Gestión turística y tecnologías de la información y la comunicación (TIC): El nuevo enfoque de los destinos inteligentes

Este artículo analiza el nuevo enfoque de los destinos inteligentes para la gestión turística a escala local. Se estudia su génesis y se realiza una propuesta de conceptualización desde una perspectiva sistémica, que se contrasta con la gestión turística actual mediante una encuesta a municipios turísticos de la Comunidad Valenciana. Los resultados ponen de relieve que no se ha asumido como objetivo de gestión el enfoque de los destinos inteligentes, el cual requiere un proyecto global que incorpore cinco ámbitos fundamentales: gobernanza, sostenibilidad, conectividad, sistema de información e innovación. No obstante, la complejidad asociada a este enfoque hace aconsejable un desarrollo de tipo flexible, escalable y adaptado a cada entorno territorial.

Ecosystem structure, function, and composition in rangelands are negatively affected by livestock grazing

Reports of positive or neutral effects of grazing on plant species richness have prompted calls for livestock grazing to be used as a tool for managing land for conservation. Grazing effects, however, are likely to vary among different response variables, types, and intensity of grazing, and across abiotic conditions. We aimed to examine how grazing affects ecosystem structure, function, and composition. We compiled a database of 7615 records reporting an effect of grazing by sheep and cattle on 278 biotic and abiotic response variables for published studies across Australia. Using these data, we derived three ecosystem measures based on structure, function, and composition, which were compared against six contrasts of grazing pressure, ranging from low to heavy, two different herbivores (sheep, cattle), and across three different climatic zones. Grazing reduced structure (by 35%), function (24%), and composition (10%). Structure and function (but not composition) declined more when grazed by sheep and cattle together than sheep alone. Grazing reduced plant biomass (40%), animal richness (15%), and plant and animal abundance, and plant and litter cover (25%), but had no effect on plant richness nor soil function. The negative effects of grazing on plant biomass, plant cover, and soil function were more pronounced in drier environments. Grazing effects on plant and animal richness and composition were constant, or even declined, with increasing aridity. Our study represents a comprehensive continental assessment of the implications of grazing for managing Australian rangelands. Grazing effects were largely negative, even at very low levels of grazing. Overall, our results suggest that livestock grazing in Australia is unlikely to produce positive outcomes for ecosystem structure, function, and composition or even as a blanket conservation tool unless reduction in specific response variables is an explicit management objective.

Theory for designing nature reserves for single species

We examine the question of the optimal number of reserves that should be established to maximize the persistence of a species. We assume that the mean time to extinction of a single population increases as a power of the habitat area, that there is a certain amount of habitat to be reserved, and that the aim is to determine how this habitat is most efficiently divided. The optimal configuration depends on whether the management objective is to maximize the mean time to extinction or minimize the risk of extinction. When maximizing the mean time to extinction, the optimal number of independent reserves does not depend on the amount of available habitat for the reserve system. In contrast, the risk of extinction is minimized when individual reserves are equal to the optimal patch size, making the optimal number of reserves linearly proportional to the amount of available habitat. A model that includes dispersal and correlation in the incidence of extinction demonstrates the importance of considering the relative rate at which these two factors decrease with distance between reserves. A small number of reserves is optimal when the mean time to extinction increases rapidly with habitat area or when risks of extinction are high.

Control of structured populations by harvest

It has long been recognized that demographic structure within a population can significantly affect the likely outcomes of harvest. Many studies have focussed on equilibrium dynamics and maximization of the value of the harvest taken. However, in some cases the management objective is to maintain the population at a abundance that is significantly below the carrying capacity. Achieving such an objective by harvest can be complicated by the presence of significant structure (age or stage) in the target population. in such cases, optimal harvest strategies must account for differences among age- or stage-classes of individuals in their relative contribution to the demography of the population. In addition, structured populations are also characterized by transient non-linear dynamics following perturbation, such that even under an equilibrium harvest, the population may exhibit significant momentum, increasing or decreasing before cessation of growth. Using simple linear time-invariant models, we show that if harvest levels are set dynamically (e.g., annually) then transient effects can be as or more important than equilibrium outcomes. We show that appropriate harvest rates can be complicated by uncertainty about the demographic structure of the population, or limited control over the structure of the harvest taken. (c) 2006 Elsevier B.V. All rights reserved.

Smart demand side management of low-voltage distribution networks using multi-objective decision making

A novel intelligent online demand side management system is proposed for peak load management in low-voltage distribution networks. This method uses low-cost controllers with low-bandwidth two-way communication installed in custumers’ premises and at distribution transformers to manage the peak load while maximising customer satisfaction. A multi-objective decision making process is proposed to select the load(s) to be delayed or controlled. The efficacy of the proposed control system is verified by simulation of three different feeder types.

Voltage regulation, power balancing and battery storage discharge control by smart demand side management and multi-objective decision making

A novel intelligent online demand side management system is proposed for peak load management. The method also regulates the network voltage, balances the power in three phases and coordinates the battery storage discharge within the network. This method uses low cost controllers with low bandwidth two-way communication installed in costumers' premises and at distribution transformers to manage the peak load while maximizing customer satisfaction. A multi-objective decision making process is proposed to select the load(s) to be delayed or controlled. The efficacy of the proposed control system is verified through an event-based developed simulation in Matlab.

Non-monetised multi-objective decision making system for road management

This paper presents a performance-based optimisation approach for conducting trade-off analysis between safety (roads) and condition (bridges and roads). Safety was based on potential for improvement (PFI). Road condition was based on surface distresses and bridge condition was based on apparent age per subcomponent. The analysis uses a non-monetised optimisation that expanded upon classical Pareto optimality by observing performance across time. It was found that achievement of good results was conditioned by the availability of early age treatments and impacted by a frontier effect preventing the optimisation algorithm from realising of the long-term benefits of deploying actions when approaching the end of the analysis period. A disaggregated bridge condition index proved capable of improving levels of service in bridge subcomponents.

Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

Dynamic SLA management with forecasting using multi-objective optimization

Cost-efficient operation while satisfying performance and availability guarantees in Service Level Agreements (SLAs) is a challenge for Cloud Computing, as these are potentially conflicting objectives. We present a framework for SLA management based on multi-objective optimization. The framework features a forecasting model for determining the best virtual machine-to-host allocation given the need to minimize SLA violations, energy consumption and resource wasting. A comprehensive SLA management solution is proposed that uses event processing for monitoring and enables dynamic provisioning of virtual machines onto the physical infrastructure. We validated our implementation against serveral standard heuristics and were able to show that our approach is significantly better.

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

In several regions of the world, climate change is expected to have severe impacts on agricultural systems. Changes in land management are one way to adapt to future climatic conditions, including land-use changes and local adjustments of agricultural practices. In previous studies, options for adaptation have mostly been explored by testing alternative scenarios. Systematic explorations of land management possibilities using optimization approaches were so far mainly restricted to studies of land and resource management under constant climatic conditions. In this study, we bridge this gap and exploit the benefits of multi-objective regional optimization for identifying optimum land management adaptations to climate change. We design a multi-objective optimization routine that integrates a generic crop model and considers two climate scenarios for 2050 in a meso-scale catchment on the Swiss Central Plateau with already limited water resources. The results indicate that adaptation will be necessary in the study area to cope with a decrease in productivity by 0–10 %, an increase in soil loss by 25–35 %, and an increase in N-leaching by 30–45 %. Adaptation options identified here exhibit conflicts between productivity and environmental goals, but compromises are possible. Necessary management changes include (i) adjustments of crop shares, i.e. increasing the proportion of early harvested winter cereals at the expense of irrigated spring crops, (ii) widespread use of reduced tillage, (iii) allocation of irrigated areas to soils with low water-retention capacity at lower elevations, and (iv) conversion of some pre-alpine grasslands to croplands.

Toward more effective management : the role of objective setting and monitoring : training materials /

"The work involved in developing and producing these materials was performed pursuant to Contract No. HEW-100-75-0055 with the U.S. Department of Health, Education, and Welfare ..."

Temperature management for heterogeneous multi-core FPGAs using adaptive evolutionary multi-objective approaches

Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionary algorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objective optimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.

Control layer resource management in SDN-IoT networks using multi-objective constraint

Software Defined Networking (SDN) and Internet of Things (IoT) integration has thrown many critical challenges. Specifically, in heterogeneous SDN-IoT ecosystem, optimized resources utilization and effective management at the control layer is very difficult. This mainly affects the application specific Quality of Service (QoS) and energy consumption of the IoT network. Motivated from this, we propose a new Resource Management (RM) method at the control layer, in distributed SDN-IoT networks. This paper starts with reasons that why at control layer RM is more complex in the SDN-IoT ecosystem. After-that, we highlight motivated examples that necessitate to investigate new RM methods in SDN-IoT context. Further, we propose a novel method to compute controller performance. Theoretical analysis is conducted to prove that the proposed method is better than the existing methods.