Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Capital, Political Efficacy and Chinese Residents’ Willingness to Participate in Food Safety Governance

Residents play an important role as one of the main actors in food safety governance. To build a pattern of food safety risk co-governance, the positive and effective participation of residents is vital. This study first establishes a comprehensive analysis framework combining social capital theory and political efficacy theory. Data from a survey of 714 residents in Shandong Province, China, were analysed through structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) to examine the causal relationship between residents’ willingness to participate and its driving factors. The results indicated that: (1) reciprocity norm, institutional trust and social engagement have significant positive effects on willingness to participate; (2) political efficacy has a partial mediating effect in the relationship between social capital and willingness to participate; (3) fsQCA findings have four solutions to achieving residents’ strong willingness to participate; reciprocity norm, institutional trust and political efficacy are the core elements that affected residents’ high willingness to participate, whereas social engagement and sociodemographic variables are the non-core variables. Therefore, we put forward suggestions for improving residents’ willingness to participate in food safety governance, including improving the appeal expression and feedback mechanism, cultivating residents’ social capital and paying attention to the superposition effect of social capital and political efficacy.
Chiral Random Matrix Theory and Chiral Perturbation Theory

Spontaneous breaking of chiral symmetry in QCD has traditionally been inferred indirectly through low-energy theorems and comparison with experiments. Thanks to the understanding of an unexpected connection between chiral Random Matrix Theory and chiral Perturbation Theory, the spontaneous breaking of chiral symmetry in QCD can now be shown unequivocally from first principles and lattice simulations. In these lectures I give an introduction to the subject, starting with an elementary discussion of spontaneous breaking of global symmetries.
The Magnetic Field Analytical Calculation of Permanent Magnet Vernier Synchronous Machine With Saddle-Shaped Permanent Magnets

This article proposes a novel permanent magnet Vernier synchronous machine (PMVSM) with saddle-shaped PMs. The torque of the PMVSM with saddle-shaped PMs is increased by 35.4% and 45.6% compared with the PMVSM with V-shaped PMs and tile-shaped PMs, respectively. The torque ripple of the PMVSM with saddle-shaped PMs is reduced by 0.1% and 1.7% compared with the PMVSM with V-shaped PMs and tile-shaped PMs, respectively. To facilitate the analysis, the saddle-shaped PMs are equivalently transformed to the fan-shaped PMs, which ensures that the pole arc angles and air-gap magnetic flux are unchanged. The rotor flux saturation region is modeled to improve the analytical calculation accuracy. The Laplace and Poisson equations are established for each part of the machine, the general solutions of the respective parts are solved, and the boundary conditions and intersection conditions are utilized to solve the factors. Compared with the finite-element method (FEM) results, the torque difference is $0.32~ \mathrm {N}\cdot \mathrm {m}$ , and the torque ripple difference is 0.2%. The correctness of the analytical calculation is verified by comparing with the FEM results. The auxiliary slot angle and slot depth can be optimized, resulting in a higher torque and a lower torque ripple.
Synthesis of high-resolution fundus fluorescein angiography images using generative adversarial networks and image stitching

Although fundus fluorescein angiography is an imaging modality that supports ophthalmic diagnosis, it requires the intravenous injection of harmful fluorescein dye. We propose the synthesis of fluorescein angiography images from fundus structure images to avoid injection. Specifically, we automatically synthesize high-resolution fundus fluorescein angiography images through an algorithm that integrates a generative adversarial networks and image stitching and enhancement. By evaluating the peak signal-to-noise ratio and structural similarity index of the proposed algorithm, pix2pix, and cycleGAN, we confirmed the superior performance of our proposal. To further validate the proposed algorithm, we compared the fundus fluorescein angiography images synthesized by our algorithm, pix2pix, and cycleGAN. The experimental results show that our algorithm provides the highest resolution and quality in the synthesis of fluorescein angiography images from fundus structure images among the evaluated methods.
Distributed Adaptive Nonlinear Control with Fusion Least-Squares

This paper is concerned with the adaptive consensus tracking control problem for strict-feedback nonlinear multiagent systems with parameter uncertainty under both fixed and switching topologies. When the topology is fixed, we first propose a novel distributed fusion leastsquares algorithm without regressor filtering, which has a clear advantage that the estimate of each agent converges to the true parameter value under a weak cooperative persistent excitation condition. Then, we design a new adaptive consensus tracking control law to guarantee that each agent can asymptotically track the reference trajectory. After that, we generalize the corresponding results to the switching topology case. Finally, two examples are given to demonstrate the theoretical results.
Hyperspectral Image Denoising With Weighted Nonlocal Low-Rank Model and Adaptive Total Variation Regularization

Hyperspectral image (HSI) is always corrupted by various types of noises during image capturing, such as Gaussian noise, stripe noise, deadline noise, impulse noise, and more. Such complicated noise significantly degrades imaging quality and thus limits the performance of downstream vision tasks. Current HSI denoising methods tackle this problem by modeling either the spectral–spatial prior of HSI or the noise characteristic of HSI, and few works consider the two aspects simultaneously. In this article, we propose a new HSI denoising method by simultaneously modeling the HSI prior and the HSI noise characteristic. Specifically, we first utilize the nonindependent and identically distributed (non-i.i.d.) mixture of Gaussian (MoG) assumptions to characterize the complex noise, which corresponds to optimizing a weighted fidelity function. Second, we exploit HSI’s nonlocal similarity and spatial–spectral correlation priors by applying a nonlocal low-rank model. Third, we design an adaptive edge-preserving total variation (TV) regularization term to characterize the nonlocal smooth property of HSI. Finally, we propose a new denoising model and develop an effective alternating direction method of multipliers (ADMM) algorithm to solve it. Extensive experiments on simulated data and real data substantiate the superiority of the proposed method beyond state-of-the-art.
Modelling of perforation process of wellbore pipes of geological wells using shaped charges

The paper shows the results of computer simulation of holes punching in steel pipes of oil-gas wells using cumulative charges. The holes make possible quick re-circulation of an exploited medium and renewal of oil-field development. Because of the conditions in a well (large depth, high pressure), limited place for the charges, and requirement of punching of the holes with precisely determined diameter and depth, specially designed charges must be applied. They have to generate a punching element (puncher) that is something between shaped charge jet and explosively formed projectile (EFP). Analyses of punching abilities of such charges (especially in a complex system of wellbore pipes and layers of sand, borehole fluid, and air filling and separating them) are possible only when the methods of computer physics are applied. The results of computer simulation based on a new version of free particle method [11, 14] are given and their comparison with experimental results is presented. New version of a free particle method makes possible computer simulation of the problem with very complex initial-boundary conditions.
Market orientation, learning orientation, and innovation capabilities in SMEs: Case study from Iran's SMEs

Increasing market changes has led to a need for increased innovation and, therefore, programs of innovation in SMEs. The interrelationships among a firm’s market-orientation, learning-orientation, and innovativeness are an important research area for investigators in the literature of management, strategy, and marketing. However, most of the empirical studies were conducted in large-scale firms in developed countries and ignored small and medium-sized business (SMEs) in general, and in developing countries in particular. This paper examine the nomological relations among market-orientation, learning-orientation and innovativeness in medium-sized business (SMEs) of developing countries. The study involves a questionnaire-based survey of managers from small-sized-firms operating in Iran. These were subjected to a structural equation modeling (SEM) analysis. The results show that; firm learning-orientation positively influences firm innovativeness; firm market-orientation positively impacts firm learning orientation; firm learning-orientation mediates the relationship between firm market-orientation and firm innovativeness.
The formulae-as-types notion of construction

The ultimate goal was to develop a notion of construction suitable for the interpretation of intuitionistic mathematics. The notion of construction developed in the notes is certainly too crude for that, so the use of the word construction is not very appropriate. However, the terminology has been kept in order to preserve the original title and also to preserve the character of the notes. The title has a second defect; namely, a type should be regarded as a abstract object whereas a formula is the name of a type.
No-hair theorems for black holes in the Abelian Higgs model

Motivated by the study of holographic superconductors, we generalize no-hair theorems for minimally coupled scalar fields charged under an Abelian gauge field, in arbitrary dimensions and with arbitrary horizon topology. We first present a straightforward generalization of no-hair theorems for neutral scalar hair. We then consider the existence of extremal black holes with scalar hair, and in the case of horizons with zero or positive curvature, provide a bound on the mass and charge of the scalar field that are necessary for the scalar hair to develop.
ALMA OBSERVATION OF 158 μm [C ii] LINE AND DUST CONTINUUM OF A Z = 7 NORMALLY STAR-FORMING GALAXY IN THE EPOCH OF REIONIZATION

We present ALMA observations of the [C ii] line and far-infrared (FIR) continuum of a normally star-forming galaxy in the reionization epoch, the z = 6.96 Lyα emitter (LAE) IOK-1. Probing to sensitivities of σline = 240 μJy beam−1 (40 km s−1 channel) and σcont = 21 μJy beam−1, we found the galaxy undetected in both [C ii] and continuum. Comparison of ultraviolet (UV)–FIR spectral energy distribution (SED) of IOK-1, including our ALMA limit, with those of several types of local galaxies (including the effects of the cosmic microwave background, CMB, on the FIR continuum) suggests that IOK-1 is similar to local dwarf/irregular galaxies in SED shape rather than highly dusty/obscured galaxies. Moreover, our 3σ FIR continuum limit, corrected for CMB effects, implies intrinsic dust mass Mdust < 6.4 × 107 M☉, FIR luminosity LFIR < 3.7 × 1010 L☉ (42.5–122.5 μm), total IR luminosity LIR < 5.7 × 1010 L☉ (8–1000 μm), and dust-obscured star formation rate (SFR) < 10 M☉ yr−1, if we assume that IOK-1 has a dust temperature and emissivity index typical of local dwarf galaxies. This SFR is 2.4 times lower than one estimated from the UV continuum, suggesting that <29% of the star formation is obscured by dust. Meanwhile, our 3σ [C ii] flux limit translates into [C ii] luminosity, L[C ii] < 3.4 × 107 L☉. Locations of IOK-1 and previously observed LAEs on the L[C ii] versus SFR and L[C ii]/LFIR versus LFIR diagrams imply that LAEs in the reionization epoch have significantly lower gas and dust enrichment than AGN-powered systems and starbursts at similar/lower redshifts, as well as local star-forming galaxies.
Impact of incomplete planned experiences on tourist satisfaction: a case of Tokyo Disneyland

ABSTRACT This study aims to explore the background factors that affect the relationship between IPE, which are tourism experiences caused by unexpected circumstances, and the evaluation of tourism experiences from the perspective of the customer journey. Qualitative research using customer journey mapping was conducted to analyze factors that influence the relationship between IPE and tourist satisfaction. The results suggest that the factors of “assumption of behavior on the day of visit,” “assumption of satisfaction on the day of visit,” and “assumption of the timing of the next visit” with respect to the pre-visit stage and “actions to implement the plan” and “actions to change plans” with respect to behavior on the day of visit may be background factors that lead to satisfaction with IPE. This study exploratively analyzes and clarifies the impact and background of tourists’ IPE on their evaluation of tourism experiences, which has not been previously examined.
The Effect of Body Fat Distribution on Systemic Sclerosis

Obesity contributes to a chronic proinflammatory state, which is a known risk factor to develop immune-mediated diseases. However, its role in systemic sclerosis (SSc) remains to be elucidated. Therefore, we conducted a two-sample mendelian randomization (2SMR) study to analyze the effect of three body fat distribution parameters in SSc. As instrumental variables, we used the allele effects described for single nucleotide polymorphisms (SNPs) in different genome-wide association studies (GWAS) for SSc, body mass index (BMI), waist-to-hip ratio (WHR) and WHR adjusted for BMI (WHRadjBMI). We performed local (pHESS) and genome-wide (LDSC) genetic correlation analyses between each of the traits and SSc and we applied several Mendelian randomization (MR) methods (i.e., random effects inverse-variance weight, MR-Egger regression, MR pleiotropy residual sum and outlier method and a multivariable model). Our results show no genetic correlation or causal relationship between any of these traits and SSc. Nevertheless, we observed a negative causal association between WHRadjBMI and SSc, which might be due to the effect of gastrointestinal complications suffered by the majority of SSc patients. In conclusion, reverse causality might be an especially difficult confounding factor to define the effect of obesity in the onset of SSc.
Time to tell a different story? Positive and negative follower perceptions of their leaders' storytelling

PurposeDefying conventional wisdom, leaders' storytelling may have ambiguous and even negative effects on followers. Build upon transformational leadership and leader-member exchange theory, the intention-perception model of storytelling in leadership provides a framework to systematically explain when and why unintended effects of storytelling happen. Despite its theoretical and practical relevance, a quantitative evaluation of the model's main assumptions, and particularly of followers' perceptions of leader storytelling, is still pending.Design/methodology/approachThe authors developed a scale to assess followers' story perceptions in study 1 (N = 79) and confirmed the scale's structure in study 2, an online cross-sectional field study (N = 60). In study 2, the authors also tested a main assumption of the intention-perception model of storytelling, that is, the relationship between followers' story perceptions and story effects, mediated via followers' affective arousal.FindingsData revealed that story effects indeed depend on followers' perception of the story. In particular, the better a leader's story met followers' needs (need-supply fit), the more adequate the input load transported by the story (story load), and the more positive followers' appraisal of their leader's story (story appraisal), the more positive affective reactions and positive effects of storytelling (transformation, leader-member exchange quality, and trust in the leader) followers perceived.Practical implicationsThe authors provide practical insights on how leaders may improve their storytelling by tailoring their stories to the expectations and needs of their followers.Originality/valueTaking a follower-centric perspective on a common leadership practice (i.e. storytelling), the present research provides first support for the intention-perception model of storytelling in leadership.
Design of Tritium Collecting System from LiPb and LiPb Dropping Experiment

Abstract In the design of the fusion blanket, it is important to gather generated tritium as quick as possible and supply them to a fuel supply system for keeping fuel cycle and reducing tritium inventories in the fusion reactor at the same time. In the advanced blanket concept which uses Lithium-Lead (LiPb) as the working fluid for heat removal, neutron shielding and tritium breeding, collection of generated tritium is thought not to be difficult as the solubility of hydrogen into the LiPb is small enough. But examination and design of these collecting systems was not fully studied. In this paper, we made the conceptual design of the tritium collecting device using vacuum sieve tray, and studied formation process of LiPb droplets by making a simple experimental device. It was found that droplets of about 0.9-mm radius were formed at 8~12-cm distance from nozzle when LiPb discharges from the nozzle with 1-mm diameter hole at pressure of 2.5×104 Pa. Using this value, it is estimated that the tritium collecting efficiency of 45% can be achieved with 1-m height single stage sieve tray at temperature of 500 °C.
MID-INFRARED PAH AND H 2 EMISSION AS A PROBE OF PHYSICAL CONDITIONS IN EXTREME PDRS

Mid-infrared (IR) observations of polycyclic aromatic hydrocarbons (PAHs) and molecular hydrogen emission are a potentially powerful tool to derive physical prop erties of dense environments irradiated by intense UV fields. We present new, spatially resolved, Spitzer mid-IR spectroscopy of the high UV-field and dense photodissocation region (PDR) around Monoceros R2, the closest ultracompact Hii region, revealing the spatial structure of ionized gas, PAHs and H2 emissions. Using a PDR model and PAH emission feature fitting algorithm, we build a comprehensive picture of the physical conditions prevailing in the region. We show that the combination of the measurement of PAH ionization fraction and of the ratio between the H2 0-0 S(3) and S(2) line intensities, respectively at 9.7 and 12.3 µm, allows to derive the fundamental parameters driving the PDR: temperature, density and UV radiation field when they fall in the ranges T = 250− 1500 K, nH = 10 4 − 10 6 cm −3 , G0 = 10 3 − 10 5 respectively. These mid-IR spectral tracers thus provide a tool to probe the similar but unresolved UV-illuminated surface of protoplanetary disks or the nuclei of starburst galaxies. Subject headings:ISM: lines and bands — ISM: molecules — infrared: ISM
Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.
MCF-Net: Fusion Network of Facial and Scene Features for Expression Recognition in the Wild

Nowadays, the facial expression recognition (FER) task has transitioned from a laboratory-controlled scenario to in-the-wild conditions. However, recognizing facial expressions in the wild is challenging due to factors such as variant backgrounds, low-quality facial images, and the subjectiveness of annotators. Therefore, deep neural networks have increasingly been leveraged to learn discriminative representations for FER. In this work, we propose the Multi-cues Fusion Net (MCF-Net), a novel deep learning model with a two-stream structure for FER. Our model first proposes a two-stream coding network to extract face and scene representations. Then, an adaptive fusion module is employed to fuse the two different representations for final recognition. In the face coding stream, a Sparse Mask Attention Learning (SMAL) module is utilized to adaptively generate the corresponding sparse face mask according to the input image. Meanwhile, we employ a Multi-scale Attention (MSA) module for extracting fine-grained feature subsets, which can obtain richer multi-scale interaction information. In the scene coding stream, a Relational Attention (RA) module is applied to construct the hidden relationship between the face and contextual features of non-facial regions by capturing the pairwise similarity. In order to verify the effectiveness and accuracy of our model, a large number of experiments are carried out on two public large-scale static facial expression image datasets, CAER-S and NCAER-S. By comparing the performance of our MCF-Net with other methods, the proposed model achieves superior results on two in-the-wild FER benchmarks: CAER-S with an accuracy of 81.82% and NCAER-S with an accuracy of 45.59%.
Pixel-Level and Feature-Level Domain Adaptation for Heterogeneous SAR Target Recognition

The performance of synthetic aperture radar (SAR) target recognition has been substantially enhanced by deep learning technology. It is still difficult to get strong recognition performance due to the distribution differences across heterogeneous SAR images. To enhance target recognition performance in heterogeneous SAR situations, a pixel-level and feature-level domain adaptation (PFDA) approach is proposed in this letter to deal with this problem. The pixel-level translation module in the first step generates images with high visual similarity with other distributed images from one distributed image. The second step involves introducing feature alignment into the network to lower the probability distribution divergence of heterologous SAR images in the feature space and enhance recognition performance. We evaluated our method on the synthetic and measured paired labeled experiment (SAMPLE) dataset and a self-built airplane dataset to ensure its effectiveness. Compared to existing domain adaption approaches, experimental results reveal that our strategy greatly improves recognition performance and model stability for heterogeneous SAR target recognition.