Development and Characterization of Environmentally Friendly Insulation Materials for the Building Industry from Olive Pomace Waste

O. Gutierrez,R. Balart,D. Lascano,L. Quiles-Carrillo,E. Fages,L. Sanchez-Nacher 한국섬유공학회 2020 Fibers and polymers Vol.21 No.5

This work is focused on the upgrading olive oil pomace to obtain high environmentally friendly insulationmaterials for the construction and building industry. Upgrading of these wastes has been carried out by using the wet-laidtechnology in combination with different thermoplastic binder fibers both from natural and synthetic origin. After the wetlaidprocess, the obtained veils or non-wovens were subjected to different processes: a hot-press moulding process or acontinuous lamination process. The acoustic insulation properties were obtained by the Kundt’s tube while the thermalinsulating properties were obtained through determining heat transmission resistance (R) and heat absorption parameter (λ). These tests have revealed the excellent insulation properties of the olive pomace-derived materials, specifically, those basedon high surface density non-wovens. The complementary process that follows wet-laid, i.e. hot-press moulding orcontinuous lamination process has a remarkable effect on insulation behaviour. In particular, the hot-pressed materials fromolive pomace show better acoustic insulation while the continuously laminated materials offer the best thermal insulationproperties.

A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling

Gutierrez-Garcia, J.O.,Sim, K.M. North-Holland ; Elsevier Science Ltd 2013 Future generation computer systems Vol.29 No.7

The scheduling and execution of bag-of-tasks applications (BoTs) in Clouds is performed on sets of virtualized Cloud resources that start being exhausted right after their allocation disregarding whether tasks are being executed. In addition, BoTs may be executed in potentially heterogeneous sets of Cloud resources, which may be either previously allocated for a different and fixed number of hours or dynamically reallocated as needed. In this paper, a family of 14 scheduling heuristics for concurrently executing BoTs in Cloud environments is proposed. The Cloud scheduling heuristics are adapted to the resource allocation settings (e.g., 1-hour time slots) of Clouds by focusing on maximizing Cloud resource utilization based on the remaining allocation times of Cloud resources. Cloud scheduling heuristics supported by information about BoT tasks (e.g., task size) and/or Cloud resource performances are proposed. Additionally, scheduling heuristics that require no information of either Cloud resources or tasks are also proposed. The Cloud scheduling heuristics support the dynamic inclusion of new Cloud resources while scheduling and executing a given BoT without rescheduling. Furthermore, an elastic Cloud resource allocation mechanism that autonomously and dynamically reallocates Cloud resources on demand to BoT executions is proposed. Moreover, an agent-based Cloud BoT scheduling approach that supports concurrent and parallel scheduling and execution of BoTs, and concurrent and parallel dynamic selection and composition of Cloud resources (by making use of the well-known contract net protocol) from multiple and distributed Cloud providers is designed and implemented. Empirical results show that BoTs can be (i) efficiently executed by attaining similar (in some cases shorter) makespans to commonly used benchmark heuristics (e.g., Max-min), (ii) effectively executed by achieving a 100% success execution rate even with high BoT execution request rates and executing BoTs in a concurrent and parallel manner, and that (iii) BoTs are economically executed by elastically reallocating Cloud resources on demand.

Effect of Supplemental Corn Dried Distillers Grains with Solubles Fed to Beef Steers Grazing Native Rangeland during the Forage Dormant Season

Murillo, M.,Herrera, E.,Ruiz, O.,Reyes, O.,Carrete, F.O.,Gutierrez, H. Asian Australasian Association of Animal Productio 2016 Animal Bioscience Vol.29 No.5

Two experiments were conducted to evaluate the effects of the level of corn dry distillers grains with solubles (CDDGS) supplementation on growing performance, blood metabolites, digestion characteristics and ruminal fermentation patterns in steers grazing dormant forage. In Exp. 1, of growth performance, 120 steers ($204{\pm}5kg$ initial body weight [BW]) were distributed randomly into 3 groups (each of 40 steers), which were provided with the following levels of CDDGS supplement: 0%, 0.25%, or 0.50% BW. All groups of steers were grazed for 30 days in each of 3 grazing periods (March, April, and May). Approximately 1,000 ha of the land was divided with electric fencing into 3 equally sized pastures (333 ha in size). Blood samples were collected monthly from 20 steers in each grazing group for analysis of glucose (G), urea-nitrogen (UN) and non-esterified fatty acids. Final BW, average daily gain (ADG) and supplement conversion (CDDGS-C) increased with increasing levels of CDDGS supplementation (p<0.05).The CDDGS supplementation also increased the plasma G and UN concentrations (p<0.05). In Exp. 2, of digestive metabolism, 9 ruminally cannulated steers ($BW=350{\pm}3kg$) were distributed, following a completely randomized design, into groups of three in each pasture. The ruminally cannulated steers were provided the same levels of CDDGS supplementation as in the growing performance study (0%, 0.25%, and 0.50% BW), and they grazed along with the other 40 steers throughout the grazing periods. The dry matter intake, crude protein intake, neutral detergent fiber intake (NDFI), apparent digestibility of dry matter (ADDM), crude protein (ADCP) and neutral detergent fiber (ADNDF) increased with increasing levels of CDDGS supplementation (p<0.05). The ruminal degradation rates of CP (kdCP), NDF (kdNDF) and passage rate (kp) also increased with increasing levels of CDDGS supplementation (p<0.05). Ruminal ammonia nitrogen ($NH_3$-N) and propionate concentrations also increased with increasing levels of CDDGS supplementation (p<0.05). However, acetate concentrations decreased with increasing levels of CDDGS supplementation (p<0.05). Liquid dilution rate increased with increasing levels of CDDGS supplementation but ruminal liquid volume decreased (p<0.05). On the basis of these findings, we can conclude that CDDGS supplementation enhanced the productive performance of cattle grazing native rangeland without negatively affecting forage intake, glucose and urea-nitrogen blood concentrations, ruminal degradation and ruminal fermentation patterns.

IPCC AR6 WGI 아틀라스 주요 내용과 핵심 결과

윤진호,J. M. Gutierrez,R. G. Jones,G. T. Narisma,L. M. Alves,M. Amjad,I. V. Gorodetskaya,M. Grose,N. A. B. Klutse,S. Krakovska,J. Li,D. Martinez-Castro,L. O. Mearns,S. H. Mernild,T. Ngo-Duc,B. van den Hu 한국기상학회 2021 한국기상학회 학술대회 논문집 Vol.2021 No.10

이번 기후변화 보고서에서는 기존의 보고서와는 달리 Interactive Atlas 라는 온라인 표출 방식을 활용하였다. 이 챕터는 5차보고서에서는 부록의 형태였으나 6차보고서에서는 완전히 독립된 챕터로 기존보고서의 지역기후변화 진단을 포함하는 내용으로 구성되었다.

Application of a deep learning algorithm to Compton imaging of radioactive point sources with a single planar CdTe pixelated detector

G. Daniel,Y. Gutierrez,O. Limousin 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.5

Compton imaging is the main method for locating radioactive hot spots emitting high-energy gammaray photons. In particular, this imaging method is crucial when the photon energy is too high for codedmask aperture imaging methods to be effective or when a large field of view is required. Reconstructionof the photon source requires advanced Compton event processing algorithms to determine the exactposition of the source. In this study, we introduce a novel method based on a Deep Learning algorithmwith a Convolutional Neural Network (CNN) to perform Compton imaging. This algorithm is trained onsimulated data and tested on real data acquired with Caliste, a single planar CdTe pixelated detector. Weshow that performance in terms of source location accuracy is equivalent to state-of-the-art algorithms,while computa