Optimization of Deflection of a Big NEO through Impact with a Small One

Using a small near-Earth object (NEO) to impact a larger and potentially threatening NEO has been suggested as an effective method to avert a collision with Earth. This paper develops a procedure for analysis of the technique for specific NEOs. First, an optimization method is used to select a proper small body from the database. Some principles of optimality are achieved with the optimization process. Then, the orbit of the small body is changed to guarantee that it flies toward and impacts the big threatening NEO. Kinetic impact by a spacecraft is chosen as the strategy of deflecting the small body. The efficiency of this method is compared with that of a direct kinetic impact to the big NEO by a spacecraft. Finally, a case study is performed for the deflection of the Apophis NEO, and the efficiency of the method is assessed

All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison

Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topic selection, a more unobtrusive way in which the media shape opinion is via the strategic inclusion or omission of partisan events that may support one side or the other. We develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. Our experiments first validate the existence of partisan event selection, and then show that article alignment and cross-document comparison detect partisan events and article ideology better than competitive baselines. Our results reveal the high-level form of media bias, which is present even among mainstream media with strong norms of objectivity and nonpartisanship. Our codebase and dataset are available at https://github.com/launchnlp/ATC.Comment: EMNLP'23 Main Conferenc

ANALYSIS ON 113 CASES OF ADVERSE REACTIONS CAUSED BY Β-LACTAM ANTIBIOTICS

The objectives of this study were to learn about the characteristics and rules of the occurrence of adverse reactions caused by lactam antibiotics and provide a reference for clinical drug use. Methods: A retrospective study was made to analyse the 113 case reports of adverse reactions caused by β-lactam antibiotics collected in our hospital between 2007 and 2009. Results: 113 cases of ADR involved 17 kinds of β-lactam antibiotics, headed by ceftriaxone sodium. The most common manifestation was skin and accessory damage; nervous system and gastrointestinal system damage were also easier to find, and the administration route was mainly intravenous infusion. Conclusion: The clinical application of β-lactam antibiotics should pay attention to adverse reaction monitoring and rational drug use to reduce the incidence of adverse reactions

The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection

Genomic selection is a promising research area due to its practical application in breeding. In this study, impact of realized genetic relationship and linkage disequilibrium (LD) on marker density and training population size required was investigated and their impact on practical application was further discussed. This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17). Two populations were genotyped with different marker sets at different density: IBM Syn4 and IBM Syn10. A high-density marker set in Syn10 was imputed into the Syn4 population with low marker density. Seven different prediction scenarios were carried out with a random regression best linear unbiased prediction (RR-BLUP) model. The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy. Taken the short-term cost for consideration, relationship information is more valuable than LD information. Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Characterization and Mechanism of a New Superhydrophobic Deicing Coating Used for Road Pavement

Owing to its high efficiency and low environmental impact, superhydrophobic deicing coating material has a bright future for application on road pavements. In this paper, a heterogeneous nucleation ice crystal growth model is proposed, with particular focus on the effect of surface roughness and the contact angle. The ice suppression mechanism of superhydrophobic materials is determined by this model and experimentally verified. The experimental results of the water contact angle and anti-skid tests illustrated that the prepared TiO2–octadecanoic acid coating material has a contact angle greater than 150° and good skid resistance. The freezing test confirms that the applied coating on the surface can effectively delay the crystallization of water droplets and maintain the waterdrop’s semi-spherical shape after freezing. The microstructure observation demonstrates the TiO2–octadecanoic acid material has a good micro-nano mastoid structure. Consequently, the proposed coating materials could possibly be utilized for effectively enhancing the deicing performance of pavements

Key Points of Simple Cultivation Technique for Whole-plant Silage Maize in Guangxi

With the vigorous development of animal husbandry in Guangxi, feed problems have become increasingly prominent. Silage maize has the characteristics of rapid growth, high nutritional value, easy digestion and absorption, and a large amount of biological output being obtained in a short time. It is one of the ideal basic feeds for cattle and sheep and other breeding industries. Based on this, the simple cultivation technique of whole-plant silage maize was summarized from the aspects of land preparation, selection of maize variety, sowing, field management, pest control and timely harvesting, so as to provide technical reference for scientific planting of silage maize in Guangxi

SEF-UNet: advancing abdominal multi-organ segmentation with SEFormer and depthwise cascaded upsampling

The abdomen houses multiple vital organs, which are associated with various diseases posing significant risks to human health. Early detection of abdominal organ conditions allows for timely intervention and treatment, preventing deterioration of patients’ health. Segmenting abdominal organs aids physicians in more accurately diagnosing organ lesions. However, the anatomical structures of abdominal organs are relatively complex, with organs overlapping each other, sharing similar features, thereby presenting challenges for segmentation tasks. In real medical scenarios, models must demonstrate real-time and low-latency features, necessitating an improvement in segmentation accuracy while minimizing the number of parameters. Researchers have developed various methods for abdominal organ segmentation, ranging from convolutional neural networks (CNNs) to Transformers. However, these methods often encounter difficulties in accurately identifying organ segmentation boundaries. MetaFormer abstracts the framework of Transformers, excluding the multi-head Self-Attention, offering a new perspective for solving computer vision problems and overcoming the limitations of Vision Transformers and CNN backbone networks. To further enhance segmentation effectiveness, we propose a U-shaped network, integrating SEFormer and depthwise cascaded upsampling (dCUP) as the encoder and decoder, respectively, into the UNet structure, named SEF-UNet. SEFormer combines Squeeze-and-Excitation modules with depthwise separable convolutions, instantiating the MetaFormer framework, enhancing the capture of local details and texture information, thereby improving edge segmentation accuracy. dCUP further integrates shallow and deep information layers during the upsampling process. Our model significantly improves segmentation accuracy while reducing the parameter count and exhibits superior performance in segmenting organ edges that overlap each other, thereby offering potential deployment in real medical scenarios