Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Compensated Driving Circuit for Reducing Drift and Hysteresis in Force Sensing Resistors

Force Sensing Resistors (FSRs) are manufactured from a blend of conductive nanoparticles dispersed in an insulating polymer matrix. FSRs exhibit large amounts of hysteresis and drift error, but currently, a great effort is placed on improving their performance through different techniques applied during sensor manufacturing. In this article, a novel technique for improving the performance of FSRs is presented; the method can be applied to already-manufactured sensors, which is a clear benefit of the proposed procedure. The method is based on driving the sensors with a modified-astable 555 oscillator, in which the oscillation frequency is set from the sensor’s capacitance and resistance. Considering that the sensor’s capacitance and resistance have opposite signs in the drift characteristic, the driving circuit provides self-compensated force measurements over extended periods of time. The feasibility of the driving circuit to reduce hysteresis and to avoid sensitivity degradation is also tested. In order to obtain representative results, the experimental measurements from this study were performed over eight FlexiForce A201-25 sensors.
Braginskii viscosity on an unstructured, moving mesh accelerated with super-time-stepping

We present a method for efficiently modelling Braginskii viscosity on an unstructured, moving mesh. Braginskii viscosity, i.e. anisotropic transport of momentum with respect to the direction of the magnetic field, is thought to be of prime importance for studies of the weakly collisional plasma that comprises the intracluster medium (ICM) of galaxy clusters. Here, anisotropic transport of heat and momentum has been shown to have profound consequences for the stability properties of the ICM. Our new method for modelling Braginskii viscosity has been implemented in the moving mesh code arepo. We present a number of examples that serve to test the implementation and illustrate the modified dynamics found when including Braginskii viscosity in simulations. These include (but are not limited to) damping of fast magnetosonic waves, interruption of linearly polarized Alfvén waves by the firehose instability, and the inhibition of the Kelvin–Helmholtz instability by Braginskii viscosity. An explicit update of Braginskii viscosity is associated with a severe time-step constraint that scales with (Δx)2, where Δx is the grid size. In our implementation, this restrictive time-step constraint is alleviated by employing second-order accurate Runge–Kutta–Legendre super-time-stepping. We envision including Braginskii viscosity in future large-scale simulations of Kelvin–Helmholtz unstable cold fronts in cluster mergers and AGN-generated bubbles in central cluster regions.
Topological Stability: a New Algorithm for Selecting The Nearest Neighbors in Non-Linear Dimensionality Reduction Techniques

In machine learning field, dimensionality reduction is one of the important tasks. It mitigates the undesired properties of high-dimensional spaces to facilitate classification, compression, and visualization of high-dimensional data. During the last decade, researchers proposed a large number of new (nonlinear) techniques for dimensionality reduction. Most of these techniques are based on the intuition that data lies on or near a complex low-dimensional manifold that is embedded in the high-dimensional space. New techniques for dimensionality reduction aim at identifying and extracting the manifold from the high-dimensional space. Isomap is one of widely-used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional scaling). Isomap chooses the nearest neighbors based on the distance only which causes bridges and topological instability. In this paper we pay our attention to topological stability that was not considered in Isomap.because at any point on the manifold , that point and its nearest neighbors forms a vector subspace and the orthogonal to that subspace is orthogonal to all vectors spans the vector subspace. Our approach uses the point itself and its two nearest neighbors to find the bases of the subspace and the orthogonal to that subspace which belongs to the orthogonal complementary subspace. Our approach then adds new points to the two nearest neighbors based on the distance and the angle between each new point and the orthogonal to the subspace. The superior performance of the new approach in choosing the nearest neighbors is confirmed through experimental work with several datasets.
Controllable pulse patterns in fiber lasers.

An all-optical pulse power editing (PPE) technique is reported. Using the PPE technique, pulses with different peak powers are output and directed to the positive or reverse saturable absorption (SA) range of the saturable absorber. Further, under the combined action of the PPE technique and SA, four pulse patterns including dissipative soliton (DS), DS molecules, soliton compounds composed of DS and noise-like pulse (NLP), and pure NLP are controllably generated in fiber lasers. The results are conducive for developing advanced DS lasers and can further clarify the onset of pulse dynamic patterns.
Sentiment Analysis for Arabic in Social Media Network: A Systematic Mapping Study

With the expansion in tenders on the Internet and social media, Arabic Sentiment Analysis (ASA) has assumed a significant position in the field of text mining study and has since remained used to explore the sentiments of users about services, various products or topics conversed over the Internet. This mapping paper designs to comprehensively investigate the papers demographics, fertility, and directions of the ASA research domain. Furthermore, plans to analyze current ASA techniques and find movements in the research. This paper describes a systematic mapping study (SMS) of 51 primary selected studies (PSS) is handled with the approval of an evidence-based systematic method to ensure handling of all related papers. The analyzed results showed the increase of both the ASA research area and numbers of publications per year since 2015. Three main research facets were found, i.e. validation, solution, and evaluation research, with solution research becoming more treatment than another research type. Therefore numerous contribution facets were singled out. In totality, the general demographics of the ASA research field were highlighted and discussed
Coupled Systems of Differential-Algebraic and Kinetic Equations with Application to the Mathematical Modelling of Muscle Tissue

We consider a coupled system composed of a linear differential-algebraic equation (DAE) and a linear large-scale system of ordinary differential equations where the latter stands for the dynamics of numerous identical particles. Replacing the discrete particles by a kinetic equation for a particle density, we obtain in the mean-field limit the new class of partially kinetic systems. We investigate the influence of constraints on the kinetic theory of those systems and present necessary adjustments. We adapt the mean-field limit to the DAE model and show that index reduction and the mean-field limit commute. As a main result, we prove Dobrushin's stability estimate for linear systems. The estimate implies convergence of the mean-field limit and provides a rigorous link between the particle dynamics and their kinetic description. Our research is inspired by mathematical models for muscle tissue where the macroscopic behaviour is governed by the equations of continuum mechanics, often discretised by the finite element method, and the microscopic muscle contraction process is described by Huxley's sliding filament theory. The latter represents a kinetic equation that characterises the state of the actin-myosin bindings in the muscle filaments. Linear partially kinetic systems are a simplified version of such models, with focus on the constraints.
Observational signatures of wormholes with thin accretion disks

We numerically construct images of thin accretion disks in rotating wormhole backgrounds, for the Kerr-like and the Teo class of wormholes. Our construction is illustrated by two methods, a semi-analytic scheme where separated null geodesic equations obtained by analytically integrating the second order equations once are used, as well as by a numerical ray-tracing method utilizing a fourth order Runge-Kutta algorithm. Our result shows dramatic differences between accretion disk images in wormhole backgrounds, compared to black hole ones, specifically because a wormhole can in principle have accretion disks on both sides of its throat. We establish the nature of the images if the observer and the disk are on two opposite sides of the throat, and show that these can provide conclusive observational evidence of wormhole geometries.
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

While node semantics have been extensively explored in social networks, little research attention has been paid to pro le edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to pro le, due to noisy multi-modal signals and limited user-generated ground-truth labels. In this work, we aim to develop a uni ed and principled frame- work that can pro le user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data. Our framework is also exible towards limited or missing supervision. Speci cally, we assume a latent distribution of multiple relations underlying each user link, and learn them with multi-modal graph edge variational autoencoders. We encode the network data with a graph convolutional network, and decode arbitrary signals with multiple reconstruction networks. Extensive experiments and case studies on two public DBLP author networks and two internal LinkedIn member networks demonstrate the superior e ectiveness and e ciency of our proposed model.
Structure of Debt Maturity across Firm Types C ü neyt

We investigate if and when the leading theories of debt maturity are useful in understanding the maturity choices of nonfinancial firms in a major developing economy, Turkey. Unlike most research, we use a dataset that provides financial information on not only large, publicly-traded firms but also small, privately-held firms across a wide variety of industries. Our strongest finding is that firms that have high leverage also have long debt maturity. Size, asset maturity, and credit quality are also important, although results depend on the type of firm group considered. The stability of the economic environment as measured by inflation and interest rate volatility also influences debt maturity decisions. Our findings are broadly consistent with the liquidity risk theory. The agency theory is also partially useful in understanding firms’ maturity decisions, particularly for mediumand largesized, publicly-traded firms. The signaling theory is most useful when the sample consists of large, publicly-traded firms. We find little evidence that taxes matter for maturity decisions. Our findings also provide some evidence that borrower-lender relationships matter for debt maturity structures.
The validity of unlicensed spectrum for future local highcapacity services

Unlicensed spectrum indeed initiates high-data rate wireless services with the combination of the great success of Wi-Fi technology. Interestingly, the local high data rate services are deployed and invested by non-traditional local actors, e.g., facility owners who have local fixed line infrastructure. Motivated by the great success of the Wi-Fi eco-system, there are growing interests from various regulatory initiatives on short-range indoor shared spectrum access to continuously foster new business innovations and local investment by new players. Despite of flexible spectrum access and almost no regulatory management overhead, it is still not so clear that the traditional unlicensed approach can work for future high-capacity services where require extremely denser deployment than today. In this paper, we aim to discuss the validity of the traditional unlicensed approach for the new local operators in an economic aspect. We evaluate the required deployment cost of conventional Wi-Fi system and compare it with a hypothetical cellular-like system with marginal regulatory coordination. We found that the traditional node-level etiquettes in unlicensed band work as system design constraints, leading to too conservative full distributed systems. Although the current unlicensed band approach is the lowest cost solution for relatively low-capacity services, it may not be work at future high-capacity provisioning. Thus, regulations need to be designed to allow more coordinated systems such as cellular-like technologies with certain inter-network regulation.
Interoperability of Digitizing Systems Based on Semantic Grid

With the growth of the complex multi-disciplinary collaborative design product demand, the collaborative environment and functionality interoperability of the product design put forward higher requirements. To solve the issues of information-sharing among heterogeneous digitizing systems, this paper introduces semantic grid combined by semantic Web and grid technology, and proposes the semantic grid hierarchy model supporting interoperability, and establishes digital system interoperability integration framework based on semantic grid, and achieves a prototype system on this basis.
Detecting anthropogenic carbon dioxide uptake and ocean acidification in the North Atlantic Ocean

Abstract. Fossil fuel use, cement manufacture and land-use changes are the primary sources of anthropogenic carbon dioxide (CO2) to the atmosphere, with the ocean absorbing approximately 30% (Sabine et al., 2004). Ocean uptake and chemical equilibration of anthropogenic CO2 with seawater results in a gradual reduction in seawater pH and saturation states (Ω) for calcium carbonate (CaCO3) minerals in a process termed ocean acidification. Assessing the present and future impact of ocean acidification on marine ecosystems requires detection of the multi-decadal rate of change across ocean basins and at ocean time-series sites. Here, we show the longest continuous record of ocean CO2 changes and ocean acidification in the North Atlantic subtropical gyre near Bermuda from 1983–2011. Dissolved inorganic carbon (DIC) and partial pressure of CO2 (pCO2) increased in surface seawater by ~40 μmol kg−1 and ~50 μatm (~20%), respectively. Increasing Revelle factor (β) values imply that the capacity of North Atlantic surface waters to absorb CO2 has also diminished. As indicators of ocean acidification, seawater pH decreased by ~0.05 (0.0017 yr−1) and ω values by ~7–8%. Such data provide critically needed multi-decadal information for assessing the North Atlantic Ocean CO2 sink and the pH changes that determine marine ecosystem responses to ocean acidification.
City Event Management System Based on Multiple Data Source

Since IBM proposed the concept of "Smarter Planet", the construction of "Smarter City" has been spread all over the world. Our country's construction in "Smarter City" is just at the beginning, and it appears with serious information silos, although data has been simply integrated, there still exists the problem of single source of data, fixed monitoring position and details can be hardly found. In response to these problems, this paper presents the design of City Event Management System, which integrates the data from public monitoring facilities, social media and crowd sourcing. Meanwhile, topic model has been used to extract city events, these events can be classified in different categories by matching with the predefined repository. The proposed system pushes events as well as relative viable solutions to users. Hence, problems of the fixed monitoring positions are solved via users' activity, and their high sensitivity of city events. Issues on single source of data are solved by the data integration of social media and crowd sourcing. Challenges of information silos are overcame by strength the data mobility.
High-performance navigation and rendering of very-large scale landscape and seascape

With fast development of graphics hardware, the current algorithms on rendering of landscape and seascape, which are focused on precise view-frustum culling and complicated decision of level of detail (LOD), lead the CPU to be the bottleneck of system. This paper presents a new framework based on some new data structures and new rendering algorithms for navigation and rendering of very large-scale landscape and seascape. Firstly, we construct a new terrain data model, which consists of terrain tile pyramid and terrain summary pyramid. Secondly, in order to stitch the terrain tile boundaries seamlessly, the index template is introduced, and an algorithm of LOD based on "remarkability" is described in detail. Finally, Perlin noise is used to simulate the ocean surface and a framework of navigation of very large-scale scene is completed. Experimental results show that this system can satisfy the requirements of real-time navigation of large-scale landscape and seascape.
Evaluation of the Concentration of Some Toxic Metals in Dietary Red Palm Oil

Palm oil has been part of human diet for more than 5000 years. For generations, it has been revered as both a nutritious food and a valuable medicine. However, care should be taken to evaluate the purity and safety of this nutritional and medicinal agent to the human system. This study investigated the concentration of cadmium, chromium, mercury, lead, arsenic and nickel in twenty-five samples of ready-to consume palm oil that were bought randomly from different markets in Lagos, Nigeria. The samples were digested with aqua-regia (2:1:2 HNO3:HClO4:H2SO4 respectively) and the digested samples were analyzed using Atomic Absorption Spectroscopy. There was no detectable mercury in one of the twenty-five samples analysed. However, all the samples contained a detectable amount of each of the other five metals analysed. The Oral Component Limit (OCL) for cadmium, chromium, mercury, lead, arsenic and nickel as stated by USP are 0. 5μg/g, 25 μg/g, 1.5 μg/g, 1 μg/g, 1.5 μg/g and 25 μg/g respectively. The samples contained detectable amounts of the metals although with values below the USP standard OCL. The results obtained from this study suggest that the palm oil samples analysed are save for consumption in terms of the metals of interest analysed.
A New EAAODV Routing Protocol based on Mobile Agent

This paper proposes a new routing scheme for mobile Ad hoc networks . EAAODV (Energy-Aware AODV), which combines the on-demand routing capability of Ad hoc On-Demand Distance Vector routing protocol with a distributed topology discovery mechanism - mobile agents. Utilizing the interior characteristics of the mobile agent technology, EAAODV obtains the dynamic energy distribution in the network, thus reaching the balance condition. EAAODV eliminates the problem of low efficiency and link breaking in routing due to the energy exhaustion of some nodes, which exists in the traditional protocols. This paper analyzes the scheme by means of both mathematics and simulation technologies. Results show that EAAODV hybrid protocol proposed in this paper is able to achieve a longer surviving network duration and a larger throughput compared to the traditional AODV protocols.
An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations

In the absence of actual suspended sediment concentration (SSC) measurements, hydrologists have used sediment rating (sediment transport) curves to estimate (predict) SSCs for subsequent flux calculations. Various evaluations of the sediment rating‐curve method were made using data from long‐term, daily sediment‐measuring sites within large (>1 000 000 km2), medium (<1 000 000 to >1000 km2), and small (<1000 km2) river basins in the USA and Europe relative to the estimation of suspended sediment fluxes. The evaluations address such issues as the accuracy of flux estimations for various levels of temporal resolution as well as the impact of sampling frequency on the magnitude of flux estimation errors.
MAPPING (SLAM) USING DIFFERENTIAL DRIVE MOBILE ROBOT

Simultaneous Localization and Mapping (SLAM) problem is a well-known problem in robotics, where a robot has to localize itself and map its environment simultaneously. Particle filter (PF) is one of the most adapted estimation algorithms for SLAM apart from Kalman filter (KF) and Extended Kalman Filter (EKF). In this work, particle filter algorithm has been successfully implemented using a simple differential drive mobile robot called epuck. The performance of the algorithm implemented is analyzed via varied number of particles. From simulation, accuracy of the resulting maps differed according to the number of particles used. The Root Mean Squared Error (RMSE) of a larger number of particles is smaller compared to a lower number of particles after a period of time.
Long-Term Temporal Variability of the Freshwater Discharge and Water Levels at Patos Lagoon, Rio Grande do Sul, Brazil

The aim of this study is to investigate the importance of freshwater discharge as a physical forcing in Patos Lagoon at timescales longer than one year, as well as identify the temporal variability of the dominant processes in freshwater discharge and water levels along the Patos Lagoon. Due to its proximity to the mouth, the water level at the estuary is influenced by the remote effects associated with the adjacent ocean circulation and wave climatology, reducing the observed correlation. At the lagoonar region a high correlation is expected because interannual data is being used, reducing the influence of the wind. Cross wavelet technique is applied to examine the coherence and phase between interannual time-series (South Oscillation Index, freshwater discharge and water levels). The freshwater discharge of the main tributaries and water levels in Patos Lagoon are influenced by ENSO on interannual scales (cycles between 3.8 and 6 years). Therefore, El Nino events are associated with high mean values of freshwater discharge and water levels above the mean. On the other hand, La Nina events are associated with low mean values of freshwater discharge and water levels below the mean.
Intelligent Search Agents for Web-Based Intelligent Tutoring Systems

In this paper, after the introduction of ontology-based domain knowledge bases and domain knowledge representation, a formal query model of intelligent search agents that can search over distributed knowledge bases with heterogeneity for Web-based intelligent tutoring systems is proposed; and the searching process of these agents is analyzed. Ontologies are introduced to describe the semantic information of knowledge bases in order to express fully the semantics of queries in our Web-based intelligent tutoring system. This model improves the relevance of the searching results. At the same time, it reflects the dynamic change of the information in the knowledge bases and reduces the consistency and efficiency problem caused by using globe ontology.