established to forecast for each cluster of the resulting clusters based on two-step clustering algorithm, incorporating sales features into the C-XGBoost model as influencing factors of forecasting. Secondly, an A-XGBoost model is used to forecast the tendency with the ARIMA model for the linear part and the XGBoost model for the nonlinear part. The final results are summed by assigning weights to forecasting results of the C-XGBoost and A-XGBoost models. By comparison with the ARIMA, XGBoost, C-XGBoost, and A-XGBoost models using data from Jollychic cross-border e-commerce platform, the C-A-XGBoost is proved to outperform than other four models.
DC voltage margin adaptive droop control strategy of VSC‐MTDC systems

In this study, direct current (DC) voltage margin control strategy and droop control strategy of voltage source converter multi-terminal direct current (VSC-MTDC) are introduced firstly, and according to the characteristics of two control strategies, an adaptive droop control strategy of DC voltage margin is proposed. When the power fluctuation of DC grid is relatively smaller, some of converter stations which connect to the power generations systems are involved in power coordination to balance the power margin of the entire DC grid. When the power of DC grid suddenly changes and the power fluctuation is relatively larger, the converter stations that adopt the adaptive droop control strategy adjust power and can avoid some of them to lose the adjust ability due to full load and power huge fluctuation as far as possible. Finally, the simulation based on MATLAB validates the effectiveness of the strategy.
The tail empirical process for long memory stochastic volatility models with leverage

We consider tail empirical processes of long memory stochastic volatility models with heavy tails and leverage. We study the limiting behaviour of the tail empirical process with both fixed and random levels. We show a dichotomous behaviour for the tail empirical process with fixed levels, according to the interplay between the long memory parameter and the tail index; leverage does not play a role. On the other hand, the tail empirical process with random levels is not affected by either long memory or leverage. The tail empirical process with random levels is used to construct a family of estimators of the tail index, including the famous Hill estimator and harmonic mean estimators. The paper can be viewed as an extension of [21] while the presence of leverage in the model creates additional theoretical problems, the limiting behaviour remains unchanged.
Treball final de grau

Over the past few years, quantum computing has become more plausible due to the great advances in technology. While quantum computers are on their birth, the underlying mathematics have evolved to the point of proving that some quantum algorithms can solve problems that were unsolvable in classic computers. In order to implement these algorithms in a real machine, it is important to develop efficient ways to do it. The Solovay-Kitaev Theorem states that is possible. This work pretends to offer a complete review of the SolovayKitaev Theorem giving all the necessary tools to prove it. Moreover, we offer a brief introduction to the standard mathematical model of quantum computing, based on unitary operations.
Estimation of CO2 Emissions from Wildfires Using OCO-2 Data

The biomass burning model (BBM) has been the most widely used method for estimation of trace gas emissions. Due to the difficulty and variability in obtaining various necessary parameters of BBM, a new method is needed to quickly and accurately calculate the trace gas emissions from wildfires. Here, we used satellite data from the Orbiting Carbon Observatory-2 (OCO-2) to calculate CO2 emissions from wildfires (the OCO-2 model). Four active wildfires in Siberia were selected in which OCO-2 points intersecting with smoke plumes identified by Aqua MODIS (MODerate-resolution Imaging Spectroradiometer) images. MODIS band 8, band 21 and MISR (Multi-angle Imaging SpectroRadiometer) data were used to identify the smoke plume area, burned area and smoke plume height, respectively. By contrast with BBM, which calculates CO2 emissions based on the bottom–top mode, the OCO-2 model estimates CO2 emissions based on the top–bottom mode. We used a linear regression model to compute CO2 concentration (XCO2) for each smoke plume pixel and then calculated CO2 emissions for each wildfire point. The CO2 mass of each smoke plume pixel was added to obtain the CO2 emissions from wildfires. After verifying our results with the BBM, we found that the biases were between 25.76% and 157.11% for the four active fires. The OCO-2 model displays the advantages of remote-sensing technology and is a useful tool for fire-emission monitoring, although we note some of its disadvantages. This study proposed a new perspective to estimate CO2 emissions from wildfire and effectively expands the applied range of OCO-2 satellite data.
Traceable multicomponent force and torque measurement

Ein Gerat, das fur die Messung von Kraft- und Drehmomentvektoren eingesetzt wird und ein Kalibriersystem integriert, wird in dieser Arbeit beschrieben. Die Krafte und Drehmomente werden auf die Messung von Position, Winkel, elektrischer Spannung, elektrischem Widerstand und Zeit zuruckgefuhrt. Hinsichtlich fundamentaler Naturkonstanten konnen die Krafte und Drehmomente auf die Planck Konstante h, die Lichtgeschwindigkeit c und die Frequenz des Hyperfeinubergangs von Caesium [Delta]v_Cs zuruckgefuhrt werden. Das Messprinzip basiert auf dem Prinzip der elektromagnetischen Kraftkompensation und die Kalibrierung auf dem Kibble-Waagen Prinzip. Mehrere Schwierigkeiten und Einschrankungen von traditionellen Kalibrierverfahren fur Mehrkomponenten-Kraft- und Drehmomentaufnehmer werden mit diesem System uberwunden und neue Moglichkeiten werden vorgeschlagen, wie z.B. die automatische Inprozess-Kalibrierung. Mithilfe einer sorgfaltigen Unsicherheitsanalyse wird die erreichbare Unsicherheit fur die Kraft- und Drehmomentmessung ausgewertet und die Einschrankungen identifiziert, die durch die verschiedenen Komponenten des Systems verursacht werden. Ein Prototyp wird vorgestellt, der die Kraft- und Drehmomentmessung im Bereich vom 2.2 N und 0.11 N m mit einer relativen Standardmessunsicherheit von 44 ppm bzw. 460 ppm ermoglicht. Weiterhin wurde das System durch die Messung einer bekannten Kraft uberpruft, die von einem kalibrierten Testgewicht erzeugt wird. Experimentelle Ergebnisse wurden durch die Anwendung von Mehrkomponentenaufnehmern in der Lorentzkraft-Anemometrie, Mikrobearbeitung und fur die Identifikation von Kraft- und Drehmomentmesssystemen erzielt. Auf Verbesserungsmoglichkeiten fur die weitere Reduzierung der Unsicherheit bei der Kraft- und Drehmomentmessung mit dem Gerat wird eingegangen.