Plusieurs auteurs ont proposé récemment des modèles et des algorithmes pour l'estimation nonparamétrique de mélanges multivariés finis dont l'identifiabilité n'est pas toujours assurée. Entre les ...modèles considérés, l'hypothèse des coordonnées indépendantes conditionnelles à la sous-population de provenance des individus fait l'objet d'une attention croissante, en raison des développements théoriques et pratiques envisageables, particulièrement avec la multiplicité des variables qui entrent en jeu dans le framework statistique moderne. Dans ce travail, nous considérons d'abord un modèle plus général supposant l'indépendance, conditionnellement à la composante, de blocs multivariés de coordonnées au lieu de coordonnées univariées, permettant toute structure de dépendance à l'intérieur de ces blocs. Par conséquent, les fonctions de densité des blocs sont complètement multivariées et non paramétriques. Nous présentons des arguments d'identifiabilité et introduisons pour l'estimation dans ce modèle deux algorithmes méthodologiques dont les procédures de calcul ressemblent à un véritable algorithme EM mais incluent une étape additionnelle d'estimation de densité: un algorithme rapide montrant l'efficacité empirique sans justification théorique, et un algorithme lissé possédant une propriété de monotonie comme certain algorithme EM, mais plus exigeant en terme de calcul. Nous discutons également les méthodes efficaces en temps de calcul pour l'estimation et proposons quelques stratégies. Ensuite, nous considérons une extension multivariée des modèles de mélange utilisés dans le cadre de tests d'hypothèses multiples, permettant une nouvelle version multivariée de contrôle du False Discovery Rate. Nous proposons une version contrainte de notre algorithme précédent, adaptée spécialement à ce modèle. Le comportement des algorithmes de type EM que nous proposons est étudié numériquement dans plusieurs expérimentations de Monte Carlo et sur des données réelles de grande dimension et comparé avec les méthodes existantes dans la littérature. En n, les codes de nos nouveaux algorithmes sont progressivement ajoutés sous forme de nouvelles fonctions dans le package en libre accès mixtools pour le logiciel de statistique R.
Recently several authors have proposed models and estimation algorithms for finite nonparametric multivariate mixtures, whose identifiability is typically not obvious. Among the considered models, the assumption of independent coordinates conditional on the subpopulation from which each observation is drawn is subject of an increasing attention, in view of the theoretical and practical developments it allows, particularly with multiplicity of variables coming into play in the modern statistical framework. In this work we first consider a more general model assuming independence, conditional on the component, of multivariate blocks of coordinates instead of univariate coordinates, allowing for any dependence structure within these blocks. Consequently, the density functions of these blocks are completely multivariate and nonparametric. We present identifiability arguments and introduce for estimation in this model two methodological algorithms whose computational procedures resemble a true EM algorithm but include an additional density estimation step: a fast algorithm showing empirical efficiency without theoretical justification, and a smoothed algorithm possessing a monotony property as any EM algorithm does, but more computationally demanding. We also discuss computationally efficient methods for estimation and derive some strategies. Next, we consider a multivariate extension of the mixture models used in the framework of multiple hypothesis testings, allowing for a new multivariate version of the False Discovery Rate control. We propose a constrained version of our previous algorithm, specifically designed for this model. The behavior of the EM-type algorithms we propose is studied numerically through several Monte Carlo experiments and high dimensional real data, and compared with existing methods in the literature. Finally, the codes of our new algorithms are progressively implemented as new functions in the publicly-available package mixtools for the R statistical software.
This paper aims to investigate the effect of generic strategy on R&D spending and the impact of R&D spending on firms’ performance conditional on their strategic position. This empirical study uses ...accounting data of 597 listed Taiwanese firms in the manufacturing industry from 2013 to 2017. The data was obtained from Taiwan Economic Journal (TEJ) database. The results indicate that firms that adopt a differentiation strategy have more R&D spending than companies with a cost leadership strategy. Furthermore, the authors find that R&D spending positively affects firms’ performance if they pursue a differentiation strategy. Meanwhile, the relationship between R&D spending and firm performance forms an inverted U-shape for those who adopt a cost leadership strategy. First, for firms adopting the differentiation strategy, the investment in R&D is critical because the more investment on R&D these firms spend, the better performance they will gain. Second, for firms with a cost-leadership strategy, R&D spending is also essential to improve efficiency. However, they should allocate the budgets wisely and reasonably, as controlling cost is the main focus of this strategy to keep their competitive advantages. This study examines the relationship between R&D spending, business strategy, and firm performance in Taiwan. Further, the study suggests that manufacturing firms in Taiwan allocate their resources wisely and efficiently according to their system.
Recommender systems are developed to personalize services for each user. The focus of recommender systems is to accurately discover the unknown preferences of users. To address this issue, ...neighbor-based recommender systems compute a weighted average of neighbors’ known preferences to predict the unknown preferences of an active user. Its weights represent the influence of neighbors’ preferences on the active user’s ones. Previous research estimates these weights solely based on users’ observed preferences. However, preferences are commonly expressed through numerical ratings, presenting a challenge for users to grasp the rating scale and assign the most precise numerical values. Therefore, the observed rating data is characterized by sparsity, a lack of precision, and insufficient detail. In modern recommender systems, besides collecting ratings, it’s entirely possible to gather various types of information. One valuable data source proven to enhance recommender systems significantly is textual reviews authored by users following their experiences with items. In this study, we utilize Bert models to derive user vectors from observed textual reviews. These vectors are then employed to estimate weights between the active user and each neighbor in a neighbor-based recommender system. In contrast to numerical ratings, textual reviews provide a more detailed and precise representation of users’ preferences. This contributes to improving the performance of neighbor-based recommender systems.
Ecteinascidin 743 (Et-743, trabectidin or Yondelis®) possesses an impressive antitumor activity that it was approved for treatments of several cancer types worldwide. Since this natural product only ...presents as a trace amount in the nature, the main supply of this drug for research and commercial use is from laboratory synthesis. Many syntheses of Et-743 have been reported including three total syntheses, two formal syntheses and two semisyntheses. The biological activities of fennebricin B were unknown due to the scarcity of this natural product. However, fennebricin B share a common pentacyclic core with Et-743, thus may also possess interesting biological activities. Our group completed our formal synthesis of the natural product in 2008, featuring the Pictet-Spengler reaction to construct the pentacyclic core of Et-743. Our work, however, also produced both desired and undesired pentacycle without selectivity. We herein described an improved formal synthesis of Et-743 employing bromine auxiliary to generate the pentacylic core of Et-743 with the desired regioisomer as the only product. This approach was also utilized in the synthetic studies toward the total synthesis of fennebricin B.
Highlights: • We have demonstrated a facile method to prepare Fe{sub 2}O{sub 3} nanoparticles. • The gas sensing properties of α-Fe{sub 2}O{sub 3} have been invested. • The results show potential ...application of α-Fe{sub 2}O{sub 3} NPs for CO sensors in environmental monitoring. - Abstract: Iron oxide nanoparticles (NPs) were prepared via a simple hydrothermal method for high performance CO gas sensor. The synthesized α-Fe{sub 2}O{sub 3} NPs were characterized by X-ray diffraction, nitrogen adsorption/desorption isotherm, scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), and selected area electron diffraction (SAED). The SEM, TEM results revealed that obtained α-Fe{sub 2}O{sub 3} particles had a peanut-like geometry with hemispherical ends. The response of the α-Fe{sub 2}O{sub 3} NPs based sensor to carbon monoxide (CO) and various concentrations of other gases were measured at different temperatures. It found that the sensor based on the peanut-like α-Fe{sub 2}O{sub 3} NPs exhibited high response, fast response–recovery, and good selectivity to CO at 300 °C. The experimental results clearly demonstrated the potential application of α-Fe{sub 2}O{sub 3} NPs as a good sensing material in the fabrication of CO sensor.
In this paper, we present an approach to assess nurses’ skills by using activity recognition in the context of Endotracheal Suctioning (ES) performed by nurses which is an important nursing activity. ...Our proposed structure for skill assessment hinges on three aspects: the activity order, suction time, and the smoothness exhibited during the suctioning process. Our order score algorithm works correctly in ground truth and identifies correctly mistakes on Not remembering to remove PPE before auscultation in activity recognition results compared to a professional nurse's evaluation. The recognized suction time is similar to the ground truth with only 1 to 2 seconds. The analysis of suctioning smoothness shows a similar trend to force data that nurses performed ES more smoothly by putting less pressure on the catheter than students. To recognize ES activities, we extract pose skeletons from multi-view videos, using a dataset including nurses and nursing students performing ES. Our methodology involves extracting pose skeletons from front and back views and enhancing model performance with skip frames, post-processing, and using micro labels for training, then evaluating with macro labels. After using multi-view data and training with micro labels, our proposed method improves the accuracy by 4% and the F1-score by 9%. By combining multi-view pose extraction, advanced post-processing, and a nuanced skill assessment framework, our work contributes to advancing activity recognition in endotracheal suctioning, fostering a deeper understanding of nurses' proficiency in this critical medical procedure.
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