49 research outputs found
Targeted aspect based multimodal sentiment analysis:an attention capsule extraction and multi-head fusion network
Multimodal sentiment analysis has currently identified its significance in a
variety of domains. For the purpose of sentiment analysis, different aspects of
distinguishing modalities, which correspond to one target, are processed and
analyzed. In this work, we propose the targeted aspect-based multimodal
sentiment analysis (TABMSA) for the first time. Furthermore, an attention
capsule extraction and multi-head fusion network (EF-Net) on the task of TABMSA
is devised. The multi-head attention (MHA) based network and the ResNet-152 are
employed to deal with texts and images, respectively. The integration of MHA
and capsule network aims to capture the interaction among the multimodal
inputs. In addition to the targeted aspect, the information from the context
and the image is also incorporated for sentiment delivered. We evaluate the
proposed model on two manually annotated datasets. the experimental results
demonstrate the effectiveness of our proposed model for this new task
A Novel Multimodal Collaborative Drone-Assisted VANET Networking Model
Drones can be used in many assistance roles in complex communication situations and play key roles as aerial wireless relays to help terrestrial network communications. Although a great deal of emphasis has been placed on the drone-assisted networks, the majority of existing works often focus on routing protocols and do not fully exploit the drones’ superiority and flexibility. To fill in this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed according to the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones simultaneously, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on a real-world floating car data of Chengdu city in China, extensive experiments are carried out to examine the performance of the proposed MNA-optimized drone-assisted VANET considering the processed mobility models. The results demonstrate that our scheme outperforms its counterparts in terms of the average number of hops, improved average packet delivery ratio, and throughput of the global test space
A Novel Nomad Migration-Inspired Algorithm for Global Optimization
Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex parameters setting-up make the existed algorithms hard for most users who are not specializing in NIC, to understand and use. To alleviate these limitations, this paper devises a succinct and efficient optimization algorithm called Nomad Algorithm (NA). It is inspired by the migratory behaviour of nomadic tribes on the prairie. Extensive experiments are implemented with respects to accuracy, rate, stability, and cost of optimization. Mathematical proof is given to guarantee the global convergence, and the nonparametric tests are conducted to confirm the significance of experiment results. The statistical results of optimization accuracy denote NA outperforms its rivals for most cases (23/28) by orders of magnitude significantly. It is considered as a promising optimizer with excellent efficiency and adaptability
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Stabilization of eptifibatide by cosolvents
Eptifibatide is a potent and highly specific inhibitor of platelet receptor glycoprotein IIb/IIIa and is indicated in the treatment of acute coronary syndrome. The commercial product Integrilin® (eptifibatide) Injection requires a cold/refrigerator storage condition. In an effort to improve the drug stability for room temperature storage and transportation, this study proposed a semi-aqueous formulation that contains 2 mg/ml dose, 10% ethanol, 40% propylene glycol and 50% 0.025 M citrate buffer. A carefully designed stability study was conducted in the pH range 4.25-6.25 under accelerated temperatures: 48°C, 60°C, 72.5°C. The results indicate that the proposed semi-aqueous vehicles greatly increased eptifibatide stability in comparison with aqueous vehicles. The pH-rate profiles of eptifibatide are V-shaped with the curves for semi-aqueous vehicles lower all over the test pH range. The pH of drug maximum stability is 5.25 in the aqueous vehicle, and it is shifted to 5.75 in the semi-aqueous vehicle. Studies indicate that eptifibatide degradation may involve a few different mechanisms: the specific acid catalyzed hydrolysis which is dominant in the acidic region, and a pH-dependent oxidation which is likely to be dominant in the basic region of the test pH range. The predicted drug shelf-life T90 at 25°C shows that an almost 2-fold increase can be achieved by formulating eptifibatide in the semi-aqueous vehicle, which is 60 months at its maximum stability pH 5.75 as opposed to the 33 months in the aqueous vehicle at its maximum stability pH 5.25