Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impacts of Battery Capacity Degradation on Optimizing BEBs Transit System Configuration

Implementing battery electric buses (BEBs) in transit is considered a sustainable mobility means. However, various planning and operational challenges impede the full adoption of BEBs. Currently, BEB system optimization practice assumes that BEBs will deliver the same performance (battery capacity) over the entire service lifespan. However, BEBs' batteries will degrade over time, leading to a limited operating range. As such, the present study addresses the impact of BEBs' battery degradation on the BEB system infrastructure and component sizing optimization. The study develops a multi-stage optimization model to optimize BEB infrastructure and component sizing, considering battery degradation over the transit system service lifespan. The trade-off between adding charging infrastructure and/or battery replacement is assessed. The results show that adding more charging piles is more cost-effective than replacing the batteries for small hup-and-spoke transit networks. The results also show some insights to improve the adoption of BEBs in transit.
Inward Pointing Trajectories, Normality of the Maximum Principle and the Non Occurrence of the Lavrentieff Phenomenon in Optimal Control under State Constraints

Journal of Convex Analysis 20 (2013), No. 4, [final page numbers not yet available] Copyright Heldermann Verlag 2013 Inward Pointing Trajectories, Normality of the Maximum Principle and the Non Occurrence of the Lavrentieff Phenomenon in Optimal Control under State Constraints Helene Frankowska Institut de Mathematiques, Universite P. et M. Curie, Case 247 -- 4 place Jussieu, 75252 Paris, France frankowska@math.jussieu. fr Daniela Tonon Institut de Mathematiques, Universite P. et M. Curie, Case 247 -- 4 place Jussieu, 75252 Paris, France tonondaniela83@gmail.com It is well known that every strong local minimizer of the Bolza problem under state constraints satisfies a constrained maximum principle. In the absence of constraints qualifications the maximum principle may be abnormal, that is, not involving the cost functions. Normality of the maximum principle can be investigated by studying reachable sets of an associated linear system under linearized state constraints. In this paper we provide sufficient conditions for the existence of solutions to such system and apply them to guarantee the non occurrence of the Lavrentieff phenomenon in optimal control under state constraints.
Close Binaries and the Abundance Discrepancy Problem in Planetary Nebulae

Motivated by the recent establishment of a connection between central star binarity and extreme abundance discrepancies in planetary nebulae, we have carried out a spectroscopic survey targeting planetary nebula with binary central stars and previously unmeasured recombination line abundances. We have discovered seven new extreme abundance discrepancies, confirming that binarity is key to understanding the abundance discrepancy problem. Analysis of all 15 objects with a binary central star and a measured abundance discrepancy suggests a cut-off period of about 1.15 days, below which extreme abundance discrepancies are found.
Density Functional Theory Study of Adding Ionic Liquid to Aqueous Ammonia System

Aqueous ammonia is a commonly working fluid used in absorption refrigeration cycles, and high energy consumption exists during the working process due to incomplete separation of ammonia and water. To address this problem, several cations and anions are selected to form ionic liquids in this paper. Then these substances are added into the ammonia-water system to form ternary systems. The energy analysis between different particles of new systems is carried out by using the density flooding theory. The results indicate that dimethylimidazolium-dimethylphosphate has a high affinity for water and shows a more obvious potential to promote ammonia to separate from water in the energy calculation, and the theoretically predicted results are also consistent with the experimental data in the literature.
Model simulation of jet precession in quasar PG 1302-102

Context. The study of periodic (or quasi-periodic) variabilities in optical and radio bands and quasi-periodic radio-jet swings are important to further our understanding of the physical processes in blazars. Among these the correlation between the periodic or quasi-periodic phenomena in radio and optical bands is particularly significant, because it can provide unique information about the relativistic jets and central engines in the nuclei of blazars. Aims. We aim to investigate the possibility that the radio jet swing on parsec scales observed in PG 1302-102 (z = 0.278) is a quasi-periodic phenomenon and study its correlation with the periodic optical variability claimed in a recently published work, seeking evidence for a binary black hole system. Methods. The precessing jet-nozzle model proposed in our previous works was applied to simulate the kinematics of the superluminal components. It is shown that the inner-jet kinematic features can well be explained in terms of the precessing nozzle model. Results. Based on the model simulation (model fitting) of the inner kinematics for its six superluminal components, a precession period of ~5.1583 ± 0.5 yr is derived for the radio jet swing and the kinematics of all the six components are consistently interpreted. The similarity between the radio jet precession period and the optical period found in its optical light curve may be physically significant. Both periodic behaviors in radio and optical bands could be explained in terms of the orbital motion of a black hole binary, if the orbital plane makes large inclinations to the sky plane: the orbital motion of the primary hole produces the periodic jet swing and the orbital motion of the secondary hole produces the periodic optical variability as suggested in the literature. Thus the total mass and the mass ratio of the binary are estimated. Conclusions. Based on this analysis, we show that PG 1302-102 might have a supermassive black hole binary existing in its nucleus and it is starting to enter its inspiral phase of merging. Gravitational radiation would start to dominate the energy-momentum loss for its orbital shrinkage.
Observation of the Falling Motion of Early Snow Flakes: Part I. Relationship between the Free-fall Pattern and the Number and Shape of Component Snow Crystals@@@第1部:自由落下パターンと構成雪結晶の数および形との関係

Free-fall pattern of early snow flakes,which were composed of two to six crystals, were observed by a stereophotogrammetric method. The considerable part (about 80%) of analysed snow flakes showed the fall pattern of spiral type or rotation type. On the other hand, the stable motion (the fall pattern without horizontal movements) was only observed in the small number of snow flakes composed of two crystals. Whether those snow flakes show the stable motion or not depend on Reynolds number of mean vertical motion, the combined state and the ratio of sizes of two component crystals. The nondimensional frequency and the period of spiral motion increased in proportion to Reynolds number and the nondimensional amplitude of spiral path, respectively. Moreover, the displacement velocity of spiral axis increased proportionally to the falling velocity of early snow flakes.
The influence of convective blueshift on radial velocities of F, G, and K stars

Context. Apparent radial velocity (RV) signals induced by stellar surface features such as spots and plages can result in a false planet detection or hide the presence of an orbiting planet. Our ability to detect rocky exoplanets is currently limited by our understanding of such stellar signals. Aims. We model RV variations caused by active regions on the stellar surface of typical exoplanet-hosting stars of spectral type F, G, and K. We aim to understand how the stellar magnetic field strength, convective blueshift, and spot temperatures can influence RV signals caused by active regions. Methods. We use magneto-hydrodynamic (MHD) simulations for stars with spectral types F3V, a G2V, and a K5V. We quantify the impact of the magnetic field strength inside active regions on the RV measurement using the magnetic and non-magnetic FeI lines at 6165 Å and 6173 Å. We also quantify the impact of spot temperature and convective blueshift on the measured RV values. Results. Increasing the magnetic field strength increases the efficiency to suppress convection in active regions which results in an asymmetry between redand blueshifted parts of the RV curves. A stronger suppression of convection also leads to an observed increase in RV amplitude for stronger magnetic fields. The MHD simulations predict convective motions to be faster in hotter stars. The suppression of faster convection leads to a stronger RV amplitude increase in hotter stars when the magnetic field is increased. While suppression of convection increases the asymmetry in RV curves,c a decreasing spot temperature counteracts this effect. When using observed temperatures for dark spots in our simulations we find that convective blueshift effects are negligible.
Improving the performance of OLSRouting using PHY-IP information for realistic V2V/MANETs

Optimized routing algorithms are of most importance for Mobile Ad-hoc NETworks. The routing protocol offers a route to the destination and adapts the path when the network changes. The popular routing algorithm, Optimized Link State Routing (OLSRv2) is evaluated in realistic real time emulations using the open source EMANE platform and the open Anglova scenario. The effect of fading is analyzed to account for the mobile wireless environment. OLSR estimates the quality of each link by processing received HELLO packets. OLSR has limited performance in scenario with fading and can lead to a poor completion ratio: 67% for the 24-node Anglova company 1 scenario. The proposed solution called OLSR+PHY uses the physical layer estimate of the Signal to Interference and Noise Ratio (SINR) to drop the HELLO packets received with poor SINR. OLSR+PHY mitigates routes flapping and data packet loss. The completion ratio of acked-messages is improved to 79% while the Round Trip Time is only increased from 0.6 to 1s.
Analysis of rounding error accumulation in Conjugate Gradients to improve the maximal attainable accuracy of pipelined CG

Pipelined Krylov solvers typically offer better scalability in the strong scaling limit compared to standard Krylov methods. The synchronization bottleneck is mitigated by overlapping time-consuming global communications with useful computations in the algorithm. However, to achieve this communication hiding strategy, pipelined methods feature multiple recurrence relations on additional auxiliary variables to update the guess for the solution. This paper aims at studying the influence of rounding errors on the convergence of the pipelined Conjugate Gradient method. It is analyzed why rounding effects have a significantly larger impact on the maximal attainable accuracy of the pipelined CG algorithm compared to the traditional CG method. Furthermore, an algebraic model for the accumulation of rounding errors throughout the (pipelined) CG algorithm is derived. Based on this rounding error model, we then propose an automated residual replacement strategy to reduce the effect of rounding errors on the final iterative solution. The resulting pipelined CG method with automated residual replacement improves the maximal attainable accuracy of pipelined CG to a precision comparable to that of standard CG, while maintaining the efficient parallel performance of the pipelined method.
Improved Simultaneous Perturbation Stochastic Approximation-based Consensus Algorithm for Tracking*

In this paper, we consider a distributed stochastic optimization problem where the goal is to cooperatively minimize a non-stationary mean-risk functional. Such problem is an integral part of many important problems in wireless networks, transportation systems, sensor networks, and others. In particular, we focus on the reduction of computational effort needed to achieve a certain level of accuracy. Thus, we propose an improved Simultaneous Perturbation Stochastic Approximation-based consensus algorithm that achieves better accuracy in contrast to an existing solution over the same time horizon and provide its theoretical analysis. We also show the convergence to a bound for mean-squared errors of estimates. The simulation validates the new algorithm in a multi-sensor multi-target problem.
An Approach for Renewable-Energy-Source-Selection Problems Based on Choquet Integral Operator of MIFS

Many criteria must be taken into account while selecting the best renewable energy source (RES), which necessitates a sophisticated multi-criteria decision-making (MCDM) procedure. Conflicting norms, as well as insufficient and inaccurate information, make this endeavour challenging. The theory of moderator-intuitionistic-fuzzy-set (MIFS), a generalization of intuitionistic fuzzy sets (IFSs) has been developed to handle these uncertainties. The MIFS also helps to get a higher degree of precision in the unpredictable behaviours due to the moderator parameter. In this paper, we suggest a technique for choosing the best RES inside MIFS architecture. Our strategy entails creating new aggregation operator namely the MIF Choquet Integral (MIFCI) by utilizing the Choquet operator into MIFS information. Finally, we provide an approach to address the RES-selection issues using the developed operator.
Fatigue assessment of offshore tubular structures: Using Dirlik’s method to establish the procedure

Fatigue assessment of offshore structures has always been an important area of study due to a large number of cyclic loading acting on the structure. Traditionally, offshore structures are simulated in time-dependent dynamic analysis to calculate the stress response at critical locations of the structure. Sorting of relevant stress cycles is done using a suitable cycle counting algorithm (usually rainflow counting) and then an appropriate S-N curve and Miner’s cumulative rule are used to estimate the total fatigue damage at these critical locations. Offshore structures are usually designed for a minimum fatigue life of 20 years, which takes a lot of computational time in FEM. If loads are converted to an equivalent frequency domain and the structure is analyzed in frequency dependent dynamic analysis, computational time can be reduced. This will help engineers perform faster optimizations on new structural developments. For frequency analysis, using Dirlik’s semi-empirical method, a good estimation of fatigue damage are achieved according to literature. Application of Dirlik’s method on various structures has been validated by comparing the damage values with equivalent rainflow counting method. Unlike rainflow counting algorithm, Dirlik’s method calculates probability density function of the stress ranges with empirical formulae and uses S-N curve and Miner’s rule to estimate total fatigue damage. Dirlik’s method is easy to understand, easy to program and estimate fatigue damage with good accuracy. This might also help us in calculating fatigue damage in the stinger, due to bow deflection. To fully understand the method of dynamic frequency analysis with Dirlik’s method, the objective of the research is to study its feasibility on simple models by conducting simulations in FEM and to establish the fatigue calculation procedure. Two models are selected in this research, a cantilever beam and a simple frame model (for tubular joints). Simulation on both the models are conducted in frequency analysis and fatigue estimation is done using Dirlik’s method. To verify the estimated damage values, equivalent time-dependent analyses are also conducted with rainflow counting method. Comparable fatigue damage results are achieved for cantilever beam but, for frame model, results obtained could not be compared due to issue found by conducting detailed analysis. Although, the overall objective of the research is not fully complete, results and observations found in this research will be helpful for conducting additional simulations with different strategies to verify the frame model. With this research, a firm basis is developed which could motivate the company to further investigate the method.
Context-aware Service Recommendation based on Knowledge Graph Embedding (Extended Abstract)

As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, w.r. t. their context requirements (e.g., invocation time, location, social profiles, connectivity). However, current CASR lacks a rich context modelling and does not allow for multi-relational interactions between users and services in different contexts. We propose a context-sensitive service recommendation, by constructing a contextual service knowledge graph (C-SKG), which we translated into a low-dimensional vector space to facilitate its processing. Dilated Recurrent Neural Networks are applied to allow a context-aware C-SKG embedding, based on the principles of subgraph-aware proximity. A recommendation algorithm, finally, returns the top-rated services w.r. t. the target user’s context and the proximity degrees.
Deep Neural Network based Algorithm for Stroke Prediction

A Stroke is a medical condition where the brain's blood supply pathways burst, causing brain damage. When the brain's flow of blood and other supplies is hampered, adverse consequences may occur. According to the World Health Organization (WHO), stroke is the most common reason for mortality as well as disability. Rapid detection of the diverse warning symptoms of a stroke can assist in reducing the harsh-ness. This work presents a stroke predictive analysis procedure based on a deep learning model in which machine learning techniques are widely used. A convolutional neural network with an effective pre-processing step is employed in developing the model to accomplish the goal. Since the dataset contains several imbalanced data, the pre-processing model can effectively reduce considering the issue that improves the overall accuracy. According to experimental results, the proposed model achieves 96.8 percent accuracy, which is superior to the state-of-the-art model.
Big Data: An Intensive Review of its Technical Uses in Various Applications and its Strategies

Thousands of interconnected sensors that are embedded into things like cellular telephones, cashier computers, cars, or weather forecasting are being used by organizations in an expanding number of ways to learn more about their clients, competitors, and activities. This expansion begs the issue of just how businesses handle these Big Data—or constantly growing volumes of data—in a manageable way. As a result, this publication's objective is to pinpoint the many Big Data techniques that a corporation may use as well as a collection of organizational contingencies elements that affect method selection. To do this, we analyzed the body of knowledge in the disciplines of Big Data analytics, systems engineering, and actionable insights. We then consolidated our results into a probability matrix that might aid operators in selecting an appropriate Big Data strategy. The hybrid algorithm, which combines conventional rdbms architecture with Dremel processes, is the option we found to be most often useful for businesses undertaking Big Data analytics, even if each methodology might be advantageous under specific company situations.
Global existence and boundedness in a parabolic-elliptic Keller-Segel system with general sensitivity

This paper is concerned with the parabolic-elliptic Keller-Segel system with signal-dependent sensitivity $\chi(v)$, \begin{align*} \begin{cases} u_t=\Delta u - \nabla \cdot ( u \nabla \chi(v)) &\mathrm{in}\ \Omega\times(0,\infty), \\ 0=\Delta v -v+u &\mathrm{in}\ \Omega\times(0,\infty), \end{cases} \end{align*} under homogeneous Neumann boundary condition in a smoothly bounded domain $\Omega \subset \mathbb{R}^2$ with nonnegative initial data $u_0 \in C^{0}(\overline{\Omega})$, $\not\equiv 0$. In the special case $\chi(v)=\chi_0 \log v\, (\chi_0>0)$, global existence and boundedness of the solution to the system were proved under some smallness condition on $\chi_0$ by Biler (1999) and Fujie, Winkler and Yokota (2015). In the present work, global existence and boundedness in the system will be established for general sensitivity $\chi$ satisfying $\chi'>0$ and $\chi'(s) \to 0 $ as $s\to \infty$. In particular, this establishes global existence and boundedness in the case $\chi(v)=\chi_0\log v$ with large $\chi_0>0$. Moreover, although the methods in the previous results are effective for only few specific cases, the present method can be applied to more general cases requiring only the essential conditions. Actually, our condition is necessary, since there are many radial blow-up solutions in the case $\inf_{s>0} \chi^\prime (s) >0$.
Progress in sensor performance testing, modeling and range prediction using the TOD method: an overview

The Triangle Orientation Discrimination (TOD) methodology includes i) a widely applicable, accurate end-to-end EO/IR sensor test, ii) an image-based sensor system model and iii) a Target Acquisition (TA) range model. The method has been extensively validated against TA field performance for a wide variety of well- and under-sampled imagers, systems with advanced image processing techniques such as dynamic super resolution and local adaptive contrast enhancement, and sensors showing smear or noise drift, for both static and dynamic test stimuli and as a function of target contrast. Recently, significant progress has been made in various directions. Dedicated visual and NIR test charts for lab and field testing are available and thermal test benches are on the market. Automated sensor testing using an objective synthetic human observer is within reach. Both an analytical and an image-based TOD model have recently been developed and are being implemented in the European Target Acquisition model ECOMOS and in the EOSTAR TDA. Further, the methodology is being applied for design optimization of high-end security camera systems. Finally, results from a recent perception study suggest that DRI ranges for real targets can be predicted by replacing the relevant distinctive target features by TOD test patterns of the same characteristic size and contrast, enabling a new TA modeling approach. This paper provides an overview.
Audio inpainting based on joint-sparse modeling

We present a new framework for the restoration of missing samples in audio signals. It consists in locating audio frames that share similar sparse structures and in applying a joint-sparse algorithm to estimate the missing samples. Such similar frames are found in audio signals due to the signals' intrinsic structures: across channels, in the temporal neighboring of each frame and, since patterns are repeated non-locally. We propose a fast and robust strategy for locating the similar frames by introducing a spectral cosine similarity that is more suitable than the usual correlation similarity. We present and compare the inpainting versions of three known joint-sparse algorithms and show how they lead to a better reconstruction of the missing parts. Experimental results reveal that by selecting only a few similar frames, joint-sparse audio inpainting outperform the state-of-the-art OMP inpainting method by up to 5 dB, and that improvements cumulatively result from non-local and inter-channel joint decomposition.