Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on sales strategy of electric vehicle target customers based on machine learning algorithm

Due to the influence of various subjective and objective factors, there is great uncertainty in consumers' willingness to purchase new energy vehicles. In this study, it is hoped that based on customer satisfaction and personal characteristic information, the main factors affecting whether the target customers are willing to purchase new energy vehicles are explored, so that corresponding sales strategies can be formulated. In this study, two different embedding methods based on penalty terms and tree models are used for feature selection, the former using three models LR, LASSO and SVM, and the latter using RF and LightGBM models for a total of five models for machine learning to find out the relevant features that affect the sales of different brands, and the voting method is applied to the selected features, and the results are found. The battery technology performance of electric vehicles, comfort, annual mortgage of target customers and the ratio of auto loan to annual household income have a significant impact on the sales of all three brands. In addition, affordability, safety and customer's work situation also had different degrees of influence on sales of the different brands. A multilayer perceptron (BP neural network) prediction model is built for each brand's target customers to predict their purchase intention. The prediction accuracy of the network is improved by balancing the positive and negative samples so that the ratio of the two types of samples in the dataset is approximately 1:1. The test set is also used for real-time testing to determine the fit status of the dataset. The accuracy of the final model was above 80% on both the training and test sets, and the model predicted the 1st, 5th, 6th, 7th, 12th, and 12th samples. The model predicted the purchase intention of the 1st, 5th, 6th, 7th, 12th and 13th customers.
Perfectly matched layer method in the finite‐difference time‐domain and frequency‐domain calculations

A complex‐coordinate method known under the guise of the perfectly matched layer (PML) method for treating unbounded domains in computational electrodynamics is related to similar techniques in fluid dynamics and classical quantum theory. It may also find use in electronic‐structure finite‐difference simulations. Straightforward transfer of the PML formulation to other fields does not seem feasible, however, since it is a unique feature of electrodynamics – the natural invariance – that allows analytic trick of complex coordinate scaling to be represented as pure modification of local material parameters within thin layers of space adjacent to the computational domain boundaries, i.e., the PMLs. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Non-resonant optical modulation of quantum cascade laser and its application potential in infrared spectroscopy

Based on the nature of ultra-fast carrier life time in semiconductor quantum well, optical modulation of quantum cascade laser offers an unique way to control intersubband transition through interband transition. This method circumvents the problem of parasitic effects associated with electrical modulation, resulting in a high modulation bandwidth. In addition it allows for fast wavelength modulation on standard type quantum cascade lasers by directly injecting charge carriers to laser active region with near-infrared optical excitation. Here, we demonstrate the first infrared spectroscopic measurement conducted with this all-optical modulation approach. Using wavelength modulation spectroscopy, a 1st order derivative spectrum of methanol vapor gas is observed. Optically based wavelength modulation up to 200 MHz is purely induced by pumping the front facet of quantum cascade laser with an intensity-modulated 1550 nm DFB laser. Compared with conventional direct absorption approach, the noise equivalent sensitivity is improved by a factor of 10 by adding optical modulation in a non-optimized system.
Association between Perceived Health-Related Quality of Life and Depression with Frailty in the FRASNET Study

Frailty is a major challenge facing the aging world. The phenotype of the frail subject is still far from being satisfactorily defined. We report data on mood, cognition, and quality of life (QoL) in relation to anamnestic factors, health, and socio-economic status in the FRASNET geriatric population (1204 subjects in stable health conditions), which is an observational cohort study that includes fairly balanced groups of Italian frail (421, 35%), pre-frail (449, 37.3%) and robust (334, 27.7%) subjects. A conditional inference tree analysis revealed a substantial influence of psychological variables on frailty. The physical indicator of QoL (Short Form Survey-36-Physical Component Summary, SF-36-PCS) was the predominant variable in the full model (threshold at 39.9, p < 0.001): higher frailty was found in subjects with a caregiver and lower SF-36-PCS. Frailty was also associated with the mental indicator of QoL (Short Form Survey-36-Mental Component Summary, SF-36-MCS), depression (Geriatric Depression Scale, GDS-15), leisure activities, and level of education. In support of the prominent role of inflammation in aging and mental illness, the SF-36-PCS score was correlated with the blood concentration of C-X-C motif chemokine ligand 10 (CXCL10) (r Pearson −0.355, p = 0.015), a critical signal in cell senescence and inflammaging, while the rs7567647 variant in FN1 gene encoding a glycoprotein in the extracellular matrix was significantly associated with frailty in a multivariable model (p = 0.0006). The perception of health-related QoL and subclinical depression contribute to frailty. Their assessment could improve the identification of older patients at increased risk of adverse outcomes.
Minimum Torque and Minimum Dissipation Black Hole-Driven Winds

This article is an investigation of paired winds of magnetized plasma on the magnetic flux tubes that thread a rotating black hole. The paired wind system includes an ingoing accretion wind and an outgoing magnetically and centrifugally slung wind. We consider wind systems in which the field-line angular velocity, ΩF, is established by the causal physics of plasma injection into the azimuthally symmetric magnetic flux tubes, ΩF ≪ ΩH, where ΩH is the angular velocity of the black hole. There is another school of thought that holds that ΩF ≈ ΩH/2 is the proper choice of field-line angular velocity. However, it has been argued previously by this author that there is no known microphysics associated with the gravitational field of a rotating black hole that can produce the predominantly field-aligned poloidal current flows associated with ΩF ≈ ΩH/2. Furthermore, the detailed structure of MHD waves in the background of the geometry of the hole argues against the causal structure implicit in assuming ΩF ≈ ΩH/2. Consequently, for the purposes of this paper we assume that the relatively low angular velocity of the injected plasma sets ΩF ≪ ΩH throughout the following. Two families of solutions are analyzed. A "minimum dissipation solution," which obeys the perfect MHD approximation everywhere, is shown to be the analog of the Blandford-Znajek solution for an imposed value of ΩF ≪ ΩH. A second paired-wind system is studied in which the outgoing wind is the perfect MHD "minimum torque solution. " The paired ingoing wind in the perfect MHD limit cannot reach the event horizon. It is shown that the ingoing perfect MHD wind joins onto a deflagration wind. It is argued that the second paired-wind system is physically more reasonable on the basis of causality, minimum energy, and minimum dissipation principles. The minimum dissipation aspect of the discussion requires a general relativistic understanding of entropy generation in the event horizon.
A Tutorial on Bayesian Analysis of Count Data Using JAGS

In behavioral studies, the frequency of a particular behavior or event is often collected and the acquired data are referred to as count data. This tutorial introduces readers to Poisson regression models which is a more appropriate approach for such data. Meanwhile, count data with excessive zeros often occur in behavioral studies and models such as zero-inflated or hurdle models can be employed for handling zero-inflation in the count data. In this tutorial, we aim to cover the necessary fundamentals for these methods and equip readers with application tools of JAGS. Examples of the implementation of the models in JAGS from within R are provided for demonstration purposes.
Galvanic corrosion induced degredation of tensile properties in micromachined polycrystalline silicon

Immersion of polycrystalline silicon in hydrofluoric acid-based solutions is often utilized in microsystem fabrication to liberate mechanical structures. The authors demonstrate, using microfabricated tensile specimens, that such etching can cause a catastrophic reduction in tensile strength and elastic modulus in silicon galvanically coupled to a metallic layer, such as commonly used gold. Galvanically corroded silicon exhibits grain-boundary attack leading to intergranular fracture and/or generalized material removal. The severity of damage and corresponding losses in strength and modulus depend on etch duration and etch chemistry. In contrast, without a metallic layer, uncorroded silicon fails transgranularly and independent of etch duration or chemistry.
Computational Evidence for Laboratory Diagnostic Pathways: Extracting Predictive Analytes for Myocardial Ischemia from Routine Hospital Data

Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, patient follow-ups, and overall patient care, and should therefore have underlying standardized, evidence-based recommendations. Currently, laboratory parameters and their significance are treated differently depending on expert opinions, clinical environment, and varying hospital guidelines. In our study, we aimed to demonstrate the capability of a set of algorithms to identify predictive analytes for a specific diagnosis. As an illustration of our proposed methodology, we examined the analytes associated with myocardial ischemia; it was a well-researched diagnosis and provides a substrate for comparison. We intend to present a toolset that will boost the evolution of evidence-based laboratory diagnostics and, therefore, improve patient care. Methods: The data we used consisted of preexisting, anonymized recordings from the emergency ward involving all patient cases with a measured value for troponin T. We used multiple imputation technique, orthogonal data augmentation, and Bayesian Model Averaging to create predictive models for myocardial ischemia. Each model incorporated different analytes as cofactors. In examining these models further, we could then conclude the predictive importance of each analyte in question. Results: The used algorithms extracted troponin T as a highly predictive analyte for myocardial ischemia. As this is a known relationship, we saw the predictive importance of troponin T as a proof of concept, suggesting a functioning method. Additionally, we could demonstrate the algorithm’s capabilities to extract known risk factors of myocardial ischemia from the data. Conclusion: In this pilot study, we chose an assembly of algorithms to analyze the value of analytes in predicting myocardial ischemia. By providing reliable correlations between the analytes and the diagnosis of myocardial ischemia, we demonstrated the possibilities to create unbiased computational-based guidelines for laboratory diagnostics by using computational power in today’s era of digitalization.
Memory Carousel: LLVM-Based Bitwise Wear Leveling for Nonvolatile Main Memory

Emerging nonvolatile memory yields, alongside many advantages, technical shortcomings, such as reduced cell lifetime. Although many wear-leveling approaches exist to extend the lifetime of such memories, usually a tradeoff for the granularity of wear leveling has to be made. Due to iterative write schemes (repeatedly sense and write), wear out of memory in certain systems is directly dependent on the written bit value and thus can be highly imbalanced, requiring dedicated bit-wise wear leveling. Such a bit-wise wear leveling so far has only be proposed together with a special hardware support. However, if no dedicated hardware solutions are available, especially for commercial off-the-shelf systems with nonvolatile memories, a software solution can be crucial for the system lifetime. In this work, we propose entirely software-based bit-wise wear leveling, where the position of bits within CPU words in the main memory is rotated on a regular basis. We leverage the LLVM intermediate representation to adjust load and store operations of the application with a custom compiler pass. Experimental evaluation shows that the lifetime by applying local rotation within the CPU word can be extended by a factor of up to $21\times $ . We also show that our method can incorporate with coarser-grained wear leveling, e.g., on block granularity and assist achievement of higher lifetime improvements.
Wavelet-based Time Series Cluster Analysis of Mortgage Risk

In this paper, we implement the discrete wavelet transform (DWT) to categorize mortgages based on the payment history. The wavelet transform is applied to the time series of payments to perform a multiresolution analysis. The resulting wavelet coefﬁcients are used to cluster loans into three rating groups by using the various kMeans clustering methods. The ﬁrst model proposed uses the wavelet coefﬁcients corresponding to each scale to cluster the time series. The second model improves upon the ﬁrst, by using an iterative procedure derived from the i-kMeans model that uses the ﬁnal centers from clustering the higher scale coefﬁcients to initialize the clustering for the next level. The third model makes use of the energy decomposition of wavelet coef-ﬁcients to cluster the payment histories. It is seen that the k-Means algorithm fails to converge at lower scales when random centers are used. The i-kMeans algorithm addresses this problem by using cluster assignments from higher levels to initialize the centroids for the k-Means algorithm applied to lower level coefﬁcients. The clusters are evaluated by observing the cluster assignments at the time of default. The clusters formed by the i-kMeans algorithm provide better separation than using individual-level coefﬁcients while clustering based on energy decomposition manages to group all the defaults in a single cluster but has large numbers of false positives.
Optimization of surface morphology to reduce the effect of grain boundaries and contact resistance in small molecule based thin film transistors

Grain boundaries in organic thin film have been controlled by engineering the different growth conditions in such a way that it reduces the contact resistance and enhances the carrier mobility in p-type copper phthalocyanine and n-type copper hexadecafluoro phthalocyanine based organic thin film transistors. Reduced effect of grain boundary has been demonstrated by temperature dependence of charge carrier mobility and other transport parameters. A correlation has been established between contact resistance and certain thin film morphology, achieved by varying different growth conditions.
Network Intrusion Detection System Based on One-Dimensional Convolutional Neural Networks

Network Intrusion leaks the personal information of network users on a large scale, causing serious security risks. It is of great significance to the Intrusion Detection Systems (IDS) to find abnormal traffic from a huge database in time. Traditional machine learning methods to detect abnormal network traffic usually need to manually extract features from the dataset, which is time-consuming and has low accuracy. This paper proposes a deep learning-based abnormal traffic detection method based on an Improved One-Dimensional Convolutional Neural Networks (ICNN-1D) to detect abnormal network traffic, which greatly improves the extraction accuracy of abnormal traffic features and improves the identification of attack traffic. CNN applies multiple filters (convolution kernels) to the raw pixel data of an image to extract and learn higher-level features. After multiple convolutions, the characteristic graph with the same number of categories as the number of samples is obtained. The experimental results on the dataset CIC-IDS2017 show that the accuracy of the hybrid algorithm is 99.8%. Compared with other learning algorithms, the accuracy of our method greatly improves, and the operation time has been reduced.
Theory of resonant scattering from spherical cavities in elastic and viscoelastic media

Plane incident p‐waves which propagate through continuous (possibly absorptive) media are scattered by a fluid‐filled spherical cavity contained in it. Using an approach familiar in nuclear scattering theory but novel to acoustics and elastodynamics, it is possible to express the scattering amplitudes (or the partial waves contained in them) for the scattered p‐and s‐waves in a form which clearly exhibits their dependence on two interacting contributions, one being the broad background of an ideally soft cavity, and the other the superimposed narrow spikes due to resonances excited in the cavity fluid. The cavity appears as a perfectly soft obstacle to the incident waves at all frequencies except in the near vicinity of the cavity eigenfrequencies where there is wave penetration into the filler fluid. When this happens, the interference of this wave with the ’’potential scattering’’ of the background is seen to cause the fluctuating character of the amplitudes (or cross section). We have numerically compu.
A Flexible Full-Wave Analysis of Multilayered AMC Using an Aperture Oriented Approach

A full-wave analysis of printed artificial magnetic conductors (AMC), consisting of multilayered array of metal patches in a stratified dielectric medium, is presented. The approach is based on the Method of Moments, using as unknowns the magnetic currents over the apertures. This approach is effective, irrespective of the metallization shapes and of the dielectric slabs configuration. A comparison with results derived from commercial software will assess the procedure.
Fibers of aligned single-walled carbon nanotubes: Polarized Raman spectroscopy

To probe the one-dimensional nature of single-wall carbon nanotubes (SWNTs) in bulk samples, we have devised a simple method for generating fibers of aligned SWNTs. We measured polarization-dependent Raman spectra on the oriented fibers. Contrary to what is expected from their theoretically assigned vibration-mode symmetries, all the Raman line intensities are observed to decrease in nearly equal amounts for the 647.1 nm laser excitation polarized perpendicular to the fiber axis versus that polarized parallel to the fiber axis. The effect is explained as a loss of resonance Raman scattering for the perpendicular polarization case.
The crosstalk between gut barrier impairment, mitochondrial dysfunction, and microbiota alterations in people living with HIV

Functional and structural damage of the intestinal mucosal barrier significantly contribute to translocation of gut microbial products into the bloodstream and are largely involved in HIV‐1 associated chronic immune activation. This microbial translocation is largely due to a progressive exhaustion of intestinal macrophage phagocytic function, which leads to extracellular accumulation of microbial derived components and results in HIV‐1 disease progression. This study aims to better understand whether the modulation of gut microbiota promotes an intestinal immune restoration in people living with HIV (PLWH). Long‐term virologically suppressed PLWH underwent blood, colonic, and fecal sampling before (T0) and after 6 months (T6) of oral bacteriotherapy. Age‐ and gender‐matched uninfected controls (UC) were also included. 16S rRNA gene sequencing was applied to all participants' fecal microbiota. Apoptosis machinery, mitochondria, and apical junctional complex (AJC) morphology and physiological functions were analyzed in gut biopsies. At T0, PLWH showed a different pattern of gut microbial flora composition, lower levels of occludin (p = 0.002) and zonulin (p = 0.01), higher claudin‐2 levels (p = 0.002), a reduction of mitochondria number (p = 0.002), and diameter (p = 0.002), as well as increased levels of lipopolysaccharide (LPS) (p = 0.018) and cCK18 (p = 0.011), compared to UC. At T6, an increase in size (p = 0.005) and number (p = 0.008) of mitochondria, as well as amelioration in AJC structures (p < 0.0001) were observed. Restoration of bacterial richness (Simpson index) and biodiversity (Shannon index) was observed in all PLWH receiving oral bacteriotherapy (p < 0.05). Increased mitochondria size (p = 0.005) and number (p = 0.008) and amelioration of AJC structure (p < 0.0001) were found at T6 compared to T0. Moreover, increased occludin and zonulin concentration were observed in PLWH intestinal tracts and decreased levels of claudin‐2, LPS, and cCK18 were found after oral bacteriotherapy (T0 vs. T6, p < 0.05 for all these measures). Oral bacteriotherapy supplementation might restore the balance of intestinal flora and support the structural and functional recovery of the gut mucosa in antiretroviral therapy treated PLWH.
A Complete Framework for Model-Free Difference-in-Differences Estimation

We propose a complete framework for data-driven difference-in-differences analysis with covariates, in particular nonparametric estimation and testing. We start with simultaneously choosing confounders and a scale of the outcome along identification conditions. We estimate first heterogeneous treat-ment effects stratified along the covariates, then the average effect(s) for the treated. We provide the asymptotic and finite sample behavior of our estimators and tests, bootstrap procedures for their standard errors and p-values, and an automatic bandwidth choice. The pertinence of our methods is shown with a study of the impact of the Deferred Action for Childhood Arrivals program on educational outcomes for non-citizen immigrants in the US.
Facile Entry to Pharmaceutically Important 3-Difluoromethyl-Quinoxalin-2-Ones Enabled by Visible-Light-Driven Difluoromethylation of Quinoxalin-2-Ones

CF2H moiety has a significant potential utility in drug design and discovery, and the incorporation of CF2H into biologically active molecules represents an important and efficient strategy for seeking lead compounds and drug candidates. On the other hand, quinoxalin-2-one is of great interest to pharmaceutical chemists as a common skeleton frequently occurring in plenty of natural products and bioactive compounds. Herein, we reported a practical and efficient protocol for the synthesis of 3-CF2H-quinoxalin-2-ones. Thus, in the presence of 3 mol% of photocatalyst and S-(difluoromethyl)sulfonium salt as difluoromethyl radical sources, a wide range of quinoxalin-2-ones readily underwent a visible-light redox-catalyzed difluoromethylation reaction, to deliver structurally diverse 3-difluoromethyl-quinoxalin-2-ones. We believe that this would facilitate increasing chances and possibilities for seeking potential lead compounds and drug candidates and further boost the development of fluorine-containing pharmaceuticals.