The HOST-EXAM (Harmonizing Optimal Strategy for Treatment of Coronary Artery Disease-Extended Antiplatelet Monotherapy) trial showed superior efficacy and safety of clopidogrel monotherapy compared with aspirin monotherapy during the chronic maintenance period after percutaneous coronary intervention (PCI).
Abstract Background The United States requires a patent linkage system in other countries as part of free trade agreements. However, introducing a patent linkage system could be a significant barrier to the timely approval of generic drugs. This study aimed to evaluate the perceived impact of the patent linkage system in South Korea held by domestic manufacturers and analyze variations in evaluating the system according to the characteristics of domestic manufacturers. Methods In 2020, we conducted a questionnaire survey of 39 domestic manufacturers. The survey consisted of perceptions of the system, factors affecting patent challenges, and the perceived impact of the system. A 5-point Likert scale was used to rate each item. Domestic manufacturers were categorized into three groups based on their experience of listing a patent and acquiring first generic exclusivity. Results More than half of the manufacturers surveyed had experience of listing a patent. The patent linkage system could protect the involved patents. However, manufacturers perceived that they could successfully challenge the validity of the involved patents and then obtained market approval for generic drugs. Manufacturers responded that market size, expectations for succeeding in litigation, and expectations for manufacturing the drug were the most relevant factors when they initiated patent challenges. Manufacturers reported that the system, in particular the first generic exclusivity, enhanced the research and development capability of generic manufacturers, increased their domestic sales, and improved access to generic drugs. Conclusions The perceived impact of the patent linkage system was limited to the domestic market and generic drugs. In narrowing the impact to the effects on the domestic industry, the system had positive impacts of the system on generic manufacturers. The first generic drug exclusivity lies at the center of this positive perception. However, manufacturers perceived that the current system did not provide enough incentives for domestic manufacturers to be granted first generic drug exclusivity through patent challenges.
The ascent of internet of things (IoT) technology has increased the demand for glass electronics. However, the production of glass electronics necessitates complicated processes, including conductive materials coating and chemical vapor deposition, which entail the use of additional chemicals. Consequently, this raises environmental apprehensions concerning chemical and electronic waste. In this study, a fast, cost‐effective, and simple approach are presented to meet the growing demand for glass electronics while addressing environmental concerns associated with their production processes. The method involves converting polyimide (PI) tape into laser‐induced graphene (LIG) and transferring it onto a glass substrate using ultraviolet laser direct writing technology. This process allows for the fabrication of LIG‐embedded glass without additional chemical treatments in ambient air. Subsequently, the residual PI tape is removed, resulting in LIG‐based glass electrodes with an electrical resistivity of 1.065 × 10 −3 Ω m. These LIG electrodes demonstrate efficient functionality for window applications such as defogging, heating, temperature sensing, and solar warming, suitable for automotive and residential windows. The potential scalability of this eco‐friendly technology to IoT‐based smart and sustainable window electronics further underscores its adaptability to meet diverse user needs.
AbstractIntroduction: The sales patterns of original drugs after patent expiration in Korea differ from those in other countries. This study investigates how they differ. Methods: Using data from the Ministry of Food and Drug Safety, original drugs whose patents expired in 2012–2018 were divided into two groups depending on the generic drug approval. The differences in the attributes of each variable were analyzed. Additionally, we used IQVIA data to determine the market share and growth rate of 48 original drugs over five years from the launch of the first generic drug. Results: The average sales before the patent expiration of original drugs without a generic drug and the average sales before the first generic drug launch of original drugs with generic drugs were KRW 2.9 and 22.6 billion, respectively. The sales volume of off-patent original drugs in the fifth year had an average growth rate of 150.6% compared with that before the first generic drug launch, indicating a different trend from other countries. The average market share of off-patent original drugs in the same molecule market in the fifth year was 70.6%, which was higher than previously reported research results in Korea and other countries. Conclusion: This study analyzed the trend of original drugs’ sales volume, value, growth, and market share after the launch of a first generic drug. Furthermore, it demonstrated different patterns from those of other countries.
Traffic light detection plays a central role in Advanced Driver Assistance System (ADAS). For an autonomous vehicles to move smoothly and safely on the road, not only it is crucial for an ego vehicle to detect all presented traffic light candidates, but also to not yield any false positives. It is necessary for Convolutional Neural Networks (CNNs) to attend more focus on important features and suppress non-useful details through activations. To carry out such task, we propose a novel network that employs continuous Conditional Random Fields (CRFs) to fuse multi-scale information from different layers of a CNN, guided by attention modules. Extensive experiments are conducted on traffic light detection dataset, which we have acquired with own our autonomous driving platform SNUver. Results indicate that by incorporating attention-guided CRF module inside the network, the network focuses more on regions with traffic lights and thereby improves accuracy and recall rate.
Abstract Deep learning has shown excellent performance in numerous machine-learning tasks, but one practical obstacle in deep learning is that the amount of computation and required memory is huge. Model compression, especially in deep learning, is very useful because it saves memory and reduces storage size while maintaining model performance. Model compression in a layered network structure aims to reduce the number of edges by pruning weights that are deemed unnecessary during the calculation. However, existing weight pruning methods perform a layer-by-layer reduction, which requires a predefined removal-ratio constraint for each layer. Layer-by-layer removal ratios must be structurally specified depending on the task, causing a sharp increase in the training time due to a large number of tuning parameters. Thus, such a layer-by-layer strategy is hardly feasible for deep layered models. Our proposed method aims to perform weight pruning in a deep layered network, while producing similar performance, by setting a global removal ratio for the entire model without prior knowledge of the structural characteristics. Our experiments with the proposed method show reliable and high-quality performance, obviating layer-by-layer removal ratios. Furthermore, experiments with increasing layers yield a pattern in the pruned weights that could provide an insight into the layers’ structural importance. The experiment with the LeNet-5 model using MNIST data results in a higher compression ratio of 98.8% for the proposed method, outperforming existing pruning algorithms. In the Resnet-56 experiment, the performance change according to removal ratios of 10–90% is investigated, and a higher removal ratio is achieved compared to other tested models. We also demonstrate the effectiveness of the proposed method with YOLOv4, a real-life object-detection model requiring substantial computation.
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