Possibilities and problems with the use of models as a communication tool in water resource management

Politicians and policy-makers, as well as modellers, often nurses an expectation that model derived results is an objective source of information that can be used to support decisions. However, several prerequisites have to be dealt with in order to ensure that models can be used as legitimate and efficient tools in water resource management. Based on empirical material from recent studies on the use of models in stakeholder dialogues, mainly focusing on catchment nutrient transport, two central problems are identified: (a) Models are laden with choices and thus depend on assumptions and priorities of modellers. (b) There are several factors that influence ability and willingness of stakeholders (as information recovers) to criticize or accept results of the modelling exercise. Recognized factors likely to influence stakeholders' acceptance of model derived results include issues at stake, stakeholders' ability to criticize model derived information, and their trust in the institutions that have developed or applied the used models. Identified prerequisites for successful use of models in integrated water resource management include: consideration of user relevance, awareness of and preparedness to handle constraints linked to communication of model-based results, transparency of used models and data and of involved uncertainties, mutual respect between experts and stakeholders and between involved stakeholder groups, a robust institutional network, and sufficient time for dialogues. Development and use of strategies for participatory modelling, based on a continuous dialogue between experts and stakeholders is recommended as a way to facilitate that the prerequisites for a successful use of models in water resource management are fulfilled.     

Extracting trust information from security system of a service

The issue of trust is a research problem in emerging open environments, such as ubiquitous networks. Such environments are highly dynamic and they contain diverse number of services and autonomous entities. Entities in open environments have different security needs from services. Trust computations related to the security systems of services necessitate information that meets needs of each entity. Obtaining such information is a challenging issue for entities. In this paper, we propose a model for extracting trust information from the security system of a service based on the needs of an entity. We formally represent security policies and security systems to extract trust information according to needs of an entity. The formal representation ensures an entity to extract trust information about a security property of a service and trust information about whole security system of the service. The proposed model is applied to Dental Clinic Patient Service as a case study with two scenarios. The scenarios are analyzed experimentally with simulations. The experimental evaluation shows that the proposed model provides trust information related to the security system of a service based on the needs of an entity and it is applicable in emerging open environments.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

A modified genetic algorithm for forecasting fuzzy time series

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.     

Robust multilayer neural network based on median neuron model

Multilayer perceptron has been widely used in time series forecasting for last two decades. However, it is a well-known fact that the forecasting performance of multilayer perceptron is negatively affected when data have outliers and this is an important problem. In recent years, some alternative neuron models such as generalized-mean neuron, geometric mean neuron, and single multiplicative neuron have been also proposed in the literature. However, it is expected that forecasting performance of artificial neural network approaches based on these neuron models can be also negatively affected by outliers since the aggregation function employed in these models is based on mean value. In this study, a new multilayer feed forward neural network, which is called median neuron model multilayer feed forward (MNM-MFF) model, is proposed in order to deal with this problem caused by outliers and to reach high accuracy level. In the proposed model, unlike other models suggested in the literature, MNM which has median-based aggregation function is employed. MNM is also firstly defined in this study. MNM-MFF is a robust neural network method since aggregation functions in MNM-MFF are based on median, which is not affected much by outliers. In addition, to train MNM-MFF model, particle swarm optimization method was utilized. MNM-MFF was applied to two well-known time series in order to evaluate the performance of the proposed approach. As a result of the implementation, it was observed that the proposed MNM-MFF model has high forecasting accuracy and it is not affected by outlier as much as multilayer perceptron model. Proposed method brings improvement in 7 % for data without outlier, in 90 % for data with outlier, in 95 % for data with bigger outlier.     

Unveiling the presence of mixed oxidation states of Europium in Li7+δEuxLa3−δZr2−δO12−δ garnet and its impact on the Li-ion conductivity

Recently, lanthanides have been employed by researchers to examine their impact on the structure and properties of Li₇La₃Zr₂O₁₂ (LLZO) garnets. In this regard, we developed Europium oxide (Eu₂O₃) doped LLZO (Li_7+δEu_xLa_3−δZr_2−δO_12−δ) solid electrolyte which demonstrates a cubic phase with the symmetry of Iad (No.230) at room temperature. In this investigation, different concentrations of Eu ranging from 0.1 to 0.6 atoms per formula unit (pfu) were doped into Li₇La₃Zr₂O₁₂ to evaluate the impact of Eu on the stability of the cubic phase and thereby the ionic conductivity. The results unveiled that upon doping Eu³⁺ ions, the Eu²⁺ state is also formed and is then self-doped into the structure in which Rietveld refinement coupled with XPS, EPR, and solid-state NMR suggests that Eu³⁺ ions most probably partially occupy Zr⁴⁺ (16a) site, the Eu²⁺ ions occupy La³⁺ (24d) site, and the Li⁺ ions occupy two different sites (24d and 96h). It was further found that such a site preference induces distortion at LaO₈ polyhedrons opening up the neck for Li-ions diffusion, thereby enhancing the ionic conductivity. Moreover, it was revealed that Li-ions probably hop from 96h to 24d and then to 96h site to generate the Li-ion movement. Overall, by introducing Eu ions into the LLZO structure, an enhanced bulk ionic conductivity of 0.30 × 10⁻³ S/cm at 298 K with a minimum electronic conductivity of 2.547 × 10⁻⁹ S/cm at 298 K was achieved.     

Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application

Principal Component Analysis (PCA) is a statistical process monitoring technique that has been widely used in industrial applications. PCA methods for Fault Detection (FD) use data collected from a steady-state process to monitor T² and Q statistics with a fixed threshold. For the systems where transient values of the processes must be taken into account, the usage of a fixed threshold in PCA method causes false alarms and missing data that significantly compromise the reliability of the monitoring systems. In the present article, a new PCA method based on variance sensitive adaptive threshold (T_vsa) is proposed to overcome false alarms which occur in the transient states according to changing process conditions and the missing data problem. The proposed method is implemented and validated experimentally on an electromechanical system. The method is compared with the conventional monitoring methods. Experimental tests and tabulated results confirm the fact that the proposed method is applicable and effective for both the steady-state and transient operations and gives early warning to operators.     

Analysis of autocatalytic networks in biology

Autocatalytic networks, in particular the glycolytic pathway, constitute an important part of the cell metabolism. Changes in the concentration of metabolites and catalyzing enzymes during the lifetime of the cell can lead to perturbations from its nominal operating condition. We investigate the effects of such perturbations on stability properties, e.g., the extent of regions of attraction, of a particular family of autocatalytic network models. Numerical experiments demonstrate that systems that are robust with respect to perturbations in the parameter space have an easily “verifiable” (in terms of proof complexity) region of attraction properties. Motivated by the computational complexity of optimization-based formulations, we take a compositional approach and exploit a natural decomposition of the system, induced by the underlying biological structure, into a feedback interconnection of two input–output subsystems: a small subsystem with complicating nonlinearities and a large subsystem with simple dynamics. This decomposition simplifies the analysis of large pathways by assembling region of attraction certificates based on the input–output properties of the subsystems. It enables numerical as well as analytical construction of block-diagonal Lyapunov functions for a large family of autocatalytic pathways.     

The influence of nitrogen doping on reduced graphene oxide as highly cyclable Li-ion battery anode with enhanced performance

A straightforward, one-step route has been established to fabricate reduced- (rGO) and nitrogen-doped reduced graphene oxide (NrGO) with remarkable lithium-ion storage properties. The graphene oxide (GO) was synthesized as starting material by improved Hummers’ method. Thereafter, thermally annealing GO with NH₃ at elevated temperature to synthesize NrGO was yielded a more open structure with nitrogen sites suitable for enhanced Li intercalation. NrGO exhibited a reversible capacity of 240 mAhg^?1 at 10 Ag^-1 after 500 cycles with 90% capacity retention, which is the best result achieved among graphene oxide-based anodes at this current density. In contrast to rGO, NrGO cells exhibited a gradually increasing capacity profile, reaching up to 114% of the initial capacity at 0.1, 2, and 10 Ag^-1 current densities. Results showed that high occupancy of pyridinic N within NrGO enhanced battery performance and cell kinetics upon cycling which offers long-time operability at high current density.     

Latent heat energy storage characteristics of building composites of bentonite clay and pumice sand with different organic PCMs

In the present work, six new kinds of building composite PCMs (BCPCMs), PS/octadecane, BC/octadecane, PS/CA–MA, BC/CA–MA, PS/PEG1000, and BC/PEG1000 composites, were prepared by using vacuum impregnation method. The maximum percent of PCM in the composites was assigned to be 12, 13, 18, 23, 30, and 42 wt%, respectively. The form‐stable BCPCMs were characterized using SEM, FT‐IR, DSC, and TG analysis techniques. The characterization results showed the existence of homogenous dispersion of the PCM into the PBM matrixes. The DSC measurements indicated that the melting temperatures of the form‐stable BCPCMs are in the range of 20–33°C while they have latent heats of melting in the range of about 28–55 J/g. These results make them promising BCPCMs for low temperature‐passive TES applications in buildings. Thermal cycling test indicated that the prepared BCPCMs have good thermal reliability and chemical stability. TG analysis proved that the prepared BCPCMs have good thermal durability. In addition, the thermal conductivity of BCPCMs was enhanced considerably by addition of expanded graphite (EG). The improvement in thermal conductivity of the BCPCMs caused appreciably reduction in their melting times. Copyright © 2014 John Wiley & Sons, Ltd.