Miracle-dz/UniFAR-FTUs huggingface dataset:
The association between cigarette smoking and health care service utilization among middle-aged and elderly adults in China

Objective To assess the associations between tobacco use and health care service utilization in Chinese individuals aged more or equal to 40 years old. Method This research was a cross-sectional study using data from eight provinces in China, and the final sample consisted of 4,733 observations (4,749 participants) aged more or equal to 40 years old. The dependent variable was health care utilization measured by outpatient and inpatient service utilization. Descriptive statistics were used to summarize the socio-demographic characteristics of the sample according to smoking status. The association between tobacco use and health care service utilization was examined by an instrumental variable (IV) probit model. Results Of the respondents interviewed in 2020, 3,116 (65.84%) were never smokers, 654 (13.82%) were smokers with the smoking index (SI) <400, and 963(20.34%) were smokers with SI≥400. Smokers with SI <400 reported a 6.80% higher probability of using outpatient services. Smokers with SI <400 and SI≥400 reported a 3.10 and 4.20% higher average probability of using ≥3 outpatient visits than never smokers, respectively. Additionally, smokers with SI <400 and SI≥400 reported a 6.30 and 6.20% higher average probability of using inpatient services than those who had not smoked. Moreover, smokers with SI≥400 were more likely to have had ≥2 hospital visits than nonsmokers. Conclusions Smokers make greater use of health care services. Control of smoking may ease the burden of related health care utilization.
Packet Data Flow Control in Evolved WCDMA Networks

The key idea of the new, shared high-capacity channel HSDPA, is to adapt the transmission rate to fast variations in the current radio conditions, thus enabling download peak data rates much higher than what WCDMA can offer today. This has induced a need for data that traverses the mobile network to be intermediately buffered in the Radio Base Station, RBS. A scheduling algorithm then basically selects the user with the most beneficial instantaneous radio conditions for access to the high-speed channel and transmission of its data over the air interface.The purpose of this thesis is to design a flow control algorithm for the transmission of data packets between the network node directly above the RBS, the RNC, and the RBS. This flow control algorithm should keep the level of the buffers in the RBS on such a level that the air interface may be fully utilized. Yet it is not desirable with large buffers since e.g., this induces longer round-trip times as well as loss of all data in the buffers whenever the user moves to another cell and a handover is performed. Theoretical argumentations and simulations show that both of these requirements may be met, even though it is a balancing act.Suggested is a control-theoretic framework in which the level in the RBS buffers are kept sufficiently large by taking into account predictions of future outflow over air and by using methods to compensate for outstanding data on the transport network. This makes it possible to keep the buffer levels stable and high enough to fully utilize the air interface. By using a more flexible adaptive control algorithm, it is shown possible to reach an even higher utilization of the air interface with the same or even lower buffering, which reduces the amount of data lost upon handovers. This loss is shown to be even more reduced by actively taking system messages about upcoming handover events into account as well.
Mountain Rescuers through the computation of Sample Entropy

In the present study we propose a novel method to automatically assess the quality of ECG signals collected through a wearable device in typical mountain rescuers activities. ECGs signals have been obtained during sessions of programmed field tests at the Bormio Ski Resort (Valtellina, Lombardy, Italy) in the month of March. Here, following the defined protocol, a group of 15 mountain rescuers has carried out daily rescuers’ activities, while wearing wearable textile system by Smartex Srl. The test protocol was designed to simulate the real physiological demands of mountain rescuers during their emergency deployments. Among the activities performed rescuers had to walk up and down hill in snow-covered trails, carrying stretchers onto which simulated victims were located etc… To infer the quality of ECG signals recorded we developed an algorithm for the automatic evaluation of collected signal deterioration. This method is based on the analysis of regularity of ECGs’ P-QRS-T complexes pattern. To estimate the maintenance of typical ECGs pattern shape, Sample Entropy (SampEn) was computed in moving fixed-length windows sliding along the signal, obtained after applying wavelet transform of the row ECG. The SampEn indices series was then thresholded to spot ECG points where P-QRS-T complexes were more or less easy to identify, respect to points where signal quality was completely deteriorated. Moreover, we evaluated signal quality maintenance while performing low and high intensity activities.
Personalized Fashion Recommendation Using Nearest Neighbor PageRank Algorithm

E-Commerce has seen a lot of growth over the past decade. With an increase in commodities, especially fashion accessories and clothing items in the online-market, a need for an efficient recommendation system arises for better information filtering. Several different apparel recommendation systems already exist in the literature. However, as time passes, new challenges are arising, such as computational complexity and an exponential increase in data. Also, due to fast-changing trends, the recommendation model is required to update frequently. This work proposes an improvised collaborative-filtering based recommendation system. A ranking algorithm, Nearest Neighbor PageRank (NNPR), is developed that uses the nearest neighbors of the user along with the PageRank algorithm to generate personalized recommendations. The proposed model, is evaluated in comparison with Alternating Least Square (ALS) algorithm. The experiments are conducted on Amazon Fashion Review Dataset, and the results of this experiment are recorded in Hit-Rate (HR) and Mean-Reciprocal Ranking (MRR). It is observed, that NNPR performs better than ALS in both Active User and Cold Start scenarios. Moreover, the hybrid model ALSNNPR improves the performance of ALS using NNPR as a ranking algorithm.
Ontology-Based Situation Awareness for Air and Space Traffic Management

The rapid technological and business advances of point-to-point space transport prompt the need for an integrated Air Traffic Management (ATM) and Space Traffic Management (STM) framework. The introduction of a more flexible airspace and vehicle/trajectory management services aims to harmonize the requirements of multi-domain and multi-entity stakeholders. The ATM-STM integration problem involves related air-and-space transport issues such as separation assurance/collision avoidance,, airspace capacity management, atmospheric pollution and emissions, and cybersecurity. To meet the ATM-STM integration challenges, ontologies are an attractive approach to enact and enhance situational awareness in such an integrated ATM-STM domain. In fact, the Federal Administration Agency (FAA) NextGen (Next Generation Air Transport Management) and SESAR (Single European Sky ATM Research) programs, as well as NASA, have proposed the use of ontologies to represent knowledge in the rapidly evolving ATM context. This paper presents a discussion on considerations to develop an Ontological Situation Awareness (OSAW) approach, which could be applied to an integrated air-and-space operational domain. These considerations include (1) both operational/technical challenges and opportunities, (2) the adoption of Artificial Intelligence (AI) and the associated impacts Cyber-Physical Systems (CPS) certifiability, and (3) contributions to sustainability using the OSAW. Realistic scenarios are presented to demonstrate the possible uses of the OSAW approach and how a Space Avionics Analytics Ontology (SAAO) can contribute to the development of an OSAW system for multi-domain traffic management. The SAAO also considers aircraft and spacecraft which are manned/unmanned with various degrees of automation (categories defined in line with current aviation/aerospace industry standards) and trusted autonomy (as an attribute of the applicable automation categories).
Insights on exploring a small-scale electric bicycle sharing data set

Cities can benefit to a great extent from the deployment of bicycle-sharing services (BSS). Conceived as being an alternative for taxi services and public transport, BSSs are used by daily commuters, as well as tourists. Open data sharing policies in the US allow data scientists to make use of relatively detailed and anonymized data extracts of often station-based, bicycle-sharing services. Origin destination matrices (OD matrices) allow the representation of flows while leaving out detailed information on the specific trajectory and the traffic control that influences individual movements. This paper aims at reviewing the existing methods for analyzing sharing services that operate predominantly using GPS data. We aim to use unsupervised clustering learning techniques to analyze GPS data and develop insights. We found that combining more than one clustering technique is more effective as compared to individual techniques. We discuss the merits and demerits of individual and combined techniques and their relevance in analyzing bike-sharing GPS data.
Fashion Inspired by Architecture: The Interrelationship between Mashrabiya and Fashion World

The relationship between architecture and fashion fields have received many studies. Some researches have considered them as parallel worlds, while others have proven that the link is an interrelationship. Generally, architecture and fashion have many common sources: art, science, and technology. This makes them sometimes close worlds, and in many other cases, they are mixed. However, it is wise to assume that the best situation when one field becomes a source to another, and they support each other within a logical visible relationship. Architecture gives inspiration to fashion, per contra fashion achieves notability for an architectural figure or element. Mashrabiya (The traditional Arab oriel window that works as an environmental architectural element) has entered into fashion field through one of the new approaches of the heritage restoration movement. This approach has presented unique fashion shows and unparalleled jewellery designs, while made Mashrabiya a popular name in non-architectural surroundings. While the environmental functions of Mashrabiya are used in many contemporary projects without acknowledging its original name "Mashrabiya". Moreover, other projects dealt with it using a new name like ‘geometric panels', fashion has maintained the authenticity of the name and its orientalism. This article tries to show that Mashrabiya is one of the architectural element which gives inspiration to the fashion world. The article also presents examples of fashion and jewellery design inspired from the Mashrabiya element.
Linear Embedding-based High-dimensional Batch Bayesian Optimization without Reconstruction Mappings

The optimization of high-dimensional black-box functions is a challenging problem. When a low-dimensional linear embedding structure can be assumed, existing Bayesian optimization (BO) methods often transform the original problem into optimization in a low-dimensional space. They exploit the low-dimensional structure and reduce the computational burden. However, we reveal that this approach could be limited or inefficient in exploring the high-dimensional space mainly due to the biased reconstruction of the high-dimensional queries from the low-dimensional queries. In this paper, we investigate a simple alternative approach: tackling the problem in the original high-dimensional space using the information from the learned low-dimensional structure. We provide a theoretical analysis of the exploration ability. Furthermore, we show that our method is applicable to batch optimization problems with thousands of dimensions without any computational difficulty. We demonstrate the effectiveness of our method on high-dimensional benchmarks and a real-world function.
Comparative Study among Approaches based in Fuzzy Systems and Artificial Neural Networks to Estimate Importance of Comments about Products and Services

The evolution of e-commerce and On-line Social Networks has contributed to the increase of the information available, making the task of analyzing the reviews manually almost impossible for the buying a product or service decisionmaking process. Due to the amount of information, the creation of automatic methods of knowledge extraction and data mining has become necessary. Currently, to facilitate the analysis of reviews some websites use filters such as votes by utility or by stars. However, the use of these filters is not a good practice because they may exclude reviews that have recently been submitted to the voting process, besides the possibility of the user overestimate or underestimate the review with attribution of stars. One possible solution is to filter the reviews based on their textual descriptions, author information and others measures. Sousa [1] proposed an approach, called TOP(X), to estimate the degree of importance of reviews using a Fuzzy System with three input variables: author reputation, extraction of tuples and richness analyzer and an output variable: degree of importance of the review. Although the approach presented good results, some problems were pending of resolution and improvements, besides the possibility to change the computational model used. This work proposes adaptations in two input variables, namely: quantity of tuples and vocabulary richness and the building of new approaches using computational models based on Fuzzy Systems and Artificial Neural Networks (ANN). In addition, a comparison was made among the proposed approaches through statistical measures. Experiments performed in the hotel-domain showed that the approach using Fuzzy System obtained better results when detecting the most important reviews, without considering the semantic orientation of the comments. However, the approach using Multi-Layer Perceptron (MLP) Artificial Neural Networks obtained better results when is known the semantic orientation of the review.
The planktic foraminifer Planorotalites in the Tethyan middle Eocene

Morphologically uniform, low-trochospiral foraminifera are a common, though not very well-preserved component of foraminiferal assemblages across the interval including the Middle Eocene Climatic Optimum (MECO) at the Alano (northeastern Italy) and Monte Cagnero (central Italy) Tethyan sections. The morphology of these specimens, combined with their state of preservation, made it difficult to assign them to a benthic or planktic group. We measured their stable isotope (oxygen and carbon) composition and compared them with those of trochospiral, epifaunal benthic (Nuttallides truempyi (Nuttall, 1930)) and planktic foraminifera (Acarinina and Subbotina) in the same samples. Despite the preservation, the isotopic analysis showed that the low-trochospiral forms were undoubtedly planktic foraminifera calcifying in the mixed layer. We assign them to the species Planorotalites capdevilensis (Cushman & Bermudez, 1949). Their abundance, distribution and stable isotope values suggest that P. capdevilensis may have been mixed-layer dwellers without photosymbionts, adapted to slightly more mesotrophic conditions than symbiont-bearing, larger acarininids and morozovelloidids. The oxygen isotope data for benthic and planktic species in the two sections are similar, but the carbon isotopic values of all foraminifera are 1.0–1.5 lower at the Alano section than at Monte Cagnero, probably because the Alano sediments were deposited in the more restricted Belluno Basin, the Monte Cagnero sediments in the more open oceanic setting of the Umbria-Marche Basin. Supplementary material: The full dataset is available at http://www.geolsoc. org.uk/SUP18868
UAlacant word-level and phrase-level machine translation quality estimation systems at WMT 2016

This paper describes the Universitat d’Alacant submissions (labeled as UAlacant) to the machine translation quality estimation (MTQE) shared task at WMT 2016, where we have participated in the word-level and phrase-level MTQE subtasks. Our systems use external sources of bilingual information as a black box to spot sub-segment correspondences between the source segment and the translation hypothesis. For our submissions, two sources of bilingual information have been used: machine translation (Lucy LT KWIK Translator and Google Translate) and the bilingual concordancer Reverso Context. Building upon the word-level approach implemented for WMT 2015, a method for phrase-based MTQE is proposed which builds on the probabilities obtained for word-level MTQE. For each sub-task we have submitted two systems: one using the features produced exclusively based on online sources of bilingual information, and one combining them with the baseline features provided by the organisers of the task.
Innovación social en las infraestructuras emergentes de innovación en países de renta media: Caso Guadalajara, México

La presente investigacion es pionera en el analisis de infraestructuras de innovacion para el impacto social en paises de renta media, ya que, a pesar de la considerable produccion de literatura sobre modelos de innovacion y temas relacionados con la innovacion social, se ve limitada la produccion academica alrededor de “innovacion social en infraestructuras emergentes”. Por lo tanto, atender el enfoque del impacto social de las infraestructuras de innovacion es una oportunidad de abonar al entendimiento de lo que esta sucediendo en varias partes del mundo, con fenomenos emergentes que extienden los patrones tradicionales de innovacion. La innovacion social y las infraestructuras emergentes de innovacion son los conceptos clave de esta investigacion. Se entiende a la innovacion social como un proceso de aparicion y adopcion de estrategias creativas que reconfiguran las relaciones sociales para dar respuesta a una problematica social, abriendo paso a un cambio positivo en la inclusion, calidad de vida, servicios, empoderamiento de la ciudadania; e introduciendo nuevos productos, procesos o servicios basados en tecnologia innovadora. Por su parte, las infraestructuras emergentes de innovacion se conciben como un concepto fundamentalmente relacional, y se visualizan como espacios para incentivar la actividad innovadora, a traves de la articulacion de recursos materiales, humanos y economicos dispuestos en eventos como challenges, bootcamps, hackatones, workshops, festivales de innovacion, entre otros. En general, la investigacion da cuenta de proyectos que dejan ver a la innovacion social como el resultado de destrezas individuales, o la suma de una serie de aptitudes individuales y no una competencia social compartida. Se encontro, por un lado, al conocimiento cientifico tecnologico y al conocimiento previo de los integrantes de los proyectos, como las variables que estructuran la innovacion; y por el otro lado, al conocimiento social y al conocimiento interdisciplinario como las variables que consolidan la innovacion. Por lo que se propone un trabajo interdisciplinario e intersectorial en las infraestructuras para vincular el conocimiento social y el conocimiento cientifico tecnologico, con la intencion de favorecer la concrecion de la innovacion social. Estas proposiciones de estudio se interpretaron desde enfoques multidisciplinares y en terminos de la configuracion de las acciones de los actores, dando cuenta del dinamismo de los patrones de la innovacion social implicados en los sistemas de actividad de una region y/o ciudad; colaborando asi en la construccion de un marco teorico mas amplio de los llamados “Regional Innovation Studies” con una orientacion a los esquemas emergentes de innovacion social y su relacion con “Knowledge Locations in Cities”, “Territorial Innovation Models”, “Collaborative Networked” y “Third Generation Science Parks 3GSP”.
Estimation of rate coefficients and branching ratios for gas-phase reactions of OH with aromatic organic compounds for use in automated mechanism construction

Abstract. Reaction with the hydroxyl (OH) radical is the dominant removal process for volatile organic compounds (VOCs) in the atmosphere. Rate coefficients for the reactions of OH with VOCs are therefore essential parameters for chemical mechanisms used in chemistry transport models, and are required more generally for impact assessments involving estimation of atmospheric lifetimes or oxidation rates for VOCs. A structure–activity relationship (SAR) method is presented for the reactions of OH with aromatic organic compounds, with the reactions of aliphatic organic compounds considered in the preceding companion paper. The SAR is optimized using a preferred set of data including reactions of OH with 67 monocyclic aromatic hydrocarbons and oxygenated organic compounds. In each case, the rate coefficient is defined in terms of a summation of partial rate coefficients for H abstraction or OH addition at each relevant site in the given organic compound, so that the attack distribution is defined. The SAR can therefore guide the representation of the OH reactions in the next generation of explicit detailed chemical mechanisms. Rules governing the representation of the reactions of the product radicals under tropospheric conditions are also summarized, specifically the rapid reaction sequences initiated by their reactions with O2.