Miracle-dz/UniFAR-FTUs huggingface dataset:
Blade shape optimization of an aircraft propeller using space mapping surrogates

Propeller performance greatly influences the overall efficiency of the turboprop engines. The aim of this study is to perform a propeller blade shape optimization for maximum aerodynamic efficiency with a minimal number of high-fidelity model evaluations. A physics-based surrogate approach exploiting space mapping is employed for the design process. A space mapping algorithm is utilized, for the first time in the field of propeller design, to link two of the most common propeller analysis models: the classical blade-element momentum theory to be the coarse model; and the high-fidelity computational fluid dynamics tool as the fine model. The numerical computational fluid dynamics simulations are performed using the finite-volume discretization of the Reynolds-averaged Navier–Stokes equations on an adaptive unstructured grid. The optimum design is obtained after few iterations with only 56 computationally expensive computational fluid dynamics simulations. Furthermore, an optimization method based on design of experiments and kriging response surface is used to validate the results and compare the computational efficiency of the two techniques. The results show that space mapping is more computationally efficient.
Graphene Composites for Lead Ions Removal from Aqueous Solutions

The indiscriminate disposal of non-biodegradable, heavy metal ionic pollutants from various sources, such as refineries, pulp industries, lead batteries, dyes, and other industrial effluents, into the aquatic environment is highly dangerous to the human health as well as to the environment. Among other heavy metals, lead (Pb(II)) ions are some of the most toxic pollutants generated from both anthropogenic and natural sources in very large amounts. Adsorption is the simplest, efficient and economic water decontamination technology. Hence, nanoadsorbents are a major focus of current research for the effective and selective removal of Pb(II) metal ions from aqueous solution. Nanoadsorbents based on graphene and its derivatives play a major role in the effective removal of toxic Pb(II) metal ions. This paper summarizes the applicability of graphene and functionalized graphene-based composite materials as Pb(II) ions adsorbent from aqueous solutions. In addition, the synthetic routes, adsorption process, conditions, as well as kinetic studies have been reviewed.
Computing 3-D From-Region Visibility Using Visibility Integrity

Visibility integrity (VI) is a measurement of similarity between the visibilities of regions. It can be used to approximate the visibility of coherently moving targets, called <italic>group visibility</italic>. It has been shown that computing visibility integrity using agglomerative clustering takes <inline-formula><tex-math notation="LaTeX">$O(n^{4}\log {n})$</tex-math></inline-formula> for <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> samples. Here, we present a method that speeds up the computation of visibility integrity and reduces the time complexity from <inline-formula><tex-math notation="LaTeX">$O(n^{4}\log {n})$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(n^2)$</tex-math></inline-formula>. Based on the idea of <italic>visibility integrity</italic>, we show for the first time that the visibility-integrity roadmap (VIR), a data structure that partitions a space into zones, can be calculated efficiently in 3-D. More specifically, we offer two different offline approaches, a naive one and a kernel-based one, to compute a VIR. In addition, we demonstrate how to apply a VIR to solve group visibility and group following problems in 3-D. We propose a planning algorithm for the camera to maintain visibility of group targets by querying the VIR. We evaluate our approach in different 3-D simulation environments and compare it to other planning methods.
Data-Driven Sensitivity Coefficients Estimation for Cooperative Control of PV Inverters

This paper introduces a data-driven method to compute voltage magnitude, and power loss sensitivity coefficients in unbalanced low voltage network with high photovoltaic (PV) distributed generation penetration. Implementation of the proposed method is based on the Least-Squares estimator and does not require knowledge of network model parameters, but only requires measurement of voltage magnitude, active, and reactive power. To overcome the data collinearity problem, the sensitivity coefficients are estimated using ridge regression. To track changes in the network operating conditions, the coefficients are updated using the Recursive Least-Squares and to avoid instability in the estimation process during low excitation periods, the method uses the concept of directional forgetting in which old data are discarded only when new information are available. Effectiveness of the proposed method is demonstrated by a case study where the impact of active and reactive power injection is estimated. Besides that, it is also shown how sensitivity coefficients can be useful to establish how each photovoltaic inverter can contribute to set a cooperative control, by achieving losses minimization and ensuring voltage level within limits set by the standards.
An Integrator-Backstepping Control Approach for Three-Dimensional Needle Steering

In this paper, we design a set of two-dimensional (2-D) needle steering controllers used to minimize the 3-D deflection of a flexible, bevel-tipped needle. The controllers are based on a nonlinear design tool known as integrator-backstepping. The needle's deflection is split into its two 2-D planar problems, each of which is then governed by its own, separate controller. One controller, called vertical deflection control (VDC), steers the needle so that it deflects primarily along the vertical plane. The second controller, called horizontal deflection control (HDC), steers the needle so that it deflects primarily along the horizontal plane. Our 3-D steering algorithm combines the effect of these two controllers based on the current magnitude of the deflection along each plane. Using an 18 gauge brachytherapy needle, we tested our proposed method on a phantom tissue composed of liquid plastic, and a two-layer biological tissue formed of gelatin and ex vivo beef. Without needle steering, the average needle deflection was 11.2 mm. Using the proposed 3-D needle steering technique, the deflection decreased to an average of 0.5 mm.
An Integrated Liquid Metal Thermal Switch for Active Thermal Management of Electronics

Heat dissipation is a key obstacle to achieving reliable, high-power-density electronic systems. Thermal devices capable of actively managing heat transfer are desired to enable heat dissipation optimization and enhanced reliability through device isothermalization. Here, we develop a millimeter-scale liquid metal droplet thermal switch capable of controlling heat transfer spatially and temporally. We demonstrate the thermal switch by integrating it with gallium nitride (GaN) devices mounted on a printed circuit board (PCB) and measure heat transfer and temperature of each device for a variety of switch positions and heat dissipation levels. When integrated with a single GaN device (2.6 mm $\times4.6$ mm face area) dissipating 1.8 W, the thermal switch shows the ability to actively control heat transfer by conducting 1.3 W in the ON mode with the GaN device at 51 °C ± 1 °C, and 0.5 W in the OFF mode with the GaN device at 95 °C ± 1 °C. To elucidate the heat transfer physics, we developed a 1-D system thermal resistance model in conjunction with an independent 3-D finite-element method (FEM) simulation, showing excellent agreement with our experimental data. Finally, we demonstrated that when the switch is integrated with two GaN devices, the switch can balance the device heat transfer rate and enhance junction temperature uniformity and system reliability by lowering the device-to-device temperature difference from > 10 °C (no switch) to 0 °C.
RuCoLA: Russian Corpus of Linguistic Acceptability

Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers.However, the application scope of LA in languages other than English is limited due to the lack of high-quality resources.To this end, we introduce the Russian Corpus of Linguistic Acceptability (RuCoLA), built from the ground up under the well-established binary LA approach. RuCoLA consists of 9.8k in-domain sentences from linguistic publications and 3.6k out-of-domain sentences produced by generative models. The out-of-domain set is created to facilitate the practical use of acceptability for improving language generation.Our paper describes the data collection protocol and presents a fine-grained analysis of acceptability classification experiments with a range of baseline approaches.In particular, we demonstrate that the most widely used language models still fall behind humans by a large margin, especially when detecting morphological and semantic errors. We release RuCoLA, the code of experiments, and a public leaderboard to assess the linguistic competence of language models for Russian.
Trends and Development of Sliding Mode Control Applications for Renewable Energy Systems

Based on the matured theoretical framework of sliding mode control for varied, nonlinear, and unpredictable systems, practical designs of sliding mode control have been developed to suit the purpose of controlling power converters under various operating conditions. These design guidelines are particularly valuable for emerging technologies with renewable energy sources. This paper presents a discussion on the recent development of sliding mode control applications for renewable energy systems, and further examines the current trends of achieving efficiency improvement of renewable energy systems and load protections against large overshoot/undershoot in transient states, by utilizing the fast-dynamic-tracking capability of the sliding mode control. Three comparative case studies between the sliding mode control and proportional-integral control involving, namely, a low-power wind energy conversion system, a series-series-compensated wireless power transfer system, and a multiple energy storage system in a direct current (DC) microgrid, are provided.
How Project Management and Top Management Involvement Affect the Innovativeness of Professional Service Organizations—An Empirical Study on Hospitals

This study investigates how top management involvement and project management professionalization affect firm innovativeness. We focus on hospitals as representative of professional service organizations characterized by pluralistic individual and organizational interests. A multiyear analysis of project management survey and objective performance data of 90 hospitals indicates that top management involvement in innovation projects reduces hospital innovativeness, and project management professionalization increases hospital innovativeness. However, increasing project management professionalization simultaneously strengthens top management involvement’s effectiveness. The results contribute to our understanding of potential interactions and different effects of project management efforts at different organizational levels in professional service organizations.
Design and optimization of a fiber optic data link for new generation on-board SAR processing architectures

A fiber optic digital link for on-board data handling is modeled, designed and optimized in this paper. Design requirements and constraints relevant to the link, which is in the frame of novel on-board processing architectures, are discussed. Two possible link configurations are investigated, showing their advantages and disadvantages. An accurate mathematical model of each link component and the entire system is reported and results of link simulation based on those models are presented. Finally, some details on the optimized design are provided.
The Problem of History in the Age of Abundance

Copyright© 2017 The Chronicle of Higher Education. This text-only version of the article is presented with permission for scholarly sharing purposes only. All other use requires permission of the YGS Group. To view the original see: http://www.chronicle. com/article/The-Problem-of-History-in-the/238600
Analysis of Battery Reduction for an Improved Opportunistic Wireless-Charged Electric Bus

As an attractive alternative to the traditional plug-in charged electric vehicles (EVs), wireless-charged EVs have recently been in the spotlight. Opportunistically charged utilizing the wireless-charging infrastructure installed under the road at bus stops, an electric bus can have a smaller and lighter battery pack. In this paper, an improved opportunistic wireless-charging system (OWCS) for electric bus is introduced, which includes the opportunistic stationary wireless-charging system (OSWCS) and opportunistic hybrid wireless-charging system (OHWCS) consisting of stationary wireless-charging and dynamic wireless-charging. A general battery reduction model is established for the opportunistic wireless-charged electric bus (OWCEB). Two different battery-reduction models are built separately for OWCEB on account of the characteristics of OSWCS and OHWCS. Additionally, the cost saving models including the production cost saving, the operation cost saving and total cost saving are established. Then, the mathematical models are demonstrated with a numerical example intuitively. Furthermore, we analyze several parameters that influence the effectiveness of battery reduction due to the application of an opportunistic wireless-charging system on an electric bus. Finally, some points worth discussing in this work are performed.
Nonlinear Backstepping Control of a Quadrotor-Slung Load System

This paper presents a nonlinear backstepping controller designed for an underactuated quadrotor-slung load system that drives the load to a predefined trajectory. The quadrotor and the load are modeled as a rigid body and a point mass, respectively. The controller is designed through Lyapunov and backstepping techniques, and the control laws for thrust and angular velocity are presented, which guarantee the closed-loop system to be asymptotically stable. Experimental results are provided to assess the validity and performance of the proposed controller.
Position-based impedance force controller with sensorless force estimation

Purpose This paper aims to purpose an improved sensorless position-based force controller in gravitational direction for applications including polishing, milling and deburring. Design/methodology/approach The first issue is the external force/torque estimation at end-effector. By using motor’s current information and Moore-Penrose generalized inverse matrix, it can be derived from the external torques of every joints for nonsingular cases. The second issue is the force control strategy which is based on position-based impedance control model. Two novel improvements were made to achieve a better performance. One is combination of impedance control and explicit force control. The other one is the real-time prediction of the surface’s shape allowing the controller adaptive to arbitrary surfaces. Findings The result of validation experiments indicates that the estimation of external force and prediction of surface’s shape are credible, and the position-based constant contact force controller in gravitational direction is functional. The accuracy of force tracking is adequate for targeted applications such as polishing, deburring and milling. Originality/value The value of this paper lies in three aspects which are sensorless external force estimation, the combination of impedance control and explicit force control and the independence of surface shape information achieved by real-time surface prediction.
Energy Retrofitting Effects on the Energy Flexibility of Dwellings

Electrification of the built environment is foreseen as a main driver for energy transition for more effective, electric renewable capacity firming. Direct and on-time use of electricity is the best way to integrate them, but the current energy demand of residential building stock is often mainly fuel-based. Switching from fuel to electric-driven heating systems could play a key role. Yet, it implies modifications in the building stock due to the change in the temperature of the supplied heat by new heat pumps compared to existing boilers and in power demand to the electricity meter. Conventional energy retrofitting scenarios are usually evaluated in terms of cost-effective energy saving, while the effects on the electrification and flexibility are neglected. In this paper, the improvement of the building envelope and the installations of electric-driven space heating and domestic hot water production systems is analyzed for 419 dwellings. The dwellings database was built by means of a survey among the students attending the Faculty of Architecture at Sapienza University of Rome. A set of key performance indicators were selected for energy and environmental performance. The changes in the energy flexibility led to the viable participation of all the dwellings to a demand response programme.
Acquisition and Repeatability of High-Frequency Distortion Product Otoacoustic Emissions Using Two Different Calibration Methods in Newborns

Distortion-product otoacoustic emissions (DPOAEs) elicited with high-frequency (HF; up to 16 kHz) stimuli are measurable and repeatable in normal-hearing adults and children, adult patients, and are sensitive to ototoxic insults in adults. However, objective tests for monitoring basal cochlear function in those too young to respond subjectively need to be developed. DPOAE levels recorded at frequencies <10 kHz are well characterized, but DPOAE levels measured up to 16 kHz do not exist for newborns. The goal of the current study is to determine if HF DPOAEs are measurable and repeatable in newborns. DPOAEs were measured from 2–16 kHz (f2/f1 of 1.22; L1/L2 = 65/55 dB SPL) using two different calibration methods (forward pressure level—FPL and in-the-ear—SPL) in 26 newborns. To assess repeatability, the probe was removed then re-inserted for a second round of testing. Results indicate that HF DPOAEs can be evoked and are repeatable in newborns and the use of FPL calibration shows promise for measuring HF responses and maximizing repeatability. To be implemented in monitoring programs where the highest frequencies with responses are continuously tested, stimulus parameters used to evoke newborn HF DPOAEs and calibration methods need further exploration.
Evaluation of English Subject Learning of Senior High School in Mataram City, Indonesia.

The aim of this research is to evaluate the English learning program of senior high school in Mataram based on the Regulation of Minister of Education and Culture of the Republic of Indonesia, Number 81A of 2013 on Implementation of Curriculum. This research is evaluation program research with the quantitative descriptive approach, and the evaluation model used Stake’s Countenance. The component of evaluation includes antecedents, transactions, and outputs. Data collection used lesson plan analyzing, observation, questionnaire, and document. The conclusions of this study are. (1) The input (antecedents), the planning of English learning include in very good category (100 percent). It is found that the lesson plan is in accordance with the standard of learning process. (2) The process (transaction), the implementation of English learning include in good category (59.66 percent). There is suitability between implementation of learning and standard of implementation learning process. (3) Student’ learning result met the criteria of minimum completeness, with the actuality of completeness include in good category (98.37 percent).
An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

The precondition of well testing interpretation is to determine the appropriate well testing model. In numerous attempts in the past, automatic classification and identification of well testing plots have been limited to fully connected neural networks (FCNN). Compared with FCNN, the convolutional neural network (CNN) has a better performance in the domain of image recognition. Utilizing the newly proposed CNN, we develop a new automatic identification approach to evaluate the type of well testing curves. The field data in tight reservoirs such as the Ordos Basin exhibit various well test models. With those models, the corresponding well test curves are chosen as training samples. One-hot encoding, Xavier normal initialization, regularization technique, and Adam algorithm are combined to optimize the established model. The evaluation results show that the CNN has a better result when the ReLU function is used. For the learning rate and dropout rate, the optimized values respectively are 0.005 and 0.4. Meanwhile, when the number of training samples was greater than 2000, the performance of the established CNN tended to be stable. Compared with the FCNN of similar structure, the CNN is more suitable for classification of well testing plots. What is more, the practical application shows that the CNN can successfully classify 21 of the 25 cases.
Robustness of Coherence in Noisy Scale-Free Networks and Applications to Identification of Influential Spreaders

This brief investigates robustness of the consensus characterized as coherence in the noisy scale-free networks under average degree, random nodal failures, and target attacks. A new centrality index named as leader centrality is proposed to identify more influential spreaders based on the coherence. It is shown that the leaderless and leader–follower consensus is better at the same network size with higher average degree. The obtained results reveal that the consensus becomes worse under the target attacks where the nodes are removed by the descending order of degree, closeness and eigenvector centrality. It is numerically demonstrated that the leader centrality is more effective to identify important spreaders by simulating the spreading process of an SIR epidemic model.
Butyronitrile-Based Electrolytes for Fast Charging of Lithium-Ion Batteries

After determining the optimum composition of the butyronitrile: ethylene carbonate: fluoroethylene carbonate (BN:EC:FEC) solvent/co-solvent/additive mixture, the resulting electrolyte formulation (1M LiPF6 in BN:EC (9:1) + 3% FEC) was evaluated in terms of ionic conductivity and the electrochemical stability window, as well as galvanostatic cycling performance in NMC/graphite cells. This cell chemistry results in remarkable fast charging, required, for instance, for automotive applications. In addition, a good long-term cycling behavior lasts for 1000 charge/discharge cycles and improved ionic conductivity compared to the benchmark counterpart was achieved. XPS sputter depth profiling analysis proved the beneficial behavior of the tuned BN-based electrolyte on the graphite surface, by confirming the formation of an effective solid electrolyte interphase (SEI).
Constrained Model Predictive Control in Nine-Phase Induction Motor Drives

The advent of powerful digital signal processors has recently permitted the real-time implementation of model predictive control (MPC) in high-performance electric drives. Nevertheless, the use of MPC together with multiphase systems is increasingly challenging as the number of phases gets higher. On the positive side, the redundancy provided by the extra phases also opens the possibility to further optimize the control action. This paper describes the implementation of MPC for nine-phase drives using a three-step approach with an initial discarding of the switching states, a dynamic selection of the voltage vectors using hard constraints, and an improved performance including soft constraints. Experimental results confirm the ability of the proposed MPC to highly reduce the computational burden and switching frequency, while maintaining satisfactory steady state and dynamic performance.