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Higher vector efficiency of fluidic thrust vectoring (FTV) technology results in less requirement on secondary flow mass, which helps to reduce the influence of secondary flow on the performance of an aero-engine. In the paper, a new concept of FTV, named as a hybrid shock vector control (SVC) nozzle, was proposed to promote the vector efficiency of a SVC nozzle. It adopts a rotatable valve with a secondary flow injection to enhance the jet penetration, so as to improve the vector performance. The flow characteristics of a hybrid SVC nozzle were investigated numerically by solving 2D RANS equations. The influence of secondary pressure ratio (SPR) and rotatable valve angle on vector performance were conducted. Then, the coupling performance of a hybrid SVC nozzle and an aero-engine was estimated, by using the approximate model of a hybrid SVC nozzle and the performance simulation model of an aero-engine. Results show that, a desirable vector efficiency of 2.96 º/ %-ω (the vector angle achieved by using secondary flow of 1% of primary flow) of a hybrid SVC nozzle was obtained. In the coupling progress, when a secondary flow of 5.3% of primary flow was extracted from fan exit to a hybrid SVC nozzle, a vector angle of 14.1°, and a vector efficiency of 2.91º/ %-ω were achieved. Meanwhile the thrust of the aero-engine thrust decreased by 5.6% and the specific fuel consumption (SFC) increased by 0.5%.

Shock vectoring control (SVC) is an important method of fluidic thrust vectoring (FTV) for aero-engine exhaust system. It behaves better on nozzle of high pressure ratio, and is considered as an alternative TV technology for a future aero-engine with high thrust-to-weight ratio. In this paper, the flow mechanism and vector performance, including the vector angle (δp) and thrust coefficient (Cfg), of 2D and axisymmetric SVC nozzles were investigated after the validation of turbulence models by experimental data. The influence of aerodynamic parameters, e.g. nozzle pressure ratio (NPR), secondary pressure ratio (SPR) and free-stream Ma number (M∞) on flow characteristics and vector performance were studied numerically, and results show that unbalanced pressure distributions on nozzle internal walls determine δp, while shock waves dominate thrust loss, referring to Cfg. The “pressure release mechanism” of an axisymmetric SVC nozzle causes vector angle about 16.54% smaller than that of a 2D SVC nozzle at NPR of 6. The induced shock wave interacts with nozzle upper wall at SPR of 1.5, and results in the δp of a 2D SVC nozzle 12% smaller. A new parameter (Fy,modi) of side-force was redefined for free-stream conditions, taking the pressure distributions on nozzle external walls into account. Results indicate that pressure connection on nozzle external walls of an axisymmetric SVC nozzle causes vector performance better at M∞ >0.3 and the δp is about 11.2% larger at transonic conditions of M∞ of 0.9 and 1.1.

Summary 1. Vector‐borne pathogens and parasites can induce changes in the phenotypes of their hosts that influence the frequency and nature of host–vector interactions and hence transmission, as documented by both empirical and theoretical studies. To the extent that implications for transmission play a significant role in shaping the evolution of parasite effects on host phenotypes, we may hypothesize that parasites exhibiting similar transmission mechanisms – and thus profiting from similar patterns of interaction among hosts and vectors – will have correspondingly similar effects on relevant host traits. Here, we explore this hypothesis through a survey and synthesis of literature on interactions among plant viruses, their hosts, and insect vectors. 2. Insect‐vectored plant viruses that differ in their modes of transmission benefit from different patterns of interaction among host plants and vectors. The transmission of persistently transmitted (PT) viruses requires that vectors feed on an infected host for a sustained period to acquire and circulate (and sometimes replicate) virions, then disperse to a new, healthy host. In contrast, non‐persistently transmitted (NPT) viruses are effectively transmitted when vectors briefly probe infected hosts, acquiring virions, then rapidly disperse. 3. Based on these observations, and empirical evidence from our previous work, we hypothesized that PT and NPT viruses will exhibit different effects on aspects of host phenotypes that mediate vector attraction to, arrestment on and dispersal from infected plants. Specifically, we predicted that both PT and NPT viruses would tend to enhance vector attraction to infected hosts, but that they would have contrasting effects on vector settling and feeding preferences and on vector performance, with PT viruses tending to improve host quality for vectors and promote long‐term feeding and NPT viruses tending to reduce plant quality and promote rapid dispersal. 4. We evaluated these hypotheses through an analysis of existing literature and found patterns broadly consistent with our expectations. This literature synthesis, together with evidence from other disease systems, suggests that transmission mechanisms may indeed be an important factor influencing the manipulative strategies of vector‐borne pathogens, with significant implications for managing viral diseases in agriculture and understanding their impacts on natural plant communities. Lay Summary

Plant-mediated interactions between begomoviruses and whiteflies exert important influences on the population dynamics of vectors and the epidemiology of plant diseases. In this article, we synthesize the relevant literature to identify patterns to the interactions. We then review studies on the ecological, biochemical and molecular mechanisms underlying the interactions and finally elaborate on the most interesting issues for future research. The interactions between begomoviruses and the insect vector, the whitefly Bemisia tabaci, via their shared host plants can be mutualistic, neutral or negative. However, in contrast to a pattern of improved performance of vectors on virus-infected plants that has been observed with persistently transmitted RNA viruses, the number of cases exhibiting mutualistic, neutral or negative effects in the indirect interactions between begomoviruses and whiteflies appear evenly distributed. With regard to the mechanisms of plant-mediated positive effects on whiteflies, two case studies indicate that suppression of plant defence and/or alteration in plant nutrition as a result of virus infection can be important. Our review shows that we are only just beginning to understand the tripartite interactions between begomoviruses, whiteflies and plants. Future efforts in this area should try to expand the number and diversity of pathosystems for investigation to reveal the patterns of interactions, to investigate the molecular and biochemical mechanisms of the interactions using a multidisciplinary approach, and to examine the virus–plant–vector interactions in the field and in natural plant communities.

Packet classification is a computationally intensive, highly parallelizable task in many advanced network systems like high-speed routers and firewalls that enable different functionalities through discriminating incoming traffic. Recently, graphics processing units (GPUs) have been exploited as efficient accelerators for parallel implementation of software classifiers. The aggregated bit vector is a highly parallelizable packet classification algorithm. In this work, first we present a parallel kernel for running this algorithm on GPUs. Next, we adapt an asymptotic analysis method which predicts any empirical result of the proposed kernel. Experimental results not only confirm the efficiency of the proposed parallel kernel but also reveal the accuracy of the analysis method in predicting important trends in experimental results.

Nowadays, customer expectations are increasing and organizations are prone to operate in an uncertain environment. Under this uncertain environment, the ultimate success of the firm depends on its ability to integrate business processes among supply chain partners. Supply chain management emphasizes cross-functional links to improve the competitive strategy of organizations. Now, companies are moving from decoupled decision processes towards more integrated design and control of their components to achieve the strategic fit. In this paper, a new approach is developed to design a multi-echelon, multi-facility, and multi-product supply chain in fuzzy environment. In fuzzy environment, mixed integer programming problem is formulated through fuzzy goal programming in strategic level with supply chain cost and volume flexibility as fuzzy goals. These fuzzy goals are aggregated using minimum operator. In tactical level, continuous review policy for controlling raw material inventories in supplier echelon and controlling finished product inventories in plant as well as distribution center echelon is considered as fuzzy goals. A non-linear programming model is formulated through fuzzy goal programming using minimum operator in the tactical level. The proposed approach is illustrated with a numerical example.

This paper studies a multi-agent scheduling problem on two identical parallel machines. There are g agents, and each agent’s objective is to minimize its makespan. We present an approximation algorithm such that the performance ratio of the makespan achieved by our algorithm relative to the minimum makespan is no more than i + 1 6 for the i th ( i = 1 , 2 , … , g ) completed agent. Moreover, we show that the performance ratio is tight.

Purpose Importance-performance analysis (IPA) is an effective tool for firms to prioritise service quality attributes, but has limitations in evaluating and enhancing service quality within a competitive environment. The purpose of this paper is to present an evolved model of IPA – importance-performance benchmark vectors (IPBV) – as a benchmarking tool and investigate its applicability in the context of hotel service quality. Design/methodology/approach Empirical studies based on self-completion survey data from 150 customers of two full-service hotels in Taiwan were conducted in to examine the practical utility of IPBV. Findings Eight key benchmark typologies were identified and expressed as vectors in the IPBV model which are as follows: “sustainable advantage”, “potential strength”, “false advantage or outstanding advantage”, “cease-fire competition”, “false disadvantage or on-hand disadvantage”, “potential weakness”, “dangerous warning” and “head-on competition”. Research limitations/implications The paper extends the methodology to more cases, and other service industries to test further the discriminatory power of the model and to explore the descriptors in the IPBV vector model. Alternative seven-point or nine-point Likert scales could be explored to test the discriminant validity using means. The alternative IPA diagonal approach focussing on GAP analysis may reveal alternative interpretations for the IPBV vector model. Other extended models of IPA, which include competitor analysis, should be compared in practice using a data set where both quantitative and qualitative data could be generated. Practical implications The paper proposes the two-dimensional IPBV model which retains the advantages of IPA, but also includes competitor or benchmark comparisons which enable organisations to analyse their relative competitive position. The two-part model provides both quantitative information and qualitative interpretation of relativities. The graphical matrix models provide simple quantitative analysis of attributes, whilst the IPBV vector model provides qualitative interpretations of the eight competitive market positions. Vector analysis enables the development of competitive strategies relative to benchmarks, or within a competitive set. Importance is retained and means that organisations can benchmark against a range of competitors prioritising specific attributes for resource allocation. Social implications The interpretive utility of the model should be explored with practitioners and decision makers in the service industries. The model has been designed for practical use in industry to inform operational and strategic decision making, its usefulness in practice should be explored and the attitudes of practitioners to the model should be tested. Originality/value Traditional approaches to benchmarking have adopted a one-dimensional approach that does not include a measure of the relative importance of the service quality dimensions in specific markets. This research develops a two-dimensional advanced model of IPA, called IPBV, which is based on vector relationships between key attributes of service quality. These vectors are explored and described in competitive terms and the model is discussed with regard to its implications for industry, practitioners and researchers.

Power amplifiers are one of the costly devices in wireless communication systems and exhibit non-linearity. The problem of non-linearity in the power amplifier categorises in two disturbance impacts, output distortion and in-band distortion which lead to adjacent channel interference and constellation deviation, respectively. In order to gain maximum efficiency of the power amplifier and reduce interference, the authors propose a new digital pre-distortion scheme called complex gain convergence (CGC). The proposed scheme is compared with indirect learning architecture and validated against an laterally diffused metal oxide semiconductor (LDMOS) power amplifier. The outcome of applying CGC scheme shows enhancement in adjacent channel leakage ratio and error vector magnitude performance while the complexity is even lower. An experimental demonstration with the actual power amplifier is also presented.

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms.