Structural and Morphological Characterization of Micro and Nanofibers Produced by Electrospinning and Solution Blow Spinning: A Comparative Study

Nonwoven mats of poly(lactic acid) (PLA), poly(ethylene oxide) (PEO), and poly(ε-caprolactone) (PCL) were prepared at a nano- and submicron scale by solution blow spinning (SBS) and electrospinning in order to compare crystalline structure and morphology developed by both processes during fiber formation. Polymer solutions were characterized by rheometry and tensiometry. Spun fibers were characterized by several analytical steps. SEM analyses showed that both solution blow spun and electrospun fibers had similar morphology. Absence of residual solvents and characteristic infrared bands in the solution blow spun fibers for PLA, PCL, and PEO was confirmed by FTIR studies. XRD diffraction patterns for solution blow spun and electrospun mats revealed some differences related to distinct mechanisms of fiber formation developed by each process. Significant differences in thermal behavior by DSC were observed between cast films of PLA, PCL, and PEO and their corresponding spun nanofibers. Furthermore, the average contact angles for spun PLA and PCL were higher than for electrospun mats, whereas it was slightly lower for PEO. When comparing electrospun and solution blow spun fibers, it was possible to verify that fiber morphology and physical properties depended both on the spinning technique and type of polymer

An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings

An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints related to the string length. A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition. Besides, it reduces the complexity of the string recognition task considerably since it drops out classical steps, in special preprocessing, segmentation, and a set of classifiers devoted to strings with a specific length

Uma Proposta para Classificação de Famílias de Programas Maliciosos baseada em Texturas

Classificação de malware utilizando análise de texturas é uma abordagem que tem sido amplamente empregada por diversos autores para resolver o problema de categorização de famílias. Interpretar um arquivo binário como imagem pode trazer vantagens, por exemplo, evitar as atuais técnicas de ofuscação usadas por malware. Ao longo dos últimos anos, foram propostos diferentes classificadores e descritores de textura para alcançar altas taxas de acurácia. Neste artigo são realizados experimentos utilizando Random Forest para classificação em um dataset público e em um local, apresentando uma discussão sobre o uso de datasets não apropriados pela literatura para construir classificadores genéricos de malware