Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Surface-mounted and Switched-flux PM Machines Accounting for Thermal Characteristics and Limits

This paper analyses the electromagnetic performances and thermal characteristics of surface-mounted permanent magnet (PM) and switched-flux (SF) PM machines. Based on the 12 stator slots/10 rotor poles combination and the same space envelope, the SPM and SFPM machines are optimised for maximum torques, with and without restricting the same PM consumption. The thermal characteristics of stator and rotor PM machines are investigated for machines with natural cooling accounting for thermal limits of winding insulation and magnet with different temperature grades. It is shown that when the SFPM machine is designed to produce a torque similar to the SPM machine, its PM consumption will be increased by >50%. Although the higher temperature-grade PM material together with the F-class winding insulation could improve the torque capability, the maximum torques of the SFPM machines are still lower than that of the SPM counterpart due to the thermal limits with natural cooling.
A Novel Modified PLL-based Position Estimator in SynRMs Injection-based Sensorless Control

For the purpose of improving the dynamic performance of the injection-based sensorless control for synchronous reluctance machines, a novel phase-locked loop (PLL)-based position estimator has been proposed. The new estimator is modified from the conventional proportional-integral-based PLL by adding a feedforward path as a new control degree of freedom (DOF). Hence, the dynamic performance could be improved by designing the 2DOF controllers. One of the controllers could eliminate the adverse effect of the preceded filter, while the other dominates the cutoff frequency of the estimator. Therefore, the proposed estimator can be treated as a kind of first-order low pass filter with less “aggressive” for position and speed tracking. Finally, the effectiveness of the proposed method has been verified on the simulations and experiments.
Virtual Humans

Imagine a simulated world where the characters you interact with are almost human - they converse with you in English, they understand the world they are in, can reason about what to do, and they exhibit emotions. Some of these characters may be your friends, while others will oppose you. Unlike current video garnes, being successful in this world won't just be a matter of who is quickest on the draw or most adroit at solving puzzles, instead it will be the person who understands the social fabric and cultural context and can use interpersonal skills most effectively. Such a simulation could open up whole new horizons for education, entertainment and simulation. And, given recent advances in AI and graphics, it may not be too far off. Virtual humans are computer-generated characters that can take the part of humans in a variety of limited contexts. These can include acting as role-players in simulations and training systems (Johnson, Rickel & Lester 2000; Swartout et al. 2005: Traum et al. 2005; Johnson, Vilhjalmsson & Marsella 2005), where they play a variety of parts, such as acting as friendly or hostile forces, or locals in the environment. Other uses for virtual humans include acting as museum guides (Gustafson & Bell 2000), marketing assistants (Cassell, Bickmore et al. 2000) or characters in entertainment systems, where the advent of video games such as The Sims 2 makes clear the growing interest of the computer game industry in virtual humans (see also (Mateas & Stern 2003)).
Collaborative Filtering Recommendation Algorithm Combining Tag Relevance and Score Differences

The aim of this study is to propose a collaborative filtering recommendation algorithm that addresses the issue of inaccurate user preference mining in traditional collaborative filtering algorithms. The algorithm combines tag correlation and score differences to achieve accurate recommendations. It incorporates Newton's law of cooling to model the attenuation of users' interest in tags over time, and calculates user interest based on tag correlations. Additionally, it considers the maximum difference in user ratings from historical records, taking into account the impact of these factors on item ratings. The algorithm also considers the rating time to capture recent user preferences. Finally, it performs a weighted fusion of user label interest similarity and rating difference similarity to identify the most similar neighbors and make item predictions, scores, and recommendations. Experimental results demonstrate that this algorithm significantly improves recommendation accuracy.
ENTICING JAPANESE TOURISTS BACK TO HAWAII: ARE CASINO'S THE ANSWER?

With the explosion of casino development in the Asia Pacific as well as the United States, casino gambling has become more culturally embraced as a recreational activity. The purpose of this study was to re-examine the legalization of casino gambling in Hawaii from Japanese-speaking tourists’ perspective and compare these results from a previous study completed a decade ago. With the continuing decrease of Japanese visitors to Hawaii, and with the importance of the Japanese market to Hawaii’s economy, state officials are considering other forms of tourism, such as casino tourism, to help revive the Japanese tourism market and increase Hawaii’s global competitiveness. By analyzing the responses of the current Japanese-speaking tourists to Hawaii and comparing these responses to the Japanese tourists surveyed 10 years ago (1996), this research attempted to determine if the Japanese tourists in 2006 have changed their perception on the legalization of casino gambling in Hawaii. Although the findings of this study indicated that the 2006 Japanesespeaking tourists have changed their perspective from past studies on the effects that the legalization of casino gambling would have on Hawaii, future research should focus on other segments of the tourist market. INTRODUCTION Hawaii is among the world’s most famous tourist destinations. The Hawaiian Islands are visited by over seven million tourists per year and contribute to Hawaii’s economy with approximately $12 billion in total expenditure per year (Arakawa, 2006). Not surprisingly, tourism remains the main industry for the state, and leaves Hawaii heavily dependent on it (Schmitt, 2002). Being the only American island state and with its location, Hawaii’s popularity as a tourist destination attracts both U.S. mainlanders and international visitors, with the bulk of the latter coming from Japan. The Japanese visitors have traditionally dominated Hawaii tourism with the 1990’s setting record high counts, however the numbers have decreased steadily since then. In the year 1997, Japanese arrivals to Hawaii peaked with 2.2 million visitors (DBEDT, 2004). Approximately one-third of the total 6.7 million tourists visiting Hawaii in 1997 were Japanese (Yamanouchi, 2004). Since the 1997 peak, the number of Japanese tourists traveling to Hawaii has dwindled with only 1.5 million visiting in 2005 (DBEDT, 2006).
Grain growth of ice doped with soluble impurities

Abstract. The grain size of polycrystalline ice affects key parameters related to the dynamics of ice masses, such as the rheological and dielectric properties of terrestrial ice as well as the ice shells of icy satellites. To investigate the effect of soluble impurities on the grain-growth kinetics of polycrystalline ice, we conducted annealing experiments on polycrystalline ice samples doped with different concentrations of KCl (10−2, 10−3, 10−4 and 10−5 mol L−1) or MgSO4 (10−2 and 10−5 mol L−1). Samples were annealed for a maximum of 100 h at a hydrostatic confining pressure of 20 MPa (corresponding to a depth of about 2 km) and different constant temperatures of 268, 263, 258 and 253 K (corresponding to −5, −10, −15 and −20 °C, respectively). After each experiment, images of a polished sample surface were obtained using an optical microscope equipped with a cold stage. With grain boundaries detected, grains were reconstructed from the images, and an average grain size was determined for each sample. Normal grain growth occurred in all samples. Grain-size data are interpreted using the following grain-growth model: dn-d0n=kt (d: grain size; d0: starting grain size; n: grain-growth exponent; k: growth constant; t: duration). Values of the best-fit grain-growth exponent, n, for all samples range from 2.6 to 6.2, with an average value of 4.7. Pure ice exhibits 3.1 ⩽n⩽ 4.6, with an average value of 3.8. Above the eutectic point, soluble impurities enhance grain growth, as a melt phase is formed, and it could provide a fast diffusion pathway. Below the eutectic point, soluble impurities impede grain growth probably via the formation of salt hydrates that could pin the grain boundaries. Close to the eutectic point, the grain growth of doped ice is similar to pure ice. Natural ice is impure, often containing air bubbles and soluble impurities, and is usually subjected to a hydrostatic pressure. Our data set will provide new insights into the evolution of grain size within and the dynamics of natural ice masses.
Ecological Indicator Values of Europe (EIVE) 1.0: a powerful open-access tool for vegetation scientists

Background: Ecological indicator values (EIVs) have a long tradition in vegetation ecological research in Europe. EIVs characterise the ecological optimum of species along major environmental gradients using ordinal scales. Calculating mean indicator values per plot is an effective way of bioindication. Following first systems in Russia and Central Europe, about two dozen EIV systems have been published for various parts of Europe. Aims: As there was no EIV system available at European scale that could be used for broad- scale analyses, e.g. in the context of the European Vegetation Archive (EVA), we develop such a system for the first time for the vascular plants of Europe. Location: Europe. Methods: We compiled all national and major regional EIV systems and harmonized their plant nomenclature with a newly developed contemporary European taxonomic backbone (EuroSL 1.0). Using regression, we rescaled the individual EIV systems for the main parameters to continent-wide quasi-metric scales, ranging from 1 to 99. The data from each individual system were then translated into a probability curve approximated with a normal distribution, weighed with the logarithm of the area represented and summed up across the systems. From the European density curve we extracted then a mean and a variance, which characterise the distribution of this species along this particular ecological gradient. Results and conclusions: Our consensus approach of integrating the expert knowledge of all existing EIV systems allowed deriving the first consistent description of the ecological behaviour for a significant part of the European vascular flora. The resulting Ecological Indicator Values of Europe (EIVE) 1.0 will be published open access to allow bioindication beyond country borders. Future releases of EIVE might contain more parameters, non- vascular plants and regionalisation or could be re-adjusted and extended to hitherto non- covered species through co-occurrence data from EVA.
Beyond Connectivity: Artificial Intelligence and the Internationalisation of Digital Firms

The potential impact of artificial intelligence/machine learning technologies (AI/ML) on opportunities for born-digital firms to adopt new strategies for internationalisation has only recently begun to be systematically examined. In this paper we argue that by creating a digital space where users interact both with the entrant firm and between themselves, that a born-digital firm can engineer a situation where users reveal their preferences implicitly (unintentionally) through their behaviour. By deploying AI/ML tools to analyse data generated by these successive interactions, a firm can tailor its offering to an increasingly accurate picture of each individual user’s preferences. This, in turn, enables the firm to create additional value by personalising the offering to the individual user when it enters a new market, based solely on the results of its digital interactions. As a result, foreign direct investment aimed at helping the firm learn experientially about country differences, hitherto a core element of internationalisation theories, becomes unnecessary. The use of country proxies for differences in user preferences between target markets becomes redundant, being replaced by personalisation based on segments-of-one. We illustrate these propositions with reference to value-creation activities of the short-video streaming born-digital TikTok.
Study on, fractal features of soil microaggregates during different restoration stages in the loess hilly region

The composition of soil microaggregate and condition of soil structure are important factors to affect soil character and fertility and soil microaggregate in different restoration year have different fractal dimension feature.The particle size component and fractal dimension of soil microaggregats as well as the relationship between fractal dimension and soil physical and chemical characters were studied using fractal dimension theory under different restoration stages of grasslands in the Hilly Loess Region.The objective of this study was to apply the fractal dimension theory to soil microaggregate and soil nutrient investigation and supply a new tool for grassland ecosystem evaluation.The results show that the longer the restoration time,the less the fractal dimension of soil microaggragates in the upper layer;the higher the sand content,the higher the fractal dimension;however,the clay content had an opposite trend and the silt content had no significant correlation with the fractal dimension.The fractal dimension could effectively represent the trends of soil texture and soil nutrients under different restoration stages.Positive correlation existed between fractal dimension and bulk density,non-capillary porosity,porosity ratio,total phosphorus,available potassium,and ammoniac nitrogen,while fractal dimension had negative correlation with capillary porosity,soil organic matter,total nitrogen,available nitrogen,and nitrate nitrogen.The research suggests that the fractal dimension of soil microaggregats could be a comprehension index of soil quantity evaluation.
FANNG-MPC: Framework for Artificial Neural Networks and Generic MPC

—In this work, we introduce FANNG-MPC , a versatile secure multi-party computation framework capable to offer active security for privacy-preserving machine learning as a service (MLaaS). Derived from the now deprecated SCALE-MAMBA , FANNG is a data-oriented fork, featuring novel set of libraries and instructions for realizing private neural networks, effectively reviving the popular framework. To the best of our knowledge, FANNG is the first MPC framework to offer actively secure MLaaS in the dishonest majority setting, specifically two parties. FANNG goes beyond SCALE-MAMBA by decoupling offline and online phases and materializing the dealer model in software, enabling a separate set of entities to produce offline material. The framework incorporates database support, a new instruction set for pre-processed material, including garbled circuits and convolutional and matrix multiplication triples. FANNG also implements novel private comparison protocols and an optimized library supporting Neural Network functionality. All our theoretical claims are substantiated by an extensive evaluation using an open-sourced implementation, including the private evaluation of popular neural networks like LeNet and VGG16.
The prognostic value of Forkhead box P3 regulatory T cells in biliary tract cancer: A systematic review and meta-analysis

Background: This study aimed to explore the value of tumor-infiltrating Forkhead box P3(FoxP3+) regulatory T cells (Tregs) in evaluating the prognosis of biliary tract cancer. Methods: Four electronic databases were searched using 2 computers: PubMed, Embase, Web of Science, and Cochrane Library. The vocabulary and syntax were adapted according to the database. Two researchers independently selected the studies, collected information, and assessed the risk of bias. The Meta-analysis was performed using STATA 17.0, and HR and its corresponding 95% CI were used to evaluate the correlation between FoxP3+ Tregs and the overall survival of patients with biliary tract cancer. In addition, the quality of the included studies was evaluated. Results: Ten articles were included in this study. The results of the meta-analysis showed that patients with high FoxP3+ Tregs infiltration had worse overall survival (OS) (HR = 1.34,95% CI 1.16 to 1.71; P < .001). Subgroup analysis of gallbladder carcinoma and cholangiocarcinoma showed that the high infiltration of FoxP3+ Tregs was significantly correlated with the OS of the former (HR = 1.55,95% CI 1.11 to 2.00; P < .001), but not with the OS of the latter (HR = 1.00,95% CI 0.62 to 1.38; P > .05). Conclusions: Our meta-analysis reveals that high infiltration of FoxP3 + Tregs is significantly associated with reduced overall survival in gallbladder carcinoma, endorsing their use as a prognostic biomarker for this subtype. In contrast, no significant prognostic correlation was identified for FoxP3+ Tregs in cholangiocarcinoma, indicating the need for subtype-specific evaluation of their prognostic relevance in biliary tract cancers.
The path selection of financial performance improvement of pharmaceutical companies under the perspective of social responsibility

This paper uses 47 Chinese listed pharmaceutical manufacturing companies in 2019–2021 as a research sample to explore the path selection of the multidimensional linkage effect of social responsibility on pharmaceutical companies to improve their financial performance based on stakeholder theory and using the fsQCA method. It is found that: the combination of different dimensions of social responsibility can improve the financial performance of enterprises, and none of the individual dimensions of social responsibility is necessary for high financial performance; there are three different groupings that can help improve the financial performance of pharmaceutical enterprises, namely, shareholder-government-driven, shareholder-creditor-driven, and shareholder-employee-driven. employee-driven, where social responsibility to shareholders is key and there is a significant substitution effect between the social responsibility of employees and creditor and government portfolios. The findings reveal the linkage effect between the social responsibility of pharmaceutical companies and different stakeholders and can give relevant suggestions to pharmaceutical companies, i.e., to reasonably allocate social responsibility among different stakeholders according to their own situations to achieve the improvement of financial performance of pharmaceutical companies.
Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees

: Heteroscedastic Gaussian processes (HGPs) are kernel-based, non-parametric models that can be used to infer nonlinear functions with time-varying noise. In robotics, they can be employed for learning from demonstration as motion primitives, i.e. as a model of the trajectories to be executed by the robot. HGPs provide variance estimates around the reference signal modeling the trajectory, capturing both the predictive uncertainty and the motion variability. However, similarly to standard Gaussian processes they suffer from a cubic complexity in the number of training points, due to the inversion of the kernel matrix. The uncertainty can be leveraged for more complex learning tasks, such as inferring the variable impedance profile required from a robotic manipulator. However, suitable approximations are needed to make HGPs scalable, at the price of potentially worsening the posterior mean and variance profiles. Motivated by these observations, we study the combination of HGPs and random features, which are a popular, data-independent approximation strategy of kernel functions. In a theoretical analysis, we provide novel guarantees on the approximation error of the HGP posterior due to random features. Moreover, we validate this scalable motion primitive on real robot data, related to the problem of variable impedance learning. In this way, we show that random features offer a viable and theoretically sound alternative for speeding up the trajectory processing, without sacrificing accuracy.
SPAN-100 Performance of Machine Learning versus with Large-Vessel Acute Ischemic Stroke: Prediction of Clinical Outcome in Patients

BACKGROUND AND PURPOSE: Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic stroke patients (AIS). Our aim was to select the most relevant features in terms of outcome prediction on the basis of machine learning algorithms for patients with acute ischemic stroke and to compare the performance between multiple models and the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale (SPAN-100) index model. MATERIALS AND METHODS: A retrospective multicenter cohort of 1431 patients with acute ischemic stroke was subdivided into recanalized and nonrecanalized patients. Extreme Gradient Boosting machine learning models were built to predict the mRS score at 90days using clinical, imaging, combined, and best-performing features. Feature selection was performed using the relative weight and frequency of occurrence in the models. The model with the best performance was compared with the SPAN-100 index model using area under the receiver operating curve analysis. RESULTS: In 3 groups of patients, the baseline NIHSS was the most signi ﬁ cant predictor of outcome among all the parameters, with relative weights of 0.36 (cid:1) 0.69; ischemic core volume on CTP ranked as the most important imaging biomarker with relative weights of 0.29 (cid:1) 0.47. The model with the best-performing features had a better performance than the other machine learning models. The area under the curve of the model with the best-performing features was higher than SPAN-100 model and reached statistical signi ﬁ cance for the total ( P , .05) and the nonrecanalized patients ( P , .001). CONCLUSIONS: Machine learning – based feature selection can identify parameters with higher performance in outcome prediction. Machine learning models with the best-performing features, especially advanced CTP data, had superior performance of the recovery
Examining the role of female education on son preference among Nepalese women

This paper investigates the impact of female education on son preferences in Nepal. It analyzes changing trends in son preferences in children among Nepalese women from 1996 to 2022 based on data from the Nepal Demographic and Health Surveys (NDHS). Using multivariate and multilevel regression, the study explores the roles of both individual and contextual education in reducing son preference. In addition to individual female education, a novel contribution of this research is the identification of a compositional effect of education on son preference. Furthermore, the study investigates the relationship between women’s son preferences and their partner’s education to check whether the latter also influences the gender preference of women in children. The positive association between the compositional effect of higher education and lower son preference suggests the need for policymakers to prioritize female education as one of the key investments to reduce son preference. The results highlight the importance of future research on how education’s individual and compositional effects influence the sex ratio at birth. This is crucial for making informed decisions about including individual and compositional education effects in population projection models to better project future sex ratio at birth and population structure.