The characteristics and performance of international joint ventures in Thailand

The importance of strategic alliances in the form of international joint ventures (IJVs) is growing in the present international business environment where competition is on a global scale. A review of the IJV literature, especially in developing countries, shows an over-emphasis on China and the NIEs (the first tier newly-industrialising economies: Taiwan, Singapore, Hong Kong, and South Korea). To date, relatively little attention has been paid to the ASEAN4 countries (the high-performing economies of the Association of Southeast Asian Nations: Thailand, Malaysia, the Philippines, and Indonesia), even though since the 1990s this region has had one of the fastest growing economies in the world. This study provides new empirically based insights into the under-researched phenomenon of IJV formation in the South East Asian region. The study takes Thailand as an example of the ASEAN4 countries. Drawing on an unpublished official database of international joint ventures (IJVs) and a survey of managers of IJVs operating in Thailand, the study sets out to identify recent trends in Thai international joint ventures (IJVs), explore the motives and contributions of firms that participate in such IJVs and examine some of the factors that influence their performance. The study provides for the first time an in-depth analysis of a key dimension of Foreign Direct Investment (FDI) in Thailand

A Framework to Estimate the Key Point Within an Object Based on a Deep Learning Object Detection

Automatic identification of key points within objects is crucial in various application domains. This paper presents a novel framework for accurately estimating the key point within an object by leveraging deep neural network-based object detection. The proposed framework is built upon a training dataset annotated with four non-overlapping bounding boxes, one of which shares a coordinate with the key point. These bounding boxes collectively cover the entire object, enabling automatic annotation if region annotations around the key point exist. The trained object detector is then utilized to generate detection results, which are subsequently post-processed to estimate the key point. To validate the effectiveness of the framework, experiments were conducted using two distinct datasets: cross-sectional images of a parawood log and pupil images. The experimental results demonstrate that our proposed framework surpasses previously proposed approaches in terms of precision, recall, F1-score, and other domain-specific metrics. The improvement in performance can be attributed to the unique annotation strategy and the fusion of object detection and key point estimation within a unified deep learning framework. The contribution of this study lies in introducing a novel framework for closely estimating key points within objects based on deep neural network-based object detection. By leveraging annotated training data and post-processing techniques, our approach achieves superior performance compared to existing methods. This work fills a critical gap in the field by integrating object detection and key point estimation, which has received limited attention in previous research. Our framework provides valuable insights and advancements in key point estimation techniques, offering potential applications in precise object analysis and understanding. Doi: 10.28991/HIJ-2023-04-01-08 Full Text: PD

A Framework to Create a Deep Learning Detector from a Small Dataset: A Case of Parawood Pith Estimation

A deep learning-based object detector has been successfully applied to all application areas. It has high immunity to variations in illumination and deviations among objects. One weakness of the detector is that it requires a huge, undefinable dataset for training the detector to avoid overtraining and make it deployable. This research proposes a framework to create a deep learning-based object detector with a limited-sized dataset. The framework is based on training the detector with the regions surrounding an object that typically contain various features over a more extensive area than the object itself. Our proposed algorithm further post-processes the detection results to identify the object. The framework is applied to the problem of wood pith approximation. The YOLO v3 framework was employed to create the detector with all default hyperparameters based on the transfer learning approach. A wood pith dataset with only 150 images is used to create the detector with a ratio between training to testing of 90:10. Several experiments were performed to compare the detection results from different approaches to preparing the regions surrounding a pith, i.e., all regions, only close neighbors, and only diagonal neighbors around a pith. The best experiment result shows that the framework outperforms the typical approach to create the detector with approximately twice the detection precision at a relative average error. Doi: 10.28991/ESJ-2023-07-01-017 Full Text: PD

Comparison between constant methanol feed and on-line monitoring feed control for recombinant human growth hormone production by Pichia pastoris KM71

Two methanol feeding methods, namely constant methanol feed and on-line monitoring feed control by methanol sensor were investigated to improve the production of recombinant human growth hormone (rhGH) in high cell density cultivation of Pichia pastoris KM71 in 2 L bioreactor. The yeast utilized glycerol as a carbon source for cell growth and yeast cells were accumulated to high cell density. Cell dry weight concentration around 140 to 150 g/l was obtained before entering the methanol induction period. Methanol was applied to express rhGH after high cell accumulation. The constant methanol feed rate at 0.009 l/h was applied to the cultivation to compare with the on-line monitoring feed control of methanol concentration. The highest amount of rhGH around 501 mg/l was obtained by using on-line monitoring feed control of methanol at the set point of methanol concentration at 4.0 g/l. After achieving the best result from the on-line monitoring feed control method, the experiment was further carried out to investigate the optimal methanol concentration. On-line controlling of methanol concentrations at 2.0, 4.0 and 8.0 g/l were investigated. The result shows that high amount of rhGH was achieved by controlling the methanol level at the set point of 4.0 g/l.Key words: Recombinant human growth hormone, Pichia pastoris, fed-batch cultivation, methanol feeding control