Miracle-dz/UniFAR-FTUs huggingface dataset:
Property Alignment of Linked Data Based on Similarity between Functions

Owing to the complex structure and multi-meaning, property alignment is generally regarded as a challenging problem in the context of linked data. In this paper, we propose a novel method to align properties between datasets of linked data. Considering the role of properties in RDF triples, we regard all properties of linked data as property functions, and convert the problem of property alignment to the similarity evaluation between property functions, while the equivalent instances as inputs of property functions. Based on the similarity of property functions, the property alignment process of linked data is introduced. In order to prove the validity, we use the method to align properties in five representative domains between DBpeida and YAGO, DBpedia and LinkedGeoData respectively. The experimental results show that our method is independent of the property naming rules and can retrieve some matching properties ignored by other methods. In addition, our method requires fewer entity co-reference links than the link statistical approach.
Re-Examining the Reliability and Validity of the SWPBIS Tiered Fidelity Inventory

School-wide positive behavior interventions and supports (SWPBIS) is widely implemented across the United States. Evidence suggests that SWPBIS, when implemented with fidelity, has a significant and positive impact on schools, including lower suspensions and higher reading and mathematics achievement. A critical component of this research is measuring fidelity of SWPBIS implementation in schools. In this study, we re-evaluated the reliability and validity of a widely used fidelity tool, the Tiered Fidelity Inventory (TFI), within a single-state sample. Using data from 693 schools in the U.S. state of California, we found a hierarchical factor structure based on subscales and tiers fit the data best when controlling for the multilevel nature of the data; however, there was no relation between the latent fidelity variables and changes in suspensions. Implications of these results are discussed, including different approaches to scoring and conceptualizing fidelity of implementation for SWPBIS, along with considerations for future research using the TFI.
Uncovering Resilience Disparities among Religious Groups in Israel’s Mass COVID-19 Vaccination Drive: Lessons Learned to the Post-COVID Era

The aim of the current study was to examine the emotional resilience, satisfaction with life, social support, and anxiety during the vaccination process of the Israeli population after the end of the third lockdown, according to religiosity degree. We hypothesized that a higher degree of religiosity (ultra-Orthodox and religious participants) would be associated with higher levels of resilience and with lower levels of anxiety than in secular individuals. In addition, it was hypothesized that satisfaction with life, social support, anxiety, and religiosity will predict resilience and anxiety. Nine hundred and ninety-three native Jewish Hebrew-speaking respondents representing ultra-Orthodox, religious, observant, and secular Jews participated in this study. Ultra-Orthodox participants showed higher resilience and satisfaction with life than other groups, and lower levels of anxiety. Satisfaction with life and social support predicted higher resilience. It is suggested that religious faith as well as satisfaction with life may provide a source of strength and resilience in stressful life events.
Parametric level-set inverse problems with stochastic background estimation

We study parametric shape reconstruction inverse problems in which the object of interest is embedded in a heterogeneous background medium that is known only approximately. We model the background medium as a Gaussian random field and pose shape reconstruction as a stochastic programming problem in which we seek to minimize the expected value, with respect to the background field, of a stochastic objective function. We develop a computationally efficient algorithm based on the sample average approximation that reduces the effect of uncertainty in the background medium on shape recovery. We demonstrate that by using accelerated stochastic gradient descent, we can apply our method to large-scale problems. The capabilities of our method are demonstrated on a simple two-dimensional model problem and in a more demanding application to a three-dimensional inverse conductivity problem in geophysical imaging.
Factors affecting the mainstreaming of climate change adaptation in municipal policy and practice: a systematic review

ABSTRACT Local governments have a vital climate change adaptation role. However, major breakdowns in the ability of local governments to mainstream adaptation responses have been widely observed. Using a Systematic Quantitative Literature Review method, we assessed 131 original research articles published 2005–2020 to answer three key questions: What trends are evident in the global literature that explain adaptation mainstreaming efforts in municipal policy and practice? What factors are conceptualized as shaping adaptation mainstreaming in local government? Which elements can be considered key to advancing adaptation mainstreaming in municipal policy and practice? We find two overarching factors affect outcomes – authority to adapt and capacity to adapt. Authority to adapt refers to the authorizing environment – or mandate – from national or sub-national government, or from local government leaders, for adaptation action by a municipal administration. Capacity to adapt refers to access to resources, professional networks, and supportive organizational systems and culture, that enable local government adaptation. We find lack of support from local government elected leaders is the most frequently identified barrier to municipal adaptation. Yet, few empirical studies offer deep insight into the factors that inform and influence leadership support for municipal adaptation. Further, we find local government capacity to adapt is largely treated as a singular capacity, held constant throughout the policy cycle. We find limited exploration of the capacities vital to each stage of the policy cycle and the configuration of factors that support adaptation outcomes. We devise a conceptual framework explaining how issues of authority and capacity can interact and influence each other and what they encompass. Such a framework has broader utility for policy development and importantly for implementation at the local level. Key policy insights Municipal adaptation can be strengthened through expanded understanding of, and attention to, the factors that inform local leaders’ decisions on adaptation, notably clustered around authority and capacity to adapt. Policymakers are encouraged to actively consider the differing capacities needed to progress adaptation through each stage of the policy cycle. Using an implementation lens to evaluate adaptation practice could support documentation of planning-to-implementation gaps and ways of bridging these gaps for enhanced municipal adaptation outcomes. GRAPHICAL ABSTRACT
Effect of camel milk and pistachio-based product on cancer-induced sarcopenia in Sprague Dawley rats

In the current study, we investigated the role of camel milk and pistachio in the muscle growth of rats with cancer-induced sarcopenia. Female Sprague Dawley rats weighing 180 – 200 grams were selected for a bioefficacy trial for 13 weeks and divided into four groups: G1 (negative control), G2 (positive control), G3 (normal diet + camel milk), and G4 (normal diet + camel milk and pistachio based product). Tumors were induced in rats by administering 50 mg/kg of 7,12-dimethylbenz(a)anthracene via gastrogavage followed by chemotherapy in G2, G3, and G4. Histopathological analysis, muscle strength analysis, muscle fiber histology and renal function tests were performed. The administration of camel milk and pistachio-based product (CMP) significantly (P<0.01) improved muscle strength in the treatment group. Muscle mass analysis of the extensor digitorum longus (EDL) and soleus muscle showed significant (P<0.01) improvement in the treatment group. Muscle fiber diameter also significantly (P<0.01) increased in the treatment group. Camel milk and pistachio-based product (CMP) have shown promising effects against cancer-induced sarcopenia in rat modelling by enhancing muscle function and strength.
Teeth contact behavior and load distribution of gear couplings.

The teeth contact conditions of gear couplings change with rotation during transmitting power between misaligned shafts. On this problem, tooth interference, load distribution and so on have been analyzed. The results obtained are not always correct because the analysis has been performed on the pitch circle. Then the contact behavior is investigated over whole tooth surface. Assuming the same rotational speed of hub and sleeve, the position of hub surface relative to sleeve surface is calculated at any rotated angle and the magnitude of interference is derived as the difference between both positions. By follow-up the position of minimum clearance at every rotated angle, a path of contact on a tooth surface can be obtained. Further, the number of teeth in contact and the load distribution on teeth are calculated based on above results.
Improved stepdown methods for asymptotic control of generalized error rates

This paper proposes new stepdown methods for testing multiple hypotheses and constructing confidence intervals while controlling the Familywise Error Rate and other generalized error rates. One method is a refinement of Romano and Wolf’s StepM (2005, Econometrica) that also removes inequalities that fall outside any n 1=2 -neighborhood of binding; it has the advantage that the threshold construction is incorporated into the stepdown procedure so it accounts for the number of total hypotheses (leading to better size control than some alternative methods) and excludes more nonbinding inequalities (leading to higher power). This method can also be used to test multiple inequality hypotheses simultaneously and construct confidence intervals for partially identified parameters. The paper presents methods for controlling the k-familywise error rate and the False Discovery Proportion for families of one and two-sided hypotheses as well. The paper also provides Monte Carlo evidence that the methods perform well in finite samples and demonstrates their application in an empirical analysis of hedge fund returns.
Application of Artiﬁcial Intelligence for Fraudulent Banking Operations Recognition

: This study considers the task of applying artiﬁcial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientiﬁc novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artiﬁcial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artiﬁcial intelligence algorithms is a topical issue in our digital society.
AEGAN: Generating imperceptible face synthesis via autoencoder‐based generative adversarial network

Face recognition (FR) systems based on convolutional neural networks have shown excellent performance in human face inference. However, some malicious users may exploit such powerful systems to identify others' face images disclosed by victims' social network accounts, consequently obtaining private information. To address this emerging issue, synthesizing face protection images with visual and protective effects is essential. However, existing face protection methods encounter three critical problems: poor visual effect, limited protective effect, and trade‐off between visual and protective effects. To address these challenges, we propose a novel face protection approach in this article. Specifically, we design a generative adversarial network (GAN) framework with an autoencoder (AEGAN) as the generator to synthesize the protection images. It is worth noting that we introduce an interpolation upsampling module in the decoder in order to let the synthesized protection images evade recognition by powerful convolution‐based FR systems. Furthermore, we introduce an attention module with a perceptual loss in AEGAN to enhance the visual effects of synthesized images by AEGAN. Extensive experiments have shown that AEGAN not only can maintain the comfortable visual quality of synthesized images but also prevent the recognition of commercial FR systems, including Baidu and iKLYTEK.
The Risk of Cardiovascular Disease among Male and Female Participants Treated for Gout in the Multi-Ethnic Study of Atherosclerosis (MESA)

(1) The Multi-Ethnic Study of Atherosclerosis (MESA) is a multi-center longitudinal cohort study designed to investigate the risk factors associated with the incidence of CVD. The purpose of this study is to examine the impact of gout on incident CVD. (2) Participants reporting the use of gout-specific medications (urate lowering drugs or colchicine) were compared with non-users. Kaplan–Meier survival curves and multivariable models to control for known CV risk factors evaluated hazard ratios (HR) between participants taking gout medications versus those not taking gout medications. (3) For the 6734 participants, analyses were stratified by gender owing to a gout-gender interaction. For the 164 male and 59 female participants taking gout medications, Kaplan–Meier (unadjusted) survival curves demonstrate that participants taking gout medications have higher rates of CVD than participants not taking gout medications, particularly for women. After controlling for known CV risk factors, the adjusted HR for female participants taking gout medications was 1.79 (0.99, 3.23), p = 0.05; the adjusted HR for male participants on gout medications was 1.20 (0.81, 1.77), p = 0.36; (4) Participants treated for gout in this study have many comorbid conditions with known CVD risk factors, making it difficult to confirm the independent effect of gout on CVD. There are a paucity of data on women with gout. These findings suggest that there is a clinically meaningful and potentially greater risk of CVD among women with gout as compared to men.
The Demagnetization Phenomenon in PM Machines: Principles, Modeling, and Design Considerations

Permanent Magnet (PM) machines have attracted much attention due to their high power-torque density, high efficiency, high dynamic response performance, high power factor, and simple structure. Even though there are some uncertainties about their reliable chain supply, PM machines still are the first choice for high-performance applications. Therefore, paying attention to the development and improvement of the performance of these types of electric machines is of great importance. One of the challenges of the PM machines is their vulnerability to Irreversible Demagnetization (ID), especially for applications such as Electric Vehicles (EV), in which the working temperature and Magnetic Motive Force (MMF) produced by stator winding are higher than in conventional applications. Therefore, the ID phenomenon must be regarded as a leading design and control parameter for PM machine designers. The ID can be defined as an irreversible reduction of the remnant flux density of the PM or the EMF of the PM machine due to severe temperature conditions, high external Demagnetization Field (DF), or non-proper design of PM machines, resulting in degradation in performance of the PM machines. The lack of a good source that gives a deep inside into this phenomenon in designing and developing PM machines encourages the author to conduct a comprehensive literature review. For proper design of the PM machines in terms of ID, it is necessary to consider four critical steps, i.e.: (a) accurate non-linear modelling of the PM demagnetization curve, (b) analytical model taking into account local ID, (c) geometry optimization methods targeting ID, (d) accurate experimental testing to evaluate the ID fault-tolerant capability of the manufactured PM machines. In this paper, the above four aspects are surveyed exclusively, and the advantages and disadvantages of the existing methods in these areas are highlighted. Furthermore, design tips towards the design of ID fault-tolerant PM machines and other research opportunities to enhance the reliability of PMs machines are also presented.
Computational analysis for temperature separation and correlations prediction for dual-inlet-sections vortex tube

Abstract This article intends to examine the cooling capacity enhancement for the Ranque-Hilsch vortex tube when two inlet sections are introduced compared to a single inlet section along with the optimum distance between the intake sections by computational fluid dynamics (CFD) approach. Air is taken as working fluid in a steady-state, 3-D, axisymmetric, Ranque-Hilsch vortex tube. ANSYS FLUENT software has been used to simulate flow using standard k-ɛ turbulent model. The numerical analysis is accomplished with variable intake pressure and validated with the experimental results. The numerical investigation shows that using the two inlet sections decreases the fluid temperature at the cold outlet as compared to single inlet section, but the cold and hot exit fluid temperature increases by increasing the distance between the two sections. The optimum cold outlet temperature value occurs at a dimensionless distance between intake sections of ζ/L = 0.075 and a temperature difference of 19.27 K at cold exit between single and double inlet section of vortex tube has been found from the present study. A correlation has been developed for the hot temperature gradient (ΔTh) and cold temperature gradient (ΔTc) by considering the operating parameters such as mass fraction and inlet pressure.
Study on Evaluation and Diagnosis Method for Local University Capacity in Science and Technology Innovation

The paper begins with the definition of scientific and technological innovation ability of local universities,then analyzes its diagnostic typology,and elaborates emphatically the diagnostic method-statistical analysis method based on normal distribution and have given the implementation concrete step and analysis basic mentality in view of this appraisal and the diagnosis method.It tries to provide the data analysis and the management decision-making method for studing on science and technology innovation ability construction of local universities.
The RITMARE Italian Fixed-Point Observatory Network (IFON) for marine environmental monitoring: a case study

ABSTRACT The Italian Fixed-Point Observatory Network (IFON) integrates well-established coastal and ocean infrastructures (buoys, platforms, moorings, mast platforms, etc.), most of them providing real-time multidisciplinary monitoring for a number of marine and atmospheric variables. Here, we describe the network characteristics and then discuss an example of its operation during the cold spell of winter 2012. One of the goals of the Italian Flagship Project Ricerca Italiana per il mare (RITMARE) is to create a common, validated IFON database able to fulfil both public and private demands, including validation of remotely sensed data and numerical models, environmental planning and management, and time-series analysis of climate and oceanographic data.
Genetic Divergence and Population Structure of Xanthomonas albilineans Strains Infecting Saccharum spp. Hybrid and Saccharum officinarum

Leaf scald caused by Xanthomonas albilineans (Xa) is a major bacterial disease in sugarcane that represents a threat to the global sugar industry. Little is known about the population structure and genetic evolution of this pathogen. In this study, 39 Xa strains were collected from 6 provinces in China. Of these strains, 15 and 24 were isolated from Saccharum spp. hybrid and S. officinarum plants, respectively. Based on multilocus sequence analysis (MLSA), with five housekeeping genes, these strains were clustered into two distinct phylogenetic groups (I and II). Group I included 26 strains from 2 host plants, Saccharum spp. hybrid and S. officinarum collected from 6 provinces, while Group II consisted of 13 strains from S. officinarum plants in the Zhejiang province. Among the 39 Xa strains, nucleotide sequence identities from 5 housekeeping genes were: ABC (99.6–100%), gyrB (99.3–100%), rpoD (98.4–100%), atpD (97.0–100%), and glnA (97.6–100%). These strains were clustered into six groups (A–F), based on the rep-PCR fingerprinting, using primers for ERIC2, BOX A1R, and (GTG)5. UPGMA and PCoA analyses revealed that group A had the most strains (24), followed by group C with 11 strains, while there was 1 strain each in groups B and D–F. Neutral tests showed that the Xa population in S. officinarum had a trend toward population expansion. Selection pressure analysis showed purification selection on five concatenated housekeeping genes from all tested strains. Significant genetic differentiation and infrequent gene flow were found between two Xa populations hosted in Saccharum spp. hybrids and S. officinarum. Altogether, these results provide evidence of obvious genetic divergence and population structures among Xa strains from China.
Study on the Percentage of Trend Component in a Hydrological Time Series Based on Mann-Kendall Method

The researches of trend in a hydrological series usually focus on detecting whether the trend characteristic is significant and judging whether the trend characteristic is increasing or decreasing.The paper proposes a concept about the weight of trend component in a hydrological time series which is usually decomposed as trend,period,random component and so on.The calculation formula of the weight of trend component is derived based on Mann-Kendall method.The results show that the difference of dual number p and p_0 affects directly the significant level of the trend,and the weight of trend component depends on the value of(p-p_0) and the sequence length of series.It also reveals the effects of the sequence length on the weight of trend component for a series with significant trending level.Firstly,it verifies the conclusion that at least 10 samples are necessary to conduct the trend analysis using Mann-Kendall method.Secondly,the weight decreases accordingly with the increase of the sequence length,which also indicates that whether the Mann-Kendall method is applicable for the analysis of trend characteristic should be further studied when the sequence length exceeds a certain range in a hydrological time series.The annual runoff series of Zhuqi Gauging Station in the Minjiang River Watershed from 1936 to 2007,about 72 years,has been taken as an example.The results show that the trend of the series is decreasing,but not significantly.And the weight of the trend component for the runoff series is 24.73%,which is nearly 1/4.