In‐stream construction‐induced suspended sediment in riverine ecosystems

Construction activities within the wetted‐perimeter of rivers, referred to as in‐stream construction, are prevalent economically and environmentally motivated activities having direct interactions with sensitive lotic environments. Currently, there is a paucity of research related to in‐stream construction activities and their effects on aquatic ecosystems. In‐stream construction‐induced suspended sediment may result in harmful effects to aquatic flora and fauna. Regulatory frameworks worldwide focus primarily on concentration, with limited consideration for duration and no consideration for spatial extents of suspended sediment exposures. This research develops theoretical concentration, duration, and spatial extent exposure risk relationships for riverine ecosystems to demonstrate the influence of each mechanism during typical in‐stream construction activities. To reduce exposure risk, concentration and duration may be considered pragmatically, based on anticipated activity characteristics and site conditions. Spatial exposure characteristics are important to consider, as illustrated by our finding that activities located near the channel centerline may result in greater exposure risk than similar activities conducted near the streambank. Current regulatory frameworks worldwide do not sufficiently consider all exposure risk mechanisms present during in‐stream construction‐induced suspended sediment releases, possibly inhibiting efforts to reduce adverse environmental effects. This research improves our understanding of suspended sediment in lotic environments and may help environmental managers better evaluate and manage in‐stream construction activities.     

Reducing model checking commitments for agent communication to model checking ARCTL and GCTL*

Social commitments have been extensively and effectively used to represent and model business contracts among autonomous agents having competing objectives in a variety of areas (e.g., modeling business processes and commitment-based protocols). However, the formal verification of social commitments and their fulfillment is still an active research topic. This paper presents CTLC⁺ that modifies CTLC, a temporal logic of commitments for agent communication that extends computation tree logic (CTL) logic to allow reasoning about communicating commitments and their fulfillment. The verification technique is based on reducing the problem of model checking CTLC⁺ into the problem of model checking ARCTL (the combination of CTL with action formulae) and the problem of model checking GCTL^* (a generalized version of CTL^* with action formulae) in order to respectively use the extended NuSMV symbolic model checker and the CWB-NC automata-based model checker as a benchmark. We also prove that the reduction techniques are sound and the complexity of model checking CTLC⁺ for concurrent programs with respect to the size of the components of these programs and the length of the formula is PSPACE-complete. This matches the complexity of model checking CTL for concurrent programs as shown by Kupferman et al. We finally provide two case studies taken from business domain along with their respective implementations and experimental results to illustrate the effectiveness and efficiency of the proposed technique. The first one is about the NetBill protocol and the second one considers the Contract Net protocol.     

Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms

A fundamental challenge in the design of Wireless Sensor Networks (WSNs) is to maximize their lifetimes especially when they have a limited and non-replenishable energy supply. To extend the network lifetime, power management and energy-efficient communication techniques at all layers become necessary. In this paper, we present solutions for the data gathering and routing problem with in-network aggregation in WSNs. Our objective is to maximize the network lifetime by utilizing data aggregation and in-network processing techniques. We particularly focus on the joint problem of optimal data routing with data aggregation en route such that the above mentioned objective is achieved. We present Grid-based Routing and Aggregator Selection Scheme (GRASS), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality. GRASS embodies optimal (exact) as well as heuristic approaches to find the minimum number of aggregation points while routing data to the Base-Station (BS) such that the network lifetime is maximized. Our results show that, when compared to other schemes, GRASS improves system lifetime with acceptable levels of latency in data aggregation and without sacrificing data quality.     

Central Aggregator Intrusion Detection System for Denial of Service Attacks

Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles (EVs) to be used by the smart grid through the central aggregator. Since the central aggregator is connected to the smart grid through a wireless network, it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system. However, existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network. In this paper, the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated and an intrusion detection system for the vehicle-to-grid network is proposed. The proposed system, central aggregator–intrusion detection system (CA-IDS), works as a security gateway for EVs to analyze and monitor incoming traffic for possible DoS attacks. EVs are registered with a Central Aggregator (CAG) to exchange authenticated messages, and malicious EVs are added to a blacklist for violating a set of predefined policies to limit their interaction with the CAG. A denial of service (DoS) attack is simulated at CAG in a vehicle-to-grid (V2G) network manipulating various network parameters such as transmission overhead, receiving capacity of destination, average packet size, and channel availability. The proposed system is compared with existing intrusion detection systems using different parameters such as throughput, jitter, and accuracy. The analysis shows that the proposed system has a higher throughput, lower jitter, and higher accuracy as compared to the existing schemes.     

Effect of uncoated and coated diamond on the compressive properties of porous aluminium composites

Porous aluminium composites are structural and functional materials that have vast potential, due to their lightweight and high energy absorption capacity, especially in automotive and aerospace applications. In this study, the effect of varying content of uncoated and titanium coated diamond particles on the compressive properties of porous aluminium composite was investigated. The composites were developed using powder metallurgy technique and porosity was attained by using polymethylmethacrylate (30 wt.%) as space holder material. The morphology of the pores was found to replicate the shape and size of polymethylmethacrylate particles, that were uniformly distributed in the composites. X-ray diffraction analysis confirmed formation of aluminium carbide in uncoated diamond-based aluminium composites while negligible amount was present in titanium coated porous composites during sintering. The porosities of composites decreased with an increase in diamond content due to the incomplete decomposition of polymethylmethacrylate particles. Moreover, the maximum plateau stress and energy absorption capacity of 9.96 MPa and 1.7 Mj/m³ were obtained for the composites with 8 wt.% of titanium coated diamond particles. Thus, coating inhibits the formation of undesirable compounds and contributes to better interfacial bonding between matrix and reinforcement.     

A taxonomy of argumentation models used for knowledge representation

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.     

Model updating of multistory shear buildings for simultaneous identification of mass,stiffness and damping matrices using two different soft-computing methods

In this research, two novel methods for simultaneous identification of mass–damping–stiffness of shear buildings are proposed. The first method presents a procedure to estimate the natural frequencies, modal damping ratios, and modal shapes of shear buildings from their forced vibration responses. To estimate the coefficient matrices of a state-space model, an auto-regressive exogenous excitation (ARX) model cooperating with a neural network concept is employed. The modal parameters of the structure are then evaluated from the eigenparameters of the coefficient matrix of the model. Finally, modal parameters are used to identify the physical/structural (i.e., mass, damping, and stiffness) matrices of the structure. In the second method, a direct strategy of physical/structural identification is developed from the dynamic responses of the structure without any eigenvalue analysis or optimization processes that are usually necessary in inverse problems. This method modifies the governing equations of motion based on relative responses of consecutive stories such that the new set of equations can be implemented in a cluster of artificial neural networks. The number of neural networks is equal to the number of degree-of-freedom of the structure. It is shown the noise effects may partially be eliminated by using high-order finite impulse response (FIR) filters in both methods. Finally, the feasibility and accuracy of the presented model updating methods are examined through numerical studies on multistory shear buildings using the simulated records with various noise levels. The excellent agreement of the obtained results with those of the finite element models shows the feasibility of the proposed methods.     

Model checking communicative agent-based systems

Model checking is a formal technique used to verify communication protocols against given properties. In this paper, we propose a new model checking algorithm aims at verifying systems designed as a set of autonomous interacting agents. These software agents are equipped with knowledge and beliefs and interact with each other according to protocols governed by a set of logical rules. We present a tableauased version of this algorithm and provide the soundness, completeness, termination and complexity results. A case study about an agent-based negotiation protocol and its implementation are also described.     

Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm

Since approximately 90% of the people with PD (Parkinson’s disease) suffer from speech disorders including disorders of laryngeal, respiratory and articulatory function, using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. All previous works are done to distinguish healthy people from people with Parkinson’s disease (PWP). In this paper, we propose to go further by multiclass classification with three classes of Parkinson stages and healthy control. So we have used 40 features dataset, all the features are analyzed and 9 features are selected to classify PWP subjects in four classes, based on unified Parkinson’s disease Rating Scale (UPDRS). Various classifiers are used and their comparison is done to find out which one gives the best results. Results show that the subspace discriminant reach more than 93% overall classification accuracy.