The importance of high crop residue demand on biogas plant site selection, scaling and feedstock allocation – A regional scale concept in a Hungarian study area

In regions characterised by intensive agriculture, livestock manure is a commonly used feedstock for biogas production. Due to its expensive transportation, manure sources are often the sole criteria during biogas plant site selection, regarding feedstock supply. Encouraging biogas plant operators to use larger amounts of crop residues in the feedstock is favourable from an energy management viewpoint, but its spatial projection on resource logistics and its significance on biogas plant selection is less investigated. In this study, scenarios were created with different feedstock compositions considering constant manure and varying crop residue ratios. Based on their potential biogas yields and the location of livestock farms, a manure source-oriented site selection and facility scaling was made in a Hungarian study area. The applied GIS-based feedstock allocation and logistic analysis defined the crop acquisition possibilities and optimal transportation routes, assuming multiple resource-competitive biogas plants. The results indicate that feedstock composition can indirectly impact the site selection procedure and supply security if high crop residue demand is considered. Resource acquisition possibilities and economic feasibility are significantly affected by the location and density of the proposed biogas plants and their relative position to the crop supply areas. Due to the geographical heterogeneity of the supply side and the demand points, the transportation costs of crop residues and the digestate exceed those of the manure in all scenarios, which draws attention to the importance of spatial availability of crop residues during biogas plant site selection and scaling

A knowledge-based decision support system for roofing materials selection and cost estimating: a conceptual framework and data modelling

A plethora of materials is available to the modern day house designer but selecting the appropriate material is a complex task. It requires synthesising a multitude of performance criteria such as initial cost, maintenance cost, thermal performance and sustainability among others. This research aims to develop a Knowledge-based Decision support System for Material Selection (KDSMS) that facilitates the selection of optimal material for different sub elements of a roof design. The proposed system also has a facility for estimating roof cost based on the identified criteria. This paper presents the data modelling conceptual framework for the proposed system. The roof sub elements are modelled on the Building Cost Information Service (BCIS) Standard Form of Cost Analysis. This model consists of a knowledge base and a database to store different types of roofing materials with their corresponding performance characteristics and rankings. The system s knowledge is elicited from an extensive review of literature and the use of a domain expert forum. The proposed system employs the multi criteria decision method of TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution), to resolve the materials selection and optimisation problem. The KDSMS is currently being developed for the housing sector of Northern Ireland

Judgment and Choice in Personnel Selection

[Excerpt] Imagine that you have set out to buy a used car. You examine eight cars before making your choice, test driving some of them and rejecting others at first glance (due for example to excessive rust). A researcher asks you to rate each of the eight cars in terms of overall quality. The researcher proceeds to sharply criticize you for carrying out an unsystematic search process. Your failure to test-drive every car and to ask the same questions to the dealers about each car has caused you to do a poor job of rank-ordering the cars. You respond that, since you could only afford one car, you had no interest in rank-ordering or in assigning ratings to the entire set of cars. It seems unfair to be criticized for poor performance of a task which was unrelated to your original mission of buying the best used car available. This paper explores the possibility that a similar misspecification of the goals of employee selection has caused researchers to criticize selectors for behavior which may not adversely affect the goal of hiring the best individual from among a group of candidates

Scaling of the risk landscape drives optimal life history strategies and the evolution of grazing

Consumers face numerous risks that can be minimized by incorporating different life-history strategies. How much and when a consumer adds to its energetic reserves or invests in reproduction are key behavioral and physiological adaptations that structure much of how organisms interact. Here we develop a theoretical framework that explicitly accounts for stochastic fluctuations of an individual consumer's energetic reserves while foraging and reproducing on a landscape with resources that range from uniformly distributed to highly clustered. First, we show that optimal life-history strategies vary in response to changes in the mean productivity of the resource landscape, where depleted environments promote reproduction at lower energetic states, greater investment in each reproduction event, and smaller litter sizes. We then show that if resource variance scales with body size due to landscape clustering, consumers that forage for clustered foods are susceptible to strong Allee effects, increasing extinction risk. Finally, we show that the proposed relationship between consumer body size, resource clustering, and Allee effect-induced population instability offers key ecological insights into the evolution of large-bodied grazing herbivores from small-bodied browsing ancestors.Comment: 9 pages, 5 figures, 3 Supplementary Appendices, 2 Supplementary Figure

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Internal efficiency of nutrient utilization: what is it and how to measure it during vegetative plant growth?

Efficient use of the resources required by plants to sustain crop production is considered an important objective in agriculture. In this context, the idea of developing crops with an enhanced ability to utilize mineral nutrients already taken up by roots has been proposed. In recent years powerful tools that allow the association of phenotypic variation with high-resolution genetic maps of crop plants have also emerged. To take advantage of these tools, accurate methods are needed to estimate the internal efficiency of nutrient utilization (ENU) at the whole-plant level, which requires using suitable conceptual and experimental approaches. Here we highlight some inconsistencies in the definitions of ENU commonly used for ENU ‘phenotyping’ at the vegetative stage and suggest that it would be convenient to adopt a dynamic definition. The idea that ENU should provide information about the relationship between carbon and mineral nutrient economies mainly during the period under which growth is actually affected by low internal nutrient concentration is here advocated as a guide for the selection of adequate operational ENU formulae for the vegetative stage. The desirability of using experimental approaches that allow removal of the influence of nutrient acquisition efficiency on ENU estimations is highlighted. It is proposed that the use of simulation models could help refine the conclusions obtained through these experimental procedures. Some potential limitations in breeding for high ENU are also considered.Fil: Santa Maria, Guillermo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Moriconi, Jorge Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Oliferuk, Sonia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentin

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

Optimal and Myopic Information Acquisition

We consider the problem of optimal dynamic information acquisition from many correlated information sources. Each period, the decision-maker jointly takes an action and allocates a fixed number of observations across the available sources. His payoff depends on the actions taken and on an unknown state. In the canonical setting of jointly normal information sources, we show that the optimal dynamic information acquisition rule proceeds myopically after finitely many periods. If signals are acquired in large blocks each period, then the optimal rule turns out to be myopic from period 1. These results demonstrate the possibility of robust and "simple" optimal information acquisition, and simplify the analysis of dynamic information acquisition in a widely used informational environment