Glycans are the most abundant fundamental biomolecules, but profiling glycans is challenging due to their structural complexity. To address this, a novel glycan detection platform is developed by integrating surface‐enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. Boronic acid receptors bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. Unlike prior studies that focus on designing a boronic acid with high binding selectivity toward a target glycan, this sensor is designed to analyze overall changes in spectral patterns using machine learning algorithms. For proof‐of‐concept, 4‐mercaptophenylboronic acid (4MBA) and 1‐thianthrenylboronic acid (1TBA) are used for glycan detection. The sensing platform successfully recognizes the stereoisomers and the structural isomers with different glycosidic linkages. The collective spectra that combine the spectra from both boronic acid receptors improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. This low‐cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent comparative glycan screening in standard biological laboratories.
The physical properties required in polypropylene composites (PPCs) vary depending on the purpose of use. In the manufacturing of PPCs, it is crucial to determine the types and quantities of numerous reinforcements to meet the required physical properties. Owing to industrial complexity, most PPC manufacturers produce the composites repeatedly until the desired physical properties are obtained. Hence, to reduce trial and error, we developed prediction models for the physical properties of PPCs based on commercial recipe data. The recipe data included information about five physical properties of composites manufactured using 90 materials. In complex industrial environments, because one recipe is usually composed of 2–12 materials, numerous combinations of data sets are created. It causes the lack of the same material combination data sets and thus makes it difficult to develop a good performance model. Therefore, a novel categorization process is suggested as data preprocessing to overcome the data imbalance problem. The models for predicting the five physical properties (flexural strength, melting index, tensile strength, specific gravity, and flexural modulus) were developed using random forest, and the performance of the prediction models was improved via hyperparameter optimization. Furthermore, the effects of the materials on the performance of the models were numerically described through variable importance analysis. Finally, a software was developed to implement the prediction models in the industry. The software was applied to a commercial composite and achieved high accuracy, demonstrating the effectiveness of this study. Thus, the software suggests decision‐making solutions to save cost and time by reducing the trial and error in the industrial environment with high complexity.
Packing algorithms are broadly used to avoid anti-malware systems, and the proportion of packed malware has been growing rapidly. However, just a few studies have been conducted on detection various types of packing algorithms in a systemic way. Following this understanding, we elaborate a method to classify packing algorithms of a given executable into three categories: single-layer packing, re-packing, or multi-layer packing. We convert entropy values of the executable file loaded into memory into symbolic representations, for which we used SAX (Symbolic Aggregate Approximation). Based on experiments of 2196 programs and 19 packing algorithms, we identify that precision (97.7%), accuracy (97.5%), and recall ( 96.8%) of our method are respectively high to confirm that entropy analysis is applicable in identifying packing algorithms.
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