Emulating cellular automata in chemical reaction–diffusion networks

Chemical reactions and diffusion can produce a wide variety of static or transient spatial patterns in the concentrations of chemical species. Little is known, however, about what dynamical patterns of concentrations can be reliably programmed into such reaction–diffusion systems. Here we show that given simple, periodic inputs, chemical reactions and diffusion can reliably emulate the dynamics of a deterministic cellular automaton, and can therefore be programmed to produce a wide range of complex, discrete dynamics. We describe a modular reaction–diffusion program that orchestrates each of the fundamental operations of a cellular automaton: storage of cell state, communication between neighboring cells, and calculation of cells’ subsequent states. Starting from a pattern that encodes an automaton’s initial state, the concentration of a “state” species evolves in space and time according to the automaton’s specified rules. To show that the reaction–diffusion program we describe produces the target dynamics, we simulate the reaction–diffusion network for two simple one-dimensional cellular automata using coupled partial differential equations. Reaction–diffusion based cellular automata could potentially be built in vitro using networks of DNA molecules that interact via branch migration processes and could in principle perform universal computation, storing their state as a pattern of molecular concentrations, or deliver spatiotemporal instructions encoded in concentrations to direct the behavior of intelligent materials.     

A proxy-based integrated cache consistency and mobility management scheme for client–server applications in Mobile IP systems

In this paper, we investigate a proxy-based integrated cache consistency and mobility management scheme for supporting client–server applications in Mobile IP systems with the objective to minimize the overall network traffic generated. Our cache consistency management scheme is based on a stateful strategy by which cache invalidation messages are asynchronously sent by the server to a mobile host (MH) whenever data objects cached at the MH have been updated. We use a per-user proxy to buffer invalidation messages to allow the MH to disconnect arbitrarily and to reduce the number of uplink requests when the MH is reconnected. Moreover, the user proxy takes the responsibility of mobility management to further reduce the network traffic. We investigate a design by which the MH’s proxy serves as a gateway foreign agent (GFA) as in the MIP Regional Registration protocol to keep track of the address of the MH in a region, with the proxy migrating with the MH when the MH crosses a regional area. We identify the optimal regional area size under which the overall network traffic cost, due to cache consistency management, mobility management, and query requests/replies, is minimized. The integrated cache consistency and mobility management scheme is demonstrated to outperform MIPv6, no-proxy and/or no-cache schemes, as well as a decoupled scheme that optimally but separately manages mobility and service activities in Mobile IPv6 environments.     

A novel trust management scheme based on Dempster–Shafer evidence theory for malicious nodes detection in wireless sensor networks

With the development of Internet technology, social network has become an important application in the network life. However, due to the rapid increase in the number of users, the influx of a variety of bad information is brought up as well as the existence of malicious users. Therefore, it is emergent to design a valid management scheme for user’s authentication to ensure the normal operation of social networks. Node trust evaluation is an effective method to deal with typical network attacks in wireless sensor networks. In order to solve the problem of quantification and uncertainty of trust, a novel trust management scheme based on Dempster–Shafer evidence theory for malicious nodes detection is proposed in this paper. Firstly, by taking into account spatiotemporal correlation of the data collected by sensor nodes in adjacent area, the trust degree can be estimated. Secondly, according to the D–S theory, the trust model is established to count the number of interactive behavior of trust, distrust or uncertainty, further to evaluate the direct trust value and indirect trust value. Then, a flexible synthesis method is adopted to calculate the overall trust to identify the malicious nodes. The simulation results show that the proposed scheme has obvious advantages over the traditional methods in the identification of malicious node and data fusion accuracy, and can obtain good scalability.

     

A two-level location–allocation problem in designing local access fiber optic networks

This paper deals with a two-level facility location–allocation problem on tree topology arising from the design of access networks. The access network design problem seeks to find an optimal location of switch and allocation of demands such that the total cost of switch and fiber cable is minimized, while satisfying switch port constraint, switch capacity constraint, and no-split routing constraint. The problem is formulated as a mixed-integer programming model and alternative formulations are developed by the reformulation-linearization technique (RLT) for improving computational effectiveness. By exploiting the tree structure of the problem, we develop some valid inequalities that have complementary strength and devise separation problems for finding effective valid inequalities that cut off fractional LP solutions. Also, we develop an effective MIP-based tree partitioning heuristic for finding good quality solutions for large size problems. Computational results demonstrate the efficacy of the valid inequalities and the proposed heuristic.     

ALCA: agent learning–based clustering algorithm in vehicular ad hoc networks

Vehicular ad hoc network (VANET) is an emerging technology which can be used in various applications such as intelligent transport technology, safety applications, etc. But one of the major issues in VANETs is how to cluster the vehicles on the road for efficient operations such as routing, mobility management and generating safety alarms. Clustering of vehicles has been widely used for routing and data dissemination in VANETs. But due to the high mobility of the vehicles/nodes on the road, it is quite difficult to find the exact route in VANETs. Keeping in view of the above issue, in this paper, we propose a new agent learning–based clustering and routing in VANETs. Agents learn from the environment in which they are deployed, and accordingly, their action performed is rewarded or penalized with certain values. Each agent performs its task in collaboration with the other agents, i.e. agents communicate with each other in collaborative manner for information sharing. The deployed agents estimate the mobility of the vehicles, and based upon their learning, clustering of vehicles is performed. An Agent Learning–based Algorithm for Clustering is proposed. The performance of the proposed scheme is evaluated using extensive simulation with respect to the various metrics such as message transmission ratio, percentage of connectivity, node participation, cluster head duration, and connectivity preservation ratio. The results obtained show that the proposed scheme is effective in performing fast clustering and converges quickly to the final solution.     

An evolutionary approach for multi-objective optimization of the integrated location–inventory distribution network problem in vendor-managed inventory

Vendor-managed inventory (VMI) is one of the emerging solutions for improving the supply chain efficiency. It gives the supplier the responsibility to monitor and decide the inventory replenishments of their customers. In this paper, an integrated location–inventory distribution network problem which integrates the effects of facility location, distribution, and inventory issues is formulated under the VMI setup. We presented a Multi-Objective Location–Inventory Problem (MOLIP) model and investigated the possibility of a multi-objective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA2) for solving MOLIP. To assess the performance of our approach, we conduct computational experiments with certain criteria. The potential of the proposed approach is demonstrated by comparing to a well-known multi-objective evolutionary algorithm. Computational results have presented promise solutions for different sizes of problems and proved to be an innovative and efficient approach for many difficult-to-solve problems.     

W~(1,2)(Ω)-and X~(1,2)(Ω)-stability of reactiondiffusion cellular neural networks with delay

With Poincare's inequality and auxiliary function applied in a class of retarded cel-lular neural networks with reaction-diffusion, the conditions of the systems' W1,2(Ω)-exponential and X1,2(Ω)-asmptotic stability are obtained. The stability con-ditions containing diffusion term are different from those obtained in the previous papers in their exponential stability conditions. One example is given to illustrate the feasibility of this method in the end.     

Combining simulated annealing with Lagrangian relaxation and weighted Dantzig–Wolfe decomposition for integrated design decisions in wireless sensor networks

A Wireless Sensor Network (WSN) is the outcome of the collaborative effort of multi-functional, low-power, low-cost, tiny electronic devices called sensors. Their ability to work autonomously provides a distributed environment capable to monitor even remote or inaccessible areas, which explains the wide application range of WSNs. There are four main issues in the design of a WSN: determining sensor locations (deployment), scheduling sensors, finding sink locations, and obtaining sensor-to-sink data routes. Sensors have very limited energy resources and their efficient management becomes critical for elongating network lifetime. As a result, most of the works on optimal WSN design are concerned with efficient energy usage. Unfortunately, only a few of them use an integrated approach and try to address these four issues simultaneously. In this work we also follow this line of research and develop first a monolithic mixed-integer linear programming model that maximizes network lifetime by optimally determining sensor and sink locations, sensor-to-sink data flows, active and stand-by periods of the sensors subject to data flow conservation, energy consumption and budget usage constraints. Then we propose a nested solution method consisting of two procedures: simulated annealing that performs search for the best sink locations in the outer level and Lagrangian relaxation based heuristic employed with weighted Dantzig–Wolfe decomposition for the multiplier update in the inner level, which determines sensor locations, activity schedules of the sensors and data flows routes. We demonstrate the efficiency and accuracy of the new approach on randomly generated instances by extensive numerical experiments.     

High temporal resolution rainfall–runoff modeling using long-short-term-memory (LSTM) networks

Neural Computing and Applications - Accurate and efficient models for rainfall–runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are...     

An internet of things–based personal device for diabetes therapy management in ambient assisted living (AAL)

Diabetes therapy management in AAL environments, such as old people and diabetes patients homes, is a very difficult task since many factors affect a patient’s blood sugar levels. Factors such as illness, treatments, physical and psychological stress, physical activity, drugs, intravenous fluids and change in the meal plan cause unpredictable and potentially dangerous fluctuations in blood sugar levels. Right now, operations related to dosage are based on insulin infusion protocol boards, which are provided by physicians to the patients. These boards are not considering very influential factors such as glycemic index from the diet, consequently patients need to estimate the dosage leading to dose error, which culminates in hyperglycemia and hypoglycemia episode. Therefore, right insulin infusion calculation needs to be supported by the next generation of personal-care devices. For this reason, a personal device has been developed to assist and consider more factors in the insulin therapy dosage calculation. The proposed solution is based on Internet of things in order to, on the one hand, support a patient’s profile management architecture based on personal RFID cards and, on the other hand, provide global connectivity between the developed patient’s personal device based on 6LoWPAN, nurses/physicians desktop application to manage personal health cards, glycemic index information system, and patient’s web portal. This solution has been evaluated by a multidisciplinary group formed by patients, physicians, and nurses.     

A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme

Control Chart Pattern Recognition (CCPR) is a critical task in Statistical Process Control (SPC). Abnormal patterns exhibited in control charts can be associated with certain assignable causes adversely affecting the process stability. Abundant literature treats the detection of different Control Chart Patterns (CCPs). In fact, numerous CCPR studies have been developed according to various objectives and hypotheses. Despite the widespread literature on this topic, efforts to review and analyze research on CCPR are very limited. For this reason, this survey paper proposes a new conceptual classification scheme, based on content analysis method, to classify past and current developments in CCPR research. More than 120 papers published on CCPR studies within 1991–2010 were classified and analyzed. Major findings of this survey include the following. (1) The most popular CCPR studies deal with independently and identically distributed process data. (2) Some recent studies on identification of mean shifts or/and variance shifts of a multivariate process are based on innovative techniques. (3) The percentage of studies that address concurrent pattern identification is increasing. (4) The majority of the reviewed articles use Artificial Neural Network (ANN) approach. Feature-based techniques, in particular wavelet-denoise, are investigated for improving the recognition performance of ANN. For the same reason, there is a general trend followed by many authors who propose hybrid, modular and integrated ANN recognizer designs combined with decision tree learning, particle swarm optimization, etc. (5) There are two main categories of performance criteria used to evaluate CCPR approaches: statistical criteria that are related to two conventional Average Run Length (ARL) measures, and recognition-accuracy criteria, which are not based on these ARL measures. The most applied criteria are recognition-accuracy criteria, mainly for ANN-based approaches. Performance criteria which are related to ARL measures are insufficient and inappropriate in the case of concurrent pattern identification. Finally, this paper briefly discusses some future research directions and our perspectives.     

Team training in the skies: does crew resource management (CRM) training work?

The aviation community has invested great amounts of money and effort into crew resource management (CRM) training. Using D. L. Kirkpatrick's (1976) framework for evaluating training, we reviewed 58 published accounts of CRM training to determine its effectiveness within aviation. Results indicated that CRM training generally produced positive reactions, enhanced learning, and promoted desired behavioral changes. However, we cannot ascertain whether CRM has an effect on an organization's bottom line (i.e., safety). We discuss the state of the literature with regard to evaluation of CRM training programs and, as a result, call for the need to conduct systematic, multilevel evaluation efforts that will show the true effectiveness of CRM training. As many evaluations do not collect data across levels (as suggested by D. L. Kirkpatrick, 1976, and by G. M. Alliger, S. I. Tannenbaum, W. Bennett, Jr., & H. Traver, 1997), the impact of CRM cannot be truly determined; thus more and better evaluations are needed and should be demanded.     

A novel synchronization scheme for grid-connected converters by using adaptive linear optimal filter based PLL (ALOF–PLL)

This paper proposes a novel grid synchronization scheme using a new phase-locked loop (PLL) scheme based on the adaptive linear optimal filtering (ALOF) technique. The problem formulation of the proposed ALOF is based on decomposing grid voltage signal into inner product of two vectors, namely, the vector of trigonometric functions and the vector of coefficients, corresponding to the input vector and the weight vector of the closed-loop adaptation algorithm by using least-mean-square (LMS) optimization algorithm. The coefficient of the fundamental component of the grid voltage is used as input signal for the PLL and the phase angles of the trigonometric functions are obtained from the output of the PLL recursively. The mathematical derivation of the weights updating law and the stability analysis of the ALOF–PLL are presented. Besides, the parameter selection for optimal performance is also discussed in terms of continuous domain (s-domain) analysis, discrete domain (z-domain) analysis and time domain simulations. The proposed ALOF–PLL shows the characteristic of band-pass filter at fundamental frequency and a notch filter at the harmonic frequencies. Finally, a detailed comparison with the existing single-phase and three-phase grid synchronization methods is also presented, and the proposed ALOF–PLL is found to have overwhelming advantages over the existing grid synchronization methods in terms of tracking accuracy, dynamic response and immunity to grid voltage disturbances, such as voltage sag/swell, phase-angle jump, harmonics, unbalance, random noises and frequency jump, etc. The validity and effectiveness of the ALOF–PLL is substantially confirmed by the extensive simulation results obtained from Matlab/Simulink.     

Integrating imaging spectroscopy (445–2543 nm) and geographic information systems for post-disaster management: a case of hailstorm damage in Sydney

This paper demonstrates a methodology for the analysis and integration of airborne hyperspectral sensor data (445–2543?nm) with GIS data in order to develop a vulnerability map which has the potential to assist in decision making during post-disaster emergency operations. Hailstorms pose a threat to people as well as property in Sydney, Australia. Emergency planning demands current, large-scale spatio-temporal information on urban areas that may be susceptible to hailstones. Several regions, dominated by less resistant roofing materials, have a higher vulnerability to hailstorm damage than others. Post-disaster operations must focus on allocating dynamic resources to these areas. Remote sensing data, particularly airborne hyperspectral sensor data, consist of spectral bands with narrow bandwidths, and have the potential to quantify and distinguish between urban features such as roofing materials and other man-made features. A spectral library of surface materials from urban areas was created by using a full range spectroradiometer. The image was atmospherically corrected using the empirical line method. A spectral angle mapper (SAM) method, which is an automated method for comparing image spectra to laboratory spectra, was used to develop a classification map that shows the distribution of roofing materials with different resistances to hailstones. Surface truthing yielded high percentage accuracy. Spatial overlay technique was performed in a GIS environment where several types of cartographic data such as special hazard locations, population density, data about less mobile people and the street network were overlaid on the classified geo-referenced hyperspectral image. The integrated database product, which merges high quality spectral information and cartographic GIS data, has vast potential to assist emergency organizations, city planners and decision makers in formulating plans and strategies for resource management.     

What were we thinking? A scoping review of crisis management pandemic literature (1984–2019)

Pandemics are now the focus of research attention in the fields of preparedness and crisis management. As pandemics are some of the largest crises to occur, an important question becomes ‘what were the field of crisis management thinking about pandemic management’. This paper investigates how the field of crisis management have incorporated the body of knowledge arising from pandemics into its science (from 1984 to 2019). We performed a scoping review of 4 journals on crisis management and what they have written about pandemics (230 papers). The findings are summarized in eight different categories. The main result is that the field of crisis management have shown sparse interest in pandemics. We attribute this to factors such as fragmentation of academic sciences when the problem-solving needs integration, perceived incommensurability and the organization of attention. We argue that the coronavirus disease 2019 pandemic can provide a basis for posing new questions in research on, and the political debate around, societal vulnerability at large and not only restricted to recent experiences of particular crises. Finally, we argue that this will need a stronger integration of research strands and communities, which in turn require the ability to ‘connect the dots’ between different sources of knowledge.     

Topology optimization of neural networks based on a coupled genetic algorithm and particle swarm optimization techniques (c-GA–PSO-NN)

In this short paper, a coupled genetic algorithm and particle swarm optimization technique was used to supervise neural networks where the applied operators and connections of layers were tracked by genetic algorithm and numeric values of biases and weights of layers were examined by particle swarm optimization to modify the optimal network topology. The method was applied for a previously studied case, and results were analyzed. The convergence to the optimal topology was highly fast and efficient, and the obtained weights and biases revealed great reliability in reproduction of data. The optimal topology of neural networks was obtained only after seven iterations, and an average square of the correlation (R ²) of 0.9989 was obtained for the studied cases. The proposed method can be used for fast and reliable topology optimization of neural networks.

     

Estimating soil moisture using Temperature–Vegetation Dryness Index (TVDI) in the Huang-huai-hai (HHH) plain

Soil moisture is an important indicator to describe soil conditions, and can also provide information on crop water stress and yield estimation. The combination of vegetation index (VI) and land surface temperature (LST) can provide useful information on estimation soil moisture status at regional scale. In this paper, the Huang-huai-hai (HHH) plain, an important food production area in China was selected as the study area. The potential of Temperature–Vegetation Dryness Index (TVDI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data in assessing soil moisture was investigated in this region. The 16-day composite MODIS Vegetation Index product (MOD13A2) and 8-day composite MODIS temperature product (MOD11A2) were used to calculate the TVDI. Correlation and regression analysis was carried out to relate the TVDI against in-situ soil moisture measurements data during the main growth stages of winter wheat/summer maize. The results show that a significantly negative relationship exists between the TVDI and in-situ measurements at different soil depths, but the relationship at 10–20 cm depth (R ²?=?0.43) is the closest. The spatial and temporal patterns in the TVDI were also analysed. The temporal evolution of the retrieved soil moisture was consistent with crop phenological development, and the spatial distribution of retrieved soil moisture accorded with the distribution of precipitation during the whole crop growing seasons. The TVDI index was shown to be feasible for monitoring the surface soil moisture dynamically during the crop growing seasons in the HHH plain.