Stephany C. de Rezende^1,2, João A. Pinto^1,3, Isabel P. Fernandes^1,3, Fernanda V. Leimann^1,2* and Maria-Filomena Barreiro^1,3*

Journal of Renewable Materials, Vol.6, No.7, pp. 715-723, 2018, DOI:10.32604/JRM.2018.00028

Abstract Pine-fruit shell (PFS) is a lignocellulosic residue derived from the fruit of Araucaria angustifolia, a coniferous tree native of South America, part of a whole vegetation of the Atlantic Forest, found in the South and Southwest of Brazil. In this work PFS will be characterized and used in the production of PFS-based polyols through oxypropylation. Three series were chosen (PFS/propylene oxide (PO) (w/v, g/mL) of 30/70, 20/80 and 10/90) with four catalyst levels (5%, 10%, 15% and 20%, (w/w, PFS based)). Oxypropylation occurred at moderate conditions of temperature, pressure and time giving rise to liquid… More >

Chengsheng Yuan^1,2,3, Xinting Li³, Q. M. Jonathan Wu³, Jin Li^4,5, Xingming Sun^1,2

CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 357-372, 2017, DOI:10.3970/cmc.2017.053.357

Abstract Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints, which are made of common fingerprint materials, such as silicon, latex, etc. Thus, to protect our privacy, many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint. Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features, but these methods normally destroy or lose spatial information between pixels. Different from existing methods, convolutional neural network (CNN) can generate high-level semantic representations by learning and concatenating low-level edge and shape features from… More >

Rubí-Arriaga M, A González-Huerta, I Martínez-De La Cruz, O Franco-Mora, JF Ramírez-Dávila, JA López-Sandoval, GV Hernández-Flores

Phyton-International Journal of Experimental Botany, Vol.83, pp. 203-211, 2014, DOI:10.32604/phyton.2014.83.203

Abstract Sultepec, State of Mexico, located on the central part of Mexico, belongs to the Physiographic province “Sierra Madre del Sur” and to the Subprovince “Depresión del Balsas”. Although it is known for its floristic richness, it lacks an inventory of vascular plants, including the fruit species. The aim of this work was to elaborate a database including family, scientific name, local name, biological form, origin, use, management, production and service of the fruit species. Plants were collected continuously from June 2010 to June 2011. Subsequently, they were determined in the herbarium “Eizi Matuda” (CODAGEM) from… More >

A. Rama Mohan Rao¹, S. Krishna Kumar¹, K. Lakshmi¹

Structural Durability & Health Monitoring, Vol.8, No.3, pp. 271-294, 2012, DOI:10.32604/sdhm.2012.008.271

Abstract Current advancements in structural health monitoring, sensor and sensor network technologies have encouraged using large number of sensor networks in monitoring spatially large civil structures like bridges. Large amount of spatial information obtained from these sensor networks will enhance the reliability in truly assessing the state of the health of the structure. However, if sensors go faulty during operation, the feature extraction techniques embedded into SHM scheme may lead to an erroneous conclusion and often end up with false alarms. Hence it is highly desirable to robustly detect the faulty sensors, isolate and correct the… More >

Fausto Pedro García Márquez¹

Structural Durability & Health Monitoring, Vol.6, No.2, pp. 89-100, 2010, DOI:10.3970/sdhm.2010.006.089

Abstract This paper presents a simple graphic method for detecting and classifying faults in point mechanisms based on the study of some statistical parameters of the force and current signals of the point machine. Principal Components Analysis (PCA) employed in order to reduce the number of these parameters. PCA is utilised in this paper for modifying the parameter dataset, and reducing the coordinate system by linear transformation. It is then possible to plot the new coordinate system in 2 or 3 dimensions, where the faults can be detected and identified. In this work most of the More >

Chang Kook Oh¹, Hoon Sohn¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.8, No.3, pp. 91-100, 2008, DOI:10.3970/icces.2008.008.091

Abstract Structural Health Monitoring (SHM) is concerned with identifying damage based on measurements obtained from structures being monitored. For the civil structures exposed to time-varying environmental and operational conditions, it is inevitable that environmental and operational variability produces an adverse effect on the dynamic behaviors of the structures. Since the signals are measured under the influence of these varying conditions, normalizing the data to distinguish the effects of damage from those caused by the environmental and operational variations is important in order to achieve successful structural health monitoring goals. In this paper, kernel principal component analysis More >