Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Features of Cluster Headache: A Hospital-Based Study in Taiwan

Most previous studies on cluster headache (CH) focus on Western populations. This study aimed to investigate the clinical characteristics of CH in a neurology outpatient population in Taiwan. A cross-sectional survey was conducted from July 2015 to June 2019 in a medical college affiliated with a tertiary care hospital (Tri-Service General Hospital) in Taiwan. All consecutive patients reporting headache as their chief complaint were asked to participate in a face-to-face interview with a qualified headache specialist and to complete a detailed self-administered questionnaire. The diagnosis of CH was made according to the Third edition of the International Classification of Headache Disorders. The subjects comprised 80 consecutive new CH patients (13 women and 67 men; ratio, 1:5). The mean age at presentation was 36.0 ± 10.8 years (range, 16–64 years), mean age at onset was 27.2 ± 12.1 years (range, 5–65 years), and mean time lag before diagnosis was 9.3 ± 10.5 years (range, 0–46.4 years). Of the total CH patients, 25.3% reported feelings of restlessness during headache episodes. A seasonal predilection was reported by 18% of the CH patients. The use of tobacco was the most common (44/80 patients). Chronic CH was only observed in 5% of the patients and only one patient (1.3%) reported both a positive family history for CH and aura. Features of CH in Taiwanese patients differed from that of Caucasian patients; a lower prevalence of chronic CH, positive family history of CH, and occurrence of aura may be less common in the former than in the latter.
The Role of Eye Gaze in Regulating Turn Taking in Conversations: A Systematized Review of Methods and Findings

Eye gaze plays an important role in communication but understanding of its actual function or functions and the methods used to elucidate this have varied considerably. This systematized review was undertaken to summarize both the proposed functions of eye gaze in conversations of healthy adults and the methodological approaches employed. The eligibility criteria were restricted to a healthy adult population and excluded studies that manipulated eye gaze behavior. A total of 29 articles—quantitative, qualitative and mixed methods were returned, with a wide range of methodological designs. The main areas of variability related to number of conversants, their familiarity and status, conversation topic, data collection tools—video and eye tracking—and definitions of eye gaze. The findings confirm that eye gaze facilitates turn yielding, plays a role in speech monitoring, prevents and repairs conversation breakdowns and facilitates intentional and unintentional speech interruptions. These findings were remarkably consistent given the variability in methods across the 29 articles. However, in relation to turn initiation, the results were less consistent, requiring further investigation. This review provides a starting point for future studies to make informed decisions about study methods for examining eye gaze and selecting variables of interest.
Chemical constituents of of Scrophularia ninpoensis

OBJECTIVE To study the chemical constituents of the ethyl acetate extracts of Scrophularia ninpoensis Hemsl.METHODS Compounds were isolated by ethyl acetate extract,silica gel,Sephadex LH-20 chromatographic and preparation TLC methods,and then identified by spectrum.RESUTLS Six compounds were isolated from the ethyl acetate extract of S.ninpoensis Hemsl.they were:eucalyptolic acid(Ⅰ),buergerisides A1(Ⅱ),protocatechuic acid(Ⅲ),5-hydroxymethyl-2-furaldehyde(Ⅳ),cinnamic acid(Ⅴ),β-sitosterol(Ⅵ). CONCLUSION compoundsⅠ,Ⅱ,Ⅲ are first found in S.ninpoensis Hemsl.
Roles of Different Artifacts in Enterprise Architecture Practice: An Exploratory Study

Enterprise architecture (EA) is a description of an enterprise from an integrated business and IT perspective consisting of multiple diverse documents, or artifacts. However, the existing EA literature does not offer any comprehensive theories explaining the practical usage and roles of individual EA artifacts constituting EA. To address this gap, based on five case studies of established EA practices and confirmatory interviews with ten EA experts, we develop a descriptive theory explaining the roles of different EA artifacts in an EA practice. The resulting theory articulates six general types of EA artifacts (Considerations, Designs, Landscapes, Outlines, Standards and Visions) and explains their type-specific practical roles, including their informational contents, typical usage scenarios and ensuing organizational benefits. This paper presents the first available theory describing the usage of EA artifacts in organizations and suggests that EA scholars should switch their focus from studying EA in general to studying individual EA artifacts.
More Personalized, More Useful? Reinvestigating Recommendation Mechanisms in E-Commerce

ABSTRACT To what extent should firms invest in personalized recommendation mechanisms, and are all personalized recommendations equally welcomed by online consumers? To answer these questions through the lens of resource matching theory, we investigate users’ perceptions of three types of personalized recommendations: one-to-all (nonpersonalized), one-to-many (partially personalized), and one-to-one (most personalized). Using both experimental and configurational analysis approaches, our study posits that online consumers differently experience each type of personalized recommendation and their resource matching sources (familiarity, complexity, external information) in various shopping contexts. Our study abductively formulates several theoretical propositions regarding the usefulness of each personalized recommendation. We show empirical evidence that the most personalized recommendation is not always perceived to be as useful as conventionally believed. In particular, highly personalized recommendation is found to be useful for recommending simple technology products for experienced customers. Ironically, a partially personalized recommendation, one-to-many, is perceived as the most useful mechanism for recommending complicated technology products. Based on our findings, we suggest that e-commerce vendors consider the three resource matching dimensions to avoid collecting more than enough customer data, thus enabling adequately personalized recommendation results on their online digital platforms.
Financial Incentives and Intention to Subscribe to Data-Driven Health Plans

Over the last years, health insurance companies have displayed considerable dynamism in integrating quantified-self-devices (such as smartwatches and activity trackers) in their business models to create data-driven health plans built around these systems. In order to motivate consumers to participate in these programs — and share their data — health insurance companies often make use of financial incentives. Yet, there is little evidence on the effects of discounts or rewards on individual intention to subscribe to data-driven health plans. In particular, little is known about the type of consumers for which financial incentives serve as a trigger for participation. In this paper, we thus report results from a survey made in Switzerland, which constitues a representative context of consumers’ choice in a liberal health insurance market, about consumers’ intention to participate in (incentivized) data-driven health plans. By doing so, we seek to lay the foundations for a better comprehension of individuals’ aspirations and drivers to engage into these programs.
Unsupervised DNF Blocking for Efficient Linking of Knowledge Graphs and Tables

Entity Resolution (ER) is the problem of identifying co-referent entity pairs across datasets, including knowledge graphs (KGs). ER is an important prerequisite in many applied KG search and analytics pipelines, with a typical workflow comprising two steps. In the first ’blocking’ step, entities are mapped to blocks. Blocking is necessary for preempting comparing all possible pairs of entities, as (in the second ‘similarity’ step) only entities within blocks are paired and compared, allowing for significant computational savings with a minimal loss of performance. Unfortunately, learning a blocking scheme in an unsupervised fashion is a non-trivial problem, and it has not been properly explored for heterogeneous, semi-structured datasets, such as are prevalent in industrial and Web applications. This article presents an unsupervised algorithmic pipeline for learning Disjunctive Normal Form (DNF) blocking schemes on KGs, as well as structurally heterogeneous tables that may not share a common schema. We evaluate the approach on six real-world dataset pairs, and show that it is competitive with supervised and semi-supervised baselines.
Modeling of Information Processes in Social Networks

In order to model information dissemination in social networks, a special methodology of sampling statistical data formation has been implemented. The probability distribution laws of various characteristics of personal and group accounts in four social networks are investigated. Stochastic aspects of interrelations between these indicators were analyzed. The classification of groups of social network users is proposed, and their characteristic features and main empirical regularities of mutual transitions are marked. Regression models of forecasting changes in the number of users of the selected groups have been obtained.
Enhanced adsorptive removal of anionic and cationic dyes from single or mixed dye solutions using MOF PCN-222

Metal–organic frameworks (MOFs) are attractive for the removal of industrial dyes from aqueous pollution. However, effective simultaneous removal of oppositely charged dye ions is still a challenge. We find that zirconium–metalloporphyrin mesoMOF (PCN-222/MOF-545) exhibits excellent adsorption/removal capacities for numerous anionic and cationic dyes individually and together in solution. PCN-222 is fabricated by solvothermal synthesis and characterized by powder XRD and FT-IR methods to confirm its structure. N2 adsorption/desorption indicates PCN-222 has a large pore size of 3.2 nm and a surface area of 2336 m2 g−1. The zeta potential measurement shows that PCN-222 has a slight basic isoelectric point at pH 8 with appropriate potentials of 23.5 and −13.6 mV in the range of pH 3–10. These features facilitate the dual-function of PCN-222 toward anionic and cationic dyes, or even their complexes. The results show that PCN-222 has a maximum loading efficacy of 906 mg g−1 for anionic methylene blue (MB) and 589 mg g−1 for cationic methyl orange (MO) in a single dye system. Interestingly, when MB and MO co-exist in solution, they mutually enhance the capability by 36.8% (1239 mg g−1) for MB and 73.5% (1022 mg g−1) for MO. The adsorption kinetics, isotherms, stability and reusability of PCN-222 are reported. The results reveal that no significant changes of either crystal structure or loading capacity are observed up to eight recycles during the removal of MO and MB in solution. The adsorption efficacies of eight representative dyes are presented. Compared with previously published data, the capacity of PCN-222 ranks at the top. We prepared a facile adsorption chromatography column to demonstrate the broad application potentials of PCN-222 in dye removal from aqueous pollution. Finally, we propose a push–pull mechanism to explain the mutual enhancement of adsorption in a mixture dye solution.
The Decline of User Experience in Transition from Automated Driving to Manual Driving

Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving affect user experience and how that experience correlates to take-over performance. In the study 20 participants experienced using an automated driving system during rush-hour traffic in the San Francisco Bay Area, CA, USA. The automated driving system was available in congested traffic situations and when active, the participants could engage in non-driving related activities. The participants were interviewed afterwards regarding their experience of the transitions. The findings show that most of the participants experienced the transition from automated driving to manual driving as negative. Their user experience seems to be shaped by several reasons that differ in temporality and are derived from different phases during the transition process. The results regarding correlation between participants’ experience and take-over performance are inconclusive, but some trends were identified. The study highlights the need for new design solutions that do not only improve drivers’ take-over performance, but also enhance user experience during take-over requests from automated to manual driving.
A network model that combines latent factors and sparse graphs

We propose a combined model, which integrates the latent factor model and a sparse graphical model, for network data. It is noticed that neither a latent factor model nor a sparse graphical model alone may be sufficient to capture the structure of the data. The proposed model has a latent (i.e., factor analysis) model to represent the main trends (a.k.a., factors), and a sparse graphical component that captures the remaining ad‐hoc dependence. Model selection and parameter estimation are carried out simultaneously via a penalized likelihood approach. The convexity of the objective function allows us to develop an efficient algorithm, while the penalty terms push towards low‐dimensional latent components and a sparse graphical structure. The effectiveness of our model is demonstrated via simulation studies, and the model is also applied to four real datasets: Zachary's Karate club data, Kreb's U.S. political book dataset ( http://www.orgnet.com), U.S. political blog dataset , and citation network of statisticians; showing meaningful performances in practical situations.
Exploring Decentralized Autonomous Organizations: Towards Shared Interests and 'Code is Constitution'

In recent years, scholarly interest research on blockchain technology steadily increased. While the underlying technology matures, observed problems in the field show questions of governance to remain crucial, even though scarcely studied empirically. One approach of solving these problems can be seen in decentralized autonomous organizations, which describes a new type of organizing that is grounded on consensus-based, distributed autonomy. The governance peculiarities of DAOs is fairly unexplored, and this is where this research commences. In an exploratory multiple case study consisting of three popular DAOs Aragon, Tezos, and DFINITY, their governance peculiarities are worked out by analyzing grey literature to understand stakeholder interests, incentivization, control, and coordination mechanisms, technical considerations, and external influences from off-chain entities. In the context of an on-and-off-chain continuum, it appears that DAOs provide mechanisms that might enable autonomous decision-making but, at the same time, find themselves strongly influenced by the interests of various stakeholders.
Hybrid System Combination Framework for Uyghur-Chinese Machine Translation

Both the statistical machine translation (SMT) model and neural machine translation (NMT) model are the representative models in Uyghur–Chinese machine translation tasks with their own merits. Thus, it will be a promising direction to combine the advantages of them to further improve the translation performance. In this paper, we present a hybrid framework of developing a system combination for a Uyghur–Chinese machine translation task that works in three layers to achieve better translation results. In the first layer, we construct various machine translation systems including SMT and NMT. In the second layer, the outputs of multiple systems are combined to leverage the advantage of SMT and NMT models by using a multi-source-based system combination approach and the voting-based system combination approaches. Moreover, instead of selecting an individual system’s combined outputs as the final results, we transmit the outputs of the first layer and the second layer into the final layer to make a better prediction. Experiment results on the Uyghur–Chinese translation task show that the proposed framework can significantly outperform the baseline systems in terms of both the accuracy and fluency, which achieves a better performance by 1.75 BLEU points compared with the best individual system and by 0.66 BLEU points compared with the conventional system combination methods, respectively.
Emergent Robotic Personality Traits via Agent-Based Simulation of Abstract Social Environments

This paper discusses the creation of an agent-based simulation model for interactive robotic faces, built based on data from physical human–robot interaction experiments, to explore hypotheses around how we might create emergent robotic personality traits, rather than pre-scripted ones based on programmatic rules. If an agent/robot can visually attend and behaviorally respond to social cues in its environment, and that environment varies, then idiosyncratic behavior that forms the basis of what we call a “personality” should theoretically be emergent. Here, we evaluate the stability of behavioral learning convergence in such social environments to test this idea. We conduct over 2000 separate simulations of an agent-based model in scaled-down, abstracted forms of the environment, each one representing an “experiment”, to see how different parameters interact to affect this process. Our findings suggest that there may be systematic dynamics in the learning patterns of an agent/robot in social environments, as well as significant interaction effects between the environmental setup and agent perceptual model. Furthermore, learning from deltas (Markovian approach) was more effective than only considering the current state space. We discuss the implications for HRI research, the design of interactive robotic faces, and the development of more robust theoretical frameworks of
Effect of Ozone Treatment on Postharvest Physiology and Decay of 'Dangshansu' Pear During in Cold Storage

In this study,the effect of ozone on postharvest physiology and storage diseases of ' Dangshansu' pear during cold storageC slowly cooled to 0±0.5℃) was investigated.During cold storageC slowly cooled to 0±0.5℃),the fruits were treated with 4 mg/m3 ozone for three hours every day in entire storage,the physiological indicators and decay incidence were measured regularly,In addition,its inhibitory effect on Penicillium expansum,Physalospora piricola and Monilinia fructigena(causes of Penicillium,Ring spot and Brown rot,respectively) was studied both in vitro and in vivo.It was found that ozone remarkablyinhibited respiration rate,delayed the decrease of total soluble solids(TSS),titratable acidity CTA) and inhibited the increase of malondiadehyde(MDA) content,reduced the rate of fruit decay effectively,but a little effect on fruit firmness.Furthermore,ozone-enrichment resulted in a substantial decline in spore production as well as visible lesion development in all treated.Taken as a whole, the results imply that the technology that ozone gas was used in treatmeting 'Dangshansu' pear in the cold storage not only significantly maintained higher storage quality,but also reduced the incidence of decay.
Electronic, magnetic and thermodynamic properties of magnetic shape memory alloys from first principles

Der magnetische Formgedachtnis-Effekt ist eine funktionale Eigenschaft die von wissenschaftlicher, aber auch industrieller Bedeutung ist. In magnetischen Formgedachtnis-Legierungen lassen sich durch das Anlegen moderater magnetischer Felder makroskopisch messbare Verzerrungen erzielen. Die Physik magnetischer Formgedachtnis-Materialien wird durch die Wechselwirkung magnetischer und struktureller Phasen bestimmt, da die Materialien sowohl einen ferromagnetischen als auch einen martensitischen Phasenubergang zeigen. Die Heusler-Legierung Ni2MnGa ist ein vielversprechendes magnetisches Formgedachtnis-Material, dass allerdings eine fur industrielle Anwendungen zu niedrige martensitische Ubergangstemperatur aufweist und vergleichsweise sprode ist. In der vorliegenden Arbeit wurden die bisherigen in der Literatur erwahnten Untersuchungen an Ni2MnGa durch Ab Initio Untersuchungen zum Einfluss der Beitrage von Gitterschwingungen, magnetischen Anregungen, elektronischen Korrelationen und Fermiflachennesting auf den Martensit-Ubergang erweitert. Es wird gezeigt, dass der martensitische Phasenubergang von mehreren Beitragen getrieben wird. In einem nachsten Schritt wurden Mangan-reiche Zusammensetzungen der Legierungssysteme Ni-Mn-Z (Z = Ga, In, Sn, Sb) untersucht, die hohe martensitische Ubergangstemperaturen aufweisen. Es zeigt sich, dass ein direkter Ansatz, der die Energiedifferenzen zwischen Austenit und Martensit, die in den Dichtefunktionalrechnungen auftreten, mit der Ubergangstemperatur identifiziert zu qualitativen und teilweise sogar quantitativen Ubereinstimmungen mit experimentellen Ergebnissen fuhrt. Der Ansatz, der nicht-stochiometrische Zusammensetzungen uber Superzellen berucksichtigt, ermoglicht die Berechnung vollstandiger Phasendiagramme mittels Ab Initio Methoden. Die dazu erforderliche Rechenleistung steht in einem angemessenen Verhaltnis zu den erzielbaren Resultaten. In einem weiteren Schritt wurden die vorgestellten Methoden auf die Legierungssysteme Ni-Pt-Mn-Z (Z = Ga, Sn) angewandt, die noch nicht ausfuhrlich in der Literatur behandelt wurden. Die partielle Substitution von Nickel durch Platin ist von besonderem Interesse, da diese die Valenzelektronenkonzentration nicht verandert. Es zeigt sich, dass durch das Hinzufugen von Platin hohere Ubergangstemperaturen erwartet werden. Kurzlich veroffentlichte experimentelle Arbeiten bestatigen die vielversprechenden Eigenschaften dieser Legierungssysteme. Eine weitere magnetische Formgedachtnis-Legierung die im Rahmen dieser Arbeit untersucht wurde ist Co-Ni-Ga. Dieses System zeichnet sich durch die Abwesenheit weicher Phononen und antiferromagnetischer Wechselwirkungen aus, die in Ni-Mn-Ga beobachtet werden. Die Tendenz, dass die erwarteten martensitischen Ubergangstemperaturen bei Erhohung der Valenzelektronenkonzentration ansteigen, lasst sich hingegen auch in Co-Ni-Ga beobachten.
Preparation of micronized ciprofloxacin by reactive precipitation

A simple method for formation of micronized ciprofloxacin particles by reactive precipitation has been developed.The effects of varying five critical parameters-molar ratio of reactants,concentration of NaOH solution,reaction temperature,stirring speed and drying method-on particle size and morphology are described.The optimum conditions for preparation of micronized ciprofloxacin were found to be: molar ratio of reactants in the range 8-10,stirring speed 15000 r/min,and concentration of NaOH 0.01 mol/L.The particle size of ciprofloxacin dry power shown by SEM can reach 1-2 μm. IR spectroscopy and X-ray diffraction showed that the molecular structure and crystallinity of ciprofloxacin did not change during the process.