Defense Suppression through Interplant Communication Depends on the Attacking Herbivore Species

In response to herbivory, plants emit volatile compounds that play important roles in plant defense. Herbivore-induced plant volatiles (HIPVs) can deter herbivores, recruit natural enemies, and warn other plants of possible herbivore attack. Following HIPV detection, neighboring plants often respond by enhancing their anti-herbivore defenses, but a recent study found that herbivores can manipulate HIPV-interplant communication for their own benefit and suppress defenses in neighboring plants. Herbivores induce species-specific blends of HIPVs and how these different blends affect the specificity of plant defense responses remains unclear. Here we assessed how HIPVs from zucchini plants (Cucurbita pepo) challenged with different herbivore species affect resistance in neighboring plants. Volatile “emitter” plants were damaged by one of three herbivore species: saltmarsh caterpillars (Estigmene acrea), squash bugs (Anasa tristis), or striped cucumber beetles (Acalymma vittatum), or were left as undamaged controls. Neighboring “receiver” plants were exposed to HIPVs or control volatiles and then challenged by the associated herbivore species. As measures of plant resistance, we quantified herbivore feeding damage and defense-related phytohormones in receivers. We found that the three herbivore species induced different HIPV blends from squash plants. HIPVs induced by saltmarsh caterpillars suppressed defenses in receivers, leading to greater herbivory and lower defense induction compared to controls. In contrast, HIPVs induced by cucumber beetles and squash bugs did not affect plant resistance to subsequent herbivory in receivers. Our study shows that herbivore species identity affects volatile-mediated interplant communication in zucchini, revealing a new example of herbivore defense suppression through volatile cues.

     

Process design methodology for energy‐efficient processes operating below and across ambient temperature

This article presents a targeting and design methodology that can be implemented for any process where pressure‐based exergy, also known as mechanical exergy, has an important contribution to the total exergy conversion and transfer. However, in this article it is applied to processes that operate at sub‐ambient conditions, or processes where the ambient conditions are crossed. Exergy efficiencies, new Exergetic Composite Curves, Cascades, and Extended Grid Diagrams are tools that had to be implemented, improved, or invented, to develop a methodology with considerable potential for energy‐efficient process design. The appropriate placement (correct integration) of compressors and expanders in heat exchanger networks is also analyzed to minimize the number of units. An example is used to demonstrate the methodology, where several simplifying assumptions are made to facilitate understanding and to explain the design method. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2324–2340, 2016     

Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle

Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques.

     

Importance of organizational structure for TQM success and customer satisfaction

Wireless Networks - This paper reports a structural equation model to relate three critical success factors for total quality management (TQM) (i.e. managerial commitment, role of quality...     

Multiperiod optimal planning of thermal generation using cross decomposition

This work addresses the Multiperiod Optimal Planning of Thermal Generation (MOPTG). The model considered is based on a Unit Commitment Problem that has multiperiod character and determines the start up and shut down schedules of thermal plants considering the line capacity limits of transmission and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem (MINLP) with binary variables. To reduce the computational time caused by the large number of time periods and electric generation nodes we apply the Generalized Cross Decomposition [1, 2]. The later exploits the structure of the problem to reduce solution time by decomposing the MOPTG into a primal subproblem, which is a Non Linear Problem (NLP), a dual subproblem, which is a MINLP, and a Mixed Integer Problem (MIP) called master problem. The approach is compared with Lagrangean Relaxation [3] and Generalized Benders Decomposition [4], To demonstrate the efficiency of the proposed decomposition strategy we present numerical results obtained for three test systems. The computational experiments show the superiority of the Cross Decomposition approach.     

Reliable and secure data transfer in IoT networks

With the rapid technological improvements in mobile devices and their inclusion in Internet of Things (IoT), secure key management becomes mandatory to ensure security of information exchange. For instance, IoT applications, such as smart health-care and smart homes, provide automated services to the users with less or no user intervention. As these application use user-sensitive data, ensuring their security and privacy should be paramount, especially during the key management process. However, traditional approaches for key management will not suit well in IoT environment because of the inherent resource constraint property of IoT devices. In this paper, we propose a novel distributed key management scheme for IoT ecosystem. The proposed scheme efficiently provides security to IoT devices by delegating most of the resource consuming cryptographic processing to a local entity. This entity coordinates with other peer entities to provide a distributed key as well as an authentication mechanism to network devices. In particular, the proposed scheme exploits the advantages of mobile agents by deploying them in different subnetworks as and when required: (1) to process the cryptography work for the IoT devices, and (2) to act as an local authenticated entity to perform fast authentication process. To verify the effectiveness and correctness of our proposed scheme, we have simulated it in a large IoT scenario and evaluated against relevant metrics that includes user mobility, certification generation time, and communication overhead.

     

A new algorithm for optimization of quality of service in peer to peer wireless mesh networks

Wireless Networks - Nowadays, wireless mesh networks are known as important parts of different commercial, scientific, and industrial processes. Their prevalence increases day-by-day and the future...     

Efficiency analysis for stochastic dynamic facility layout problem using meta-heuristic,data envelopment analysis and machine learning

The facility layout problem (FLP) is a combinatorial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedural design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three-stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learning (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simulated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta-heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (efficiency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA and improves the prediction accuracy of layouts. A Gaussian distribution product demand dataset for time period T = 5 and facility size N = 12 is used to prove the effectiveness of the methodology.     

Effect of soil and a nonionic surfactant on BTE-oX and MTBE biodegradation kinetics.

The biodegradation kinetics of BTE-oX and MTBE, mixed all together, in the presence of 905 mg/L VSS of BTEX-acclimated biomass was evaluated. Effects of soil and Tergitol NP-10 in aqueous samples on substrate biodegradation rates were also evaluated. Biodegradation kinetics was evaluated for 36 hours, every 6 hours. MTBE biodegradation followed a first-order one-phase kinetic model in all samples, whereas benzene, toluene and ethylbenzene biodegradation followed a first-order two-phase kinetic model in all samples. O-xylene biodegradation followed a first-order two-phase kinetic model in the presence of biomass only. Interestingly, o-xylene biodegradation was able to switch to a first-order one-phase kinetic model when either soil or soil and Tergitol NP-10 were added. The presence of soil in aqueous samples retarded benzene, toluene and ethylbenzene removal rates. O-xylene and MTBE removal rates were enhanced by soil. The addition of Tergitol NP-10 to aqueous samples containing soil had a positive effect on substrate removal rate in all samples. Substrate percent removals ranged 77-99.8% for benzene, toluene and ethylbenzene. O-xylene and MTBE percent removals ranged 50.1-65.3% and 9.9-43.0%, respectively.