Effect of conspecific and heterospecific feces on foraging and oviposition of two predatory ladybirds: role of fecal cues in predator avoidance

Growing evidence suggests a flow of chemical information from higher to lower trophic levels that affects foraging and oviposition of prey in response to potential risks from predators. This was investigated in two species of ladybird predators of aphids, Harmonia axyridis and Propylea japonica. H. axyridis is known to be the stronger intraguild predator and P. japonica to be the more frequent intraguild prey in interactions of these two species. These ladybirds share aphid prey on mugworts, hibiscus, and Italian ryegrasses in fields of northern Japan but largely avoid each other on the same plant. Fecal cues of these ladybird predators were found to contribute in their assessment of predation risk from conspecific and heterospecific competitors in common habitats. Gravid females of H. axyridis reduced rates of feeding and oviposition when exposed to feces of conspecifics, but not when exposed to feces of P. japonica. In contrast, gravid females of P. japonica reduced feeding and oviposition when exposed to feces of both H. axyridis and its own species. Females of both ladybird species exhibited similar behavior in response to water extracts of feces. For P. japonica, the influence of heterospecific feces was greater than that of conspecific feces. Our results demonstrate that feces of ladybirds contain odors that have the potential to deter the feeding and oviposition activities of conspecific as well as heterospecific ladybirds. Such deterrence allows these insects to avoid predation risk. Differences in responses of the two predators are discussed.     

Chemical reaction and Dufour effects on nonlinear free convection heat and mass transfer flow near a vertical moving porous plate

A theoretical analysis is made for steady fully developed free convection and mass transfer flow near an infinite vertical moving porous plate by taking into consideration the first‐order chemical reaction and Dufour effects. The mathematical model responsible for the present physical situation is based on the nonlinear density variation with temperature as well as nonlinear density variation with concentration. Exact solutions are derived for heat mass and momentum equations under relevant boundary conditions. The dimensionless velocity, temperature, and concentration are presented in terms of exponential functions. The impact of controlling parameters such as Dufour number (diffusion thermo effect), chemical reaction parameter, Prandlt number, Schmidt number, on velocity, temperature, Nusselt number, and skin friction are discussed with the aid of line graphs, contours, and tables. The analysis of the result shows that Nusselt number, skin friction, and velocity increases with increase in Dufour number. Furthermore, velocity and skin friction are higher in case of nonlinear convection in comparison to linear convection.     

Transient free-convective flow of reactive viscous fluid in a vertical channel

A numerical study is reported to investigate the transient free-convective flow of reactive viscous fluid in a vertical channel formed by two infinite vertical parallel plates. A theoretical analysis is also presented to find the expressions for temperature, velocity, skin-friction and Nusselt number for the steady fully developed flow using perturbation technique. During the course of numerical computation, an excellent agreement is found between steady state solutions and transient solution at large value of time.     

Dropwise Condensation Studies on Multiple Scales

Recent advances in nanotechnology, chemical/physical texturing and thin film coating technology generate definite possibilities for sustaining a dropwise mode of condensation for much longer durations than was previously possible. The availability of superior experimental techniques also leads to deeper understanding of the process parameters controlling the relevant transport phenomena, the distinguishing feature of which is the involvement of a hierarchy of length/time scales, proceeding from nuclei formation, to clusters, all the way to macroscopic droplet ensemble, drop coalescence, and subsequent dynamics. This paper is an attempt to connect and present a holistic framework of modeling and studying dropwise condensation at these multiple scales. After a review of the literature, discussions on the following problems are presented: (i) atomistic modeling of nucleation; (ii) droplet–substrate interaction; (iii) surface preparation; (iv) simulation of fluid motion inside sliding drops; (v) experimental determination of the local/ average heat transfer coefficient; and (vi) a macroscopic model of the complete dropwise condensation process underneath horizontal and inclined surfaces. The study indicates that hierarchal modeling is indeed the way forward to capture the complete process dynamics. The microscopic phenomena at the three-phase contact line, leading to the apparent droplet contact angle, influence the shear stress and heat transfer. The nucleation theory captures the quasi-steady-state behavior quite satisfactorily, although the early atomistic nucleation was not seen to have a profound bearing on the steady-state behavior. The latter is strongly governed by the coalescence dynamics. Visual observation of dropwise condensation provides important information for building hierarchical models.     

Linear and nonlinear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water — A case study

The paper describes linear and nonlinear modeling for simultaneous prediction of the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the river water using the set of independent measured variables. Partial least squares (PLS2) regression and feed forward back propagation artificial neural networks (FFBP ANNs) modeling methods were applied to predict the DO and BOD levels using eleven input variables measured monthly in the river water at eight different sites over a period of ten years. The performance of the models was assessed through the root mean squared error (RMSE), the bias, the standard error of prediction (SEP), the coefficient of determination (R²), the Nash-Sutcliffe coefficient of efficiency (E_f), and the accuracy factor (A_f), computed from the measured and model-predicted values of the dependent variables (DO, BOD). Goodness of the model fit to the data was also evaluated through the relationship between the residuals and the model predicted values of DO and BOD, respectively. Although, the model predicted values of DO and BOD by both the linear (PLS2) and nonlinear (ANN) models were in good agreement with their respective measured values in the river water, the nonlinear model (ANN) performed relatively better than the linear one. Relative importance and contribution of the input variables to the identified ANN model was evaluated through the partitioning approach. The developed models can be used as tool for the water quality prediction.     

Stability Analysis of an Underactuated Autonomous Underwater Vehicle Using Extended-Routh’s Stability Method

In this paper, a new approach to stability analysis of nonlinear dynamics of an underactuated autonomous underwater vehicle (AUV) is presented. AUV is a highly nonlinear robotic system whose dynamic model includes coupled terms due to the hydrodynamic damping factors. It is difficult to analyze the stability of a nonlinear dynamical system through Routh’s stability approach because it contains nonlinear dynamic parameters owing to hydrodynamic damping coefficients. It is also difficult to analyze the stability of AUVs using Lyapunov’s criterion and LaSalle’s invariance principle. In this paper, we proposed the extended-Routh’s stability approach to verify the stability of such nonlinear dynamic systems. This extended-Routh’s stability approach is much easier as compared to the other existing methods. Numerical simulations are presented to demonstrate the efficacy of the proposed stability verification of the nonlinear dynamic systems, e.g., an AUV system dynamics.     

Charge Transfer Modulation in Vanadium-Doped WS2/Bi2O2Se Heterostructures

The field of photovoltaics is revolutionized in recent years by the development of two–dimensional (2D) type-II heterostructures. These heterostructures are made up of two different materials with different electronic properties, which allows for the capture of a broader spectrum of solar energy than traditional photovoltaic devices. In this study, the potential of vanadium (V)-doped WS₂ is investigated, hereafter labeled V-WS₂, in combination with air-stable Bi₂O₂Se for use in high-performance photovoltaic devices. Various techniques are used to confirm the charge transfer of these heterostructures, including photoluminescence (PL) and Raman spectroscopy, along with Kelvin probe force microscopy (KPFM). The results show that the PL is quenched by 40%, 95%, and 97% for WS₂/Bi₂O₂Se, 0.4 at.% V-WS₂/Bi₂O₂Se, and 2 at.% V-WS₂/Bi₂O₂Se, respectively, indicating a superior charge transfer in V-WS₂/Bi₂O₂Se compared to pristine WS₂/Bi₂O₂Se. The exciton binding energies for WS₂/Bi₂O₂Se, 0.4 at.% V-WS₂/Bi₂O₂Se and 2 at.% V-WS₂/Bi₂O₂Se heterostructures are estimated to be ≈130, 100, and 80 meV, respectively, which is much lower than that for monolayer WS₂. These findings confirm that by incorporating V-doped WS₂, charge transfer in WS₂/Bi₂O₂Se heterostructures can be tuned, providing a novel light-harvesting technique for the development of the next generation of photovoltaic devices based on V-doped transition metal dichalcogenides (TMDCs)/Bi₂O₂Se.     

Synthesis of iron-copper alloy using electrical discharge machining

The present work investigates the novel route for the synthesis of Fe-Cu alloy using electric discharge machining (EDM). The Synthesis of Fe-Cu alloy is difficult by equilibrium processes because of their immiscible nature. An attempt was made to investigate the synthesis of Fe-Cu alloy by EDM process where the discharge can lead to a very high temperature and subsequent quenching to result in alloy formation. The electrode was made up of copper and die steel was used as workpiece. The characterization of generated debris was carried out by X-ray diffraction analysis, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and transmission electron microscopy (TEM). The nano-phase granular particles of Fe-Cu alloy were confirmed by TEM and selected area diffraction pattern analysis. SEM morphology results reveal that the generated particles were both, spherical and non-spherical shape and size ranging between 50?nm and 30?µm. The EDS analysis indicates that the spherical particles were Fe-rich and non-spherical particles were Cu-rich.     

Prominent feature extraction for review analysis: an empirical study

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.