Miracle-dz/UniFAR-FTUs huggingface dataset:
Leachate Flow in Leakage Collection Layers Due to Defects in Geomembrane Liners

This paper provides analytical and graphical solutions related to the flow of leachate in a leakage collection layer due to defects in the overlying liner (i.e. the primary liner of a double liner system). The defects are assumed to be small (e.g. holes in geomembrane liners). It is shown that leachate flows in a zone of the leakage collection layer (the wetted zone) that is limited by a parabola. A simple relationship is established between the rate of leachate migration through the defect and the maximum thickness of leachate in the leakage collection layer; this relationship depends on the hydraulic conductivity (but not on the slope) of the leakage collection layer. Equations are provided to calculate the average head of leachate on top of the liner underlying the leakage collection layer (i.e. the secondary liner of a double liner system), which is useful for calculating the rate of leachate migration through that liner. Finally, the case of several leaks randomly distributed is considered, and equat.
A new slew rate enhancement technique for operational transconductance amplifiers

This paper presents a new symmetric slew rate enhancement (SRE) circuit suitable for operational transconductance amplifiers (OTAs). The proposed SRE circuit is composed of two auxiliary circuits which are automatically activated during fast signal transitions (slewing) by comparing the input and output signals. The auxiliary circuits pump required current in/out the load capacitance. In this way, the slew rate of the amplifier is significantly improved without any extra static power dissipation. The proposed technique can be applied to a variety of operational amplifiers. However, incorporating the proposed SRE circuit into a folded‐cascode amplifier (FCA) results in 7× slew rate improvement and 66.6% settling time reduction, while the frequency response and small‐signal operation of the amplifier are not degraded. The proposed SRE circuit is designed and simulated in a 0.13‐μm Complementary Metal‐Oxide‐Semiconductor (CMOS) technology. Post‐layout simulation results show that the circuit has a slew rate of 1465 V/μs and 9.75‐ns settling time for a total 3‐pF load capacitance while the power consumption is 841 μW at 1.2‐V supply voltage; that indicates 7‐time SR improvement with lower power dissipation compared to the conventional FCA and an excellent FOML of 6.26 ([V/μs] × pF/μA). Moreover, simulation results of the circuit for worst corner and temperature cases as well as 10% supply variation verify the robustness of the circuit against PVT changes.
Dynamic performance of field-regulated synchronous machines

The paper analyses the influence of various types of field regulators on the dynamic performance of synchronous generators. Both conventional and dual-excitation generators are considered, with basic controls provided by voltage and angle regulators, which may have proportionate and derivative feedback. In the case of dual-excitation machines, both bus-rotor-angle and terminal-rotor-angle signals are employed in the quadrature-axis regulator. Stability is initially defined by computed regulation curves, and additional information is then obtained by calculating the variations of the synchronising- and damping-torque coefficients (Ts and TD) with regulator gain. These graphs are superimposed on the regulation curves, to show how Ts and TD determine the dynamic stability of the machine, and to illustrate the underlying reasons for stability.
Leveraging Six Sigma disciplines to drive improvement

Six Sigma provides the opportunity and discipline to eliminate mistakes, improve morale, and save money. Doing things right and keeping them consistent are the ideas behind Six-Sigma. A fundamental objective of Six Sigma is to achieve customer satisfaction with continuous improvement in quality. Process control and manufacturing variation reduction are important but companies often discover that the majority of their quality problems were actually created during the design process. An example of improving manufacturing process capability to give bottom line cost savings and customer satisfaction is presented in this paper. The methodology to increase system robustness through Design for Six Sigma (DFSS) is presented and demonstrated through the extension of the case study of crankshaft journal lobing design robustness improvements.
scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. In our benchmark for blood-derived and tumor-infiltrating immune cells, scGate outperforms SingleR, a state-of-the-art classifier for single-cell data. scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from complex scRNA-seq datasets. Availability R package source code and reproducible tutorials are available at https://github.com/carmonalab/scGate
An Approach To Discrete Spectrum Correction Based on Energy Centroid

This paper proves that the energy centre of symmetric window function is the coordinate origin in discrete spectrum analysis. The correction formula of frequency, amplitude and phase can be obtained using this character. Simulation demonstrates that this method can correct averaged power spectrum directly with simple algorithm and fast processing speed, and the interference of negative or narrow interval frequencies has little influence on correction precision. This method is suitable for all kinds of symmetric window. But it is not suited with dense frequencies signal or continuous spectrum.
Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis

Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning’s n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘n’. First, an objective function is designed for measuring the quality of ‘candidate’ Manning’s coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers ‘Paglia’ and ‘Aniene’. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models.
An out-of-distribution-aware autoencoder model for reduced chemical kinetics

While detailed chemical kinetic models have been successful in representing rates of chemical reactions in continuum scale computational fluid dynamics (CFD) simulations, applying the models in simulations for engineering device conditions is computationally prohibitive. To reduce the cost, data-driven methods, e.g., autoencoders, have been used to construct reduced chemical kinetic models for CFD simulations. Despite their success, data-driven methods rely heavily on training data sets and can be unreliable when used in out-of-distribution (OOD) regions (i.e., when extrapolating outside of the training set). In this paper, we present an enhanced autoencoder model for combustion chemical kinetics with uncertainty quantification to enable the detection of model usage in OOD regions, and thereby creating an OOD-aware autoencoder model that contributes to more robust CFD simulations of reacting flows. We first demonstrate the effectiveness of the method in OOD detection in two well-known datasets, MNIST and Fashion-MNIST, in comparison with the deep ensemble method, and then present the OOD-aware autoencoder for reduced chemistry model in syngas combustion.
The Impact of Restaurant Recommendation Information and Recommendation Agent in the Tourism Website on the Satisfaction, Continuous Usage, and Destination Visit Intention

Enjoying local food could be one of the motives for tourism, and local food and restaurant recommendation information would be important for tourists to decide their destination. Recommendation agents are the sorting and searching function to find the best local food and restaurant among the complexity of information, and they could also be helpful for the tourist to decide their destination. Online tourism websites (e.g., Ctrip.com) started to provide restaurant recommendations containing food-related information and recommendation agents to attract tourists. However, few studies have investigated their impact on the destination visit intention of potential Chinese tourists. This study aims to empirically validate how restaurant recommendation information, including food-related information and recommendation agents, could impact online tourists’ reactions, such as satisfaction, continuous website usage, and destination visits. We developed our hypothesis based on the information system (IS) success model. We gathered 202 data points from potential tourists using quasi-experimental methods, and these data were analyzed by the PLS algorithm. The results indicate that restaurant recommendation information and recommendation agents significantly increase the perceived information quality and perceived system quality. Increased perceived information quality and system quality could significantly increase potential tourists’ satisfaction, website continuous usage intention, and destination visit intention. The results of this study could contribute to making tourism websites more attractive by using local food and restaurant information and recommendation agents.
Remarks on emotion recognition from multi-modal bio-potential signals

This paper proposes an emotion recognition system from multi-modal bio-potential signals. For emotion recognition, two types of classifier: neural network (NN) and support vector machine (SVM) are designed and investigated. Using gathered data under psychological emotion stimulation experiments, the classifiers are trained and tested. In experiments of recognizing two emotion: pleasure and unpleasure, recognition rates of 62.3% with the NN classifier and 59.7% with the SVM classifier are achieved. The experimental result shows that using multi-modal bio-potential signals is feasible and that NN and/or SVM are well suited for emotion recognition tasks.
From Constituency to UD-Style Dependency: Building the First Conversion Tool of Turkish

This paper deliberates on the process of building the first constituency-to-dependency conversion tool of Turkish. The starting point of this work is a previous study in which 10,000 phrase structure trees were manually transformed into Turkish from the original PennTreebank corpus. Within the scope of this project, these Turkish phrase structure trees were automatically converted into UD-style dependency structures, using both a rule-based algorithm and a machine learning algorithm specific to the requirements of the Turkish language. The results of both algorithms were compared and the machine learning approach proved to be more accurate than the rule-based algorithm. The output was revised by a team of linguists. The refined versions were taken as gold standard annotations for the evaluation of the algorithms. In addition to its contribution to the UD Project with a large dataset of 10,000 Turkish dependency trees, this project also fulfills the important gap of a Turkish conversion tool, enabling the quick compilation of dependency corpora which can be used for the training of better dependency parsers.
Accurate wire-grid modelling of buried conducting cylindrical scatterers

This work is focused on the wire-grid modelling of 2D (infinitely long) buried conducting objects. A circular cylinder is considered, embedded in a dielectric half-space and illuminated by a transverse magnetic monochromatic plane wave. The scattered near field is calculated by using the cylindrical-wave approach. This rigorous technique is briefly resumed in the paper, and its convergence properties in wire-grid modelling are discussed. Numerical results obtained for different wire-grid models are compared with the exact solution. It is shown that the well-known same-area criterion, in the considered cases, yields affordable results. However, it is not the optimum, and better results are obtained with a wire radius shorter than what was suggested by the rule.
Selective MOCVD growth of InGaAs/GaAs and InGaAs/InP quantum dots employing diblock copolymer nanopatterning

The conventional approach to fabricate semiconductor based QDs is based on the Stranski-Krastnow (SK) growth mode, which has enjoyed considerable success in device applications. However, the SK QD approach is complicated by the randomness of the QD size distribution and inherent presence of the wetting layer. Carrier leakage to the wetting layer has been identified as one of the underlying causes for low optical gain and high temperature sensitivity in diode lasers. To fully exploit the potential advantages of ideal Quantum Dots (i.e. full 3D carrier confinement), elimination of the wetting layer and a uniform mono-modal QD size distribution is needed. Nanopatterning with selective MOCVD QD growth has potential for achieving a higher degree of control over the QD formation, compared with the SK process. Furthermore, the problematic wetting layer states are eliminated and improved optical gain is expected. The QD patterning is prepared by dense nanoscale (20-30 nm diameter) diblock copolymer lithography, which consists of perpendicularly ordered cylindrical domains of polystyrene-block-poly(methylmethacrylate) (PS-b-PMMA) matrix. For selective MOCVD growth, a dielectric template mask was utilized and the polymer patterning is transferred on it. The resulting GaAs QD densities are larger than 5×1010/cm2, comparable to SK growth mode, with a nearly monomodal QD size distribution. Variable temperature PL has been used to characterize the optical properties of capped InGaAs QDs on GaAs (λ ~ 1.1 μm) and InP (λ ~ 1.5 μm) substrates.
A CRLH TL Transmission Line Structure and its Application to Bandpass Filter Design

In this paper, a novel bandpass filter (BPF) using the composite right/left-handed transmission line (CRLH-TL) theory is presented.The composite right/left-handed TL with the high-pass characters of left-handed transmission line (LH-TL) and the low-pass characters of right-handed transmission line (RH-TL) are used to construct the bandpass filter.Using this theory,we design a bandpass filter which have an obvious band pass response with a wide passband range from 5.1to 12.9GHz and a low insertion loss of less than 3.1dB. The relative bandwidth is close to 110%. Simulation using ADS demonstrated the viability of the approach.
Long period grating made by rocking a single mode fiber

Long period gratings (LPGs) can be made by rocking a single mode fiber and at the same time periodically scanning a focused CO2 beam across the fiber. A 15 period LPG with ~ 3 degree rocking angle and 1.3 mm pitch exhibits a narrow bandwidth of 4.8 nm and a resonant attenuation of 33.5 dB. The novel LPG has a temperature sensitivity of -77 pm/°C and a strain sensitivity of -0.14 nm/mε , and is insensitive to twist.
Circulating zero-sequence current control of parallel three-phase inverters

A novel circulating zero-sequence current-control method for common DC-source parallel inverter systems is proposed and implemented in FPGA. The follower inverter can closely track the three-phase discontinuous PWM modulation waveforms of the master inverter and the circulating current caused by asynchronous modulation waveforms can be reduced. The controller is implemented in FPGA, and the requirement of high-speed control and measurement can be satisfied without any extra burden added to the processor of the drive system. The theoretical analysis of the experimental results verifies the performance of the proposed circulating-current-control method.
A Hybrid Algorithm for the Multi-Objective Time Dependent Vehicle Routing Problem

This paper presents a real road network case based on the time dependent vehicle routing problem with time windows (TDVRPTW), which involves optimally routing a fleet of vehicles with fixed capacity when traffic conditions are time dependent and services at customers are only available in their own time tables. A hybrid algorithm based on the Genetic Algorithm (GA) and the Multi Ant Colony System (MACS) is introduced in order to find optimal solutions that minimize two hierarchical objectives: the number of tours and the total travel cost. The test results show that the integrated algorithm outperforms both of its traditional ones in terms of the convergence speed towards optimal solutions.
Assessing tropical cyclone compound flood risk using hydrodynamic modelling: a case study in Haikou City, China

Abstract. The co-occurrence of storm tide and rainstorm during tropical cyclones (TCs) can lead to compound flooding in low-lying coastal regions. The assessment of TC compound flood risk can provide vital insight for research on coastal flooding prevention. This study investigates TC compound flooding by constructing a storm surge model and overland flooding model using Delft3D Flexible Mesh (DFM), illustrating the serious consequences from the perspective of storm tide. Based on the probability distribution of storm tide, this study regards TC1415 as the 100-year event, TC6311 as the 50-year event, TC8616 as the 25-year event, TC8007 as the 10-year event, and TC7109 as the 5-year event. The results indicate that the coastal area is a major floodplain, primarily due to storm tide, with the inundation severity positively correlated with the height of the storm tide. For 100-year TC event, the inundation area with a depth above 1.0 m increases by approximately 2.5 times when compared with 5-year TC event. The comparison of single-driven flood (storm tide flooding and rainstorm inundation) and compound flood hazards shows that simply accumulating every single-driven flood hazard to define the compound flood hazard may cause underestimation. For future research on compound flooding, copula function can be adopted to investigate the joint occurrence of storm tide and rainstorm to reveal the severity of extreme TC flood hazards.
Development of biomimetic quadruped walking robot with 2-DOF waist joint

This paper presented a novel bio-mimetic quadruped walking robot with 2-DOF (Degree Of Freedom) waist joint, which connects the front and the rear parts of the body. The waist-jointed walking robot can guarantee more stable and more animal-like gait than that of a conventional single-rigid-body walking robot. The developed robot, called ELIRO-II (Eating LIzard RObot version 2), can bend its body from side to side by using 1-DOF passive waist joint while the legs is transferred, thereby increasing the stride and speed of the robot. In addition, ELIRO-II has one more active DOF to bend its body up and down, which increases the mobility in irregular terrain such as slope and stairs. We design the mechanical structure of the robot, which is small and light to have high mobility. This research described characteristics of the 2-DOF waists joint and leg mechanism as well as a hardware and software of the controller of ELIRO-II.
Research on Moderating Effects of Job Satisfaction in Employee Turnover Intention of Insurance Enterprise

With the development of insurance enterprises, the frequent employee turnover increases the human cost in insurance enterprise. It also reduces profit of insurance enterprise. This study conducts hierarchical regression analysis based on the sample of insurance enterprise employees. It will research the regulation of job satisfaction for turnover intention in insurance enterprises. This study will help the human resource management in insurance enterprise.
Wideband signal detection using a Nyquist folding analog-to-information receiver in multipath fading environment

The need to efficiently and effectively monitor the frequency spectrum for identification of unoccupied bands is essential in communication systems such as Cognitive Radio (CR), battlefield communications, etc. The Nyquist Folding Analog-to-Information Receiver (NYFR) which is based on the theory of Compressed Sensing has been proposed recently to address this problem in a sparse environment. Although, typical CS techniques, involve random projections followed by a computationally intensive signal reconstruction process, the methods used in NYFR does not requires the laborious l1 minimization algorithm. The NYFR performs analog compression via a non-uniform sampling process that induces a chirp-like modulation on each received signal. Signal parameters can simply be determined by using timefrequency analysis techniques without full signal reconstruction. This paper revisits the detection problem of using NYFR for information recovery for appropriate frequency detection when the original signal in the presence of both the additive white Gaussian noise and Rice multipath fading. An automatic detection algorithm was also developed to determine the detected frequency parameters without looking at the FFT spectrogram plot.
Manufacturing Process Design Based on Mental and Physical Workload Analysis

Human factor is an important issue in industrial engineering system. Mental and physical states in the manufacturing process greatly influence workers performance. In this study, EEG data were recorded to analyze mental workload and EMG data to analyze physical workload when a worker was tasks in the production line. Video camera was used to record the manufacturing process simultaneously. We could detect the time periods within which the workers mental workload or physical workload was at a high level. By tracking the operating process through the video, we were able to find the potential problems leading to high workload, and thus optimize the design of manufacturing process or machine in order to decrease mental and physical workload and improve production efficiency.