CHARACTERIZATIONS OF A NEW DRY POWDER INHALER (DPI) FOR PULMONARY DRUG DELIVERY

Pulmonary drug delivery, as one of the most common alternative administration routes for injection, has been widely developed to perform effective local (e.g. asthma and chronic obstructive pulmonary disease) or systemic (proteins and peptides) therapy purpose. During this administration procedure, the therapy effects depend on many key factors, including fine powder properties, formulation of the drug powder, design of the dry powder inhaler, and operation of patients and so on. Especially, the mechanical structure of the dry powder inhaler plays the most important role, for example, the loading and emission consistency, air flow resistance, residue of fine drug powder, de-agglomeration construction, and the principle of generation of the powder aerosol and so on. In this research, based on previous fundamental study on an old dry powder inhaler, a new dry powder inhaler has been developed and characterized. Different fine drug powder formulations were prepared to investigate the enhanced performance of the new dry powder inhaler. Several experiments, such as dose uniformity, emission consistency and in vitro particle deposition and distribution test, have been conducted to evaluate the new device. All the above test results were considered as the guide to modify and redesign the new dry powder inhaler. As an attempt, the puncture diameter of the new dry powder inhaler was increased, and the effects on the drug delivery performance were also evaluated. In addition to the consideration of device design, studies on the formulation development have been carried out in this research. The effects of lactose with different particle size were compared as the carrier for the dry powder formulations. The drug formulations were prepared by different methods, using coffee grinder or rotary blender, in order to optimize the production process. The effects of different amounts of carrier and stability test for the storage of formulation have been conducted. This thesis successfully developed a new dry powder inhaler and discovered proper drug formulation for this device. With drug formulations using large lactose as carrier, the new DPI showed a good drug delivery performance with high consistency. Also a filling by hand process and a filling machine were developed and proved to be qualified for the application to dry powder inhaler

A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning

Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks. The architecture is structurally flexible and considers various interactions among tasks, which can be regarded as a generalized case of many previous works. Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others

Power-Efficient Radio Resource Allocation for Low-Medium -Altitude Aerial Platform Based TD-LTE Networks

In order to provide an increased capacity, throughput and QoS guarantee for terrestrial users in emergency scenarios, a low-medium-altitude aerial platform based time-division-duplex long term evolution (TD-LTE) system referred to as Aerial LTE, is presented in this paper. Additionally a power-efficient radio resource allocation mechanism is proposed for both the Aerial LTE downlink and uplink, which is modeled as a cooperative game. Our simulation results demonstrate that the proposed algorithm imposes an attractive tradeoff between the achievable throughput and the power consumption while ensuring fairness among users

The Mood and Physical Activity of the Tibetan and Han University Students During the COVID-19

This study aimed at exploring a relation between the mood state and physical activity (PA) among the Zang and Han university students during the COVID-19. 2382 university students in Shaanxi, Tibet, Hubei and Guizhou were recruited using a convenient sampling method to participate in this study. Independent samples t-test and ANOVA were used to compare the differences of the mood state of the college students by gender, grade, and ethnicity respectively. Pearson correlation and stepwise linear regression were conducted to examine the indicators’ relations. The results indicated that there was statistical significance in the mood state among the ethnicity, gender, and grade (p \u3c 0.05). The total emotional score tended to increase as the grade increased; There are statistical significance in PA between gender, and grade (p \u3c 0.05), while PA declining as the grade increased. However, there was no statistically significant relationship between grade and positive affect (p \u3e 0.05). There are statistically significances between all other indicators (p \u3c 0.05). The study shows that COVID-19 suppressed mood state and participation in PA among Tibetan-Han college students. PA is better for increasing mood state of the university students, which is better for positive emotion, but bad for negative emotion as the grade rises

New Product Diffusion: A Dual Word-Of-Mouth Perspective

Word-of-Mouth plays its great important role on the base of social network in affecting consumers’ shopping behaviour. However, it is little known how firms make a self-suitable marketing strategy according to both online and offline WOM effect in their product diffusion. This article investigates a new product diffusion process taking both offline WOM and online WOM effect into consideration. Specifically, we compare three marketing strategies by predicting product diffusion level during its product life cycle and assist managers to improve cost efficiency. The findings indicate that product peak sales rate and cumulative sales at peak time would be highest when managers market their products only through the Internet. However, product peak adopting time is not determined by the strategy which the manager takes but impacted by the relationship between coefficients. Parameter analysis is further provided to extract more managerial insights

Discrete Factorization Machines for Fast Feature-based Recommendation

User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss. This work is accepted by IJCAI 2018.Comment: Appeared in IJCAI 201