Miracle-dz/UniFAR-FTUs huggingface dataset:
LFFI-H: A Recommendation System Suitable for Crowdfunding Platform

The crowdfunding platform is a new financial model that helps fundraisers get the funds needed for projects. At present, people are paying more and more attention to the crowdfunding platform, and more and more projects are launched on the crowdfunding platform. Therefore, it is difficult to find the projects lenders want to invest in. Many research designs recommend models to solve this problem. However, there are two problems here: (1) Compared with the application scenarios of traditional recommendation systems, the data of crowdfunding platforms is clearer and the structure is more concise. Previous studies often ignore the characteristics of crowdfunding platform data to design recommendation models, and it is difficult to make the best use of data information. (2) Previous crowdfunding recommendation studies have not considered the influence of user hierarchy on prediction results. Therefore, this paper proposes a recommendation model for crowdfunding platform projects–latent factor feature interaction model based on users' historical investment category preferences named LFFI-H. Specifically, according to the characteristics of the crowdfunding platform, we add the features obtained from hidden factor model to the shallower position in the model, thus avoiding the loss caused by layer-by-layer transmission from the bottom layer of Deep Neural Network (DNN). Moreover, we divide the investor's investment project category preferences hierarchically, splice them with other attribute features and input them into the model. A large number of experiments on kiva public data set verify the effectiveness of this model.
Prediction of 1000-grain Weight of Rapeseed Based on Auto-encoder

DNA sequence contains a lot of genetic information, and a variety of phenotypic information of the organism can be obtained by analyzing its single nucleotide polymorphism (SNP). Experiments have shown that 1000-grain weight of rapeseed is positively correlated with its oil yield. In this paper, the 1000-grain weight of rapeseed at maturity is predicted by the genetic data of rapeseed, so as to control the oil yield of rapeseed. When analyzing and processing high-dimensional genetic data, the author proposes a deep learning method—auto-encoder for dimensionality reduction processing of high-dimensional genetic data, and compares it with the traditional principal component analysis method. As the number of features of genetic data is much higher than the number of samples, the data generated by using the auto-encoder for dimensionality reduction cannot completely present the effective information of the original genetic data, and dimension disasters are prone to occur. In view of the above problems, this paper optimized the auto-encoder network. The experimental results showed that the proposed optimization method could reduce the MAE error by 0.1344, and the MAE error was 0.3824, indicating that there was an error of 0.3824g between the predicted value and the measured value of 1000-grain weight of rapeseed. Step 2: The thousand-grain weight prediction using neural network mainly includes two parts: 1. Use dimensionality reduction neural network to reduce the dimension of high-dimensional genetic data, and extract the deep relationship in the data at the same time; 2. Use linear regression to perform 1000-grain weight prediction on the new data after dimensionality reduction.
Hyperbolic Fibonacci and Lucas Functions, "Golden" Fibonacci Goniometry, Bodnar's Geometry, and Hilbert's Fourth Problem —Part II. A New Geometric Theory of Phyllotaxis (Bodnar's Geometry)

This article refers to the “Mathematics of Harmony” by Alexey Stakhov in 2009, a new interdisciplinary direction of modern science. The main goal of the article is to describe two modern scientific discoveries–New Geometric Theory of Phyllotaxis (Bodnar’s Geometry) and Hilbert’s Fourth Problem based on the Hyperbolic Fibonacci and Lucas Functions and “Golden” Fibonacci λ-Goniometry (λ > 0 is a given positive real number). Although these discoveries refer to different areas of science (mathematics and theoretical botany), however they are based on one and the same scientific ideas-the “golden mean,” which had been introduced by Euclid in his Elements, and its generalization—the “metallic means,” which have been studied recently by Argentinian mathematician Vera Spinadel. The article is a confirmation of interdisciplinary character of the “Mathematics of Harmony”, which originates from Euclid’s Elements.
A Battery Equalization Method Based on Model Predictive Control and Disturbance Observer

Battery equalization is to maintain every cell in the same working condition and is of great significance for battery life. Multivariable coupling and model mismatch bring serious problems to battery equalization control and cause performance degradation in battery management system. Model predictive control (MPC) is applied for multivariable decoupling and disturbance observer (DOB) is also employed to deal with the model mismatch problem. Numerical simulation results demonstrate that this DOB-MPC composite control strategy can effectively improve the dynamic performance of the battery balancing process.
Visual Simulation of Tire Smoke

In recent years, the realism of video expression by 3DCG has increased remarkably. In this study, we propose a CG representation method of tire smoke based on physical simulation. Although tire smoke has appeared in various video works and 3DCG games, the mechanism of how tire smoke is generated has not been fully elucidated. We calculate the generation and conduction of heat from the rotation speed and friction between the tire and the ground, and construct a model of tire smoke generation from the viewpoint of statistical mechanics. The parameters in the mathematical model are set from actual images and observations, and we confirm that the phenomenon of tire smoke can be reproduced.
Direct determination of vocal‐tract wall impedance

The side wall of the human vocal tract yields and vibrates in response to contained sound pressure. This motion influences the acoustic resonances of the vocal tract and the vibratory behavior of the vocal cords. Accurate computer modeling of the cord/tract system therefore requires knowledge of the mechanical impedance of the yielding walls. Heretofore, this factor has only been estimated indirectly from sound wave measurements. We describe here a technique for direct measurement of the wall impedance. We also give a rationale for interpreting the low‐frequency impedance in terms of a simple mass‐compliance‐viscous loss combination. Finally, we give mechanical parameters for the vocal‐tract wall which we presently use in computer simulations.
Zonal Large-Eddy Simulation of a Fan Tip-Clearance Flow, With Evidence of Vortex Wandering

The flow in a fan test-rig is studied with combined experimental and numerical methods, with a focus on the tip-leakage flow. A zonal RANS/LES approach is introduced for the simulation: the region of interest at tip is computed with full large-eddy simulation (LES), while Reynolds-averaged Navier–Stokes (RANS) is used at inner radii. Detailed comparisons with the experiment show that the simulation gives a good description of the flow. In the region of interest at tip, a remarkable prediction of the velocity spectrum is achieved, over about six decades of energy. The simulation precisely captures both the tonal and broadband contents. Furthermore, a detailed analysis of the simulation allows identifying a tip-leakage vortex (TLV) wandering, whose influence onto the spectrum is also observed in the experiment. This phenomenon might be due to excitation by upstream turbulence from the casing boundary layer and/or the adjacent TLV. It may be a precursor of rotating instability. Finally, considering the outlet duct acoustic spectrum, the vortex wandering appears to be a major contribution to noise radiation.
Chinese Institutional Considerations for the Internationalization of the Renminbi: A Network Effect Perspective

Abstract The article discusses the internationalization of the Chinese renminbi (RMB) in light of a redefined network effects framework to incorporate an original institutionalist perspective. Contradicting common wisdom, this approach shows that rather than slowing down in 2015, the strategy to internationalize the RMB has been redirected towards the creation of network effects, thereby directly challenging the institutions—such as the petrodollar system, creditworthiness, among others—that support the United States dollar’s hegemony.
Aplicação de Testes Adaptativos Computadorizados em Modelos de Desdobramento Graduado Generalizados

Resumo. Quando um pesquisador deseja determinar o grau ou a intensidade dos sintomas de um paciente aplica um questionário de papel e caneta com uma série de perguntas pré-definidas, o mesmo acontece quando um pedagogo busca determinar a habilidade de um aluno em uma determinada área do conhecimento. Na quase totalidade dos casos, tais questionários são demasiadamente extenuantes para o entrevistado e, devido a isso, correm o risco de não representar de forma significativa o estado do paciente ou o grau de conhecimento do aluno. Uma alternativa para este cenário é a construção de um Computerized Adaptive Testing CAT. O CAT procura otimizar o teste para cada respondente, buscando estimar a caracteŕıstica predominante de um indiv́ıduo a cada item respondido. Neste artigo, o CAT desenvolvido não utilizará a Teoria Clássica de Medidas, que considera apenas a proporção de acertos e erros em uma prova. Em vez disso, será utilizada a Teoria de Resposta ao Item (TRI), que apresenta a vantagem de fazer comparações entre traços latentes de indiv́ıduos de populações diferentes quando são submetidos a testes ou questionários que tenham alguns itens comuns e permite, ainda, a comparação de indiv́ıduos da mesma população submetidos a testes totalmente diferentes. Isto é posśıvel porque a TRI tem como elementos centrais os itens e não o teste ou questionários como um todo. A TRI apresenta modelos matemáticos que já foram amplamente abordados pela literatura, tais como os modelos acumulativos, devido à sua facilidade de implementação. No entanto, outros modelos não apresentam a mesma facilidade de implementação, como os modelos de desdobramentos. Este trabalho propõe a construção de um CAT utilizando um modelo de desdobramento graduado generalizado, visto que este modelo pode ser utilizado tanto para respostas dicotômicas como para respostas politômicas ordinais.
A Domains of Power Analysis of the Narratives of Women of Color on Community College STEM Education Pathways

Informed by Black feminist thought and intersectional feminism, this study examined the lived experiences of 12 Women of Color on community college STEM education pathways. Participants were current and former community college STEM students, whose narrative interviews were analyzed using Patricia Hill Collins’s domains of power framework. Findings suggest that rather than participants’ experiences being isolated cases, they form a part of a larger pattern of realities that reveal multiple forms of power operating in STEM and beyond. Participants’ experiences of racism and heterosexism intersected with other identities and backgrounds such as being a transfer student, an undocumented student, and a parent. These overlapping and multilayered manifestations of power were found to impact individuals’ abilities to successfully navigate STEM in higher education. While the women of this study critically analyzed their experiences of power and resisted oppression, all stakeholders in the STEM education community must work toward an understanding and dismantling of oppressive power in STEM.
Magnetic field-modulated photo-thermo-electric effect in Fe/GaAs film

Ferromagnet/semiconductor heterostructure, such as Fe/GaAs, is always one of the key issues in spintronics due to its prerequisite for the realization of spin sensitive devices. In this letter, a lateral photoelectric effect (LPE) was observed in Fe/GaAs. Our results show that the sensitivity was not related to laser wavelength, but only proportional to laser power, suggesting that the lateral photovoltage was induced by photo-thermo-electric effect. Moreover, we also observe that the voltage signal increases with the increase in applied field due to decreasing scattering probability for spin-polarized electrons. Our finding of LPE adds another functionality to the Fe/GaAs system and will be useful in development of spin-polarized voltage devices.
Employee adaptability skills for Industry 4.0 success: a road map

ABSTRACT Industry 4.0 revolutionizes the concept of automation and digitization within the organization leading to immense interest in academia and industry. The digitization of an organization in terms of horizontal, vertical, and end-to-end integration would change the roles an employee discharges in an organization. The main purpose of the paper is to successfully develop an employee adaptability road map for the successful implementation of Industry 4.0. This paper critically analyses the previous studies to develop a roadmap of employee adaptability skills for the successful implementation of Industry 4.0. This study uses deductive methodology using a systematic literature review to group and thematically analyse 52 articles to develop the conceptual model. Besides, this study also ranks these dimensions using an empirical study and finds the most critical employee adaptability dimensions. This is the first systematic literature review and empirical study carried out on employee adaptability skills in Industry 4.0.
NMDA receptor misalignment in iPSC-derived neurons from a multi-generational family with inherited Creutzfeldt-Jakob disease

The most common subtype of genetic prion disease is caused by the E200K mutation of the prion protein. We have obtained samples from 22 members of a multi-generational Israeli family harboring this mutation, and generated a library of induced pluripotent stem cells (iPSCs) representing nine carriers and four non-carriers. Whole-exome sequencing was performed on all individuals. A comparison of neurons derived from E200K iPSCs to those from non-carriers revealed the presence of several disease-relevant phenotypes. Neurons from E200K carriers were found to contain thioflavin S-positive accumulations of PrP in their cell bodies. In addition, these neurons displayed disruptions of NMDA receptor/PSD95 co-localization at postsynaptic sites. Our study shows that iPSC-derived neurons, which express physiologically relevant levels of mutant PrP in a human neuronal context, can model certain aspects of human prion disease, offering a powerful platform for investigating pathological mechanisms and testing potential therapeutics.
Multilocus typing of Lachancea thermotolerans for wine fermentation monitoring

Climate change is causing a lack of acidity during winemaking and oenologists use several solutions to cope with such a problem. Lachancea thermotolerans, which has the potential to tolerate the harsh physicochemical conditions of wine, has emerged as a promising alternative for pH management during winemaking and, currently, it is the most valuable yeast used for acidity control in wine. In this work an amenable method for L. thermotolerans genotyping based on a multiplexed microsatellite amplification in 6 different loci was developed. This specific and sensitive method was used to distinguish between 103 collection strains obtained from different geographical and isolation sources, and then challenged against a 429 L. thermotolerans isolates from several wineries and harvests. The procedure was also tested for fermentation monitoring and strain implantation. The procedure was conceived to simplify the methodology available for L. thermotolerans genotyping, making it easy for applying in wine-related laboratories. This method can be applied to distinguish between L. thermotolerans strains in selection programs and to follow implantation of inoculated strains during winemaking with optimal results.
Single Cell transcriptional analysis of ex vivo models of cortical and hippocampal development identifies unique longitudinal trends

Postnatal cortical and hippocampal mouse primary neuronal cultures are powerful and widely-used models of neuronal activity and neurological disease. While this model is frequently used to recapitulate what is seen in vivo, how the transcriptomic profiles of neuronal networks change over development is not fully understood. We use single-cell transcriptomics to provide a view of neuronal network establishment and maturation. Our data highlight region-specific differences and suggest how cell populations program the transcriptome in these brain regions. We demonstrate that patterns of expression markedly differ between and within neurological diseases, and explore why these differences are found and how well they compare to other models. In particular, we show significant expression differences between genes associated with epilepsy, autism spectrum disorder, and other neurological disorders. Collectively, our study provides novel insights on this popular model of development and disease that will better inform design for drug discovery and therapeutic intervention. Graphical Abstract (A) Schematic representing select gene expression progression through neuronal network maturation from human cortical organoids (3- and 6-Month Organoid), newborn mice (P0 Mouse), immature ex vivo cortex derived cultures (DIV 3 ex vivo), functionally mature ex vivo cortex derived cultures (DIV15-31 ex vivo), and adult mice (P56 Mouse). Color represents proportion of excitatory neurons with detectable expression for selected representative genes Mapk10, Igfbp2, which increase and decrease through network maturation, respectively. (B) Schematic representing divergent expression patterns between genes associated with epilepsy and ASD through network maturation between the organoids and ex vivo cultures shown in (A). Color scales represent the change in the percentile, in respect to all genes, of the proportion of excitatory neurons with detectable expression.
Surface models to constrain three-dimensional sparse SAR reconstruction

Sparse data collection geometries represent a significant challenge to high-resolution three-dimensional SAR imaging. In particular, sparse sampling can lead to significant sidelobe structure in high-resolution reconstructions. To help constrain volumetric SAR reconstructions, we have introduced a surface model. We justify this based on physical phenomenology: higher frequency SAR systems exhibit only limited surface penetration. In our method, we jointly estimate the surface models and reconstructions, significantly reducing sidelobing artifacts in comparison with traditional reconstructions. Our paper and presentation illustrate reconstructions both with and without surface models to demonstrate the potential improvement.
Ecological drivers and evolutionary consequences of torpor in Andean hummingbirds

Daily torpor allows endotherms to save energy during energetically stressful (e.g., cold) conditions. Although studies on avian torpor have mostly been conducted under laboratory conditions, information on the usage of torpor in the wild is limited to few, predominantly temperate-zone species. We studied torpor under seminatural conditions from 249 individuals from 29 hummingbird species across a 1,920 m elevational gradient in the western Andes of Colombia using cloacal thermistors. Small birds used torpor more often than large birds, but only at low ambient temperatures, where torpor was prolonged. We also found effects of proxy variables for body condition and energy expenditure on the use of torpor, its characteristics, and impacts. Our results suggest that judicious deployment and subtle modifications of torpor, as well as phylogenetic variation in propensity for torpor, have allowed only certain clades of hummingbirds to colonize and persist in the harsh climate of the high Andes.
Combining education scenarios and modalities: the contextual dimension of health sciences professors’ learning ecologies (Combinando escenarios y modalidades de formación: la dimensión contextual de las ecologías de aprendizaje del docente universitario de ciencias de la salud)

ABSTRACT Professional learning processes occur in many scenarios in which there are a variety of different elements that play key roles. The concept of learning ecologies is particularly useful for understanding how these complex processes happen, although it is still an embryonic line of research whose potential has not been fully explored. The objective of this study was to identify the elements of the contextual dimension of learning ecologies. This qualitative research was performed via a case study. Five professors of the health sciences field at the University of A Coruña constitute the case. Data were collected via in-depth interviews and analysed using Atlas.ti software. The results showed that there were three key elements in the contextual dimension: Activities, Interactions and Resources. Being aware of the components that make up their own ecology will help professors maintain and enhance it and, therefore, help them achieve their learning objectives.
Homeostasis in Networks with Multiple Inputs

Homeostasis, also known as adaptation, refers to the ability of a system to counteract persistent external disturbances and tightly control the output of a key observable. Existing studies on homeostasis in network dynamics have mainly focused on ‘perfect adaptation’ in deterministic single-input single-output networks where the disturbances are scalar and affect the network dynamics via a pre-specified input node. In this paper we provide a full classification of all possible network topologies capable of generating infinitesimal homeostasis in arbitrarily large and complex multiple-input parameter networks. Working in the framework of ‘infinitesimal homeostasis’ allows us to make no assumption about how the components are interconnected and the functional form of the associated differential equations, apart from being compatible with the network architecture. Remarkably, we show that there are just three distinct ‘mechanisms’ that generate infinitesimal homeostasis. Each of these three mechanisms generates a rich class of well-defined network topologies – called homeostasis subnetworks. Most importantly, we show that these classes of homeostasis subnetworks provides a topological basis for the classification of ‘homeostasis types’: the full set of all possible multiple-input parameter networks can be uniquely decomposed into these special homeostasis subnetworks. We build on previous work that treated the cases of single-input node and multiple-input node, both with a single scalar input parameter. Furthermore, we identify a new phenomenon that occurs in the multiparameter setting, that we call homeostasis mode interaction, in analogy with the well-known characteristic of multiparameter bifurcation theory.