Application of Complex Networks Theory in Urban Traffic Network Researches

Networks and Spatial Economics - Complex network theory is a multidisciplinary research direction of complexity science which has experienced a rapid surge of interest over the last two decades....     

Anomaly Detection and Reliability Analysis of Groundwater by Crude Monte Carlo and Importance Sampling Approaches

In this paper, Crude Monte Carlo method and importance sampling are utilized to determine the reliability of long-term changes in groundwater level. Furthermore, different data analysis methods are used to determine the abnormal patterns and to investigate the cause of spatial variations of failure probability. For this purpose, three methods including robust covariance, one-class SVM, and Isolation Forest are applied to define the decision function. In the preliminary detection of the outliers, DFFITS and COOK measures are used to confirm the existence of abnormal plains in a two-dimensional space. The validity of prediction results is verified through the developed method of uncertain monitoring by selecting the most significant outlier points. In addition, the abnormal pattern detection methods are compared using non-random pattern discovery decision functions. The reliability analysis of groundwater is conducted during the two periods from 1994 to 2007 and 2008 to 2021. In the second period, parts of the eastern part of the northwest, central parts of the desert of Iran, and areas from west-southwest and east-south-east to other regions exposed to a lower probability of passing through the critical conditions. In contrast, the outcomes confirm the occurrence of drought with probability more than 80% for most of the plains. Eventually, the importance sampling method showed the closest relation in the correct distribution of the decision function. In contrast, due to the cluster shape and density of the outliers, the upper part of the decision function was determined with high certainty in the discovery of abnormal plains.     

The use of domination and legitimation in information systems implementation

In this paper, we present the results of a longitudinal case study on information systems (IS) implementation conducted in a community healthcare organization. Using structuration theory as a sensitizing framework, we highlight how the information technology (IT) Management improved their influence through gaining legitimation from other organizational stakeholders, and how the nature of this evolved over time. Our results highlight how an appropriate, sophisticated use of what Giddens refers to as the duality of structure contributed to the consolidation of the IT Manager's credibility and authority. We also report on how the IT Management had most of their actions legitimated as an integral element of their actions. The results also highlight the distributed nature of power, such that even those at the lower end of organizational hierarchy were able to influence the success or failure of IS implementation.     

Efficient Simplification Methods for Generating High Quality LODs of 3D Meshes

Two simplification algorithms are proposed for automatic decimation of polygonal models,and for generating their LODs.Each algorithm orders vertices according to their priority values and then removes them iteratively.For setting the priority value of each vertex,exploiting normal field of its one-ring neighborhood,we introduce a new measure of geometric fidelity that reflects well the local geometric features of the vertex.After a vertex is selected,using other measures of geometric distortion that are ...     

Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction

Abstract: Electromyography gives an electrical representation of neuromuscular activation associated with a contracting muscle. The electromyography signal acquires noise while travelling though different media. The wavelet transform is employed for removing noise from surface electromyography (SEMG) and higher order statistics are applied for analysing the signal. With the appropriate choice of wavelet, it is possible to remove interference noise (denoise) effectively in order to analyse the SEMG. Daubechies wavelets (db2, db4, db5, db6, db8), symmlet (sym4, sym5) and the orthogonal Meyer (dmey) wavelet can efficiently remove noise from the recorded SEMG signals. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square difference and signal-to-noise ratio values. Results for both root mean square difference and signal-to-noise ratio show that wavelet db2 performs denoising best out of the wavelets. Furthermore, the higher order statistics method is applied for SEMG signal analysis because of its unique properties when applied to random time series, such as parameter estimation, testing of Gaussianity and linearity, deterministic and non-deterministic signal detection etc. Gaussianity and linearity tests as part of higher order statistics are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. According to the results, the SEMG signal becomes less Gaussian and more linear with increased force.     

A hybrid genetic algorithm for machine-part grouping

Cellular manufacturing (CM), which incorporates the flexibility of job shops and high production rate of flow lines, has been seen as a promising alternative for batch type production. Although CM provides great benefits, the design of CM is quite complex for real world problems. The main problem in the design of a CM is the formation of machine groups and corresponding part families. This paper aims at developing an approach that combines a local search heuristic (LSH) with genetic algorithm (GA). The approach has been tested on a number of problems from the literature. The results show that new approach not only converges to the best solution in the earlier generations but also produces solutions that are as accurate as any of the results reported, so far, in literature. Also, in certain cases the results produced by this technique are even better than the previously reported results.     

Neural network based model predictive control for a steel pickling process

A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an industrial system such as nonlinear dynamics and multi-effects among variables. In the modeling, multiple input, single-output recurrent neural network subsystem models are developed using input–output data sets obtaining from mathematical model simulation. The Levenberg–Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. The proposed algorithm is tested for control of a steel pickling process in several cases in simulation such as for set point tracking, disturbance, model mismatch and presence of noise. The results for the neural network model predictive control (NNMPC) overall show better performance in the control of the system over the conventional PI controller in all cases.     

Scalable and efficient key management for heterogeneous sensor networks

As typical wireless sensor networks (WSNs) have resource limitations, predistribution of secret keys is possibly the most practical approach for secure network communications. In this paper, we propose a key management scheme based on random key predistribution for heterogeneous wireless sensor networks (HSNs). As large-scale homogeneous networks suffer from high costs of communication, computation, and storage requirements, the HSNs are preferred because they provide better performance and security solutions for scalable applications in dynamic environments. We consider hierarchical HSN consisting of a small number high-end sensors and a large number of low-end sensors. To address storage overhead problem in the constraint sensor nodes, we incorporate a key generation process, where instead of generating a large pool of random keys, a key pool is represented by a small number of generation keys. For a given generation key and a publicly known seed value, a keyed-hash function generates a key chain; these key chains collectively make a key pool. As dynamic network topology is native to WSNs, the proposed scheme allows dynamic addition and removal of nodes. This paper also reports the implementation and the performance of the proposed scheme on Crossbow’s MicaZ motes running TinyOS. The results indicate that the proposed scheme can be applied efficiently in resource-constrained sensor networks. We evaluate the computation and storage costs of two keyed-hash algorithms for key chain generation, HMAC-SHA1 and HMAC-MD5.

MoSx@NiO Composite Nanostructures: An Advanced Nonprecious Catalyst for Hydrogen Evolution Reaction in Alkaline Media

The design of the earth‐abundant, nonprecious, efficient, and stable electrocatalysts for efficient hydrogen evolution reaction (HER) in alkaline media is a hot research topic in the field of renewable energies. A heterostructured system composed of MoS_x deposited on NiO nanostructures (MoS_x@NiO) as a robust catalyst for water splitting is proposed here. NiO nanosponges are applied as cocatalyst for MoS₂ in alkaline media. Both NiO and MoS₂@NiO composites are prepared by a hydrothermal method. The NiO nanostructures exhibit sponge‐like morphology and are completely covered by the sheet‐like MoS₂. The NiO and MoS₂ exhibit cubic and hexagonal phases, respectively. In the MoS_x@NiO composite, the HER experiment in 1 m KOH electrolyte results in a low overpotential (406 mV) to produce 10 mA cm^?2 current density. The Tafel slope for that case is 43 mV per decade, which is the lowest ever achieved for MoS₂‐based electrocatalyst in alkaline media. The catalyst is highly stable for at least 13 h, with no decrease in the current density. This simple, cost‐effective, and environmentally friendly methodology can pave the way for exploitation of MoS_x@NiO composite catalysts not only for water splitting, but also for other applications such as lithium ion batteries, and fuel cells.