Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Predicting potential customer needs and wants for agile design and manufacture in an industry 4.0 environment

Manufacturing is currently experiencing a paradigm shift in the way that products are designed, produced and serviced. Such changes are brought about mainly by the extensive use of the Internet and digital technologies. As a result of this shift, a new industrial revolution is emerging, termed “Industry 4.0” (i4), which promises to accommodate mass customisation at a mass production cost. For i4 to become a reality, however, multiple challenges need to be addressed, highlighting the need for design for agile manufacturing and, for this, a framework capable of integrating big data analytics arising from the service end, business informatics through the manufacturing process, and artificial intelligence (AI) for the entire manufacturing value chain. This thesis attempts to address these issues, with a focus on the need for design for agile manufacturing. First, the state of the art in this field of research is reviewed on combining cutting-edge technologies in digital manufacturing with big data analysed to support agile manufacturing. Then, the work is focused on developing an AI-based framework to address one of the customisation issues in smart design and agile manufacturing, that is, prediction of potential customer needs and wants. With this framework, an AI-based approach is developed to predict design attributes that would help manufacturers to decide the best virtual designs to meet emerging customer needs and wants predictively. In particular, various machine learning approaches are developed to help explain at least 85% of the design variance when building a model to predict potential customer needs and wants. These approaches include k-means clustering, self-organizing maps, fuzzy k-means clustering, and decision trees, all supporting a vector machine to evaluate and extract conscious and subconscious customer needs and wants. A model capable of accurately predicting customer needs and wants for at least 85% of classified design attributes is thus obtained. Further, an analysis capable of determining the best design attributes and features for predicting customer needs and wants is also achieved. As the information analysed can be utilized to advise the selection of desired attributes, it is fed back in a closed-loop of the manufacturing value chain: design → manufacture → management/service → → → design... For this, a total of 4 case studies are undertaken to test and demonstrate the efficacy and effectiveness of the framework developed. These case studies include: 1) an evaluation model of consumer cars with multiple attributes including categorical and numerical ones; 2) specifications of automotive vehicles in terms of various characteristics including categorical and numerical instances; 3) fuel consumptions of various car models and makes, taking into account a desire for low fuel costs and low CO2 emissions; and 4) computer parts design for recommending the best design attributes when buying a computer. The results show that the decision trees, as a machine learning approach, work best in predicting customer needs and wants for smart design. With the tested framework and methodology, this thesis overall presents a holistic attempt to addressing the missing gap between manufacture and customisation, that is meeting customer needs and wants. Effective ways of achieving customization for i4 and smart manufacturing are identified. This is achieved through predicting potential customer needs and wants and applying the prediction at the product design stage for agile manufacturing to meet individual requirements at a mass production cost. Such agility is one key element in realising Industry 4.0. At the end, this thesis contributes to improving the process of analysing the data to predict potential customer needs and wants to be used as inputs to customizing product designs agilely

Relation of BMI to a dual-energy X-ray absorptiometry measure of fatness

Dual-energy X-ray absorptiometry (DXA) is a valid technique for measuring the fat, bone and lean (muscle, organs and water) masses of the body. We evaluated relationships of BMI (kg/m2) with independent measurements of fat and lean masses using DXA in 226 adult volunteers. The evaluation was an application of a general approach to compositional data which has not previously been used for describing body composition. Using traditional regression analyses, when lean mass was held constant, BMI varied with fat mass (men r 0·75, P < 0·05 ; women r 0·85, P < 0·05); when fat mass was held constant, BMI varied with lean mass (men r 0·63, P < 0·05; women r 0·47, P < 0·05). In contrast, a regression model for compositional data revealed that BMI was: (a) strongly associated with log fat mass in both sexes (b1 4·86, P < 0·001 for all women and b1 5·96, P < 0·001 for all men); (b) not associated with bone mass, except in older men; (c) related to lean mass in women but not in men (b3 −4·04, P < 0·001 for all women and b1 −2·59, P < 0·15 for all men). Women with higher BMI tended to have more fat mass and more lean mass than women with lower BMI. Men with higher BMI had more fat mass but similar lean mass to men with lower BMI. Investigators need to be alert to the inaccuracy of BMI to assign a fatness risk factor to individuals, especially among wome

A finite strain, finite band method for modeling ductile fracture

We present a finite deformation generalization of the finite thickness embedded discontinuity formulation presented in our previous paper [A.E. Huespe, A. Needleman, J. Oliver, P.J. Sánchez, A finite thickness band method for ductile fracture analysis, Int. J. Plasticity 25 (2009) 2349–2365]. In this framework the transition from a weak discontinuity to a strong discontinuity can occur using a single constitutive relation which is of importance in a range of applications, in particular ductile fracture, where localization typically precedes the creation of new free surface. An embedded weak discontinuity is introduced when the loss of ellipticity condition is met. The resulting localized deformation band is given a specified thickness which introduces a length scale thus providing a regularization of the post-localization response. The methodology is illustrated through several example problems emphasizing finite deformation effects including the development of a cup-cone failure in round bar tension.A.E.H. and P.J.S. are grateful for financial support from ANPCyT and CONICET of Argentina through grants: PICT 2006-01232, PICT 2008-1228 and PIP 112-200901-00341. J.O. is grateful for financial support from the Spanish Ministry of Science and Innovation and the Catalan Government Research Department, under grants BIA2008-00411 and 2009 SGR 1510, respectively.Peer ReviewedPostprint (author's final draft

A finite thickness band method for ductile fracture analysis

We present a finite element method with a finite thickness embedded weak discontinuity to analyze ductile fracture problems. The formulation is restricted to small geometry changes. The material response is characterized by a constitutive relation for a progressively cavitating elastic–plastic solid. As voids nucleate, grow and coalesce, the stiffness of the material degrades. An embedded weak discontinuity is introduced when the condition for loss of ellipticity is met. The resulting localized deformation band is given a specified thickness which introduces a length scale thus providing a regularization of the post-localization response. Also since the constitutive relation for a progressively cavitation solid is used inside the band in the post-localization regime, the traction-opening relation across the band depends on the stress triaxiality. The methodology is illustrated through several example problems including mode I crack growth and localization and failure in notched bars. Various finite element meshes and values of the thickness of the localization band are used in the calculations to illustrate the convergence with mesh refinement and the dependence on the value chosen for the localization band thickness.Peer ReviewedPostprint (author’s final draft

Morphological and physiological responses of Galapagos endemic tree Croton scouleri to site conditions varying through its altitudinal range

: Galapagos is a natural laboratory offering a great opportunity to study plants functional traits. This study characterises morphological and ecophysiological responses of Croton scouleri, an endemic tree that habits from humid and upper elevations to semiarid lowlands, throughout an altitudinal gradient and in a manipulative experiment. Croton scouleri trees were gradually smaller with less total leaf area due to a gradual reduction in mean leaf size, and they folded their leaves at lower elevations. These results were also recorded after cutting every deep root. Two physiological traits that allowed Croton scouleri to avoid damages to the photosynthetic apparatus were detected between 30 and 150 m a.s.l. Lower variable fluorescence (Fv) and basal fluorescence (F0) keeping constant maximum photochemical efficiency of PSII (Fv/Fm) denoted a drop in chlorophyll concentration. Concomitantly, the recorded increase in the Quantum efficiency of PSII ( PSII) with similar Fv/Fm means that Croton scouleri could be using cyclic electron transport as photoprotective mechanism. On the other hand, a deep root system to reach the water table allowed Croton scouleri to behave as a drought-avoider, which was reflected in: (1) unvarying water status Leaf Water Content and Relative Water Content were always higher than 69 and 58%, respectively; (2) stable and low photoinhibition levels; and (3) unvarying leaf area index. However, Croton scouleri was not able to avoid drought at altitudes lower 30 m a.s.l. where similar responses to those recorded after root cutting were recorded.Junta de Andalucía AI60/0

Prolactin is a strong candidate for the regulation of luteal steroidogenesis in vizcachas (Lagostomus maximus)

Prolactin (PRL) is essential for the maintenance of the corpora lutea and the production of progesterone (P4) during gestation of mice and rats, which makes it a key factor for their successful reproduction. Unlike these rodents and the vast majority of mammals, female vizcachas (Lagostomus maximus) have a peculiar reproductive biology characterized by an ovulatory event during pregnancy that generates secondary corpora lutea with a consequent increment of the circulating P4. We found that, although the expression of pituitary PRL increased steadily during pregnancy, its ovarian receptor (PRLR) reached its maximum in midpregnancy and drastically decreased at term pregnancy. The luteinizing hormone receptor (LHR) exhibited a similar profile than PRLR. Maximum P4 and LH blood levels were recorded at midpregnancy as well. Remarkably, the P4-sinthesizing enzyme 3β-HSD accompanied the expression pattern of PRLR/LHR throughout gestation. Instead, the luteolytic enzyme 20α-HSD showed low expression at early and midpregnancy, but reached its maximum at the end of gestation, when PRLR/LHR/3ß-HSD expressions and circulating P4 were minimal. In conclusion, both the PRLR and LHR expressions in the ovary would define the success of gestation in vizcachas by modulating the levels of 20α-HSD and 3ß-HSD, which ultimately determine the level of serum P4 throughout gestation.Fil: Proietto, Sofia. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cortasa, Santiago Andrés. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Corso, María Clara. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Inserra, Pablo Ignacio Felipe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; ArgentinaFil: Charif, Santiago Elías. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schmidt, Alan Raul. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Di Giorgio, Noelia Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Lux Lantos, V.. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Vitullo, Alfredo Daniel. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Dorfman, Verónica Berta. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Halperin, Julia. Universidad Maimónides. Área de Investigaciones Biomédicas y Biotecnológicas. Centro de Estudios Biomédicos, Biotecnológicos, Ambientales y de Diagnóstico. Departamento de Estudios Biomédicos y Biotecnológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Water point mapping en Tiraque

En este artículo se describe la implementación de la metodología del Water Point Mapping (WPM) con el fin de estudiar el acceso sostenible al agua potable y al saneamiento básico e higiene en el municipio de Tiraque (Cochabamba, Bolivia) . E l WPM se fundamenta en realiza r un mapeo exhaustivo de los puntos de agua “mejorados”, y en este caso se ha complementado el mapeo con un muestreo aleatorio de casas para obtener información relacionada con el saneamiento y las prácticas higiénicas. Por lo tanto, el estudio usa dos fuentes de información diferentes: (i) el punto de agua, y (ii) la familia; y el análisis de datos posterior se plantea a tres escalas distintas: (i) la comunidad, (ii) el distrito, y (iii) la municipalidad. En base a toda la información recogida se han identificado y analizado un conjunto reducido de indicadores críticos para la e valuación del sector desde diferentes perspectivas (disponibilidad de infraestructura, estado de los puntos de agua y de las letrinas, calidad del agua, nivel de servicio, etc.). El estudio concluye que e l uso adecuado de un número limitado de indicadores (obtenidos gracias al WPM ) permite diseñar políticas de forma participada a escala local, así como planificar las inversiones necesarias para mejorar la situación de acceso al agua potable y al saneamiento básicoPeer ReviewedPostprint (published version

Fatigue Characterization Of A High-Performance Steel Fiber Reinforced Concrete (HPFRC) By Means Of Compressive, Flexural, And Z-Type Shear Tests

The use of fiber-reinforced concretes (FRC) for infrastructures subject to fatigue loading can result into an extension of their service life by providing enhanced ductility and toughness. The cyclic actions might affect the fiber-matrix interface and it is necessary to assess to what extent the degradation hinders the mechanical properties of these materials. Currently, the only predictive models for fatigue life and performance reduction are empirical. Therefore, a mechanical characterization is required for any mix whose composition and performance might differ from the one pertinent to the database the models are based on. This work presents the effect of high-cycle fatigue on a high-performance fiber-reinforced concrete (HPFRC) with hybrid fiber reinforcement. The material was characterized under compressive, flexural, and shear loads at various stress ranges. The Palmgren-Miner rule was applied to predict the fatigue life of the material. The results showed the effects of fatigue loading on the strength of the material. The compressive strength remained constant in most cases, while the flexural and the shear performances were slightly reduced by the cycling process. The predictive capacity of the P-M model proved to be reliable only in limited scenarios