Hybrid Petri-nets for modelling and performance evaluation of supply chains

Modelling and analysis of complex and co-ordinated supply chains is a crucial task due to its inherent complexity and uncertainty. Therefore, the current research direction is to devise an efficient modelling technique that maps the dynamics of a real life supply chain and assists industrial practitioners in evaluating and comparing their network with other competing networks. Here an effective modelling technique, the hybrid Petri-net, is proposed to efficiently handle the dynamic behaviour of the supply chain. This modelling methodology embeds two enticing features, i.e. cost and batch sizes, in deterministic and stochastic Petri-net for the modelling and performance evaluation of supply chain networks. The model is subsequently used for risk management to investigate the issues of supply chain vulnerability and risk that has become a major research subject in recent years. In the test bed, a simple productive supply chain and an industrial supply chain are modelled with fundamental inventory replenishment policy. Subsequently, its performance is evaluated along with the identification and assessment of risk factors using analytical and simulation techniques respectively. Thus, this paper presents a complete package for industrial practitioners to model, evaluate performance and manage risky events in a supply chain.     

Tackling representation,annotation and classification challenges for temporal knowledge base population

Temporal Information Extraction (TIE) plays an important role in many natural language processing and database applications. Temporal slot filling (TSF) is a new and ambitious TIE task prepared for the knowledge base population (KBP2011) track of NIST Text Analysis Conference. TSF requires systems to discover temporally bound facts about entities and their attributes in order to populate a structured knowledge base. In this paper, we will provide an overview of the unique challenges of this new task and our novel approaches to address these challenges. We present challenges from three perspectives: (1) Temporal information representation: We will review the relevant linguistic semantic theories of temporal information and their limitations, motivating the need to develop a new (4-tuple) representation framework for the task. (2) Annotation acquisition: The lack of substantial labeled training data for supervised learning is a limiting factor in the design of TSF systems. Our work examines the use of multi-class logistic regression methods to improve the labeling quality of training data obtained by distant supervision. (3) Temporal information classification: Another key challenge lies in capturing relations between salient text elements separated by a long context. We develop two approaches for temporal classification and combine them through cross-document aggregation: a flat approach that uses lexical context and shallow dependency features and a structured approach that captures long syntactic contexts by using a dependency path kernel tailored for this task. Experimental results demonstrated that our annotation enhancement approach dramatically increased the speed of the training procedure (by almost 100 times), and that the flat and structured classification approaches were complementary, together yielding a state-of-the-art TSF system.     

A methodology to design virtual cellular manufacturing systems

Virtual Cellular Manufacturing System is a new type of cellular system that emerged as a promising alternative for designing cells in real time. The problem is complex and time consuming NP-hard problem in its nature. This study was conducted to design virtual cells that maximizes similarity index and minimizes lead time. A mathematical model and an effective solution procedure were proposed. Several randomly generated data sets were used along a case study to test the performance of the proposed approach. The effects of inherent parameters of virtual systems were investigated and compared by using particular performance measures.     

Energy-Effective and Secure Data Transfer Scheme for Mobile Nodes in Smart City Applications

Wireless Personal Communications - Mobile nodes are deployed at different locations in the smart city to collect meaningful information so captured data can be used as an input in different smart...     

On the interplay of cell thickness and optimum period of silicon thin‐film solar cells: light trapping and plasmonic losses

Light trapping and photon management in honeycomb‐textured microcrystalline silicon solar cells are investigated experimentally and by modeling of the manufacturing process and the optical wave propagation. The solar cells on honeycomb‐textured substrates exhibit short circuit current densities exceeding 30 mA/cm² and energy conversion efficiencies of up to 11.0%. By controlling the fabrication process, the period and height of the honeycomb‐textured substrates are varied. The influence of the honeycomb substrate morphology on the interfaces of the individual solar cell layers and the quantum efficiency is determined. The optical wave propagation is calculated using 3D finite difference time domain simulations. A very good agreement between the optical simulation and experimental results is obtained. Strategies are discussed on how to increase the short circuit current density beyond 30 mA/cm². In particular, the influence of plasmonic losses of the textured silver (Ag) reflector on the short circuit current and quantum efficiency of the solar cell is discussed. Finally, solar cell structures with reduced plasmonic losses are proposed. Copyright © 2015 John Wiley & Sons, Ltd.     

Advances in multi-agency disaster management: Key elements in disaster research

Multi-agency disaster management requires collaboration among geographically distributed public and private organizations to enable a rapid and effective response to an unexpected event. Many disaster management systems often lack the capability to cope with the complexity and uncertainty. In this introduction to the special issues on advances in multi-agency disaster management we discuss the role of information, enterprise architecture, coordination and related human efforts aimed at improving multi-agency disaster management. The paper concludes that although there is a common body of knowledge, disaster management is still an under-developed area. There is a need to relate practice and theory by using human-centered approaches such that disaster management can realize its full potential.     

Weyl Semimetals as Hydrogen Evolution Catalysts

The search for highly efficient and low‐cost catalysts is one of the main driving forces in catalytic chemistry. Current strategies for the catalyst design focus on increasing the number and activity of local catalytic sites, such as the edge sites of molybdenum disulfides in the hydrogen evolution reaction (HER). Here, the study proposes and demonstrates a different principle that goes beyond local site optimization by utilizing topological electronic states to spur catalytic activity. For HER, excellent catalysts have been found among the transition‐metal monopnictides—NbP, TaP, NbAs, and TaAs—which are recently discovered to be topological Weyl semimetals. Here the study shows that the combination of robust topological surface states and large room temperature carrier mobility, both of which originate from bulk Dirac bands of the Weyl semimetal, is a recipe for high activity HER catalysts. This approach has the potential to go beyond graphene based composite photocatalysts where graphene simply provides a high mobility medium without any active catalytic sites that have been found in these topological materials. Thus, the work provides a guiding principle for the discovery of novel catalysts from the emerging field of topological materials.     

Solving the design of distributed layout problem using forecast windows: A hybrid algorithm approach

In today’s competitive environment, manufacturing facilities have to be more responsive to the frequent changes in product mix and demand by realigning their organizational structure for minimizing material handling cost. However, manufacturing firms are reluctant to modify the layout as it leads to operation disruption and excess rearrangement cost. In this paper, we present an alternative approach for designing a multi-period layout (i.e., distributed layout) that maintains a tradeoff between re-layout cost and cost of excess material handling. Obtaining an optimal solution to distributed layout problem is generally a difficult task, owing to larger size of quadratic assignment problem. In order to overcome the aforementioned drawback, a meta-heuristic, named ‘CSO-DLP’ (Clonal Symbiotic Operated-Distributed Layout Planning) is developed for designing a distributed layout that jointly determines the arrangement of department and flow allocation among them. It inherits its trait from Symbiotic algorithm and Clonal algorithm. In addition to these; the concept of ‘forecast window’ is used, which evaluates the layout for varying number of periods at a given time. The proposed meta-heuristic is applied on a benchmark dataset and the effect of system parameters, such as rearrangement cost, department disintegration, and duplication are investigated and benchmarked in this paper.