Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial econometric analysis of cultural tourism development quality in the Yangtze River Delta

ABSTRACT On the basis of constructing the index system of measuring cultural tourism development quality (CTDQ), this paper explores the spatiotemporal evolution of CTDQ and its influencing factors in the Yangtze River Delta of China by using quantitative methods. The results show that the CTDQ was at a medium level with a Z-shaped spatial rising trend and significant agglomeration. The CTDQ of 41 cities had significant spatial spillover effect under the interaction of multiple determinants and can be divided into four types. The findings have implications for local governments to achieve high-quality innovative development of cultural tourism and maintain tourism sustainability.
Effect of Lime, Inorganic and Organic Sources on Soil Quality and Yield and Proximate Composition of Elephant Foot Yam - Black Gram System in Alfisols

ABSTRACT A field experiment was conducted during 2015–18 to study the effect of integrated use of lime, inorganic and organic manures on soil health and yield performance of elephant foot yam (Amorphophallus paenifolius L) – black gram (Vigna mungo L) system in an acid Alfisol of Eastern India. Significantly highest mean corm yield of elephant foot yam (22.72 t ha−1), grain and haulm yields of black gram (7.33 and 23.87 q ha−1, respectively) were recorded due to integrated application of lime + FYM + ½ NPK + MgSO4. Graded doses of NPK showed a corm yield response of 29, 70 and 105% in elephant foot yam due to addition of 50, 100, and 150% of NPK over control, respectively. Among the organic sources, incorporation of vermicompost showed the highest corm yield (13.92 t ha−1) with a yield response of 46% over control at par with neem cake (13.60 t ha−1). The bio-chemical constituents in the corms of elephant foot yam were found highest due to integrated use of lime + FYM + ½ NPK + Borax. The soil fungi and actinomycetes were dominant in the soils and are involved in nutrient transformations. Highest dehydrogenase activity (0.521 µg TPF hr−1 g−1), fluorescein diacetate hydrolysis assay (0.709 µg TPF hr−1 g−1), and urease activity (142 µg NH4-N g−1 hr−1) were observed due to integrated application of lime + FYM + ½ NPK + MgSO4. Integrated application of lime, organic manure and half of the recommended doses of NPK in combination with secondary/micro nutrients not only sustains the soil quality but also enhanced the productivity of elephant foot yam – black gram cropping system in Alfisols.
Composition of particulate matter during a wildfire smoke episode in an urban area

Abstract The composition of wildfire smoke particulate matter (PM) was investigated during a 2018 smoke episode in the San Francisco Bay Area and compared to non-wildfire periods. Scanning electron microscopy (SEM) of passive PM samples deployed at four sites exhibited a higher concentration of submicron, spherical, carbonaceous PM (“tar balls”) during the wildfire period compared to the non-wildfire period. Coarse ash aluminosilicates and potassium bearing particles were enhanced as well. Disproportionately more UV light was absorbed by PM during the wildfire period than during the non-wildfire period, indicating a higher organic content consistent with the observed tar balls. An indoor PM sample from a residence operating a medium-efficiency furnace filter during the smoke episode exhibited fine, carbonaceous PM comparable to outdoors, but reduced coarse ash PM. Indoor/outdoor ratios for PM2.5, PM10-2.5, and PM10 were 0.6, 0.1, and 0.5 respectively. SEM of the wildfire furnace filter revealed a greater proportion of tar balls compared to a non-wildfire filter. Relatively rare particles enriched in Cu, Zn, Sn and Pb were observed in wildfire samples, but not in non-wildfire samples. Higher concentrations of Cu, Zn, and Pb during the wildfire were confirmed by inductively coupled plasma mass spectrometry (ICP-MS) of the furnace filter. The use of low-cost passive samplers enabled rapid deployment at multiple locations and indoors and outdoors during the wildfire episode. These measurements suggest smoke impacts were regional in scale and similar across sites, though local variations in PM2.5 magnitudes, particle types, and confounding by local sources were observed. Copyright © 2021 American Association for Aerosol Research
Open science reforms: Strengths, challenges, and future directions

Abstract In this article, I comment on the potential benefits and limitations of open science reforms for improving the transparency and accountability of research, and enhancing the credibility of research findings within communities of policy and practice. Specifically, I discuss the role of replication and reproducibility of research in promoting better quality studies, the identification of generalizable principles, and relevance for practitioners and policymakers. Second, I suggest that greater attention to theory might contribute to the impact of open science practices, and discuss ways in which theory has implications for sampling, measurement, and research design. Ambiguities concerning the aims of preregistration and registered reports also are highlighted. In conclusion, I discuss structural roadblocks to open science reform and reflect on the relevance of these reforms for educational psychology.
Rule-Violation in Trempealeau National Wildlife Refuge is Predicted by Environmental Characteristics

ABSTRACT Outdoor recreation in natural areas is a popular activity among people of various backgrounds. However, how recreationists use natural spaces has implications on the well-being of life in these spaces. Nonconformist behaviors, or actions that go against the rules or expectations of an area, are of special interest due to the higher potential for these types of behaviors to have harmful impacts on the ecosystem. Understanding the environmental characteristics that are predictive of nonconformist behavior can be especially valuable to land managers interested in curbing such behaviors in a targeted way. In this study, Trempealeau National Wildlife Refuge in Wisconsin was surveyed with the objective of locating signs of nonconformist behaviors. Such instances were recorded using a GPS unit and analyzed using MaxEnt software to determine the extent to which environmental predictors influenced where these behaviors occurred, and to predict where in the refuge nonconformist behaviors are likely to occur in the future. Close proximity to trails and parking lots, along with the elevation in a given area, proved to be the factors that had the greatest influence on locations of nonconformist behavior. By determining the factors most associated with where people break refuge rules, these results can contribute to proper management strategies at Trempealeau National Wildlife Refuge and other wildlife refuges.
Discovery of potential novel microsomal triglyceride transfer protein inhibitors via virtual screening of pharmacophore modelling and molecular docking

Abstract In order to identify potential natural inhibitors against the microsomal triglyceride transfer protein (MTP), HipHop models were generated using 20 known inhibitors from the Binding Database. Using evaluation indicators, the best hypothesis model, Hypo1, was selected and utilised to screen the Traditional Chinese Medicine Database, which resulted in a hit list of 58 drug-like compounds. A homology model of MTP was built by MODELLER and was minimised by CHARMm force field. It was then validated by Ramachandran plot and Verify-3D so as to obtain a stable structure, which was further used to refine the 58 hits using molecular docking studies. Then, five compounds with higher docking scores which satisfied the docking requirements were discovered. Among them, Ginkgetin and Dauricine were most likely to be candidates that exhibition inhibiting effect on MTP. The screening strategy in this study is relatively new to the discovery of MTP inhibitors in medicinal chemistry. Moreover, it is important to note that, lomitapide, an approved MTP inhibitor, fits well with Hypo1 as well as our homology model of MTP, which confirmed the rationality of our studies. The results indicated the applicability of molecular modeling for the discovery of potential natural MTP inhibitors from traditional Chinese herbs.
Effects of solar noise on the detection range performance of a laser spot tracker

Abstract. The effects of solar spectral irradiance on the performance of laser spot tracker are analyzed. When the entrance pupil diameter of the receiver increases, the field of view decreases, but the detection range increases. If the angle between the receiver and transmitter and target increases from 0 deg to 60 deg, the detection range decreases by approximately 23%. Due to solar noise’s effects caused by solar spectral irradiance, the detection range decreases by approximately 33% during the day, more than during the night, and the laser spot tracker’s altitude decreases by approximately 13% at 2000 m, more than at 0 m. When the solar intensity’s variation increases from 1% to 5%, the detection range decreases by 54%.
The Ties That Bind: Do Brand Attachment and Brand Passion Translate Into Consumer Purchase Intention?

Abstract Purpose: Drawing from attachment theory and categorization theory, the present study aims to investigate the effects of brand attachment and brand passion on consumer purchase intention, and to explore the moderation effect of product involvement (i.e.a low-involvement convenience product vs. a high-involvement shopping product) in these relationships. Design/methodology/approach: To bridge this gap, we recruited n = 205 young consumers to test the hypotheses using AMOS 24.0 and SPSS 24.0. Exploratory factor analysis, confirmatory factor analysis, structural equation modeling, and moderation analysis techniques were used as data analysis methods. Findings: Results show that when brand attachment and brand passion were assessed, the brand passion has the highest effect on purchase intention. Moreover, our data reveal that brand attachment is more likely to lead to consumer purchase intention for convenience products, while brand passion is more promising for increasing consumer purchase intention for high-involve ment shopping products. Finally, we provide a detailed discussion of how these results can be applied to both research and practice. Implications: This study offers recommendations for how practitioners can strengthen purchase intentions of convenience and shopping brands in emerging markets. Originality/value: This study is the first to prove that brand attachment is a driver of purchase intention of low-involvement convenience brands, whereas brand passion is a more prominent predictor of the purchase intention of high-involvement shopping brands.
Molecular Identification and Antifungal Susceptibility Patterns of Dermatophytes Isolated from Companion Animals with Clinical Symptoms of Dermatophytosis

Abstract Introduction Dermatophytosis is a common skin disease in cats and dogs caused by Microsporum and Trichophyton fungi. Species identification and knowledge of their antifungal susceptibility are therapeutically and epidemiologically important. This study assessed the prevalence of feline and canine dermatophytosis in Iran, identified the aetiological agents molecularly and tested their antifungal susceptibility. Material and Methods A total of 308 companion animals (134 dogs and 174 cats) with skin lesions were examined from March 2015 to March 2018. Hair and skin samples were examined by microscopy with 20% KOH and cultured on Sabouraud dextrose agar with cycloheximide and chloramphenicol. Fungal isolates were confirmed by sequencing of the internal transcribed spacer (ITS) r-DNA region. The antifungal susceptibility of dermatophytes was tested by broth microdilution assay using standard drugs. Results Dermatophytes were found in 130 (42.2%) samples, 62 of them feline and 68 canine. Based on sequencing of all strains, M. canis (78.5%, P<0.05), M. gypseum (10.7%), and T. mentagrophytes (10.7%) were the dermatophytes isolated. The non-dermatophyte species Nannizziopsis vriesii was also isolated from two feline dermatomycosis cases. Dogs and cats younger than one year (61.5%) showed a statistically significantly higher prevalence of infection (P<0.05). Caspofungin produced the lowest geometric mean MIC at 0.0018 μg/mL, followed by ketoconazole, terbinafine, itraconazole, miconazole, griseofulvin, clotrimazole and fluconazole, in a 0.038–1.53 μg/mL range. Conclusion This is the first molecular study to identify the causes of pet dermatophytosis in north-western Iran. ITS-PCR was shown to be a useful and reliable method for the identification of closely related species of dermatophytes in clinical and epidemiological settings. The lowest MIC of caspofungin indicated that this drug was the most potent in vitro.
Investigation of the Prevalence of Mycoplasma Ovipneumoniae in Southern Xinjiang, China

Abstract Introduction It is very important to monitor the infection of Mycoplasma ovipneumoniae as a potential threat to the sheep industry. Southern Xinjiang is a major sheep breeding base in China, however, there is no relevant information concerning the infection of the region’s ovine stock with this bacteria at present. This study aimed to address this knowledge gap. Material and Methods A total of 824 nasal swabs and the lungs of six sheep that died of pneumonia were collected in four regions between 2018 and 2020. Primers specific for M. ovipneumoniae and universal ones for the genus were used for PCR. Sequencing was undertaken of 159 universal primer-positive samples (153 nasal swabs and 6 lungs) and of 84 specific primer-positive samples (80 nasal swabs, 20 per region; and 4 lungs, 1 per region). The lungs were also sampled for the isolation of M. ovipneumoniae. A phylogenetic tree based on partial sequences of the Mycoplasma 16S rRNA gene was built. Results The overall nasal swab positive rate for M. ovipneumoniae was 40.78%; the rate of animals older than 12 months was significantly different to those of younger sheep (< 3 months, 53.39%; 3 – 12 months, 46.01%; >12 months, 31.76%). Four strains of M. ovipneumoniae were isolated from six lungs. Phylogenetic analysis indicated their origin outside southern Xinjiang. Two other species were also detected: M. arginine and M. conjunctivae. Conclusion Our survey indicated that a high level of M. ovipneumoniae asymptomatic colonisation in sheep, especially in lambs, affects southern Xinjiang and also confirmed the existence of M. conjunctivae and M. arginine. Our results showed that the health of sheep in southern Xinjiang is facing a great threat, and relevant prevention and control measures should be strengthened.
Detection of COVID-19 in chest X-ray images using transfer learning with deep convolutional neural network

Over the years, Computer-Aided Diagnosis (CAD) systems have been proving their effectiveness in classifying many pathologies. With the advent of the COVID-19 pandemic, new systems were developed quickly. The chest radiography is one of the least expensive among the imaging exams that assist in the detection of COVID-19. Despite not having high sensitivity for pattern detection compared to other tests - such as ground-glass opacities in computed tomography - this test helps screen infected patients. Therefore, in this work, we propose a methodology for detecting COVID-19 in chest radiography considering three possible scenarios: the healthy, presence of COVID-19, and presence of other pathologies. We developed the methodology by evaluating transfer learning techniques in five well know pre-trained Convolutional Neural Networks (CNNs) architectures. For training CNNs, we used 1,932 healthy images, 3,651 of other pathologies, and 1,436 images related to the presence of COVID-19. We obtained an accuracy of 94.36% in the scenario COVID-19 vs. healthy, 99.80% for COVID-19 vs. others pathologies, and 95.01% differentiating in three classes. The results are considered promising when compared to state of the art since the database used in this work has the largest number of examples for the class COVID-19.
"I can show what I really like.": Eliciting Preferences via Quadratic Voting

Surveys are a common instrument to gauge self-reported opinions from the crowd for scholars in the CSCW community, the social sciences, and many other research areas. Researchers often use surveys to prioritize a subset of given options when there are resource constraints. Over the past century, researchers have developed a wide range of surveying techniques, including one of the most popular instruments, the Likert ordinal scale, to elicit individual preferences. However, the challenge to elicit accurate and rich self-reported responses with surveys in a resource-constrained context still persists today. In this study, we examine Quadratic Voting (QV), a voting mechanism powered by the affordances of a modern computer and straddles ratings and rankings approaches, as an alternative online survey technique. We argue that QV could elicit more accurate self-reported responses compared to the Likert scale when the goal is to understand relative preferences under resource constraints. We conducted two randomized controlled experiments on Amazon Mechanical Turk, one in the context of public opinion polling and the other in a human-computer interaction user study. Based on our Bayesian analysis results, a QV survey with a sufficient amount of voice credits, aligned significantly closer to participants' incentive-compatible behaviors than a Likert scale survey, with a medium to high effect size. In addition, we extended QV's application scenario from typical public policy and education research to a problem setting familiar to the CSCW community: a prototypical HCI user study. Our experiment results, QV survey design, and QV interface serve as a stepping stone for CSCW researchers to further explore this surveying methodology in their studies and encourage decision-makers from other communities to consider QV as a promising alternative.
Errors in Geotargeted Display Advertising

The rise of geotargeted online advertising has disrupted the business model of local journalism, but it remains ambiguous whether online advertising platforms can effectively reach local audiences. To address this ambiguity, we present a focused study auditing the positional accuracy of geotargeted display advertisements on Google. We measure the frequency and severity of geotargeting errors by targeting display ads to random ZIP codes across the United States, collecting self-reported location information from users who click on the advertisement. We find evidence that geotargeting errors are common, but minor in terms of advertising goals. While 41% of respondents lived outside the target ZIP code, only 11% lived outside the target county, and only 2% lived outside the target state. We also present details regarding a high volume of suspicious clicks in our data, which made the cost per sample extremely expensive. The paper concludes by discussing implications for advertisers, the business of local journalism, and future research.
Named Entity Recognition and Relation Extraction

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.
Testing the tests: simulation of rankings to compare statistical significance tests in information retrieval evaluation

Null Hypothesis Significance Testing (NHST) has been recurrently employed as the reference framework to assess the difference in performance between Information Retrieval (IR) systems. IR practitioners customarily apply significance tests, such as the t-test, the Wilcoxon Signed Rank test, the Permutation test, the Sign test or the Bootstrap test. However, the question of which of these tests is the most reliable in IR experimentation is still controversial. Different authors have tried to shed light on this issue, but their conclusions are not in agreement. In this paper, we present a new methodology for assessing the behavior of significance tests in typical ranking tasks. Our method creates models from the search systems and uses those models to simulate different inputs to the significance tests. With such an approach, we can control the experimental conditions and run experiments with full knowledge about the truth or falseness of the null hypothesis. Following our methodology, we computed a series of simulations that estimate the proportion of Type I and Type II errors made by different tests. Results conclusively suggest that the Wilcoxon test is the most reliable test and, thus, IR practitioners should adopt it as the reference tool to assess differences between IR systems.
Location Privacy-preserving Mechanisms in Location-based Services

Location-based services (LBSs) provide enhanced functionality and convenience of ubiquitous computing, but they open up new vulnerabilities that can be utilized to violate the users’ privacy. The leakage of private location data in the LBS context has drawn significant attention from academics and industry due to its importance, leading to numerous research efforts aiming to confront the related challenges. However, to the best of our knowledge, none of relevant studies have performed a qualitative and quantitative comparison and analysis of the complex topic of designing countermeasures and discussed the viability of their use with different kinds of services and the potential elements that could be deployed to meet new challenges. Accordingly, the purpose of this survey is to examine the privacy-preserving techniques in LBSs. We categorize and provide an inside-out review of the existing techniques. Performing a retrospective analysis of several typical studies in each category, we summarize their basic principles and recent advances. Additionally, we highlight the use of privacy-preserving techniques in LBSs for enabling new research opportunities. Providing an up-to-date and comprehensive overview of existing studies, this survey may further stimulate new research efforts into this promising field.