Superior performance of engineered mannitol as a carrier in dry powder inhalations containing salbutamol sulphate

The use of freeze drying technique can constitute an important step used in the pharmaceutical industry towards preparing freeze dried carrier particles which could help to solve some problems connected to drug-carrier dry powder aerosol formulations

On generalized Lagrange–Hermite–Bernoulli and related polynomials

We introduce a new class of generalized polynomials, ascribed to the family of Hermite, Lagrange, Bernoulli, Miller–Lee, and Laguerre polynomials and of their associated forms. These polynomials can be expressed in the form of generating functions, which allow a high degree of exibility for the formulation of the relevant theory. We develop a point of view based on generating relations, exploited in the past, to study some aspects of the theory of special functions. We propose a fairly general analysis allowing a transparent link between different forms of special polynomials

Using TF-IDF n-gram and word embedding cluster ensembles for author profiling: Notebook for PAN at CLEF 2017

This paper presents our approach and results for the 2017 PAN Author Profiling Shared Task. Language-specific corpora were provided for four langauges: Spanish, English, Portuguese, and Arabic. Each corpus consisted of tweets authored by a number of Twitter users labeled with their gender and the specific variant of their language which was used in the documents (e.g. Brazilian or European Portuguese). The task was to develop a system to infer the same attributes for unseen Twitter users. Our system employs an ensemble of two probabilistic classifiers: a Logistic regression classifier trained on TF-IDF transformed n-grams and a Gaussian Process classifier trained on word embedding clusters derived for an additional, external corpus of tweets

Prediction model for pile construction productivity rate utilizing the multiple linear regression technique

Productivity is a prevalent requirement in the bridge construction industry. The precision of productivity rate measurement is significantly influenced by the ability to recognize and implement the critical factors that impact the productivity rate. However, the significance of productivity in cost reduction and profit generation is fundamental to every construction industry. Bored piles are critical components in the foundation of transportation bridges. The productivity estimation processes for piles are influenced by a multitude of factors, leading to several challenges for estimators in terms of time and cost. Hence, the present study aims to diagnose these issues and evaluate the rate of productivity in pile construction through the utilization of the multiple linear regression (MLR) Technique. The data for this study was gathered via designated questionnaires, on-site interviews, and telephone inquiries to professionals affiliated with various construction firms. A selection of nine factors that have the greatest influence on the productivity of construction have been identified. These factors are considered autonomous variables that have an impact on the rate of pile productivity. The construction productivity rate, which is impacted by the influencing factors, is the dependent variable. An equal number of 84 questionnaire samples were utilized to construct each of the influencing factors incorporated in this model. The work measurement form was designed to collect real-time primary data from the construction site, six data samples for each factor were obtained from different bridge and overpass projects to verify the effectiveness and performance of the model. The study revealed that the multi-linear regression model has a high level of prediction for productivity, with an accuracy rate of 92.93%. Additionally, the correlation coefficient was determined to be 99%. The results indicated a robust correlation among the independent variables in the constructed model, and the predictions generated by the model matched what was observed