Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Optimization of Securities Combination

On the basis of two securities selection, the fuzzy concept was applied to the portfolio composition selection. According to the hope, object and permitting difference given by investor, the membership was taken as object function. The models of the linear membership function and the non linear membership function were discussed. When the membership function takes value between [0,1], the solution of two models has no big difference and reflects,the satisfatory degree of the customer. The parameter of the non linear membership function corresponds to the fuzzy scale of the investor's profit rate and the risk. These values are smaller and the fuzzy scale is smaller compared with the linear membership function. It is well shown that the investor takes the value intention for the object value . For the data of ten stock profit rate in Shen Zhen stock market, the evolution programming is adopted to wage the optimization calculation and to verify the effectiveness of the model.
Does coat colour influence survival? A test in a cyclic population of snowshoe hares

Some mammal species inhabiting high-latitude biomes have evolved a seasonal moulting pattern that improves camouflage via white coats in winter and brown coats in summer. In many high-latitude and high-altitude areas, the duration and depth of snow cover has been substantially reduced in the last five decades. This reduction in depth and duration of snow cover may create a mismatch between coat colour and colour of the background environment, and potentially reduce the survival rate of species that depend on crypsis. We used long-term (1977–2020) field data and capture–mark–recapture models to test the hypothesis that whiteness of the coat influences winter apparent survival in a cyclic population of snowshoe hares (Lepus americanus) at Kluane, Yukon, Canada. Whiteness of the snowshoe hare coat in autumn declined during this study, and snowshoe hares with a greater proportion of whiteness in their coats in autumn survived better during winter. However, whiteness of the coat in spring did not affect subsequent summer survival. These results are consistent with the hypothesis that the timing of coat colour change in autumn can reduce overwinter survival. Because declines in cyclic snowshoe hare populations are strongly affected by low winter survival, the timing of coat colour change may adversely affect snowshoe hare population dynamics as climate change continues.
Integrated CO2 capture and reverse water–gas shift reaction over CeO2-CaO dual functional materials

Achieving carbon neutrality is one of the most important tasks to meet the environmental challenges due to excessive CO2 emissions. Integrated CO2 capture and utilization (ICCU) represents an effective process for direct utilization of CO2-contained exhaust gas (e.g. flue gas), in which converting the captured CO2 into CO via reverse water–gas shift (RWGS) reaction is a promising route. The dual functional materials (DFMs), containing CO2 adsorbents and catalysts, are widely applied to achieve ICCU. The conventional active metals (Ni, Fe, etc.)-based DFMs and non-transition metal DFMs (e.g. CaO) are restricted by low CO selectivity, catalytic efficiency or CO generation in the CO2 capture step. To address the above obstructs in the application of DFMs, the metal oxides-based DFMs, MOx-CaO (M = Al, Ce, Ti or Zr), are synthesized and evaluated. The CeO2-CaO outperformed the other metal oxides-based DFMs and possessed significantly improved catalytic performance. It is found that 33% CeO2-CaO DFM displayed approximately 49% CO2 conversion and approximately 100% CO selectivity in integrated CO2 capture and reverse water–gas shift reaction (ICCU-RWGS) at 650°C, while CaO-alone only achieved approximately 20% CO2 conversion at the same condition. The surface basicity of CeO2 is revealed to contribute to the improved catalytic performance by enhancing CO2 chemisorption and activation in the hydrogenation step. Furthermore, CeO2-CaO material possessed excellent cycle stability in 20 cycles ICCU-RWGS, achieving a sustainable and high-efficient performance in CO2 conversion and CO selectivity.
The emotions experienced by family medicine residents and interns during their clinical trainings: a qualitative study

Abstract Background: The family medicine residents and final year medical students are challenged with increased workload and they experience various emotions during their clinical trainings. They are confronted with uncertainties in their role descriptions and they witness illness, suffering and deaths as part of their everyday duties which may lead to burnout. Only several studies have focused on these experiences to find out what the family medicine residents and medical students were literally feeling. Aim: The aim of this study was to explore the family medicine residents’ and final year medical students’ emotions during their clinical trainings. Method: This qualitative study was performed with 15 family medicine residents and 24 final-year medical students using a convenience sample from two medical faculties to explore and analyze their emotions. Data were gathered by means of focus group interviews, including six interviews conducted and recorded through online meetings. Data were analyzed for themes using a thematic analysis approach. Since the interviews reached saturation in terms of content, the interviews were terminated at the end of sixth focus group meetings. Each interview took an average of 45–60 min. Results: Three main themes emerged from the data regarding residents’ and interns’ emotions. These were the “clinical climate’s role”, “emotions during patient encounters” and “coping strategies with negative emotions”. The most commonly encountered emotions were tension and anxiety followed by frustration and uncertainty. Conclusions: The family medicine residents and final-year medical students are challenged with emotions during their clinical trainings. Therefore, medical educators have to be aware of the need to support them in reflecting their emotions by prioritizing residents’and interns’ well-being.
Penetrative convection: heat transport with marginal stability assumption

Abstract This paper investigates heat transport in penetrative convection with a marginally stable temporal-horizontal-averaged field or background field. Assuming that the background field is steady and is stabilised by the nonlinear perturbation terms, we obtain an eigenvalue problem with an unknown background temperature $\tau$ by truncating the nonlinear terms. Using a piecewise profile for $\tau$, we derived an analytical scaling law for heat transport in penetrative convection as $Ra\rightarrow \infty$: $Nu=(1/8)(1-T_M)^{5/3}Ra^{1/3}$ ($Nu$ is the Nusselt number; $Ra$ is the Rayleigh number and $T_M$ corresponds to the temperature at which the density is maximal). A conditional lower bound on $Nu$, under the marginal stability assumption, is then derived from a variational problem. All the solutions to the full system should deliver a higher heat flux than the lower bound if they satisfy the marginal stability assumption. However, data from the present direct numerical simulations and previous optimal steady solutions by Ding & Wu (J. Fluid Mech., vol. 920, 2021, A48) exhibit smaller $Nu$ than the lower bound at large $Ra$, indicating that these averaged fields are over-stabilised by the nonlinear terms. To incorporate a more physically plausible constraint to bound heat transport, an alternative approach, i.e. the quasilinear approach is invoked which delivers the highest heat transport and agrees well with Veronis's assumption, i.e. $Nu\sim Ra^{1/3}$ (Astrophys. J., vol. 137, 1963, p. 641). Interestingly, the background temperature $\tau$ yielded by the quasilinear approach can be non-unique when instability is subcritical.
Fiber Bragg Grating-Based Force-Torque Sensor with Six Degrees of Freedom

Robotic control and force-feedback applications require multi-axial force and torque sensing. One possible implementation of future sensors is seen in fiber optic force torque sensors, since the signal demodulation may be located in some distance to the actual sensor and they also do not have to include any magnetic material. This article presents a fiber Bragg grating-based force and torque sensor with six degrees of freedom. The general setup resembles a Stewart platform. Its connecting beams are formed by the fiber used to measure the deformation of the transducer. The element creating stiffness may be of arbitrary form. We demonstrate how the sensor is realized and show results of all six force and torque measurements. We present a theoretical model of the sensor. The results in this work demonstrate the feasibility of a fiber-optic force-torque sensor.
Facial Expressions as an Index of Listening Difficulty and Emotional Response

Abstract Knowledge about listening difficulty experienced during a task can be used to better understand speech perception processes, to guide amplification outcomes, and can be used by individuals to decide whether to participate in communication. Another factor affecting these decisions is individuals' emotional response which has not been measured objectively previously. In this study, we describe a novel method of measuring listening difficulty and affect of individuals in adverse listening situations using automatic facial expression algorithm. The purpose of our study was to determine if facial expressions of confusion and frustration are sensitive to changes in listening difficulty. We recorded speech recognition scores, facial expressions, subjective listening effort scores, and subjective emotional responses in 33 young participants with normal hearing. We used the signal-to-noise ratios of −1, +2, and +5 dB SNR and quiet conditions to vary the difficulty level. We found that facial expression of confusion and frustration increased with increase in difficulty level, but not with change in each level. We also found a relationship between facial expressions and both subjective emotion ratings and subjective listening effort. Emotional responses in the form of facial expressions show promise as a measure of affect and listening difficulty. Further research is needed to determine the specific contribution of affect to communication in challenging listening environments.
Research on Intelligent Routing Technology Based on Improved DQN

With the rise of artificial intelligence, intelligent routing technology has become a research hotspot in the current academic circles. In view of the problems of poor load balancing ability of traditional routing algorithms and difficulty in guaranteeing quality of service (QoS), this paper proposes an intelligent routing algorithm DQN-Route based on deep reinforcement learning (DRL). The algorithm adopts the deep Q network (DQN) as the training framework, and introduces the convolutional neural network (CNN) as the neural network. Based on the algorithm advantages of DQN processing continuous state space, as well as the local perception and parameter sharing capabilities of CNN, we can input the dynamically changing network state into the neural network for training. After the algorithm training converges, the action value output by the neural network is used as the network link weight to realize the dynamic adjustment of the routing strategy. Finally, the DQN-Route routing algorithm is compared with the OSPF, ECMP and Q-Learning routing algorithms respectively. The results show that the DQN-Route has better convergence, and compared with the Q-Learning routing algorithm, DQN-Route reduces the delay by 14.13%, increases the throughput by 11.34%, and reduces the packet loss rate by 9.17%.
A Region-Based Point Cloud Down Sampling Technique for Scene Rendering Augmentation of Synthetic 3D-Modeled Objects

3D modeling is the process of creating and designing 3-dimensional objects used to represent real objects or physical environments. It is used for game development, information, educational and communication materials that are developed and deployed efficiently in PCs or Mobile devices with at least 8GB of RAM, 4 Cores CPU, etc. To increase efficiency in development and deployment, point cloud downsampling is developed without sacrificing accuracy or quality of object classification of 3D objects created through 3D modeling softwares. The combination of native voxel approaches with the region-based adaptation of down-sampling unordered point clouds for potential augmentation was successfully done in rendering scenes using synthetic 3D Model Objects.
CRP2.0: A Fast and Robust Cooperation between Routing and Placement in Advanced Technology Nodes

Traditionally, the placement and routing stages of a physical design are performed separately. Because of the additional complexities arising in advanced technology nodes, they have become more interdependent. Therefore, creating efficient cooperation between the routing and placement steps has become an important topic in Electronic Design Automation (EDA). In this article, a framework that allows cooperation between routing and placement is proposed. The main objective of the proposed framework is to improve the detailed routing solution by combining routing and placement. The core of this framework is the Cooperation between Routing and Placement (CRP2.0)1 engine including techniques to combine routing and placement. The key contributions of CRP2.0 include an Integer Linear Programming (ILP)-based Detailed Placement (ILP-DP), net classification, and two Cost and Net Caching techniques. The efficacy of the proposed framework is evaluated on the official ACM/IEEE International Symposium on Physical Design (ISPD) 2018 and 2019 contest benchmarks. In this article, we show that by using the Cost Caching technique, the global routing runtime compared with state-of-the-art algorithms was reduced by 28.56%, on average. Moreover, numerical results show that when working with advanced technology nodes, the proposed framework can improve the detailed routing score by an average of 0.3% while only moving 0.7% of the cells, on average. The proposed engine can be employed as an add-on to the physical design flow between the global routing and detailed routing steps.
wganBCS: Block-wise Image Compressive Sensing: Model using Wasserstein GAN

Block-wise compressive sensing (BCS) uses a fix size sensing matrix to raster-scan the entire image to acquire the signal block by block. Compared with traditional image compressive sensing methods, BCS can greatly reduce the memory consumption of sensing matrix. However, BCS paradigm still suffers from two issues. One is that block-wise sensing causes heavy block effect on the reconstructed image, which leads to degradation in the image quality metrics. Another is that the state of art block wise image compressive sensing methods use mean square error loss function to optimize their models, which causes the reconstructed images over smoothed. In this paper, we incorporate generative adversarial training into BCS paradigm and propose a new block wise image compressive sensing and reconstruction model called wganBCS, which a combination of traditional L2 loss and the Wasserstein loss are used to optimize the model. We use a modified Wasserstein GAN (WGAN) network to deal with the block effect caused by the block wise compressive sensing. Specifically speaking, at the first stage training, the generator network will focus on minimize the mean square error loss to keep the overall pixel accuracy between reconstructed images and their ground truths. Then the output images from the generator are sent to a critic network to calculate the Wasserstein distance between the ground truth images, the generator network will minimize this Wasserstein distance to keep the reconstructed images visually authentic to ground truth images. Experimental result shows that our model is superior both in visual authenticity and the image quality metrics compared to the most state of art image compressive sensing methods.
Remote Sensing of Clouds and Fog with a 1.4-mm Radar

Abstract A recently developed 1.4 mm wavelengh incoherent radar has potential for remote sensing of low reflectivity atmospheric targets for ranges up to several kilometers. Power output of 60 W is achieved using an Extended Interaction Oscillator (EIO). Preliminary reflectivity measurements of clouds and fog for ranges between 36 and 1900 meters are believed to be the first such measurements at this wavelength Limitations on the accuracy of the reflectivity measurements are discussed, highlighting uncertainties due to highly variable attenuation.
Research on a Hyperplane Decomposition NMF Algorithm Applied to Speech Signal Separation

Non-negative Matrix Factorization (NMF) solves the single-channel speech separation problem. However, problem solving mostly separates the pure speech signal from the mixed speech signal with noise. This paper proposes an NMF algorithm based on hyperplane decomposition to separate speech for this problem. Based on the NMF algorithm, the observed signal is decomposed into different hyperplanes, a nonlinear mapping between the amplitude of the observed signal and the amplitude of the signal to be separated is constructed, and the speech signal is reconstructed according to the amplitude spectrum of the pure speech signal to realize the separation of the two original speech signals. The correlation coefficients between the reconstructed speech signal and the original speech signal after decomposition into different numbers of hyperplanes are compared and analyzed. The effects of different numbers of hyperplanes on the separation effect of speech signals are discussed. The study shows that when the number of hyperplanes is 20, the similarity coefficient after speech separation is 0.8356, and the separation effect is better. Meanwhile, the algorithm proposed in this paper can obtain more extracted features, which can be used as the training data of deep learning algorithm and provide some technical reference for speech separation.
Low-light Image Enhancement via a Frequency-based Model with Structure and Texture Decomposition

This article proposes a frequency-based structure and texture decomposition model in a Retinex-based framework for low-light image enhancement and noise suppression. First, we utilize the total variation-based noise estimation to decompose the observed image into low-frequency and high-frequency components. Second, we use a Gaussian kernel for noise suppression in the high-frequency layer. Third, we propose a frequency-based structure and texture decomposition method to achieve low-light enhancement. We extract texture and structure priors by using the high-frequency layer and a low-frequency layer, respectively. We present an optimization problem and solve it with the augmented Lagrange multiplier to generate a balance between structure and texture in the reflectance map. Our experimental results reveal that the proposed method can achieve superior performance in naturalness preservation and detail retention compared with state-of-the-art algorithms for low-light image enhancement. Our code is available on the following website.1
End-to-End Delay Performance for Mobile Cyber-Physical Systems with Edge Computing

In this letter, we investigate the end-to-end (E2E) delay performance for mobile cyber-physical systems (M-CPS) with edge computing. The traditional delay analysis is based on flow conservation law, whereas the flow is not conserved in the M-CPS with edge computing due to the existence of edge computing node and remote controller. To tackle this problem, we introduce two scaling factors to ensure the flow conservation, then the E2E delay bound of M-CPS with edge computing is derived via stochastic network calculus. In addition, to cater for the delay demand for M-CPS stability, the stable-aware delay bound is obtained. Numerical results show the gap between the analytic bound and simulation can be narrower as the average signal-to-noise (SNR) growing.
INVESTIGATION OF CONTINUOUS WAVE NEAR INFRARED OPTICAL TOMOGRAPHY BASED ON GRADIENT OPTIMIZATION SCHEMES

Two gradient-based optimization schemes were investigated for continuous wave near infrared optical tomography. The reason of slow convergence rate and low spatial resolution for conventional optimization schemes was analyzed. A spatial location weighted gradient-based optimization scheme was adopted to reduce the computation burden and increase the reconstruction precision. The reconstruction results confirm that the spatial location weighted optimization method offers a more efficient approach to the continuous wave optical imaging problem than conventional methods.
Fabrication of 2.5 GHz SAW retiming filter with SiO/sub 2//ZnO/diamond structure

The variety of layered structures with a diamond film are expected to be applied for high-frequency SAW devices due to their high velocities. The SiO/sub 2//ZnO/IDT/diamond structure is one of the promising, especially for high-frequency and narrow-band filters. In this paper, the 1st mode SAW in the SiO/sub 2//ZnO/IDT/diamond structure was experimentally studied, and confirmed that it can provide zero temperature coefficient of frequency (TCF) as well as high velocity of 9,000 m/s. A 2.5 GHz narrow-band SAW filter was successfully fabricated with this structure with IDTs of 0.9 /spl mu/m finger width, and it was shown that this filter has practical and superior characteristics for the 2.5 GHz retiming filter. Its insertion loss was 10 dB that is smaller than ever reported with quartz.
Establishing decision tree-based short-term default credit risk assessment models

ABSTRACT Traditional credit risk assessment models do not consider the time factor; they only think of whether a customer will default, but not the when to default. The result cannot provide a manager to make the profit-maximum decision. Actually, even if a customer defaults, the financial institution still can gain profit in some conditions. Nowadays, most research applied the Cox proportional hazards model into their credit scoring models, predicting the time when a customer is most likely to default, to solve the credit risk assessment problem. However, in order to fully utilize the fully dynamic capability of the Cox proportional hazards model, time-varying macroeconomic variables are required which involve more advanced data collection. Since short-term default cases are the ones that bring a great loss for a financial institution, instead of predicting when a loan will default, a loan manager is more interested in identifying those applications which may default within a short period of time when approving loan applications. This paper proposes a decision tree-based short-term default credit risk assessment model to assess the credit risk. The goal is to use the decision tree to filter the short-term default to produce a highly accurate model that could distinguish default lending. This paper integrates bootstrap aggregating (Bagging) with a synthetic minority over-sampling technique (SMOTE) into the credit risk model to improve the decision tree stability and its performance on unbalanced data. Finally, a real case of small and medium enterprise loan data that has been drawn from a local financial institution located in Taiwan is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed approach with the logistic regression and Cox proportional hazards models, it was found that the classifying recall rate and precision rate of the proposed model was obviously superior to the logistic regression and Cox proportional hazards models.