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The inverse kinematics in robot manipulator have to handle the arctangent and arccosine function. However, the two functions are complicated and need much computation time so that it is difficult to be realized in the typical processing system. The purpose of this paper is to solve this problem by using Field Programmable Gate Array (FPGA) to speed up the computation power.

The Taylor series expansion method is firstly applied to transfer arctangent and arccosine function to a polynomial form. And Look-Up Table (LUT) is used to store the parameters of the polynomial form. Then the behavior of the computation algorithm is described by Very high-speed IC Hardware Description Language (VHDL) and a co-simulation using ModelSim and Simulink is applied to evaluate the correctness of the VHDL code.

The computation time of arctangent and arccosine function using by FPGA need only 320 and 420 ns, respectively, and the accuracy is <0.01°.

Fast computation in arctangent and arccosine function can speed up the motion response of the real robot system when it needs to perform the inverse kinematics function.

This is the first time such to combine the Taylor series method and LUT method in the computation the arctangent and arccosine function as well as to implement it with FPGA.

Despite the extensive academic interest in social media sentiment for financial fields, multimodal data in the stock market has been neglected. The purpose of this paper is to explore the influence of multimodal social media data on stock performance, and investigate the underlying mechanism of two forms of social media data, i.e. text and pictures.

This research employs panel vector autoregressive models to quantify the effect of the sentiment derived from two modalities in social media, i.e. text information and picture information. Through the models, the authors examine the short-term and long-term associations between social media sentiment and stock performance, measured by three metrics. Specifically, the authors design an enhanced sentiment analysis method, integrating random walk and word embeddings through Global Vectors for Word Representation (GloVe), to construct a domain-specific lexicon and apply it to textual sentiment analysis. Secondly, the authors exploit a deep learning framework based on convolutional neural networks to analyze the sentiment in picture data.

The empirical results derived from vector autoregressive models reveal that both measures of the sentiment extracted from textual information and pictorial information in social media are significant leading indicators of stock performance. Moreover, pictorial information and textual information have similar relationships with stock performance.

To the best of the authors’ knowledge, this is the first study that incorporates multimodal social media data for sentiment analysis, which is valuable in understanding pictures of social media data. The study offers significant implications for researchers and practitioners. This research informs researchers on the attention of multimodal social media data. The study’s findings provide some managerial recommendations, e.g. watching not only words but also pictures in social media.

In this paper, a combined TODIM-BSC method with the neutrosophical approach for evaluating the performance of a private insurance company has been proposed. In other words, first, using the BSC technique, and identify the performance evaluation indicators, then evaluate the performance of the insurance company's agencies and rank them with the TODIM decision-making method.

The insurance industry has a special prestige and importance in domestic and foreign trade. The evaluation of insurance companies, in addition to informing the stakeholders, increases competition, industry dynamism, sustainable and balanced development of society. The purpose of this paper is to establish a model for evaluating the performance of private insurance companies by adopting multiple-attribute decision-making and Balanced Scorecard (BSC) with single-values neutrosophic numbers (SVNNs) which will be applied by considering a set of indicators and alternatives deliberated with different viewpoints.

A case study of the private insurance agencies in one of the provinces of Iran based on 26 criteria of agencies is used to confirm the practicality and effectiveness of the proposed model. Finally, there was a discussion about why the results are logical, which shows the strength and robustness of the proposed framework.

To the best of our knowledge, no study has been performed to evaluate the performance of a real-world problem with the integrated TODIM-BSC method in a neutrophilic environment. Therefore, this paper can be effective in bridging the existing research gap and expanding our knowledge of the discussion of evaluating the performance of organizations or companies. Besides, by using these results, the authors can help the planners of these companies as well as similar organizations in attracting satisfaction and retaining target customers.